The objectives of the research were to investigate the causes of the violet-red discolouration during processing of canned Palmyra palm and to identify preventive methods without the use of chemical additives. Palmyra palm flesh with (13-21 %) /without some peel left on was tested for the source of violet-red discolouration during blanching. It was found that blanching in distilled water had no effect on discolouration when blanching conditions were at 75, 85, and 95°C for 7 min but the violet-red discolouration occurred only when tap water was used for blanching at 95°C for 7 min. Moreover, the addition of bicarbonate (NaHCO 3 ) in distilled water to imitate higher alkalinity of tap water during blanching could contribute to the violet-red discolouration. Thus the observed cut off alkalinity of the blanching water wherein not to expect any discolouration was found to be about 100 ppm. After sterilization, there was no violet-red discolouration from Palmyra palm flesh without any peel left on even though it had a preceding treatment of blanching in highly alkaline tap water.
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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