comparison of on-line nir spectrometer with thief … · data regarding the end-point of the...

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 13 - HPLC data for blend 2 at 12 and 15 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 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


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


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



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



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


RSD Value


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


Blend 1

0,5

0,6

0,7

0,8

0,9

1

1 3 5 7

R2 X


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.

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

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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.


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.


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

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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

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LC) o

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Time (minutes)



45


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).


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

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(%LC

) o

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Time (minutes)



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

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(%LC

) o

f th

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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

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-0,1

0,2

0,5

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Sco

res

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PC

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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.

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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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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.

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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.

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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

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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.

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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.

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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.

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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.

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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

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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

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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.

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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.

62

9. Bibliographic References

[1] O. Scheibelhofer, N. Balak, D. M. Koller, and J. G. Khinast, “Spatially resolved monitoring of powder mixing processes via multiple NIR-probes,” Powder Technol., vol. 243, pp. 161–170, 2013.

[2] H. Wu, M. White, and M. Khan, “An Integrated Process Analytical Technology (PAT) Approach for Process Dynamics-Related Measurement Error Evaluation and Process Design Space Development of a Pharmaceutical Powder Blending Bed,” Org. Process Res. Dev., vol. 19, no. 1, pp. 215–226, 2015.

[3] F. J. Muzzio, P. Robinson, C. Wightman, and D. Brone, “Sampling Practices in Powder Blending,” Internaltional J. Pharm., vol. 155, no. 2, pp. 153–178, 1997.

[4] U.S. Department of Health and Human Services - Food and Drug Administration, “Guidance for Industry: PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance,” 2004. [Online]. Available: https://www.fda.gov/downloads/drugs/guidances/ucm070305.pdf. [Accessed: 08-Oct-2017].

[5] C. K. Lai, D. Holt, J. C. Leung, C. L. Cooney, G. K. Raju, and P. Hansen, “Real Time and Noninvasive Monitoring of Dry Powder Blend Homogeneity,” AIChE J., vol. 47, no. 11, pp. 2618–2622, 2001.

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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


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


Blend 2




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


Blend 2




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


Blend 3




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


Blend 3




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


Blend 3




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.

comparison of on-line nir spectrometer with thief … · data regarding the end-point of the...

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