It is important for rice breeding and quality evaluation to predict the protein content of brown rice rapidly and easily. In this study, near-infrared spectroscopy (NIRS) was utilized to establish a model for detecting crude protein content in brown rice based on 349 samples prepared from three kinds of brown rice, and the performance of the model was evaluated. Improved interval partial least squares (iPLS) was used to divide and screen different feature intervals after spectral preprocessing in this research. On this basis, competitive adaptive reweighted sampling (CARS) optimized the selected feature intervals, and finally 14 effective spectral features concentrated in 1160 nm–1338 nm were selected from 1050 features. The above hybrid feature selection has more advantages than the single selection. Support vector regression (SVR) calibration model was established, and partial least squares regression (PLSR) model commonly used in similar studies was selected as a comparison. The optimal spectral preprocessing method was selected according to the model prediction effect. The coefficient of determination (), of cross-validation set (), root mean square error of prediction (), and relative percent difference () evaluated for the prediction model reached 0.9185, 0.8876, 0.2040% and 3.5194, respectively. The results showed that the designed method can be used for the rapid determination of the crude protein content of brown rice, providing a convenient, efficient and non-destructive method for related detection.

中文翻译：

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基于混合特征选择的近红外光谱定量检测糙米中粗蛋白

快速、简便地预测糙米的蛋白质含量对于水稻育种和品质评价具有重要意义。本研究基于3种糙米制备的349个样品，利用近红外光谱（NIRS）建立了检测糙米粗蛋白含量的模型，并对模型的性能进行了评价。本研究采用改进的区间偏最小二乘法（iPLS）对光谱预处理后的不同特征区间进行划分和筛选。在此基础上，竞争性自适应重加权采样（CARS）对选定的特征区间进行优化，最终从1050个特征中筛选出集中在1160 nm~1338 nm的14个有效光谱特征。上述混合特征选择比单一选择更有优势。建立支持向量回归（SVR）校准模型，并选择同类研究中常用的偏最小二乘回归（PLSR）模型作为比较。根据模型预测效果选择最优的光谱预处理方法。预测模型评估的判定系数()、交叉验证集()、预测均方根误差()和相对百分比差异()分别达到0.9185、0.8876、0.2040%和3.5194。结果表明,所设计的方法可用于糙米粗蛋白含量的快速测定,为相关检测提供了一种便捷、高效、无损的方法。