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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

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 p...

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Autores principales: Aït-Kaddour, Abderrahmane, Andueza, Donato, Dubost, Annabelle, Roger, Jean-Michel, Hocquette, Jean-François, Listrat, Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555109/
https://www.ncbi.nlm.nih.gov/pubmed/32911633
http://dx.doi.org/10.3390/foods9091254
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author Aït-Kaddour, Abderrahmane
Andueza, Donato
Dubost, Annabelle
Roger, Jean-Michel
Hocquette, Jean-François
Listrat, Anne
author_facet Aït-Kaddour, Abderrahmane
Andueza, Donato
Dubost, Annabelle
Roger, Jean-Michel
Hocquette, Jean-François
Listrat, Anne
author_sort Aït-Kaddour, Abderrahmane
collection PubMed
description 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 (R(2) test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R(2)P = 0.99 and RMSEP = 0.103 g × 100 g(−1) DM.
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spelling pubmed-75551092020-10-14 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 Aït-Kaddour, Abderrahmane Andueza, Donato Dubost, Annabelle Roger, Jean-Michel Hocquette, Jean-François Listrat, Anne Foods Article 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 (R(2) test > 0.90) for practical applications. For example, considering the prediction of IMF, the regression model giving the best ANN model exhibited R(2)P = 0.99 and RMSEP = 0.103 g × 100 g(−1) DM. MDPI 2020-09-08 /pmc/articles/PMC7555109/ /pubmed/32911633 http://dx.doi.org/10.3390/foods9091254 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aït-Kaddour, Abderrahmane
Andueza, Donato
Dubost, Annabelle
Roger, Jean-Michel
Hocquette, Jean-François
Listrat, Anne
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555109/
https://www.ncbi.nlm.nih.gov/pubmed/32911633
http://dx.doi.org/10.3390/foods9091254
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