Characterization and Clustering Analysis

Michael BorutaIndustrial Solutions Manager

Optical Spectroscopy Product Manager

Outline

• Background

• Algorithm

• Analysis & Review

• Examples

Background

• The most common use of cluster analysis is classification.

• Several assumptions– No prior judgments used to organize the data (un-supervised

clustering)

– Each member belongs to one and only one group

• Several questions– What will be used to measure the similarity

– How are classes formed & defined

– What inferences can be drawn regarding their significance

???

Euclidean Distance

Total area = 369

Total area = 927

HQI = 54.4

1st Derivative Euclidean Distance

Total area = 7.46

Total area = 6.83HQI = 97.96

Gap Analysis

Gap= 4.3; Gap % = 11.5

Gap= 11.8; Gap % = 31.1

Gap= 3.4; Gap % = 9.0

CH3

CH3

CH3

CH3

CH3

PVA sample

Example 1: XRPD Polymorphs

Example 1: XRPD Polymorphs

Data Review

Spectral Overlays

Graph

Overlay Legend

Nearest Neighbors Table

DSC curve

TGA curve

Image

Analysis/Review

Example 2: C-13 NMR Polymers

Example 3: IR Polymers

Example 3: IR Polymers

Example 3: IR Polymers

Example 4: IR Oils

All spectra

Example 4: IR Oils

Groups 1 and 2

Example 4: IR Oils

Summary

• Clustering available for several spectroscopy types; IR, Raman, C13, H1, & XRPD

• Clustering assumes each member can be in only one cluster

• Data analysis/review can merge or split clusters, or move members from one cluster to another

• Clustering can be used for many types of classification problems;

– Comparing competitive products

– Salts and polymorphs

– Classifying polymer types

– Chemical imaging analysis

• Once classifications exist, new samples can be compared to existing clusters