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

Abstract

Research on fatty acids (FA) is important because their intake is related to human health. NIRS can be a useful tool to estimate the FA of beef but due to the high moisture and the high absorbance of water makes it difficult to calibrate the analyses. This work evaluated near-infrared reflectance spectroscopy as a tool to assess the total fatty acid composition and the phospholipid fraction of fatty acids of beef using freeze-dried meat. An average of 22 unrelated pure breed young bulls from 15 European breeds were reared on a common concentrate-based diet. A total of 332 longissimus thoracis steaks were analysed for fatty acid composition and a freeze-dried sample was subjected to near-infrared spectral analysis. 220 samples (67%) were used as a calibration set with the remaining 110 (33%) being used for validation of the models obtained. There was a large variation in the total FA concentration across the animals giving a good data set for the analysis and whilst the coefficient of variation was nearly 68% for the monounsaturated FA it was only 27% for the polyunsaturated fatty acids (PUFA). PLS method was used to develop the prediction models. The models for the phospholipid fraction had a low R2p and high standard error, while models for neutral lipid had the best performance, in general. It was not possible to obtain a good prediction of many individual PUFA concentrations being present at low concentrations and less variable than other FA. The best models were developed for Total FA, saturated FA, 9c18:1 and 16:1 with R2p greater than 0.76. This study indicates that NIRS is a feasible and useful tool for screening purposes and it has the potential to predict most of the FA of freeze-dried beef.

Nutritional Quality of Meat from Barren Merino Ewes in Comparison to Meat from Traditional Lambs

“…The results (shown in Table 6) agree with those found by other authors for the Merino breed [41][42][43][44]. We found an effect of the type of animal in 23 of the 41 fatty acids detected and, in general, the content of almost all of the fatty acids was higher in ewes' meat than in lambs' meat, probably due to the higher fat content of the former [45]. Numerous studies have indicated that increased intramuscular fat content is associated with an increase in total saturated fatty acids and a decrease in polyunsaturated fatty acids [46].…”

Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition

“…A significant number of spectra peaks were detected at 1454 nm (combination of C-H stretching and first overtone stretching) and at 1930 nm (combination of N-H stretching and O-H stretching), mostly associated with protein, water, sugar, and starch concentrations [ 80 , 81 ]. The second overtone of C-H stretching at 1205 nm, and the first overtone of C-H stretching between 1727 and 1797 nm are usually associated with fat and fatty acid content [ 82 , 83 ]. The predictability of fat content in living mealworm larvae is very good, but it could probably be improved by creating a larger variety of data for fat content within the calibration, as other studies have shown if more data points are available, a higher model performance is reached [ 84 , 85 ].…”

Prediction of the intramuscular fat and protein content of freeze‐dried ground meat from cattle and sheep using near‐infrared spectroscopy (NIRS)

“…While the freeze drying and grinding of samples is more expensive and labour intensive than analysing samples on a fresh basis, it is a preparation method already required by many wet chemistry assays carried out by researchers (AOAC, 2006, 2012). It has also been shown to improve the accuracy of NIRS predictions over scans performed on a fresh meat (Prieto et al ., 2017; Coombs et al ., 2021), as it eliminates problems such as water banding and sample heterogeneity (Prieto et al ., 2017; Ripoll et al ., 2021).…”