@article{guzman-martinez_differentiation_2009,
title = {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},
author = {Rocío Guzmán-Martínez and MT Osorio and JM Zumalacárregui and Rocío Alaiz-Rodríguez and SB Engelsen and Javier Mateo},
url = {https://www.sciencedirect.com/science/article/pii/S0309174009001119},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
journal = {Meat science},
volume = {83},
number = {1},
pages = {140–147},
abstract = {FT-IR spectroscopy was evaluated to distinguish suckling lamb carcasses based on rearing systems (ewe’s milk vs. milk replacer). Fat samples (perirenal and omental) were analyzed for fatty acid composition, achieving perfect discrimination using PLS regression. FT-IR spectra were also analyzed using PLS and ANN models, with ANN providing better classification. Perirenal fat was perfectly classified, while omental fat had misclassification rates of 9–13%.},
note = {Publisher: Elsevier},
keywords = {Artificial Neural Networks, Ewe Milk, Fatty Acids, FT-IR, Meat Authenticity, Milk Replacers, Omental Fat, Perirenal Fat, Rearing System},
pubstate = {published},
tppubtype = {article}
}
FT-IR spectroscopy was evaluated to distinguish suckling lamb carcasses based on rearing systems (ewe’s milk vs. milk replacer). Fat samples (perirenal and omental) were analyzed for fatty acid composition, achieving perfect discrimination using PLS regression. FT-IR spectra were also analyzed using PLS and ANN models, with ANN providing better classification. Perirenal fat was perfectly classified, while omental fat had misclassification rates of 9–13%.