Hyperspectral data for predicting moisture content and distribution in scallops during continuous and intermittent drying

With the development of optical and spectroscopy sensors, hyperspectral technology has been wildly used in the quality monitoring of food processing. Drying is one of the most commonly used processes in food processing. Moisture content (MC) and moisture distribution (MD) are very important in evaluating a drying technique and the quality of dried final products. This study aimed to investigate the feasibility of using hyperspectral data (387.1-1024.7 nm) to predict MC and realize visualization of MD during constant and intermittent drying, leading to determine the optimal drying in a nondestructive and convenient way. Two characteristic wavelength (CW) selected methods were established: regression coefficients (RC) and two-dimensional correlation spectroscopy (2 D-COS), resulting in 4 (405.7, 513.2, 606.9 and 967.1 nm) and 3 (402.3, 511.5 and 965.2 nm) CWs, respectively. The partial least-square regression (PLSR) models (FW-PLSR, RC-PLSR, and 2 D-COS-PLSR) for MC prediction were built using FW and CW. Comparing the performance of the models, RC-PLSR model was determined to be optimal (variable number = 4, R-C (2)=0.9731, R-CV (2) = 0.9703, R-P (2)=0.9662, RMSEC = 0.0335, RMSECV = 0.0355, RMSEP = 0.0353, and RPD = 5.5198). The visualization maps for MC and MD were generated by the optimized model, which revealed that the intermittent drying process of drying for 4 h with 1 h tempering could improve the product quality of scallops. This study proposed the method for predicting MC and MD and highlighted the potential of hyperspectral technology in the monitoring of constant and intermittent drying in scallop.

Automated summary

This study aimed to investigate the feasibility of using hyperspectral data to predict moisture content and visualize moisture distribution during drying processes. By establishing regression coefficients and two-dimensional correlation spectroscopy models, the optimal model for moisture content prediction was determined. The visualization maps generated by the model revealed the optimal drying process.