Application of similarity parameters to discriminate and classify herbal medicines

Transcription

1 CHIMIOMETRIE XVII January 18-2th, Namur, Belgium Application of similarity parameters to discriminate and classify herbal medicines Johan Viaene 1, Goedele Alaerts 1, Bieke Dejaegher 1,2 Yvan Vander Heyden 1 1 Department of Analytical Chemistry and Pharmaceutical Technology (FABI), Center for Pharmaceutical Research (CePhar), Vrije Universiteit Brussel (VUB), Laarbeeklaan 13, B-19 Brussels, Belgium 2 Laboratory of Instrumental Analysis and Bioelectrochemistry, Institute of Pharmacy, Université Libre de Bruxelles (ULB), Campus Plaine CP25/6, Boulevard du Triomphe, B-15 Brussels, Belgium

2 Introduction Chromatographic fingerprints

3 Introduction Chromatographic fingerprints Applications Similarity analysis Distance- and correlation based parameters a Euclidean distance congruence coefficient Standardized Euclidean distance Mahalanobis distance correlation coefficient Adapted Similarity score a Alaerts et al., Journal of Chromatography B, 91 (212) 61 7

4 Case study 3 Curcuma types in Pharmacopoeia of the People s Republic of China: 32 Rhizoma Curcumae longae (Jianghuang) samples Species: C. longa Plant part: dried rhizome 29 Rhizoma Curcumae (Ezhu) samples Species: C. phaeocaulis/c. kwangsiensis/c. wenyujin Plant part: dried rhizome 32 Radix Curcumae (Yujin) samples Species: C. wenyujin/c. longa/c.kwansiensis/c.phaeocaulis Plant part: dried root tuber

5 Case study 3 Curcuma types in Pharmacopoeia of the People s Republic of China: 32 Rhizoma Curcumae longae (Jianghuang) samples Species: C. longa Plant part: dried rhizome 29 Rhizoma Curcumae (Ezhu) samples Species: C. phaeocaulis/c. kwangsiensis/c. wenyujin Plant part: dried rhizome 3 Traditional Chinese Medicines with indications 32 Radix Curcumae (Yujin) samples Species: C. wenyujin/c. longa/c.kwansiensis/c.phaeocaulis Plant part: dried root tuber

6 Aim Similarity parameters (Euclidean distance) applicable to distinguish the 3 types of Curcuma samples?

7 Registration of fingerprint data Preparation of extracts 1.5 g powdered and sieved 2. ml extraction solvent: ethanol/water (8/2 V/V) 3 min ultrasonic extraction Filter extract + adjust volume to 25. ml (extraction solvent)

8 Registration of fingerprint data High Pressure Liquid Chromatography LaChrom Elite VWR Hitachi (L-213 pump, L-22 autosampler, L-235 column oven, L-2455 diode array detector) 4 coupled Chromolith Performance RP-18e C18 columns (1 x 4.6 mm) + guard column at 35 C Injection volume: 1 µl Mobile phases: water (A) and acetonitrile (B), both +.5% trifluoroacetic acid Gradient: min: 5% B; 5 min: 33.5%B; 36 min: 6%B; 5-6 min: Flow rate: 1mL/min Detection wave length: 214 nm Each sample analyzed in duplicate Daily blank injection in duplicate

9 Preprocessing Blank correction of the fingerprints Correlation optimized warping (COW) per Curcuma type Alignment of the 3 types (COW)

10 Absobance (mau) Absobance (mau) Absobance (mau) Fingerprint data analysis BLANK-CORRECTED DATA 18 Fingerprints of Clon raw data C. longa Time (min) 16 Fingerprints of Crhi raw data C. rhizoma Time (min) 1 Fingerprints of Crad raw data C. radix Time (min)

11 Fingerprint data analysis DATA AFTER COW BLANK-CORRECTED DATA Curcuma longa fingerprints after warping C. longa 8 6 Absorbance (mau) Absobance (mau) Fingerprints of Clon raw data Time (min) C. rhizoma Time (min) Curcuma rhizoma fingerprints after warping 16 Absorbance (mau) Absobance (mau) Fingerprints of Crhi raw data 3 Time (min) Time (min) Curcuma radix fingerprints after warping Fingerprints of Crad raw data C. radix 2 Absorbance (mau) Absobance (mau) Time (min) Time (min)

12 Division in calibration and test set Per sample: calculation of average (n=2) fingerprint Division samples in calibration and test set: duplex algorithm Check distribution calibration & test set samples: Principal Component Analysis

13 PC2 Fingerprint data analysis DISTRIBUTION CALIBRATION & TEST SET FINGERPRINTS IN PC1-PC2 SPACE C. longa cal C. rhizoma cal C. radix cal C. longa test C. rhizoma test C. radix test PC1

14 PC2 Fingerprint data analysis DISTRIBUTION CALIBRATION & TEST SET FINGERPRINTS IN PC1-PC2 SPACE C. longa cal C. longa test PC1

15 PC2 Fingerprint data analysis DISTRIBUTION CALIBRATION & TEST SET FINGERPRINTS IN PC1-PC2 SPACE PC C. rhizoma cal C. rhizoma test 19

16 PC2 Fingerprint data analysis DISTRIBUTION CALIBRATION & TEST SET FINGERPRINTS IN PC1-PC2 SPACE C. radix cal C. radix test PC1

17 Similarity analysis Calculation of Euclidean distance (d) between each pair of fingerprints

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19 Similarity analysis Upper Warning and Control limits (UWL & UCL a ) for d per C. type UWL = d x s d 97.5% of d values per C. type < UWL C.type UCL = d +3.9 x s d 99.85% of d values per C. type < UCL C.type a Alaerts et al., Journal of Chromatography B, 91 (212) 61 7

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22 UWL + UCL C. longa

23 UWL + UCL C. longa

24 UWL + UCL C. longa UWL+UCL C. rhizoma

25 UWL + UCL C. longa UWL+UCL C. rhizoma

26 UWL + UCL C. longa UWL+UCL C. rhizoma UWL + UCL C. radix

27 Similarity analysis How to use these limits?

28 Similarity analysis How to use these limits? Green horizontals: UWL C.longa Red horizontals: UCL C.longa d from C. longa calibration samples d from C. rhizoma calibration samples d from C. radix calibration samples to C. longa calibration samples to C. longa calibration samples to C. longa calibration samples Global behavior relative to UWL C. longa and UCL C. longa differs per C. type!!!

29 Similarity analysis How to use these limits? Green horizontals: UWL C.rhizoma Red horizontals: UCL C.rhizoma d from C. longa calibration samples d from C. rhizoma calibration samples d from C. radix calibration samples to C. rhizoma calibration samples to C. rhizoma calibration samples to C. rhizoma calibration samples Global behavior relative to UWL C. rhizoma and UCL C. rhizoma differs per C. type!!!

30 Similarity analysis How to use these limits? Green horizontals: UWL C.radix Red horizontals: UCL C.radix d from C. longa calibration samples d from C. rhizoma calibration samples d from C. radix calibration samples to C. radix calibration samples to C. radix calibration samples to C. radix calibration samples Global behavior relative to UWL C. radix and UCL C. radix differs per C. type!!!

31 Absorbance (mau) Fingerprint data analysis Similarity analysis Identification of outliers d of C. radix calibration samples to C. radix calibration samples Green horizontals: WL C. radix Red horizontals: CL C. radix 3 25 OUTLIERS! Curcuma radix fingerprints after warping Time (min)

32 Similarity analysis Identification of outliers d of C. radix calibration samples to C. radix calibration samples Green horizontals: WL C. radix Red horizontals: CL C. radix

33 Similarity analysis Deriving discrimination/classification rules Sample Known Class % UCL C. longa exceedings % UWL C. longa exceedings % UCL C. rhizoma exceedings % UWL C. rhizoma exceedings % UCL C. radix exceedings % UWL C. radix exceedings 1 C. longa C. longa C. longa C. longa C. longa C. longa C. longa C. longa

34 Similarity analysis Deriving discrimination/classification rules To distinguish C. samples only UCL values necessary C. longa sample if: d to 1% of C. longa calibration samples exceeds UCL C. longa d to > 1% of C. rhizoma calibration samples exceeds UCL C. rhizoma d to > 1% of C. radix calibration samples exceeds UCL C. radix C. rhizoma sample if: d to 1% of C. rhizoma calibration samples exceeds UCL C. rhizoma d to > 1% of C. radix calibration samples exceeds UCL C.radix C. radix sample if: d to > 1% of C. longa calibration samples exceeds UCL C.longa d to 1% of C. rhizoma calibration samples exceeds UCL C. rhizoma d to 1% of C. radix calibration samples exceeds UCL C. radix Samples not compliant to these rules: outliers

35 Application of Euclidean distance based classification/ discrimination rules and comparison with classical discrimination/classification methods Linear and Quadratic Discriminant Analysis Soft Independent Modelling by Class Analogy (nearest class) DISCRIMINATION/ CLASSIFICATION TECHNIQUE CCR CALIBRATION SET CCR TEST SET EUCLIDEAN DISTANCE LDA QDA SIMCA DISCRIMINATION/ CLASSIFICATION TECHNIQUE NUMBER OF RETAINED LV/PC LDA 16 QDA 1 SIMCA 1 (C. longa), 2 (C. rhizoma), 3 (C. radix) Euclidean distance based classification best discriminant analysis technique (QDA)

36 Conclusions Euclidean distance was succesfully used to eliminate outliers in the Radix Curcumae fingerprints Rizoma Curcumae longae, Rhizoma Curcumae and Radix Curcumae samples can be distinguished based on Euclidean distances Euclidean distance based discrimination/classification performs almost equal to best discrimination technique

37 Acknowledgements To the co-authors for their contributions! To you for your kind attention