Comprehensive chemometrics: chemical and biochemical data analysis

Editors in Chief: Steven D Brown, Romà Tauler and Beata Walczak

Elsevier 2009, four volumes, 2896 ppISBN: 9780444527028€1360

Reviewed by Chris Burgess

Spectroscopists, like chromatographers, produce large quantities of raw data. Data reduction into something more meaningful is the goal. Selecting the right tool for the job is the challenge. Inevitably the right tool involves heavy duty mathematical and statistical expertise and hence chemometrics.

Version 4 of the Pirouette software from Infometrix has been enhanced to support process monitoring and control applications. An update of the installer was also required to handle Windows 7 32-bit and 64-bit operating systems.

A.M.C. DaviesNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: td@nnirc.co.ukTom FearnDepartment of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: tom@stats.ucl.ac.uk

Introduction

This column has been developed from two recent publications by Tom.1,2 My thanks to NIR Publications for allowing us to use Reference 1 essentially unchanged.

Tony

It is common practice in near infrared (NIR) calibration to apply pre-treatments designed to correct for the scatter effects usually seen in absorbance data. These pre-treatments can interfere with interpretation of the spectra. This is illustrated here with the aid of two rather extreme artificial examples.

An electroscalpel offers surgeons advantages, and now a German–Hungarian research team has developed a mass-spectrometry-based technique by which tissues can be analysed during surgery.

Umetrics has announced the release of its SIMCA Batch On-Line Version 3.3, a multivariate analysis software package, which performs batch process monitoring and MSPC and provides an easy to interpret batch fingerprint for the running batch. This version, which is compatible with SIMCA-P+, Version 12, allows comparisons between a running batch and a training set batch, plotting of two process variables on the same chart and has improved memory use and catch-up functionality.

Umetrics Inc Issue: 21/01 RSN: 124

MTT Multantiv Oy has announced that it has granted Bristol-Myers Squibb non-exclusive licensing to its patent rights (US Patent 6,629,041) for methods of multivariate calibration. www.multantiv.fi

A.M.C. DaviesNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: td@nnirc.co.ukTom FearnDepartment of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: tom@stats.ucl.ac.uk

Introduction

In our previous column1 we introduced CVA, one of the very early applications of multivariate analysis (1930s). In this column we will discuss SIMCA (officially it is Soft Independent Modelling of Class Analogies, but no one uses the long form!). SIMCA was invented 30 years later2 by another pioneer, Svante Wold (the man who coined the word “chemometrics”).

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A.M.C. Daviesa and Tom FearnbaNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: td@nnirc.co.ukbDepartment of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: tom@stats.ucl.ac.uk

Introduction

In our previous column we introduced some distance statistics that have been used for comparing spectra. These calculations provide univariate answers from multivariate data in a single step. This may be adequate for some problems but often we need to employ some multivariate mathematics before the reduction to a univariate answer.

SIMCA-P+ Version 12 has been released by Umetrics. This multivariate analysis software performs cluster analysis and PLS regression with classification trees. It has full OPLS and O2PLS functionalities and improved support for hierarchical PCA and PLS. The analysis of batch data is improved through summarised contribution plots that can be resolved to the level of individual variables. Local centring, trimming and Winsorising can be made per phase. The software is 21 CFR Part 11 compliant for use in the pharmaceutical and biotech industries from R&D to manufacturing.Umetrics IncIssue: 20/04 RSN:

A.M.C. DaviesNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: td@nnirc.co.ukTom FearnDepartment of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: tom@stats.ucl.ac.uk

More “Back-to-basics”

In December 2004 I made the decision that this column should make a return visit to topics in quantitative analysis that had been covered (or sometimes just mentioned) in previous columns. Within seconds of that decision I realised that we would need to treat qualitative analy­sis to the same revision. The quantitative aspects turned out to be a three year marathon∗ journey but we have at last arrived at the start of what was conceived as “Part 2”.

I have been working on problems in qualitative analysis for 40 years! That’s before chemometrics as a topic began and I regard them as being much more demanding than quantitative analysis. There are several reasons for this, some more obvious than others:

qualitative analysis is not a single problem, some humans are very good at looking at spectra and making qualitative decisions, solutions require more statistics than are needed for quantitative analysis. Full Text (655.99 Kb)

A.M.C. Daviesa and Tom FearnbaNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: td@nnirc.co.ukbDepartment of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: tom@stats.ucl.ac.uk

Introduction

The starting point for this column is the last one from last year! In that column TD began what was intended to be a two-part story but has, with this column, become a story in four parts. There were required topics we had not covered and we need to begin this column by completing the discussion of removing multiplicative effects by describing what the two most popular methods do to NIR spectra.

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A.M.C. Daviesa and Tom FearnbaNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. E-mail: td@nnirc.co.ukbDepartment of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK. E-mail: tom@stats.ucl.ac.uk

Introduction

The last TD column showed the effect of calculating second derivatives on a set of 100 spectra, which will be the starting point for this column. In order to remind you where we had got to, the last graph is plotted again as Figure 1. You may have been surprised that it was suggested that these second derivative spectra still contain irrelevant variability. The plan was that in this column we would discuss the two most popular methods of removing what is known as “multiplicative effects”, but we realised that in order to understand why we want to remove it you need to know where it has come from.

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A.M.C. DaviesNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. td@nnirc.co.uk

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A.M.C. DaviesNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK. td@nnirc.co.uk

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N.M. Faber,a,* and R. RajkóbaChemometric Consultant, Rubensstraat 7, 6717 VD Ede, The Netherlands. nmf@chemometry.combDepartment of Unit Operations and Food Engineering, Szeged College of Food Engineering, University of Szeged, H-6701 Szeged, POB 433, Hungary

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Pittcon 2007 has launched a new, re-designed website. www.pittcon.org

A.M.C. DaviesNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UK.

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A.M.C. Daviesa and Tom FearnbaNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UKbDepartment of Statistical Science, University College London, Gower Street, London, UK

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A.M.C. Daviesa and Tom FearnbaNorwich Near Infrared Consultancy, 75 Intwood Road, Cringleford, Norwich NR4 6AA, UKbDepartment of Statistical Science, University College London, Gower Street, London, UK

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