| 基于近红外光谱的高粱籽粒直链淀粉、支链淀粉含量检测模型的构建与应用 |
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| 引用本文： | 张北举,陈松树,李魁印,李鲁华,徐如宏,安畅,熊富敏,张燕,董俐利,任明见.基于近红外光谱的高粱籽粒直链淀粉、支链淀粉含量检测模型的构建与应用[J].中国农业科学,2022,55(1):26-35. |
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| 作者姓名： | 张北举 陈松树 李魁印 李鲁华 徐如宏 安畅 熊富敏 张燕 董俐利 任明见 |
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| 作者单位： | 贵州大学农学院/国家小麦改良中心贵州分中心,贵阳 550025 |
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| 基金项目： | 贵州省特色杂粮现代农业产业技术体系建设专项(黔财农[2019]15号);酒用高粱良种繁殖及配套栽培技术试验研究(700484192124);贵州酒用高粱品种选育研究(GNW2020GD001) |
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| 摘 要： | 目的]高粱是酿酒和饲料的主要原料之一,其籽粒直链淀粉含量与支链淀粉含量的比值大小与白酒品质及饲料质量密切相关.传统的高粱成分化学检测方法已不适合高通量测试,采用改进最小二乘法(modified PLS)对高粱样品的近红外光谱图进行光谱预处理、得分处理和结果监控建立高粱籽粒直链淀粉、支链淀粉含量的预测模型,旨在得到一种...
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| 关 键 词： | 近红外光谱 高粱 直链淀粉 支链淀粉 改进最小二乘法 |
| 收稿时间： | 2021-06-01 |
Construction and Application of Detection Model for Amylose and Amylopectin Content in Sorghum Grains Based on Near Infrared Spectroscopy |
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| ZHANG BeiJu,CHEN SongShu,LI KuiYin,LI LuHua,XU RuHong,AN Chang,XIONG FuMin,ZHANG Yan,DONG LiLi,REN MingJian.Construction and Application of Detection Model for Amylose and Amylopectin Content in Sorghum Grains Based on Near Infrared Spectroscopy[J].Scientia Agricultura Sinica,2022,55(1):26-35. |
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| Authors: | ZHANG BeiJu CHEN SongShu LI KuiYin LI LuHua XU RuHong AN Chang XIONG FuMin ZHANG Yan DONG LiLi REN MingJian |
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| Institution: | College of Agriculture, Guizhou University/Guizhou Branch of National Wheat Improvement Center, Guiyang 550025 |
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| Abstract: | 【Objective】 Sorghum is one of the main raw materials for wine making and feed. The ratio of amylose content to amylopectin content in its grains is closely related to liquor quality and feed quality. Traditional chemical detection methods of sorghum components are no longer suitable for high-throughput testing. Modified PLS is used to perform spectral preprocessing, score processing and result monitoring on the near-infrared spectra of sorghum samples to establish sorghum grain amylose and amylopectin. The prediction model of amylose content aims to obtain a fast, efficient and low-cost detection method, laying the foundation for genetic improvement and quality analysis of sorghum. 【Method】 From 450 sorghum resources, 112 representative varieties were selected as calibration set and verification set. The chemical values of amylose and amylopectin content in 112 sorghum varieties were measured, and near-infrared spectra with wavelengths of 850-1 048 nm were collected, and the spectrum was scanned data matrix and chemical data calculated score (PL1) processing and interpreting the differences between the spectra, and eliminating abnormal species with Global H (GH) greater than 3 to reduce modeling errors. Modified PLS regression technology is used for modeling, and different calibration models are established through different scattering processing and derivative processing methods. Determine the best model according to the cross-validation standard deviation (SECV) and cross-validation correlation coefficient (1-VR), and perform result monitoring and non-parametric testing to evaluate the predictive performance of the model.【Result】 The near-infrared prediction model SECV of amylose is 2.7732, 1-VR is 0.9503, and the correlation coefficient (RSQ) is 0.9688. Bias=0.229<2.7732(SECV)×0.6, that is, the deviation (Bias) is less than 0.6 times of the calibration model SECV; the predicted standard deviation (SEP)=1.266<2.7732(SECV)×1.3=3.60516, that is, the SEP is less than the calibration. The model SECV is 1.3 times, 11.01(SD)-10.81(SD)=0.2<11.02(SD)×0.2=2.204, that is, the difference between the standard deviation (SD) of the chemical data and the near-infrared prediction data is less than 20% of the chemical data SD. The near-infrared prediction model SECV of amylopectin is 1.7516, 1-VR is 0.8818, and RSQ is 0.9127. Bias=-0.014<1.7516(SECV)×0.6 means that Bias is less than 0.6 times of SECV of calibration model, SEP=1.316<1.7516(SECV)×1.3=2.2708 means SEP is less than 1.3 times of SECV of calibration model, 5.30-5.29=0.01<5.30×0.2=1.06, that is, the difference between the chemical data and the near-infrared prediction data SD is less than 20% of the chemical data SD. Using 30 sorghum grains outside the model to conduct a two-pair sample non-parametric test on the validity of the model, the results showed that the difference between the measured and predicted values of amylose content and amylopectin content was not significant (P=0.262>0.05; P=0.992>0.05).【Conclusion】 The established near-infrared model has high accuracy and good stability, can accurately and quickly detect the content of amylose and amylopectin in sorghum, and can be used for the genetic improvement of sorghum and the detection of sorghum quality. |
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| Keywords: | near infrared spectroscopy sorghum amylose amylopectin improved least squares method |
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