Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis

Abstract Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430–1010 nm) coupled with Linear Deep Neural...

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Autores principales:	Marzieh Moosavi-Nasab, Sara Khoshnoudi-Nia, Zohreh Azimifar, Shima Kamyab
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a747cbff8b844f3387d4838f0832f720
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spelling	oai:doaj.org-article:a747cbff8b844f3387d4838f0832f7202021-12-02T13:30:11ZEvaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis10.1038/s41598-021-84659-y2045-2322https://doaj.org/article/a747cbff8b844f3387d4838f0832f7202021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84659-yhttps://doaj.org/toc/2045-2322Abstract Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430–1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R2p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R2 p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.Marzieh Moosavi-NasabSara Khoshnoudi-NiaZohreh AzimifarShima KamyabNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marzieh Moosavi-Nasab Sara Khoshnoudi-Nia Zohreh Azimifar Shima Kamyab Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
description	Abstract Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430–1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R2p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R2 p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.
format	article
author	Marzieh Moosavi-Nasab Sara Khoshnoudi-Nia Zohreh Azimifar Shima Kamyab
author_facet	Marzieh Moosavi-Nasab Sara Khoshnoudi-Nia Zohreh Azimifar Shima Kamyab
author_sort	Marzieh Moosavi-Nasab
title	Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
title_short	Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
title_full	Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
title_fullStr	Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
title_full_unstemmed	Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
title_sort	evaluation of the total volatile basic nitrogen (tvb-n) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a747cbff8b844f3387d4838f0832f720
work_keys_str_mv	AT marziehmoosavinasab evaluationofthetotalvolatilebasicnitrogentvbncontentinfishfilletsusinghyperspectralimagingcoupledwithdeeplearningneuralnetworkandmetaanalysis AT sarakhoshnoudinia evaluationofthetotalvolatilebasicnitrogentvbncontentinfishfilletsusinghyperspectralimagingcoupledwithdeeplearningneuralnetworkandmetaanalysis AT zohrehazimifar evaluationofthetotalvolatilebasicnitrogentvbncontentinfishfilletsusinghyperspectralimagingcoupledwithdeeplearningneuralnetworkandmetaanalysis AT shimakamyab evaluationofthetotalvolatilebasicnitrogentvbncontentinfishfilletsusinghyperspectralimagingcoupledwithdeeplearningneuralnetworkandmetaanalysis
_version_	1718392992901890048

Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis

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