Abstract
Moisture content (MC) of rough rice directly affects rice quality and its market value. This study applied spectroscopy both in visible 400-700 nm and NIR 700-1050 nm bands to record spectrum of rough rice single kernel (SK). Tainan No.11 medium rice randomly collected from field. After machine harvested, it was used in the tests and they were conditioned by oven to five MC levels ranging from 10.2 to 35.9%. Two regression methods, multiple linear regressions (MLR) and partial least square regression (PLSR), were applied to develop calibration models. Among 7 tested models were found that PLSR model of first differential with 21 gap points, which are rc=0.98, SEC=1.1% for calibration and rp=0.96, SEP=1.9% for prediction. The results suggested average accuracy for the best model was about 98.4% in 400-1050 nm wavelength.
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