Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial

- Class-modelling performs verification of compliance by defining multivariate spaces.
- Models built in such a way are free from the distribution of non-target samples.
- Discriminant approaches for one-class problems usually lead to biased solutions.
- Several graphical tools may aid model optimisation and validation stages.
- Rigorous class-modelling should be optimised by considering only sensitivity.

Qualitative data modelling is a fundamental branch of pattern recognition, with many applications in analytical chemistry, and embraces two main families: discriminant and class-modelling methods. The first strategy is appropriate when at least two classes are meaningfully defined in the problem under study, while the second strategy is the right choice when the focus is on a single class. For this reason, class-modelling methods are also referred to as one-class classifiers.Although, in the food analytical field, most of the issues would be properly addressed by class-modelling strategies, the use of such techniques is rather limited and, in many cases, discriminant methods are forcedly used for one-class problems, introducing a bias in the outcomes.Key aspects related to the development, optimisation and validation of suitable class models for the characterisation of food products are critically analysed and discussed.

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