Multivariate Data Analysis (Chemometrics)

  • Sylvie Roussel
  • Sébastien Preys
  • Fabien Chauchard
  • Jordane Lallemand
Part of the Food Engineering Series book series (FSES)


Chemometrics plays a key role in PAT strategies. It is essential in understanding and diagnosing real-time processes, and keeping them under multivariate statistical control. This chapter will cover design of experiments, exploratory analysis, quantitative predictive modelling, classification, multivariate process monitoring and multi-block and multi-way analyses. The objective of the chapter is to describe chemometrics methods with a main focus on understanding, interpretation and evaluating the usefulness of the results.


Support Vector Machine Fractional Factorial Design Principal Component Regression Process Analytical Technology Multivariate Curve Resolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors want to thank Dr. Mazerolles from INRA for his multi-block section review, Dr. Williams from the Canadian Grain Commission, CAMO (Oslo, Norway) and Dr. Guillaume from Cemagref for authorising the usage of their data.


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Copyright information

© Springer Science+Business Media, New York 2014

Authors and Affiliations

  • Sylvie Roussel
    • 1
  • Sébastien Preys
    • 1
  • Fabien Chauchard
    • 2
  • Jordane Lallemand
    • 1
  1. 1.OndalysClapiersFrance
  2. 2.IndatechClapiersFrance

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