Qualitative pattern recognition in chemistry: Theoretical background and practical guidelines
作者： Paolo Oliveri , Cristina Malegori , Eleonora Mustorgi , Monica Casale
DOI: 10.1016/J.MICROC.2020.105725
关键词: Artificial intelligence 、 Pattern recognition 、 Discriminant 、 Pattern recognition (psychology) 、 Experimental data 、 Qualitative research 、 Focus (computing) 、 Chemistry (relationship) 、 Class membership
摘要: Abstract Qualitative pattern recognition methods find important applications in the chemometric sector to extract structured information from complex experimental data. Two main strategies can be distinguished: unsupervised analysis, aimed at investigating on presence of groupings within samples analysed, and supervised predicting class membership new samples. Supervised qualitative are, turn, divided two families: discriminant class-modelling methods. The first ones require least classes defined, while second are suitable also for one-class classification. features each strategy, with a focus advantages limitations, described compared. New trends methods, as well recent attempts force behave ones, vice versa, critically presented.
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