Online Prediction of Physico-Chemical Quality Attributes of Beef Using Visible—Near-Infrared Spectroscopy and Chemometrics

Abstract

The potential of visible–near-infrared (Vis–NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (R2C) and cross-validation (R2CV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (R2CV: 0.91 (quartering, 24 h) and R2CV: 0.96 (LTL muscle, 48 h)) and drip loss (R2CV: 0.82 (quartering, 24 h) and R2CV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis–NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods.

Predicting the Content of 20 Minerals in Beef by Different Portable Near-Infrared (NIR) Spectrometers

“…There are several other recent studies on the accuracy of predicting meat characteristics using NIR spectroscopy [ 26 , 27 ]. The best results were obtained predicting the chemical composition of organic matter of meat and the color traits, whereas modest results were obtained for the other physical traits, such as drip and cooking losses and meat tenderness [ 13 , 28 , 29 , 30 ]. Although prediction of mineral content is mainly correlative in nature, several studies on NIRS predictions of minerals have been published in the field of food and agriculture [ 3 , 31 ].…”

Emerging nondestructive approaches for meat quality and safety evaluation—A review

“…There are currently many methods and tools applied to monitor and determine the quality and safety of different meat types (beef, pork, lamb, and chicken) such as WBSF (de Nadai Bonin et al, 2020), pH meter (Sahar et al, 2019), texture analyzer (Sujiwo et al, 2019), and HPLC F I G U R E 1 General illustration of e-nose system applied for meat quality detection (Lu et al, 2017). As a whole, chemical and instrumentation tests are very precise and well standardized; however, due to their cost and drudgery associated with their use, they are not suitable for modern and adaptive, large-scale meat facility where high throughput and scale is required, and quick feedback is desired.…”

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

“…However, several studies reported low predictive ability (R 2 cv ≤ 0.29 or R 2 p ≤ 0.30) in beef, pork, and lamb meat at research laboratories or commercial abattoirs (Fiorentini et al, 2017;Andersen et al, 2018;Patel et al, 2021;Savoia et al, 2021); this is likely due to narrower ranges in pH values compared with the 2 above-mentioned studies. moderate NIRS predictability for beef L* (R 2 cv = 0.33 to 0.49 or R 2 p = 0.42 to 0.52), a* (R 2 cv = 0.07 to 0.32 or R 2 p = 0.52 to 0.71), and b* (R 2 cv = 0.19 to 0.39 or R 2 p = 0.35 to 0.63; Sahar et al, 2019;Patel et al, 2021;Savoia et al, 2021) as well as for pork L* (R 2 cv = 0.63), a* (R 2 cv = 0.72), and b* color values (R 2 cv = 0.65; Furtado et al, 2019). These differences among studies for NIRS predictability of color values could be partly due to instrument variations.…”