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基于近红外快速检测技术的谷子淀粉多样性分析及模型构建

王若楠1,2,**，张颖星1,2,**，于筱菡2，刘少雄2，王跃1,2，薛亚鹏1,2，辛旭霞1,2，张莉1,*，刘敏轩2,*   

  1. 1山西农业大学农学院, 山西晋中 030801; 2中国农业科学院作物科学研究所, 北京 100081
  • 收稿日期:2024-11-22 修回日期:2025-03-26 接受日期:2025-03-26 网络出版日期:2025-04-02

Near-infrared spectroscopic evaluation of starch diversity and model construction in Foxtail millet

WANG Ruo-Nan1,2,**,ZHANG Ying-Xing1,2,**,YU Xiao-Han2,LIU Shao-Xiong2,WANG Yue1,2,XUE Ya-Peng1,2,XIN Xu-Xia1,2,ZHANG Li1,*,LIU Min-Xuan2,*   

  1. 1 College of Agronomy, Shanxi Agricultural University, Jinzhong 030801, Shanxi, China; 2 Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
  • Received:2024-11-22 Revised:2025-03-26 Accepted:2025-03-26 Published online:2025-04-02
  • Supported by:
    本研究由国家重点研发计划项目“杂粮抗逆抗病种质资源的系统挖掘与精准鉴定”(2023YFD1202703-2), 国家自然科学基金项目“种质资源遗传基础解析”(32241041), 财政部和农业农村部国家现代农业产业技术体系建设专项(CARS-06-14.5-A2)资助。

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摘要：

建立谷子种质资源品质性状的快速、高效检测对挖掘具有优异品质性状的资源具有重要意义。本研究选取来自于国内外不同生态区谷子种质资源657份，采用双波长法测定种子的直链淀粉、支链淀粉和总淀粉含量，在此基础上选择550份种质用Unscrambler X 10.4化学计量软件构建直链淀粉、支链淀粉和总淀粉的近红外模型，利用标准正常化结合散射处理(SNV and Detrend)和一阶求导导数处理参数对原始光谱进行预处理，用偏最小二乘法(PLS)构建光谱模型。试验结果表明，657份谷子种质的直链淀粉含量为2.99%~22.40%，平均值为16.25%；支链淀粉含量为52.77%~76.09%，平均值为59.56%；总淀粉含量为62.53%~83.31%，平均值为75.81%；直支比为0.04~0.40，平均值为0.28。国外种质的直链淀粉和总淀粉的变异系数最高，分别为30.08%、5.07%；与国内种质相比，国外种质的平均支链淀粉和总淀粉含量最低，平均值为59.20%、75.19%，幅度范围为54.65%~65.76%、64.65%~82.38%。国内5个生态区谷子种质的淀粉含量差异明显，内蒙古高原区直链淀粉、总淀粉和直支比的变异系数最高，分别为29.40%、4.07%、30.77%；东北春谷区种质的支链淀粉的变异系数最高，为6.00%；南方谷子栽培区种质的直链淀粉、支链淀粉、总淀粉和直支比的变异系数最低，分别为8.21%、4.40%、2.97%和10.71%。本研究筛选出的高直支比和高支链淀粉含量在前5名的材料均来自于华北夏谷区、黄土高原区和东北春谷区，其中黄土高原区二毛尖的直支比和直链淀粉最高(0.40、22.40%)，华北夏谷区半芒红谷的支链淀粉含量最高(76.09%)。直链淀粉、支链淀粉和总淀粉的近红外预测模型校正相关系数(R2cal)分别为0.910、0.848和0.717，交叉验证决定系数(R2cv)分别为0.902、0.830和0.675，外部验证决定系数(R2val)分别为0.903、0.826和0.702，定标标准误差(SEC)分别为1.156、1.234和1.367，交叉检验标准误差(RMSECV)分别为1.208、1.288和1.471，验证标准偏差(RMSEP)分别为1.130、1.260和1.649，外部验证相对分析误差(RPD)分别为3.415、2.539和1.765，最佳因子分别为9、10、10。研究表明，国内外不同生态区的谷子种质在淀粉含量上呈显著多样性，且本研究开发的近红外光谱(NIRS)模型可用于预测谷子的直链淀粉和支链淀粉含量，总淀粉含量虽然可以粗略预测，但仍需进一步的调整和优化以提高准确性。

关键词: 谷子, 不同生态区, 近红外光谱, 直链淀粉, 支链淀粉

Abstract:

Developing rapid and efficient methods for assessing quality traits in foxtail millet germplasm is crucial for identifying superior genetic resources. In this study, we analyzed 657 foxtail millet germplasms from diverse ecological regions, both domestic and international. The double-wavelength method was used to determine amylose, amylopectin, and total starch contents in seeds. Of these, 550 germplasms were selected to develop near-infrared spectroscopy (NIRS) models for predicting amylose, amylopectin, and total starch contents using Unscrambler X 10.4 chemometric software. Standard normal variate combined with scatter correction and first derivative transformation were applied for spectral preprocessing, and partial least squares regression was used to construct the predictive models. The results showed that amylose content across the 657 germplasms ranged from 2.99% to 22.40% (mean: 16.25%), amylopectin content from 52.77% to 76.09% (mean: 59.56%), total starch content from 62.53% to 83.31% (mean: 75.81%), and the amylose-to-amylopectin ratio from 0.04 to 0.40 (mean: 0.28). Among all tested germplasms, foreign accessions exhibited the highest coefficients of variation (CVs) for amylose (30.08%) and total starch (5.07%). Compared to domestic germplasms, foreign germplasms had lower average amylopectin (59.20%) and total starch (75.19%) contents, with ranges of 54.65%–65.76% and 64.65%–82.38%, respectively. Significant differences in starch content were observed among foxtail millet germplasms from five domestic ecological regions. The Inner Mongolia Plateau region exhibited the highest CVs for amylose content (29.40%), total starch content (4.07%), and the amylose-to-amylopectin ratio (30.77%) among all domestic regions. The highest CV for amylopectin content (6.00%) was observed in the Northeast Spring Millet region, whereas the Southern region exhibited the lowest CVs for amylose (8.21%), amylopectin (4.40%), total starch (2.97%), and the amylose-to-amylopectin ratio (10.71%). Germplasms with the highest amylose-to-amylopectin ratios and amylopectin contents were primarily from the North China Summer Millet region, Loess Plateau region, and Northeast Spring Millet region. Notably, Ermaojian from the Loess Plateau region had the highest amylose-to-amylopectin ratio (0.40) and amylose content (22.40%), while Banmanghonggu from the North China Summer Millet region had the highest amylopectin content (76.09%). The NIRS models developed for amylose, amylopectin, and total starch contents achieved calibration correlation coefficients of 0.910, 0.848, and 0.717, respectively; cross-validation determination coefficients of 0.902, 0.830, and 0.675; and external validation determination coefficients of 0.903, 0.826, and 0.702. The standard errors of calibration were 1.156, 1.234, and 1.367, while the root mean square errors of cross-validation were 1.208, 1.288, and 1.471, and the root mean square errors of prediction were 1.130, 1.260, and 1.649, respectively. The ratio of performance to deviation for external validation was 3.415, 2.539, and 1.765, with optimal factor numbers of 9, 10, and 10, respectively. This study highlights the substantial variation in starch content among foxtail millet germplasms from different ecological regions, both domestically and internationally. The NIRS models developed here are effective for predicting amylose and amylopectin contents in foxtail millet. However, further refinement is needed to improve the accuracy of total starch content predictions.

Key words: foxtail millet, different ecological regions, near infrared spectroscopy, amylose, amylopectin

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