Near Infrared Spectroscopy for Classification of Iberian Pig Carcasses Using an Artificial Neural Network

Abstract

Artificial neural networks (ANNs) have demonstrated their usefulness in near infrared (NIR) reflection and transmittance spectroscopy for quantitative prediction. The new approach presented here considers the use of ANNs for qualitative classification. Four forms of neural networks (a competitive network using the learning vector quantisation, LVQ learning rule; a backpropagation network using the extended delta-bar-delta, EDBD rule; a network with direct random search, DRS; and a simple competitive linear network, CL) have been tested for classification of 118 fat samples from Iberian pig carcasses into three different price groups. An ANN using the LVQ learning rule has been found to be the best in terms of classification error size. The classification ability of the LVQ network has been evaluated against discriminant analysis, one of the most used methods for NIR spectroscopic qualitative analysis.