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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, SemimembranosusandBiceps 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 (R(2)test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited (RP)-P-2 = 0.99 and RMSEP = 0.103 g x 100 g(-1)DM.
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https://hal.inrae.fr/hal-02945250
Déposant : Sabine Rossi <>
Soumis le : mardi 22 septembre 2020 - 10:36:07
Dernière modification le : jeudi 5 novembre 2020 - 03:08:54

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Abderrahmane Aït-Kaddour, Donato Andueza, Annabelle Dubost, Jean-Michel Roger, Jean-François Hocquette, et al.. 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, MDPI, 2020, 9 (9), pp.1254. ⟨10.3390/foods9091254⟩. ⟨hal-02945250⟩

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