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首页> 外文期刊>Annals of Agricultural Science >DETECTING RICE QUALITY AS INFLUENCED BY HULLING USING VISIBLE AND NEAR-INFRARED SPECTROSCOPY
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DETECTING RICE QUALITY AS INFLUENCED BY HULLING USING VISIBLE AND NEAR-INFRARED SPECTROSCOPY

机译:使用可见和近红外光谱检测稻谷质量

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Visible and near-infrared (V1S/N1R) spectros-copy calibration models for rice taste evaluation were developed Sakha, 101 short-grain rice variety. The best performance calibration model was obtained from original spectra of whole grain milled rice using multiple linear regression (MLR) analysis. The correlation coefficient (R2) and the standard error of prediction (SEP) of the validation set was 0.83 and 0.31, respectively for estimated taste value. Near-infrared transmission (NIRT) spectroscopy wasused in an attempt to predict moisture content, protein content and amylose content from un-dried whole grain rough and milled rice spectra. Using partial least squares calibration models obtained from un-dried whole grain rough and milled rice spectra,the coefficient of determination (R~2) and the standard error of prediction (SEP) of the validation set were R~2 = 0.96 and SEP = 0.46 for rough rice moisture content, R~2 = 0.82 and SEP = 0.3.1 for brown rice protein content, ~R2 = 0.87 and SEP = 0.29 for milled rice protein content, and R2 = 0.04 and SEP = 0.25 for milled rice amylose content. The results of the validation indicated that NIRT could be used to determine moisture content and protein content but not the amylose content. Thus, NIRT technology may be used to classify un-dried rough rice into qualitative groups such as high protein content rice and low protein content rice upon arrival at a rice-drying facility after harvesting. The re-sults also indicated that VIS/NIR technology could be used for classifying rice samples into qualitative groups, such as poor taste, better taste and the best taste. However, taste of rice and its acceptability need to be considered to adopt lower degreeof milling. A predicted equation was obtained for rice taste value depending on some physico-chemical composition such as protein, amylose, moisture content, fat acid and taste value from the experiment data. According to the principle of the taste analyzer, rice taste value may be directly calculated with the content of the compositions measured at laboratory.
机译:开发了101种短粒大米Sakha,开发了用于米味评估的可见光和近红外(V1S / N1R)光谱复制校准模型。使用多元线性回归(MLR)分析从全谷物碾米的原始光谱中获得最佳性能的校准模型。验证值的相关系数(R2)和预测的标准预测误差(SEP)分别为0.83和0.31。为了从未干燥的全谷物粗粉和碾米粉中预测水分含量,蛋白质含量和直链淀粉含量,尝试使用近红外透射(NIRT)光谱。使用从未干燥的全谷物粗粉和碾米粉光谱中获得的偏最小二乘校正模型,验证集的测定系数(R〜2)和预测标准误(SEP)为R〜2 = 0.96和SEP =糙米水分含量为0.46,糙米蛋白含量为R〜2 = 0.82和SEP = 0.3.1,碾米蛋白含量为〜R2 = 0.87和SEP = 0.29,碾米淀粉含量为R2 = 0.04和SEP = 0.25内容。验证结果表明,NIRT可用于确定水分含量和蛋白质含量,但不能用于测定直链淀粉含量。因此,NIRT技术可用于将未干燥的糙米收割后到达水稻干燥设备时,将其归类为定性组,例如高蛋白含量的水稻和低蛋白含量的水稻。研究结果还表明,VIS / NIR技术可用于将大米样品分类为定性组,例如口感差,口感好和口感好。但是,米粉的口味及其可接受性需要考虑采用较低的研磨度。根据实验数据,根据蛋白质,直链淀粉,水分,脂肪酸和口味值等一些理化成分,获得了米味值的预测方程。根据味道分析仪的原理,大米的味道值可以直接用实验室测量的组合物含量来计算。

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