Differentiation of perirenal and omental fat quality of suckling lambs according to the rearing system from Fourier transforms mid-infrared spectra using partial least squares and artificial neural networks analysis

• Published in: Meat science Vol. 83; no. 1; pp. 140 - 147
• Main Authors: Osorio, M.T., Zumalacárregui, J.M., Alaiz-Rodríguez, R., Guzman-Martínez, R., Engelsen, S.B., Mateo, J.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.09.2009; [Amsterdam]: Elsevier Science; Elsevier
• Subjects: animal growth; animal housing; animal performance; Artificial neural networks; backfat; Biological and medical sciences; body fat; dairy farming; discriminant analysis; Ewe milk; fat deposit; fatty acid composition; Fatty acids; Food industries; Fourier transform infrared spectroscopy; FT-IR; Fundamental and applied biological sciences. Psychology; lamb carcasses; lamb feeding; lamb meat; lambs; least squares; lipid content; Meat and meat product industries; Meat authenticity; meat composition; milk replacer; Milk replacers; neural networks; Omental fat; Perirenal fat; principal component analysis; product authenticity; rearing; Rearing system; suckling; Milk replacers; Omental fat; Rearing system; Perirenal fat; Artificial neural networks; Meat authenticity; FT-IR; Fatty acids; Ewe milk; Fourier transformation; Meat product; Lipids; Neural network; Meat; Infrared spectrometry; Quality; Fat; Differentiation; Adulteration
• ISSN: 0309-1740; 1873-4138
• DOI: 10.1016/j.meatsci.2009.04.013

Fourier transform mid-infrared (FT-IR) spectroscopy was evaluated as a tool to discriminate between carcasses of suckling lambs according to the rearing system. Fat samples (39 perirenal and 67 omental) were collected from carcasses of lambs from up to three sheep dairy farms, reared on either ewes milk (EM) or milk replacer (MR). Fatty acid composition of the samples from each fat deposit was first analyzed and, when discriminant-partial least squares regression (PLS) was applied, a perfect discrimination between rearing systems could be established. Additionally, FT-IR spectra of fat samples were obtained and discriminant-PLS and artificial neural network (ANN) based analysis were applied to data sets, the latter using principal component analysis (PCA) or support vector machines (SVM) as processing procedure. Perirenal fat samples were perfectly discriminated from their FT-IR spectra. However, analysis of omental fat showed misclassification rates of 9–13%, with the ANN approach showing a higher discrimination power.