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Abstract：
The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.
摘要：
通过机械榨油过程中提高采油率，可以在一定程度上克服食用芥子油供需之间的巨大差距。据报道，芥菜种子的微波（MW）预处理可以对机械可表达油的可用性产生积极影响。以床厚和暴露时间为变量，使用高光谱成像（HSI）来了解微波（MW）处理种子中油的空间传播变化，使用可见近红外(可见近红外，400-1000nm）和短波红外（SWIR，1000-1700nm）系统。使用化学计量学技术分析光谱数据，例如偏最小二乘判别分析（PLS-DA）和回归（PLSR），以开发预测模型。PLS-DA模型显示出强大的能力，以96.6和99.5％的高精度水平，从对照样品进行不同MW预处理的芥菜种子的Vis-NIR和SWIR-HSI分类，分别。用SWIR-HSI光谱数据建立的PLSR模型预测(R2>0.90)油含量和油酸等脂肪酸成分,芥酸,饱和脂肪酸，和PUFA最接近分析技术获得的结果。然而,使用Vis-NIR光谱数据时,这些预测(R2>0.70)的准确性较低.
