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Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning

Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live c...

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Autores principales: Cao, Yi-Ming, Zhang, Yan, Yu, Shuang-Ting, Wang, Kai-Kuo, Chen, Ying-Jie, Xu, Zi-Ming, Ma, Zi-Yao, Chen, Hong-Lu, Wang, Qi, Zhao, Ran, Sun, Xiao-Qing, Li, Jiong-Tang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486347/
https://www.ncbi.nlm.nih.gov/pubmed/37685087
http://dx.doi.org/10.3390/foods12173154
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author Cao, Yi-Ming
Zhang, Yan
Yu, Shuang-Ting
Wang, Kai-Kuo
Chen, Ying-Jie
Xu, Zi-Ming
Ma, Zi-Yao
Chen, Hong-Lu
Wang, Qi
Zhao, Ran
Sun, Xiao-Qing
Li, Jiong-Tang
author_facet Cao, Yi-Ming
Zhang, Yan
Yu, Shuang-Ting
Wang, Kai-Kuo
Chen, Ying-Jie
Xu, Zi-Ming
Ma, Zi-Yao
Chen, Hong-Lu
Wang, Qi
Zhao, Ran
Sun, Xiao-Qing
Li, Jiong-Tang
author_sort Cao, Yi-Ming
collection PubMed
description Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (r(p)) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (r(p) ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (r(p) ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (r(p) ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp.
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spelling pubmed-104863472023-09-09 Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning Cao, Yi-Ming Zhang, Yan Yu, Shuang-Ting Wang, Kai-Kuo Chen, Ying-Jie Xu, Zi-Ming Ma, Zi-Yao Chen, Hong-Lu Wang, Qi Zhao, Ran Sun, Xiao-Qing Li, Jiong-Tang Foods Article Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (r(p)) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (r(p) ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (r(p) ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (r(p) ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp. MDPI 2023-08-22 /pmc/articles/PMC10486347/ /pubmed/37685087 http://dx.doi.org/10.3390/foods12173154 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cao, Yi-Ming
Zhang, Yan
Yu, Shuang-Ting
Wang, Kai-Kuo
Chen, Ying-Jie
Xu, Zi-Ming
Ma, Zi-Yao
Chen, Hong-Lu
Wang, Qi
Zhao, Ran
Sun, Xiao-Qing
Li, Jiong-Tang
Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
title Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
title_full Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
title_fullStr Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
title_full_unstemmed Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
title_short Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning
title_sort rapid and non-invasive assessment of texture profile analysis of common carp (cyprinus carpio l.) using hyperspectral imaging and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486347/
https://www.ncbi.nlm.nih.gov/pubmed/37685087
http://dx.doi.org/10.3390/foods12173154
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