Visible and Near-Infrared Multispectral Features in Conjunction with Artificial Neural Network and Partial Least Squares for Predicting Biochemical and Micro-Structural Features of Beef Muscles

Abstract

The objective of this study was to determine the potential of multispectral imaging (MSI) data recorded in the visible and near infrared electromagnetic regions to predict the structural features of intramuscular connective tissue, the proportion of intramuscular fat (IMF), and some characteristic parameters of muscle fibers involved in beef sensory quality. In order to do this, samples from three muscles (Longissimus thoracis, Semimembranosus and Biceps femoris) of animals belonging to three breeds (Aberdeen Angus, Limousine, and Blonde d’Aquitaine) were used (120 samples). After the acquisition of images by MSI and segmentation of their morphological parameters, a back propagation artificial neural network (ANN) model coupled with partial least squares was applied to predict the muscular parameters cited above. The results presented a high accuracy and are promising (R2 test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R2P = 0.99 and RMSEP = 0.103 g × 100 g−1 DM.

The one-humped camel: The animal of future, potential alternative red meat, technological suitability and future perspectives

“…With the ancestral breeding methods, the meat producing ability of camels is limited by modest growth rates (500 g/day) ( Kadim et al 2008 ). On the one hand, the application of new technologies used for cattle meats are also being considered for evaluating camel meat (fiber optic spectrophotometry, video image analysis, probes optics, electromechanical probe) ( Craigie et al 2012 ; Aït-Kaddour et al 2020 ; Dlouha et al 2021 ). In other hand, emerging meat-processing technologies (High-pressure processing, electronic nose, Ohmic heating, pulsed electric field …) are some of these new technologies perfectly applicable to the meat of camelids ( Varghese et al 2014 ; Smetana et al 2019 ; Sarno et al 2020 ; Bhat et al 2019 ).…”

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

“…Near-infrared spectroscopy has been used for many years to measure the chemical composition of raw materials in the agri-food industry because it is a rapid, clean and accurate tool. The meat industry routinely uses infrared spectroscopy to analyse the chemical composition of meats [ 1 , 2 , 3 , 4 , 5 , 6 ].…”