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A COMBINE-MOUNTED NIR SPECTROSCOPY-BASED SENSOR FOR SINGLE RICE KERNEL PROTEIN CONTENT MEASUREMENT

机译:基于组合的基于NIR光谱的传感器,用于单米核蛋白质含量测量

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It is useful to develop the monitoring technology of spatial variability of the rice quality when a combine harvests within fields. The moisture content and the protein content of kernels are important information for the unhulled rice. In addition, it is necessary to research the nondestructive measurement of single kernels in order to separate the rice depending on the quality of kernel for the future. Therefore near-infrared (NIR) spectroscopy was selected in this study. The NIR transmittance spectra of single kernels of unhulled rice (rough rice) were measured just after the harvest. The moisture content of these kernels was determined by oven method, and the reference protein content was obtained by determination of total nitrogen using a CN coder. The measured NIR transmittance spectra were applied the multiple linear regression, the principle component regression, and the partial least squares regression in order to identify the regression models. As the results, the best coefficient of determination (r{sup}2) and the standard error of prediction (SEP) of validation set were r{sup}2=0.76 and SEP=2.60 for the moisture content, r{sup}2=0.22 and SEP=0.53 for the protein content. To make a kind of possibility for this technique monitoring on combine, more improved calibration model has to be developed.
机译:当田地内结合收获时,开发稻米品质的空间变异监测技术是有用的。水分含量和粒子的蛋白质含量是未硫化米的重要信息。此外,有必要研究单核的非破坏性测量,以便根据未来内核的质量分离米饭。因此,在本研究中选择了近红外(NIR)光谱。在收获之后,测量了无菌水稻(粗糙米)的单颗粒的NIR透射率光谱。这些核的水分含量通过烘箱方法测定,通过CN编码器测定总氮来获得参考蛋白质含量。测量的NIR透射谱被应用于多元线性回归,原理分量回归和部分最小二乘回归,以便识别回归模型。结果,验证集的最佳确定系数(R {sup} 2)和预测的标准误差为湿度含量的r {sup} 2 = 0.76和sep = 2.60,R {sup} 2蛋白质含量= 0.22和SEP = 0.53。为了使这种技术的可能性在组合上进行监控,必须开发更多改进的校准模型。

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