title: Artificial neural networks in foodstuff analyses: Trends and perspectives A review.
abstract: Artificial neural networks are a family of non-linear computational methods, loosely inspired by the human brain, that have found application in an increasing number of fields of analytical chemistry and specifically of food control. In this review, the main neural network architectures are described and examples of their application to solve food analytical problems are presented, together with some considerations about their uses and misuses.
authors.affiliation: Dipartimento di Chimica - Sapienza Università di Roma, P.le Aldo Moro 5, I-00185 Rome, Italy. fmmonet@homail.com
authors.forename: Federico
authors.initials: F
authors.lastname: Marini
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001330:Electronic Data Processing; D005504:Food Analysis; D005524:Food Technology / Q000639:trends*; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics