Application of artificial neural networks to determine the authentication of fattening diets of Iberian pigs according to their triacylglycerol profiles

Abstract

The triacylglycerols in the subcutaneous fat from Iberian pigs reared on four different feeding types, Montanera, Recebo, extensive Cebo and intensive Cebo, have been determined by gas chromatography with a flame ionization detector. Analyses were performed in a column coated with a bonded stationary phase (50% phenyl-50% methylpolysiloxane) with hydrogen as the carrier gas. Lipids were extracted by melting the subcutaneous fat in a microwave oven and then filtering and dissolving it in hexane. A total amount of 2783 samples from several campaigns were considered. Using the triacylglycerols as chemical descriptors, a study on the discriminating power to differentiate samples according to the pig feeding type and system was performed. With this aim, pattern recognition techniques, such as linear discriminant analysis (LDA) and multilayer perceptron artificial neural networks (MLPANN), have been used. ANN performed better than LDA, with a mean prediction ability of approximately 97% in the differentiation of fattening diets such as Montanera, extensive Cebo and intensive Cebo. In the case of including the recebo fattening diet, the model presents a mean performance of 82%. The differentiation of fattening systems has also been achieved by means of ANN, with a mean performance of 96%.