赵冰, 王爱文, 赵华. 2022: 应用近红外光谱和化学计量法测定甜玉米种子活力. 南方农业学报, 53(7): 1875-1882. DOI: 10.3969/j.issn.2095-1191.2022.07.009
引用本文: 赵冰, 王爱文, 赵华. 2022: 应用近红外光谱和化学计量法测定甜玉米种子活力. 南方农业学报, 53(7): 1875-1882. DOI: 10.3969/j.issn.2095-1191.2022.07.009
ZHAO Bing, WANG Ai-wen, ZHAO Hua. 2022: Determination of sweet corn seed vigor by near infrared spectra and chemometrics. Journal of Southern Agriculture, 53(7): 1875-1882. DOI: 10.3969/j.issn.2095-1191.2022.07.009
Citation: ZHAO Bing, WANG Ai-wen, ZHAO Hua. 2022: Determination of sweet corn seed vigor by near infrared spectra and chemometrics. Journal of Southern Agriculture, 53(7): 1875-1882. DOI: 10.3969/j.issn.2095-1191.2022.07.009

应用近红外光谱和化学计量法测定甜玉米种子活力

Determination of sweet corn seed vigor by near infrared spectra and chemometrics

  • 摘要: 【目的】建立一种利用近红外光谱和化学计量学检测甜玉米种子活力指数的方法,为种子批量无损筛选提供新方法。【方法】在反射和透射模式下分别收集甜玉米种子的近红外光谱,采用主成分分析和蒙特卡罗交叉验证方法对异常值进行识别与剔除;选取最合适的预处理方法和变量选择方法,建立并选取最优偏最小二乘法预测模型。【结果】对于漫反射活力指数定量分析模型,采用532份样品进行建模研究,其最佳预处理方法为多项式平滑导数(Savitzky-Golay derivative,SG)+均值中心化(Mean Center,MC),最佳变量选择方法为竞争自适应重加权抽样(Competitive adaptive reweighted sampling,CARS),其模型的性能参数校正相关系数(Rc)、交互验证相关系数(Rcv)、预测相关系数(Rp)、校正均方根误差(RMSEC)、交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.826、0.783、0.663、0.137、0.151和0.199。对于透射活力指数定量分析模型,采用415份样品进行研究,其最佳预处理方法为SG一阶导数平滑,最佳变量选择方法为相关系数法(Correlation coefficients,CC),模型的性能参数RcRcvRp、RMSEC、RMSECV和RMSEP分别为0.783、0.680、0.728、0.121、0.142和0.133,该模型不存在过拟合现象,说明光谱采集的透射模型可能更适合测定种子活力指数。【结论】透射光谱可获得更多有关甜玉米种子活力的信息,透射模块是光谱采集预测种子活力的较好方法。

     

    Abstract: 【Objective】To develop a new method to measure sweet corn seed vigor by near infrared(NIR) spectroscopy and chemometrics, so as to provide a new method for seed screening in the seed industry.【Method】Near infrared spectra of sweet corn seed under reflection and transmission modes were recorded. Then outliers were identified and eliminated by principal component analysis(PCA) and Monte Carlo cross validation methods. The proper preprocessing methods and variables selection methods were applied to establish and select the partial least squares(PLS) prediction model.【Result】For the vitality index quantitative analysis model of diffuse reflection, a total of 532 samples were used for modeling study. The best preprocessing method was Savitzky-Golay derivative(SG) +Mean Center(MC), and the optimal variable selection method was competitive adaptive reweighted sampling(CARS). The correction correlation coefficient(Rc), cross validation correlation coefficient(Rcv), prediction correlation coefficient(Rp), root mean square error of correction(RMSEC), root mean square error of cross validation(RMSECV) and root mean square error of prediction(RMSEP) of the model were 0.826, 0.783, 0.663, 0.137, 0.151 and 0.199, respectively. For the vitality index quantitative analysis model of transmission, a total of 415 samples were used for modeling study. And the best preprocessing method was SG first derivative smoothing method, and the optimal variable selection method was correlation coefficients(CC). The model performance parameters Rc, Rcv, Rp, RMSEC, RMSECV and RMSEP were 0.783, 0.680, 0.728, 0.121, 0.142 and 0.133, respectively, there was no over-fitting in this model, indicating that the transmission model of spectral acquisition might be more suitable for the determination of seed vigor index.【Conclusion】More information about sweet corn seed vigor can be obtained through transmission spectrum, and transmission module is a better method for spectral acquisition and prediction of seed vigor.

     

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