基于近红外光谱的稻种秕谷含量等级快速判别

doi:10.3964/j.issn.1000-0593(2025)03-0692-08

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摘要：为实现水稻考种中秕谷含量的快速有效检测，基于近红外光谱技术，构建了稻种秕谷含量判别模型。制作不同秕谷含量的稻种样品并采集其近红外光谱数据；为提高模型判别精度，选取两种不同组合Savitzky-Golay平滑(SG)+多元散射校正(MSC)+多项式基线校正(PBC)和SG+标准正态变量转换(SNV)+多项式基线校正(PBC)的预处理方法降噪处理；为减少光谱中冗余信息对模型运算速度和预测精度的影响，利用连续投影算法(SPA)、竞争自适应重加权采样(CARS)和主成分分析(PCA)三种方法对预处理后的光谱进行特征波长变量提取，并建立支持向量机(SVM)、K-最近邻算法(KNN)、决策树(DT)、线性判别分析(LDA)、偏最小二乘判别分析(PLS-DA)、朴素贝叶斯(NB)等6种稻种秕谷含量等级判别模型。试验结果表明，在SG+SNV+PBC预处理方法下，使用CARS方法选择158个特征波长，KNN模型的测试集等级判别准确率可达99.47%。表明近红外光谱技术为实现稻种秕谷含量等级判别提供了一种可行的方法，为实现稻种品质的无损检测提供了支撑。

关键词：稻种；秕谷；含量等级判别；近红外光谱

Abstract：A model for determining empty grain content in rice seed examination was established based on near-infrared spectroscopy to rapidly and effectively detect empty grains in rice seeds. Firstly, rice samples with different empty grain contents were prepared, and their near-infrared spectral data were collected. To improve the discrimination accuracy of the model, two different combinations of preprocessing methods, including Savitzky-Golay smoothing (SG)+multiplicative scatter correction (MSC)+polynomial baseline correction (PBC) and Savitzky-Golay smoothing (SG)+standard normal variate transformation (SNV)+polynomial baseline correction (PBC) were selected for noise reduction. Besides, three methods of sequential projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA) were used to extract the characteristic wavelength variables of the preprocessed spectra, thereby reducing the impact of redundant information in the spectra on the model computation speed and prediction accuracy. Then, based on support vector machine (SVM), K-nearest neighbor algorithm (KNN), decision tree (DT), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and naive Bayes (NB), 6 different identification models for empty grain content of rice seeds were established. Experimental results show that after SG+SNV+PBC preprocessing, the performance of the identification model is better than that of without preprocessing and SG+MSC+PBC. The 158 bands were selected based on the CARS combination SG+SNV+PBC preprocessing band selection. The KNN model established using the selected bands has a better prediction effect, where the testing set identification accuracy of the KNN model could reach 98.47%. The research indicates that near-infrared spectroscopy technology provides a feasible method for discriminating rice seed husk content grades, which provides theoretical support for the non-destructive testing of rice seed quality.

Key words：Rice seed; Empty grain; Content grade discriminatio; Near-infrared spectroscopyn

收稿日期: 2024-01-15 修订日期: 2024-06-14

中图分类号:

O657.33

基金资助: 国家自然科学基金项目(32201665), 安徽省自然科学基金面上项目(2208085ME131)，国家重点研发计划项目(2022YFD2001801-3), 安徽重点研发项目(202204c06020018)资助

通讯作者: 朱德泉 E-mail: zhudequan@ahau.edu.cn

作者简介: 廖娟，女，1986年生，安徽农业大学工学院副教授 e-mail: liaojuan@ahau.edu.cn

引用本文:

廖娟，曹佳雯，田泽丰，刘晓丽，杨玉青，邹禹，王玉伟，朱德泉. 基于近红外光谱的稻种秕谷含量等级快速判别[J]. 光谱学与光谱分析, 2025, 45(03): 692-699.
LIAO Juan, CAO Jia-wen, TIAN Ze-feng, LIU Xiao-li, YANG Yu-qing, ZOU Yu, WANG Yu-wei, ZHU De-quan. Rapid Discrimination Method for Empty Grain Content Grade of Rice Seed Based on Near-Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(03): 692-699.

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