Wei Xiao, Kong Dandan, Zhu Shiping, Li Song, Zhou Shengling, Wu Weiji
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
College of Engineering and Technology, Southwest University, Chongqing, China.
Front Plant Sci. 2022 Mar 11;13:823865. doi: 10.3389/fpls.2022.823865. eCollection 2022.
Different soybean varieties vary greatly in their nutritional value and composition. Screening for superior varieties is also essential for the development of the soybean seed industry. The objective of the paper was to analyze the feasibility of terahertz (THz) frequency-domain spectroscopy and chemometrics for soybean variety identification. Meanwhile, a grey wolf optimizer-support vector machine (GWO-SVM) soybean variety identification model was proposed. Firstly, the THz frequency-domain spectra of experimental samples (6 varieties, 270 in total) were collected. Principal component analysis (PCA) was used to analyze the THz spectra. After that, 203 samples from the calibration set were used to establish a soybean variety identification model. Finally, 67 samples from the test set were used for prediction validation. The experimental results demonstrated that THz frequency-domain spectroscopy combined with GWO-SVM could quickly and accurately identify soybean varieties. Compared with discriminant partial least squares (DPLS) and particles swarm optimization support vector machine, GWO-SVM combined with the second derivative could establish a better soybean variety identification model. The overall correct identification rate of its prediction set was 97.01%.
不同大豆品种在营养价值和成分上差异很大。筛选优良品种对大豆种业发展也至关重要。本文的目的是分析太赫兹（THz）频域光谱和化学计量学用于大豆品种鉴别的可行性。同时，提出了一种灰狼优化器-支持向量机（GWO-SVM）大豆品种鉴别模型。首先，采集了实验样本（共6个品种，270份）的太赫兹频域光谱。采用主成分分析（PCA）对太赫兹光谱进行分析。之后，利用校准集中的203个样本建立大豆品种鉴别模型。最后，使用测试集中的67个样本进行预测验证。实验结果表明，太赫兹频域光谱结合GWO-SVM能够快速、准确地鉴别大豆品种。与判别偏最小二乘法（DPLS）和粒子群优化支持向量机相比，GWO-SVM结合二阶导数能够建立更好的大豆品种鉴别模型。其预测集的总体正确识别率为97.01%。
