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Generalized Rank Annihilation Factor Analysis Anal Chem 58(1986)496. E Sanchez B R Kowalski.

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Presentation on theme: "Generalized Rank Annihilation Factor Analysis Anal Chem 58(1986)496. E Sanchez B R Kowalski."— Presentation transcript:

1 Generalized Rank Annihilation Factor Analysis Anal Chem 58(1986)496. E Sanchez B R Kowalski

2 Bilinear data 2 1 3 2.1.2.3.2 e (Excit.) f (Emiss.) X 1 (Fluoresc.)  = One component  Rank =1 Conc. 0.40.81.20.8 0.20.40.60.4 0.61.21.81.2

3 21 12 31 10 03.1.2.3.2.4.3 21.1 +13.2 21.1 +13.2 11.1 +23.2 31.2 +13.3 Two components EFConc.  = X 2 (Fluoresc.)

4 0.2 +0.60.4 +1.20.6 +1.20.4 +.9 0.1 +1.20.2 +2.40.3 +2.40.2 +1.8 0.3 +0.60.6 +1.20.9 +1.20.6 +0.9 Two components X 2 = Rank = 2 0.40.81.20.8 0.20.40.60.4 0.61.21.81.2 one component (calibration matrix) Rank = 1 X 1 = Lorber, 1984 X 2 - 0.5 X 1 = E Rank=2Rank=1 Quantification of one component.

5 0.2 +0.60.4 +1.20.6 +1.20.4 +0.9 0.1 +1.20.2 +2.40.3 +2.40.2 +1.8 0.3 +0.60.6 +1.20.9 +1.20.6 +0.9 Two components(sample) X 2 = Rank = 2 0.4+0.20.8+0.41.2+0.40.8+0.3 0.2+0.40.4+0.80.6+0.80.4+0.6 0.6+0.21.2+0.41.8+0.41.2+0.3 Two components(calibration) Rank = 2 X 3 = What about quantific. of more than one component?

6 Generalized RAFA [Anal Chem 1986, 58, 496-499. B.R. Kowalski] 1. Non-iterative [Lorber, 1984]. 2. Simultaneous detn. of analytes using Just one bilinear calibration spectrum from one mixture of standards. a.Bilinear spectrum of each analyte b. Relative conc.s

7 Theory E  F T = X 2 E  F T = X 1 sample : Calibration :  E = X 2 (F T ) +  -1  E = X 1 (F T ) +  -1 X 1 (F T ) +  -1 = X 2 (F T ) +  -1 X 1 Z = U S V T Z  -1  Z = V S -1 Z * (definition) Common F and E Trilinearity

8 X 1 V S -1 Z * = U S V T V S -1 Z *  -1  I I U T X 1 V S -1 Z * = Z *  -1  R V = V  (eigenvector analysis) F T = (V S -1 Z * ) + E = U Z *  -1  -1  =  ?

9 Simult. detn. of two acids in a sample H 2 A  HA  A H 2 B  HB  B using pH-metric titration

10 H 2 A  HA  A H 2 B  HB  A sample C 0A ? C 0B ? H 2 A  HA  A H 2 B  HB  A calibr. C 0A =0.02 M C 0B =0.04 M Data matrices

11 sample calibration

12 Only HA - and HB - are optically active.

13 sample calibration

14 [Zstar,λ]=eig(Usm‘ * Xcl‘ * Vsm* inv(Ssm)) [Usm,Ssm,Vsm] = svd(Xsm')

15 0.6669 1.9998 λ=λ= (C oA ) cl (C oA ) sm (C oB ) cl (C oB ) sm, (C oA ) cl =0.02 M => (C oA ) sm =0.03 M, (C oB ) cl =0.04 M => (C oB ) sm =0.02 M 0.03 0.02 β =β = 15

16 F = pinv( Vsm * inv( Ssm ) * Zstar) Conc. profiles

17 E = Usm * Zstar * inv(β) spectral profiles

18

19 What if: The calibration sample includes some components that are not present in unknown sample, And there be some components in unknown sample not present in the calibration sample. HPLC-DAD chromatogram for A,B, and C (as CL), for ?,?,and ? (as SM) Example: The General Condition

20 Xcl C Acl = 1 mM C Bcl = 3 mM C Ccl = 2 mM

21 Xsm ?, ?, and ?,..

22 [Zstar,λ]=eig(Utot‘ * Xsm‘ * Vtot* inv(Stot)) [Utot,Stot,Vtot] = svd(Xtot') Xtot = Xcl + Xsm The total space, rank =4 (includes A, B, C,and D)

23 0.9999 0 0 0 0 0.0003 0 0 0 0 0.5000 0 0 0 0 0.3334 λ= β / ( β + ξ ) C?sm C?sm+C?cl =0.9999 0.0003 0.5000 0.3334 C?cl=0 Only in sm C?sm=0 Only in cl C?sm= C?cl 2C?sm= C?cl C B A D CBsm= 3 mM CCsm= 1 mM

24 F = pinv( Vtot * inv( Stot ) * Zstar) Conc. profiles

25 E = Utot * Zstar spectral profiles

26 Non-bilinear RA Analyte detn...in the presence of unaccounted spectral interference.. Rank for the pure component >1

27 H2A  HA  A

28 One compon, but Rank=…3 Xcl

29 H 2 A and H 2 B

30 Rank(Xsm)=5 H 2 A and H 2 B Interference

31 Conc. Prof.s Spect. Prof.s

32 0.94150000 00.0003000 00-0.00300 0002.00440 00002.0010 λ of H 2 B

33

34 Direct Exponential Curve Resolution Algorithm J. Chemom. 14 (2000) 213-227. DECRADECRA

35 Model base: an exponential decay 162 54 18 6 2 162 54 18 6 2 x2x2 x1x1 162/54= 54/18= 18/6= 6/2= 3 3 3 3 shift x

36 C 1 = e –k t C 2 = e –k (t+S) C1C1 C2C2  =  =  =  e –kt +k(t+S) = e –k S e –k t e –k (t+S)  k = ln( ) / S x 2 : x 1 : Shift

37 Shift=7 x2x2 x1x1 1 st Ord Data From 1 sample

38 k = ln(  ) / 7 =0.1

39 cPcP sPTsPT cQcQ sQTsQT cRcR sRTsRT =++ X Expon. Decay 2 st Ord Data From 1 sample

40 Trilinear structure N X1X1 X2X2 =+ E Gives k1 and k2 X 2-way (MN) X 3-way ((M-S) N 2) Stacking E F λ 1 M-S 1+S M

41 Decomposition of a number of colorants to colorless products.. A  A’ B  B’ C  C’ … 1 st order reactions

42

43 svd(X)= 6279.5 294.0 34.4 0.7 0.6 … Three components

44 Shift = 10 min

45 Estimated F

46 estmated E

47 k = ln( λ ) / shift 3.320100 02.22550 001.4918 λ = 0.1200 00.080 000.04

48

49 A consecutive reaction:

50 No Expon. Decaying concn. A  B  D k1k2

51 Reaction model First order, consecutive C A,i = C A,0 e –k1 ti C B,i = (e –k1 ti - e –k2 ti ) k1 C B,0 k2- k1 C D,i = C A,0 - C A,i - C B,i A  B  D k1k2 Columns of C matrix cAcA cBcB cDcD

52 X* = c A s A T + c B s B T + c D s D T + c L s L T = (e -k1 t ) s A T + k(e -k1 t ) s B T - k(e -k2 t ) s B T - (e -k1 t )s D T + e 0 t s D T - k(e -k1 t )s D T + k(e -k2 t )s D T + e 0 t s L T = e -k1 t ( s A + k s B - s D – k s D ) T + e -k2 t ( - k s B + ks D ) T + e 0 t (s D + s L ) T Sum of exponentially decaying functions

53 Unique decomp. But not result into actual spectra and concn. profiles = e -k1 t ( s P + k s Q - s R – k s R ) T + e -k2 t ( - k s Q + ks R ) T + e 0 t (s R + s L ) T e 1 e 2 e 3

54 Trilinear structure 1 M-s 1+s M N X1X1 X2X2 =+ X 2-way X 3-way E Gives k1 and k2 (MN) ((M-S) N 2) Stacking E F λ Expon. Decaying

55 X* fAfA eATeAT fBfB eBTeBT fDfD eDTeDT =+ ++ 1..11..1 N+1 eLTeLT =[0 0.. 0 1] fLfL =[e 0 e 0.. e 0 ]

56 What if : Not applying the ones column?

57 An Example for consecutive reaction

58

59

60 0.9999700 05.770 0019.848 λ=λ= k = ln( ) / shift 0.000000 00.07010 000.1195

61 Not proper pure spectra !

62 Not proper pure conc. Prof.s !

63 What about estimation of spectral and concentration profiles?

64

65 An NMR example

66 PGSE NMR Pulsed Gradient Spin Echo NMR A Mixture, with exponential decay of the contribution of each component A series of spectra A function of diffusion coefficient of component

67 Low-MW Poly(dimethylsiloxane) PDMS

68

69 MRI 14 images (echo times (TEs) from 15 to 210ms) Exponential decay of signal from each component =f(sp.-sp. relax. Time of compon.)

70

71 Thanks


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