Detection of moisture content in salted sea cucumbers by hyperspectral and low field nuclear magnetic resonance based on deep learning network framework

The accurate control of moisture content (MC) during the processing of sea cucumber is beneficial to improve the taste of sea cucumber and maintain its nutritional value, which is directly related to the quality and shelf life of sea cucumber. The purpose of this study is to explore the feasibility using deep learning (DL) to realize rapid nondestructive detection of MC in salted sea cucumbers based on hyperspectral imaging (HSI) and low field nuclear magnetic resonance (LF-NMR) data. Firstly, three Cuckoo Search (CS) dimensionality reduction algorithms (Traditional-CS, Binary-CS and Chaotic-CS) were combined with DL framework respectively using HSI and LF-NMR data to establish prediction models, which proved the feasibility of DL framework in predicting the MC of sea cucumbers, and Chaotic-CS algorithm was selected as the optimal dimensionality reduction algorithm. Then, the MC visualization based on HSI and LF-NMR data was realized respectively to detect the migration and decrease of MC. Finally, using both HSI and LF-NMR data, the advantages of the models based on Fusion-net DL (FDL) framework were discussed, which showed better performance than the single-data models, with RC2 of 0.9929, RMSEC of 0.0016, RP2 of 0.9936 and RPD of 12.5041. In summary, the rapid nondestructive detection of MC in salted sea cucumbers could be realized by HSI and LF-NMR data based on DL framework, and the advantage of data fusion detection based on FDL framework was verified.