1

Analytical Chemistryand Multi-Block Modelingfor Improved NIR Spectral

Interpretation

Charles E. (Chuck) Miller, Ph.D.

Eigenvector Research, Inc.

©Copyright 2008

Eigenvector Research, Inc.

No part of this material may be photocopied or

reproduced in any form without prior written

consent from Eigenvector Research, Inc.

22

Outline

• Background- the Value of (NIR) Understanding

• Combining Analytical Chemistry and Multi-block

modeling

• Contrast with NIR Correlation Spectroscopy

• Case study I: NIR/DSC of polyethylene films

• Case study II: NIR/NIR FT-Raman of RIM

polyurethanes

• Conclusions

33

The value of (NIR) understanding

• NIR Technology is practically useful

• Empirical modeling (Chemometrics) enabling

• In principle, fundamental spectroscopy knowledge not required

• In practice, it can be very valuable!

• Guides the empirical modeling process

• Re-enforces validation of final models

1200 1400 1600 1800 2000 22000

0.2

0.4

0.6

0.8

1

1.2

wavelength (nm)

44

Empirical Modeling for Understanding

• Implicit

• Use the same data as is used to develop deployed methods

• Ex. Interpreting PCA/PLS loadings “spectra”

1200 1400 1600 1800 2000 2200

-0.2

-0.1

0

0.1

0.2

0.3

wavelength (nm)

RO

TA

TE

D P

C 2

Lo

ad

ing

Variables/Loadings Plot for PolyU_NIR_reflectance

• Explicit

• Design and execute a separate experiment specifically forunderstanding (vs. method development)

• Facilitated by: NIR’s non-invasiveness, and digitization of other analytical technologies

55

Analytical Chemistry and Multi-Block (PLS2) Modeling

X (NIR)Y (other

MV mtd)

T

P

T

Q

Y: Other multivariate

analytical methods-

run on identical

samples (DSC, Raman,

rheology…)

Scores are identical for both blocks!

PLS2

T

PLS2: finding

variability

sources in X

that are

correlated to Y

66

PLS2 (vs Correlation Spectroscopy!)

• Sample “perturbations” do not

have to be continuous and

univariate

• Can be discrete chemical changes

• Explicitly account for covariance

within both sets of variables (NIR

and the reference method)

• Can explore common and

uncommon covariance between

blocks

• Both provide problem-specific

understanding

Nabet et al., Appl. Spec., Volume 51, Number 4, 1997

77

NIR & DSC of polyethylene films

• Duplicate LDPE/HDPE blend films obtained at 7 different compositions:

• 0, 2.5, 5, 10, 25, 50 and 100% HDPE

• NIR analysis using Technicon InfraAlyzer 500 (1100-2500nm)

• 3.5-4.5 mg sample cut from center of each film sample, and run on Mettler DSC

• 40-149 C, scan rate = 10C/min

88

NIR & DSC Data

2100 2150 2200 2250 2300 2350 2400 2450 25000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

wavelength (nm)

Sam

ple

Nam

e : D

ata

50 60 70 80 90 100 110 120 130-2.5

-2

-1.5

-1

-0.5

0

0.5

Temperature

PE

sa

mp

le n

am

e : D

ata

NIR Spectra

(MSC corrected)

DSC thermograms

(normalized to sample mass)

νas+δνs+δ

99

PLS-2 results- explained variancePercent Variance Captured by Regression

Model

-----X-Block----- -----Y-Block-----

Comp This Total This Total

---- ------- ------- ------- -------

1 86.57 86.57 63.99 63.99

2 12.02 98.59 31.06 95.05

3 0.38 98.97 1.56 96.61

4 0.39 99.36 0.36 96.97

5 0.22 99.58 0.41 97.38

There are two

independent NIR spectral

effects that co-vary with

DSC responses

There is a small effect in

the DSC data that does

not appear to have any

corresponding NIR effect

1010

PLS2 scores 1 & 2 (common components)

-0.12 -0.1 -0.08 -0.06 -0.04 -0.02 0 0.02-0.035

-0.03

-0.025

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

PLS LV1 SCORE

PL

S L

V2

SC

OR

E

0

5

10

25

50

100

2.5

x-axis zero

y-axis zero

NOTE: components already rotated to an

interpretable structure!

LV1: separates 100% HDPE

sample from the rest

LV2: aligned

with

decreasing

HDPE, for

0-50%

HDPE

samples

1111

2100 2200 2300 2400 2500-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

PLS2 loading 1

MEAN NIR

PLS2 loadings LV1

NIR

40 60 80 100 120 140-0.5

0

0.5

1

1.5

2

Temperature (C)

DSC

112.2 C: LDPE

crystallites

126.0 C:

“unhindered”

HDPE

crystallites

Looks similar to mean, with

different baseline!

Low for 100% HDPE, high for the rest (0-50% HDPE)

“special” νas+δ

• small increase in LDPE crystallites

• decrease in “UN-hindered” HDPE

crystallites

• large decrease in overall crystallinity

• opacity/scattering/baseline effect

• unique –CH2- νas+δ band for un-

hindered HDPE!

1212

PLS2 loadings LV2

40 60 80 100 120 140-1

-0.5

0

0.5

1

1.5

Temperature (C)

DSC

2100 2200 2300 2400 2500-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

PLS loading 2

MEAN NIR

123.6 C:

“hindered”

HDPE

crystallites

112.2 C: LDPE

crystallites

Inversely related to %HDPE, for 0 to 50% HDPE

• increase in LDPE crystallites

• decrease in “hindered” HDPE crystallites

• small decrease in overall crystallinity

-CH3

• increase in –CH3 bands

• shift of bands to lower nm

Shift of ν+δ bands

1313

Summary- NIR & DSC

• 100% HDPE vs. the rest

• Lot of UN-hindered HDPE crystallites!

• NIR sees this mainly via opacity/scattering effect, but

also via unique νas+δ band

• 50% to 0% HDPE

• Increase in LDPE crystallites, decrease in hindered

HDPE crystallites

• NIR sees this via increase in –CH3 bands, as well as

shifts in both ν+δ bands

1414

NIR and Raman of RIM Polyurethanes

• Polyurethanes produced via reaction-injection-molding (RIM)

• 18 RIM Sheets produced at 4 nominal compositions• 42.5, 46, 51 & 55 % hard block

• 3 samples cut f/each sheet

• NIR diffuse reflectance analysis• Technicon I/A 500

• NIR-FT-Raman of identical samples (YAG excitation)

Injection of

reactive mixture

Gate sample

Middle sample

End sample

“soft block” “hard block”

C.E. Miller, Spectrochimica Acta, 49A(5/6), 621 (1993).

1515

NIR and Raman Data

NIR Spectra (raw) Raman spectra

• MSC-corrected, then

• Augmented with intensity

of Rayleigh scattering peak

• MSC-corrected, then

• Augmented with MSC A

and B coefficients

1200 1400 1600 1800 2000 22000

0.2

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0.8

1

1.2

1.4

wavelength (nm)

Da

ta

500 1000 1500 2000 2500 30000

2

4

6

8

10

12

Raman shift (cm-1)

Da

ta

1616

Percent Variance Captured by Regression

Model

-----X-Block----- -----Y-Block-----

Comp This Total This Total

---- ------- ------- ------- -------

1 49.11 49.11 82.21 82.21

2 44.26 93.38 4.61 86.82

3 3.37 96.75 5.79 92.61

4 1.27 98.02 0.59 93.20

5 0.69 98.72 0.56 93.76

PLS-2 results- explained variance

LV1: about 50% of

NIR data and 80% of

Raman variation

LV3: weak variation in

both Raman and NIR

LV2: large NIR

variation, but small

Raman variation

1717

PLS2 scores 1 & 2 (rotated 15o)

-0.1 -0.05 0 0.05 0.1 0.15-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

LV1 score, rotated

LV

2 s

co

re, ro

tate

d

42.5

46

51

55

-0.1 -0.05 0 0.05 0.1 0.15-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

LV1 score, rotatedL

V2

score

, ro

tate

d

Gate

Middle

End

LV1: increases with

increasing hard block%

LV2: varies based on

position on sheet: gate->

middle � end

By composition By sheet position

1818

0 500 1000 1500 2000 2500 3000 3500-3

-2

-1

0

1

2

3

4

5

6

7

Raman shift (cm-1)

1000 1200 1400 1600 1800 2000 2200 2400-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

wavelength (nm)

PLS2 loadings, LV1 (rotated 15o)

NIRRaman

Increasing hard block %

• features assigned to characteristic

fundamental bands

• enables more confident assignment

of NIR features

1919

1000 1200 1400 1600 1800 2000 2200 2400-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

wavelength (nm)

LV2 loading

NIR MEAN

0 500 1000 1500 2000 2500 3000 3500-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Raman shift (cm-1)

PLS2 loadings, LV2 (rotated 15o)

NIRRaman

Sheet position effect (gate-> middle � end)

• not much, except the Rayleigh peak

intensity!

• suggests “physics” (scattering) effects,

rather than chemistry effects

• interesting loading pattern (assigned

to “scattering ability” )

• MSC A and B coefficients

Rayleigh

intensity

MSC A and

B coeffs.

2020

Summary- NIR & Raman

• Both NIR and Raman see compositional effects

• Known Raman band assignments aid NIR band

assignments

• Sheet position effects

• Larger in NIR reflectance than in Raman

• Interesting NIR loading pattern

2121

PLS2 Summary

• “3-way” interpretation: scores/loadingsA/loadingsB

• Explains variance in both blocks

• Assess both common and uncommon variability sources

• More confident assignment of NIR spectral features

• Can reveal, characterize un-designed effects in

samples

• Similar methods: Canonical Correlation (CCA)

2222

Acknowledgements

• Polyethylenes

• Statoil

• MATFORSK (Norwegian Food Research Institute)

• RIM Polyurethanes

• ICI Polyurethanes

2323

References

• DSC/NIR

• C.E. Miller, Appl. Spectrosc. 47(2), 222 (1993).

• D.C Yang, J.M. Brady, E.L. Thomas, J. Mat. Sci., 23, 2546 (1988).

• M.P. Farr, I.R. Harrison, Poly. Prepr., 31(1), 257 (1990).

• C.E. Miller, B.E. Eichinger, J. Appl. Poly. Sci., 42, 2169 (1991).

• Raman/NIR

• Ref: C.E. Miller, "NIR-FT-Raman Spectroscopy and PLS-2 Modeling for Improved Understanding of Near-infrared Spectra", Spectrochimica Acta, 49A(5/6), 621 (1993).