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

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...

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Bibliographic Details
Published in:Scientific reports 2021-03, Vol.11 (1), p.5094-5094, Article 5094
Main Authors: Moosavi-Nasab, Marzieh, Khoshnoudi-Nia, Sara, Azimifar, Zohreh, Kamyab, Shima
Format: Article
Language:English
Subjects:
631/114/2397
639/638/11
Algorithms
Deep learning
Distillation
Food quality
Food safety
Humanities and Social Sciences
Meta-analysis
multidisciplinary
Neural networks
Oncorhynchus mykiss
Predictions
Science
Science (multidisciplinary)
Wavelengths
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Summary: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 (R 2 p = 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 R 2 p  = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-84659-y