Analytical Chemistry and Multi-Block Modeling for Improved ...
Transcript of Analytical Chemistry and Multi-Block Modeling for Improved ...
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.
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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
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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)
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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
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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
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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
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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
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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
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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
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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!
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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
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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
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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
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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).
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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
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1
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wavelength (nm)
Da
ta
500 1000 1500 2000 2500 30000
2
4
6
8
10
12
Raman shift (cm-1)
Da
ta
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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
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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
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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
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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
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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)
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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).