chemometric analysis and visualization tools for ir spectral searching august 2008 presentation

7/18/2019 Chemometric Analysis and Visualization Tools for IR Spectral Searching AUGUST 2008 Presentation

http://slidepdf.com/reader/full/chemometric-analysis-and-visualization-tools-for-ir-spectral-searching-august 1/21

Marie Scandone1 and Dana Garcia2

(1) Bio-Rad Laboratories, Inc.

Informatics Division,Two Penn Center,Philadelphia, PA 19102(2) Arkema Inc.

900 First Avenue, King of Prussia, PA 19406-0936

Chemometric Analysis and VisualizationTools for IR Spectral Searching



4 /

Introduction - Cheminformatics

•Cheminformatics: IR spectral and property databases.

Traditional visualization of multiple spectra:

¾Overlay

¾Offset

¾Stacked

These plotting methods obliterate trends when viewing large

amounts of data.



5 /

Introduction – Chemometrics PCA

•Chemometric PCA: spectral decomposition to create a

simplified representation of the data using a set of

principal components (PCs).

•PCs describe the phenomena (concentration/property)

that generate the spectral variations of interest.

•PCs are used:

¾Explore & Describe Data

¾Characterize & Classify

PCA analysis using Infometrix Pirouette®



6 /

Hit List Spectra PCA Score Plot

•2 Component mixture:

¾ Two clusters determined by the characteristic IR transitions for each

mixture component

Mixture:

Component 1

Mixture:

Component 2



7 /

IntroductionIntroduction -- Overlap Density Heatmaps

(ODH)

High Overlap Density =

More Common Features

Low Overlap Density =

More Unique Features

Spectrum 1

Spectrum 2

•Visual data mining and analysis.

• Assess similarities and dissimilarities.



8 /

Overlap Density Heatmap (ODH) Display

Unique Similar

•ODH displays are obtained byselecting Hit List database spectra

resulting from a standard search.

¾ The positive or negative consensusspectra can be calculated and

searched in the databases

3500 3000 2500 2000 1500 1000 500

-1

0

3500 3000 2500 2000 1500 1000 500

-1

0



9 /

Iterative Process

Cheminformatics

Chemometric PCA

Overlap Density Heatmaps

Unknown Spectrum

Consensus Spectrum

•Progressive reduction of data complexity.



10 /

Experimental: Materials and Spectra

• Materials: commercial coating.

• Spectroscopy: ThermoFisher Avatar™ FT-IR with a diamond ATR.

• ATR spectra challenging search:¾ Subtraction can be difficult and yield inaccurate results

¾ATR correction may not improve results

4000 3500 3000 2500 2000 1500 1000

0

0.5



11 /

Experimental: Methods

•Spectrum searched against the Sadtler and Arkema

databases using SearchIt™ application.¾Euclidean Distance algorithm with 200 hits retrieved

•Resulting Hit List transferred to AnalyzeIt™ MVP

Chemometrics PCA.

•Standard chemometrics tools:¾Data pre-processing: smoothing derivative, normalization

¾Model setup: spectral range, variable selection, outlier detection

This study:

¾ 2nd Derivative, Savitzky-Golay polynomial filter smoothing

¾ MSC normalization

¾ Spectral ranges captures major spectral features



12 /

Results: Simple Search

Spectral searching did not yield any definite results.

Coating spectrum

Hit List – 1st hit

Hit List



13 /

Results: Chemometrics PCA

Transfer Hit List to MVP Chemometrics PCA

No specific clusteringto separate components

Approach: investigate score plot outer areasto look for differentiation.



14 /

Results: Chemometrics PCA and ODH

Component 1

Select samples which are different.

4000 3500 3000 2500 2000 1500 1000 500

-1

0

Obtain the Overlap Density Heatmap.

Calculate the positive Consensus spectrum.



15 /

Results: Identification Component 1

Component 1 = PVDF

The common functional group of the chosen scores is –CF2-CH2-

Simple search of the Consensusspectrum identifies PVDF.



16 /

Factor 1-20 -10 0 10 20 30

F a c t o r 2

-20

0

20

Results: Chemometrics PCA and ODH -

Component 2

Return back to the PCA analysisand select another set of samples that are different.



4000 3500 3000 2500 2000 1500 1000 500

-1

0

Component 2 =

AcrylicSearch



17 /

Results: Component 2 - Acrylic

4000 3500 3000 2500 2000 1500 10000

0.5

Identification of the acrylic although it isonly a minor component.

•PCA identified the presence of the acrylic.



18 /

Results: Chemometrics PCA and ODH -

Component 3

Return back to the PCA analysisand select another set of samples that are different.



Component 3 =

CaCO3

Factor 2

-30 -20 -10 0 10 20

F a c t o r 3

-20

-10

0

10

20

4000 3500 3000 2500 2000 1500 1000 500

-1

0

Search



19 /

Results: Cheminformatics - Component 4

4000 3500 3000 2500 2000 1500 1000

0

0.5

Remaining unassigned bands.



20 /

Results: Cheminformatics - Component 4

•Simultaneous spectral and peak search.

¾Sadtler Polymer and Monomer Databases

Component 4 = Nitr ile elastomer

Search



21 /

Conclusions

•IR spectral identification of mult i-component samples lends itself to

a combination approach using Cheminformatics, PCA, ODH and

Consensus Spectra.

•High degree of module interconnectivity.

¾Individual module provide partial answers

¾In totality a systematic and efficient analysis

• Applicable to a wide variety of spectral identification problems.

chemometric analysis and visualization tools for ir spectral searching august 2008 presentation

Documents