Artificial neural networks in chemometrics: History, examples and perspectives

Abstract Artificial neural networks (ANNs) are non-linear computational tools suitable to a great host of practical application due to their flexibility and adaptability. However, their application to the resolution of chemometric problems is relatively recent (early 1990s). In this communication, different artificial neural networks architectures are presented and their application to different kinds of chemometric problems (mainly classification and regression) is discussed by means of examples taken from the authors' experience, stressing the pros and cons of ANNs with respect to traditional chemometric techniques.

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