Noninvasive freshness evaluation of bighead carp heads based on fluorescence spectroscopy coupled with long short-term memory network: simulation of cold chains
Juan You | Zhenqian Sun | Xiaoting Li | Xiaoguo Ying | Ce Shi | Hongbing Fan
To swiftly and noninvasively assess the freshness of bighead carp heads within simulated cold chain environments, an excitation-emission matrix fluorescence spectroscopy coupled with a long short-term memory network (EEM-LSTM) model was developed. Through the parallel factor algorithm based on analysis of residuals, diagnosis of core consistency, and split-half evaluation, three key fluorescent components from fish fillets were extracted, with the most significant components linked to tryptophan and NADH, both indicative of fish freshness. The EEM-LSTM model exhibited coherent trends in freshness indicators and demonstrated exceptional predictive capabilities for four freshness indicators simultaneously, achieving R2 values exceeding 0.8840 in simulated cold chain situations. Relative errors in the supermarket direct sales cold chain were less than 10%, surpassing those of the long-distance transport cold chain. Hence, the EEM-LSTM model stands validated for predicting fish freshness in simulated cold chains, holding promise for real-world aquatic product freshness forecasting within cold chain scenarios.
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