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Chemometrics
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===Classification, pattern recognition, clustering=== Supervised multivariate classification techniques are closely related to multivariate calibration techniques in that a calibration or training set is used to develop a mathematical model capable of classifying future samples. The techniques employed in chemometrics are similar to those used in other fields β multivariate discriminant analysis, logistic regression, neural networks, regression/classification trees. The use of rank reduction techniques in conjunction with these conventional classification methods is routine in chemometrics, for example discriminant analysis on [[principal component analysis|principal components]] or [[partial least squares regression|partial least squares]] scores. A family of techniques, referred to as class-modelling or [[One-class classification|one-class classifiers]], are able to build models for an individual class of interest.<ref>{{Cite journal|last=Oliveri|first=Paolo|date=2017|title=Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues β A tutorial|journal=Analytica Chimica Acta|language=en|volume=982|pages=9β19|doi=10.1016/j.aca.2017.05.013|pmid=28734370|bibcode=2017AcAC..982....9O |hdl=11567/881059 |s2cid=10119515 |hdl-access=free}}</ref> Such methods are particularly useful in the case of quality control and authenticity verification of products. Unsupervised classification (also termed [[cluster analysis]]) is also commonly used to discover patterns in complex data sets, and again many of the core techniques used in chemometrics are common to other fields such as machine learning and statistical learning.
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