Chemometrics for Process Analytics: Data Alignment

CPAC Summer Institute June 2011

Brian G. Rohrback Infometrix, Inc., Bothell, WA

2011 CPAC Summer Institute

History of a CPAC initiative

April 2000: Analect users meeting Started with focus on on-line

Matlab computing standards March and May 2001: Pittcon

and CPAC meetings Broadened vendor interest,

formalized as a CPAC initiative 2002 - 2004: IFPAC meetings

End-users, vendors, academics: focus on accessibility, FDA

2005 - 2007: IFPAC meetings Product introductions, integration,

feedback 2008 - 2011+: IFPAC meetings

Second generation COPA products, services

• Model formats are more accessible

• Software has been created to allow use of chemometric models in more flexible and more seamless ways

2011 CPAC Summer Institute

Poll of Process Users

1. Analytical failure prediction 2. Result validation 3. More process-specific information (timely, higher quality,

more focused) 4. Simplification of procedures 5. Elimination of analytical discrepancies 6. Reduction in the lifecycle cost (cost of ownership)

Driven in part by dwindling manpower, skill sets and capabilities !

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2011 CPAC Summer Institute

The Data Processing Bottleneck

Ultimately, the task will be to quantitate or classify samples Identifying the origin of a sample Assessing some physical property or proportion Unfolding a mixture into the relative proportion of ingredients

(which can be mixtures themselves)

Accurate peak identification is undermined by retention time variability

All chromatography is plagued by peak shifting Chemometrics provides a means for significantly reducing

retention time inconsistency

The goal: simplified and objective quality control

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2011 CPAC Summer Institute

The key is multivariate instrumentation

You can’t control what you don't measure All measurements are inferentials Multiple measurements allow tighter specifications –

multivariate instruments Timeliness is key

Turnaround speed of the instrument On-line versus near-line versus off-line

Elimination of the analyst Real-Time Release – real time data analysis and automated, application-specific interpretation

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2011 CPAC Summer Institute

Chemometrics for instrumentation: the value proposition

Anything you can do to improve precision of the multivariate measurements collected by the instrument will allow you to tighten the control – essentially for free.

One way is to construct an application-specific, objective evaluation system: Experimental design Exploratory data analysis

Leading to Multivariate modeling (qualitative and quantitative analysis)

Just as key is the signal processing aspect of chemometrics to reduce instrument-derived variability Within an instrument (e.g., noise reduction) Between instruments (i.e., transfer of calibration)

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2011 CPAC Summer Institute

Alignment Technology

What if we can make all of our instruments look as much alike as possible?

Interchangeability Common interpretive base

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2011 CPAC Summer Institute

Raw process chromatograms

Full Data;2

0 10 20 30 40Time Index (E +03)

0.0

0.1

0.2

Res

pons

e

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2011 CPAC Summer Institute

The same chromatograms after alignment

Full Data;2

0 10 20 30 40Time Index (E +03)

0.0

0.1

0.2

Res

pons

e

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2011 CPAC Summer Institute

Product Development Trail

Voice Recognition Technology (academic articles) Translation into Chromatography (again academic)

“Did not work”, use of traditional technology (version 1)

Interaction with End User Group Version 2, start to deploy Identification of high value targets Recognition of barriers to large-scale deployment

Enter CPAC Contacts, expertise (publications) Low learning curve

2011 CPAC Summer Institute

38 samples of M. simiae (5 labs)

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Area Summation

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Area Summation

aligned

2011 CPAC Summer Institute

1H NMR in the Aromatic Region

NMR Spectra

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1H NMR in the Aromatic Region

NMR Spectra - Aligned

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QC of x-ray contrast agents Original HPLC data

Aligned HPLC data

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QC of x-ray contrast agents

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QC of x-ray contrast agents

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PCA as a basis for interpretation

PC1

PC2

PC3

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PC1

PC2

PC3

PCA as a basis for interpretation

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On-line Simulated Distillation

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400 Samples Un-Aligned

Same Samples Aligned

2011 CPAC Summer Institute

Comparison of PCA scores

85% of all of the variation in the raw data is due to the misaligned peaks.

Correcting for this shows us that there are three different production regimes in these data.

Before alignment After alignment

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Automated alignment works

5 year period, 6 GCs

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Automated alignment works

aligned

5 year period, 6 GCs

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Same Sample run on multiple instruments (runs 7 weeks apart)

aligned

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200

300

400

500

600

700

800

0 10 20 30 40 50 60 70 80 90 100Yield, wt%

BP,

F

2 minute run

Yield Curves Match

µGC, Lab, and Process

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2011 CPAC Summer Institute

Continuous validation of a multivariate instrument We can correct retention times to match an application-

specific relevant sample This eliminates the transfer of calibration problem in

chromatography Common regression and classification algorithms can be

applied automatically to infer physical properties or characteristics

This allows us to bring more complex analyses into on-line use

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Chemometrics for Process Analytics: Data AlignmentHistory of a CPAC initiativePoll of Process UsersThe Data Processing BottleneckThe key is multivariate instrumentationChemometrics for instrumentation: �the value propositionAlignment TechnologyRaw process chromatogramsThe same chromatograms after alignmentProduct Development Trail38 samples of M. simiae (5 labs)Area SummationArea SummationNMR SpectraNMR Spectra - AlignedQC of x-ray contrast agentsQC of x-ray contrast agentsQC of x-ray contrast agentsPCA as a basis for interpretationPCA as a basis for interpretationOn-line Simulated DistillationComparison of PCA scoresAutomated alignment worksAutomated alignment worksSame Sample run on multiple instruments� (runs 7 weeks apart)Yield Curves MatchContinuous validation of a multivariate instrument