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首页> 外文会议>Proceedings of the Fourteenth International conference on mechanization of field experiments >Nondestructive identification of hard seeds of three legume plants using near infrared spectroscopy
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Nondestructive identification of hard seeds of three legume plants using near infrared spectroscopy

机译：近红外光谱法无损鉴定三种豆科植物的硬种子

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摘要

To explore the applicability of the NIRS on nondestructive identification of hardseedness characteristics,in this study,near infrared spectroscopy (NIRS) models were established using near-infrared spectroscopy combined with Partial Least squares (DPLS) to investigate the hard seed characteristics of legume seeds,Codariocalyx motorius Glycine max Cassia tora L.and Sophora alopecuroides.120 seeds of the three species were divided into two groups:calibration set (80 samples) and validation set (40 samples),which contained the same number of hard seeds and soft seeds.The influences of replicate times,speetral region and calibration samples on the discrimination rate were compared.The results showed that with 4 000-5 000 cm-1 of spectral range,vector correction,eight main components,the predictive value of the model fitted with the true value well.Discrimination rates of calibration and validation sets in NIR models were over 85％ for Glycine max.With 4 000-8 000 cm-1 of spectral range,first-derivative speetroseopy,four main components,diserimination rates of calibration and validation sets were about 90％ for Cassia tora L.With 4 000-8 000 cm-1 of spectral range,second derivative spectroscopy,eight main components,discrimination rates of calibration and validation sets were over 95％ for Sophora alopecuroides.%为探讨近红外光谱技术在鉴定种子硬实特性上的普遍性,该文采用近红外光谱法结合偏最小二乘法建立了大豆、苦豆子和决明子单粒种子硬实特性的定性分析模型,每种种子均选择120粒种子进行近红外定性分析,种子分为建模集、检验集2组,建模集80粒,检验集40粒,各组中硬实与非硬实种子的比例为hi,比较了光谱重复次数、光谱范围以及不同建模样品的建模效果.结果表明:采用二次平均光谱所建模型的鉴别率优于单次光谱；大豆采用4 000～5 000 cm-1光谱范围,矢量校正预处理,主成分为8时,建模集与检验集鉴别率均在85％以上；决明子采用4000～8000 cm-1光谱范围,一阶导数预处理,主成分为4时,模型建模集与检验集鉴别率均在90％左右；苦豆子采用4000～8 000cm-1光谱范围,二阶导数预处理,主成分为8时,模型的建模集与检验集鉴别率均在95％以上.以上结果表明近红外光谱技术可以很好地应用于单粒种子硬实特性的判断,有助于硬实机理的深入研究.
机译：为了探索NIRS在无损鉴定种子硬度特性中的适用性，在这项研究中，使用近红外光谱结合偏最小二乘（DPLS）建立了近红外光谱（NIRS）模型，以研究豆类种子的种子硬度特征， 3种120个种子分为定标组（80个样品）和验证组（40个样品），分别包含相同数量的硬种子和软种子。比较了复制时间，峰面积和校正样品对鉴别率的影响。结果表明，在4 000-5 000 cm-1的光谱范围，矢量校正，8个主要成分的基础上，该模型的预测值符合在NIR模型中，对于Glycine max，校准和验证集的鉴别率超过了85％。光谱范围为4000-8 000 cm-1， t导数立体异构体，决明子的四个主要成分，校准和验证集的消解率约为90％。具有4 000-8 000 cm-1的光谱范围，二阶导数光谱，八个主要成分，校准的鉴别率并针对苦豆子碱的验证集超过95％。％为探讨近红外光谱技术在鉴定种子硬实特性上的普遍性，该文采用近红外光谱法结合偏最小二乘建立了大豆，苦豆子和决明子单粒种子硬实特性的定性分析模型，其中种子均选择120粒种子进行近红外定性分析，种子划分建模集，检验集2组，建模集80粒，检验集40粒，各组中硬实与非硬实种子的比例为hi，比较了光谱重复次数，光谱范围以及不同建模样品的建模效果。结果表明：采用二次平均光谱所建模型的鉴别率替代单次光谱；大豆采用4000 〜5000 cm-1光谱范围，矢量校正预处理，主成分8时，建模集与检验集鉴别率均在85％以上；决明子采用4000〜8000 cm-1光谱范围，一阶导数预处理，主成分4时，模型建模集与检验集鉴别率均在90％左右；苦豆子采用4000〜8 000cm-1光谱范围，二阶导数调制，主成分8时，模型的建模集与检验集鉴别率均在95％以上。以上结果表明近红外光谱技术可以很好地放置单粒种子硬实特性的判定，有助于硬实机理的深入研究。

著录项

  • 来源
  • 会议地点 Qingdao(CN)
  • 作者

    Zhu Liwei; Huang Yanyan; Wang Qing; Ma Hanxu; Sun Baoqi; Sun Qun;

  • 作者单位

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

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

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

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

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

    Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;

    Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology,China Agricultural University / 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

    infrared spectroscopy; seeds; technology; Glycine max; Cassia tora L; Sophora alopecuroides红外光谱; 种子; 技术; 大豆; 苦豆子; 决明子;

    机译：infrared spectroscopy; seeds; technology; Glycine max; Cassia tora L; Sophora alopecuroides%红外光谱;种子;技术;大豆;苦豆子;决明子;

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