Nondestructive determination of the freshness change in bighead carp heads under variable temperatures by using excitation-emission matrix fluorescence and back-propagation neural networks

This study established back-propagation neural networks (BPNNs) for evaluating the freshness of bighead carp (Hypophthalmichthys nobilis) heads during chilled storage via fluorescence spectroscopy using an excitation-emission matrix (EEM). The total volatile basic nitrogen (TVB-N) and total aerobic count (TAC) of fish increased obviously during storage at 0, 4, 8, 12, and 16 °C, while sensory scores decreased with increasing storage time. The EEM fluorescence intensity was measured, and its change was correlated with the freshness indicators of the samples. Three characteristic components of EEM data were extracted by parallel factor analysis, and two freshness indicators were used to construct the EEM-BPNNs model. The results demonstrated that the relative errors of the EEM-BPNNs model for TVB-N and TAC were less than 14%. This result indicated that the EEM-BPNNs model could determine the freshness of fish in cold chains in a rapid and nondestructive way.