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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

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

Chemical modeling

Fitting data to model (Hard model)

Fitting model to data (soft model)

multicomponent curve resolution
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

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0.5

=

S

N

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C

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sensors

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Multicomponent Curve Resolution
basic principles of mcr methods
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
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
MCR-ALS Theory
  • 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
MCR-ALS Theory
  • 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
MCR-ALS Theory
  • Initial estimate of C or S
  • Evolving Factor Analysis (EFA) C
  • Simple-to-use Interactive Self-Modeling Mixture Analysis (SIMPLISMA) S
mcr als theory9
MCR-ALS Theory
  • 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
Constraints in 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
Constraints in MCR-ALS
  • Peak shape constraint
  • Hard model constraint (combined hard model MCR-ALS)
slide12
Rotational Ambiguity
  • Rank Deficiency
evolving factor analysis efa
Evolving Factor Analysis(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
Basic EFA ExampleCalculate Forward Singular Values

1

___ 1st Singular Value

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0.9

----- 2nd Singular Value

SVD

...… 3rd Singular Value

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basic efa example calculate backward singular values
Basic EFA ExampleCalculate Backward Singular Values

1

1

___ 1st Singular Value

0.9

----- 2nd Singular Value

0.8

...… 3rd Singular Value

0.7

R

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SVD

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basic efa
Basic EFA
  • 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.

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basic efa17

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Basic EFA

First estimated spectra

Area under 1st forward

and 3rd backward singular

value plot. (Blue)

Compare to true component

(Black)

basic efa18

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Basic EFA

First estimated spectra

Area under 2nd forward

and 2nd backward singular

value plot. (Red)

Compare to true component

(Black)

basic efa19

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Basic EFA

First estimated spectra

Area under 3rd forward

and 1st backward singular

value plot. (Green)

Compare to true component

(Black)

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

A B C

k1

k2

slide22
Pseudo first-order reaction with respect to A
  • A + R B C
  • [R]1 k1=0.20 k2=0.02
  • [R]2 k1=0.30 k2=0.08
  • [R]3 k1=0.45 k2=0.32

k1

k2

slide29

K1=0.2

K2=0.02

K1=0.3

K2=0.08

K1=0.45

K2=0.32

slide33

K1=0.30

K2=0.08

K1=0.20

K2=0.02

K1=0.45

K2=0.32

efa analysis
EFA Analysis
  • 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
Simple-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

Variable (i.e. wavelength)

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

Absorbance spectra

Chromatographic profile

Pure object

Pure variable

slide54

1

2

slide55

35

20

slide56

µ0

0

.

.

.

.

.

.

µm

m

Mean vector

Standard deviation vector

t = 0

t = m

slide57

t = 0

t = m

µm

µ0

0

m

.

.

.

Mean vector

.

.

.

Standard deviation vector

slide58

chromatogram

Pure spectra

slide59

Pure spectra

Standard deviation

Mean

slide60

chromatogram

Mean

Standard deviation

slide61

λ1

vi

xi

µi

λ2

slide62

SIMPLISMA steps

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

slide63

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

2) Normalisation of the data matrix: Each spectrum 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

3) Determination of the weight of each spectrum, 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

When the first spectrum has been selected, 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

i=1 H=I

i=2 H=p1

i=3 H=[p1p2]

i=4 H=[p1p2p3]

slide74

*

*

slide75

*

*

slide76

*

*

example data hplc dad data of a binary mixture
Example data

HPLC-DAD data of a binary mixture

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