Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches

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 R-SVR kernel. 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 (GA). 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 (RPD) of 4.8.

Near-Infrared Reflectance Spectroscopy for Predicting the Phospholipid Fraction and the Total Fatty Acid Composition of Freeze-Dried Beef

“…Therefore, these are often not reported and published data tend to focus on the main groups of FA [ 24 , 50 , 52 , 58 ]. Most authors reported worse statistics for PUFA than for SFA and MUFA [ 14 , 48 , 53 , 55 , 59 ]. This could be explained because long-chain PUFA are mainly located in the membrane phospholipids which are quite constant because they are controlled by a complex enzymatic system, providing low variability among animals and have relatively low concentrations [ 58 ].…”

“…The poor performance of NIRS in prediction equations for FA is due to this low variability and because some FA absorbs at the same wavelengths [ 14 , 47 ]. In our data set the range of variability may result in a complex relationship between the spectra and the response variables that are not predicted under a PLS model [ 59 ]. Using the NIRS technique to predict fatty acids is hampered by the absorption of light by the C–H bonds in certain wavelengths.…”

“…This means that some individual fatty acids are not determined accurately, which could be related to similarities in the NIR absorption spectra among FA [ 48 , 53 ]. Other authors that have studied the PLS method, used in our study, fail when the relationship between spectra and the analyte of interest is non-linear [ 59 , 61 ]. Spectra collected in the reflectance mode are influenced not only by the main components of meat (water, fat, protein, etc.)…”

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

“…This condition could generate complex relationships between the spectra and the response variables that are not capable of being predicted under a PLSR model. In fact, Barragán-Hernández et al (2020) considerably improved NIRS predictability of individual and groups of FA in ground beef samples when support vector machine-learning (SVM) regression was applied (R 2 p = 0.74 to 0.99) compared with PLSR (R 2 p = 0.06 to 0.58). Therefore, regression based on SVM could be a novel alternative that manages to identify several vectors in hyperspace capable of constructing a generalizable prediction model for estimating FA profiles in beef with higher accuracy than conventional methods.…”

Research on the wavelength intersection union fusion selection method for near-infrared spectroscopy analysis of apple sugar degree

“…The results showed that the predictive accuracy of the model after screening with the SPA method was higher. In 2020, Barragá n et al [22] used GA (Genetic Algorithm) combined with PLS and Radial Kernel Support Vector Machine Regression (R-SVR) models to predict the fatty acid content in beef. After analysis, it was found that the application of GA increased the error prediction of the two models by 15% and 68% respectively.…”