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 R2p = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality.
Highlights
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 (1) investigate the potential of visible and near-infrared (VIS/NIR) hyperspectral imaging technique coupled with deep learning model to predict the total volatile basic nitrogen (TVB-N) content of rainbow trout fish, and (2) compare the performance of deep learning algorithm with partial least squares regression (PLSR) and LS-support vector machine (SVM) models established in current study and (3) meta-analysis of previous researches on the prediction of the TVB-N value in meat products using hyperspectral imaging coupled with various chemometric algorithms
The initial TVB-N content of the rainbow trout fillets was 8.70 ± 0.86 N/100 g, which significantly increased during storage time and reached to 36.79 ± 4.38 N/100 g, which this data is comparable with previous study results for rainbow trout fish fillets[22,23,24,25]
Summary
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. Hyperspectral imaging method in combination with different chemometric analysis has been applied to evaluate several freshness indicators, such as Total Volatile Basic Nitrogen (TVB-N), Trimethylamine (TMA)[5,6,7], Thiobarbituric acid reactive substances (TBARS)[8,9,10], total viable count (TVC)[11,12,13], sensory factors[5,14,15] and, etc Volatile compounds such as trimethylamine, ammonia and dimethylamine are considered as total volatile basic nitrogen (TVB-N), produced as a result of destructive activities of microorganisms and are considered as one of the most important freshness indicators to monitor the quality and safety of seafood p roducts[16]. Variable 0th day 2nd day 4th day 6th day 8th day 10th day 12th day Calibration (set) Prediction (set) All
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