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COMPARISON OF ON-LINE NIR SPECTROMETER WITH
THIEF SAMPLING IN COMBINATION WITH HPLC FOR THE
MONITORING OF BLEND UNIFORMITY
Rita Crumley Barral
Thesis to obtain the Master of Science Degree in
Pharmaceutical Engineering
Supervisors: Dr. Maria-Leonor Alvarenga
Prof. Dr. José Monteiro Cardoso de Menezes
Examination Committee
Chairperson: Prof. Dr. Pedro Paulo De Lacerda e Oliveira Santos
Supervisor: Prof. Dr. José Monteiro Cardoso de Menezes
Member of the Committee: Prof. Dr. João Pedro Martins de Almeida Lopes
October 2017
ii
Abstract
The blending of powders is a common unit operation used in the manufacture of oral solid dosage
forms. A homogenous blend is crucial to ensure the quality of the final dosage form. The conventional
approach for assessing the uniformity of the blend is by sampling with a thief probe. However, this
method is laborious and can lead to errors. With the release of the FDA's PAT initiative, techniques such
as NIRS have been proposed as an advantageous alternative to monitor this operation.
The objective of this project was to evaluate whether these two approaches provide comparable
data regarding the end-point of the blending operation. To this end, the blending process of three blends
were monitored with an NIRS attached to the lid of the blender. Additionally, the blend was stopped at
predetermined time points to perform thief sampling. Subsequently, these samples were analyzed using
an HPLC technique. Quantitative and qualitative approaches were applied on the NIRS-acquired
spectra with the aim of extracting information pertaining to the state of the blend.
The results did not reveal commonalities between the two approaches. This was most strikingly
observed in the two most similar blends. According to the RSD values derived from thief sampling these
blends were found to show similar trends. However, the NIR results showed that these blends had
different blending profiles.
Keywords: Blend Homogeneity; Multivariate Data Analysis; Near-Infrared Spectroscopy; Powder
Blending; Process Analytical Technology (PAT); Thief Sampling
iii
Resumo
A mistura de pós é uma operação unitária recorrente na produção de formas farmacêuticas
sólidas. Uma mistura homogênea é crucial para garantir a qualidade da forma farmacêutica final. A
abordagem convencional para avaliar a uniformidade da mistura é por amostragem com uma sonda.
No entanto, esta técnica consome muito tempo e pode ser errónea. Com o lançamento da iniciativa
PAT da FDA, técnicas como espectroscopia no infravermelho próximo foram propostas como uma
alternativa vantajosa para a monitorização desta operação.
O objetivo deste projeto foi avaliar se essas duas abordagens fornecem dados comparáveis
quanto ao tempo ótimo de mistura. Com esse propósito, três misturas foram monitorizadas com um
NIRS acoplado à base do misturador. Adicionalmente, a mistura foi interrompida em tempos
predeterminados para realizar a amostragem com uma sonda. Essas amostras foram posteriormente
analisadas através de uma técnica de HPLC. Além disso, abordagens quantitativas e qualitativas foram
aplicadas aos espectros adquiridos para extrair informações relativas ao estado da mistura.
Os resultados não revelaram semelhanças entre as duas abordagens. Mais notavelmente nas
duas misturas mais parecidas. De acordo com os valores de RSD obtidos pelos os dados resultantes
do HPLC as misturas mostraram tendências semelhantes. No entanto, os resultados do NIR mostraram
que as misturas tinham perfis de mistura diferentes.
Palavras-chave: Amostragem; Análise Multivariada; Espectroscopia de Infravermelho Próximo;
Homogeneidade da Mistura; Mistura de Pós; Tecnologia Analítica de Processos
iv
Table of Contents
List of Tables ..................................................................................................................................... vi
List of Figures ................................................................................................................................... vii
Nomenclature ................................................................................................................................... xii
1. Introduction ..................................................................................................................................1
1.1. Thesis Objectives and Structure...........................................................................................1
1.2. Brief Review of Research on the Application of NIRS in Blending Monitoring........................2
1.2.1. Quantitative Methods ...................................................................................................3
1.2.2. Qualitative Methods .....................................................................................................4
2. Theoretical Background ...............................................................................................................5
2.1. Blending of Dry Powder .......................................................................................................5
2.1.1. Mixing Theory ..............................................................................................................5
2.1.2. Blending Equipment .....................................................................................................6
2.1.3. Assessment of Blend Uniformity and Current Regulation ..............................................6
2.2. Process Analytical Technology ............................................................................................9
2.2.1. Food and Drug Administration ......................................................................................9
2.2.2. European Medicines Agency ...................................................................................... 10
2.3. Near Infra-Red Spectroscopy ............................................................................................. 10
2.3.1. NIRS Basics............................................................................................................... 10
2.3.2. Advantages and Drawbacks ....................................................................................... 11
2.3.3. Instrumentation and Measurement Modes .................................................................. 11
2.4. Chemometrics and Multivariate Data Analysis .................................................................... 12
2.4.1. Data Pre-Processing .................................................................................................. 13
2.4.2. Principal Component Analysis (PCA).......................................................................... 14
2.4.3. Quantitative Analysis with Partial Least Squares (PLS) Regression ............................ 15
2.4.4. Hotelling’s T-Squared Statistics .................................................................................. 16
3. Implementation .......................................................................................................................... 17
3.1. Blending and Thief Sampling ............................................................................................. 17
3.1.1. Blending Conditions and Parameters ......................................................................... 17
3.1.2. Thief Sampling ........................................................................................................... 18
v
3.2. High-Performance Liquid Chromatography ........................................................................ 19
3.2.1. Variance Component Analysis.................................................................................... 19
3.3. On-Line NIR Instrument and Spectral Measurements ......................................................... 21
3.4. Data Analysis .................................................................................................................... 22
3.4.1. Quantitative Analysis .................................................................................................. 22
3.4.2. Qualitative Analysis .................................................................................................... 26
4. Results and Discussion of HPLC Reference Data ....................................................................... 31
5. Results and Discussion of NIR Spectral Characteristics.............................................................. 35
6. Results and Discussion of the Quantitative Approach ................................................................. 39
6.1. Calibration Model Development ......................................................................................... 39
6.2. NIR-API Predicted Concentration Blending Profile ............................................................. 44
6.3. Effect of Spectral Acquisition Rate on Blend Profile ............................................................ 46
7. Results and Discussion of the Qualitative Approach ................................................................... 47
7.1. PCA Scores versus Blending Time .................................................................................... 48
7.2. Moving Block Standard Deviation....................................................................................... 54
7.3. Principal Component Score Distance Analysis ................................................................... 57
8. Conclusions and Future Work .................................................................................................... 60
9. Bibliographic References ........................................................................................................... 62
Annex A ............................................................................................................................................ 71
Annex B ............................................................................................................................................ 74
vi
List of Tables
Table 1 - Regressions used for the development of quantitative models for evaluating blend uniformity
with NIRS .............................................................................................................................3
Table 2 – List of qualitative approaches for the determination of blend homogeneity using NIRS. ........4
Table 3 - Percentage (% w/w) of the components in blend 1, 2, and 3. .............................................. 17
Table 4 – Reference data from the spectra used to develop the model. The spectra used for testing the
prediction performance of the calibration models created are highlighted in gray. ............... 23
Table 5 - Statistical parameters and number of PLS latent variables for calibration models using the
entire NIR wavelength range, without data pretreatment as well as after different spectral
pretreatments. .................................................................................................................... 40
Table 6 - Statistical parameters and number of PLS latent variables for the selected models chosen
through iPLS, without data pretreatment as well as after different spectra pretreatments..... 41
Table 7 - Statistical parameters for the selected models chosen through iPLS with 1 PLS latent variable,
without data pretreatment, as well as after different spectra pretreatments.......................... 42
Table 8 - Statistical parameters and number of PLS latent variables for the selected models of each
approach tested. ................................................................................................................ 42
Table 9 - Statistical parameters and number of PLS latent variables for the selected models of each
tested approach with 1 PLS latent variable. ........................................................................ 43
Table 10 - HPLC data for blend 1. ..................................................................................................... 71
Table 11 - HPLC data for blend 2 at 2 and 4 minutes......................................................................... 71
Table 12 - HPLC data for blend 2 at 6 and 8 minutes......................................................................... 71
Table 13 - HPLC data for blend 2 at 12 and 15 minutes. .................................................................... 72
Table 14 - HPLC data for blend 3 at 2 and 4 minutes......................................................................... 72
Table 15 - HPLC data for blend 3 at 6 and 8 minutes......................................................................... 72
Table 16 - HPLC data for blend 3 at 12 and 15 minutes. .................................................................... 73
vii
List of Figures
Figure 1 - Structure of the thesis. ........................................................................................................2
Figure 2 - Random Mix[65] ..................................................................................................................5
Figure 3 - Perfect Mix[65] ....................................................................................................................5
Figure 4 - Recommendations and acceptance criteria for the assessment of powder mix uniformity
according to the withdrawn FDA draft guidances [122], [123] and modifications to the
withdrawn FDA draft stratified sampling guidance. [81] .........................................................8
Figure 5 -FDA’s reasons for drug shortages.[83] .................................................................................9
Figure 6- Representation of configurations for spectral acquisition (transmittance, reflectance, and
transflectance)[95] .............................................................................................................. 12
Figure 7- Effect of mean centering on PCA. (a) Without mean centering, (b) With mean centering. By
applying mean centering, it allows for a better description of the variance present in the data.
[111] .................................................................................................................................. 13
Figure 8 - Mathematical representation of principal component analysis[135] .................................. 14
Figure 9 - Graphical representation of a method to determine the optimal number of latent variables, by
plotting RMSEcv versus latent variables (LV)[116] .............................................................. 15
Figure 10 - Illustration of the PharmaPicker. (a) Collection cylinder connected to the rod; (b) Collection
Cylinder, composed of the sampling cavity, outer sleeve and volume tip; (c) infographic of the
sampling system; (d) volume tips from 0.1 mL to 2.5 mL, which determine the sample
quantity.[121] ..................................................................................................................... 18
Figure 11 - Schematic of the blending parameters for the three blends. ............................................. 19
Figure 12 – Illustration of the steps taken for the variance component analysis. SSB and SSW
correspond to the sum of squares between and within location, respectively; df corresponds
to the degrees of freedom; t and r correspond to the number of sampling locations and number
of replicates, respectively; MSB and MSW correspond to the mean squares between and
within location, respectively; EMS corresponds to the expected mean squares; σ2w and σ2
B
correspond to the within and between location variance. .................................................... 20
Figure 13 - Scheme of on-line NIR spectral acquisition in 20 L bin-blender. (1) bin-blender; (2) NIR
Spectrometer; (3) rotation axis............................................................................................ 21
Figure 14 - Schematic showing how the data set was constructed for the PLS model. The blender was
stopped at pre-defined time points, and samples were removed via a thief sampler at various
locations, which are shown here as roman numerals. These are the sampling locations of
blends 2 and 3. For each time point, a mean API content (%LC) was calculated. This value
was used as a reference value for the last spectra recorded before the blender was stopped.
.......................................................................................................................................... 22
Figure 15 - Fraction of the variance of the Y variables explained by the model (R2Y) for the full spectrum
model and 10 interval models, without preprocessing, plotted against the number of PLS latent
variables. ........................................................................................................................... 24
viii
Figure 16 – Example of the iPLS method for spectra without preprocessing. The four plots represent the
iPLS models with(a) one, (b) two, (c) three, and (d) four latent variables. The red line
corresponds to the RMSECV value of the full spectra model with 2 latent variables. ........... 25
Figure 17 - The first principal component, PC1, represents the direction of maximum variance in the
data. Each observation (green circles) can be projected onto the principal component in order
to get a co-ordinate value along the PC-line. This value is known as a score. The red circle
represents the mean along PC1.[129]................................................................................. 26
Figure 18 - Diagram of the moving block standard deviation calculation process.[21] ......................... 27
Figure 19 – Effect of block size on MBSD results. Exemplified on spectra of blend 3, pretreated with an
SNV. Number of measurements included in the block varies between, (a) 5, (b) 10, (c) 15, and
(d) 20. ................................................................................................................................ 28
Figure 20 – Fraction of the variance of the X variables explained by the model (R2X) plotted against the
number of principal components for blend 1 (a), blend 2 (b), and blend 3 (c) with and without
preprocessing. ................................................................................................................... 29
Figure 21 - Schematic of the Principal Component - Score Distant Analysis (PC-SDA) approach steps
for blend preprocessed with SNV.[19] (a) Spectra; (b) Score Plot of the spectral data; (c)
Calculation of the standard deviation; (d) PCA with the successive spectra with lowest SD; (e)
PCA predicted sore plot; (f) Hotelling T2 Prediction chart. ................................................... 30
Figure 22 - Evolution of the relative standard deviation (RSD) over time for the 3 tested blends. Black
line represents an RSD of 5.0 % which, according to previous withdrawn FDA guidance [123],
corresponds to the limit below which the values indicate that the blend is uniform. .............. 31
Figure 23 – Illustration of the VCA combined with the RSD values for blend 2 (a) and blend 3 (b). The
blue and green lines correspond to the connection of the between location and within location
variance values, respectively. The black line corresponds to the calculated RSD values. .... 32
Figure 24 – Raw NIR spectra of the pure compounds in static state................................................... 35
Figure 25 - Mean of the last 10 NIR spectra collected during mixing of blends 1, 2, and 3 and the spectra
of the granule used in the 3 blends. The figure illustrates the dissimilarities between spectra
due to differing API concentrations (%). Blend 1 contained 7% of API and blends 2 and 3,
which overlap in the graph, contained 15% API. ................................................................. 36
Figure 26 –Scores and contribution plots of the granules used in blends 2 and 3. In the score graphs,
the green and blue circles correspond to the granules used in blend 2 and blend 3,
respectively. (a) and (c) correspond to the score and contribution plot of spectra without pre-
treatment, respectively. (b) and (d) correspond to the score and the contribution plot of spectra
preprocessed with a 1st derivative, respectively. The y-axis of the contribution plot Group 1
and 2 corresponds to the granules spectra of blend 2 and blend 3, respectively.................. 37
Figure 27 - Raw NIR spectra of the granules used in blend 2 and 3. The spectra of the granules used in
blend 2 are colored red. The spectra of the granules used in blend 3 are colored green. ..... 38
Figure 28 - NIR spectra of the granules used in blend 2 and 3 pretreated with a 1st Derivative. The
spectra of the granules used in blend 2 are colored red. The spectra of the granules used in
blend 3 are colored green. .................................................................................................. 38
ix
Figure 29 – Raw spectra of the runs used for model development. API content (%LC) roughly increases
in the direction of the arrow between 91.1% and 103.9%. ................................................... 39
Figure 30 – Scatter plot and regression line of predicted vs. observed Y values of the models (a) without
and (b) with variable selection. Both with 1 latent variable. .................................................. 43
Figure 31 - Predicted API concentration (%LC) from the NIR spectra acquired in Blend 1. To improve
interpretation of the predicted results, a Savitzky-Golay smoothing filter (polynomial order 1
and a frame length of 15), represented by the red line, was applied. ................................... 44
Figure 32 - Predicted API concentration (%LC) from the NIR spectra acquired in Blend 2. To improve
interpretation of the predicted results, a Savitzky-Golay smoothing filter (polynomial order 1
and a frame length of 15), represented by the red line, was applied. ................................... 44
Figure 33 - Predicted API concentration (%LC) from the NIR spectra acquired in Blend 3. To improve
interpretation of the predicted results, a Savitzky-Golay smoothing filter (polynomial order 1
and a frame length of 15), represented by the red line, was applied. ................................... 45
Figure 34 – Comparison of the predicted blending profile of the API concentration (%LC) of Blend 3 with
the reduced and the full amount of spectral data, represented by the blue and grey line,
respectively. ....................................................................................................................... 46
Figure 35 - Illustration of the spectral variation between (a) the 10 first and (b) the 10 last spectra
recorded for Blend 1. The blue line represents the mean spectrum of (a) the 10 first and (b)
the 10 last spectra collected during blend. The red lines demonstrate the variation with ±15
SD limits............................................................................................................................. 47
Figure 36 – First principal components scores of Blend 1 with and without preprocessing versus blending
time. The blue circles represent the scores, and the green line represents a Savitzky-Golay
smoothing line (polynomial order 1 and a frame length of 15), used to facilitate interpretation.
On the y axis, the variance captured by the principal component is presented as a percentage.
Plots (a), (b), (c), and (d) illustrate the scores for spectral data without preprocessing, and
preprocessed with SNV, 1st derivative, and 2nd derivative, respectively. .............................. 48
Figure 37 – First principal component scores of Blend 2 with and without preprocessing versus blending
time. The blue circles represent the scores, and the green line represents a Savitzky-Golay
smoothing line (polynomial order 1 and a frame length of 15), used to facilitate interpretation
of the trend. On the y axis, the variance captured by the principal component is presented as
a percentage. Plots (a), (b), (c), and (d) illustrate the scores for spectral data without
preprocessing, and preprocessed with SNV, 1st derivative, and 2nd derivative, respectively. 50
Figure 38 - Second principal component scores of Blend 2 with and without preprocessing versus
blending time. The blue circles represent the scores, and the green line represents a Savitzky-
Golay smoothing line (polynomial order 1 and a frame length of 15), used to facilitate
interpretation of the trend. On the y axis, the variance captured by the principal component is
presented as a percentage. Plots (a), (b), (c), and (d) illustrate the scores for spectral data
without preprocessing, and preprocessed with SNV, 1st derivative, and 2nd derivative,
respectively. ....................................................................................................................... 51
x
Figure 39 – First principal components scores of Blend 3 with and without preprocessing versus blending
time. The blue circles represent the scores, and the green line represents a Savitzky-Golay
smoothing line (polynomial order 1 and a frame length of 15), used to facilitate interpretation.
On the y axis, the variance captured by the principal component is presented as a percentage.
Plots (a), (b), (c), and (d) illustrate the scores for spectral data without preprocessing, and
preprocessed with SNV, 1st derivative, and 2nd derivative, respectively. Plot (e) is an
enlargement of (d) to reveal levels that were similarly identified in Blend 1. ......................... 52
Figure 40 – First principal components scores of spectra acquired in Blend 3 pretreated with SNV versus
blending time. Plots (a) and (b) illustrate the differences between the score plots with spectra
acquired (a) at every rotation of the blender and (b) at every second rotation. The blue circles
correspond to the scores, and the green line represents a Savitzky-Golay smoothing line
(polynomial order 1 and a frame length of 15), used to ease interpretation. ......................... 53
Figure 41 – Application of moving block standard deviation to the spectra collected in Blend 1. MBSD
was applied to spectral data without preprocessing (blue line) and with preprocessing, SNV
(green line), 1st derivative (red line), and 2nd derivative (black line). To overlap the MBSD
curves, an SNV was applied to the mean standard deviation. The vertical grey lines represent
the times the blender was restarted. ................................................................................... 54
Figure 42 - Application of moving block standard deviation to the spectra collected in Blend 2. MBSD
was applied to spectral data without preprocessing (blue line) and with preprocessing, SNV
(green line), 1st derivative (red line), and 2nd derivative (black line). To overlap the MBSD
curves, an SNV was applied to the mean standard deviation. The grey lines represent the
times the blender was restarted. ......................................................................................... 55
Figure 43 - Application of moving block standard deviation to the spectra collected in Blend 3. MBSD
was applied to spectral data without preprocessing (blue line) and with preprocessing, SNV
(green line), 1st derivative (red line), and 2nd derivative (black line). To overlap the MBSD
curves, an SNV was applied to the mean standard deviation. The grey lines represent the
times the blender restarted. ................................................................................................ 56
Figure 44 - PC-SDA with Hotelling's T2 charts for blend 1 (a) without and with preprocessing, (b) SNV,
(c) 1st Derivative, and (d) 2nd Derivative. T2critical (95%, green line) = 15,2. Dashed line
represents the time point in which the T2 Hoteling values are below the T2critical limit. ........... 57
Figure 45 - PC-SDA with Hotelling't T2 charts for blend 2; (a) without and with preprocessing, (b) SNV,
(c) 1st derivative, and (d) 2nd derivative. T2critical (95%, green line) = 15.2. The dashed line
represents the time point at which the T2 Hoteling values are consistently below the T2critical
limit. ................................................................................................................................... 58
Figure 46 - PC-SDA with Hotelling't T2 charts for blend 3; (a) without and with preprocessing, (b) SNV,
(c) 1st derivative, and (d) 2nd derivative. T2critical (95%, green line) = 15.2. The dashed line
represents the time point in which the T2 Hoteling values are consistently below the T2critical
limit. ................................................................................................................................... 59
Figure 47 - Summary of the HPLC, quantitative and qualitative results of the three blends................. 60
Figure 48 - NIR spectra of the pure components in static state preprocessed with a 1st derivative. ..... 74
xi
Figure 49 - NIR spectra of the pure components in static state preprocessed with a 2nd derivative. .... 74
xii
Nomenclature
%LC – Percent Label Claim
ISPE – International Society of
Pharmaceutical Engineering
ANDA – Abbreviated New Drug
Application
LIF – Light-Induced Fluorescence
ANN – Artificial Neural Networks
LSSVM – Least Squares Support
Vector Machines
ANOVA – Analysis of Variance
LV – Latent Variable
API – Active Pharmaceutical
Ingredient
LW-
PLS –
Locally Weighed – Partial
Least Squares
BUCU – Blend Uniformity and
Content Uniformity
m/m% – Percent by Mass
CFR – Code of Federal
Regulations
MBSD –
Moving Block Standard
Deviation
cGMP – current Good Manufacturing
Practices
MC – Mean Centering
CLS – Classical Least Squares MCR-
ALS –
Multivariate Curve Resolution
by Alternating Least Squares
CPMP – Committee for Proprietary
Medicinal Products
MgSt – Magnesium Stearate
df – Degrees of Freedom
min. – Minutes
EMA – European Medicines
Agency
MLR – Multiple Linear Regression
EMS – Expected Mean Squares
MS – Mean Squares
EU – European Union
MSB – Mean Squares Between
Location
FDA – Food and Drug
Administration
MSW –
Mean Squares Within
Location
GDP – Good Distribution Practices
NIPLS – Non-Linear Iterative Partial
Least Squares
GMP – Good Manufacturing
Practices
NIR – Near Infrared
HPLC – High-Performance Liquid
Chromatography
NIRS – Near-Infrared Spectroscopy
ICH – International Conference on
Harmonisation
nm – Nanometers
iPLS – Interval Partial Least
Squares
PAT –
Process Analytical
Technology
xiii
PC – Principal Component
SSB – Sum of Squares Between
Location
PC1 – 1st Principal Component
SSW – Sum of Squares Within
Location
PC2 – 2nd Principal Component
SSF – Sodium Stearyl Fumarate
PCA – Principal Component Analysis
UV – Unit-Variance Scaling
PCR – Principal Component
Regression
UV/VIS –
Ultraviolet–visible
spectroscopy
PC-SDA – Principal Component Scores
Distance Analysis
v/v% – Volume Percent
PLS – Partial Least Squares
VCA – Variance Component
Analysis
PQRI – Product Quality Research
Institute
w/ – With
QWP – Quality Working Party
R2 or R2cal –
Coefficient of Determination of
the Calibration Set
R2pred –
Coefficient of Determination of
the Prediction Set
R2X – Fraction of the Variation of the X
Variables by the Model
RMSEC – Root Mean Square Error of
Calibration
RMSECV – Root Mean Square Error of
Cross-Validation
RMSEP – Root Mean Square Error of
Prediction
RPM – Rotations Per Minute
RSD – Relative Standard Deviation
SD – Standard Deviation
SIMCA – Soft Independent Modeling of
Class Analogy
SIMPLISMA – Simple-To-Use Interactive Self-
Modeling Mixture Analysis
SNV – Standard Normal Variate
SS – Sum of Squares
1
1. Introduction
The process of blending powders is a complex system, one that is hard to predict, as it depends
on a range of factors, including [1]:
▪ Operating Conditions (i.e. fill level, loading order, blending time)
▪ Material Properties (i.e. particle size and shape, cohesivity, tendencies for segregation or
agglomeration)
▪ Environmental Conditions (i.e. humidity)
▪ Equipment (i.e. blender type)
All these variables can have an impact on the quality of the blend. Thus, it may be argued that
identical blends may not become uniform at similar times. Furthermore, if blend issues are translated
into content uniformity issues in the final tablet (e.g. overpotency or subpotency) [2], this might pose a
risk to patients’ health. Blend uniformity must therefore be monitored.
The conventional method of assessing blend uniformity is by sampling the blend. This method
utilizes thief probes, which are inserted into the blend to remove samples from different locations in the
blender. However, this method has been shown to extensively disturb the blend and to provide an
inaccurate representation of the state of the mixture. [3] Furthermore, Muzzio et al. [3] demonstrated
that poorly mixed systems require hundreds of samples to accurately characterize the mixture. This
might be unfeasible due to the laborious and time-consuming nature of this method.
With the release of the FDA’s PAT initiative [4], which encourages implementation of technologies
for real-time monitoring of critical quality attributes, the pharmaceutical industry has increased research
into new analytical technologies to enable assessment of blend uniformity in real-time. [5] These
technologies are more advantageous than the conventional techniques, as they are less time consuming
and are non-destructive. They also improve operator safety by lowering exposure to potent active
pharmaceuticals. [6] Several types of PAT tools have been evaluated to monitor blend uniformity,
including NIRS [7], [8], Raman [9]–[11], light-induced fluorescence (LIF) [12], [13], and thermal effusivity
[14], [15]. Out of these, NIRS has been the most studied.[16]
Nevertheless, one may question whether the conventional approach and the PAT approach
provide comparable data regarding the uniformity of the blend. This study was designed to evaluate
whether commonalities exist between these two approaches
1.1. Thesis Objectives and Structure
This project aimed to evaluate if the conventional approach of assessing blending uniformity
through powder sampling and the PAT approach using real time monitoring with NIRS showed
commonalities. Various qualitative and quantitative chemometric techniques were applied to the NIR
spectra acquired during the blending process to evaluate how the blend uniformity results differed
between the two techniques used.
2
An illustration of the structure of the thesis is presented in Figure 1. The thesis is divided into four
parts. The first part presents a literature review of the fundamentals of powder blending and current
regulations that apply to the assessment of blend uniformity. Additionally, an overview of process
analytical technology (PAT) is given, as well as a description of near-infrared spectroscopy and the
types of multivariate data analysis that are applied to it. The second part describes how this project was
implemented and the type of data analysis that was done on the NIR spectra acquired. The third part
presents the results and discussion. To facilitate interpretation of the results, this part was divided into
4 sub-parts, which can be seen in Figure 1. Lastly, the fourth part presents the conclusions and
recommendations for future work.
1.2. Brief Review of Research on the Application of NIRS in Blending
Monitoring
NIRS is a prime PAT tool since it is rapid, non-destructive, and sensitive to both chemical and
physical attributes.[17] Another advantage of this method is that, unlike the traditional thieving methods
which assume that the excipients are evenly distributed if the active pharmaceutical ingredient (API) is,
all components of the blend influence the resulting NIR spectrum and are therefore measured.[18]
Figure 1 - Structure of the thesis.
3
Various methodologies have been described for investigating the homogeneity of a blend by
NIRS. In general, NIR spectral measurements are done in two different ways: non-invasively through a
window [16], [17], [19], [20], or by insertion of a probe directly into the powder bed at a fixed position or
multiple positions [18], [21]–[23]. However, a key difference between blending uniformity studies with
NIRS is the type of data analysis and modeling strategies used to translate the NIR spectral data into
blend uniformity results. There are two main methodologies:
▪ Quantitative calibration models, which require development of a calibration model
▪ Qualitative methods, which do not involve a calibration model but generally demand that
process control limits be defined.[2] In general, the end-point is identified when a specific
value remains constant for a given number of consecutive blending observations or when
it meets a defined criterion.[24]
1.2.1. Quantitative Methods
Quantitative methods rely on developing a regression model to predict the amount(s) of the
component(s) present in the blend.[24] The main challenge with this method is to develop a robust
calibration model.[25] The calibration set should contain enough samples to encompass all possible
variance of chemical and physical characteristics. This includes, concentration variations and particle
sizes of the components. [25], [26] Zacour et. al. [27], demonstrated that in a system of four chemical
components and two physical components, a calibration set with only two levels of each component
would require a minimum a 70 independent samples. Additionally, a validation set and calibration
transfer samples would need to be generated. Taking into account all the samples required, this method
demands enormous labor and time. [25]
To acquire the calibration samples, most of the studies chose one of the following two options:
(1) stopping the blender at different time points, removing calibration samples by thieving and analyzing
them offline [28], [29]; or (2) synthesizing calibration samples in the laboratory [8], [30], [31]
The main method of constructing quantitative models has been Partial Least-Squares (PLS)
regression. [25] However, other regression methods, presented in Table 1, have been used and shown
to perform adequately.
Table 1 - Regressions used for the development of quantitative models for evaluating blend uniformity with NIRS
Method References
Partial Least-Squares (PLS) [1], [7], [8], [25]–[28], [30], [32]–[44]
Locally Weighed – PLS (LW-PLS) [25]
Principal Component Regression (PCR) [8], [37], [43]
Non-Linear Iterative PLS (NIPLS) [45]
Multiple Linear Regression (MLR) [37], [43]
Classical Least Squares (CLS) [27]
Artificial Neural Networks (ANN) [27]
Multivariate Curve Resolution by Alternating Least Squares (MCR-ALS) [46]
4
1.2.2. Qualitative Methods
Qualitative methods typically evaluate how the spectral variance evolves over time. They rely on
one of the following:[16], [47]
▪ During blending, the spectral variance is reduced as components are becoming
homogeneous
▪ Distance from an “ideal” reference spectra, which is usually spectra that represent
homogeneous blend
Compared to the quantitative methods, qualitative methods are simple to use and are quick to
implement. On the other hand, quantitative methods, despite requiring a greater modeling effort, provide
quantitative information about the state of the mixture.[48] However it should be taken into account that
quantitative methods typically only monitor the evolution of a single component. In some cases, it is of
interest to monitor the evolution of the excipients as well. To be able to create a quantitative method
that predicts the evolution of all the components of interest, there is a need for wet chemistry methods
for the analytical determination of excipients, which in some cases have not yet been developed.[24]
Thus, qualitative approaches may be applied at various stages of process development, due to
their ability to provide quick information about effects, such as changes of components in the formulation
and process variables that affect blend uniformity.[16], [19]
A summary of the different types of qualitative approaches developed is given in Table 2.
Table 2 – List of qualitative approaches for the determination of blend homogeneity using NIRS.
Method References
Change in absorbance of one of the components [49]
Average standard deviation of spectra [50], [51]
MBSD of PC scores [52]
Moving Block Standard Deviation (MBSD) of Spectra [17], [18], [20], [52]–[54]
Dissimilarity between spectra and ideal mixture [51], [18]
Mean square of differences between consecutive spectra [22]
Euclidean distance between spectra [51]
Chi-Square Analysis [55]
Principal Component Analysis (PCA) [51], [20], [56], [57]
Principal Component Score Distance Analysis (PC-SDA) [19]
SIMPLISMA [56]
Bootstrap error-adjusted single-sample technique [55]
SIMCA [18], [20]
Moving F-Test [16]
Caterpillar [58], [59]
5
2. Theoretical Background
2.1. Blending of Dry Powder
2.1.1. Mixing Theory
Mixing of powders is a key step in the manufacture of virtually all solid dosage forms, including
tablets and capsules. The purpose of the mixing operation is to reduce inhomogeneities in the blend to
an acceptable level, so to ensure that each final dosage contains almost exactly the same amount of
the active pharmaceutical ingredient (API). Unlike molecules in a liquid, which in time will mix
spontaneously by a diffusion mechanism, powder particles do not spontaneously mix. Therefore, mixing
of powders can only occur if energy is put into the process, usually with the aid of an agitating impeller,
gas flow, or rotational motion of a container. [60]–[64]
The blending process produces a random redistribution of particles. A mixture is considered
“perfect” when the ratio of particles in any given sample remains constant regardless of the location that
the sample is taken from. This is shown in Figure 3, where the components are distributed as evenly as
possible. With powders this is unattainable. All that is possible to achieve is a maximum degree of
randomness, i.e., a mixture in which the probability of finding a particle of a given component is the
same at all positions in the mixture (Figure 2). [65]
There are three main mechanisms by which powder mixing occurs, namely convection, shear,
and diffusion. Convective mixing, also referred as macro-mixing, occurs when there is the motion of
large groups of particles within the mixture. This type of mixing contributes mainly to the macroscopic
mixing of powder mixtures and tends to produce a large degree of mixing quickly. However, mixing does
not occur within the group of particles moving together as a unit, and so in order to achieve a random
mixing an extended mixing time is required. Shear mixing occurs when a “layer” of material moves or
flows over another “layer”. It can enhance semi-microscopic mixing. Diffusive mixing is caused by the
motion of individual powder particles and is essential for microscopic homogenization. Diffusive mixing,
Figure 2 - Random Mix[65] Figure 3 - Perfect Mix[65]
6
although, having the potential to produce a random mixing, generally results in a low rate of mixing.[6],
[60], [62] All of these mechanisms influence the degree of randomness of the blend, however, which
one predominates will depend on various variables, such as, mixing process conditions (e.g. type of
blender) and the characteristics of the powder components.[60]
Nevertheless, during blending, it might be expected that the randomness of a mixture will
progressively increase with time, however, this isn’t generally the situation. Under certain conditions, an
optimum blending time occurs, past which the blend demonstrates a propensity to separate back into
its components, i.e. demixing. This process can be caused by segregation effects. Segregation often
occurs in free-flowing powders and is likely to happen in mixtures where the components vary in particle
size, density, and shape. Segregation will generally cause an increase in content variation between
samples taken from the mixture and may cause a batch to fail a blend or content uniformity test.[61],
[62]
2.1.2. Blending Equipment
Batch blending processes consists of three sequential steps: weighing and loading of
components, blending and discharging. Blenders come in many different designs and sizes and make
use of a range of blending mechanisms. The selection of the most appropriate blender for any given
formulation must take into consideration several aspects, such as the type of mixing mechanism desired;
contamination (e.g. dust-tight); space requirement; and ease to discharge and clean.[60], [66]
According to Muzzio et. al. [3], the two most common types of blenders used in the pharmaceutical
industry are tumbling and convective blenders. They are based on different operating principles.
Tumbling blenders accomplish mixing by allowing the powder to move within a closed vessel attached
to an axis which rotates for a specified length of time and at a set speed (rpm). [14], [22]. However, this
type of blender is not indicative for cohesive powders which present the tendency of forming large
agglomerates; because the forces generated are not sufficient to break up the agglomerates. [23], [60]
Convective blenders are composed of a stationary container and a rotating device that mixes the
powder.[3] This type of blender, unlike the tumble blender, is effective in mixing cohesive material due
to its ability to apply a high amount of shear to the powder.[8] Nonetheless, due to the limited movement
of the rotating device, “dead-spots” are difficult to remove. [14]
For a more comprehensive description of the various types of mixers, readers are referred to a
book chapter by Dickey [67].
2.1.3. Assessment of Blend Uniformity and Current Regulation
The standard batch blending procedure generally includes loading a blender, blending for a pre-
determined time span, and stopping the blender. To determine the degree of mixing obtained, it is
necessary to sample the mixture and evaluate the variation within the mix statistically. To this end,
collected samples from various locations in the blend are analyzed with analytical methods such as
UV/VIS spectroscopy or high-performance liquid chromatography (HPLC) to determine the assay of the
active ingredient(s).[20] The variations in API content from the differing sampling locations, often
expressed as a relative standard deviation (RSD), is an indicator of the homogeneity of the blend. [6],
7
[63] Numerous factors affect the assessment of blend uniformity, including the nature of the mixture, the
method of analysis, and sampling methods. Regarding sampling methods, the location of sampling,
number of samples drawn, and size of the samples to be removed are important variables.[68]
A common technique for sampling a batch blending process is by inserting a probe known as a
thief sampler. A powder thief is an equipment specially designed for taking out defined amounts of
sample from a blender. A powder thief has one or more cavities in an empty cylinder. In general, a
sample is collected by inserting the closed thief into the blend. When the insertion is complete, the
cavities are opened by twisting the cylinder and powder flows into the cavities. Subsequently, the
cylinder is twisted again to close and it is removed from the blend.[3]
Nonetheless, by taking into consideration the two “golden rules of sampling” of Allen, which are:”
Sample material when it’s in motion, and sample the entire material stream during short intervals”, [69]
both rules are violated with the use of thief probes for sampling. As a result, several papers have
demonstrated the disadvantages linked to the use of thief sampling for blend uniformity assessment.
[3], [71]–[74]. These disadvantages result from various causes, such as the distortion of the powder bed
when a sample thief probe is inserted into the mixture, and uneven flow of different powder components
into the cavities of the probe.[3], [70] As a result, the collected samples may not represent the true state
of the blend from where it was sampled.
Current Regulation
Techniques and acceptance criteria for the assessment of blend uniformity continues to be a
debated topic between the industry and health authorities.[76] The FDA has pulled back two draft
guidance documents that had provided some guides to follow. The recommendation and acceptance
criteria of these guidances are presented in Figure 4.
The first FDA guidance was the “ANDAs: Blend Uniformity Analysis” which was released in 1999
and withdrawn in 2002.[77] The pharmaceutical industry raised worries over the absence of scientific
merit of the approach defined in this guidance document.[6] As a result, in 1999, the Product Quality
Research Institute (PQRI) established the Blend Uniformity Working Group, with the purpose of
discussing recommendations on appropriate techniques to ensure blend uniformity.[78] On December
2002, PQRI submitted the group's final recommendation to the FDA [79], which formed the basis for the
FDA draft guidance for industry, “Powders Bends and Finished Dosage Units – Stratified In-Process
Dosage Unit Sampling and Assessment”, issued October 2003.
After the FDA's stratified sampling draft guidance was withdrawn in 2013, due to the fact that this
document was no longer consistent with their thinking[80]. In August 2013, the International Society for
Pharmaceutical Engineering (ISPE) sponsored the creation of the Blend Uniformity and Content
Uniformity (BUCU) group with the purpose of discussing alternative ways of assessing blend and content
uniformity.[81] In 2014, this group released a paper with recommendations for the assessment of blend
uniformity.[81] The approach is similar to the approach in the withdrawn draft guidance but with a few
key modifications.
8
Nonetheless, evaluating the uniformity of a blend continues to be a challenging time-consuming
task mainly due to erroneous nature of the sampling technique, which cause confusion on whether the
batch is truly inhomogeneous or if the results are biased due to incorrect sampling. Therefore, the
development and implementation of alternative methods which enable uniformity analysis in a non-
destructive, non-invasive and real-time basis show to be more advantageous than conventional
techniques. Here is where PAT found a generous field to enhance optimization and a better
understanding of the blending process.[18], [52]
Figure 4 - Recommendations and acceptance criteria for the assessment of powder mix uniformity according to the withdrawn FDA draft guidances [122], [123] and modifications to the withdrawn FDA draft stratified sampling guidance. [81]
9
2.2. Process Analytical Technology
Conventional pharmaceutical manufacturing is generally
accomplished using batch processing with product quality and
performance is ensured by end batch quality control testing. If
quality specifications are not met, the batch is scrapped. This
approach has been successful in providing safe
pharmaceuticals to the public. Be that as it may, over the past
decade, there have been challenges in drug shortages and
recalls due to failures in pharmaceutical quality.[82] As
presented in Figure 5, according to the US Food and Drug
Administration, nearly 64% of all drug shortages were attributed
to quality failures, particularly due to issues in facility quality and
product manufacturing.[83]
To mitigate this situation, the FDA and subsequently the European Medicines Agency (EMA) have
promoted and encouraged the adoption of science and risk-based approaches to pharmaceutical
development and manufacturing. Process Analytical Technology (PAT) is one of these approaches. [4]
2.2.1. Food and Drug Administration
In August of 2002, the FDA launched a new initiative named “Pharmaceutical Current Good
Manufacturing Practices for the 21st Century: A Risk-Based Approach”, which was intended to
“encourage the implementation of a risk-based pharmaceutical quality assessment systems” and “early
adoption of new technological advances”.[84]
Two years later, the FDA introduced a document entitled “Guidance for Industry PAT – A
Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance”. This
guidance encouraged the use of process analytical technology by the pharmaceutical industry. It defined
it as “a system for designing, analyzing, and controlling manufacturing through timely measurements
(i.e., during processing) of critical quality and performance attributes of raw and in-process materials
and processes, with the goal of ensuring final product quality.” This guidance also highlights the
necessity for better process understanding and opportunities for improving manufacturing productivity
through innovation.[4]
More recently, the FDA released a draft guidance document entitled “Advancement of Emerging
Technology Applications to Modernize the Pharmaceutical Manufacturing Base Guidance for Industry”.
This guidance is focused on facilitating the introduction of innovative manufacturing techniques as a
means to modernize pharmaceutical manufacturing.[85]
All the guidance mentioned above encourage the use of process analytical technologies. Overall,
the benefit of implementing PAT is to provide dynamic manufacturing processes which manages
variability and consistently fabricated products of a predefined quality at the end of the manufacturing
process, through the use of on-, in-, and/or at-line measurements and controls.[4]
Raw Materials
27%
Quality: Manufacturing
Issues37%
Quality: Delays/Capicity
27%
Others9%
Figure 5 -FDA’s reasons for drug shortages.[83]
10
2.2.2. European Medicines Agency
To support PAT activities in the EU, an EMA PAT team was created November 2003. The team
acts as a forum for dialogue between the Quality Working Party, the Biologics Working Party and the
GMP/GDP Inspectors Working Group with the aim of reviewing the implications of PAT ensuring that
the European regulatory framework and authorities are prepared and equipped to conduct thorough and
effective evaluations of PAT-based submissions.[86]
According to the EMA, some of the more specific objectives of the team are as follows[87]:
▪ Review of legal and procedural implications of PAT on EU regulatory system
▪ Review and comment on documents produced by other organizations
▪ Review and assess “mock” submissions of applications using PAT
▪ Develop a procedure for assessment of PAT related applications
▪ Avoidance of disharmony with other regions
▪ Identify training needs.
The EMA PAT team believes that the current regulatory framework in Europe is open to the
implementation of PAT in marketing authorization applications. Reference is made to the existing
guidance on Development of Pharmaceutics (CPMP/QWP/054/98)[88], the Note for Guidance on
Parametric Release (CPMP/QWP/3015/99)[89] and Annex 17 to the EU GMP Guide[90]. In order to
clarify the EMA PAT team´s position on a number of issues raised by the Industry, a “Question and
Answers” document[91] and a reflection paper[92] have been published. The EMA has also released a
“Guideline on the use of near-infrared spectroscopy by the pharmaceutical industry and the data
requirements for new submissions and variations“, which provides guidance on the use of NIRS for PAT
applications. [93]
2.3. Near Infra-Red Spectroscopy
Near-infrared spectroscopy (NIRS) is recognized as a powerful analytical technique, due to its
ability to make fast, nondestructive measurements that require little to no need of reagents and sample
preparation, and multivariate properties, i.e. chemical and physical data from one spectrum.[93], [94]
These characteristics of near-infrared (NIR) allow this technique to be implemented as a process
analytical technology (PAT). [95] NIR spectroscopy has been widely studied within the pharmaceutical
industry for various areas, such as counterfeit product investigation[96], drug product quality[97],
continuous process monitoring[98].
The application of NIRS for the evaluation of blend uniformity is a major subject of this thesis. In
the following chapter, theoretical aspects of NIRS will be described.
2.3.1. NIRS Basics
The near-infrared region is situated between the visible and the mid-infrared regions of the
electromagnetic spectrum. The wavelength range of NIR extends from about 750 to 2500 nm.[99] The
NIR signal is a result of the absorbance due to molecular vibrations of hydrogen bonds. Thus, the most
11
prominent absorption bands occurring in the NIR region are related to molecular overtone and
combination vibrations of -CH, -NH, -OH, and -SH functional groups.[94]
2.3.2. Advantages and Drawbacks
As mentioned, NIRS is a simple and quick analytical technique which is non-destructive, due to
the fact that the molar absorptivity in this region is weaker than the bands in the mid-infrared, which
allows for the deeper penetration of the NIR radiation into the samples, allowing measurements without
sample preparation.[94] NIRS also enables simultaneous measurements of chemical and physical
properties.[94]
However, like every technique, NIR also has some disadvantages. NIR spectra are complex and
are characterized by poor spectral selectivity due to broad overlapping bands. This property makes it
difficult to analyze characteristic peaks of components in the samples seldom using measurements at
only one wavelength.[100] Additionally, the spectral response is influenced by the physical state of the
sample (e.g. sample temperature, sample thickness, sample optical properties, moisture and residual
solvents, polymorphism, the age of samples)[99] making it more difficult to interpret the data.[100] Due
to these issues, to interpret the NIR spectra chemometric methods are often required in order to extract
the relevant information and reduce interfering variables.[94]
Another drawback is that the implementation of a NIRS analyzer requires a significant investment
of time, effort, and investment. NIRS, for the most part, is not utilized as a direct analysis technique; a
calibration may be built based on measurements with a reference method to link the information of
interest with the spectra. The construction of the calibration set can be complex and time-
consuming.[100], [101]
2.3.3. Instrumentation and Measurement Modes
A basic NIR spectrometer is mainly composed of 4 components: a light source, a monochromator,
a sample holder or a sample interface, and a detector.[94]
NIR measurements can be performed in 3 different modes: reflectance, transmittance, and
transflectance [94] (Figure 6):
▪ Transmittance Mode, the sample is placed in between the light source and detector. NIR
radiation is passed through the sample, and the light that is not absorbed by the chemical
components is collected on the detector [102]
▪ Diffuse Reflectance Mode measures the light that is reflected back to the detector after
penetrating the sample, where some radiation interacts and is absorbed by the chemical
components in the sample [102]
▪ Transflection Mode, this mode is a combination of transmittance and reflectance. The light
transmitted through the sample is reflected back, with a mirror, a second time across the
sample to the detector. [103]
For further details on principles, instrumentation, and applications of NIRS, readers are referred
to reviews by Reich [94], Luypaert et al. [100], and Blanco et al. [104].
12
2.4. Chemometrics and Multivariate Data Analysis
As previously referenced in Chapter 1.4., NIR spectra are distinguished by broad overlapping
bands which are influenced by chemical and physical characteristics of the sample. Due to a large
amount of complex data and multivariate nature of NIRS, to interpret and create models with the NIR
spectra chemometric methods are often employed.[94]
Chemometrics has been defined as a [105]: “(…) chemical discipline that uses mathematics,
statistics, and formal logic (a) to design or select optimal experimental procedures; (b) to provide
maximum relevant chemical information by analyzing chemical data; and (c) to obtain knowledge about
chemical systems”.
A relevant tool of chemometrics is multivariate data analysis. This method allows to reduce data
into a representation that uses fewer variables, yet still, express most of its information.[106] Multivariate
data analysis is commonly used for classification and regression. Classification methods such as
principal component analysis (PCA) can be used for variable reduction and exploratory data analysis
(e.g. checking for clusters and detecting trends/patterns). [107] Regression modeling is deployed to
relate two data matrices, e.g. spectral data and reference values, by a multivariate model. [94]
Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Hotelling’s T-
Squared Statistics are described in the following subsections, as they are the primary methods utilized
in the development of the thesis.
Figure 6- Representation of configurations for spectral acquisition (transmittance, reflectance, and transflectance)[95]
13
2.4.1. Data Pre-Processing
There are many external effects, such as variable physical sample properties (e.g. differing
particle size) which exert an effect on the NIR spectra.[108] In order to reduce these interfering factors,
which may complicate subsequent data analysis, mathematical corrections called data pre-treatments
are used.[94], [109] A good data pre-treatment enhances models by bringing out important variance in
the dataset. Yet, by choosing an inappropriate pre-treatment, the interpretation and quantification
analysis of data can be distorted.[109], [110]
Data pre-treatments used in the experimental section of this thesis are listed below:
▪ Mean Centering: Each variable is centered by the subtraction of its mean value across all
samples. In the case of spectral data, this is equivalent to subtracting from each sample
the mean spectrum of the data set. [111] The effect of mean centering is demonstrated in
Figure 7.
▪ Scaling: This technique is commonly used for data sets with contains variables of different
scales. The most common scaling technique is the unit-variance scaling (UV) which divides
each variable by its standard deviation.[111], [112]
▪ Standard Normal Variate (SNV): This method removes multiplicative interferences of light
scattering and particle sizes.[108] The mean of the individual spectrum data points is
subtracted from the original spectrum and then divided by the standard deviation of the
same spectrum.[18]
▪ Derivatives: This technique is used to reduce scattering effects and to improve the
resolution of overlapping bands. The drawback of differentiation is that it may amplify noise.
Therefore, derivatives are usually combined with smoothing methods, such as Savitzky-
Golay.[106], [108]
Figure 7- Effect of mean centering on PCA. (a) Without mean centering, (b) With mean centering. By applying mean centering, it allows for a better description of the variance present in the data.
[111]
14
2.4.2. Principal Component Analysis (PCA)
Since NIR data contains a vast number of spectral information, there is a need for data
compression. The best-known and most widely used data compression method is Principal Component
Analysis (PCA).[94]
PCA is defined as a mathematical procedure used to reduce the dimensionality of a data set
consisting of a large number of variables while retaining most of the variation and information present
in the dataset.[113] This is achieved by transforming the original data into orthogonal components, called
principal components (PC’s), whose linear combinations approximate the original data.[94] It is
constructed in such a way that the first PC captures the largest amount of variability present in the
dataset. The second and subsequent PCs, must be orthogonal to the previous PC and describe the
maximum amount of the residual variance.[100], [114]
PCA constructs its mathematical model by decomposing the data matrix X, represented in Figure
8:
Where X is an N x M matrix where N corresponds to samples (rows) with M measured variables
(columns). T is an N x A matrix, PT is an A x M matrix, where A is the number of calculated PCs. E is an
N x M matrix containing the PCA model residual, i.e. variance unexplained by the PCs. [109]
PCA is also referred to as a data projection technique [114]. By lowering the dimension of the
data and projecting this onto the principal components, the data set can be visualized in simpler
graphical representations, thus improving analysis of the information present in the dataset.[114] There
are various types of graphs which result from the PCA model, such as scores and loadings plot, and
Hotelling’s T2.[114]
The scores plot displays the projection of the samples in the principal components. By examining
the position of the samples in the plot, one can identify clusters, trends, and atypical observations (e.g.
outliers).[109], [114] With the loadings plot, variables which contribute more strongly to a principal
component can be identified. The position of the variable in the loading plots can also describe how the
variables are inter-related. Combining the loading and scores plot results in a biplot which is used to
interpret how the variables influence the trends or clusters seen in the scores plot. [114]
Figure 8 - Mathematical representation of principal component analysis[135]
15
2.4.3. Quantitative Analysis with Partial Least Squares (PLS) Regression
The PLS regression is the most widely used algorithm for quantitative predictions using spectral
data.[114] It works by constructing a mathematical model that correlates spectral variance with changes
in a property of interest on the sample (e.g. API concentration). [115]
PLS is similar to PCA, in the sense that each block of data is decomposed, however, unlike PCA
where the PCs are built in the direction of the maximum variance of the data set; the PLS regression
takes into account the correlation between the spectral data and the reference data; and the principal
components will be selected as the directions that maximize the covariance between both data
sets.[114]
A first step in creating a quantitative model is to develop a calibration set. [94] However, an
important limitation of the PLS regression is that the created models are only as good as data included
in the calibration set. A robust calibration model must have enough relevant and representative samples
which provide a good representation of the expected system variability that could be encountered in
future samples.[44], [114] For blend monitoring, some potential sources of variability are: (1) formulation
(e.g. concentration range of all the blend components); (2) physical properties (e.g. particle size and
shape); and (3) operating conditions. To obtain the calibration samples, it can be chosen either to
synthesize them in the laboratory or to obtain them from the actual process. [44] With the calibration set
developed, the next step is the development and validation of the multivariate model (e.g. PLS
regression).[94]
The validation of the model generally involves two steps, an internal and external validation. The
internal validation, also known as cross-validation, involves splitting the calibration set into two sets, one
is used to train the regression model and subsequently the “trained” model is applied to the remaining
portion to obtain a prediction. There are various forms of cross-validation, such as k-fold and leave-one-
out cross-validation.[114], [116] In k-fold cross-validation the data is divided into equally sized k
segments. In leave-one-out cross-validation, k equals the number of observations in the dataset, i.e.
only one observation is used to test the model.[117] The main output metrics from the internal validation
are the coefficient of determination of the calibration set, R2cal, the root mean square error of calibration,
RMSEC, and the root mean square error of the cross-validation RMSEcv. [116] The results of this
validation can, subsequently, be used to avoid over-fitting. This
achieved by plotting the RMSEcv versus the number of latent
variables to determine the optimal number of latent variables that
should be included in the model (Figure 9).[114], [116]
Figure 9 - Graphical representation of a method to determine the optimal number of latent variables, by plotting RMSEcv versus latent variables (LV)[116]
16
To further validate the calibration set, an external validation is performed to test the built model
with a data set which was not included in the calibration set. To measure the predictive ability of the
developed model, coefficient of determination of the prediction set, R2pred, and root mean square error
of prediction, RMSEP, are evaluated.[116]
Nonetheless, to find the most robust quantitative model, i.e. the model with the lowest RMSEcv
and RMSEP, one can create various models, by varying, for example, the pre-treatment and/or the
wavelength interval. [116]
2.4.4. Hotelling’s T-Squared Statistics
When the PCA model is obtained, multivariate statistical control charts can be used to monitor
processes. One commonly used chart is the Hotelling’s T squared plot.[118] Hotelling´s T squared
statistics measures the distance between the sample and the center of the PCA model.[114] Hotelling´s
T2 is defined as: [119]
𝑇𝑖2 = ∑(𝑡𝑖,𝐾 − 𝑡𝑎𝑣𝑔,𝐾)2
𝑠𝐾2
𝐾
𝐾=1
Equation 1
Where, 𝑇𝑖2 is the Hoteling’s T2 statistic for sample I; K is the number of PCs; 𝑡𝑖,𝑘 is the score value
for sample I with K components; 𝑡𝑎𝑣𝑔,𝐾 is the mean score value of principal component K; and 𝑠𝑘2 is the
variance of 𝑡𝑖,𝑘 according to the class model.
A larger T2 value indicates that the scores are much more different than those from which the
model was developed. It provides evidence that the new data is located in a region different from one
captured in the original data set used to build the PCA model.[120] To determine when larger values of
these statistics are significant, a control limit for the T2 statistic is obtained from an f-distribution. An
upper control limit 𝑇𝑈𝐶𝐿,∝2 is defined as: [119]
𝑇𝑈𝐶𝐿,∝
2 = 𝐾(𝐼 − 1)
𝐼 − 𝐾∗ 𝐹1−∝,𝐾,𝐼−𝐾 Equation 2
Where, α is a significance level, e.g. α=0.05 or 0.1; I is the number of observations, and K the
number of chosen PCs.
17
3. Implementation
3.1. Blending and Thief Sampling
3.1.1. Blending Conditions and Parameters
In this project, three blends were monitored with thief sampling and an on-line NIRS. All the blends
consisted of 4 components that are commonly used in a tablet formulation: the API (in a granulated
form); agglomerated α-lactose monohydrate - Tablettose® 100 (Meggle Pharma, Wasserburg,
Germany) which functioned as a filler; croscarmellose sodium (Ac-Di-Sol®, FMC Biopolymer, Ireland)
used as a disintegrant; and sodium stearyl fumarate or magnesium stearate (Merck KGaA, Darmstadt,
Germany) used as lubricants. Table 3 presents the weight percentages of the abovementioned
components in blends 1, 2, and 3.
The granules were produced in a fluidized-bed granulator (Glatt, GPCG 2, Germany). In every
case, the granules comprised the same components: API, lactose monohydrate, microcrystalline
cellulose, and hypromellose. The granulation parameters were also kept constant between the batches.
A major difference between the blends is the type of lubricant used. Sodium stearyl fumarate was
used in blend 1, and magnesium stearate in blends 2 and 3. Moreover, the batch of granules used
differed between the three blends.
Table 3 - Percentage (% w/w) of the components in blend 1, 2, and 3.
Component Percentage (w/w %)
Blend 1
API (granulated form) 83.3
Tablettose® 100 14.7
Croscarmellose Sodium 1.0
Sodium Stearyl Fumarate 1.0
Blends 2 and 3
API (granulated form) 83.3
Tablettose® 100 12.7
Croscarmellose Sodium 3.0
Magnesium Stearate 1.0
Figure 11 illustrates the blending parameters of the three blends tested. All the blends were
performed in a 20 L bin-blender (Servolift GmbH, Offenburg, Germany). The components were added
through the top of the blender and the fill order was kept the same for all the blends. The granulated API
was added first, then Tablettose® 100, followed by croscarmellose sodium and finally, the chosen
lubricant. Additionally, before the excipients were added in to the blender, these were passed through a
1 mm mesh sieve (Retsch, Germany). The fill level differed between the three blends. The fill levels of
blends 1, 2, and 3 were 30%, 65%, 67% (v/v), respectively. All the blends were rotated at 12 rpm for 15
minutes.
18
3.1.2. Thief Sampling
To perform thief sampling, the blender was stopped at pre-defined time points. In blend 1, the
blender was stopped at 2, 4, 6, 10, and 15 minutes. In blends 2 and 3, the blender was stopped at 2, 4,
6, 8, 12, and 15 minutes. Every time the blender was stopped, thief sampling was performed. To do the
sampling, the blender was removed from the rotating cage and transported into a laminar flow booth.
Then the lid, where the NIRS was mounted, was removed. The samples were then removed via a thief
probe (PharmaPicker®, Burkle, Bad Bellingen, Germany). The PharmaPicker® is illustrated in Figure
10.
The PharmaPicker® is a side-sampler. When it was inserted in the powder bed, the outer sleeve
raised and closed the sampling cavity. Once insertion was complete in the defined location, the cavity
was opened, and the sample was collected. After sampling, the PharmaPicker® was removed. The
powder sample was collected by unscrewing the volume tip and transferring the contents to a sample
vial for further analysis.[121]
Proposed modifications to the withdrawn FDA draft guidance [81] recommended assessment of
the effects of the size of the collected powder sample (e.g. 1-10 times the mass of the dosage unit form)
on measurements of the uniformity of the blend. Previous FDA draft guidance [122] recommended that
the sample size should be equivalent to one to three times the weight of an individual dose. Taking into
consideration that the weight of the final dosage form is 375 mg, the decision was made to remove
samples of approximately 750 mg (i.e. 2x the dosage unit form). Moreover, in each blend, sampling
locations were chosen that were representative of two depths along the axis of the blender [123], i.e.
samples were removed from the top and bottom regions of the mixer. In blend 1, samples were collected
at ten different locations to assess the blending profile. In blends 2 and 3, only six locations were chosen;
but from each location, three replicates were removed in order to perform a variance component analysis
(VCA). All the collected samples were analyzed using a high-performance liquid chromatography
(HPLC) technique to quantify the API.
Figure 10 - Illustration of the PharmaPicker. (a) Collection cylinder connected to the rod; (b) Collection Cylinder, composed of the sampling cavity, outer sleeve and volume tip; (c) infographic of the sampling system; (d) volume tips from 0.1 mL to 2.5
mL, which determine the sample quantity.[121]
19
3.2. High-Performance Liquid Chromatography
Each sample removed during blending was analyzed by HPLC. The HPLC analysis was carried
out on an Agilent 1200 HPLC system with a UV detector set at a wavelength of 275 nm.
Chromatographic separations were performed at a temperature of 25 °C and a flow rate of 1.0 mL/min
on a 2.6 µm Kinetex® XB-C18, 100 x 4.6 mm column (Phenomenex, CA, USA). Gradient elution was
used with mobile phases A and B consisting of 0.12% trifluoroacetic acid (Merck KGaA, Darmstadt,
Germany) in a mixture of water/acetonitrile (95:2) (Merck KGaA, Darmstadt, Germany) and 0.12%
trifluoroacetic acid in a mixture of water/acetonitrile (2:95), respectively. The injection volume was 5 µL.
OpenLAB CDS ChemStation software (Agilent, CA, USA) was used for data acquisition and processing.
3.2.1. Variance Component Analysis
In the BUCU’s proposed modification to the withdrawn FDA draft guidance,[81] it is recommended
that a variance component analysis (VCA) be performed when the relative standard deviation (RSD)
value of the blend samples is greater than 5.0%. The goal is to determine whether the variability present
in the sampling procedure is due to a product issue or a sampling issue. The VCA decomposes the
variance present in the sampling procedure into between-location (variability between sampling
locations) and within-location variance (variability between samples from the same sampling location).
Figure 11 - Schematic of the blending parameters for the three blends.
20
In blends 2 and 3, replicates were removed from each chosen sampling location. With these replicates,
it was possible to perform a variance component analysis.
The steps taken in the VCA are shown in Figure 12.
The first step was to organize the API content (LC%) given by the HPLC analysis. The sampling
locations are set in the columns and the replicates in rows. The next step was to calculate a one-way
ANOVA. A significance level of 0.05 was chosen. This was performed using Microsoft EXCEL (2016).
ANOVA tests the hypothesis that the means of two or more populations are equal. Thus, the null
hypothesis states that all sampling location means are equal, while the alternative hypothesis states that
there is at least one difference among the means.[124] If the F-value > Fcritical value, the null hypothesis
of equal means can be rejected.
The purpose of the VCA was to find out how much of the variance present in the sampling
procedure was due to between-location (σ2B) or within-location variance (σ2
w). For this purpose, the
expected mean square (EMS) column in the ANOVA was used. [125] Thus, the next step was setting
the MS values equal to the EMS values, and solving the expressions. Because estimates of σ2 were
being calculated, s2 was used instead. It should be taken into consideration that, when calculating s2B,
if MSB is less than MSW, the result is a negative value. Since variance cannot be negative, a negative
variance estimate is replaced by 0. This does not mean that the variance is zero. It may signify that
Figure 12 – Illustration of the steps taken for the variance component analysis. SSB and SSW correspond to the sum of squares between and within location, respectively; df corresponds to the degrees of freedom; t and r correspond to the number of sampling locations and number of replicates, respectively; MSB and MSW correspond to the mean squares
between and within location, respectively; EMS corresponds to the expected mean squares; σ2w and σ2
B correspond to the within and between location variance.
21
there was not enough information in the data to get a good estimate of σ2B.[125] Finally, the total variance
(s2
total) is calculated, and the within-location and between-location variance can be presented in terms
of percentages.[126]
3.3. On-Line NIR Instrument and Spectral Measurements
The LANCIR II® (BRUKER OPTIC GmbH, Ettllingen, Germany), which provides spectral coverage
of 1100 to 2200 nm, was the NIR spectrometer chosen for spectra acquisition. Figure 13 illustrates how
the NIRS was installed in the blender. The LANCIR II® was mounted onto the lid of the blender. The lid
had a sapphire window through which the NIRS performed reflectance measurements. The LANCIR II®
contains an acceleration sensor, which determines the position of the blender. Thus, the spectrometer
was triggered to acquire an NIR spectrum when the blender was upside down, which was the point
when the sapphire window was covered with powder. Each rotation triggers an acquisition. However,
due to an unknown problem with the NIRS, in blend 2, spectra were only acquired at every second
rotation of the blender.
The spectral data were then transferred from the NIRS via a wireless connection to a laptop
computer with the OPUS PROCESS® software (BRUKER OPTIC GmbH, Ettllingen, Germany). Before
starting each analysis, the NIRS was calibrated with a dark and white measurement. The measurements
were performed with a Spectralon® standard (Labsphere, USA). Furthermore, this same spectrometer
was used to acquire NIR spectra of the pure components which comprised the blend in a static state.
Figure 13 - Scheme of on-line NIR spectral acquisition in 20 L bin-blender. (1) bin-blender; (2) NIR Spectrometer; (3) rotation axis.
22
3.4. Data Analysis
The data acquired from the NIR measurements allow for a broad range of modeling approaches,
including chemometric and statistical tools. Both quantitative and qualitative approaches were used to
analyze the NIR spectra acquired during blending, which were used to evaluate blend homogeneity.
3.4.1. Quantitative Analysis
In this project, three different blends were monitored with an on-line NIR spectrometer for 15
minutes. As illustrated in Figure 14, the blending process was stopped at pre-determined time points, at
which samples were removed from different locations in the blender for HPLC analysis. Two statistical
parameters that output from thief sampling were the mean API content (%LC) and an RSD value. The
set used for developing the model was constructed by using the mean API content (%LC) as a reference
value for the last NIR spectra recorded before the blender was stopped. This was done for all the time
points at which the blender was stopped, resulting in 17 spectra.
Of the 17 spectra obtained, 14 were used for the calibration set and 3 for the prediction set. The
prediction set was composed of one spectra per blend and which are representative of the API content
(%LC) range present in the calibration set. Table 4 presents the corresponding data used to develop
the model.
I II III IV V VI
Time (minutes) 2 4 6
RSD Value
I II III IV
V VI I II III IV V VI
Mean API Content (%LC)
RSD Value
Mean API Content (%LC)
RSD Value
Mean API Content (%LC)
0
0,2
0,4
0,6
1100 1600 2100
Ab
sorb
ance
Wavelength (nm)
0
0,2
0,4
0,6
1100 1600 2100
Ab
sorb
ance
Wavelength (nm)
0
0,2
0,4
0,6
1100 1600 2100
Ab
sorb
ance
Wavelength (nm)
Reference
Value Reference
Value Reference
Value
Figure 14 - Schematic showing how the data set was constructed for the PLS model. The blender was stopped at pre-defined time points, and samples were removed via a thief sampler at various locations, which are shown here as roman
numerals. These are the sampling locations of blends 2 and 3. For each time point, a mean API content (%LC) was calculated. This value was used as a reference value for the last spectra recorded before the blender was stopped.
23
Table 4 – Reference data from the spectra used to develop the model. The spectra used for testing the prediction performance of the calibration models created are highlighted in gray.
The calibration models were developed using the SIMCA 14.0 Sartorius Stedim Biotech tool
(Umea, Sweden; available at http://umetrics.com/products/simca) by using partial least squares (PLS)
regression. To build the calibration model, various preprocessing techniques and spectral regions were
evaluated to find the model that best correlates the spectral data with the reference data (Y). Six
combinations of preprocessing were investigated: None, SNV, 1st Derivative, 2nd Derivative, SNV
followed by 1st Derivative, and SNV followed by 2nd Derivative. A second order polynomial and Savitzky-
Golay filter with an 11-point moving window were used for all the derivatives. Along with the
preprocessing methods, different spectral regions were also analyzed: whole spectral region, and
specific intervals chosen by performing interval partial least squares regression (iPLS).
The calibration models developed were both internally and externally validated. Each model was
evaluated with multiple metrics, including the root mean square error of cross-validation (RMSECV);
R2cal on the calibration set; and root mean square error of prediction (RMSEP) of the validation set.
SIMCA 14.0 has a default 7-round cross-validation.[128] However, due to a small calibration set,
it was decided to perform a leave-one-out cross-validation instead. The number of latent variables was
chosen by plotting the number of latent variables versus the RMSECV values and identifying which
number of LVs corresponded to the lowest RMSECV value.
API Content (%) Time (min)
1 95.4 2
Blend 1
2 91.5 4
3 93.1 6
4 91.1 10
5 95.7 15
6 103.9 2
Blend 2
7 102.1 4
8 102.4 6
9 103.6 8
10 100.4 12
11 101.5 15
12 101.7 2
Blend 3
13 101.9 4
14 100.5 6
15 102.2 8
16 101.2 12
17 100.0 15
24
Interval Partial Least Squares
The goal of iPLS is to improve the performance of the PLS model. In the iPLS approach, the data
was subdivided into non-overlapping equidistant intervals. Each interval underwent a PLS modeling and
the RMSECV was calculated for each sub-interval.[127] According to Nørgaard et al. [127] the objective
is to find which sub-interval model can compete with the performance of the full spectrum model.[127]
This method gives an overview of the spectral data and shows relevant spectral regions to calibrate the
PLS model. However, it only allows evaluation one spectral region at a time.
Nevertheless, the spectra were divided into 10 and 20 equidistant intervals. No advantage was
found, i.e. lower RMSECV values, in using the higher number of intervals: 20 intervals resulted in 12
wavelength variables per interval. Thus, the choice was made to divide the spectra into 10 intervals,
which resulted in 25 variables per interval. The iPLS approach is composed of a number of step. In the
following sections, the steps taken in this approach are presented for the spectra without preprocessing.
This procedure was repeated for the spectra with differing preprocessing techniques.
The first step was to choose how many latent variables to evaluate. To that end, the fraction of
the variance in the Y variable which was explained by the model was evaluated. As seen in Figure 15,
for all the models, most of the variance was explained with four latent variables. Thus, it was chosen to
analyze the first four latent variables.
The next step was to create a bar graph. This was achieved by dividing the spectra into 10
equidistant intervals and then creating a PLS model with each interval and calculating the RMSECV
value; the models were created with one to four latent variables. From the analysis carried out previously
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
1 2 3 4 5 6 7 8
R2Y
Latent Variables
Full Spectra Interval 1 Interval 2 Interval 3
Interval 4 Interval 5 Interval 6 Interval 7
Interval 7 Interval 8 Interval 9 Interval 10
Figure 15 - Fraction of the variance of the Y variables explained by the model (R2Y) for the full spectrum model and 10 interval models, without preprocessing, plotted against the number of PLS latent variables.
25
with full spectra models, the optimal RMSECV value for the models with and without preprocessing is
known. This value, which corresponds to the red line in the bar graphs, was used to compare the
performance of the interval models with the full-spectrum model. Lastly, the subinterval models that
compete with the full spectrum model, i.e., those that have a lower RMSECV than the full spectrum
model were identified. It can be observed in Figure 16 a) and d), which correspond to the models with 1
and 4 LV, respectively, that none of the interval models performed better than the full spectrum model.
In Figure 16 b) and c), which correspond to the models with 2 and 3 LV, respectively, only interval 9
shows a lower RMSECV value than the full spectrum model. Based on these results, interval 9 was
chosen to be compared with the iPLS results of preprocessed models.
0
0,5
1
1,5
2
2,5
3
1 2 3 4 5 6 7 8 9 10
RM
SEC
V (
%)
Interval Number
0
0,5
1
1,5
2
2,5
3
1 2 3 4 5 6 7 8 9 10
RM
SEC
V (
%)
Interval Number
0
0,5
1
1,5
2
2,5
3
1 2 3 4 5 6 7 8 9 10
RM
SEC
V (
%)
Interval Number
0
0,5
1
1,5
2
2,5
3
1 2 3 4 5 6 7 8 9 10
RM
SEC
V (
%)
Interval Number
a) b)
c) d)
Figure 16 – Example of the iPLS method for spectra without preprocessing. The four plots represent the iPLS models with(a) one, (b) two, (c) three, and (d) four latent variables. The red line corresponds to the RMSECV value of the full spectra model
with 2 latent variables.
26
3.4.2. Qualitative Analysis
Quantitative analysis focuses on the spectral variability present during the blending process.
Spectral variability was assessed using 3 different approaches. Each approach was carried out on
spectra with and without preprocessing. The rationale behind this evaluation was to observe the
influence of the preprocessing techniques on the blend uniformity information. Prior to analysis, the
spectra were mean-centered. The preprocessing methods applied were SNV, 1st derivative and 2nd
derivative (second order polynomial and Savitzky-Golay filter with an 11-point moving window).
Principal Component Scores Versus Blending Time
The NIR spectrum is characterized by its absorbances over a wavelength range (253
wavelengths). When analyzing a large quantity of NIR data, this results in a vast amount of NIR spectral
information. Therefore, PCA was employed to reduce the dimensionality of the dataset. [94]
Each spectrum is converted into a single point by plotting it in a multidimensional space.[19]
Subsequently, PCA reduces the dimension of the data by introducing a new set of orthogonal
coordinates, i.e. principal components, which are constructed in such way to express the largest amount
of variance present in the data. [94] Each single point spectrum gets its own score value.
Figure 17 illustrates how the score value is calculated. Firstly, the single-point spectrum are
projected onto the principal component. The score value will represent the distance from the mean along
the PC to the projected point.[129]
Figure 17 - The first principal component, PC1, represents the direction of maximum variance in the data. Each observation (green circles) can be projected onto the principal component in order to get a co-ordinate value along the PC-line. This
value is known as a score. The red circle represents the mean along PC1.[129]
27
Moving Block Standard Deviation
The Moving Block Standard Deviation (MBSD) approach was first mentioned by Sekulic et al.[21].
It assesses spectral variability, and thus blend homogeneity, by calculating the standard deviation of the
absorbance values over a time window or block. Figure 18 illustrates the MBSD calculation process.
The first step was to arrange the NIR data into a time by wavelength table. The next step was to
define the size of the time window or block, i.e. how many spectra will be included in the time window.
Figure 18 illustrates an MBSD for a block size of 5 spectra. A new data table is then calculated. The first
row corresponds to the standard deviation for each wavelength of the block. The next row is calculated
by moving the block down one spectrum and again determining the SD. This process is repeated until
all the spectra have been processed. Finally, a mean value is calculated for each row of the resulting
SD dataset. Subsequently, the mean SD is plotted as a function time. [18], [21]
As referenced, a variable which needs to be defined is the size of the time window or block. The
literature does not give instructions or recommendations on how to choose the size of the time window.
In this case, the size of the block was chosen by trial and error. As the aim was to compare the different
blends using the same block size, block sizes of 5, 10, 15 and 20 spectra were tested on the blend that
presented the noisiest trend, which was blend 3. Figure 19 illustrates the effect of block size on the
translation of NIR data to blend uniformity for blend 3. Overall, increased smoothness of the MBSD
curves was observed as the block size increased. The smallest block size, which included 5
observations, demonstrated a noisy trend, which is difficult to interpret. It may be assumed that the
smaller block size is more sensitive to changes in the blend. Thus, it might be argued whether these
variations are significant for the determination of blend uniformity. Differences observed between the 15
and 20 block sizes were minimal. Although a larger block size shows smoother MBSD results, it may be
neglecting some valuable information on changes in the blend. Therefore, a block size of 10 spectra
was chosen for the analysis of the tested blends.
Figure 18 - Diagram of the moving block standard deviation calculation process.[21]
28
Furthermore, another point which isn't discussed in the literature which utilizes MBSD, is how to
handle the times. When the MBSD approach is applied a block of absorbances of a specific wavelength
is reduced to one value. Thus, the choice was made to also reduce the times of a specific block to one
value. This was accomplished by applying a moving average with the same window size as the chosen
block for the spectra.
Moreover, because different preprocessing techniques were applied to the spectra, to compare
the resulting MBSD curves in the same graph, an SNV was applied to the re sulting mean SD column.
0
0,1
0,2
0,3
0,4
0 2 4 6 8 10 12 14 16
MB
SD
Time (minutes)
20 Block
0
0,1
0,2
0,3
0,4
0 2 4 6 8 10 12 14 16
MB
SD
Time (minutes)
15 Block
0
0,1
0,2
0,3
0,4
0 2 4 6 8 10 12 14 16
MB
SD
Time (minutes)
10 Block
0
0,1
0,2
0,3
0,4
0 2 4 6 8 10 12 14 16
MB
SD
Time (minutes)
5 Blocka) b)
d) c)
Figure 19 – Effect of block size on MBSD results. Exemplified on spectra of blend 3, pretreated with an SNV. Number of measurements included in the block varies between, (a) 5, (b) 10, (c) 15, and (d) 20.
29
Principal Component Score Distant Analysis
The Principal Component – Score Distance Analysis (PC-SDA) approach proposed by Puchert
et al. [19] is another method of determining when the blend may be homogeneous. It relies on comparing
the NIR spectra acquired during blending with a reference group of spectra, which represent the blend
when uniform. Figure 21 is a schematic of the steps taken to perform the PC-SDA exemplified for blend
1 preprocessed with SNV. As shown, the first step is to select the preprocessing technique. In this
project, the PC-SDA approach was applied to spectra with and without preprocessing. Subsequently, a
PCA is applied to the spectral data set in order to extract the score values. As seen in Figure 20, for
blend 1, 2, and 3, most of the variability in the data set is explained by the first three principal
components. Thus, in this project, it was chosen to evaluate the first three PC scores.
With the selected PC scores, the next step was to calculate the Euclidean distance between
successive scores. Next, a moving block standard deviation was applied to the distance values. This
resulted in a column of standard deviations. Since low standard deviation indicates less spectral
variability and therefore good homogeneity, the next step was to find the lowest standard deviation and
the spectra from which it derived. With these spectra, a PCA was performed. The remaining spectra
were projected into the PCA model that had been created. To find the spectra that have similar
characteristics to the ones that the PCA model was constructed from, a Hotelling T2 chart was employed.
The Hotelling T2 chart computes the distance between the scores of the center of the model. When that
distance is below a pre-defined limit value, in this case, T2crit (95%) limit, the blend may be considered
uniform
0,5
0,6
0,7
0,8
0,9
1
1 3 5 7R
2X
Principal Components
Blend 3
0,5
0,6
0,7
0,8
0,9
1
1 3 5 7
R2 X
Principal Components
Blend 1
0,5
0,6
0,7
0,8
0,9
1
1 3 5 7
R2 X
Principal Components
Blend 2
Figure 20 – Fraction of the variance of the X variables explained by the model (R2X) plotted against the number of principal components for blend 1 (a), blend 2 (b), and blend 3 (c) with and without preprocessing.
a) b) c)
30
MBSD n=10
-2
-1
0
1
2
1099 1399 1699 1999Ab
so
rban
ce -
SN
V
Wavelength (nm)
Spectra
0 - 7 min.
7 - 15 min.
PCA Score Plot
𝑑1 = ඥ(𝑥2 − 𝑥1)2 + (𝑦2 − 𝑦1)2 + (𝑧2 − 𝑧1)2
PCA Score Plot Successive spectra with
lowest SD
Calibration Set Prediction Set
PCA Predicted Score Plot Projection of the remainder
spectra
0
50
100
150
200
250
0 2 4 6 8 10 12 14 16
T2R
angeP
S[1
-3]
SN
V
Time (minutes)
T2
crit (95%)
Blend End-Point
Hotelling’s T2 Prediction Chart
Figure 21 - Schematic of the Principal Component - Score Distant Analysis (PC-SDA) approach steps for blend preprocessed with SNV.[19] (a) Spectra; (b) Score Plot of the spectral data; (c) Calculation of the standard deviation; (d) PCA with the
successive spectra with lowest SD; (e) PCA predicted sore plot; (f) Hotelling T2 Prediction chart.
a) b)
c)
d)
f) e)
31
4. Results and Discussion of HPLC Reference Data
To examine the progress of the mixing of the API, the blends were stopped and analyzed at
predetermined time points. Using a thief probe, samples were removed from the blender and analyzed
using an HPLC technique. The HPLC results from the various sampling locations and time points are
summarized in Annex A.
Figure 22 illustrates the evolution of the relative standard deviation (RSD) of the three tested
mixtures over the blending time. The RSD is an indicator of blend homogeneity as it offers a measure
of the variation between the different sampling locations. The previous withdrawn FDA guidance [123]
stated that, to consider a blend uniform, the RSD value should be inferior or equal to 5.0%, which is
represented by the black line in Figure 22. In Blend 1, a “zig-zag” trend of the RSD values is observed,
which appears to change direction every 4 to 5 minutes. Furthermore, the only time point at which the
blend had an RSD value inferior to 5.0% was at 15 minutes, i.e. at the end of the blending time. Blends
2 and 3 have similar tendencies except at the 4-minute time point, where blend 2 had a higher RSD
value than blend 3. In both blends, the lowest RSD value occurred at 6 minutes, after which the RSD
value of the blends appears to become constant at approximately 5.0%. Thus, it could be assumed that
the components of the blends ceased to mix after 8 minutes. Nevertheless, according to these results,
at the end of 2 minutes of mixing, the blend was more uniform, i.e. the RSD value was lower, than it was
after 15 minutes.
Figure 22 - Evolution of the relative standard deviation (RSD) over time for the 3 tested blends. Black line represents an RSD of 5.0 % which, according to previous withdrawn FDA guidance [123], corresponds to the limit below which the values
indicate that the blend is uniform.
0,0
1,0
2,0
3,0
4,0
5,0
6,0
7,0
8,0
9,0
0 2 4 6 8 10 12 14 16
RSD
(%
)
Time (minutes)
Blend 1
Blend 2
Blend 3
32
The variance present in the thief sampling results can be deconstructed into process or
sampling/assay variability.[81] To investigate the source of these values, a variance component analysis
(VCA) was performed. VCA quantifies the between-location (variability across the sampling locations)
and within-location (variability between samples from the same sampling location) variance that may be
present.[6], [78]
Figure 23 illustrates the VCA results combined with the calculated RSD values. This plot identifies
which variance, between location (blue line) or within location (green line), had a greater influence on
the calculated variance in the blend, represented by the RSD value (black line). During blending, the
between-location variance is expected to decrease as the blend becomes more homogeneous, i.e. as
the RSD value is decreasing. When the blend is homogeneous and therefore the RSD value and
between-location variance are low, within-location variance is expected to be greater.
This was observed in both blends. The between-location variance values follow the same trend
as the RSD values. Furthermore, when the RSD values were below 5.0%, the within-location variance
was greater than the between-location variance. However, in blend 2, the 4-minute time point deviates
from this trend. At this time point, the higher RSD value is associated with greater within-location
variance. This might be indicative of a sampling or analytical error.
In this analysis, it was also observed that the increased RSD values after 6 minutes were
influenced by the process and not by sampling errors. This may suggest that a segregation occurred
after 6 minutes. In blend 3, at 6 minutes, the between-location variance percentage was equal to 0%.
This does not signify that the variance is zero. It is due to a known pitfall of the equation used to calculate
the variance components, which occurs when MSB is lower than MSW (see chapter 3.4.2).
0,0
2,0
4,0
6,0
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16
RSD
(%
)
Esti
mat
ed
Var
ian
ce
Co
mp
on
ents
(%)
Time (minutes)
0,0
2,0
4,0
6,0
0
20
40
60
80
100
0 2 4 6 8 10 12 14 16
RSD
(%
)
Esti
mat
ed
Var
ian
ce
Co
mp
on
en
ts (%
)Time (minutes)
Figure 23 – Illustration of the VCA combined with the RSD values for blend 2 (a) and blend 3 (b). The blue and green lines correspond to the connection of the between location and within location variance values, respectively. The black line
corresponds to the calculated RSD values.
a) b)
33
Overall, blends 2 and 3 showed similar blend profiles. This could be due to their having
approximately the same fill level and types and amounts of components. These blends were found to
mix and become more homogeneous in the first six minutes of mixing. According to these results, it
could be assumed that 6 minutes was the optimal blending time. After that point, the blend profiles
appear to demix and subsequently cease to mix. In contrast, blend 1 had a different blending profile.
The two main differences between these blends was the fill level, which was lower in blend 1, and the
type of lubricant used, which in blend 1 was SSF instead of MgSt. In this case, it appears that blend 1
was constantly mixing and unmixing every 4 to 5 minutes, and only at the end of the blending time did
the RSD value drop below 5.0%.
Nonetheless, the relative standard deviations results were not anticipated. Ideally, the RSD value
would be expected to drop over time, with the rate of mixing being greater than the rate of demixing and
reach a constant where the two rates are balanced.[60] That was not observed in this case. These
results may be the result of several factors, such as:
▪ Flawed sampling procedure. It can be reasoned that not enough samples were removed
to properly to determine the state of the blend. According to past guidance at least 10
sampling locations should be chosen in the blender and from each location replicate
samples should be extracted (e.g. a minimum of three). Furthermore, Muzzio et al.[3]
demonstrated that the number of samples, and not the sample size, is of more importance
for characterization of the state of the mixture. Furthermore, for all the tested blends, it
would have been of interest to evaluate and determine the RSD value at time 0, with the
goal of getting some insight into the state of the mix before starting the blending process.
Not having this initial value made it impossible to determine how the mix progressed from
the start of the blending process and how it compared to the subsequent RSD values.
▪ Error associated by utilizing a thief probe. Muzzio et al.,[3] demonstrated that thief probes
disrupt the structure of the powder bed. As the thief is inserted, particles are dragged along
the path of insertion. Consequently, the collected samples might be contaminated by
particles along the sampling path. Moreover, by not having a consistent thief-sampling
technique, such as a constant thief insertion angle and velocity, this may also influence the
final results.
▪ The nature of the blend. Particle shape and size distribution can also be one of the
properties affecting the blending process. Sommier et al.[130], showed that these
differences may lead to segregation effects. Moreover, according to Twitchell,[60] blends
prone to segregation, often display to have an optimum mixing time, i.e. the uniformity of
the blend does not improve with increases in blending time. In blend 2 and 3 this was
observed.
34
35
5. Results and Discussion of NIR Spectral Characteristics
Figure 24 shows the NIR spectra of the pure compounds used in this work. As shown, all the
constituents of the blend are optically active in the NIR region; however, most show large overlapping
spectral regions. Moreover, similarities were observed between the spectra of the granule (black line)
and Tablettose® (green line). This may be due to the composition of the granule, which consisted of
Tablettose®, microcrystalline cellulose and hypromellose.
However, even though the percentage of API in the granule ranged between 7% (m/m) in the first
blend, and 15% (m/m) in the second and third blends, there were no noticeable analogous peaks
between the spectra of the granule and the API. It may be surmised that the absorption bands of the
API were overshadowed by the absorption bands of the other components that were present in a higher
percentage, such as Tablettose®, which was a main component of the granule.
In the case of overlapping broad bands, one might suggest applying derivatives with the aim of
improving peak separation. However, it was observed that the spectra of the pure components continued
to overlap after preprocessing with derivatives. The characteristics of these spectra might complicate
future quantitative and qualitative analysis of specific spectral features of an analyte. The spectra of the
pure components preprocessed with derivatives are presented in Annex B.
0
0,1
0,2
0,3
0,4
0,5
1080 1230 1380 1530 1680 1830 1980 2130
Ab
sorb
ance
Wavelength (nm)
Croscramellose Sodium
Granule
Magnesium Stearate
Sodium Stearyl Fumaral
Tablettose
API
Figure 24 – Raw NIR spectra of the pure compounds in static state.
36
Figure 25 presents the mean of the 10 last NIR spectra collected in blends 1, 2, and 3, along with
the spectra of the granule, which contains the API utilized in the three blends. Blend 1 contained 7%
(m/m) API, whereas blends 2 and 3 contained an API concentration of 15% (m/m). These differences in
API concentration can be visualized in the graph, where there is a downward drift of the spectra baseline
of a lower to a higher API concentration.
When the spectra of the granule were combined with the spectra of the blends, it was observed
that the overall form of the blend spectra is very similar to that of the granule. This is most likely because
the largest segment of the blends is composed of granules, around 83%. On one hand, this could be
favorable, since the granule can be used as an indirect measurement for indicating whether the API is
uniformly distributed in the blend, i.e. it can be assumed that the API is uniformly distributed in the blend
if the granule is as well. On the other hand, it could be argued whether the spectra acquired during
blending display interference from the other components in the blend. A possible way to circumvent this
issue is to create a quantitative model for each of the components of the blend.
In chapter 3.1, it was mentioned that the granules used in the blends were from different
granulation production batches. This may have resulted in differences between the granules due to
variability in the manufacturing process. In blends 2 and 3, the only parameter that differed was the
batch of the granule. To address this issue, the granules were further studied and compared through a
principal component analysis.
0
0,1
0,2
0,3
0,4
0,5
1080 1230 1380 1530 1680 1830 1980 2130
Ab
sorb
ance
Wavelength (nm)
Granule
Blend 1
Blend 2
Blend 3
7% API
15% API
Figure 25 - Mean of the last 10 NIR spectra collected during mixing of blends 1, 2, and 3 and the spectra of the granule used in the 3 blends. The figure illustrates the dissimilarities between spectra due to differing API concentrations (%). Blend 1
contained 7% of API and blends 2 and 3, which overlap in the graph, contained 15% API.
37
Figure 26 presents the score and contribution plots which output from the principal component
analysis. Figure 26 (a) and (b) correspond to the score plots of the spectra of the different granules
without preprocessing and preprocessed with a 1st derivative (second order polynomial, 11 moving
block), respectively. Both plots indicate that differences between these two granule batches exist.
Therefore, the next step was to identify which variables influenced the differentiation of these granule
batches. This was achieved with a contribution plot, which is shown in Figure 26 (c) and (d).
Figure 26 (c) corresponds to the granule spectra without preprocessing. After 1379 nm, all the
wavelengths were shown to contribute to the observed difference between the granules. Because
preprocessing was not applied, this may be indicative of particle size differences. A baseline shift was
also observed between the spectra of the granules in Figure 27, where the spectra of the granules from
blend 2 are below the spectra of the granules from blend 3.
Figure 26 –Scores and contribution plots of the granules used in blends 2 and 3. In the score graphs, the green and blue circles correspond to the granules used in blend 2 and blend 3, respectively. (a) and (c) correspond to the score and
contribution plot of spectra without pre-treatment, respectively. (b) and (d) correspond to the score and the contribution plot of spectra preprocessed with a 1st derivative, respectively. The y-axis of the contribution plot Group 1 and 2 corresponds
to the granules spectra of blend 2 and blend 3, respectively
38
Figure 26 (d) corresponds to granule spectra preprocessed with a 1st derivative. Preprocessing
was utilized to reduce the physical and emphasize the chemical information. Compared to the previous
contribution plot, specific bands were identified. The absorption bands that contributed most were 1392-
1488 nm; 1886-1934 nm; and 2004-2074 nm. Luypaert et al. [100] stated that NIR shows strong
absorption bands of water especially between 1400-1450 nm and 1900-1940 nm. Thus, commonalities
between contribution plot bands and the absorption bands of water were observed. As shown in Figure
28, a shift of the spectra was observed in the previously identified bands, where, again, the spectra of
the granules from blend 2 are below the spectra of the granules from blend 3. This may be indicative of
differing moisture content in the granule batches.
Because the granules are the largest component in the blends, these slight differences between
the granules might have an impact on the NIR spectra acquired during blending and, consequently, on
the results that are derived from the quantitative and qualitative approaches applied.
Figure 27 - Raw NIR spectra of the granules used in blend 2 and 3. The spectra of the granules used in blend 2 are colored red. The spectra of the granules used in blend 3 are colored green.
Figure 28 - NIR spectra of the granules used in blend 2 and 3 pretreated with a 1st Derivative. The spectra of the granules used in blend 2 are colored red. The spectra of the granules used in blend 3 are colored green.
39
6. Results and Discussion of the Quantitative Approach
6.1. Calibration Model Development
The goal of this quantitative NIR model was to predict the API concentration (%LC) from the
spectra recorded during the blending process. To this end, a calibration set was developed by combining
reference data with the NIR spectra, which is described in chapter 3.4.1. Examining the developed
calibration set, presented in Table 4, the API concentration (%LC) ranges from 91.1% to 103.9%.
However, there are no samples representative of the API concentration range from 95.7% to 100.0%.
The NIR spectrum of the data set used for model development is presented in Figure 29.
As previously mentioned, PLS models are data-based and only valid within the known space, i.e.
the model is only as good as the data included. Taking into consideration the blend thieving results,
shown in Annex A, the API content (%LC) ranged between 83% and 112%. Thus, by not having samples
representative of the range from 95.7% to 100.0%, the calibration set does not include the expected
variability of the API concentration (%LC) during the blending process. Consequently, when performing
predictions with this model, it should be taken into account that predictions outside of the known space
may result in inaccuracy.[114]
Figure 29 – Raw spectra of the runs used for model development. API content (%LC) roughly increases in the direction of the arrow between 91.1% and 103.9%.
API Content (%LC)
40
Moreover, the procedure used to create the PLS data set has a high likelihood of inaccurately
assigning reference values to the spectra. By attributing the mean API value (%LC) of the different
locations thieved in the blender as the reference value to the spectra, it is assumed that the results from
the blend thieving represent the true state of the blend. This may be erroneous. Additionally, the last
NIR spectra recorded before the blender was stopped is presumed to match the blend sampling results,
which again may be erroneous.
Nevertheless, despite the inaccuracy associated with this approach, by examining the raw
spectra included in the data set, represented in Figure 29, a correlation can be seen between the API
value (%LC) and the spectra. As the baseline shifts downwards, the API value (%LC) increases.
However, that the fact that the observed baseline offset may be caused by other factors, such as light
scattering effects, must be taken into account. These spectral variations can be minimized by applying
suitable preprocessing techniques.
To find the optimal quantitative model, several combinations of different preprocessing and
spectral regions were tested. To evaluate the performance of the PLS models, the coefficient of
determination of the calibration, R2cal, root mean square error of cross-validation, RMSECV, and of
prediction, RMSEP, were compared.
Without Variable Selection
In this approach, it was taken into account the whole spectral wavelength range to construct the
model. The number of latent variables, cross-validation regression parameters, and prediction results,
with and without data preprocessing are given in Table 5.
Table 5 - Statistical parameters and number of PLS latent variables for calibration models using the entire NIR wavelength range, without data pretreatment as well as after different spectral pretreatments.
Pre-Processing None SNV 1st
Derivative
2nd
Derivative
SNV + 1st
Derivative
SNV + 2nd
Derivative
Wavelength (nm) 1099 – 2201
Latent Variables 2 2 2 2 2 2
R2cal 0.89 0.88 0.90 0.90 0.86 0.86
RMSECV (%) 1.59 1.94 1.72 1.91 2.05 2.23
RMSEP (%) 1.01 0.58 0.90 0.59 0.85 1.40
According to the SIMCA user guide [131], a popular plot to interpret the performance of the
regression model is the observed versus predicted plot, which displays the relationship between the
observed Y and the predicted Y. A regression line may be added to this plot. An output of the regression
line is the coefficient of determination, R2, which measures the strength of the relationship between the
observed Y and the predicted Y. The R2 ranges from 0 to 1, and the closer the R2 is to 1, the stronger
the relationship. In the resulting PLS models, the R2 values are quite similar between the models, ranging
41
from 0.86 to 0.90. Thus, the R2 values were not used as a metric to compare the predictability of the
models.
An alternative metric that measures the predictive power of the models is the root mean square
error of cross-validation (RMSECV). It can be observed that the PLS model with the lowest RMSECV
value is the model in which the spectra were not preprocessed. Additionally, the RMSECV value was
found to increase when a pretreatment was applied. Considering that the objective of preprocessing is
to remove physical phenomena, such as variation of particle size present in the spectra [132], it may be
presumed that the physical variations present in the spectra were important features for better
performance of this specific model.
Taking into account the root mean square error of prediction (RMSEP) values, it was observed
that, in general, the RMSEP and RMSECV values are dissimilar. This might indicate of a lack of
robustness in the models created.
Nonetheless, when creating a model using the entire spectral range, it must be considered that
there might be spectral regions that contain noise and/or irrelevant information that might deteriorate
the model. Discarding these regions is thought to improve the performance of the PLS model. Therefore,
new models using iPLS as a variable selection technique were developed with the aim of obtaining
better results, e.g. lower RMSECV values.
With Variable Selection – iPLS
The goal of iPLS is to improve the performance of the PLS model. Using iPLS, the spectra were
split into equal intervals. Then a PLS regression model was developed and the RMSECV was calculated
for each sub-interval. This method gives an overview of the spectral data and shows relevant spectral
regions to calibrate the PLS model. Table 6 shows the results of this method (see chapter 3.4.1).
Table 6 - Statistical parameters and number of PLS latent variables for the selected models chosen through iPLS, without data pretreatment as well as after different spectra pretreatments.
Pre-Processing None SNV 1st
Derivative
2nd
Derivative
SNV + 1st
Derivative
SNV + 2nd
Derivative
Wavelength (nm) 1973-2078 1755-1860 1317-1422 1317-1422 1973-2078 1317-1422
Latent Variable 2 1 1 2 3 4
R2cal 0.91 0.86 0.90 0.90 0.90 0.90
RMSECV (%) 1.44 1.74 1.52 1.59 1.78 1.85
RMSEP (%) 1.45 1.19 1.54 0.52 0.79 0.41
42
Compared to the previous models without variable selection, the R2 and the RMSECV did not
significantly improve. Nevertheless, it was again observed that the model with the lowest RMSECV is
the one without preprocessing. However, the model with spectra pre-treated with a 1st derivative shows
R2 and RMSECV values similar to the model without preprocessing, but only with 1 LV. Generally, as
more latent variables (LV) are added, a greater amount of variance present in the data set is explained,
and this could either improve or worsen the predictive ability of the model. That being the case, to
compare the models, the latent variables of the models were all reduced to 1. The resulting statistical
metrics are presented in Table 7. By taking into account the R2 and RMSECV values, it can be observed
that the 1st derivative model has greater predictive ability, i.e. R2 closer to 1 and lowest RMSECV.
Additionally, compared to the previous models, there is less dissimillarity between the RMSEP and
RMSECV values.
Based on the results presented, the model with spectra pretreated with 1st derivative was found
to be the most suitable model for quantification of API (%LC).
Table 7 - Statistical parameters for the selected models chosen through iPLS with 1 PLS latent variable, without data pretreatment, as well as after different spectra pretreatments.
Pre-Processing None SNV 1st
Derivative
2nd
Derivative
SNV + 1st
Derivative
SNV + 2nd
Derivative
Wavelength (nm) 1973-2078 1755-1860 1317-1422 1317-1422 1973-2078 1317-1422
Latent Variable 1 1 1 1 1 1
R2cal 0.60 0.86 0.90 0.87 0.74 0.80
RMSECV (%) 2.76 1.74 1.52 1.72 2.34 2.18
RMSEP (%) 2.20 1.19 1.54 0.19 1.52 0.91
Comparison of the approaches
From the two approaches tested, the models with better predictive performance metrics, e.g. R2
closer to 1 and lower RMSECV, were chosen for further comparison. The selected models are presented
in Table 8.
Table 8 - Statistical parameters and number of PLS latent variables for the selected models of each approach tested.
Method Without Variable Selection With Variable Selection - iPLS
Pre-Processing None 1st Derivative
Wavelength (nm) 1099 - 2201 1317-1422
Latent Variables 2 1
R2cal 0.89 0.90
RMSECV (%) 1.59 1.52
RMSEP (%) 1.01 1.54
43
Table 9 - Statistical parameters and number of PLS latent variables for the selected models of each tested approach with 1 PLS latent variable.
Method Without Variable Selection With Variable Selection - iPLS
Pre-Processing None 1st Derivative
Wavelength (nm) 1099 - 2201 1317-1422
Latent Variables 1 1
R2cal 0.74 0.90
RMSEcv (%) 2.23 1.52
RMSEP (%) 2.20 1.54
Both models have similar R2 and RMSECV values. However, as seen before, the models have
a different number of PLS latent variables. The number of latent variables were reduced to 1, and the
resulting statistical metrics are presented in Table 9.
Figure 30 represents the observed vs predicted Y plot for the models with and without variable
selection. When a latent variable was removed in the without variable selection model, the performance
of the model worsened: the RMSECV increased from 1.59% to 2.23% and the R2 decreased from 0.89
to 0.74. Another point in favor of the with variable selection model is the small difference between the
RMSECV and RMSEP. Furthermore, in Figure 30, the model with variable selection was found to fit the
set of data better.
Based on the results, the model developed with variable selection was chosen for the following
analysis of the quantification and prediction of the API concentration (%LC) in blends.
Figure 30 – Scatter plot and regression line of predicted vs. observed Y values of the models (a) without and (b) with variable selection. Both with 1 latent variable.
44
6.2. NIR-API Predicted Concentration Blending Profile
The previously chosen PLS model was applied to the spectra acquired during blending with the
goal of predicting the API concentration (%LC) and identifying the blending end-point.
Blend 1
Figure 31 presents the predicted API concentration (%LC) from the acquired NIR spectra over
the blending time of blend 1. To display the predominant trend of the predicted API concentration (%LC)
over time, a Savitzky-Golay smoothing filter (polynomial order 1 and a frame length of 15), represented
as the red line in the graph, was applied. It can be observed that the predicted API (%LC) concentration
decreased from time point 0 to 6 minutes and subsequently increased and then stabilized from the 8-
minute mark till the end of the blending process. This plateau might be indicative of a homogeneous
blend; however, this could not be confirmed with the HPLC results. Overall, there were no observable
similarities between the HPLC and the NIR predicted results.
89
91
93
95
97
0 2 4 6 8 10 12 14 16
Pre
dic
ted
AP
I Co
nce
ntr
ati
on
(%
LC) o
f th
e ac
qu
ire
d N
IR S
pec
tra
Time (minutes)
Figure 31 - Predicted API concentration (%LC) from the NIR spectra acquired in Blend 1. To improve interpretation of the predicted results, a Savitzky-Golay smoothing filter (polynomial order 1 and a frame length of 15), represented
by the red line, was applied.
99
100
101
102
103
104
105
0 2 4 6 8 10 12 14 16
Pre
dic
ted
AP
I Co
nce
ntr
ati
on
(%
LC) o
f th
e ac
qu
ire
d N
IR s
pe
ctra
Time (minutes)
Figure 32 - Predicted API concentration (%LC) from the NIR spectra acquired in Blend 2. To improve interpretation of the predicted results, a Savitzky-Golay smoothing filter (polynomial order 1 and a frame length of 15), represented
by the red line, was applied.
45
Figure 32 presents the predicted API concentration (%LC) from the acquired NIR spectra over
the blending time of blend 2. Compared to the previous result, this blend shows a different trend. In this
case, the trend stayed mainly constant at 102% over the blending time. Moreover, the variation mainly
stays between 100% and 104% and never dropped for a prolonged period of time. Overall, there were
no obvious similarities between the predicted and HPLC results. However, it should be noted that in
blend 2, spectra were only recorded at every second rotation of the blender, instead of at every rotation,
as in blend 1 and 3. Due to this, it may be presumed that the number of spectra recorded during the
blending process had an influence on the predicted results from the acquired spectra. By “looking” into
the blending process at a lower rate, valuable information about the changes of the blend are lost. This
issue is further explored in the sub-chapter that follows (see chapter 6.3).
Figure 33 presents the predicted API concentration (%LC) from the acquired NIR spectra over
the blending time of blend 3. Similarly, to blend 2, the predicted API concentration(%LC) trend stayed
mostly constant over the blending time. Furthermore, the variation never reduced for a prolonged period
of time. Nonetheless, this blend profile continues to show no similarities between the NIR predicted and
HPLC results.
Overall, the predicted NIR results showed no similarity to the HPLC-determined results.
Furthermore, in all the blends, a high level of variation was observed between subsequent predicted
values. Nonetheless, all the results presented should be taken with a degree of skepticism. It must be
acknowledged that the HPLC results may not represent the true state of the blend, which may explain
the discrepancies observed between the predicted and HPLC results. It should also be kept in mind that
there is a high degree of error associated with the PLS model developed, since representative samples
of all the possible variations of the API concentration (%LC) are not available and there is a high
probability of inaccurately assigning reference values to the spectra.
99
100
101
102
103
104
105
0 2 4 6 8 10 12 14 16
Pre
dic
ted
AP
I Co
nce
ntr
ati
on
(%LC
) o
f th
e ac
qu
ire
d N
IR s
pe
ctra
Time (minutes)
Figure 33 - Predicted API concentration (%LC) from the NIR spectra acquired in Blend 3. To improve interpretation of the predicted results, a Savitzky-Golay smoothing filter (polynomial order 1 and a frame length of 15), represented
by the red line, was applied.
46
6.3. Effect of Spectral Acquisition Rate on Blend Profile
In these experiments, NIR spectra were acquired by mounting an NIR spectrometer in a
sapphire window present on the cover of a bin-blender. The NIR spectra were acquired when the blender
was in an inverted position, with the powder fully covering the sapphire window. This resulted in an NIR
spectrum with every rotation of the blender. As mentioned in chapter 3.1, in blend 2 spectra were only
recorded at every second rotation of the blender, instead of at every rotation as in blend 1 and 3. To
investigate whether the reduction in the number of spectra acquired during blending affects the NIR
predicted results, blend 3 was chosen to simulate the experimental conditions of blend 2 due to its
similarity to blend 2. Only the spectra acquired at every second rotation during blend 3 were considered
for comparison.
Figure 34 illustrates the comparison between the predicted API concentration (%LC) from the
acquired NIR spectra over the blending time of blend 3 with spectra acquired at every rotation (every ~5
seconds), represented by the grey line, and at every second rotation (~ every 10 seconds), represented
by the blue line. It can be observed that the blending profile with the reduced spectral acquisition rate
lost some variation, which is to be expected. Overall, however, there were no significant changes in the
trend of predicted API concentration (%LC) results with spectral data acquired at ~ every 5 seconds or
~ every 10 seconds. However, the possibility that some blending variability is lost cannot be excluded.
Thus, further study of the effect of the acquisition rate is recommended.
Figure 34 – Comparison of the predicted blending profile of the API concentration (%LC) of Blend 3 with the reduced and the full amount of spectral data, represented by the blue and grey line, respectively.
99
100
101
102
103
104
105
0 2 4 6 8 10 12 14 16
Pre
dic
ted
AP
I Co
nce
ntr
ati
on
(%LC
) o
f th
e
acq
uir
ed
NIR
sp
ectr
a
Time (minutes)
Acquisition At Every Second Rotation Acquisition At Every Rotation
47
7. Results and Discussion of the Qualitative Approach
Qualitative methods used to monitor blending processes rely on the variation between spectra.
During initial blending, a large degree of spectral variation is expected, which subsequently begins to
decrease as the components in the blender become more uniform. Therefore, when a minimum or
stationary variation between successive spectra is attained, the blend endpoint is deemed to have been
reached. [59], [133]
This is exemplified for Blend 1 in Figure 35, which illustrates the spectral variance of the first
and last 10 spectra collected during the blending process, the variation from spectrum to spectrum is
shown with ± 15 SD limits, chosen only for representative purposes. In the first 10 NIR spectra acquired,
there are broad standard deviation (SD) limits, which indicates a large variation between the 10 first
spectra. In the end of the 15-minute blend, i.e. in the last 10 acquired spectra, the SD limits tighten,
which signifies decreased spectral variation.
In this project, three different qualitative approaches were evaluated: (1) analysis of spectral
variance by evaluating the how PCA scores trend over time, (2) moving block standard deviation
(MBSD); and (3) principal component score distance analysis (PC-SDA).
a) b)
Figure 35 - Illustration of the spectral variation between (a) the 10 first and (b) the 10 last spectra recorded for Blend 1. The blue line represents the mean spectrum of (a) the 10 first and (b) the 10 last spectra collected during blend. The red lines
demonstrate the variation with ±15 SD limits.
-2
-1
0
1
2
1090 1590 2090
Ab
sorb
ance
-SN
V
Wavelength (nm)
-2
-1
0
1
2
1090 1590 2090
Ab
sorb
ance
-SN
V
Wavelength (nm)
48
7.1. PCA Scores versus Blending Time
As previously mentioned, PCA score values represent the distance of the projected observation
to the mean along the principal component. By plotting the scores of a principal component versus time
it is possible to observe how the scores change in position along the principal component over the
blending time.
Blend 1
Figure 36 illustrates how the scores vary along the blending time for blend 1. When evaluating
the score plot, it can be seen that when the scores achieve a constant trend, the blend may be uniform,
i.e. when the value of the score between observations becomes constant, it may represent that the
observations have similar spectral characteristics, and thus that the blend is homogeneous.
In the score plot without data preprocessing, Figure 36 (a), a slight variation in the score values
was observed throughout the blending time. However, after approximately 7 minutes, the score values
varied less between each other and began to trend in a constant manner. The score plots with
preprocessed spectra showed similar trends. Overall, the score values varied in the beginning of the
blend and then started to become constant after approximately 7 minutes.
a) b)
c) d)
Figure 36 – First principal components scores of Blend 1 with and without preprocessing versus blending time. The blue circles represent the scores, and the green line represents a Savitzky-Golay smoothing line (polynomial order 1 and a frame length of 15), used to facilitate interpretation. On the y axis, the variance captured by the principal component is presented
as a percentage. Plots (a), (b), (c), and (d) illustrate the scores for spectral data without preprocessing, and preprocessed with SNV, 1st derivative, and 2nd derivative, respectively.
-0,7
-0,4
-0,1
0,2
0,5
0 2 4 6 8 10 12 14 16
Sco
res
on
PC
1 (9
7%)
No
Pre
-Pro
cess
ing
Time (minutes)
-0,7
-0,4
-0,1
0,2
0,5
0 2 4 6 8 10 12 14 16
Sco
res
on
PC
1 (6
6%)
SNV
Time (minutes)
-0,7
-0,4
-0,1
0,2
0,5
0 2 4 6 8 10 12 14 16
Sco
res
on
PC
1 (6
3%)
1st
Der
ivat
ive
Time (minutes)
-0,7
-0,4
-0,1
0,2
0,5
0 2 4 6 8 10 12 14 16
Sco
res
on
PC
1 (6
9%)
2nd
Der
ivat
ive
Time (minutes)
49
In all the plots, especially those where preprocessing was applied, distinct levels were observed in
which the scores values maintained a constant trend. This might indicate that the spectra were alike in
these time periods. Furthermore, taking into consideration that the blender was stopped at the time
points 2, 4, 6, 10, and 15 minutes, it can be perceived that the observed drift between levels coincides
with the times the blender was stopped. These levels and drift between levels seem to be uncommon.
Therefore, the steps taken during the experiments need to be taken into account.
The NIRS was attached to a sapphire window present in the lid of the blender. Whenever the
blender was stopped, the lid was removed, in order to perform the thief sampling. When the sampling
was concluded, the sapphire window was cleaned and the lid was positioned back in the blender, and
the blending and monitoring with NIRS were continued. These steps constitute disruptive factors that
may explain the observed trend of the scores, as described below:
▪ The NIRS only acquires spectra when the blender is in an inverted position. Thus, these
measurements only consider the powder particles that are near the lid. Moreover, it has been
noted by Corredor et al.[134] that the depth of penetration of NIR light in reflectance mode
ranges from 0.5 to 2.5 mm. A possible explanation for why the scores seem to stay constant in
the observed levels could be the sticking of powders to the window. If the depth of penetration
is short and the window is covered with the same layer of powder, the acquired NIR spectra
will likely be similar, and consequently, similar score values may be observed. Moreover, if the
assumption of powder sticking is correct, it may explain the observed drift between levels. By
removing the powder stuck to the window, which was disabling the acquisition of NIR spectra
of the mix during blending, it may be presumed that only the first acquired spectra when the
blending was reinitiated truly represented the state of the blend. Thus, drifts between levels of
similar score values are to be expected due to the inherent changes of the state of the blend.
▪ To perform the thief sampling, the blender had to be transported into a laminar flow booth.
When the sampling was concluded, the blender was transported back into the rotating cage.
This movement may cause shaking and vibration of the powder particles and induce a slight
degree of segregation. Furthermore, the procedure of thief sampling has been shown to
extensively disrupt the structure of the powder mixture.[3] Thus, the possibility that these steps
introduced some disruptive factors to the blends cannot be excluded. This could explain the
observed drift between levels, as the blend distribution may not be the same as when the
blender was no longer being monitored with NIRS.
Nevertheless, most of the score plots appear to show a constant trend after approximately 7 to 8
minutes. This could indicate that the blend is homogeneous after this time point. Moreover, these results
show a commonality with NIR predicted API content (%LC) (Figure 31). Both become constant after the
same amount of time. However, taking into consideration the HPLC results, more specifically the RSD
values, this assumption cannot be confirmed.
50
Blend 2
Figure 37 presents score plots of the first principal component for blend 2 with and without
preprocessing. It can be observed that most of the score plots, except for the one preprocessed with a
2nd derivative, have similar trends.
In contrast to what was observed in blend 1, it is difficult to identify a region where successive
scores had similar values for an extended period of time. In this case, the score plots, except for the
one preprocessed with a 2nd derivative, show a constant trend after 2 minutes. Nevertheless, there is a
high variability between successive score values. The only time interval in which a reduction in the
variance occurs is between approximately 11 and 14 minutes. To further analyze this blend, the score
plots of the second principal component were also evaluated.
-0,7
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0,2
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Sco
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on
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1 (6
4%)
SNV
Time (minutes)
a) b)
c) d)
Figure 37 – First principal component scores of Blend 2 with and without preprocessing versus blending time. The blue circles represent the scores, and the green line represents a Savitzky-Golay smoothing line (polynomial order 1 and a frame length
of 15), used to facilitate interpretation of the trend. On the y axis, the variance captured by the principal component is presented as a percentage. Plots (a), (b), (c), and (d) illustrate the scores for spectral data without preprocessing, and
preprocessed with SNV, 1st derivative, and 2nd derivative, respectively.
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riva
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51
Figure 38 presents score plots of the second principal component for blend 2 with and without
preprocessing. Overall, the score plots of the second principal component did not improve interpretability
of the scores.
In the score plots of blend 2, it was difficult to identify a specific trend of scores. Additionally, a
high variance between successive scores was observed, which was not reduced when preprocessing
was applied. In the first component, a region was identified where the spectra seem to cluster, which
corresponds to the time interval between 11 and 14 minutes. This was also observed in the second PC
scores preprocessed with a 2nd derivative. Taking into consideration that in blend 2 a spectrum was only
collected on every 2nd rotation of the blender, it may be argued that the observed high variance between
successive spectra is due to the lower rate of spectral acquisition. By only having a spectrum acquired
approximately every 10 seconds during the blending time, it might be presumed that there are significant
changes between the successively acquired spectra, which would be demonstrated by higher variations
between successive scores. This assumption is further investigated in the following analysis of blend 3.
However, these results showed no commonality with the HPLC results, especially with regard to the
evolution of the RSD value over time.
-0,7
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-0,1
0,2
0,5
0 2 4 6 8 10 12 14 16
Sco
res
on
PC
2 (1
6%)
SNV
Time (minutes)
Figure 38 - Second principal component scores of Blend 2 with and without preprocessing versus blending time. The blue circles represent the scores, and the green line represents a Savitzky-Golay smoothing line (polynomial order 1 and a frame length of 15), used to facilitate interpretation of the trend. On the y axis, the variance captured by the principal component
is presented as a percentage. Plots (a), (b), (c), and (d) illustrate the scores for spectral data without preprocessing, and preprocessed with SNV, 1st derivative, and 2nd derivative, respectively.
-0,7
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%)
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9%)
2nd
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Time (minutes)
a) b)
c) d)
52
Blend 3
Figure 39 shows the score plots of data with and without preprocessing of blend 3. In the score
plot without data preprocessing, Figure 39 (a), the trend of the score values slightly varied from the
beginning to end of the blend, mostly hovering around the score value of zero. However, when
preprocessing was applied to the spectral data, a different score trend was observed. The trends in
these score plots are similar. The score values seem to slowly trend upward or downward; however, the
trend of the scores does not appear to become constant in any period of time. Furthermore, by zooming
-0,2
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Sco
res
on
PC
1 (6
4%)
2nd
Der
ivat
ive
Time (minutes)
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0,2
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3%)
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Time (minutes)
-0,7
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-0,1
0,2
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Sco
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1 (7
1%)
SNV
Time (minutes)
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Sco
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1st
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Time (minutes)
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Sco
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1 64
%)
2nd
De
riva
tive
Time (minutes)
a) b)
e)
c) d)
Figure 39 – First principal components scores of Blend 3 with and without preprocessing versus blending time. The blue circles represent the scores, and the green line represents a Savitzky-Golay smoothing line (polynomial order 1 and a frame length of 15), used to facilitate interpretation. On the y axis, the variance captured by the principal component is presented
as a percentage. Plots (a), (b), (c), and (d) illustrate the scores for spectral data without preprocessing, and preprocessed with SNV, 1st derivative, and 2nd derivative, respectively. Plot (e) is an enlargement of (d) to reveal levels that were similarly
identified in Blend 1.
53
in to the score plot from spectra pretreated with a 2nd derivative, Figure 39 (e), levels where the scores
had similar values were identified. This was also observed and discussed in the score plots of blend 1.
In this case, it was difficult to identify a time interval in which the blend could be uniform. Thus, it could
be argued that this blend needed to be mixed longer. Nevertheless, as in blend 2, these results showed
no commonality with the HPLC results, especially with regard to the evolution of the RSD values over
time.
Blend 3 With Reduced Number of Spectra
In the score plots of blend 2, a higher variation between successive scores was observed
compared to blends 1 and 3. A possible explanation for this observation is the fact that blend 2 had a
lower NIR spectra acquisition rate than blend 1 and 3. Blend 3 was chosen to further investigate this
assumption due to its similarity to blend 2 with respect to the amount and type of components present
in the blend. The number of spectra acquired during blend 3 was reduced to simulate the experimental
conditions of blend 2, i.e. only the spectra from every 10 seconds instead 5 were considered.
Figure 40 illustrates the differences between the score plots that were calculated with spectra
acquired at every rotation of the blender and at every second rotation. For representational purposes,
only the score plots with SNV as pretreatment are displayed. Overall, the general trend of the scores
did not significantly change when the number of acquired spectra was reduced. There was also no
increase in the variance between successive scores. Considering these results, the lower acquisition
rate does not appear to be correlated with an increase in variation between successive scores.
Knowing that blends 2 and 3 were nearly alike raises the question of why their blend profiles
were so different. The only identifiable difference between these blends is the batch of the granule. In
chapter 5, it was observed that the granules in these two blends displayed different properties. Because
the granules represented a major part of the blends, it might be assumed that the differences between
the two granule batches had an impact on the NIR spectra acquired during blending.
-0,7
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1 -
SNV
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-0,4
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0,2
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Sco
res
on
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1 -
SNV
Time (minutes)
Figure 40 – First principal components scores of spectra acquired in Blend 3 pretreated with SNV versus blending time. Plots (a) and (b) illustrate the differences between the score plots with spectra acquired (a) at every rotation of the blender and
(b) at every second rotation. The blue circles correspond to the scores, and the green line represents a Savitzky-Golay smoothing line (polynomial order 1 and a frame length of 15), used to ease interpretation.
a) b)
54
7.2. Moving Block Standard Deviation
A common approach to monitoring blend homogeneity with NIR data is by calculating the
Moving Block Standard Deviation (MBSD). This is carried out by calculating the standard deviation of
the absorbance values over a time window or block. According to this method, the blend may be
considered uniform, when the standard deviation reaches a minimum.
A typical MBSD curve initially shows a large mean SD value, which subsequently decreases
over the blending time. A large and low mean SD is indicative of large and low spectral variation between
a consecutive set of spectra. It is presumed that the blend is homogeneous when the mean SD profile
reaches a minimum value. [16]
Blend 1
Figure 41 illustrates the MBSD curves for spectra of blend 1 with and without preprocessing. It
can be observed that all the MBSD curves are quite similar, especially those that were preprocessed.
Overall, the standard deviation reaches a minimum value and a steady state is reached after
approximately 11 minutes.
Before this time point, the MBSD shows a sort of “peak and valley” trend. In other words, the
spectral variance appears to fluctuate between higher values, corresponding to the peaks, and lower
values, corresponding to the valleys. The standard deviation value of these peaks lowers over the
blending time. These observations coincide with what was observed in the score plots. The valleys
correspond to the observed levels where the score values remained constant. Thus, a lower standard
deviation is observed. The peaks correspond to drift between levels, where the scores jumped to a
different value. The presence of these levels and drift between levels were previously discussed in
chapter 7.1. However, the reason why the standard deviation of the peaks decreased, or why the drift
between levels also decreased over the blending time, was not explored. As previously stated, a
possible explanation for why the spectral variance decreased during blending is that it is due to powder
Figure 41 – Application of moving block standard deviation to the spectra collected in Blend 1. MBSD was applied to spectral data without preprocessing (blue line) and with preprocessing, SNV (green line), 1st derivative (red line), and 2nd derivative
(black line). To overlap the MBSD curves, an SNV was applied to the mean standard deviation. The vertical grey lines represent the times the blender was restarted.
-3
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0
1,5
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0 2 4 6 8 10 12 14 16
SNV
No
rmal
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d M
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Time (minutes)
No Pre-Processing SNV 1st Derivative 2nd Derivative
55
sticking to the sapphire window. Whenever the blender was stopped, the sapphire window was cleaned.
Because the window was cleaned before reinitiating the mixing, it could be assumed that only the first
set of spectra acquired accurately represent the state of the blend. Thus, the reduction in peak size may
indicate that the blend is in fact becoming more uniform. However, because the blender was not stopped
during the time interval of 10 to 15 minutes, it could be argued that the observed steady state is a result
of the powder sticking to the sapphire window through which the NIR spectra are acquired rather than
an indication of a homogeneous blend.
No observable commonalities between the MBSD curves and the HPLC results were found.
Considering the results presented, it could be assumed that the blend is homogeneous after 11 minutes.
From the HPLC results, it can only be concluded that the blend may be homogeneous at the end of the
15 minutes.
Blend 2
Figure 42 shows the MBSD curves for spectra of blend 2 with and without preprocessing.
Similar to what was previously seen for blend 1, the MBSD curves with and without preprocessing are
very alike. Additionally, the standard deviation reaches a minimum value at approximately 11 minutes.
However, in contrast to blend 1, after that point the standard deviation does not trend in a steady manner.
Furthermore, the “peak and valley” trend was not observed. In this case, the standard deviation
remained constant before dropping at 11 minutes. There are several factors that might explain why the
MBSD curve for blend 2 is different from that of blend 1, such as (1) the higher fill level; (2) a slower NIR
spectra acquisition rate; and (3) different granule batches and lubricants.
Nonetheless, similar to blend 1, commonalities between the MBSD curves and the scores
versus blending time plots were observed. In the score plots, the time interval between 11 and 13
Figure 42 - Application of moving block standard deviation to the spectra collected in Blend 2. MBSD was applied to spectral data without preprocessing (blue line) and with preprocessing, SNV (green line), 1st derivative (red line), and 2nd derivative (black line). To overlap the MBSD curves, an SNV was applied to the mean standard deviation. The grey lines represent the
times the blender was restarted.
-3
-1,5
0
1,5
3
4,5
0 2 4 6 8 10 12 14 16
SNV
No
rmal
ized
MB
SD
Time (minutes)
No Pre-Processing SNV 1st Derivative 2nd Derivative
56
minutes was a period where the scores had similar values. This same period is also identified in the
MBSD curve, where it is associated with less spectral variation.
Nevertheless, once again, no observable connection between the MBSD curves and the HPLC
results were found. Moreover, according to the RSD values, the blend should have been homogeneous
at 6 minutes. After this time point, the RSD values indicate that the blend is not homogeneous. This is
contrary to what is observed in the MBSD results. These results indicate that the blend might be
homogeneous after 11 minutes.
Blend 3
Figure 43 shows the MBSD curves for blend 3 spectra with and without preprocessing. Similar
to what was seen in blends 1 and 2, there are no distinguishable differences between the MBSD curves
of the spectra with and without preprocessing.
In this case, the standard deviation drops to a minimum value after approximately 9 minutes.
Like blend 1, a “peak and valley” trend is also observed, with the peaks roughly coinciding with the times
when the blender was restarted. However, compared to blend 1, the standard deviation of the peaks
does not decrease over time. In this case, an abrupt decrease in the spectral variance was observed
between the peaks at 8 and 12 minutes. Possible explanations for the “peak and valley” trend have been
previously discussed.
Nevertheless, similar to blend 2, no observable connection between the MBSD curves and the
HPLC results was found.
Figure 43 - Application of moving block standard deviation to the spectra collected in Blend 3. MBSD was applied to spectral data without preprocessing (blue line) and with preprocessing, SNV (green line), 1st derivative (red line), and 2nd derivative (black line). To overlap the MBSD curves, an SNV was applied to the mean standard deviation. The grey lines represent the
times the blender restarted.
-3
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0
1,5
3
4,5
0 2 4 6 8 10 12 14 16
SNV
No
rmal
ize
d M
BSD
Time (minutes)
No Pre-Processing SNV 1st Derivative 2nd Derivative
57
7.3. Principal Component Score Distance Analysis
In the PC-SDA approach, a PCA model is constructed with spectra that demonstrated less
spectral variability. It is assumed that the spectra chosen to create the PCA model represent a
homogeneous blend. In the next step, each spectrum, i.e. observation, is projected onto the model and
a predicted Hotelling T2 chart is generated. The Hotelling T2 values that are below T2critical correspond to
scores that are close to the center of the model, i.e. they have spectral characteristics similar to the
spectra used to create the PCA model and may be considered spectra that represent uniform blend.
Blend 1
Figure 44 presents the predicted Hotelling’s T2 charts for blend 1, with and without
preprocessing. Overall, it can be observed that in blend 1, approximately 6 minutes are needed to
achieve Ti2 values consistently below the T2
critical limit, except for the spectra preprocessed with a 1st
derivative, which needs 10 minutes. The Hotelling’s T2 profiles with and without preprocessing are
similar. However, there is a noticeable difference when preprocessing is applied. The time interval
between 2 and 4 minutes is below or close to the T2critical limit, which indicates that the blend might have
been homogeneous during this time interval. In the score versus blending time plots, it can be observed
0
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60
80
100
120
140
0 2 4 6 8 10 12 14 16
T2R
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PS[
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Time (minutes)
0
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250
0 2 4 6 8 10 12 14 16
T2R
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Time (minutes)
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0 2 4 6 8 10 12 14 16
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Time (minutes)
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200
0 2 4 6 8 10 12 14 16
T2R
ange
PS[
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] 2n
d D
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e
Time (minutes)
Figure 44 - PC-SDA with Hotelling's T2 charts for blend 1 (a) without and with preprocessing, (b) SNV, (c) 1st Derivative, and (d) 2nd Derivative. T2
critical (95%, green line) = 15,2. Dashed line represents the time point in which the T2 Hoteling values are below the T2
critical limit.
a) b)
c) d)
58
that when preprocessing was applied, the score values of the 2 to 4-minute time interval are similar to
the score values after 6 minutes. Thus, the spectra acquired between 2 and 4 minutes appear to be
similar to the spectra used to create the PCA model. Furthermore, the drops in Ti2 values observed at
the time points when the blender was restarted are represented in the scores by the drift between levels.
Overall, the Hotelling’s T2 plots show commonalities with the score versus time plots, which is to be
expected.
Compared to MBSD curves, this approach establishes that the blend is uniform at an earlier
time. However, a disadvantage of the MBSD approach is that the time point at which the blend may be
considered uniform is depends solely on the person interpreting the results. This is due to the lack of a
“minimum SD value” and a statistical rationale to indicate at which point the blend may be considered
uniform with any degree of certainty.
Blend 2
Figure 45 illustrates the predicted Hotelling’s T2 charts for blend 2, with and without
preprocessing. In this case, the Hotelling’s T2 plot with spectra without preprocessing does not identify
any interval of time in which the blend may be homogeneous. The Ti2 values, which are below the T2
critical
limits, represent the spectra used to create the PCA model, which is inherently always below the T2critical
limits. The Hotelling’s T2 plot generated with preprocessed spectra show similar trends. The Ti2 values
are consistently below the T2critical limit after approximately 10 minutes. This is similar to the results
0
100
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300
400
500
600
700
0 2 4 6 8 10 12 14 16
T2R
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PS[
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Time (minutes)
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60
80
100
0 2 4 6 8 10 12 14 16
T2R
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Time (minutes)
0
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30
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50
60
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80
0 2 4 6 8 10 12 14 16
T2R
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Time (minutes)
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50
60
0 2 4 6 8 10 12 14 16
T2R
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] 2n
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Time (minutes)
Figure 45 - PC-SDA with Hotelling't T2 charts for blend 2; (a) without and with preprocessing, (b) SNV, (c) 1st derivative, and (d) 2nd derivative. T2
critical (95%, green line) = 15.2. The dashed line represents the time point at which the T2 Hoteling values are consistently below the T2
critical limit.
a) b)
c) d)
59
obtained in the previously applied approaches. In the score versus time plots, the time interval between
10 and 14 minutes was identified as the period in which the score values were similar. It was also
observed that, after 14 minutes, subsequent score values begin to vary. This can also be observed in
the Hotelling T2 plot, where after 14 minutes the Ti2 values begin to increase.
Blend 3
Figure 46 shows the predicted Hotelling’s T2 charts for blend 3, with and without preprocessing.
Similar to blend 2, the Hotelling’s T2 plot with spectra without preprocessing only identifies the blend as
uniform in an interval of time that is mostly composed of the observations used to create the PCA model.
When preprocessing is applied, the Ti2 values are found to be consistently below the T2
critical limit
after approximately 9 minutes. Similar results were obtained in the MBSD curves. In the score versus
time plot, it was difficult to pinpoint a region in which the scores trended in a steady manner. Moreover,
as can be seen in Figure 46 (b) - (d), the distances to the center of the model decreased. This was also
observed in the score versus time plot. The distance between subsequent scores decreased. It may be
assumed that the pretreatments applied in (b) through (d) reduced the spectral variability.
0
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400
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T2R
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] 2n
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Time (minutes)
Figure 46 - PC-SDA with Hotelling't T2 charts for blend 3; (a) without and with preprocessing, (b) SNV, (c) 1st derivative, and (d) 2nd derivative. T2
critical (95%, green line) = 15.2. The dashed line represents the time point in which the T2 Hoteling values are consistently below the T2
critical limit.
a) b)
c) d)
60
8. Conclusions and Future Work
The main goal of this project was to evaluate whether there exist any commonalities between the
traditional method of assessing a blend through sampling and real-time monitoring with NIRS as a PAT
tool. To this end, three extragranular blends were monitored using these two methods.
In order to extract information pertaining to the uniformity of the blend, both quantitative and
qualitative methods were applied to the spectra acquired during blending. In these methods, the blend
was considered uniform when specific values remained constant for a prolonged period of time
(quantitative approach, scores, and MBSD), or when it met a defined criterion (PC-SDA). Figure 47
presents a summary of the HPLC, quantitative, and qualitative results of the three blends.
Overall, the NIR results showed no commonalities with the sampling results, as seen in Figure
47. However, some commonalities between quantitative and qualitative approaches were observed,
especially within the qualitative methods. Moreover, in blends 2 and 3, it was difficult to identify a time
Figure 47 - Summary of the HPLC, quantitative and qualitative results of the three blends.
61
point at which the blend could be considered uniform, especially in the quantitative approach and the
score values versus blending time plots. This is due to the fact that, for some of the methods applied,
the time point at which the blend can be considered uniform is depends solely on the person interpreting
the results.
Blends 2 and 3 were similar blends, the only factors that differed between these blends was the
batch of the granule and the acquisition rate of the NIR spectra. The blend sampling results showed that
these blends had similar blend profiles. However, the NIR results did not. Although the qualitative
methods indicated that these blends had similar end-points, they showed different blend profiles, which
raised the question of why this occurred.
To investigate whether the acquisition rate was the disruptive variable, the number of spectra
acquired during blend 3 were reduced in order to simulate the same acquisition rate of blend 2. Overall,
it was observed that some variation was lost; however, it did maintain a similar trend. Thus, it was
presumed that the acquisition rate was not the reason why the trajectories of the blends were different.
Another plausible reason was the difference between the granule batches. Therefore, a PCA was
performed with the NIR spectra of the granules used in these two blends. The granules used in blends
2 and 3 were found to be different, as they clustered in different groups. Furthermore, by analyzing the
contribution plot, it appeared that these granules differed in particle size and moisture content. As nearly
80% (w/w) of the blend was composed of granules, and it was also observed that the spectra acquired
during blending were similar to those of the pure granule, these differences might be the cause for the
different trends of blends 2 and 3. Thus, future work is recommended to investigate the effect of different
granule batches on NIR results.
At the same time, one needs to consider some of the limitations presented in the project that may
have biased the results. If this is experiment were to be repeated these limitations should be reduced.
Thus, the following points should be taken into consideration in any future work:
▪ To better assess the state of the blend, as many samples as possible should be taken. At least 30
samples of the blend should be taken from 10 sampling locations with 3 replicates from each
location. However, the experimental conditions must also be considered, such as; the size of the
blender and its fill level. In some cases, it may not be feasible to identify 10 sampling locations.
Another factor that must be considered is the time needed to analyze each sample removed, e.g.
assay the active ingredient.
▪ To develop a better PLS model, the data set used to train the model should contain enough
samples that are representative of most of the variations of the API value (%LC) over the blending
time.
▪ An alternative to the "stop and start" procedure used during the blending should be studied. A
possible alternative would be to perform multiple blends in which their blending time would increase
incrementally from blend to blend.
▪ Solutions to reduce the likelihood of powder sticking to the window from which the NIR spectra are
acquired should be studied.
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9. Bibliographic References
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71
Annex A
Table 10 - HPLC data for blend 1.
Blend 1
t=2 minutes t=4 minutes t=6 minutes t=10 minutes t=15 minutes
Position API Content (%)
1 87,3 91,5 91,2 83,4 94,7
2 106,3 86,5 90,5 100,9 97,1
3 87,9 85,2 93,3 95,9 94,7
4 90,3 93,1 92,8 94,0 87,2
5 96,1 100,5 104,3 100,1 95,8
6 101,1 87,0 91,0 89,7 93,3
7 105,5 96,8 99,6 91,1 101,4
8 101,1 90,6 84,7 82,0 103,9
9 94,3 95,5 91,0 86,2 97,2
10 84,5 88,7 92,5 87,2 91,8
Table 11 - HPLC data for blend 2 at 2 and 4 minutes.
Blend 2
t=2 minutes t=4 minutes
1st Replicate 2nd Replicate 3rd Replicate 1st Replicate 2nd Replicate 3rd Replicate
Position API Content (%)
1 101,1 109,0 108,1 99,3 103,3 83,5
2 111,5 111,5 107,9 107,8 110,5 105,1
3 104,1 105,8 103,6 110,7 106,5 101,7
4 101,7 103,5 106,1 102,5 105,5 104,2
5 93,9 95,7 98,9 94,8 98,0 96,5
6 102,4 104,6 102,0 97,8 103,9 105,8
Table 12 - HPLC data for blend 2 at 6 and 8 minutes.
Blend 2
t=6 minutes t=8 minutes
1st Replicate 2nd Replicate 3rd Replicate 1st Replicate 2nd Replicate 3rd Replicate
Position API Content (%)
1 98,7 105,1 105,2 111,8 104,7 100,8
2 100,1 102,0 113,1 104,7 114,2 110,5
3 101,0 105,4 106,7 103,7 102,1 108,4
4 100,1 106,0 105,1 100,1 101,2 102,3
5 92,5 98,5 94,2 91,5 98,4 103,4
6 104,7 103,2 102,0 101,8 104,1 100,9
72
Table 13 - HPLC data for blend 2 at 12 and 15 minutes.
Blend 2
t=12 minutes t=15 minutes
1st Replicate 2nd Replicate 3rd Replicate 1st Replicate 2nd Replicate 3rd Replicate
Position API Content (%)
1 100,8 103,0 103,7 104,6 103,0 102,8
2 106,6 109,2 105,5 103,7 103,9 107,5
3 99,7 96,6 93,8 99,4 96,6 95,2
4 97,3 99,9 100,9 102,0 102,0 103,8
5 89,6 94,2 93,9 92,4 93,0 93,7
6 103,4 105,7 104,4 105,1 106,3 111,7
Table 14 - HPLC data for blend 3 at 2 and 4 minutes.
Blend 3
t=2 minutes t=4 minutes
1st Replicate 2nd Replicate 3rd Replicate 1st Replicate 2nd Replicate 3rd Replicate
Position API Content (%)
1 99,3 102,3 105,5 100,0 107,7 105,5
2 96,0 94,9 96,4 107,0 103,9 101,5
3 106,8 107,0 107,0 102,9 102,7 100,5
4 98,7 106,2 99,0 100,2 102,8 102,1
5 99,0 105,1 102,4 100,0 99,8 104,4
6 102,5 104,0 99,1 97,0 97,7 99,9
Table 15 - HPLC data for blend 3 at 6 and 8 minutes.
Blend 3
t=6 minutes t=8 minutes
1st Replicate 2nd Replicate 3rd Replicate 1st Replicate 2nd Replicate 3rd Replicate
Position API Content (%)
1 98,9 103,6 102,5 99,8 101,0 105,5
2 98,0 98,4 100,0 109,6 103,8 100,6
3 98,4 103,2 99,6 102,2 107,7 105,6
4 101,4 98,8 107,2 104,4 103,4 108,6
5 96,1 99,4 102,7 95,1 98,5 96,3
6 96,7 105,6 97,5 96,1 102,2 98,6
73
Table 16 - HPLC data for blend 3 at 12 and 15 minutes.
Blend 3
t=12 minutes t=15 minutes
1st Replicate 2nd Replicate 3rd Replicate 1st Replicate 2nd Replicate 3rd Replicate
Position API Content (%)
1 99,2 98,6 106,2 99,7 101,9 102,2
2 103,9 108,4 104,9 104,6 101,7 103,9
3 99,5 97,3 97,3 103,6 96,5 98,5
4 97,5 104,8 99,6 97,6 100,8 104,0
5 93,9 94,3 93,2 92,8 90,9 93,7
6 102,0 110,6 110,4 100,4 106,9 101,2
74
Annex B
-8E-03
-3E-03
2E-03
7E-03
1E-02
1115 1215 1315 1415 1515 1615 1715 1815 1915 2015 2115
Ab
sorb
ance
-1s
t D
eriv
ativ
e
Wavelength (nm)
Tablettose
Croscarmellose
SSF
MgSt
Granule
-6E-03
-4E-03
-2E-03
0E+00
2E-03
1115 1215 1315 1415 1515 1615 1715 1815 1915 2015 2115
Ab
sorb
ance
-2n
d D
eriv
ativ
e
Wavelength (nm)
Tablettose
Croscarmellose
SSF
MgSt
Granule
Figure 48 - NIR spectra of the pure components in static state preprocessed with a 1st derivative.
Figure 49 - NIR spectra of the pure components in static state preprocessed with a 2nd derivative.
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