Article(id=1307627328893383061, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1307627292386153084, articleNumber=null, orderNo=null, doi=10.7606/j.issn.1009-1041.2025.11.09, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734537600000, receivedDateStr=2024-12-19, revisedDate=1741795200000, revisedDateStr=2025-03-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1789692249749, onlineDateStr=2026-09-18, pubDate=1763136000000, pubDateStr=2025-11-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789692249749, onlineIssueDateStr=2026-09-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789692249749, creator=13701087609, updateTime=1789692249749, updator=13701087609, issue=Issue{id=1307627292386153084, tenantId=1146029695717560320, journalId=1303687221787852879, year='2025', volume='45', issue='11', pageStart='F2', pageEnd='1588', issueExtLink='null', onlineDate='null', pubDate='1763136000000', pubDateStr='2025-11-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789692241045, creator='13701087609', updateTime=1789722017969, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1307752187061363394, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1307627292386153084, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1307752187061363395, tenantId=1146029695717560320, journalId=1303687221787852879, issueId=1307627292386153084, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1521, endPage=1527, ext={EN=ArticleExt(id=1307627329077932438, articleId=1307627328893383061, tenantId=1146029695717560320, journalId=1303687221787852879, language=EN, title=Prediction of Wheat Quality Based on GlutoPeak, columnId=1307627250845770215, journalTitle=Journal of Triticeae Crops, columnName=Genetics & Breedin, runingTitle=null, highlight=null, articleAbstract=

In order to explore the feasibility of using GlutoPeak test (GPT) for rapid evaluation of wheat quality, flour samples of 150 wheat varieties (lines) were tested for the main quality indicators, including GPT indicators, sedimentation, gluten content, and dough rheological properties. The correlations between GPT indicators and other parameters were evaluated. The results showed that among the tested indicators of the wheat flour samples, water absorption had the smallest coefficient of variation (4.69%), while dough development time had the largest coefficient of variation (82.08%). The coefficients of variation for peak maximum time, startup energy, and plateau energy were all above 40%. Correlation analysis showed that there were significant correlations between the GPT indicators and dough rheological properties. The seven GPT indicators included peak maximum time (PMT), brabender units at the maximum (BEM), ante maximum (AM), post maximum (PM), startup energy (A1), plateau energy (A2) and aggregation energy (A3). The PMT showed a highly significant positive correlation with sedimentation and a highly significant negative correlation with wet gluten content, while the BEM, AM, PM and A3 exhibited a highly significant positive correlation with both sedimentation and wet gluten content. The AM, PM and A3 showed a highly significant positive correlation with water absorption and development time. The PMT had a highly significant negative correlation with water absorption, while it showed highly significant positive correlations with development time and stability time. The PMT, AM and PM were significantly positively correlated with stretch area and max resistance. Then, stepwise linear regression models were developed using the GPT indices to predict other parameters. The coefficients of determination were all significant at the 0.01 level, and the GPT indicators had high coefficients of determination (R2>0.6) for other parameters, such as stretch area, max resistance, gluten index, and stability time. A3 can predict sedimentation and wet gluten content, while PMT, AM and PM can predict stability time, stretch area, and gluten index among dough indicators.

, authors=Xiaoli LI, Yang ZHANG, Lanfang JIANG, Jianyu HAO, Min WANG, Xiaofei MA, Dingyi ZHANG, Hutai JI, authorsList=Xiaoli LI, Yang ZHANG, Lanfang JIANG, Jianyu HAO, Min WANG, Xiaofei MA, Dingyi ZHANG, Hutai JI, authorCompany=null, correspAuthors=Hutai JI, 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=1307627329396699543, articleId=1307627328893383061, tenantId=1146029695717560320, journalId=1303687221787852879, language=CN, title=基于面筋聚集仪的小麦粉品质快速检测方法研究, columnId=1305133119284212678, journalTitle=麦类作物学报, columnName=遗传育种, runingTitle=null, highlight=null, articleAbstract=

为探究面筋聚集仪测试(GPT)在快速预测小麦加工品质方面的可行性,以150份小麦品种(系)面粉样品为材料,对面筋聚集指标、沉降值、面筋含量、面团流变学指标等主要面粉质量参数进行检测,并分析面筋聚集指标与其他被测指标的相关性。结果表明,供试小麦粉被测指标中,吸水率的变异系数最小(4.69%),面团形成时间的变异系数最大(82.08%),峰值时间(PMT)、启动能量(A1)、稳定能量(A2)的变异系数均大于40%。PMT、峰值扭矩(BEM)、峰前值(AM)、峰后值(PM)、A1、A2和聚集能量(A3)共7个GPT指标与其他被测指标存在不同程度的相关性,其中PMT与沉降值呈极显著正相关(P<0.01),与湿面筋含量呈极显著负相关;BEM、AM、PM、A3与沉降值和湿面筋含量均呈极显著正相关;AM、PM和A3与吸水率、形成时间等粉质仪指标均呈极显著正相关;PMT与吸水率呈极显著负相关,与形成时间和稳定时间呈极显著正相关;PMT、AM和PM与拉伸面积、最大拉伸阻力呈极显著正相关。利用GPT特性建立与其他被测品质指标的逐步线性回归方程,决定系数均达到0.01显著水平,其中拉伸面积、最大拉伸阻力、面筋指数、稳定时间等指标的决定系数较高(R2>0.6)。A3可以预测沉降值、湿面筋含量等指标,PMT、AM、PM可以预测稳定时间、拉伸面积、面筋指数等面团指标。

, authors=李晓丽, 张洋, 姜兰芳, 郝建宇, 王敏, 马小飞, 张定一, 姬虎太, authorsList=李晓丽, 张洋, 姜兰芳, 郝建宇, 王敏, 马小飞, 张定一, 姬虎太, authorCompany=null, correspAuthors=姬虎太, authorNote=

E-mail: (李晓丽)

, correspAuthorsNote=
E-mail: (姬虎太)
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基于面筋聚集仪的小麦粉品质快速检测方法研究
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李晓丽 , 张洋 , 姜兰芳 , 郝建宇 , 王敏 , 马小飞 , 张定一 , 姬虎太
麦类作物学报 | 遗传育种 2025,45(11): 1521-1527
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麦类作物学报 |遗传育种 2025 , 45 (11) : 1521 -1527
基于面筋聚集仪的小麦粉品质快速检测方法研究
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E-mail: (李晓丽)

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李晓丽 , 张洋, 姜兰芳, 郝建宇, 王敏, 马小飞, 张定一, 姬虎太
作者信息
  • 山西农业大学小麦研究所,山西 临汾 041000
通讯作者:
E-mail: (姬虎太)
作者简介:

E-mail: (李晓丽)

Prediction of Wheat Quality Based on GlutoPeak
Xiaoli LI , Yang ZHANG, Lanfang JIANG, Jianyu HAO, Min WANG, Xiaofei MA, Dingyi ZHANG, Hutai JI
Affiliations
  • Institute of Wheat Research, Shanxi Agricultural University, Linfen, Shanxi 041000, China
出版时间: 2025-11-15 doi: 10.7606/j.issn.1009-1041.2025.11.09
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为探究面筋聚集仪测试(GPT)在快速预测小麦加工品质方面的可行性,以150份小麦品种(系)面粉样品为材料,对面筋聚集指标、沉降值、面筋含量、面团流变学指标等主要面粉质量参数进行检测,并分析面筋聚集指标与其他被测指标的相关性。结果表明,供试小麦粉被测指标中,吸水率的变异系数最小(4.69%),面团形成时间的变异系数最大(82.08%),峰值时间(PMT)、启动能量(A1)、稳定能量(A2)的变异系数均大于40%。PMT、峰值扭矩(BEM)、峰前值(AM)、峰后值(PM)、A1、A2和聚集能量(A3)共7个GPT指标与其他被测指标存在不同程度的相关性,其中PMT与沉降值呈极显著正相关(P<0.01),与湿面筋含量呈极显著负相关;BEM、AM、PM、A3与沉降值和湿面筋含量均呈极显著正相关;AM、PM和A3与吸水率、形成时间等粉质仪指标均呈极显著正相关;PMT与吸水率呈极显著负相关,与形成时间和稳定时间呈极显著正相关;PMT、AM和PM与拉伸面积、最大拉伸阻力呈极显著正相关。利用GPT特性建立与其他被测品质指标的逐步线性回归方程,决定系数均达到0.01显著水平,其中拉伸面积、最大拉伸阻力、面筋指数、稳定时间等指标的决定系数较高(R2>0.6)。A3可以预测沉降值、湿面筋含量等指标,PMT、AM、PM可以预测稳定时间、拉伸面积、面筋指数等面团指标。

面筋聚集仪  /  品质分析  /  小麦

In order to explore the feasibility of using GlutoPeak test (GPT) for rapid evaluation of wheat quality, flour samples of 150 wheat varieties (lines) were tested for the main quality indicators, including GPT indicators, sedimentation, gluten content, and dough rheological properties. The correlations between GPT indicators and other parameters were evaluated. The results showed that among the tested indicators of the wheat flour samples, water absorption had the smallest coefficient of variation (4.69%), while dough development time had the largest coefficient of variation (82.08%). The coefficients of variation for peak maximum time, startup energy, and plateau energy were all above 40%. Correlation analysis showed that there were significant correlations between the GPT indicators and dough rheological properties. The seven GPT indicators included peak maximum time (PMT), brabender units at the maximum (BEM), ante maximum (AM), post maximum (PM), startup energy (A1), plateau energy (A2) and aggregation energy (A3). The PMT showed a highly significant positive correlation with sedimentation and a highly significant negative correlation with wet gluten content, while the BEM, AM, PM and A3 exhibited a highly significant positive correlation with both sedimentation and wet gluten content. The AM, PM and A3 showed a highly significant positive correlation with water absorption and development time. The PMT had a highly significant negative correlation with water absorption, while it showed highly significant positive correlations with development time and stability time. The PMT, AM and PM were significantly positively correlated with stretch area and max resistance. Then, stepwise linear regression models were developed using the GPT indices to predict other parameters. The coefficients of determination were all significant at the 0.01 level, and the GPT indicators had high coefficients of determination (R2>0.6) for other parameters, such as stretch area, max resistance, gluten index, and stability time. A3 can predict sedimentation and wet gluten content, while PMT, AM and PM can predict stability time, stretch area, and gluten index among dough indicators.

GlutoPeak test  /  Quality analysis  /  Wheat
李晓丽, 张洋, 姜兰芳, 郝建宇, 王敏, 马小飞, 张定一, 姬虎太. 基于面筋聚集仪的小麦粉品质快速检测方法研究. 麦类作物学报, 2025 , 45 (11) : 1521 -1527 . DOI: 10.7606/j.issn.1009-1041.2025.11.09
Xiaoli LI, Yang ZHANG, Lanfang JIANG, Jianyu HAO, Min WANG, Xiaofei MA, Dingyi ZHANG, Hutai JI. Prediction of Wheat Quality Based on GlutoPeak[J]. Journal of Triticeae Crops, 2025 , 45 (11) : 1521 -1527 . DOI: 10.7606/j.issn.1009-1041.2025.11.09
  • 山西省基础研究计划项目(202303021212092)
  • 山西省种业创新良种联合攻关项目(NYGG26)
  • 国家现代农业产业技术体系项目(CAR-03-53)
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2025年第45卷第11期
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doi: 10.7606/j.issn.1009-1041.2025.11.09
  • 接收时间:2024-12-19
  • 首发时间:2026-09-18
  • 出版时间:2025-11-15
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出版历史
  • 收稿日期:2024-12-19
  • 修回日期:2025-03-13
基金
山西省基础研究计划项目(202303021212092)
山西省种业创新良种联合攻关项目(NYGG26)
国家现代农业产业技术体系项目(CAR-03-53)
作者信息
    山西农业大学小麦研究所,山西 临汾 041000

通讯作者:

E-mail: (姬虎太)
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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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