光谱学与光谱分析, 2011, 31 (10): 2706, 网络出版: 2011-11-09  

应用近红外光谱技术定量分析杂交玉米纯度的研究

Quantitative Analysis of Hybrid Maize Seed Purity Using Near Infrared Spectroscopy
作者单位
1 中国农业大学农学与生物技术学院植物遗传育种学系/农业部基因组学与遗传改良重点实验室/北京市作物遗传改良重点实验室, 北京100193
2 中国农业大学信息与电气工程学院, 北京100193
摘要
利用近红外光谱分析技术结合定量偏最小二乘法对农大108玉米的纯度进行了定量测定, 首先通过在农大108杂交种子加入不同量的母本178种子, 获得纯度60%~100%范围内的样本123份, 然后测定粉碎后样本的光谱, 根据2: 1的比例划分建模集和检验集。 结果表明: 6 000~10 000 cm-1为适宜的建模光谱范围, 主成分为8时, 建模集内部交叉验证的决定系数达96.61%、 校正标准差(SEC)2.15%, 平均相对误差(RSD)2.04%; 检验集的决定系数达到97.67%, 校正标准差(SEP)1.78%, 平均相对误差(RSD)1.94%。 采用该方法建模时, 采用不同比例的建模样品和检验样品, 建模集平均决定系数为96.21%, 校正标准差2.29%, 平均相对误差为2.81%。 检验集的平均决定系数为95.75%, 预测标准差2.23%, 平均相对误差为2.73%, 进一步证明模型的稳定性。
Abstract
A quantitative identification model for testing the purity of hybrid maize seeds was built by near infrared reflectance spectroscopy with quantitative partial least squares (QPLS). The NIR spectra of 123 seeds powder samples (Nongda108 and mother178) with the purity of 60%~100% were collected using MPA spectrometer. All samples were divided into two groups: calibration set (82 samples) and validation set (41 samples). Synergy interval partial least squares (SiPLSu) was used for selecting effective spectral regions and building models. The influences of different spectral regions and different calibration samples on the prediction results and different main components were compared. The result showed that the spectral regions 6 000~8 000, 6 000~9 000 and 6 000~10 000 cm-1 all had better prediction results (R2 over 95%). Spectral region 6 000~10 000 cm-1 was regarded the optimum spectral region for building the model with less main components(8), and the determination coefficient (R2) of calibration and validation sets were 96.61% and 97.67% respectively, SEC (standard error of calibration) and SEP (standard error of prediction) were 2.15% and 1.78% respectively, RSDs (relative standard deviation) were 2.04% and 1.94% respectively. Even with different calibration samples, the average determination coefficients (R2) of calibration and validation sets were 96.21% and 95.75%, SEC (standard error of calibration) and SEP (standard error of prediction) were 2.29% and 2.23% respectively, RSDs (relative standard deviation) were 2.81% and 2.73% respectively, which further proved the model’s stability. With the increase in the number of main components, the identification rates in calibration set and validation set gradually increased, when the number of main components reached 8, the model determination coefficients reached the best (96.61% and 97.67%), and related coefficients of true value and predicted value were 98.29% and 98.87% respectively. The results have important value for rapid and accurate testing of hybrid maize seed purity.

黄艳艳, 朱丽伟, 马晗煦, 李军会, 孙宝启, 孙群. 应用近红外光谱技术定量分析杂交玉米纯度的研究[J]. 光谱学与光谱分析, 2011, 31(10): 2706. HUANG Yan-yan, ZHU Li-wei, MA Han-xu, LI Jun-hui, SUN Bao-qi, SUN Qun. Quantitative Analysis of Hybrid Maize Seed Purity Using Near Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2011, 31(10): 2706.

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