Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction
Wilson Barragán-Hernández1, Liliana Mahecha-Ledesma2, William Burgos-Paz3
1Red de Ganadería y Especies Menores, Centro de Investigación El Nus, Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA), San Roque, Antioquia, Colombia.
Abstract:
This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.
