Initial estimates for mcr als method efa and simplisma
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7 th Iranian Workshop on Chemometrics 3-5 February 2008. Initial estimates for MCR-ALS method: EFA and SIMPLISMA. Bahram Hemmateenejad Chemistry Department, Shiraz University, Shiraz, Iran E-mail: [email protected] Chemical modeling. Fitting data to model (Hard model)

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Initial estimates for mcr als method efa and simplisma l.jpg

7th Iranian Workshop on Chemometrics

3-5 February 2008

Initial estimates for MCR-ALS method: EFA and SIMPLISMA

Bahram Hemmateenejad

Chemistry Department, Shiraz University, Shiraz, Iran

E-mail: [email protected]


Chemical modeling l.jpg

Chemical modeling

Fitting data to model (Hard model)

Fitting model to data (soft model)


Multicomponent curve resolution l.jpg

Goal: Given an I x J matrix, D, of N species, determine N and the pure spectra of each specie.

Model: DIxJ = CIxNSNxJ

Common assumptions:

Non-negative spectra and concentrations

Unimodal concentrations

Kinetic profiles

1

0.5

=

S

N

x

J

0

30

60

20

C

40

10

20

I

x

N

0

0

samples

sensors

D

I

x

J

Multicomponent Curve Resolution


Basic principles of mcr methods l.jpg
Basic Principles of MCR methods

PCA: D=TP

Beer-Lambert: D=CS

In MCR we want to reach from PCA to Beer-Lambert

  • D= TP = TRR-1P, R: rotation matrix

  • D = (TR)(R-1P)

  • C=TR, S=R-1P

  • The critical step is calculation of R


Multivariate curve resolution alternative least squares mcr als l.jpg
Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS)

  • Developed by R. Tauler and A. de Juan

  • Fully soft modeling method

  • Chemical and physical constraints

  • Data augmentation

  • Combined hard model

  • Tauler R, Kowalski B, Fleming S, ANALYTICAL CHEMISTRY 65 (15): 2040-2047, 1993.

  • de Juan A, Tauler R, CRITICAL REVIEWS IN ANALYTICAL CHEMISTRY 36 (3-4): 163-176 2006


Mcr als theory l.jpg
MCR-ALS Theory (MCR-ALS)

  • Widely Applied to spectroscopic methods

    • UV/Vis. Absorbance spectra

    • UV-Vis. Luminescence spectra

    • Vibration Spectra

    • NMR spectra

    • Circular Dichroism

  • Electrochemical data are also analyzed


Mcr als theory7 l.jpg
MCR-ALS Theory (MCR-ALS)

  • In the case of spectroscopic data

  • Beer-Lambert Law for a mixture

  • D(mn) absorbance data of k absorbing species

    D = CS

  • C(mk) concentration profile

  • S(kn) pure spectra


Mcr als theory8 l.jpg
MCR-ALS Theory (MCR-ALS)

  • Initial estimate of C or S

  • Evolving Factor Analysis (EFA) C

  • Simple-to-use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) S


Mcr als theory9 l.jpg
MCR-ALS Theory (MCR-ALS)

  • Consider we have initial estimate of C (Cint)

  • Determination of the chemical rank

  • Least square solution for S: S=Cint+ D

  • Least square solution for C: C=DS+

  • Reproducing of Dc: Dc=CS

  • Calculating lack of fit error (LOF)

    Go to step 2


Constraints in mcr als l.jpg
Constraints in MCR-ALS (MCR-ALS)

  • Non-negativity (non-zero concentrations and absorbencies)

  • Unimodality (unimodal concentration profiles). Its rarely applied to pure spectra

  • Closure (the law of mass conservation or mass balance equation for a closed system)

  • Selectivity in concentration profiles (if some selective zooms are available)

  • Selectivity in pure spectra (if the pure spectra of a chemical species, i.e. reactant or product, are known)


Constraints in mcr als11 l.jpg
Constraints in MCR-ALS (MCR-ALS)

  • Peak shape constraint

  • Hard model constraint (combined hard model MCR-ALS)


Slide12 l.jpg


Evolving factor analysis efa l.jpg
Evolving Factor Analysis (MCR-ALS)(EFA)

  • Gives a raw estimate of concentration profiles

  • Repeated Factor analysis on evolving submatrices

  • Gampp H, Maeder M, Meyer CI, Zuberbuhler AD, CHIMIA 39 (10): 315-317 1985

  • Maeder M, Zuberbuhler AD, ANALYTICA CHIMICA ACTA 181: 287-291, 1986

  • Gampp H, Maeder M, Meyer CJ, Zuberbuhler AD, TALANTA 33 (12): 943-951, 1986


Basic efa example calculate forward singular values l.jpg
Basic EFA Example (MCR-ALS)Calculate Forward Singular Values

1

___ 1st Singular Value

1

0.9

----- 2nd Singular Value

SVD

...… 3rd Singular Value

0.8

S

0.7

i

R

0.6

i

0.5

0.4

0.3

0.2

0.1

I

0

0

5

10

15

20

25

I samples


Basic efa example calculate backward singular values l.jpg
Basic EFA Example (MCR-ALS)Calculate Backward Singular Values

1

1

___ 1st Singular Value

0.9

----- 2nd Singular Value

0.8

...… 3rd Singular Value

0.7

R

0.6

0.5

0.4

i

SVD

0.3

S

0.2

i

0.1

I

0

0

5

10

15

20

25

I

samples


Basic efa l.jpg
Basic EFA (MCR-ALS)

  • Use ‘forward’ and ‘backward’ singular values to estimate initial concentration profiles

  • Area under both nth forward and (K-n+1)th backward singular values is estimate for initial concentration of nth component.

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

5

10

15

20

25

I

samples


Basic efa17 l.jpg

1 (MCR-ALS)

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

5

10

15

20

25

samples

I

Basic EFA

First estimated spectra

Area under 1st forward

and 3rd backward singular

value plot. (Blue)

Compare to true component

(Black)


Basic efa18 l.jpg

1 (MCR-ALS)

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

5

10

15

20

25

I

samples

Basic EFA

First estimated spectra

Area under 2nd forward

and 2nd backward singular

value plot. (Red)

Compare to true component

(Black)


Basic efa19 l.jpg

1 (MCR-ALS)

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

5

10

15

20

25

I

samples

Basic EFA

First estimated spectra

Area under 3rd forward

and 1st backward singular

value plot. (Green)

Compare to true component

(Black)


Example data l.jpg
Example data (MCR-ALS)

  • Spectrophotometric monitoring of the kinetic of a consecutive first order reaction of the form of

    A B C

k1

k2


Slide22 l.jpg

k1

k2


Slide29 l.jpg

K1=0.2 (MCR-ALS)

K2=0.02

K1=0.3

K2=0.08

K1=0.45

K2=0.32


Slide33 l.jpg

K1=0.30 (MCR-ALS)

K2=0.08

K1=0.20

K2=0.02

K1=0.45

K2=0.32


Slide34 l.jpg

Noisy data (MCR-ALS)


Efa analysis l.jpg
EFA Analysis (MCR-ALS)

  • The m.file is downloadable from the MCR-ALS home page:

    http://www.ub.edu/mcr/welcome.html


Simpl e to use i nteractive s elf modeling m ixture a nalysis simplisma l.jpg
Simpl (MCR-ALS)e-to-use Interactive Self-Modeling Mixture Analysis (SIMPLISMA)

W. Windigm J. Guilment, Anal. Chem. 1991, 63, 1425-1432.

F.C. Sanchez, D.L. Massart, Anal. Chim. Acta 1994, 298, 331-339.


Slide52 l.jpg

Variable (i.e. wavelength) (MCR-ALS)

Object (i.e. time or sample)

Data matrix

  • SIMPLISMA is based on the selection of what are called pure variables or pure objects.

  • A pure variable is a wavelength at which only one of the compounds in the system is absorbing.

  • A pure object is an analysis time at which only one compound is eluting.


Slide53 l.jpg

Absorbance spectra (MCR-ALS)

Chromatographic profile

Pure object

Pure variable


Slide54 l.jpg

(MCR-ALS)1

2


Slide55 l.jpg

35 (MCR-ALS)

20


Slide56 l.jpg

µ (MCR-ALS)0

0

.

.

.

.

.

.

µm

m

Mean vector

Standard deviation vector

t = 0

t = m


Slide57 l.jpg

(MCR-ALS)

t = 0

t = m

µm

µ0

0

m

.

.

.

Mean vector

.

.

.

Standard deviation vector


Slide58 l.jpg

chromatogram (MCR-ALS)

Pure spectra


Slide59 l.jpg

Pure spectra (MCR-ALS)

Standard deviation

Mean


Slide60 l.jpg

chromatogram (MCR-ALS)

Mean

Standard deviation


Slide61 l.jpg

λ (MCR-ALS)1

vi

xi

µi

λ2


Slide62 l.jpg

SIMPLISMA steps (MCR-ALS)

1) The ratio between the standard deviation, σi, and the mean, μi, of each spectrum is determined


Slide63 l.jpg

To avoid attributing a high purity value to spectra with low mean absorbances, i.e., to noise spectra, an offset is included in the denominator

0<offset<3


Slide64 l.jpg

2) Normalisation of the data matrix: Each spectrum mean absorbances, i.e., to noise spectra, an offset is included in the denominator xiis normalised by dividing each element of a row xijby the length of the row ||xi||:

When an offset is added, the same offset is also included in the normalisation of the spectra.


Slide65 l.jpg

3) Determination of the weight of each spectrum, mean absorbances, i.e., to noise spectra, an offset is included in the denominator wi.The weight is defined as the determinant of the dispersion matrix of Yi, which contains the normalised spectra that have already been selected and each individual normalised spectrum ziof the complete data matrix.

Yi = [Zi H]

Initially, when no spectrum has been selected, each Yi contains only one column, zi(H=1), and the weight of each spectrum is equal to the square of the length of the normalized spectrum


Slide66 l.jpg

When the first spectrum has been selected, mean absorbances, i.e., to noise spectra, an offset is included in the denominator p1, each matrix Yi consists of two columns: p1 and each individual spectrum zi, and the weight is equal to

Yi = [Zi p1]

When two spectra have been selected, pl and p2, each Yi consists of those two selected spectra and each individual zi, and so on.

Yi = [Zi [p1 p2]]


Slide67 l.jpg

i=1 mean absorbances, i.e., to noise spectra, an offset is included in the denominator H=I

i=2 H=p1

i=3 H=[p1p2]

i=4 H=[p1p2p3]


Slide72 l.jpg

Offset=0 mean absorbances, i.e., to noise spectra, an offset is included in the denominator


Slide73 l.jpg

Offset=1 mean absorbances, i.e., to noise spectra, an offset is included in the denominator


Slide74 l.jpg

* mean absorbances, i.e., to noise spectra, an offset is included in the denominator

*


Slide75 l.jpg

* mean absorbances, i.e., to noise spectra, an offset is included in the denominator

*


Slide76 l.jpg

* mean absorbances, i.e., to noise spectra, an offset is included in the denominator

*


Example data hplc dad data of a binary mixture l.jpg

Example data mean absorbances, i.e., to noise spectra, an offset is included in the denominator

HPLC-DAD data of a binary mixture


Slide78 l.jpg

chromatogram mean absorbances, i.e., to noise spectra, an offset is included in the denominator


Slide79 l.jpg

Pure spectra mean absorbances, i.e., to noise spectra, an offset is included in the denominator


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