Article(id=1309574986596188713, tenantId=1146029695717560320, journalId=1308397196074385509, issueId=1309574965775659485, articleNumber=null, orderNo=null, doi=10.11733/j.issn.1007-0435.2026.08.013, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1772899200000, receivedDateStr=2026-03-08, revisedDate=1776700800000, revisedDateStr=2026-04-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1790156607531, onlineDateStr=2026-09-23, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790156607531, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790156607531, creator=13701087609, updateTime=1790156607531, updator=13701087609, issue=Issue{id=1309574965775659485, tenantId=1146029695717560320, journalId=1308397196074385509, year='2026', volume='34', issue='8', pageStart='2753', pageEnd='3140', issueExtLink='null', onlineDate='null', pubDate='1786723200000', pubDateStr='2026-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790156602567, creator='13701087609', updateTime=1790156935415, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309576361992352087, tenantId=1146029695717560320, journalId=1308397196074385509, issueId=1309574965775659485, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309576361992352088, tenantId=1146029695717560320, journalId=1308397196074385509, issueId=1309574965775659485, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2889, endPage=2898, ext={EN=ArticleExt(id=1309574990438171179, articleId=1309574986596188713, tenantId=1146029695717560320, journalId=1308397196074385509, language=EN, title=Near-Infrared Spectroscopy Models for Assessing Oat Hay Quality Across Diverse Environments, columnId=null, journalTitle=Acta Agrestia Sinica, columnName=null, runingTitle=null, highlight=null, articleAbstract=To develop a rapid quality detection model for oat (Avena sativa) hay applicable across environments, 849 samples were collected at three sites (Baoding and Zhangjiakou in Hebei, Guyuan in Ningxia) for two years. Crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), soluble sugars (SS), and total potassium (K) were measured. Prediction models were established using near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR). All five traits showed broad, near-normal distributions, indicating good sample representativeness. The CP, SS, and K models performed well, with cross-validation coefficients (R2) ≥ 0.89, ratio of performance to deviation (RPD) ≥ 3.0, and validation set correlations (r) of 0.84, 0.86, and 0.83, respectively, demonstrating strong external predictive ability. The NDF and ADF models achieved R2 ≥ 0.85 and RPD ≥ 2.5, with r values of 0.78 and 0.79, meeting requirements for routine screening. Based on model predictions and forage quality standards, 56 high-quality forage oat accessions were identified. This study provides an efficient technical platform for high-throughput phenotyping and quality-based breeding in oat germplasm., authors=XUE Min1, LI Jia-ruo1, GUO Yu-fan1, MENG Yu1, WANG Yu1, HU Xiao-ying1, DU Hui-long1, HE Qiang1, authorsList=XUE Min, LI Jia-ruo, GUO Yu-fan, MENG Yu, WANG Yu, HU Xiao-ying, DU Hui-long, HE Qiang, authorCompany=1 College of Life Sciences, Hebei University, Baoding, Hebei Province 071000, China, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309574986986258986, articleId=1309574986596188713, tenantId=1146029695717560320, journalId=1308397196074385509, language=CN, title=多环境燕麦干草主要品质成分近红外光谱模型构建及应用, columnId=1309565024058835950, journalTitle=草地学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=为构建适用于多环境的燕麦(Avena sativa L.)干草品质快速检测模型,本研究收集了河北保定、张家口和宁夏固原两年三点的849份样品,测定粗蛋白(Crude protein,CP)、中性洗涤纤维(Neutral detergent fiber,NDF)、酸性洗涤纤维(Acid detergent fiber,ADF)、可溶性糖(Soluble sugar,SS)和全钾(Total potassium,K)含量,并结合近红外光谱与偏最小二乘法建立预测模型。结果表明:五项指标均呈宽范围正态分布,代表性广泛。CP,SS和K模型预测精度较高,交叉验证决定系数(R²)均≥0.89,相对分析误差(Ratio of performance to deviation,RPD)≥3.0,验证集相关系数(r)分别为0.84,0.86和0.83,外部验证结果可靠。NDF和ADF模型的R² ≥0.85,RPD≥2.5,验证集r分别为0.78和0.79,能满足常规筛查需求。基于模型预测结果与饲草品质标准,筛选出56份优质饲用燕麦种质。本研究为燕麦种质资源的高通量表型鉴定及品质育种提供高效技术支撑。, authors=薛敏1, 李嘉若1, 郭玉帆1, 孟雨1, 王渝1, 胡晓莹1, 杜会龙1, 何强1, authorsList=薛敏, 李嘉若, 郭玉帆, 孟雨, 王渝, 胡晓莹, 杜会龙, 何强, authorCompany=1 河北大学生命科学学院, 河北 保定 071000, correspAuthors=null, authorNote=薛敏(2002-),女,汉族,陕西宝鸡人,硕士研究生,主要从事饲草品质性状的鉴定与评价研究,E-mail:2036778897@qq.com;李嘉若(2002-),女,汉族,河北沧州人,硕士研究生,主要从事燕麦基因组学研究,E-mail:2332896189@qq.com, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=WudalGYdlV8JANTvoONHUg==, pdfFileSize=2258278, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null)}, authors=[Author(id=1320569606759100647, tenantId=1146029695717560320, journalId=null, articleId=1309574986596188713, orderNo=null, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=null, email=null, emailSecond=null, emailThird=null, correspondingAuthor=null, authorType=null, ext={EN=AuthorExt(id=null, tenantId=null, 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SANADYA S K,SOOD V K,SHARMA G,et al. Adaptation of oat genotypes in organic and conventionally managed fields in the northwestern Himalayas[J]. Agronomy Journal,2024,116(2):433-445
TULU A,DIRIBSA M,TEMESGEN W. Evaluation of seven oat (Avena sativa) genotypes for biomass yield and quality parameters under different locations of western Oromia,Ethiopia[J]. Advances in Agriculture,2020,2020(1):8822344
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WANG J W,TAO F. Application research progress of near infrared spectroscopy technology in agricultural products detection[J]. Anhui Agricultural Science Bulletin,2021,27(17):155-158王建伟,陶飞. 近红外光谱技术在农产品检测中的应用研究进展[J]. 安徽农学通报,2021,27(17):155-158
WANG C,MA D M,LI Y,et al. Near infrared spectroscopic model for the detection of the main nutrient components of oat hay[J]. Acta Agrestia Sinica,2022,30(10):2645-2651王储,麻冬梅,李跃,等. 检测燕麦干草主要营养成分含量的近红外光谱分析模型[J]. 草地学报,2022,30(10):2645-2651
ZHANG S Y,XIONG A R,PAN Y C,et al. Near infrared prediction model establishment for conventional nutrient contents of oat grass[J]. Chinese Journal of Animal Nutrition,2022,34(2):1334-1342张书阅,熊安然,潘予琮,等. 燕麦草常规营养成分含量近红外预测模型的建立[J]. 动物营养学报,2022,34(2):1334-1342
LI J F,XIANG Y,WANG Y,et al. Establishment of a rapid detection model for polysaccharide content in Alhagi sparsifolia honey based on near-infrared spectroscopy technology[J]. Food and Fermentation Industries,2025,51(24):342-348李进发,向阳,王宇,等. 基于近红外光谱技术建立刺糖中多糖含量的快速检测模型[J]. 食品与发酵工业,2025,51(24):342-348
LI Q Y,YANG X P,YANG F Y,et al. Rapid detection of mulberry silage quality based on near-infrared characteristic spectral interval extraction[J]. Acta Agrestia Sinica,202634,34(5):1878-1888李沁雨,杨雪萍,杨富裕,等. 特征光谱提取法在饲料桑青贮品质近红外光谱快速检测技术中的应用[J]. 草地学报,2026,34,34(5):1878-1888
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WANG T,ZHENG Y,XU L L,et al. Comprehensive comparison on different wavelength selection methods using several near-infrared spectral datasets with different dimensionalities[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2025,331:125767
HE C,JIANG X G,LI B,et al. A method based on local linear reconstruction combined with spectral information entropy for constructing and updating apple origin discrimination models[J]. Journal of Food Measurement and Characterization,2024,18(7):6365-6377
DING C M,LI K,LIU F,et al. Advancements in the application of near-infrared spectroscopy in complex compositional samples[J]. Chemical Reagents,2025,47(7):18-27丁超民,李轲,刘帆,等. 近红外光谱技术在复杂组分样品中的应用进展[J]. 化学试剂,2025,47(7):18-27
DAI Y T,HAN Q. Cross-regional adaptability of spectral models for avocado stem-end rot detection[J]. Laser Technology,2025,49(6):822-828戴雨涛,韩强. 跨区域鳄梨茎端腐烂光谱模型适应性研究[J]. 激光技术,2025,49(6):822-828
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薛敏, 李嘉若, 郭玉帆, 孟雨, 王渝, 胡晓莹, 杜会龙, 何强
草地学报 | 研究论文 2026,34(8): 2889-2898
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草地学报 |研究论文 2026 , 34 (8) : 2889 -2898
多环境燕麦干草主要品质成分近红外光谱模型构建及应用
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薛敏, 李嘉若, 郭玉帆, 孟雨, 王渝, 胡晓莹, 杜会龙, 何强
作者信息
作者简介:
薛敏(2002-),女,汉族,陕西宝鸡人,硕士研究生,主要从事饲草品质性状的鉴定与评价研究,E-mail:2036778897@qq.com;李嘉若(2002-),女,汉族,河北沧州人,硕士研究生,主要从事燕麦基因组学研究,E-mail:2332896189@qq.com
Near-Infrared Spectroscopy Models for Assessing Oat Hay Quality Across Diverse Environments
XUE Min, LI Jia-ruo, GUO Yu-fan, MENG Yu, WANG Yu, HU Xiao-ying, DU Hui-long, HE Qiang
Affiliations
    1 College of Life Sciences, Hebei University, Baoding, Hebei Province 071000, China
doi: 10.11733/j.issn.1007-0435.2026.08.013
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为构建适用于多环境的燕麦(Avena sativa L.)干草品质快速检测模型,本研究收集了河北保定、张家口和宁夏固原两年三点的849份样品,测定粗蛋白(Crude protein,CP)、中性洗涤纤维(Neutral detergent fiber,NDF)、酸性洗涤纤维(Acid detergent fiber,ADF)、可溶性糖(Soluble sugar,SS)和全钾(Total potassium,K)含量,并结合近红外光谱与偏最小二乘法建立预测模型。结果表明:五项指标均呈宽范围正态分布,代表性广泛。CP,SS和K模型预测精度较高,交叉验证决定系数(R²)均≥0.89,相对分析误差(Ratio of performance to deviation,RPD)≥3.0,验证集相关系数(r)分别为0.84,0.86和0.83,外部验证结果可靠。NDF和ADF模型的R² ≥0.85,RPD≥2.5,验证集r分别为0.78和0.79,能满足常规筛查需求。基于模型预测结果与饲草品质标准,筛选出56份优质饲用燕麦种质。本研究为燕麦种质资源的高通量表型鉴定及品质育种提供高效技术支撑。
燕麦干草  /  近红外光谱  /  品质成分  /  预测模型  /  偏最小二乘法  /  种质资源
To develop a rapid quality detection model for oat (Avena sativa) hay applicable across environments, 849 samples were collected at three sites (Baoding and Zhangjiakou in Hebei, Guyuan in Ningxia) for two years. Crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), soluble sugars (SS), and total potassium (K) were measured. Prediction models were established using near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR). All five traits showed broad, near-normal distributions, indicating good sample representativeness. The CP, SS, and K models performed well, with cross-validation coefficients (R2) ≥ 0.89, ratio of performance to deviation (RPD) ≥ 3.0, and validation set correlations (r) of 0.84, 0.86, and 0.83, respectively, demonstrating strong external predictive ability. The NDF and ADF models achieved R2 ≥ 0.85 and RPD ≥ 2.5, with r values of 0.78 and 0.79, meeting requirements for routine screening. Based on model predictions and forage quality standards, 56 high-quality forage oat accessions were identified. This study provides an efficient technical platform for high-throughput phenotyping and quality-based breeding in oat germplasm.
Oat hay  /  Near-infrared spectroscopy  /  Quality components  /  Prediction model  /  Partial least squares  /  Germplasm resources
薛敏, 李嘉若, 郭玉帆, 孟雨, 王渝, 胡晓莹, 杜会龙, 何强. 多环境燕麦干草主要品质成分近红外光谱模型构建及应用. 草地学报, 2026 , 34 (8) : 2889 -2898 . DOI: 10.11733/j.issn.1007-0435.2026.08.013
XUE Min, LI Jia-ruo, GUO Yu-fan, MENG Yu, WANG Yu, HU Xiao-ying, DU Hui-long, HE Qiang. Near-Infrared Spectroscopy Models for Assessing Oat Hay Quality Across Diverse Environments[J]. Acta Agrestia Sinica, 2026 , 34 (8) : 2889 -2898 . DOI: 10.11733/j.issn.1007-0435.2026.08.013

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WANG C,MA D M,LI Y,et al. Near infrared spectroscopic model for the detection of the main nutrient components of oat hay[J]. Acta Agrestia Sinica,2022,30(10):2645-2651王储,麻冬梅,李跃,等. 检测燕麦干草主要营养成分含量的近红外光谱分析模型[J]. 草地学报,2022,30(10):2645-2651
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LI J F,XIANG Y,WANG Y,et al. Establishment of a rapid detection model for polysaccharide content in Alhagi sparsifolia honey based on near-infrared spectroscopy technology[J]. Food and Fermentation Industries,2025,51(24):342-348李进发,向阳,王宇,等. 基于近红外光谱技术建立刺糖中多糖含量的快速检测模型[J]. 食品与发酵工业,2025,51(24):342-348
LI Q Y,YANG X P,YANG F Y,et al. Rapid detection of mulberry silage quality based on near-infrared characteristic spectral interval extraction[J]. Acta Agrestia Sinica,202634,34(5):1878-1888李沁雨,杨雪萍,杨富裕,等. 特征光谱提取法在饲料桑青贮品质近红外光谱快速检测技术中的应用[J]. 草地学报,2026,34,34(5):1878-1888
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CHEN P,DAI J W,LI J Y,et al. Progress of chemometric methods in near-infrared spectroscopy[J]. Chemical Reagents,2023,45(6):105-112陈瀑,戴嘉伟,李敬岩,等. 近红外光谱分析中的化学计量学方法进展[J]. 化学试剂,2023,45(6):105-112
WANG T,ZHENG Y,XU L L,et al. Comprehensive comparison on different wavelength selection methods using several near-infrared spectral datasets with different dimensionalities[J]. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2025,331:125767
HE C,JIANG X G,LI B,et al. A method based on local linear reconstruction combined with spectral information entropy for constructing and updating apple origin discrimination models[J]. Journal of Food Measurement and Characterization,2024,18(7):6365-6377
DING C M,LI K,LIU F,et al. Advancements in the application of near-infrared spectroscopy in complex compositional samples[J]. Chemical Reagents,2025,47(7):18-27丁超民,李轲,刘帆,等. 近红外光谱技术在复杂组分样品中的应用进展[J]. 化学试剂,2025,47(7):18-27
DAI Y T,HAN Q. Cross-regional adaptability of spectral models for avocado stem-end rot detection[J]. Laser Technology,2025,49(6):822-828戴雨涛,韩强. 跨区域鳄梨茎端腐烂光谱模型适应性研究[J]. 激光技术,2025,49(6):822-828
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WANG G Q,WANG F,CHEN D,et al. A novel method for the determination of inorganic ions in complex plant samples by near infrared spectroscopy[J]. Spectroscopy and Spectral Analysis,2004,24(12):1540-1542王国庆,王芳,陈达,等. 近红外光谱技术用于复杂植物样品中无机离子测定的新方法[J]. 光谱学与光谱分析,2004,24(12):1540-1542
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2026年第34卷第8期
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doi: 10.11733/j.issn.1007-0435.2026.08.013
  • 接收时间:2026-03-08
  • 首发时间:2026-09-23
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  • 收稿日期:2026-03-08
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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