The fruits are regarded as one of the world's top 10 super foods by the New York Times ( Hwang and Ko, 2010), and can be consumed raw or incorporated into various processed foods, such as jams, jellies, muffins and cakes, as functional ingredients to attract consumer interest ( Lee and Lee, 2007 Lee et al., 2008). 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.īlueberries (Ericaceae and Chlorella) are mainly distributed in North America but cultivated throughout the world ( Westwood, 1993). 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.
The accuracy of the MC prediction model pretreated by the multiplicative scatter correction method was the highest at 0.9302. To develop a prediction model for blueberry MC, 150 hyperspectral images were obtained from 30 samples per group. Regression coefficients were high at 706, 790, 827, 868, and 894 nm, corresponding to water molecules and carbohydrates (830-840 nm). The PLS discriminant analysis prediction accuracy of smoothing the pre-processed data was the highest. Blueberries (n=820) were dried at 35☌ for 0 (control), 3, 6, 9 and 12 days. 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.
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