Hyperspectral imaging technigue for monitoring moisture content of blueberry during the drying process

Abstract

Changes in the moisture content (MC) of blueberries during drying was monitored by hyperspectral image analysis, and the degree of drying was determined using the partial least squares (PLS) model. Blueberries (n=820) were dried at 35°C for 0 (control), 3, 6, 9 and 12 days. The PLS discriminant analysis prediction accuracy of smoothing the pre-processed data was the highest. Regression coefficients were high at 706, 790, 827, 868, and 894 nm, corresponding to water molecules and carbohydrates (830-840 nm). To develop a prediction model for blueberry MC, 150 hyperspectral images were obtained from 30 samples per group. The MC of each group was also analyzed. The accuracy of the MC prediction model pretreated by the multiplicative scatter correction method was the highest at 0.9302. As indicated by Pearson’s correlation analysis, the blueberry MC showed a high correlation of 0.95 with the total soluble solid contents, brightness, and total flavonoid contents. These results suggest that hyperspectral imaging techniques can be used to predict and monitor various quality characteristics as well as the MC of blueberries during drying.