Determination of moisture content in rice using non-destructive short-wave near infrared spectroscopy

Abstract

Determination of indices of the indigenous west Sumatran rice varieties was done to rapidly evaluate its moisture contents (MC) by means of non-destructive evaluation. The objective of this study was to identify the MC of two indigenous rice from west Sumatra, Indonesia, namely Junjuangan and Mundam, cultivars. The evaluation was rapidly performed by means of non-destructive evaluation using 1000-2500 nm short wave infrared (SWIR) spectral assessment. The paddy grains samples with identical MC were put into 10 cm petri dish and measured using SWIR spectrophotometer. The grains’ actual MC was then measured by primary method, based on weight measurement. In this study, the spectral data of the grains was then processed by means of Principal Component Analysis (PCA) before correlated with its MCs by Partial Least Square (PLS) method. The model calibration obtained for SWIR spectrophotometer showed correlation of 0.826 and 0.955, with root mean squared error calibration (RMSEC) of 2.97 and 1.4 for Junjuangan and Mundam rice respectively. Moreover, model validation produced correlation of 0.788 and 0.968, RMSEP of 3.8 and 1.29, and bias of 0.193 and 0.171 for Junjuangan and Mundam rice, respectively. The results indicated that the MC of paddy grains could be precisely identified by means of non-destructive evaluation using spectral analysis.Determination of indices of the indigenous west Sumatran rice varieties was done to rapidly evaluate its moisture contents (MC) by means of non-destructive evaluation. The objective of this study was to identify the MC of two indigenous rice from west Sumatra, Indonesia, namely Junjuangan and Mundam, cultivars. The evaluation was rapidly performed by means of non-destructive evaluation using 1000-2500 nm short wave infrared (SWIR) spectral assessment. The paddy grains samples with identical MC were put into 10 cm petri dish and measured using SWIR spectrophotometer. The grains’ actual MC was then measured by primary method, based on weight measurement. In this study, the spectral data of the grains was then processed by means of Principal Component Analysis (PCA) before correlated with its MCs by Partial Least Square (PLS) method. The model calibration obtained for SWIR spectrophotometer showed correlation of 0.826 and 0.955, with root mean squared error calibration (RMSEC) of 2.97 and 1.4 for Junjuangan ...