Marzieh Moosavi-Nasab1, Sara Khoshnoudi-Nia2, Zohreh Azimifar3, Shima Kamyab3. 1. Seafood Processing Research Group, Department of Food Science and Technology, School of Agriculture, Shiraz University, P.O. Box 71441-65186, Shiraz, Iran. marzieh.moosavi-nasab@mail.mcgill.ca. 2. Seafood Processing Research Group, School of Agriculture, Shiraz University, P.O. Box 71441-65186, Shiraz, Iran. 3. Department of Computer Science and Engineering, Shiraz University, P.O. Box 71936-16548, Shiraz, Iran.
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.
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 n class="Chemical">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.