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Quantitative analysis of seed purity for maize using near infrared spectroscopy

机译：利用近红外光谱对玉米种子纯度进行定量分析

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摘要

应用近红外光谱分析技术结合定量偏最小二乘法对先玉335杂交种纯度进行了定量分析,将不同年份和来源的杂交种和其母本种子粉碎后混合,按0.5％的梯度获得纯度80～100％范围内的样本123份(每梯度按年份和来源设置3个重复)后采集光谱.结果表明:采用散射校正预处理,4 000～8 000 cm-1光谱范围时建模效果较适宜(建模集:检验集=3∶1),建模集内部交叉决定系数达96.06％,校正标准差1.18％,平均相对误差1.03％；检验集的决定系数均达到95.02％,校正标准差1.28％,平均相对误差1.12％.采用不同比例的建模样品和检验样品时,建模集和检验集的决定系数均在94％以上,证明了近红外光谱技术定量测定玉米杂交种纯度的可行性以及所建模型的稳定性.%A quantitative identification model for testing the purity of Xianyu335 hybrid maize seed was built by near intrared reflectance spectruseopy (NIRS) with quantitative partial least squares (QPLS).By grinding and mixing maize hybrid seeds of different years and sources with their female parent seeds,123 samples were obtained with a 0.5％ gradient and purity within the range of 80％-100％ (three replicates of every year and every source in each gradient) and the spectra of the samples were collected.The results showed as following:through implementation of scatter correction pretreatment,the wave number range of 4 000-8 000 cm-1 was appropriate for modeling (calibration sets:validation set =3:1); the internal cross coefficient of determination (R2) for the calibration set reached 96.06％; the standard error of calibration (SEC) was 1.18％; and the Average absolute relative deviation (AARD) was 1.03％.Further,the R2 for the validation set was 95.02％; the SEC was 1.28％; and the AARD was 1.12％.Results of using different ratios of the modeling samples and testing samples showed that the R2 of the calibration set and validation set were all greater than 94％,indicating the feasibility and the stability of NIRS to quantitatively determine the purity of maize hybrid.
机译：应用近红外光谱分析技术结合定量偏最小二乘法对先玉335杂交种纯度进行了定量分析,将不同年份和来源的杂交种和其母本种子粉碎后混合,按0.5％的梯度获得纯度80～100％范围内的样本123份(每梯度按年份和来源设置3个重复)后采集光谱.结果表明:采用散射校正预处理,4 000～8 000 cm-1光谱范围时建模效果较适宜(建模集:检验集=3∶1),建模集内部交叉决定系数达96.06％,校正标准差1.18％,平均相对误差1.03％；检验集的决定系数均达到95.02％,校正标准差1.28％,平均相对误差1.12％.采用不同比例的建模样品和检验样品时,建模集和检验集的决定系数均在94％以上,证明了近红外光谱技术定量测定玉米杂交种纯度的可行性以及所建模型的稳定性.%A quantitative identification model for testing the purity of Xianyu335 hybrid maize seed was built by near intrared reflectance spectruseopy (NIRS) with quantitative partial least squares (QPLS).By grinding and mixing maize hybrid seeds of different years and sources with their female parent seeds,123 samples were obtained with a 0.5％ gradient and purity within the range of 80％-100％ (three replicates of every year and every source in each gradient) and the spectra of the samples were collected.The results showed as following:through implementation of scatter correction pretreatment,the wave number range of 4 000-8 000 cm-1 was appropriate for modeling (calibration sets:validation set =3:1); the internal cross coefficient of determination (R2) for the calibration set reached 96.06％; the standard error of calibration (SEC) was 1.18％; and the Average absolute relative deviation (AARD) was 1.03％.Further,the R2 for the validation set was 95.02％; the SEC was 1.28％; and the AARD was 1.12％.Results of using different ratios of the modeling samples and testing samples showed that the R2 of the calibration set and validation set were all greater than 94％,indicating the feasibility and the stability of NIRS to quantitatively determine the purity of maize hybrid.

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  • 来源
  • 会议地点 Qingdao(CN)
  • 作者

    Wang Qing; Xue Weiqing; Ma Hanxu; Li Junhui; Sun Baoqi; Sun Qun;

  • 作者单位

    Department of Plant Genetics and Breeding, College of Agricult.ure and Biotechnology, China Agricultural University/Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China;

    Department of Plant Genetics and Breeding, College of Agricult.ure and Biotechnology, China Agricultural University/Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China;

    Department of Plant Genetics and Breeding, College of Agricult.ure and Biotechnology, China Agricultural University/Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China;

    College of lnformation and Electrical Engineering, China Agricultural University, Beijing l00193, China;

    Beijing Forestry Academy Science, Beijing l00097, China;

    Department of Plant Genetics and Breeding, College of Agricult.ure and Biotechnology, China Agricultural University/Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture/Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 农业生产作业机械化工艺组织;
  • 关键词

    近红外光谱; 最小二乘法; 模型; 玉米杂交种; 纯度near infrared spectroscopy; least squares approximations; models; hybrid maize seed; purity;

    机译：近红外光谱;最小二乘法;模型;玉米杂交种;纯度%near infrared spectroscopy; least squares approximations; models; hybrid maize seed; purity;

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