quantitative analysis of polymorphic mixtures using infrared spectroscopy ir spectroscopy...
TRANSCRIPT
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QUANTITATIVE ANALYSIS OF POLYMORPHIC MIXTURES USING
INFRARED SPECTROSCOPY
• IR Spectroscopy• Calibration
– Homogeneous Solid-State Mixtures– Multivariate Calibration Concepts– IR Data Collection
• Examples
Thomas M. NiemczykDepartment of ChemistryUniversity of New Mexico
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IR SPECTROSCOPY
T = A = - LOG T
A = bC10000 cm-1 → 400 cm-1
4000 → 400 cm-1 Fundamentals10000 → 4000 cm-1 Overtones,
Combinations
OII
Sample
I I0
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3500 3000 2500 2000 1500 1000 500
FREQUENCY (cm-1)
-0.1
0.4
0.9
1.4
-LO
G(R
/R 0)
FAFB
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ADVANTAGES OF APPLYING MULTIVARIATE STATISTICS TO
SPECTRAL DATA• Greater Precision (Increased Sensitivity)• Greater Accuracy• Increased Reliability (Outlier Diagnostics)• Quantitative Determination Can be Made
in the Presence of Multiple Unknown Interferences
• New Range of Problems Can be Addressed
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FREQUENCY
AB
SOR
BA
NC
E
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QUANTITATIVE ANALYSIS• Design Experiment• Prepare Samples• Collect and Assemble IR Data• Preprocess Data
– Mean Center, Baseline– Smoothe, Derivative– Scatter Correct– Frequency Select
• Develop Calibration Model– Validate Model
• Determine Concentration in Unknowns
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IMPORTANCE OF STATISTICAL EXPERIMENTAL DESIGNS
• Efficient Use of a Limited Number of Samples• Eliminate Spurious Correlations With Orthogonal
Designs• Necessary to Avoid Modeling Drift• Can Aid in the Detection of Outliers• Can Assure that Deviations From Linearity are
Modeled• Can Yield Realistic Estimates of Future
Prediction Ability
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CALIBRATION DATA• Spectral Calibration Often Limited by
Accuracy and Precision of the Reference Methods
• Calibration Samples Must Span the Range of Variation Expected in Unknowns
• Concentration Range Must be Large Relative to Precision of Reference Method
• Avoid Correlation Between Components• Use Statistical Calibration Designs
Whenever Possible
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QUANTITATIVE ANALYSIS• Design Experiment• Prepare Samples• Collect and Assemble IR Data• Preprocess Data
– Mean Center, Baseline– Smooth, Derivative– Scatter Correct– Frequency Select
• Develop Calibration Model– Validate Model
• Determine Concentration in Unknowns
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MAKING A 1% SAMPLE
10.0 mgm 1.000 gm
DIFFICULT TO PRODUCE HOMGENEOUS MIXTURE
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MIX EQUAL AMOUNTS
MAKING A 1% SAMPLE
10 mgm1.00 gm
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0.990 gm
0.020 gm
SECOND ADDITION
MIX THUROUGHLY
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CONTINUE ADDING AND MIXING EQUAL AMOUNTS
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QUANTITATIVE ANALYSIS• Design Experiment• Prepare Samples• Collect and Assemble IR Data• Preprocess Data
– Mean Center, Baseline– Smooth, Derivative– Scatter Correct– Frequency Select
• Develop Calibration Model– Validate Model
• Determine Concentration in Unknowns
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IR SAMPLING METHODS• KBr Disk
Not Appropriate for Polymorphs (?)Poor Quantitative Results
• Attenuated Total ReflectanceQuick and EasyQuantitative Solids Analysis (?)
• Nujol MullTakes PracticeGood Quantitative Results
• Diffuse Reflectance (DRIFT)Good Quantitative Results
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Sample
Nujol
ControlBaselinePathlength
Io I
KBr Mull
b (path length)
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DRIFT SAMPLINGSample KBr
RD
RS
Ro: KBr, Gold Mirror
RD: Sample
“A” = - log
IO
O
D
RR
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QUANTITATIVE ANALYSIS• Design Experiment• Prepare Samples• Collect and Assemble IR Data• Preprocess Data
– Mean Center, Baseline– Smooth, Derivative– Scatter Correct– Frequency Select
• Develop Calibration Model– Validate Model
• Determine Concentration in Unknowns
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MULTIVARIATE CALIBRATION• Focus on Factor Analysis Methods
– Partial-Least-Squares (PLS)– Principal Component Regression (PCR)
• “Full-Spectrum” Methods• Optimal Number of Factors Determined
Empirically• Knowledge of All Spectrally Important
Components Not Required– Baseline Variations– Temperature– Unknown Sample Component(s)
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PLS MODEL
A = TB + EA
c = Tv + ec
Spectral Decomposition Maximizes Covariance Between A and c
Unknown Predictiona = tuB + eu
cu = tuV
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X Y
Z
(0,0,0)
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XY
Z
(0,0,0)
PC2
PC1
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QUANTITATIVE ANALYSIS• Design Experiment• Prepare Samples• Collect and Assemble IR Data• Preprocess Data
– Mean Center, Baseline– Smooth, Derivative– Scatter Correct– Frequency Select
• Develop Calibration Model– Validate Model
• Determine Concentration in Unknowns
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EVALUATION OF THE CALIBRATION DATA
CALIBRATION SET VALIDATION SET
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CROSS VALIDATION EVALUATION OF THE CALIBRATION DATA
CALIBRATION DATA PREDICTION SAMPLES
A. LEAVING OUT HALF THE SAMPLES AT A TIME
B. LEAVING OUT ONE SAMPLE SAMPLE AT A TIME
1 2
3 4 5 6 7 8
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IMPORTANCE OF CROSS VALIDATION
• Needed to Select the Optimal Calibration Model– Determine Prediction Residual Error Sum of
Squares (PRESS)– Select Optimal Number of Factors Based on
PRESS• Used to Evaluate Precision of the
Multivariate Calibration Model• Important for Outlier Detection
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PLS MODEL
A = TB + EA
C = TV + ec
Spectral Decomposition Maximizes Covariance Between A and c
Unknown Predictiona = tuB + eu
cu = tuV
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NHCH3
HO
H
CH3H
NHCH3
H
HO
CH3H
(1R 2S) ephedrine (1S 2S) pseudoephedrine
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EPHEDRINE • HCL PSEUDOEPHEDRINE • HCL
R. Bergin Acta Cryst., B27, 381 (1971) Mathew & Palenik Acta Cryst., B33, 1016 (1977)
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4000 3000 2000 1000
FREQUENCY (cm-1)
0.0
0.2
0.4
0.6
0.8
1.0
-LO
G(R
/R0)
EphedrinePseudoephedrine OH---Cl 2.16 A 3273 cm-1
OH---Cl 2.38 A 3330 cm-1
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3500 3000 2500 2000 1500 1000 500
FREQUENCY (cm-1)
0.8
1.0
1.2
1.4
1.6
1.8
-LO
G(R
/R0)
E0E25E50
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SUMMARY OF 0-50% RESULTS
Frequency Region(cm-1)
Pretreatment CVSEP(wt.%)
No. PLSFactors
400-4000 Baseline 0.75 5
400-4000 MSC 2.27 3
400-4000 1st Derivative 1.46 3
950-1540 Baseline 0.74 3
950-1540 MSC 2.55 5
950-1540 1st Derivative 1.08 3
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1500 1400 1300 1200 1100 1000
FREQUENCY (cm-1)
INTE
NSI
TY (a
rb. u
nits
)
SPECTRA (base)
SPECTRA (mean centered)
FIRST LOADING VECTOR
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0 10 20 30 40 50
REFERENCE CONCENTRATION (wt%)
0
10
20
30
40
50
PRED
ICTE
D C
ON
CEN
TRAT
ION
(wt%
) CVSEP = 0.74 wt%
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SUMMARY OF 0-5% RESULTSFrequency
Region(cm-1)
Pretreatment CVSEP(wt.%)
No. PLSFactors
400-4000 Baseline 0.09 4
400-4000 MSC 0.11 6
400-4000 1st Derivative 0.16 5
400-4000 2nd Derivative 0.13 4
950-1540 Baseline 0.11 4
950-1540 MSC 0.13 6
950-1540 1st Derivative 0.11 3
950-1540 2nd Derivative 0.12 3
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0 1 2 3 4 5
REFERENCE CONCENTRATION (wt%)
0
1
2
3
4
5PR
EDIC
TED
CO
NC
ENTR
ATIO
N (w
t%)
20 SAMPLES
950-1540 CM-1
BASELINE
CVSEP = 0.11 wt%
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0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
10 15 20 25 30 35 40 45 50 55 60
NUMBER OF SAMPLES IN CALIBRATION
AVER
AGE
CVS
EP (w
t%)
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REPEAT DETERMINATIONS OF THE 2.67 wt.% SAMPLE
Experiment Std. Dev (wt.%)
No Movement
Sample In/Out
Sample In/Out – Smooth
Sample Cup Repacked
0.02
0.08
0.17
0.12
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4000 3000 2000 1000
FREQUENCY (cm -1)
0.0
0.5
1.0
1.5
2.0A
BS
OR
BA
NC
E
F1F2
CC
C
O
C
H H
CH2
N
CH3
CH3 CH2S
CH2
CH2 NH
C
CH NO2
NCH3
H
Ranitidine
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1500 1300 1100 900 700 500
FREQUENCY (cm-1)
-0.1
0.3
0.7
1.1
AB
SO
RB
AN
CE
F1F2
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0 1 2 3 4
REFERENCE CONCENTRATION (wt%)
0
1
2
3
4
PRED
ICTE
D C
ON
CEN
TRAT
ION
(wt%
) CVSEP = 0.09 wt%
1292-400 cm-1
MSC
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3500 3000 2500 2000 1500 1000 500
FREQUENCY (cm-1)
-0.1
0.4
0.9
1.4
-LO
G(R
/R 0)
FAFB
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1500 1300 1100 900 700 500
FREQUENCY (cm-1)
0.0
0.5
1.0
1.5
-LO
G(R
/R0)
FAFB
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0 1 2 3 4
REFERENCE CONCENTRATION (wt%)
0
1
2
3
4
PRED
ICTE
D C
ON
CEN
TRA
TIO
N (w
t%)
CVSEP = 0.33 wt%
983 - 1262 cm-1
1st Der. Preprocess
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NIR (~10000 to 4000 cm-1)
• Overtone and Combination Bands small– Neat samples
• Bands Broad and Overlapped– Poor Qualitative Analysis– Good Quantitative Analysis
• MVC
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E.W. Ciurczak, Appl. Spec. Rev. 23, 147 (1987)
J. Bernstein, “Polymorphism is Molecular Crystals”, Clarendon Press, 2002
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8000 7000 6000 5000 4000
FREQUENCY (cm-1)
0.0
0.2
0.4
0.6
0.8
-LO
G(R
/R0)
EphedrinePseudoephedrine
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4600 4400 4200 4000FREQUENCY (cm-1)
INTE
NSI
TY (a
rb. u
nits
)
0.1
Spectra (MSC)
Mean Centered
Loading Vector
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0 10 20 30 40 50
REFERENCE CONCENTRATION (wt%)
0
10
20
30
40
50PR
EDIC
TED
CO
NC
ENTR
ATI
ON
(wt%
) CVSEP = 3.27 wt%MSC Preprocess
3940 - 4742 cm-1
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0 1 2 3 4 5
REFERENCE CONCENTRATION (wt%)
0
1
2
3
4
5PR
EDIC
TED
CO
NC
ENTR
ATIO
N (w
t%)
CVSEP = 0.26 wt%
1st Derivative Preprocess
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CONCLUSIONS
• Number of Samples Relative to the Concentration Range is Important
• Complexity of the Spectral Data is a Factor
• Sample Prep is Critical– Homogeneous Mixtures– Baseline, Abs. Range
• NIR Useful
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FREQUENCY
AB
SOR
BA
NC
E
10 1 2 3 4 5
CONCENTRATION
AB
SOR
BA
NC
E
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0 1 2 3 4 5
CONCENTRATION
AB
SOR
BA
NC
E
AA
AM
CA CM
FREQUENCY
AB
SOR
BA
NC
E
MEASURED, A1
ANALYTE, AA
INPURITY, AI
A1 = AA + AI
1
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Conce
ntrati
on
0
1
5
1.5
0 0.5 1.5 2
ABSORBANCE 1
AB
SO
RB
AN
CE
2
FREQUENCY
AB
SO
RB
AN
CE
1 2
0.5
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FREQUENCY
AB
SO
RB
AN
CE
1 2
0
0.5
1.0
1.5
0 0.5 1.0 1.5
ABSORBANCE 1
AB
SO
RB
AN
CE
2