Artificial neural networks in foodstuff analyses: Trends and perspectives A review

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.