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This study develops a reliable radial basis function neural networks (RBFNNs) to estimate freshness for tilapia fillets stored under non-isothermal conditions by using optimal wavelengths from hyperspectral imaging (HSI). The results show that, for tilapia fillet stored at -3, 0, 4, 10, and 15 °C and non-isothermal conditions, total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and the K value increase whereas sensory scores decrease with increasing storage time. To simplify the models, nine optimal wavelengths were selected by using the successive projections algorithm (SPA), following which SPA-RBFNN models were built based on the selected wavelengths and the values of TVB-N, TAC, K, and sensory evaluations for tilapia fillets store isothermally. The ability of the models based on HSI to predict the freshness indicators were verified for tilapia fillets stored under non-isothermal conditions. HSI thus has an excellent potential for nondestructive determination of freshness in tilapia fillets., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The study assessed the feasibility of developing a machine vision system based on pupil and gill color changes in tilapia for simultaneous prediction of total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA) and total viable counts (TVC) during storage at 4°C. The pupils and gills were chosen and color space conversion among RGB, HSI and L ∗ a ∗ b ∗ color spaces was performed automatically by an image processing algorithm. Multiple regression models were established by correlating pupil and gill color parameters with TVB-N, TVC and TBA (R 2 =0.989-0.999). However, assessment of freshness based on gill color is destructive and time-consuming because gill cover must be removed before images are captured. Finally, visualization maps of spoilage based on pupil color were achieved using image algorithms. The results show that assessment of tilapia pupil color parameters using machine vision can be used as a low-cost, on-line method for predicting freshness during 4°C storage., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

This study developed principal component analysis and radial basis function neural networks (PCA-RBFNNs) for predicting freshness in tilapia fillets stored at different temperatures by integrating an electronic nose and electronic tongue. Total volatile basic nitrogen (TVB-N), total aerobic counts (TAC), and K value increased at 0, 4, 7, and 10 °C, while sensory scores decreased significantly. The electronic nose and tongue acquired the volatiles and dissolved chemical compounds in the stored samples. Gas chromatography-mass spectrometry (GC-MS) verified the changes in gas species and contents in fillets stored for different periods of time at different temperatures. PCA-RBFNNs based on data fusion were developed and presented good performance for prediction of TVB-N, TAC, K value, and sensory score in tilapia fillets. The established PCA-RBFNNs based on feature variables of the electronic nose and tongue is a promising method to predict changes in the freshness of fillets stored from 0 to 10 °C in the cold chain. [ABSTRACT FROM AUTHOR]