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

Foods

Jean-Michel Roger¹  Abderrahmane Aït-Kaddour²  Donato Andueza³  Jean-François Hocquette³  Annabelle Dubost³  Anne Listrat³ 

[1] Chem House Reasearch Group, 34060 Montpellier, France;VetAgro Sup, INRAE (National Institute for Agriculture, Food, and Environment), Université Clermont Auvergne, 63370 Lempdes, France;VetAgro Sup, UMR1213 Herbivores, INRAE, Clermont Université, Université de Lyon, 63122 Saint-Genès-Champanelle, France;

关键词: intramuscular lipids;    meat;    perimysium;    endomysium;    intramuscular connective tissue;   

DOI  :  10.3390/foods9091254

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown