Abstract
The main goal of this study was to predict the age-after-harvest of milled rice and classify it for stale or fresh rice during storage by determining the thiobarbituric acid (TBA) value non-destructively via a hyperspectral imaging (HSI). Thai jasmine rice (KDML 105 variety) was stored at 25°C, 35°C, and 50°C and randomly sampled every month for 12 months for TBA testing (for 4 months at 50°C). During storage, the chemical analysis value of TBA increased over the storage time at all storage temperatures. Hyperspectral imaging in the range 864–1695 nm was used, and partial least squares regression was used to develop multivariate calibration models. The resulting prediction model could approximate quantitative values for TBA with a ratio of performance to the deviation at 2.0 and the root mean square error of prediction of 3.20 μmol MDA/kg. Partial least squares discriminant analysis was conducted for quality analysis based on the TBA value. The age-after-harvest prediction model and the model for classifying stale or fresh rice effectively performed on milled rice, providing a total cross-validation accuracy of 98% and 100%, respectively.
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