ArticleScientific reports2021
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.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 79 citations in OpenAlex.
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- Informative Wavelength Selection for Evaluation of Bacterial Spoilage in Raw Salmon (Foods (Basel, Switzerland) · 2025Article
- Applications of Near-Infrared Spectroscopy for Nondestructive Quality Analysis of Fish and Fishery Products.Foods (Basel, Switzerland) · 2024Article
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- Colorimetric Indicator Based on Gold Nanoparticles and Sodium Alginate for Monitoring Fish Spoilage.Polymers · 2024Article
- Physicochemical properties of Kakol (Food science & nutrition · 2023Article
- Quality Evaluation of Mackerel Fillets Stored under Different Conditions by Hyperspectral Imaging Analysis.Food science of animal resources · 2023Article
- Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp (Foods (Basel, Switzerland) · 2023Article
- Determination of Freshness of Mackerel (Foods (Basel, Switzerland) · 2023Article
- Effects of E-beam irradiation on the physicochemical properties of Atlantic cod (Food bioscience · 2022Article
- Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain.Food engineering reviews · 2022Review
- Effects of immersing treatment of curcumin and piperine combined with vacuum packaging on the quality of salmon (Frontiers in nutrition · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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 (R
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Registered trials
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