The Powerful Role of Chemometrics on Analy7cal Instrumental Data in Quality Assessment of Herbal Extracts. Ernesto Marco Mar7nelli Roberto Pace
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1 The Powerful Role of Chemometrics on Analy7cal Instrumental Data in Quality Assessment of Herbal Extracts Ernesto Marco Mar7nelli Roberto Pace
2 XMETRICS THE DATA FLOOD GENERATED BY MODERN INSTRUMENTATION/OBSERVATIONS PRODUCE LARGE QUANTITY OF NUMBERS TO UNDERSTAND AND QUANTIFY PHENOMENONS AROUND US. The use of mul7variate analysis in the discipline X: Psychrometrics (used in psychology) Taxometrics (used in taxonomy) Biometrics (used in biology) Technometrics (used in engineering) Chemometrics (used in chemistry) Sta7s7cal, mathema7cal or graphical technique, considers mul7ple variables
3 CHEMOMETRICS USE OF STATISTICS AND MATHEMATICS (MAINLY MATRIX CALCULATIONS) IN CHEMICAL SCIENCES, TO MEASURE AND INTERPRET CHEMICAL DATA (ALSO USED FOR BIOLOGICAL DATA)
4 CHEMOMETRICS WHY USE CHEMOMETRICS? SIMPLIFY THE OVERALL DATA MAKING THEM EASIER TO VISUALIZE BY REDUCING THE DIMENSION CHROMATOGRAPHY AND SPECTROSCOPY WILL ALWAYS YIELD MULTIVARIATE DATA CONTAINING HIDDEN INFORMATION CURRENT NEED TO INTERPRET COMPLEX CHEMICAL AND BIOLOGICAL PHENOMENON COMPLEX SYSTEMS WITH MANY INTERACTIONS ARE COMMON IN SCIENCE
5 CHEMOMETRICS THE DATA FLOOD GENERATED BY MODERN ANALYTICAL INSTRUMENTATION PRODUCES LARGE QUANTITY OF NUMBERS TO UNDERSTAND AND QUANTIFY PHENOMENONS AROUND US. HPLC (or GC) peaks IR ( FT/IR)/ UV 10 3 DATA POINTS HPLC/GC-MS DATA POINTS NMR DATA POINTS
6 CHEMOMETRICS RECENTLY EUROPEAN PHARMACOPOEIA AND USP HAVE INTRODUCED THE USE OF CHEMOMETRICS AS POWERFUL TOOL FOR MULTIVARIATE EVALUATION OF THE ANALYTICAL DATA
7 CHEMOMETRICS AND PH.EUR CHEMOMETRIC METHODS APPLIED TO ANALYTICAL DATA PCA is the most used between the various Chemometric Methods
8 PCA The most important and essen7al mul7variate method, is named: Principal Component Analysis (PCA). With PCA, we move from the one-dimensional vision of a problem to its simplified mul7dimensional version with a minimal loss (or improved) informa7on. PCA
9 PRINCIPAL COMPONENT ANALYSIS (PCA) PCA was invented in 1901 by Karl Pearson it was later developed (and named) by Harold Hotelling in the 1930s
10 DATAMATRIX FOR PCA PROCESSING Variables, e.g. NMR or HPLC data (columns) LABEL Delphinidin-3-O-gal Delphinidin -3-O-glu Cyanidin-3-O-gal Delphinidin -3-O-ara Cyanidin -3-O-glu Petunidin-3-O-gal Cyanidin -3-O-ara Petunidin -3-O-glu Delphinidin Peonidin-3-O-gal Petunidin -3-O-ara Peonidin-3-O-glu Malvidin-3-O-gal Peonidin-3-O-ara Malvidin-3-O-glu Cyanidin Malvidina-3-O-ara Petunidin Peonidin Malvidin Samples (rows) BIL 1 5,91 6,00 3,40 5,50 3,80 1,75 2,81 3,64 0,21 0,37 1,23 1,41 1,36 0,17 3,39 0,13 0,85 0,04 0,02 0,03 BIL 2 4,09 4,75 2,46 3,38 3,33 1,33 1,93 3,19 0,90 0,29 0,84 1,49 1,07 0,13 3,16 0,79 0,66 0,36 0,14 0,33 BIL 3 5,69 5,36 3,28 4,20 3,47 1,81 2,20 3,31 0,42 0,41 0,99 1,33 1,60 0,16 2,95 0,28 0,69 0,13 0,04 0,11 BIL 4 5,66 5,32 4,02 4,59 4,16 1,85 2,88 3,62 0,29 0,45 1,17 1,81 1,49 0,20 3,77 0,21 0,90 0,09 0,03 0,07 BIL 5 5,20 5,28 3,62 4,51 4,02 1,84 2,81 3,72 0,18 0,45 1,18 1,82 1,62 0,20 4,01 0,13 0,97 0,05 0,02 0,04 BIL 6 5,44 4,96 3,46 4,20 3,50 1,76 2,47 3,27 0,21 0,41 1,10 1,55 1,45 0,18 3,44 0,12 0,88 0,05 0,02 0,04 BIL 7 5,63 5,38 3,72 4,66 3,94 1,90 2,77 3,61 0,19 0,44 1,20 1,74 1,54 0,20 3,72 0,12 0,96 0,05 0,02 0,04 BIL 8 4,90 5,21 3,45 3,86 3,90 1,67 2,44 3,50 0,17 0,44 1,01 1,73 1,39 0,19 3,52 0,12 0,80 0,05 0,02 0,04 BIL 9 5,19 4,73 3,34 4,27 3,26 1,73 2,34 3,22 0,63 0,39 1,04 1,42 1,52 0,17 3,45 0,44 0,80 0,21 0,06 0,18 VM-IND 1 5,15 5,50 3,58 4,62 3,84 1,75 2,81 3,62 0,08 0,40 1,16 1,59 1,30 0,20 3,51 0,03 0,86 0,02 0,03 0,02 VM-IND 2 4,93 5,24 3,80 4,40 4,13 1,75 2,93 3,55 0,05 0,47 1,14 1,86 1,33 0,23 3,58 0,04 0,92 0,02 0,03 0,03 VM-IND 3 5,15 5,51 3,78 4,33 4,12 1,75 2,87 3,59 0,07 0,44 1,11 1,75 1,31 0,21 3,55 0,03 0,85 0,01 0,02 0,02 VM-IND 4 5,12 5,48 3,76 4,24 4,12 1,74 2,83 3,58 0,07 0,44 1,09 1,76 1,30 0,21 3,55 0,03 0,84 0,01 0,03 0,02 VM-IND 5 4,92 4,98 3,73 4,48 3,91 1,70 2,95 3,35 0,07 0,44 1,15 1,65 1,39 0,21 3,40 0,03 0,90 0,01 0,03 0,02 VM-IND 6 4,90 4,96 3,62 4,53 3,82 1,68 2,90 3,36 0,08 0,44 1,15 1,62 1,40 0,20 3,47 0,04 0,91 0,02 0,03 0,02 VM-IND 7 4,77 5,02 3,77 4,11 4,06 1,71 2,82 3,43 0,10 0,47 1,10 1,80 1,35 0,22 3,51 0,06 0,85 0,03 0,03 0,03 VM-IND 8 4,85 5,13 3,80 4,18 4,10 1,71 2,83 3,50 0,10 0,46 1,10 1,81 1,37 0,22 3,56 0,06 0,86 0,03 0,03 0,03 VM-IND 9 4,91 5,53 3,68 4,25 4,17 1,74 2,80 3,70 0,06 0,44 1,11 1,81 1,30 0,22 3,64 0,04 0,84 0,02 0,03 0,02 VM-IND 10 4,83 5,16 3,81 4,49 4,13 1,71 2,98 3,45 0,09 0,48 1,15 1,78 1,35 0,22 3,41 0,05 0,90 0,02 0,03 0,03 VM-IND 11 4,17 4,47 3,31 3,62 3,67 1,57 2,54 3,11 0,08 0,44 1,03 1,76 1,33 0,21 3,34 0,04 0,86 0,02 0,02 0,03 VM-IND 12 4,95 5,64 3,53 4,52 4,05 1,60 2,89 3,67 0,09 0,39 1,10 1,60 1,26 0,18 3,46 0,04 0,81 0,02 0,03 0,02 VM-IND 13 4,90 5,57 3,48 4,42 4,03 1,62 2,84 3,63 0,08 0,39 1,09 1,60 1,25 0,20 3,42 0,03 0,80 0,01 0,03 0,02 VM-IND 14 4,84 5,10 3,70 4,49 4,02 1,65 2,96 3,41 0,09 0,42 1,13 1,67 1,33 0,20 3,45 0,04 0,90 0,02 0,03 0,02 VM-IND 15 4,83 5,13 3,66 4,43 4,00 1,67 2,94 3,46 0,09 0,43 1,13 1,70 1,35 0,20 3,51 0,05 0,91 0,02 0,03 0,03 VM-IND 16 4,93 5,23 3,80 4,17 4,11 1,69 2,87 3,47 0,08 0,45 1,09 1,77 1,34 0,21 3,46 0,04 0,88 0,02 0,03 0,02 VM-IND 17 4,89 5,10 3,78 4,29 4,05 1,68 2,91 3,41 0,07 0,46 1,11 1,76 1,35 0,21 3,42 0,04 0,89 0,01 0,03 0,02 VM-IND 18 4,96 5,31 3,78 4,10 4,13 1,70 2,81 3,52 0,07 0,45 1,08 1,78 1,34 0,20 3,51 0,03 0,87 0,02 0,03 0,02 VM-IND 19 4,86 5,07 3,86 4,33 4,08 1,66 2,96 3,35 0,08 0,46 1,11 1,75 1,34 0,21 3,35 0,05 0,89 0,02 0,03 0,02 VM-IND 20 4,83 5,00 3,82 4,23 4,09 1,74 2,91 3,44 0,09 0,47 1,13 1,83 1,39 0,22 3,59 0,05 0,92 0,02 0,02 0,03 VM-IND 21 4,85 5,00 3,88 4,19 4,09 1,75 2,92 3,47 0,09 0,48 1,13 1,84 1,42 0,22 3,63 0,05 0,91 0,02 0,02 0,03 VM-IND 22 4,88 5,30 3,61 4,27 4,03 1,71 2,82 3,56 0,10 0,44 1,11 1,72 1,31 0,20 3,48 0,05 0,86 0,02 0,02 0,02 VM-IND 23 4,77 4,96 3,80 4,42 4,09 1,70 3,00 3,45 0,10 0,47 1,17 1,84 1,47 0,22 3,68 0,06 0,97 0,03 0,03 0,03 VM-IND 24 4,80 4,99 3,82 4,40 4,10 1,70 3,01 3,48 0,10 0,47 1,16 1,84 1,49 0,22 3,71 0,06 0,97 0,03 0,04 0,03 VM-IND 25 4,73 5,01 3,60 4,35 3,95 1,64 2,87 3,39 0,09 0,42 1,11 1,67 1,32 0,20 3,44 0,05 0,90 0,02 0,03 0,02 VM-IND 26 4,92 5,76 3,46 4,49 4,12 1,61 2,89 3,70 0,08 0,36 1,09 1,56 1,20 0,18 3,43 0,03 0,81 0,02 0,02 0,02 VM-IND 27 4,78 5,11 3,78 4,40 4,11 1,71 2,96 3,46 0,04 0,47 1,14 1,86 1,30 0,23 3,51 0,04 0,89 0,02 0,03 0,03 VM-IND 28 4,15 4,43 3,37 3,60 3,74 1,57 2,61 3,11 0,09 0,45 1,04 1,78 1,31 0,22 3,35 0,05 0,88 0,03 0,03 0,03 VM-IND 29 4,77 4,97 3,84 4,40 4,10 1,71 3,03 3,47 0,10 0,47 1,16 1,85 1,47 0,22 3,69 0,06 0,96 0,03 0,04 0,03 VM-IND 30 4,76 4,96 3,79 4,39 4,07 1,71 2,99 3,46 0,10 0,47 1,16 1,83 1,47 0,22 3,68 0,06 0,96 0,03 0,04 0,03 VM-IND 31 4,74 5,10 3,67 4,30 3,96 1,65 2,85 3,41 0,07 0,43 1,11 1,69 1,27 0,21 3,32 0,04 0,82 0,02 0,03 0,03 VM-IND 32 4,79 5,15 3,61 4,39 3,94 1,66 2,86 3,44 0,08 0,42 1,12 1,67 1,27 0,21 3,32 0,04 0,83 0,02 0,03 0,02 VM-IND 33 4,69 4,94 3,67 4,14 3,96 1,64 2,81 3,34 0,07 0,44 1,06 1,72 1,27 0,21 3,30 0,03 0,83 0,01 0,03 0,02 VM-IND 34 4,66 5,08 3,72 4,05 4,11 1,65 2,86 3,46 0,07 0,43 1,07 1,74 1,29 0,21 3,41 0,03 0,84 0,01 0,03 0,02 VM-IND 35 4,84 5,55 3,69 4,24 4,24 1,71 2,84 3,76 0,08 0,44 1,11 1,81 1,33 0,21 3,70 0,04 0,86 0,02 0,02 0,02 VM-IND 36 4,71 5,39 3,48 4,13 4,01 1,68 2,69 3,63 0,10 0,43 1,09 1,72 1,32 0,21 3,55 0,05 0,84 0,02 0,02 0,02 VM-IND 37 4,88 5,36 3,68 4,32 4,15 1,74 2,86 3,65 0,09 0,46 1,14 1,82 1,41 0,22 3,66 0,04 0,90 0,02 0,02 0,02 VM-IND 38 5,07 5,62 3,63 4,39 4,14 1,75 2,80 3,74 0,08 0,43 1,12 1,76 1,34 0,21 3,67 0,03 0,87 0,02 0,03 0,02
11 HOW PCA WORKS ONE OBJECT CAN BE REPRESENTED AS A POINT INTO A MULTIDIMENSIONAL SPACE PCA IS BASED ON THE POSSIBILITY TO REDUCE THE NUMBER OF THE VARIABLES (X1,X2,X3, ) WITH A MINIMAL LOSS OF INFORMATION AND WITH THE POSSIBILITY TO ELIMINATE THE RANDOM VARIABILITY X3 X1 X2 X3 X1 X2
12 HOW PCA WORKS Each point can be projected down onto the direc7ons (components pc1 e pc2) which explain the maximum of the variance so that to reduce the dimension of the ORIGINAL variables PRINCIPAL COMPONENTS ASSOCIATED VARIANCE X3 PC1 PC1 = C1 * X1 + C2 * X2 + C3 * X3 PC2 = C1 * X1 + C2 * X2 + C3 * X3 V1% V2% PC3 =.. V3% PC2 X1 V1>V2>V3 X2 X = ORIGINAL VARIABLES COEFFICIENTS OF CORRELATION (LOADINGS)
13 REDUCING THE DATA DIMENSION TO PUT INTO EVIDENCE GROUPS SEPARATION Projec7on of the points down on the principal components (pc1 e pc2) which iden7fy the bidimensional space where the points present the maximum variance. X3 PC1 PC2 PC2 X1 PC1 X2
14 HERBAL EXTRACTS (MANUFACTURING PROCESS) herbal startng material (e.g. V. myr'llus L. frozen fruits) extrac7on solvent curng, extrac7ng primary extract concentrated sot extract dry extract concentra7on purificaton drying, milling standardiza7on quan7fica7on standardized, quantfied, other dry extract, refined/purified
15 HERBAL DRUG EXTRACTS They are complex mul7component mixtures (generally secondary plant metabolites) whose composi7on depends mainly on the plant species, part of the plant within a natural variability and the manufacturing process. Many substances belong to the same phytochemical class (flavonols, flavones, triterpenes, alkaloids etc.) Are present in a characteris7c pavern of cons7tuents in the same plant/part of the plant. e.g.: Poliphenols Triterpenoids
16 SYNERGIC EFFECTS MANY DATA SHOWS THAT BIOLOGICAL ACTIVITY OF THESE PREPARATIONS MIGHT RESULT FROM SYNERGY OF ACTIVE COMPOUNDS RATHER THAN FROM A SINGLE CHEMICAL ENTITY. EXAMPLES: 1. THE ANTIMICROBIAL ACTIVITY OF ALKALOID BERBERINE IS 100 TIMES ENHANCED BY 5 - METHOXYHYDNOCARPIN (5 MHC). 2. SYNERGISTIC EFFECTS ARE ALSO KNOWN TO OCCUR IN THE ACTIVITY OF GINKGO BILOBA DRY EXTRACTS FOR THEIR ANTICLASTOGENIC, ANTIOXIDANT, VASOREGULATORY, COGNITION ENHANCING, STRESS ALLEVIATING AND GENE- REGULATORY EFFECTS (CURTIS ET AL., 1999)
17 HERBAL EXTRACTS HO COOH O HO OH O OH OH HO OH OH O OR OH O HO O OH O HO 8 9 OH OH O OH O OH 10 HO HO 11 OH O OH CH3 R OH O OH HO O O O CH2R Generally it is not possible to iden7fy a substance responsible for the biological/ physiological effect: all substances generally contribute in a synergis7c way to parmacological effect and / or mi7ga7ng toxic effects. Isolated individual consttuents generally behave in different way.
18 HERBAL EXTRACTS 4 5 They are biologically ac7ve ingredients in their entrety
19 STARTING HERBAL ID TO ASSURE QUALITY OF FINAL EXTRACT
20 STARTING MATERIAL-HERBAL DRUG a) DEFINITION: A QUALITATIVE STATEMENT OF THE BOTANICAL SOURCE, PLANT PART USED AND ITS STATE (E.G. WHOLE, REDUCED, POWDERED, FRESH, DRY). IT IS ALSO IMPORTANT TO KNOW THE GEOGRAPHICAL SOURCE(S) AND THE CONDITIONS UNDER WHICH THE HERBAL SUBSTANCE IS OBTAINED. GENUS EX. VACCINIUM MYRTILLUS L. FRESCH FRUITS (FROZEN) ORIGIN: EUROPE SPECIES AUTHOR PART OF THE PLANT
21 VACCINIUM MYRTILLUS ANTHOCYANINS ARE PRESENT IN A CHARACTERISTIC PATTERN OF CONSTITUENTS 15 ANTHOCYANINS
22 VACCINIUM MYRTILLUS ANTHOCYANINS ARE PRESENT IN A CHARACTERISTIC PATTERN OF CONSTITUENTS HPLC of V.myr7llus anthocianins
23 VACCINIUM MYRTILLUS bilberries are unique berries - HPLC method is suitable to dis7nguish V.myr'llus from other berries Vaccinium myr7llus Vaccinium corymbosum, Highbush blueberry Vaccinium angus7folium, Lowbush blueberry Vaccinium macrocarpon, American cranberry
24 USE OF PCA FOR HERBAL IDENTIFICATION (SPECIES) Can be used to support Herbal Iden7fica7on for correct species when other methods are not suitable.
25 BOSWELLIA SERRATA RESIN Boswellic acids (Ac7ve triterpenoids)
26 BOSWELLIA SERRATA TYPICAL TRITERPENOIDS (HPLC) beta-ba Dehydro-Boswellic Ac betaaba LA - alpha-ba alphaaba ALA Minutes AU 210 nm betakba betaakba alphakba alphaakba Minutes AU 250 nm
27 BOSWELLIA SERRATA RESIN PC2 = C1 * X1 + C2 * X2 + C3 * X3+ +C9 X9 Boswellia spp Boswellia serrata PC1 = C1 * X1 + C2 * X2 + C3 * X3+.+C9*X9 PCA of HPLC profile (HPLC peaks areas = X vectors)
28 BOSWELLIA SERRATA RESIN PC2 = C1 * X1 + C2 * X2 + C3 * X3+ +C9 X9 PC1 = C1 * X1 + C2 * X2 + C3 * X3+.+C9*X9
29 PC3 = C1 * X1 + C2 * X2 + C3 * X3+ +C9 X9 BOSWELLIA SERRATA RESIN b1 c1 1 b2 PC2 = C1 * X1 + C2 * X2 + C3 * X3+ +C9 X9
30 BOSWELLIA SERRATA RESIN LOADINGS for PC1 VARIABLES (Boswellia serrata cons7tuents)
31 BOSWELLIA SERRATA RESIN PC1 = C1 * X1 + C2 * X2 + C3 * X3+.+C9*X9 PC2 PC1 LOADINGS for PC1
32 BOSWELLIA SERRATA RESIN VARIANCE (%) COMPONENT n.
33 PCA AS TOOL FOR OVERALL HERBAL EXTRACTS VARIABILITY EVALUATION Can be used as a powerful QUALITY CONTROL CHART to support the overall consistency of the composi7on of the extract.
34 PCA AS TOOL FOR OVERALL HERBAL EXTRACTS VARIABILITY EVALUATION Typical 1 H-NMR spectra of Vaccinium myr7llus Dry Extract aligned and normalized NMR signals
35 Typical 1 H-NMR spectra of V. myr'llus Dry Extract TYPICAL 1 H-NMR SPECTRA OF VACCINIUM MYRTILLUS DRY EXTRACT polyphenols (anthocyanins/anthocyanidins)
36 PC percent explained variance PCA AS TOOL FOR OVERALL HERBAL EXTRACTS VARIABILITY EVALUATION PCA of 1 H-NMR spectra of Vaccinium myr'llus Dry Extracts VM-IND VM-IND VM-IND 3 VM-IND 22 VM-IND VM-IND VM-IND 7 VM-IND VM-IND 5 VM-IND 146 VM-IND VM-IND VM-IND % conf ellipse 95% conf ellipse Manufacturer I VM-IND VM-IND VM-IND 21 VM-IND 1315 VM-IND VM-IND 1 VM-IND VM-IND 25 2 VM-IND 24 VM-IND BIL 8 8 BIL PC percent explained variance BIL 4 BIL 2 OTHERS BIL 7 BIL 11 BIL 6 BIL 1 BIL 12 BIL 10 BIL 9 BIL 5
37 PCA AS TOOL FOR OVERALL HERBAL EXTRACTS VARIABILITY EVALUATION PCA of 1 H-NMR spectra of Vaccinium myr7llus Dry Extracts 0.08 LOADING Others Manufacturer I LOADINGS of PC #10 4
38 USE OF PCA FOR HERBAL EXTRACTS Can be used as a powerful tool to prove/support overall phytoequivalence of two extracts
39 HERBAL DRUG EXTRACTS OTHER EXTRACTS SERENOA REPENS (W. BARTRAM) SMALL, OILY EXTRACT TOTAL FATTY ACIDS: MINIMUM 80.0 PER CENT (ANHYDROUS EXTRACT) LAURIC ACID: MINIMUM 23.0 PER CENT (ANHYDROUS EXTRACT) TOTAL STEROLS, EXPRESSED AS Β-SITOSTEROL: MINIMUM 0.20 PER CENT (ANHYDROUS EXTRACT) Β-SITOSTEROL: MINIMUM 0.10 PER CENT (ANHYDROUS EXTRACT)
40 HERBAL EXTRACTS SERENOA REPENS (W. BARTRAM) EXTRACT METHYL LINOLEATE METHYL CAPROATE METHYL LAURATE METHYL PALMITATE METHYL LINOLENATE METHYL CAPYLATE METHYL CAPRATE METHYL MYRISTATE METHYL PALMITOLEATE METHYL MARGARATE METHYL OLEATE METHYL STEARATE TMSI DER CHOLESTEROL CAMPESTEROL STIGMASTEROL Β-SITOSTEROL SITOSTANOL
41 PRINCIPAL COMPONENT ANALYSIS (PCA) SERENOA REPENS EXTRACT (CO 2 SUPERCRITICAL AND HEXANE EXTRACTS PHYTOEQUIVALENCE VERIFICATION) NMR FINGERPRINTING (QC) Coefficient of correla7on with reference NMR Supercri7cal CO2extrac7on Hexane extrac7on
42 PRINCIPAL COMPONENT ANALYSIS (PCA) SERENOA REPENS EXTRACT (CO 2 SUPERCRITICAL AND HEXANE EXTRACTS PHYTOEQUIVALENCE VERIFICATION) NMR FINGERPRINTING (QC) PCA ANALYSIS Supercri7cal CO 2 extrac7on Hexane extrac7on
43 POTENTIAL USE OF PCA Can be used to support genuinity of the extracts or To put into evidence poten7al adultera7ons
44 FINGERPRINTING VARIABILITY - PCA SERENOA REPENS EXTRACT NMR FINGERPRINTING Serenoa repens extract Olive oil, Sunflower oil
45 FINGERPRINTING VARIABILITY PCA SERENOA REPENS EXTRACT NMR FINGERPRINTING FOR DIFFERENCES ANALYSIS Serenoa repens extracts
46 USE OF PCA FOR HERBAL EXTRACTS Olive oils Other oils Serenoa repens extracts
47 PCA AS TOOL FOR TLC EVALUATION Can be used to sta7s7cally evaluate every type of complex analy7cal data including TLC images
48 PCA AS TOOL FOR TLC EVALUATION TLC - run 2 R,G,B profiles B G R
49 PCA AS TOOL FOR TLC COMPARISON R G B R G B alignment
50 CONCLUSION Principal Component Analysis (PCA) is a simple yet popular and useful linear transforma7on technique that is used in numerous different applica7ons, par7cularly those involving complex mul7component mixtures such as the analysis of complex natural products including gene expression data, fingerperin7ng evalua7on, biological fluids changes and many more allowing to think in more powerful mul7variate holis7c way than in the old and reduc7onis7c univariate ones. It has been used for regulatory purpose to support a new source of the same plant showing similar composi7on with that already approved.
51
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