Application of Chemometrics in the Analysis of Drug in Mixture or in the Presence of Degradation Products.

1
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2
APPLICATION OFCHEMOMETRIC TECHNIQUESIN
MULTICOMPONENT ANALYSIS
Presented by Mohamed Abdallah El-Sayed Agha
3

• Thesis Contents

4

• Thesis Contents

Part I Introduction General introduction Handling of multivariate data Model building Experimental design Conventional multivariate calibration methods Model validation

5

• Thesis Contents

Part II Multi-way analysis an application to a ternary mixture of ascorbic acid, paracetamol and p-aminophenol Section A Parallel factor analysis (PARAFAC) Section B Multi-way partial least squares (N-PLS)


6
Thesis Contents
Part III Wavelength selection based methods an application to a pharmaceutical ternary mixture of atenolol, hydrochlorothiazide and amiloride hydrochloride. Section A Genetic algorithms based wavelength selection method (GAs) Section B Net analyte signal (NAS) based hybrid linear analysis (HLA)
7
Part IV Conclusion Appendix References Application of stability indicating chromatographic and chemometric Methods Section A Preparation, isolation and identification of noscapine metabolites and degradates Section B Thin layer chromatographic method Section C Multivariate curve resolution (MCR) of excitation emission matrices (EEM)

8

Part I
Introduction
9


Part II
Multi-Way Analysis An application to a ternary
mixture of ascorbic acid, paracetamol and
p-aminophenol

10
What is multi-way data?
Variable 2 (K )
5 pH values
Objects (I ) sample
10 samples
Variable 1 (J )
200 wavelengths
Three dimensional matrix (array) of dimensions I
x J x K
A matrix of the dimensions 10 x 200 x 5 will be
produced
11
Data Decomposition
PCA Principal component analysis of
bilinear data
. . . . . n


(Variable 2) C
C-Loading 1
C-Loading 2
Three- way array
B-Loading 2
B-Loading 1
Object A
PARAFAC Parallel factor analysis of
trilinear data

. . . . . n

A- Loading 1
A- Loading 2
(Variable 1) B
12
Structures of The Studied Compounds
OH-
H
H
H
H
Paracetamol
Ascorbic acid
H
2
p-aminophenol
13
Part II - Section A
Parallel Factor Analysis (PARAFAC)
14
Application of Parallel Factor Analysis
(PARAFAC) on the analysis of ascorbic acid,
paracetamol and p-aminophenol

• Extraction of pure spectra of each ionization
  form
• Study of the ionic equilibria of the three
  compounds and determination of their ionization
  constants
• Quantitative determination of the three compounds

15
PARAFAC Trilinear Decomposition
X (I x J x K), F the number of factors in the
model, a, b and c are vectors of the loading
matrices A, B and C, respectively and e is the
residual error
C

B
E

A
A three-component (1,2 3) three-way PARAFAC
model for the Three-way array X
16
Absorption Spectra of Ascorbic Acid, Paracetamol
and p-aminophenol at pH 3
17
Absorption spectra of ascorbic acid (15 µg.ml-1)
at pH range 3-11

18
Absorption spectra of paracetamol (24 µg.ml-1) at
pH range 3-11
pH 3 pH 5 pH 7
pH 9 pH 11
- H H
pH 3-9
pH (11) Paracetamol
Phenate
anion
19
Absorption spectra of p-aminophenol (2.4 µg.ml-1)
at pH range 3-11
pH 3 pH 5 pH 7
pH 9 pH 11
- H H
- H H
pH (3-5)
pH (7-9)
pH (11)
Ammonium ion p-aminophenol
Phenate anion
I
II III
20


21
Estimation of Model Parameters

• Several methods are available
• Split half experiments
• Residuals
• Comparison with the external knowledge of the
  data being modelled
• Core consistency diagnostic (Corcondia)

22
Model Parameters vs. Number of Components
Residuals
Variance
Corcondia
Parameters values
Number of components
23
Estimated Pure Spectra by PARAFAC
Ascorbic acid Ascorbate Paracetam
ol Paracetamol monoanion p-aminophenol p-aminophen
ate
24
Estimated pH Profiles by PARAFAC
p-aminophenol
pKa
Ascorbic acid
pKa
paracetamol
pKa
pKb
25
Statistical Parameters of Regression Equation
Representing the Correlation of the First Loading
Against the True Concentration
Statistical Parameter Ascorbic acid Paracetamol p-aminophenol
Intercept 1.530 1.453 0.896
SE of intercept 0.016 0.014 0.008
Slope 0.045 0.745 0.055
SE of slope 0.172 0.240 0.014
Correlation coefficient 0.9996 0.9998 0.9993
SE of regression 0.128 0.179 0.011
26
Model Validation Application
Statistical parameters Ascorbic acid Paracetamol p-aminophenol
Validation set Mean SD RMSEP 99.40 0.512 0.077 100.17 0.885 0.147 100.52 1.525 0.026
Cevamol Effervescent Tablets Batch no. 406137 Mean SD RMSEP Batch no. 706169 Mean SD RMSEP 100.17 0.773 0.053 100.09 1.008 0.098 ____
Cevamol Effervescent Tablets Batch no. 406137 Mean SD RMSEP Batch no. 706169 Mean SD RMSEP 99.91 0.702 0.073 99.94 1.262 0.152 ____
27
Comparison with a reported HPLC Method
Statistical parameter Ascorbic acid  Ascorbic acid  Paracetamol  Paracetamol 
Cevamol Effervescent Tablets Batch no. 406137   HPLC PARAFAC HPLC PARAFAC
Cevamol Effervescent Tablets Batch no. 406137 Mean SD RMSEP 99.84 0.996 0.067 100.17 0.773 0.053 99.65 0.470 0.061 100.09 1.008 0.089
Cevamol Effervescent Tablets Batch no. 406137 t-test (2.306) F-test (6.380) 0.572 0.413 1.659 4.601 0.572 0.413 1.659 4.601 0.572 0.413 1.659 4.601 0.572 0.413 1.659 4.601
Batch no. 706169 Mean SD RMSEP 98.39 0.642 0.043 99.91 0.702 0.073 100.61 0.632 0.112 99.94 1.262 0.152
Batch no. 706169 t-test (2.306) F-test (6.380) 0.251 0.331 1.192 3.992 0.251 0.331 1.192 3.992 0.251 0.331 1.192 3.992 0.251 0.331 1.192 3.992
R.Thomis,R. Roets and J. Hoogmartens J.Pharm.
Sci., 731830-1833, (1984). Tabulated values
at p 0.05
28
Part II - Section B
Multi-Way Partial Least Squares (N-PLS)
29
Application of Multi-way Partial Least Squares
Method (N-PLS) on the analysis of ascorbic acid,
paracetamol and p-aminophenol

• A method that decomposes X and predicts Y
• Trilinear decomposition of data matrix is
  performed
• Each triad (score vector and two loading
  vectors) is
• considered a LV

X (I x J x K), t is score vector, wJ and wK are
the vectors of the loadings matrices is the
number of LVs and eijk contains the residuals.
30
Construction of the Calibration Models

• PLS model was constructed at each pH value (from
  3-11)
• N-PLS model was built using multi-way data (the
  three dimensional data matrix)
• The number of LVs and the validation parameters
  of the developed models were studied

31
PLS Models vs. N-PLS Model validation parameters

Ascorbic acid
Paracetamol
PLS_pH3 PLS_pH5 PLS_pH7 PLS_pH9
PLS_pH11 N-PLS
PLS_pH3 PLS_pH5 PLS_pH7
PLS_pH9 PLS_pH11 N-PLS
p-aminophenol acid
PLS_pH3 PLS_pH5 PLS_pH7
PLS_pH9 PLS_pH11 N-PLS
RMSEP Correlation coefficient
32
Analysis of Cevamol Effervescent Tablets and
Comparison with HPLC Method
  Statistical parameters Ascorbic acid Ascorbic acid Ascorbic acid Paracetamol Paracetamol Paracetamol
Batch no. 406137   PLS pH 7 N-PLS HPLC PLS pH 7 N-PLS HPLC
Batch no. 406137 Mean SD RMSEP 99.44 1.164 0.156 100.04 0.846 0.081 99.97 0.567 0.050 100.46 0.961 0.223 99.74 0.914 0.164 99.65 0.47 0.685
Batch no. 406137 t-test(2.306) F-test(6.380) 0.388 4.221 0.892 2.232   0.137 4.185 0.848 3.784  
Batch no. 706169 Mean SD RMSEP 98.99 1.179 0.092 99.70 0.885 0.067 100.56 0.607 0.078 100.13 1.279 0.201 99.73 1.168 0.197 100.20 0.598 0.086
Batch no. 706169 t-test(2.306) F-test(6.380) 0.038 3.780 0.118 2.127   0.911 4.565 0.456 3.810  
R.Thomis,R. Roets and J. Hoogmartens J.Pharm.
Sci., 731830-1833, (1984). Tabulated values
at p 0.05
33
N-PLS vs. The Conventional PLS

• The method verifies the ruggedness, as many
• compounds can be determined at different pH
• values
• Variation in pH will not affect the results as
  in
• the case with PLS
• The method can be used for the determination of
• pH sensitive drugs.

34
Part IIIWavelength Selection Based Methods
An application to a ternary mixture of atenolol,
hydrochlorothiazide and amiloride hydrochloride
35
Structures of The Studied Drugs
H
H
CL
Hydrochlorothiazide
Atenolol
Amiloride hydrochloride
36


Part III -Section A
Genetic Algorithms Based Wavelength Selection
Method (GAs)
37
Genetic Algorithms Based Wavelength
Selection Method (GAs) on the analysis of
atenolol, hydrochlorothiazide and amiloride
hydrochloride
Hydrochlorothiazide 28.8 µg.ml-1
Absorbance spectra
Atenolol 57.6 µg.ml-1
Amiloride hydrochloride 2.88 µg.ml-1
38
Genetic Algorithms
It iteratively transforms a set of mathematical
objects (population), each with an associated
fitness value, into a new population using the
Darwin theory of natural selection after certain
genetic operations, such as crossover and
mutation.

• Each spectrum is considered as chromosome
• Each wavelength is considered as a gene in
• that chromosome and encoded by 0 or 1
• 0 Omitted
• 1 Selected

39
0
1
1
0
0
1
0
0
0
0
0
0
1
1
0
1
1
0
0
0
0
0
0
1
0
1
1
1
0
1
0
0
1
1
1
0
0
1
1
0
0
0
Initial populations
PLS Modelling

• Rank fitness values
• from best to worst
• Survival of the fittest!

Fit value of different solutions
1- 0.001 2- 0.01
. n- 1
Rank of the individuals
11000111 00011101 00010111 11000111
Crossover
1110011 1111100 Mutation
GA operations
New Generation
40
Screening of GAs Parameter Settings by Plackett
- Burman Design
Trial Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Assigned variables (A-F) Unassigned variables (G-K) Percent Improvement
Trial A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K A B C D E F G H I J K Percent Improvement
1 2 3 4 5 6 7 8 9 10 11 12 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 21.53 20.67 21.53 20.65 20.76 33.50 21.83 17.50 21.23 36.98 33.87 20.01
A-population size B- wavelengths at initiation
C-maximum generations D- at convergence E-
mutation rate F-crossover type. Percent
improvement- percentage improvement of RMSEP of
GA-PLS1 in comparison with that of PLS2.
41
Significance of GAs Parameters Settings
Variable Chosen level Average effect (absolute value) Significance (plt0.1) (Ems2.042 )
Population size 16 1.188 No
wavelengths at initiation 20 -1.668 No
Maximum generations 25 -3.732 Yes
at convergence 50 1.078 No
Mutation rate 0.01 3.838 Yes
Crossover type double 0.987 No
42
The Selected Wavelengths by GAs for each compound

Atenolol Hydrochlorothiazide Amiloride
hydrochloride
The selected wavelengths are shown on the mixture
spectra
43
Model validation Application
Statistical parameters Atenolol Hydrochlorothiazide Amiloride Hydrochloride
Validation set Mean SD RMSEP 99.86 0.902 0.418 99.75 0.656 0.149 99.79 0.643 0.013
Atenoretic Capsules Batch no. 50050 Mean SD RMSEP Batch no. 40544 Mean SD RMSEP 98.56 0.319 0.718 98.85 0.449 0.299 98.90 0.524 0.026
Atenoretic Capsules Batch no. 50050 Mean SD RMSEP Batch no. 40544 Mean SD RMSEP 99.51 0.526 0.308 100.31 0.569 0.118 100.62 0.511 0.017
44
Comparison with a Reported HPLC Method
Statistical parameter Atenolol Atenolol hydrochlorothiazide hydrochlorothiazide Amiloride hydrochloride Amiloride hydrochloride
Atenoretic Capsules Batch no. 50050   HPLC GAs - PLS HPLC GAs-PLS HPLC GAs-PLS
Atenoretic Capsules Batch no. 50050 Mean SD RMSEP 98.57 0.417 0.709 98.56 0.319 0.718 99.31 0.362 0.205 98.85 0.449 0.299 99.39 0.645 0.019 98.90 0.524 0.026
Atenoretic Capsules Batch no. 50050 t-test (2.306) F-test (6.380) 0.931 0.370 0.231 1.711 1.540 1.556 0.931 0.370 0.231 1.711 1.540 1.556 0.931 0.370 0.231 1.711 1.540 1.556 0.931 0.370 0.231 1.711 1.540 1.556 0.931 0.370 0.231 1.711 1.540 1.556 0.931 0.370 0.231 1.711 1.540 1.556
Batch no. 50544 Mean SD RMSEP 100.18 0.765 0.290 99.51 0.526 0.308 100.61 0.615 0.177 100.31 0.569 0.118 100.74 0.461 0.022 100.62 0.511 0.017
Batch no. 50544 t-test (2.306) F-test (6.380) 0.154 0.444 0.707 2.113 1.169 1.229 0.154 0.444 0.707 2.113 1.169 1.229 0.154 0.444 0.707 2.113 1.169 1.229 0.154 0.444 0.707 2.113 1.169 1.229 0.154 0.444 0.707 2.113 1.169 1.229 0.154 0.444 0.707 2.113 1.169 1.229
M.S. Bhatia, S.G. Kaskhedikar and S.C.
Chaturvedi Indian Drugs, 34 576-579,
(1997). Tabulated values at p 0.05
45
Model updating
Atenolol and amiloride hydrochloride are
present in another dosage form that contains a
different compound which is chlorothalidone
Is it possible to use the
developed model without reconstruction of the
calibration samples to predict the components
of the new dosage form?

• The model must be updated to deal with the new
  dosage form by including into the model the new
  sources of data variance
• Two parameters must be optimized
• The number of samples used to update the
• model
• The RMSEP value of unknown samples

46
RMSEP vs. the number of samples used to update
the developed model
RMSEP
Sample number
47
Part III - Section B
Net Analyte Signal (NAS)-Based Hybrid Linear
Analysis (HLA)
48
Net Analyte Signal (NAS)-Based Hybrid Linear
Analysis (HLA) On the analysis of atenolol,
hydrochlorothiazide and amiloride hydrochloride
mixture
NAS was introduced by Lorber and can be defined
as

• The part of a component spectrum which is
  orthogonal to the spectra of
• the other components
• The part of the gross signal of the mixture
  spectrum that is useful for
• prediction

49
Applications of net analyte signal (NAS)

• Figures of merit
• Sensitivity
• Selectivity
• Limit of detection
• Analytical sensitivity
• Wavelength selection
• Outlier detection
• Development of new calibration models,
• e.g. hybrid linear analysis (HLA)

50
Hybrid linear analysis (HLA)

• The algorithm combines the advantage of CLS
• of knowing the pure component spectra with
• the modelling advantages of PCR and PLS of
• ignoring all other interferences
• It generates better prediction results than
• those obtained by PLS

51
Spectra of the calibration samples
The outlier plot no outlier detected
F ratios
52
Validation Parameters
53
Net Analyte Signal Regression Plot (NASRP)
Full spectrum
Selected wavelength region
54
Parameters of the of the validation samples by
the selected number of factors
Component Spectral range RMSEP REP Q2 EI
Atenolol Full spectra 0.376 0.782 0.9873 0.1974
Atenolol 250-330 nm 0.220 0.663 0.9897 0.0954
Hydrochlorothiazide Full spectra 0.234 0.546 0.9510 0.1780
Hydrochlorothiazide 331-350 nm 0.158 0.451 0.9973 0.0901
Amiloride hydrochloride Full spectra 0.018 0.045 0.9982 0.1444
Amiloride hydrochloride 286-305, 350-400 0.014 0.039 0.9983 0.0768
55
Results of analysis of atenoretic capsules by NAS
method and results of statistical comparison with
HPLC method
Statistical parameters Atenolol Atenolol Hydrochlorothiazide Hydrochlorothiazide Amiloride hydrochloride Amiloride hydrochloride
Batch no. 50050 Mean SD RMSEP HPLC NAS HPLC NAS HPLC NAS
Batch no. 50050 Mean SD RMSEP 98.57 0.417 0.709 98.87 0.913 0.728 99.31 0.362 0.197 97.96 0.350 0.483 99.36 0.584 0.019 98.80 0.435 0.028
Batch no. 50050 t-test (2.306) F-test (6.380) 0.536 4.768 0.536 4.768 0.001 1.072 0.001 1.072 0.131 1.811 0.131 1.811
Mean SD RMSEP 100.27 0.819 0.347 99.28 0.678 0.471 100.61 0.615 0.177 98.86 1.003 0.384 100.74 0.461 0.022 98.76 0.839 0.037
t-test (2.306) F-test (6.380) 0.071 1.459 0.071 1.459 0.013 2.648 0.013 2.648 0.004 3.291 0.004 3.291
Batch no.40544
M.S. Bhatia, S.G. Kaskhedikar and S.C.
Chaturvedi Indian Drugs, 34 576-579,
(1997). Tabulated values at p 0.05
56
Wavelength Selection Based Models Compared to the
Conventional PLS
PLS GA_PLS NAS
(full rage) NAS (selected regions)
PLS GA_PLS
NAS (full rage) NAS (selected regions)
PLS GA_PLS NAS
(full rage) NAS (selected regions)
Atenolol
Hydrochlorothiazide
Amiloride Hydrochloride
LVs RMSEP
57
The selected wavelengths of GAs ()
vs. NAS ()
Atenolol
Amiloride Hydrochloride
Hydrochlorothiazide
The selected wavelengths are shown on the mixture
spectra
58

Part IV Application of Chromatographic and
Chemometric Methods on the analysis of
noscapine in the presence of its metabolites and
degradates
59
Part IV - Section A
Preparation, Isolation and Identification of
Noscapine Metabolites and Degradates
60
Preparation, Isolation and Identification of
Noscapine Metabolites and Degradates

• An opioid agonist alkaloid
• It is used effectively for its antitussive
  effects in cough syrups
• It has some anticancer activity
• Unlike its related drugs, it has no significant
  pain killing properties

61
Stability of noscapine
Stability Noscapine undergo hydrolysis with
dilute sulphuric acid where cotarnine and
opionic acid are produced
Metabolites Cotarnine and meconine are the major
metabolites of noscapine, they were prepared by
treatment of noscapine with dilute nitric acid
62
Degradation of noscapine with dilute sulphuric
acid
CH3
CH3
Noscapine
CH3
H
Cotarnine
Opionic acid
63
Hydrolysis of noscapine with dilute nitric acid
CH3
Noscapine
CH3
H
Meconine
Cotarnine
64

• Hydrolysis of noscapine with sulphuric and
• nitric acids was done
• Separation was achieved using preparative
• TLC
• Identification of the separated products
• was verified by IR and MASS spectrometry

65
IR Spectrum of Noscapine
CH3
66
IR Spectrum of Cotarnine
O-H
67
IR Spectrum of Meconine
CO
68
IR Spectrum of Opionic Acid
Carboxylic O-H
CO
69
Mass Spectrum of Cotarnine (Mol. Wt. 237)
m /z 236
70
Mass Spectrum of Meconine (Mol. Wt. 194)
m/z 193
71
Mass Spectrum of Opionic (Mol. Wt. 210), Monomer
m/z 209
72
Mass Spectrum of Opionic acid anhydride (Dimmer)
m/z 400
73
Part IV - Section B
Application of Thin Layer Chromatography as a
Stability Indicating Method for the
Determination of Noscapine
74
Application of Thin Layer Chromatography as a
Stability Indicating Method for the Determination
of Noscapine
Separation of noscapine, cotarnine, meconine and
opionic acid was optimized using chloroform
methanol (100.5 v/v)
Opionic acid
Noscapine
Meconine
Cotarnine
A B C D
E F G
75
The chromatogram of noscapine in the
concentration range of 1-10 µg per band
76
Calibration curve of noscapine in the
concentration range of 1-10 µg/band
77
Chromatogram of standard noscapine (10 µg per
band)
The chromatogram of a resolved mixture
Chromatogram of the two metabolites
78
Summary of the validation parameters
Parameter Data
Linearity range (µg per band) 1.0 10.0
Coefficient 1a SE -0.063 x 103 0.035
Coefficient 2b SE -0.213 x 103 0.015
Intercept SE 0.098 x 103 0.020
Correlation Coefficient SE 0.9998 0.030
Limit of detection (µg per band) 0.180
Limit of quantitation (µg per band) 0.900
Mean RSD 100.30 0.889
Precision (RSD ) Intra-day (n 4) Intermediate precision. Inter-day (n 4) 0.890 1.030
Robustness robust
Specificity specific
Following a polynomial regression A a
x 2 bx c Where, A is the integrated peak
area, x is the concentration of noscapine
(µg/band), a and b are slope 1 and 2,
respectively, c is the intercept.
79
Results of analysis of Tusscapine syrup and
statistical comparison with HPLC method.
Batch no. 05038 Statistical parameters Noscapine Noscapine
Batch no. 05038 Statistical parameters HPLC TLC
Batch no. 05038 Mean SD RMSEP 99.89 0.753 0.075 100.13 0.683 0.046
Batch no. 05038 t-test (2.306) F-test (6.380) 0.026 1.214
Batch no. 05036 Mean SD RMSEP 99.20 0.923 0.083 99.83 0.749 0.041
Batch no. 05036 t-test (2.306) F-test (6.380) 0.274 1.519
Tusscapine syrup claimed to contain 15 mg
of noscapine per 5 ml syrup Tabulated
values at p 0.05 D.F.Chollet, C. Ruols and
V. Arena J. Chromatogr.,70181-85, (1997)
80
Part IV - Section C
Multivariate Curve Resolution (MCR) of
Excitation - Emission Matrices (EEM) of
Noscapine, Cotarnine, Meconine and Opionic Acid
81
MCR of Excitation - Emission (EEM) Matrices of
Noscapine, cotarnine, meconine and opionic acid
They are techniques that is concerned with the
extraction of the pure spectra of the components
and their corresponding concentration profiles
using ALS algorithm
MCR-ALS

• (MCR-ALS) is a powerful tool for resolving two
  and three
• way data arrays

• MCR-ALS method is based on a bilinear data
  decomposition
• D CST E
• 
  Where, D (I x J) is the data matrix, C (I
  x F) is the concentration matrix,
• 
  ST (F x J) is the pure spectral profiles and
  E (I x J) is the matrix of residuals

82

• The four compounds are fluorescent
• They are severely overlapped over wide
  excitation
• range

Emission spectra of noscapine400
ng.ml-1,cotarnine 20 ng.ml-1, meconine 20
ng.ml-1 and opionic acid 20 ng.ml-1
Scanning emission at different ?ex can be helpful
in the resolution of the four compounds
83




3D Emission Spectra of The Calibration Set
1
2
3

5
4
6
7
8
?ex 1 300 nm 2 305 nm 3 310 nm 4 315 nm 5 320 nm 6 325 nm 7 330 nm 8 335 nm


84

• PLS models at each excitation were described by
  1 LV

• MCR-ALS applied on Augmented data matrix

EX1 EX2 EX3 EX4 EX5 EX6 EX7 EX8
samples
Wavelength (nm)

• The data matrices were arranged in a row
• Matrix augmentation provides much better
  resolution of pure profiles
• (spectral and concentration) than multivariate
  curve resolution of
• individual data matrices

85
The Emission Scans of The Pure Compounds vs. The
Extracted by the Model at ?ex 300 nm
The Pure Spectra
The Extracted Spectra

86
The Resolved Emission Spectra of The Four
Compounds at Different Excitation
87
Model Parameter
parameter value
Lack of fitness (Lof , PCA) Lack of fitness (Lof , EXP) Explained variance SD 1.758 0.012 99.97 0.011
Model Validation
Statistical parameters Noscapine Cotarnine Meconine Opionic acid
Mean SD RMSEP 100.08 0.733 2.741 100.55 1.024 0.217 100.46 1.023 0.219 100.19 1.091 0.229
88
Results of analysis of Tusscapine syrup and
statistical comparison with the developed TLC
method.
Batch no. 05038 Statistical parameters Noscapine Noscapine
Batch no. 05038 Statistical parameters MCR-ALS TLC
Batch no. 05038 Mean SD RMSEP 98.89 0.753 0.004 100.13 0.683 0.046
Batch no. 05038 t-test (2.306) F-test (6.380) 0.026 1.214
Batch no. 05036 Mean SD RMSEP 99.20 0.923 0.004 98.45 0.749 0.041
Batch no. 05036 t-test (2.306) F-test (6.380) 0.274 1.519
Tabulated values at p 0.05 tusscapine syrup
claimed to contain 15 mg of noscapine per 5 ml
syrup
89
Maha Hegazy