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Nondestructive Prediction of Tilapia Fillet Freshness During Storage at Different Temperatures by Integrating an Electronic Nose and Tongue with Radial Basis Function Neural Networks.
- Source :
-
Food & Bioprocess Technology . Oct2018, Vol. 11 Issue 10, p1840-1852. 13p. - Publication Year :
- 2018
-
Abstract
- 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]
Details
- Language :
- English
- ISSN :
- 19355130
- Volume :
- 11
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Food & Bioprocess Technology
- Publication Type :
- Academic Journal
- Accession number :
- 131471196
- Full Text :
- https://doi.org/10.1007/s11947-018-2148-8