Characterization and Clustering Analysis - ACD/Labs · 2014-06-20 · Michael Boruta Industrial...
Transcript of Characterization and Clustering Analysis - ACD/Labs · 2014-06-20 · Michael Boruta Industrial...
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Characterization and Clustering Analysis
Michael BorutaIndustrial Solutions Manager
Optical Spectroscopy Product Manager
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Outline
• Background
• Algorithm
• Analysis & Review
• Examples
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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
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???
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Euclidean Distance
Total area = 369
Total area = 927
HQI = 54.4
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1st Derivative Euclidean Distance
Total area = 7.46
Total area = 6.83HQI = 97.96
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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
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Example 1: XRPD Polymorphs
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Example 1: XRPD Polymorphs
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Data Review
Spectral Overlays
Graph
Overlay Legend
Nearest Neighbors Table
DSC curve
TGA curve
Image
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Analysis/Review
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Example 2: C-13 NMR Polymers
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Example 3: IR Polymers
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Example 3: IR Polymers
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Example 3: IR Polymers
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Example 4: IR Oils
All spectra
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Example 4: IR Oils
Groups 1 and 2
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Example 4: IR Oils
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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
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