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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7555109 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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