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作物学报 ›› 2025, Vol. 51 ›› Issue (7): 1757-1768.doi: 10.3724/SP.J.1006.2025.44194

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

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 出版日期:2025-07-12 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:2025-07-12 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

[1] Volkmer J P, Sahoo D, Chin R K, Ho P L, Tang C, Kurtova A V, Willingham S B, Pazhanisamy S K, Contreras-Trujillo H, Storm T A, et al. Three differentiation states risk-stratify bladder cancer into distinct subtypes. Proc Natl Acad Sci USA, 2012, 109: 2078–2083.

[2] Trivedi A K, Arya L, Verma S K, Tyagi R K, Hemantaranjan A, Verma M, Sharma V P, Saha D. Molecular profiling of foxtail millet (Setaria italica (L.) P. Beauv) from Central Himalayan Region for genetic variability and nutritional quality. J Agric Sci, 2018, 156: 333–341.

[3] 刘建垒, 王文娟, 王瑞杰, 赵璐瑶, 常柳, 杨维巧, 张东, 孙辉, 段晓亮. 全国主要谷子品种的营养及食用品质分析. 中国粮油学报, 2022, 37(11): 227–235.

Liu J L, Wang W J, Wang R J, Zhao L Y, Chang L, Yang W Q, Zhang D, Sun H, Duan X L. Nutrition and eating quality of main foxtail millet varieties in China. J Chin Cereals Oils Assoc, 2022, 37(11): 227–235 (in Chinese with English abstract).

[4] 刘建垒, 常柳, 段晓亮, 王文娟, 孙辉. 谷子的生产概况及其保健功能与机理研究进展. 食品工业科技, 2022, 43(5): 389–395.

Liu J L, Chang L, Duan X L, Wang W J, Sun H. Foxtail millet: production status, advances in health benefits and its mechanism. Sci Technol Food Ind, 2022, 43(5): 389–395 (in Chinese with English abstract).

[5] 徐姝. 谷子淀粉的理化特性及Waxy的功能研究. 山西师范大学硕士学位论文, 山西临汾, 2022.

Xu S. The Physical and Chemical Properties of Foxtail Millet Starch and Functional Analysis of Waxy. MS Thesis of Shanxi Normal University, Linfen, Shanxi, China, 2022 (in Chinese with English abstract).

[6] 刘建垒, 常柳, 段晓亮, 洪宇, 孙辉. 小米营养成分及其贮藏加工稳定性研究进展. 中国食物与营养, 2022, 28(3): 55–62.

Liu J L, Chang L, Duan X L, Hong Y, Sun H. Research advancement on nutritional compontent of foxtail millet and their stability during storage and processing. Food Nutr China, 2022, 28(3): 55–62 (in Chinese with English abstract).

[7] 徐锡明, 张欣, 施利利, 崔晶, 丁得亮, 曲红岩, 谷守贤, 李永杰. 直链淀粉含量偏低型杂交粳稻组合的稻米品质评价. 作物杂志, 2016, (6): 44–48.

Xu X M, Zhang X, Shi L L, Cui J, Ding D L, Qu H Y, Gu S X, Li Y J. Evaluation of rice quality with low amylose content in hybrid Japonica rice combinations. Crops, 2016, (6): 44–48 (in Chinese with English abstract).

[8] 贾冠清, 刁现民. 谷子(Setaria italica(L.) P. Beauv.)作为功能基因组研究模式植物的发展现状及趋势. 生命科学, 2017, 29(3): 292–301.

Jia G Q, Diao X M. Current status and perspectives of researches on foxtail millet (Setaria italica (L.) P. beauv.): a potential model of plant functional genomics studies. Chin Bull Life Sci, 2017, 29(3): 292–301 (in Chinese with English abstract).

[9] 杨慧卿, 王根全, 郝晓芬, 程乔林, 王晓宇, 秦玉忠. 山西省谷子地方种质资源表型多样性分析. 江苏农业科学, 2022, 50(13): 20–25.

Yang H Q, Wang G Q, Hao X F, Cheng Q L, Wang X Y, Qin Y Z. Analysis on phenotypic diversity of foxtail millet local germplasm resources in Shanxi Province. Jiangsu Agric Sci, 2022, 50(13): 20–25 (in Chinese with English abstract).

[10] 相吉山, 张恒儒, 刘涵, 索良喜, 贾姝婧, 张颖, 史景奇, 胡利喆, 蔡一宁. 不同生态区谷子种质资源表型比较分析. 中国农业科技导报, 2020, 22(9): 31–41.

Xiang J S, Zhang H R, Liu H, Suo L X, Jia S J, Zhang Y, Shi J Q, Hu L Z, Cai Y N. Comparison of phenotypic traits of foxtail millet germplasm resources in different ecological regions. J Agric Sci Technol, 2020, 22(9): 31–41 (in Chinese with English abstract).

[11] 刘思辰, 曹晓宁, 温琪汾, 王海岗, 田翔, 王君杰, 陈凌, 秦慧彬, 王纶, 乔治军. 山西谷子地方品种农艺性状和品质性状的综合评价. 中国农业科学, 2020, 53: 2137–2148.

Liu S C, Cao X N, Wen Q F, Wang H G, Tian X, Wang J J, Chen L, Qin H B, Wang L, Qiao Z J. Comprehensive evaluation of agronomic traits and quality traits of foxtail millet Landrace in Shanxi. Sci Agric Sin, 2020, 53: 2137–2148 (in Chinese with English abstract).

[12] 刘敏轩, 陆平. 中国谷子育成品种维生素E含量分布规律及其与主要农艺性状和类胡萝卜素的相关性分析. 作物学报, 2013, 39: 398–408.

Liu M X, Lu P. Distribution of vitamin E content and its correlation with agronomic traits and carotenoids content in foxtail millet varieties in China. Acta Agron Sin, 2013, 39: 398–408 (in Chinese with English abstract).

[13] Liu M X, Zhang Z W, Ren G X, Zhang Q, Wang Y Y, Lu P. Evaluation of selenium and carotenoid concentrations of 200 foxtail millet accessions from China and their correlations with agronomic performance. J Integr Agric, 2016, 15: 1449–1457.

[14] 巫小建, 曾凡荣, 岳文浩, 汪军妹. 大麦籽粒总淀粉含量近红外快速无损检测模型的构建. 浙江农业科学, 2021, 62: 40–41.

Wu X J, Zeng F R, Yue W H, Wang J M. Construction of a rapid nondestructive testing model for total starch content in barley grain by near infrared spectroscopy. J Zhejiang Agric Sci, 2021, 62: 40–41 (in Chinese with English abstract).

[15] 李路, 黄汉英, 赵思明, 胡月来, 杨素仙. 大米蛋白质、脂肪、总糖、水分近红外检测模型研究. 中国粮油学报, 2017, 32(7): 121–126.

Li L, Huang H Y, Zhao S M, Hu Y L, Yang S X. NIR spectra detection model of protein, fat, total sugar and moisture in rice. J Chin Cereals Oils Assoc, 2017, 32(7): 121–126 (in Chinese with English abstract).

[16] 王勇生, 李洁, 王博, 张宇婷, 耿俊林. 基于近红外光谱扫描技术对高粱中粗脂肪、粗纤维、粗灰分含量的测定方法研究. 中国粮油学报, 2020, 35(3): 181–185.

Wang Y S, Li J, Wang B, Zhang Y T, Geng J L. Research on measurement of crude fat, crude fiber and ash contents in Sorghum using near-infrared reflectance spectroscopy method. J Chin Cereals Oils Assoc, 2020, 35(3): 181–185 (in Chinese with English abstract).

[17] 李琳琳, 金华丽, 崔彬彬, 王晓君. 基于近红外透射光谱的大豆蛋白质和粗脂肪含量快速检测. 粮食与油脂, 2014, 27(12): 57–60.

Li L L, Jin H L, Cui B B, Wang X J. Rapid determination of soybean protein and crude fat content by near-infrared transmittance spectroscopy. Cereals Oils, 2014, 27(12): 57–60 (in Chinese with English abstract).

[18] Xie L H, Tang S Q, Wei X J, Sheng Z H, Shao G N, Jiao G A, Hu S K, Wang L, Hu P S. Simultaneous determination of apparent amylose, amylose and amylopectin content and classification of waxy rice using near-infrared spectroscopy (NIRS). Food Chem, 2022, 388: 132944.

[19] 刘文丽, 严虞虞, 吴东慧, 滕明攀, 何诗慧. 近红外光谱技术无损检测大米中蛋白质. 食品工业, 2019, 40(1): 205–209.

Liu W L, Yan Y Y, Wu D H, Teng M P, He S H. Rapid and nondestructive detection of protein in rice by near infrared spectroscopy. Food Ind, 2019, 40(1): 205–209 (in Chinese with English abstract).

[20] Zhang H Y, Wang X M, Wang F, Zhao F, Li X R, Fan G Y, Zhao Z H, Guo P Y. Rapid prediction of Apparent Amylose, total starch, and crude protein by near-infrared reflectance spectroscopy for foxtail millet (Setaria italica). Cereal Chem, 2020, 97: 653–660.

[21] 田翔, 秦慧彬, 王君杰, 乔治军. 近红外光谱法快速测定小米品质. 粮食与油脂, 2021, 34(10): 145–148.

Tian X, Qin H B, Wang J J, Qiao Z J. Rapid determination of millet quality by near infrared reflectance spectrometry. Cereals Oils, 2021, 34(10): 145–148 (in Chinese with English abstract).

[22] 刁现民, 程汝宏. 十五年区试数据分析展示谷子糜子育种现状. 中国农业科学, 2017, 50: 4469–4474.

Diao X M, Cheng R H. Current breeding situation of foxtail millet and common millet in China as revealed by exploitation of 15 years regional adaptation test data. Sci Agric Sin, 2017, 50: 4469–4474 (in Chinese with English abstract).

[23] 刘建垒, 商博, 邢晓婷, 张东, 常柳, 孙辉, 段晓亮. 4种方法测定小米直链淀粉含量的比较. 食品科学, 2023, 44(12): 217–224.

Liu J L, Shang B, Xing X T, Zhang D, Chang L, Sun H, Duan X L. Comparison of four methods for the determination of the amylose content in foxtail millet. Food Sci, 2023, 44(12): 217–224 (in Chinese with English abstract).

[24] 马艳弘, 钟小仙, 乔月芳, 李亚辉, 张宏志, 李芬芳. 双波长法测定珍珠粟淀粉中直链和支链淀粉的含量. 江苏农业科学, 2016, 44(12): 331–334.

Ma Y H, Zhong X X, Qiao Y F, Li Y H, Zhang H Z, Li F F. Determination of amylose and amylopectin in pearl millet starch by dual wavelength method. Jiangsu Agric Sci, 2016, 44(12): 331–334 (in Chinese with English abstract).

[25] 王志伟, 王秀贞, 马浪, 刘婷, 唐月异, 吴琪, 孙全喜, 王传堂. 花生籽仁食用感官品质近红外分析模型构建. 花生学报, 2022, 51(3): 77–82.

Wang Z W, Wang X Z, Ma L, Liu T, Tang Y Y, Wu Q, Sun Q X, Wang C T. Construction of near infrared spectroscopy models on prediction of eating quality of peanut kernel. J Peanut Sci, 2022, 51(3): 77–82 (in Chinese with English abstract).

[26] 陈淼, 侯名语, 崔顺立, 李振, 穆国俊, 刘盈茹, 李秀坤, 刘立峰. 不同种皮颜色花生糖含量近红外模型的构建. 光谱学与光谱分析, 2022, 42: 2896–2902.

Chen M, Hou M Y, Cui S L, Li Z, Mu G J, Liu Y R, Li X K, Liu L F. Construction of near-infrared model of peanut sugar content in different seed coat colors. Spectrosc Spectr Anal, 2022, 42: 2896–2902 (in Chinese with English abstract).

[27] 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. 中国农业科学, 2022, 55: 26–35.

Zhang B J, Chen S S, Li K Y, Li L H, Xu R H, An C, Xiong F M, Zhang Y, Dong L L, Ren M J. Construction and application of detection model for amylose and amylopectin content in Sorghum grains based on near infrared spectroscopy. Sci Agric Sin, 2022, 55: 26–35 (in Chinese with English abstract).

[28] 李军涛. 近红外反射光谱快速评定玉米和小麦营养价值的研究. 中国农业大学博士学位论文, 北京, 2014.

Li J T. Study on Rapid Evaluation of Nutritional Value of Corn and Wheat by Near-infrared Reflectance Spectroscopy. PhD Dissertation of China Agricultural University, Beijing, China, 2014 (in Chinese with English abstract).

[29] Kovalenko I V, Rippke G R, Hurburgh C R. Determination of amino acid composition of soybeans (Glycine max) by near-infrared spectroscopy. J Agric Food Chem, 2006, 54: 3485–3491.

[30] Bagchi T B, Sharma S, Chattopadhyay K. Development of NIRS models to predict protein and amylose content of brown rice and proximate compositions of rice bran. Food Chem, 2016, 191: 21–27.

[31] Lebot V, Champagne A, Malapa R, Shiley D. NIR determination of major constituents in tropical root and Tuber crop flours. J Agric Food Chem, 2009, 57: 10539–10547.

[32] 王佳. 不同稻米加工成脱水方便米饭的适应性及工艺优化. 中南林业科技大学硕士学位论文, 湖南长沙, 2012.

Wang J. Adaptability and Process Optimization of Different Kinds of Rice Processed into Dehydrated Instant Rice. MS Thesis of Central South University of Forestry & Technology, Changsha, Hunan, China, 2012 (in Chinese with English abstract).

[33] Jeong H Y, Lim S T. Crystallinity and pasting properties of freeze-thawed high amylose maize starch. Starch Stärke, 2003, 55: 511–517.

[34] Li K H, Zhang T Z, Sui Z Q, Narayanamoorthy S, Jin C, Li S G, Corke H. Genetic variation in starch physicochemical properties of Chinese foxtail millet (Setaria italica Beauv.). Int J Biol Macromol, 2019, 133: 337–345.

[35] 薛亚鹏, 辛旭霞, 王若楠, 于筱菡, 刘少雄, 王瑞云, 刘敏轩. 国内外谷子资源农艺、品质性状差异分析以及遗传多样性研究. 作物学报, 2024, 50: 2468–2482.

Xue Y P, Xin X X, Wang R N, Yu X H, Liu S X, Wang R Y, Liu M X. Analysis of agronomic, quality traits and genetic diversity of domestic and foreign foxtail millet resources. Acta Agron Sin, 2024, 50: 2468–2482 (in Chinese with English abstract).

[36] 王多加, 周向阳, 金同铭, 胡祥娜, 钟娇娥, 吴启堂. 近红外光谱检测技术在农业和食品分析上的应用. 光谱学与光谱分析, 2004, 24: 447–450.

Wang D J, Zhou X Y, Jin T M, Hu X N, Zhong J E, Wu Q T. Application of near-infrared spectroscopy to agriculture and food analysis. Spectrosc Spectr Anal, 2004, 24: 447–450 (in Chinese with English abstract).

[37] 梁晓艳, 吉海彦. 近红外光谱技术在农作物品质分析方面的应用. 中国农学通报, 2006, 22(1): 366–371.

Liang X Y, Ji H Y. Applications of near infrared spectroscopy technology in analyzing the quality of crops. Chin Agric Sci Bull, 2006, 22(1): 366–371 (in Chinese with English abstract).

[38] 田翔, 刘思辰, 王海岗, 秦慧彬, 乔治军. 近红外漫反射光谱法快速检测谷子蛋白质和淀粉含量. 食品科学, 2017, 38(16): 140–144.

Tian X, Liu S C, Wang H G, Qin H B, Qiao Z J. Application of near infrared diffuse reflectance spectroscopy in rapid detection of crude protein and starch in foxtail millet. Food Sci, 2017, 38(16): 140–144 (in Chinese with English abstract).

[39] Bao J S, Cai Y Z, Corke H. Prediction of rice starch quality parameters by near-infrared reflectance spectroscopy. J Food Sci, 2001, 66: 936–939.

[40] 吕建珍, 马建萍, 赵凯, 王宏勇, 任莹, 张海颖, 独俊娥. 23个谷子育成品种的综合评价. 种子, 2020, 39(11): 126–132.

Lyu J Z, Ma J P, Zhao K, Wang H Y, Ren Y, Zhang H Y, Du J E. Comprehensive evaluation of 23 millet cultivars. Seed, 2020, 39(11): 126–132 (in Chinese with English abstract).

[41] 赵星, 张嘉楠, 张一鸣, 金欣欣, 苏俏, 宋亚辉, 李玉荣, 王瑾. 花生籽仁蔗糖含量近红外光谱快速测定方法研究. 中国油料作物学报, 2025, 47: 226–233.

Zhao X, Zhang J N, Zhang Y M, Jin X X, Su Q, Song Y H, Li Y R, Wang J. Rapid determination of sucrose content in peanut kernels by near-infrared spectroscopy. Chin J Oil Crop Sci, 2025, 47: 226–233 (in Chinese with English abstract).

[42] 余松柏, 黄张君, 吴奇霄, 贾俊杰, 王红梅, 王松涛, 沈才洪. 基于近红外光谱构建酒用高粱主要理化指标的快速无损分析模型. 食品工业科技, 2023, 44(10): 311–319.

Yu S B, Huang Z J, Wu Q X, Jia J J, Wang H M, Wang S T, Shen C H. Constructing rapid and undamaged detection models for main physicochemical indexes of brewing Sorghum based on near infrared spectrum. Sci Technol Food Ind, 2023, 44(10): 311–319 (in Chinese with English abstract).

[43] 郭岩. 谷子籽粒主要营养品质性状QTL定位及遗传分析. 西北农林科技大学硕士学位论文, 陕西杨凌, 2024.

Guo Y. QTL Mapping and Genetic Analysis of Main Nutritional Quality Traits in Foxtail Millet. MS Thesis of Northwest A&F University, Yangling, Shaanxi, China, 2024 (in Chinese with English abstract)

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