Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy

Abstract

The objective of this study was to examine the online use of near infrared reflectance (NIR) spectroscopy to estimate the concentration of individual and groups of fatty acids (FA) as well as intramuscular fat (IMF) in crossbred Aberdeen Angus (AA3) and Limousin (LIM3) cattle. This was achieved by direct application of a fibre-optic probe to the muscle immediately after exposing the meat surface in the abattoir at 48 h post mortem. Samples of M. longissimus thoracis from 88 AA3 and 106 LIM3 were scanned over the NIR spectral range from 350 to 1800 nm and samples of the M. longissimus lumborum were analysed for IMF content and FA composition. Statistically significant differences (P , 0.001) were observed in most FA between the two breeds studied, with FA concentration being higher in AA3 meat mainly. NIR calibrations, tested by cross-validation, showed moderate to high predictability in LIM3 meat samples for C16:0, C16:1, C18:0, trans11 C18:1, C18:1, C18:2 n-6, C20:1, cis9, trans11 C18:2, SFA (saturated FA), MUFA (monounsaturated FA), PUFA (polyunsaturated FA) and IMF content with R 2 (SE CV , mg/ 100 g muscle) of 0.69 (146), 0.69 (28), 0.71 (62), 0.70 (8.1), 0.76 (192), 0.65 (13), 0.71 (0.9), 0.71 (2.9), 0.68 (235), 0.75 (240), 0.64 (17) and 0.75 (477), respectively. FA such as C14:0, C18:3 n-3, C20:4 n-6, C20:5 n-3, C22:6 n-3, n-6 and n-3 were more difficult to predict by NIR in these LIM3 samples (R 2 5 0.12 to 0.62; SE CV 5 0.5 to 26 mg/100 g muscle). In contrast, NIR showed low predictability for FA in AA3 beef samples. In particular for LIM3, the correlations of NIR measurements and several FA in the range from 0.81 to 0.87 indicated that the NIR spectroscopy is a useful online technique for the early, fast and relatively inexpensive estimation of FA composition in the abattoir.

Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy1

“…Results for IMF content from our study are consistent with those reported by Prieto et al (2006) and Ripoll et al (2008) on homogenized meat samples, whereas they are greater to those reported by Prieto et al (2011) who tested the ability of NIRS on intact meat samples in the abattoir. As reported by Prieto et al (2009), the sample preparation (in particular the homogeneity of meat samples) plays an important role in the accuracy of NIRS prediction; in fact, the chemical composition in minced samples can be more accurately predicted by NIRS than in intact tissue.…”

“…Several studies have confirmed the ability of NIRS to predict the content of IMF in beef (Ripoll et al, 2008;Prieto et al, 2006Prieto et al, , 2011 using different NIRS system and meat treatments. Results for IMF content from our study are consistent with those reported by Prieto et al (2006) and Ripoll et al (2008) on homogenized meat samples, whereas they are greater to those reported by Prieto et al (2011) who tested the ability of NIRS on intact meat samples in the abattoir.…”

The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts

“…Nevertheless, no significant improvement was found compared with the use of untreated spectral data, in agreement with De Marchi et al (2011) and Cecchinato et al (2011). The previous procedures were done according to Williams (2001), Prieto et al (2011), andDe Marchi et al (2012).…”

“…This was confirmed by Liu et al (2003) who found an absorption band produced by myoglobin oxidation at 760 nm. The band at 1193 was related to C-H bonds as confirmed by several authors (Murray & Williams, 1987;Prieto et al, 2011;. Bands at approximately 1450 nm are related to water absorption and usually these signals are stronger with respect to those of other components, so that water signals can hide signals related to components such as proteins, which are usually at 1485 nm.…”

“…Partial least squares (PLS) regressions type I were used on 4 types of spectral data (Vis-NIR and NIT, intact and ground) as independent variables (Cecchinato et al, 2011;De Marchi et al, 2011;Prieto et al, 2011). Spectral data subjected to PLS produced a new and smaller set of variables called loadings (#L); the optimal number of #L was calculated as the number of #L after which the standard error of crossvalidation no longer decreased substantially.…”