首页 期刊 论文 学科刊群 资讯 加盟 关于
EN
image
Article(id=1153986713824256485, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986709126635984, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241111012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731254400000, receivedDateStr=2024-11-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753061472612, onlineDateStr=2025-07-21, pubDate=1737734400000, pubDateStr=2025-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753061472612, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753061472612, creator=13701087609, updateTime=1753061472612, updator=13701087609, issue=Issue{id=1153986709126635984, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='2', pageStart='1', pageEnd='324', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753061471492, creator=13701087609, updateTime=1760345674980, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1184538872999457117, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986709126635984, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1184538872999457118, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986709126635984, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=171, endPage=177, ext={EN=ArticleExt(id=1153986714407264744, articleId=1153986713824256485, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Non-destructive detection of Zea mays L. seed maturity based on multimodal fusion, columnId=1153986581653349021, journalTitle=Journal of Food Safety & Quality, columnName=Special Topic: Application of Modern Analysis Instrument in Food Detection, runingTitle=null, highlight=null, articleAbstract=

Objective To achieve accurate and non-destructive detection of Zea mays L. seed maturity by applying hyperspectral imaging technology combined with multimodal fusion methods. Methods Hyperspectral images of high and low maturity Zea mays L. seeds were acquired. The cascade algorithm of bootstrapping soft shrinkage and successive projections algorithm (BOSS-SPA) was used for feature wavelength extraction, while the gray-level co-occurrence matrix method (GLCM) was used for image texture feature extraction. Five feature parameters—energy, entropy, correlation, homogeneity and contrast were selected to integrate the spectra with the image data in a feature level fusion. Results The partial least squares-discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used to establish a Zea mays L. seed maturity classification model. The use of Savitzky-Golay convolution smoothing-standard normal variable transformation (SG-SNV) was identified as the best spectral preprocessing method, and the 11 wavelengths extracted using the BOSS-SPA method showed good modelling performance, and the overall recognition accuracies of the model test set based on the fused data of the spectral images all reached over 95%. Conclusion Hyperspectral technology combined with multimodal feature fusion method is expected to provide a practical reference method for non-destructive detection of Zea mays L. seed maturity.

, correspAuthors=Yuan-Yuan CHEN, 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, authorCompany=null, fund=null, authors=null, authorsList=Ke-Yi ZENG, Yu-Tong LIU, Qian ZHANG, Yuan-Yuan CHEN, Jing-Zhu WU), CN=ArticleExt(id=1153986740982374684, articleId=1153986713824256485, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=基于多模态融合的玉米种子成熟度的无损检测, columnId=1153986581842092705, journalTitle=食品安全质量检测学报, columnName=本期专题：现代分析仪器在食品检测中的应用, runingTitle=null, highlight=null, articleAbstract=

目的 应用高光谱成像技术, 结合多模态融合方法, 实现对玉米种子成熟度精准、无损检测。方法 获取高、低成熟度玉米种子高光谱图像, 采用自举软收缩算法与连续投影算法的级联算法(bootstrapping soft shrinkage-successive projections algorithm, BOSS-SPA)进行特征波长提取, 采用灰度共生矩阵法(gray-level co-occurrence matrix, GLCM)进行图像纹理特征提取, 选择能量、熵、相关性、逆方差和对比度5个特征参数, 将光谱与图像数据进行特征级融合, 利用偏最小二乘判别(partial least squares-discriminant analysis, PLS-DA)和最小二乘支持向量机(least squares support vector machine, LS-SVM)建立玉米种子成熟度分类模型。结果 确定使用SG卷积平滑-标准正态变量变换(Savitzky-Golay convolution smoothing-standard normal variable, SG-SNV)作为最佳光谱预处理方法, 采用BOSS-SPA方法提取的11个波长表现出良好建模性能, 基于光谱图像融合数据的模型测试集总体识别准确率均达到95%以上。结论 高光谱技术结合多模态特征融合方法有望成为玉米种子成熟度的无损检测提供切实可行的参考方法。

, correspAuthors=陈媛媛, authorNote=null, correspAuthorsNote=
* 陈媛媛(1979—), 女, 博士, 副教授, 主要研究方向为光电无损检测。E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=wII/6OJMXZltNUQXtIZYyg==, magXml=IEAPzB8lB3Z267Q3+MlW5A==, pdfUrl=null, pdf=K6miiD0VKZI9HqTyVoGdTw==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ZZnZhMHOw7JIV601eH40QA==, mapNumber=null, authorCompany=null, fund=null, authors=

曾柯宜(2004—), 女, 主要研究方向为机器学习。E-mail:

, authorsList=曾柯宜, 刘禹彤, 张倩, 陈媛媛, 吴静珠)}, authors=[Author(id=1184566947862692304, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=xsd_1128@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1184566948068213202, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566947862692304, language=EN, stringName=Ke-Yi ZENG, firstName=Ke-Yi, middleName=null, lastName=ZENG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1184566948143710675, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566947862692304, language=CN, stringName=曾柯宜, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048, bio={"content":"

曾柯宜(2004—), 女, 主要研究方向为机器学习。E-mail:

"}, bioImg=null, bioContent=

曾柯宜(2004—), 女, 主要研究方向为机器学习。E-mail:

, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1184566947728474572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, xref=null, ext=[AuthorCompanyExt(id=1184566947736863181, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China), AuthorCompanyExt(id=1184566947749446094, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048)])]), Author(id=1184566948311482837, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1184566948500226519, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566948311482837, language=EN, stringName=Yu-Tong LIU, firstName=Yu-Tong, middleName=null, lastName=LIU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1184566948621861336, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566948311482837, language=CN, stringName=刘禹彤, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1184566947728474572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, xref=null, ext=[AuthorCompanyExt(id=1184566947736863181, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China), AuthorCompanyExt(id=1184566947749446094, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048)])]), Author(id=1184566948693164506, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, orderNo=2, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1184566948756079068, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566948693164506, language=EN, stringName=Qian ZHANG, firstName=Qian, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1184566948839965149, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566948693164506, language=CN, stringName=张倩, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1184566947728474572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, xref=null, ext=[AuthorCompanyExt(id=1184566947736863181, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China), AuthorCompanyExt(id=1184566947749446094, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048)])]), Author(id=1184566948915462623, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, orderNo=3, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=chen.yy@163.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1184566948969988577, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566948915462623, language=EN, stringName=Yuan-Yuan CHEN, firstName=Yuan-Yuan, middleName=null, lastName=CHEN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=*, address=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1184566949062263266, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566948915462623, language=CN, stringName=陈媛媛, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=*, address=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1184566947728474572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, xref=null, ext=[AuthorCompanyExt(id=1184566947736863181, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China), AuthorCompanyExt(id=1184566947749446094, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048)])]), Author(id=1184566949150343652, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, orderNo=4, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1184566949209063910, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566949150343652, language=EN, stringName=Jing-Zhu WU, firstName=Jing-Zhu, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1184566949292949991, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, authorId=1184566949150343652, language=CN, stringName=吴静珠, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1184566947728474572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, xref=null, ext=[AuthorCompanyExt(id=1184566947736863181, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China), AuthorCompanyExt(id=1184566947749446094, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048)])])], keywords=[Keyword(id=1184566949422973416, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, orderNo=1, keyword=hyperspectral imaging), Keyword(id=1184566949477499369, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, orderNo=2, keyword=Zea mays L. seed maturity), Keyword(id=1184566949569774058, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, orderNo=3, keyword=multimodal fusion), Keyword(id=1184566949653660139, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, orderNo=4, keyword=characteristic wavelength extraction), Keyword(id=1184566949741740524, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, orderNo=5, keyword=texture feature extraction), Keyword(id=1184566949804655085, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, orderNo=1, keyword=高光谱成像), Keyword(id=1184566949955650030, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, orderNo=2, keyword=玉米种子成熟度), Keyword(id=1184566950031147503, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, orderNo=3, keyword=多模态融合), Keyword(id=1184566950102450672, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, orderNo=4, keyword=特征波长提取), Keyword(id=1184566950194725361, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, orderNo=5, keyword=纹理特征提取)], refs=[Reference(id=1184566951645954562, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2020, volume=22, issue=7, pageStart=10, pageEnd=19, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=崔爱民, 张久刚, 张虎, journalName=中国农业科技导报, refType=null, unstructuredReference=崔爱民, 张久刚, 张虎, 等. 我国玉米生产现状及发展变革[J]. 中国农业科技导报, 2020, 22(7): 10-19., articleTitle=我国玉米生产现状及发展变革, refAbstract=null), Reference(id=1184566951725646340, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2020, volume=22, issue=7, pageStart=10, pageEnd=19, url=null, language=null, rfNumber=[1], rfOrder=1, authorNames=CUI AIM, ZHANG JG, ZHANG H, journalName=Journal of Agricultural Science and Technology, refType=null, unstructuredReference=CUI AIM, ZHANG JG, ZHANG H, et al. Preliminary exploration on current situation and development of maize production in China[J]. Journal of Agricultural Science and Technology, 2020, 22(7): 10-19., articleTitle=Preliminary exploration on current situation and development of maize production in China, refAbstract=null), Reference(id=1184566951813726726, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2019, volume=24, issue=3, pageStart=219, pageEnd=238, url=null, language=null, rfNumber=[2], rfOrder=2, authorNames=刘彬, 陈柳, 高艳琼, journalName=中国农业大学学报, refType=null, unstructuredReference=刘彬, 陈柳, 高艳琼, 等. 三大粮食作物基础研究:中国开始引领世界[J]. 中国农业大学学报, 2019, 24(3): 219-238., articleTitle=三大粮食作物基础研究:中国开始引领世界, refAbstract=null), Reference(id=1184566951914390023, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2019, volume=24, issue=3, pageStart=219, pageEnd=238, url=null, language=null, rfNumber=[2], rfOrder=3, authorNames=LIU B, CHEN L, GAO YQ, journalName=Journal of China Agricultural University, refType=null, unstructuredReference=LIU B, CHEN L, GAO YQ, et al. Basic research on the three grain crops: China is beginning to lead the world[J]. Journal of China Agricultural University, 2019, 24(3): 219-238., articleTitle=Basic research on the three grain crops: China is beginning to lead the world, refAbstract=null), Reference(id=1184566951977304584, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=9, pageStart=53, pageEnd=55, url=null, language=null, rfNumber=[3], rfOrder=4, authorNames=余欣悦, journalName=现代营销(下旬刊), refType=null, unstructuredReference=余欣悦. 中国玉米产业高质量发展现状及驱动因素分析[J]. 现代营销(下旬刊), 2022(9): 53-55., articleTitle=中国玉米产业高质量发展现状及驱动因素分析, refAbstract=null), Reference(id=1184566952056996361, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=9, pageStart=53, pageEnd=55, url=null, language=null, rfNumber=[3], rfOrder=5, authorNames=YU XY, journalName=Marketing Management Review, refType=null, unstructuredReference=YU XY. High-quality development status and driving factors analysis of China's corn industry[J]. Marketing Management Review, 2022(9): 53-55., articleTitle=High-quality development status and driving factors analysis of China's corn industry, refAbstract=null), Reference(id=1184566952157659658, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=11, pageStart=222, pageEnd=224, url=null, language=null, rfNumber=[4], rfOrder=6, authorNames=杨小康, 王瑞华, journalName=农业开发与装备, refType=null, unstructuredReference=杨小康, 王瑞华. 玉米栽培新技术及病虫害防治对策探究[J]. 农业开发与装备, 2022(11): 222-224., articleTitle=玉米栽培新技术及病虫害防治对策探究, refAbstract=null), Reference(id=1184566952379957771, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=11, pageStart=222, pageEnd=224, url=null, language=null, rfNumber=[4], rfOrder=7, authorNames=YANG XK, WANG RH, journalName=Agricultural Development & Equipments, refType=null, unstructuredReference=YANG XK, WANG RH. Exploration of new maize cultivation techniques and pest and disease control strategies[J]. Agricultural Development & Equipments, 2022(11): 222-224., articleTitle=Exploration of new maize cultivation techniques and pest and disease control strategies, refAbstract=null), Reference(id=1184566952434483724, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=11, issue=4, pageStart=541, pageEnd=551, url=null, language=null, rfNumber=[5], rfOrder=8, authorNames=韦小了, 何腾兵, journalName=植物学研究, refType=null, unstructuredReference=韦小了, 何腾兵. 玉米品质的形成及其调控[J]. 植物学研究, 2022, 11(4): 541-551., articleTitle=玉米品质的形成及其调控, refAbstract=null), Reference(id=1184566952539341325, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=11, issue=4, pageStart=541, pageEnd=551, url=null, language=null, rfNumber=[5], rfOrder=9, authorNames=WEI XL, HE TB, journalName=Plant Science Journal, refType=null, unstructuredReference=WEI XL, HE TB. The formation and regulation of maize quality[J]. Plant Science Journal, 2022, 11(4): 541-551., articleTitle=The formation and regulation of maize quality, refAbstract=null), Reference(id=1184566952702919182, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2016, volume=15, issue=3, pageStart=19, pageEnd=27, url=null, language=null, rfNumber=[6], rfOrder=10, authorNames=陈龙江, MICHAEL RR, journalName=华南农业大学学报(社会科学版), refType=null, unstructuredReference=陈龙江, MICHAEL RR. 种子质量对中国玉米产出的影响[J]. 华南农业大学学报(社会科学版), 2016, 15(3): 19-27., articleTitle=种子质量对中国玉米产出的影响, refAbstract=null), Reference(id=1184566952778416655, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2016, volume=15, issue=3, pageStart=19, pageEnd=27, url=null, language=null, rfNumber=[6], rfOrder=11, authorNames=CHEN LJ, MICHAEL RR, journalName=Journal of South China Agricultural University (Social Science Edition), refType=null, unstructuredReference=CHEN LJ, MICHAEL RR. The effect of seeds quality improvement on maize yield[J]. Journal of South China Agricultural University (Social Science Edition), 2016, 15(3): 19-27., articleTitle=The effect of seeds quality improvement on maize yield, refAbstract=null), Reference(id=1184566952946188817, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=1, pageStart=60, pageEnd=62, url=null, language=null, rfNumber=[7], rfOrder=12, authorNames=李静, 柴岳, 程志斌, journalName=中国食品工业, refType=null, unstructuredReference=李静, 柴岳, 程志斌, 等. 浅析果蔬农产品快速检测技术的应用及优化研究[J]. 中国食品工业, 2023(1): 60-62, 65., articleTitle=浅析果蔬农产品快速检测技术的应用及优化研究, refAbstract=null), Reference(id=1184566953130738197, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=null, issue=1, pageStart=60, pageEnd=62, url=null, language=null, rfNumber=[7], rfOrder=13, authorNames=LI J, CHAI Y, CHENG ZB, journalName=China Food Industry, refType=null, unstructuredReference=LI J, CHAI Y, CHENG ZB, et al. A brief analysis of the application and optimization research of rapid detection technology in fruits and vegetables agricultural products[J]. China Food Industry, 2023(1): 60-62, 65., articleTitle=A brief analysis of the application and optimization research of rapid detection technology in fruits and vegetables agricultural products, refAbstract=null), Reference(id=1184566953193652759, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2020, volume=46, issue=1, pageStart=27, pageEnd=37, url=null, language=null, rfNumber=[8], rfOrder=14, authorNames=刘妍, 周新奇, 俞晓峰, journalName=浙江大学学报(农业与生命科学版), refType=null, unstructuredReference=刘妍, 周新奇, 俞晓峰, 等. 无损检测技术在果蔬品质检测中的应用研究进展[J]. 浙江大学学报(农业与生命科学版), 2020, 46(1): 27-37., articleTitle=无损检测技术在果蔬品质检测中的应用研究进展, refAbstract=null), Reference(id=1184566953306898968, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2020, volume=46, issue=1, pageStart=27, pageEnd=37, url=null, language=null, rfNumber=[8], rfOrder=15, authorNames=LIU Y, ZHOU XQ, YU XF, journalName=Journal of Zhejiang University (Agriculture and Life Sciences), refType=null, unstructuredReference=LIU Y, ZHOU XQ, YU XF, et al. Research progress of nondestructive testing techniques for fruit and vegetable quality[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2020, 46(1): 27-37., articleTitle=Research progress of nondestructive testing techniques for fruit and vegetable quality, refAbstract=null), Reference(id=1184566953453699609, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=1, pageStart=109, pageEnd=110, url=null, language=null, rfNumber=[9], rfOrder=16, authorNames=李世诚, journalName=农业工程技术, refType=null, unstructuredReference=李世诚. 光谱检测技术在农产品质量安全检测中的应用[J]. 农业工程技术, 2024, 44(1): 109-110., articleTitle=光谱检测技术在农产品质量安全检测中的应用, refAbstract=null), Reference(id=1184566953596305946, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=1, pageStart=109, pageEnd=110, url=null, language=null, rfNumber=[9], rfOrder=17, authorNames=LI SC, journalName=Agricultural Engineering Technology, refType=null, unstructuredReference=LI SC. Application of spectroscopic detection technology in the quality and safety detection of agricultural products[J]. Agricultural Engineering Technology, 2024, 44(1): 109-110., articleTitle=Application of spectroscopic detection technology in the quality and safety detection of agricultural products, refAbstract=null), Reference(id=1184566953684386331, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=11, pageStart=116, pageEnd=124, url=null, language=null, rfNumber=[10], rfOrder=18, authorNames=倪淳宇, 蒙庆华, 吴哲锋, journalName=食品安全质量检测学报, refType=null, unstructuredReference=倪淳宇, 蒙庆华, 吴哲锋, 等. 基于高光谱成像技术对番石榴可溶性固形物的快速测定[J]. 食品安全质量检测学报, 2024, 15(11): 116-124., articleTitle=基于高光谱成像技术对番石榴可溶性固形物的快速测定, refAbstract=null), Reference(id=1184566953906684444, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=11, pageStart=116, pageEnd=124, url=null, language=null, rfNumber=[10], rfOrder=19, authorNames=NI CY, MENG QH, WU ZF, journalName=Journal of Food Safety & Quality, refType=null, unstructuredReference=NI CY, MENG QH, WU ZF, et al. Rapid determination of soluble solids content in Psidium guava fruit based on hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2024, 15(11): 116-124., articleTitle=Rapid determination of soluble solids content in Psidium guava fruit based on hyperspectral imaging technology, refAbstract=null), Reference(id=1184566954036707872, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=14, issue=1, pageStart=98, pageEnd=104, url=null, language=null, rfNumber=[11], rfOrder=20, authorNames=李威, 黄云峰, 代作晓, journalName=食品安全质量检测学报, refType=null, unstructuredReference=李威, 黄云峰, 代作晓, 等. 基于高光谱成像技术无损检测芒果轻微损伤[J]. 食品安全质量检测学报, 2023, 14(1): 98-104., articleTitle=基于高光谱成像技术无损检测芒果轻微损伤, refAbstract=null), Reference(id=1184566954108011044, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=14, issue=1, pageStart=98, pageEnd=104, url=null, language=null, rfNumber=[11], rfOrder=21, authorNames=LI W, HUANG YF, DAI ZX, journalName=Journal of Food Safety & Quality, refType=null, unstructuredReference=LI W, HUANG YF, DAI ZX, et al. Non-destructive detection of minor damage in mangoes based on hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2023, 14(1): 98-104., articleTitle=Non-destructive detection of minor damage in mangoes based on hyperspectral imaging technology, refAbstract=null), Reference(id=1184566954238034472, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=14, issue=4, pageStart=596, pageEnd=616, url=null, language=null, rfNumber=[12], rfOrder=22, authorNames=KANG ZL, ZHANG YC, CHEN L, journalName=Food Engineering Reviews, refType=null, unstructuredReference=KANG ZL, ZHANG YC, CHEN L, et al. Advances in machine learning and hyperspectral imaging in the food supply chain[J]. Food Engineering Reviews, 2022, 14(4): 596-616., articleTitle=Advances in machine learning and hyperspectral imaging in the food supply chain, refAbstract=null), Reference(id=1184566954338697770, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2021, volume=11, issue=1, pageStart=6512, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=23, authorNames=MELIT DEVASSY B, GEORGE S, journalName=Scientific Reports, refType=null, unstructuredReference=MELIT DEVASSY B, GEORGE S. Forensic analysis of beverage stains using hyperspectral imaging[J]. Scientific Reports, 2021, 11(1): 6512., articleTitle=Forensic analysis of beverage stains using hyperspectral imaging, refAbstract=null), Reference(id=1184566954414195244, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2021, volume=221, issue=null, pageStart=121567, pageEnd=null, url=null, language=null, rfNumber=[14], rfOrder=24, authorNames=DAIKOS O, SCHERZER T, journalName=Talanta, refType=null, unstructuredReference=DAIKOS O, SCHERZER T. Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging[J]. Talanta, 2021, 221: 121567., articleTitle=Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging, refAbstract=null), Reference(id=1184566954556801583, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2019, volume=167, issue=null, pageStart=105053, pageEnd=null, url=null, language=null, rfNumber=[15], rfOrder=25, authorNames=GU X, WANG Y, SUN Q, journalName=Computers and Electronics in Agriculture, refType=null, unstructuredReference=GU X, WANG Y, SUN Q, et al. Hyperspectral inversion of soil organic matter content in cultivated land based on wavelet transform[J]. Computers and Electronics in Agriculture, 2019, 167: 105053., articleTitle=Hyperspectral inversion of soil organic matter content in cultivated land based on wavelet transform, refAbstract=null), Reference(id=1184566954665853490, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=125, issue=null, pageStart=104279, pageEnd=null, url=null, language=null, rfNumber=[16], rfOrder=26, authorNames=ZHANG L, ZHANG Q, WU J, journalName=Infrared Physics and Technology, refType=null, unstructuredReference=ZHANG L, ZHANG Q, WU J, et al. Moisture detection of single corn seed based on hyperspectral imaging and deep learning[J]. Infrared Physics and Technology, 2022, 125: 104279., articleTitle=Moisture detection of single corn seed based on hyperspectral imaging and deep learning, refAbstract=null), Reference(id=1184566954791682613, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=44, issue=2, pageStart=159, pageEnd=165, url=null, language=null, rfNumber=[17], rfOrder=27, authorNames=杨欢, 罗斌, 张晗, journalName=江苏大学学报(自然科学版), refType=null, unstructuredReference=杨欢, 罗斌, 张晗, 等. 基于高光谱成像技术和IRIV算法的玉米种子品种纯度识别[J]. 江苏大学学报(自然科学版), 2023, 44(2): 159-165., articleTitle=基于高光谱成像技术和IRIV算法的玉米种子品种纯度识别, refAbstract=null), Reference(id=1184566954850402872, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=44, issue=2, pageStart=159, pageEnd=165, url=null, language=null, rfNumber=[17], rfOrder=28, authorNames=YANG H, LUO B, ZHANG H, journalName=Journal of Jiangsu University (Natural Science Edition), refType=null, unstructuredReference=YANG H, LUO B, ZHANG H, et al. Recognition of maize seed variety purity based on hyperspectral imaging technology and IRIV algorithm[J]. Journal of Jiangsu University (Natural Science Edition), 2023, 44(2): 159-165., articleTitle=Recognition of maize seed variety purity based on hyperspectral imaging technology and IRIV algorithm, refAbstract=null), Reference(id=1184566954900734522, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2016, volume=75, issue=null, pageStart=173, pageEnd=179, url=null, language=null, rfNumber=[18], rfOrder=29, authorNames=AMBROSE A, KANDPAL ML, KIM SM, journalName=Infrared Physics and Technology, refType=null, unstructuredReference=AMBROSE A, KANDPAL ML, KIM SM, et al. High speed measurement of corn seed viability using hyperspectral imaging[J]. Infrared Physics and Technology, 2016, 75: 173-179., articleTitle=High speed measurement of corn seed viability using hyperspectral imaging, refAbstract=null), Reference(id=1184566954972037691, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2021, volume=112, issue=null, pageStart=103596, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=30, authorNames=WANG Z, TIAN X, FAN S, journalName=Infrared Physics and Technology, refType=null, unstructuredReference=WANG Z, TIAN X, FAN S, et al. Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models[J]. Infrared Physics and Technology, 2021, 112: 103596., articleTitle=Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models, refAbstract=null), Reference(id=1184566955026563645, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2016, volume=36, issue=12, pageStart=4028, pageEnd=4033, url=null, language=null, rfNumber=[20], rfOrder=31, authorNames=杨小玲, 由昭红, 成芳, journalName=光谱学与光谱分析, refType=null, unstructuredReference=杨小玲, 由昭红, 成芳. 高光谱成像技术检测玉米种子成熟度[J]. 光谱学与光谱分析, 2016, 36(12): 4028-4033., articleTitle=高光谱成像技术检测玉米种子成熟度, refAbstract=null), Reference(id=1184566955089478209, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2016, volume=36, issue=12, pageStart=4028, pageEnd=4033, url=null, language=null, rfNumber=[20], rfOrder=32, authorNames=YANG XL, YOU ZH, CHENG F, journalName=Spectroscopy and Spectral Analysis, refType=null, unstructuredReference=YANG XL, YOU ZH, CHENG F. Study on identification of immature corn seed using hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2016, 36(12): 4028-4033., articleTitle=Study on identification of immature corn seed using hyperspectral imaging, refAbstract=null), Reference(id=1184566955160781379, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=26, issue=8, pageStart=1504, pageEnd=1529, url=null, language=null, rfNumber=[21], rfOrder=33, authorNames=苏红军, journalName=遥感学报, refType=null, unstructuredReference=苏红军. 高光谱遥感影像降维: 进展、挑战与展望[J]. 遥感学报, 2022, 26(8): 1504-1529., articleTitle=高光谱遥感影像降维: 进展、挑战与展望, refAbstract=null), Reference(id=1184566955253056069, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=26, issue=8, pageStart=1504, pageEnd=1529, url=null, language=null, rfNumber=[21], rfOrder=34, authorNames=SU HJ, journalName=National Remote Sensing Bulletin, refType=null, unstructuredReference=SU HJ. Dimensionality reduction for hyperspectral remote sensing: advances, challenges, and prospects[J]. National Remote Sensing Bulletin, 2022, 26(8): 1504-1529., articleTitle=Dimensionality reduction for hyperspectral remote sensing: advances, challenges, and prospects, refAbstract=null), Reference(id=1184566955307582024, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=2022, issue=null, pageStart=9813841, pageEnd=null, url=null, language=null, rfNumber=[22], rfOrder=35, authorNames=XIAO Q, TANG W, ZHANG C, journalName=Plant Phenomics, refType=null, unstructuredReference=XIAO Q, TANG W, ZHANG C, et al. Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves[J]. Plant Phenomics, 2022, 2022: 9813841., articleTitle=Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves, refAbstract=null), Reference(id=1184566955366302282, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=1989, volume=45, issue=5, pageStart=772, pageEnd=777, url=null, language=null, rfNumber=[23], rfOrder=36, authorNames=BARNES RJ, DHANOA MS, SUSAN, journalName=Applied Spectroscopy Reviews, refType=null, unstructuredReference=BARNES RJ, DHANOA MS, SUSAN, et al. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy Reviews, 1989, 45(5): 772-777., articleTitle=Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra, refAbstract=null), Reference(id=1184566955429216844, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2016, volume=908, issue=null, pageStart=63, pageEnd=74, url=null, language=null, rfNumber=[24], rfOrder=37, authorNames=DENG BC, YUN YH, CAO DS, journalName=Analytica Chimica Acta, refType=null, unstructuredReference=DENG BC, YUN YH, CAO DS, et al. A bootstrapping soft shrinkage approach for variable selection in chemical modeling[J]. Analytica Chimica Acta, 2016, 908: 63-74., articleTitle=A bootstrapping soft shrinkage approach for variable selection in chemical modeling, refAbstract=null), Reference(id=1184566955496325712, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=200, issue=null, pageStart=107231, pageEnd=null, url=null, language=null, rfNumber=[25], rfOrder=38, authorNames=ZHANG Q, HUANG W, WANG Q, journalName=Computers and Electronics in Agriculture, refType=null, unstructuredReference=ZHANG Q, HUANG W, WANG Q, et al. Detection of pears with moldy core using online full-transmittance spectroscopy combined with supervised classifier comparison and variable optimization[J]. Computers and Electronics in Agriculture, 2022, 200: 107231., articleTitle=Detection of pears with moldy core using online full-transmittance spectroscopy combined with supervised classifier comparison and variable optimization, refAbstract=null), Reference(id=1184566955550851666, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=104, issue=4, pageStart=901, pageEnd=909, url=null, language=null, rfNumber=[26], rfOrder=39, authorNames=KINI MGR, BHANDARKAR R, journalName=Journal of the Institution of Engineers (India): Series B, refType=null, unstructuredReference=KINI MGR, BHANDARKAR R. Quality assessment of seed using supervised machine learning technique[J]. Journal of the Institution of Engineers (India): Series B, 2023, 104(4): 901-909., articleTitle=Quality assessment of seed using supervised machine learning technique, refAbstract=null), Reference(id=1184566955622154835, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=1999, volume=9, issue=3, pageStart=293, pageEnd=300, url=null, language=null, rfNumber=[27], rfOrder=40, authorNames=SUYKENS JA, VANDEWALLE J, journalName=Neural Processing Letters, refType=null, unstructuredReference=SUYKENS JA, VANDEWALLE J. Least squares support vector machine classifers[J]. Neural Processing Letters, 1999, 9(3): 293-300., articleTitle=Least squares support vector machine classifers, refAbstract=null), Reference(id=1184566955664097877, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2023, volume=34, issue=1, pageStart=5, pageEnd=12, url=null, language=null, rfNumber=[28], rfOrder=41, authorNames=MOINDJIÉ IA, DABO-NIANG S, PREDA C, journalName=Statistics and Computing, refType=null, unstructuredReference=MOINDJIÉ IA, DABO-NIANG S, PREDA C. Classification of multivariate functional data on different domains with partial least squares approaches[J]. Statistics and Computing, 2023, 34(1): 5-12., articleTitle=Classification of multivariate functional data on different domains with partial least squares approaches, refAbstract=null), Reference(id=1184566955743789654, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=40, issue=16, pageStart=261, pageEnd=268, url=null, language=null, rfNumber=[29], rfOrder=42, authorNames=夏宇, 孟静武, 罗斌, journalName=农业工程学报, refType=null, unstructuredReference=夏宇, 孟静武, 罗斌, 等. 高光谱结合理化参数跨品种识别玉米种子贮藏年份[J]. 农业工程学报, 2024, 40(16): 261-268., articleTitle=高光谱结合理化参数跨品种识别玉米种子贮藏年份, refAbstract=null), Reference(id=1184566955836064344, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=40, issue=16, pageStart=261, pageEnd=268, url=null, language=null, rfNumber=[29], rfOrder=43, authorNames=XIA Y, MENG JW, LUO B, journalName=Transactions of the Chinese Society of Agricultural Engineering, refType=null, unstructuredReference=XIA Y, MENG JW, LUO B, et al. Cross-species identification of maize seed storage year by hyperspectral combination of physicochemical parameters[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(16): 261-268., articleTitle=Cross-species identification of maize seed storage year by hyperspectral combination of physicochemical parameters, refAbstract=null), Reference(id=1184566955919950426, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2019, volume=52, issue=2, pageStart=249, pageEnd=267, url=null, language=null, rfNumber=[30], rfOrder=44, authorNames=LI J, YU XN, GE WZ, journalName=Analytical Letters, refType=null, unstructuredReference=LI J, YU XN, GE WZ, et al. Qualitative analysis of maize haploid kernels based on calibration transfer by near-infrared spectroscopy[J]. Analytical Letters, 2019, 52(2): 249-267., articleTitle=Qualitative analysis of maize haploid kernels based on calibration transfer by near-infrared spectroscopy, refAbstract=null), Reference(id=1184566956070945373, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2022, volume=25, issue=1, pageStart=1351, pageEnd=1364, url=null, language=null, rfNumber=[31], rfOrder=45, authorNames=DARFOUR B, AYEH EA, ODOI KM, journalName=International Journal of Food Properties, refType=null, unstructuredReference=DARFOUR B, AYEH EA, ODOI KM, et al. Physical characteristics of maize grain as influenced by varietal and moisture differences[J]. International Journal of Food Properties, 2022, 25(1): 1351-1364., articleTitle=Physical characteristics of maize grain as influenced by varietal and moisture differences, refAbstract=null), Reference(id=1184566956171608670, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2019, volume=9, issue=1, pageStart=1, pageEnd=17, url=null, language=null, rfNumber=[32], rfOrder=46, authorNames=MA LJ, PENG YF, PEI YL, journalName=Scientific Reports, refType=null, unstructuredReference=MA LJ, PENG YF, PEI YL, et al. Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds[J]. Scientific Reports, 2019, 9(1): 1-17., articleTitle=Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds, refAbstract=null), Reference(id=1184566956251300448, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=3, pageStart=722, pageEnd=730, url=null, language=null, rfNumber=[33], rfOrder=47, authorNames=李煜, 毕卫红, 孙建成, journalName=光谱学与光谱分析, refType=null, unstructuredReference=李煜, 毕卫红, 孙建成, 等. 紫外-可见吸收光谱结合化学计量学算法的水体总有机碳浓度快速检测[J]. 光谱学与光谱分析, 2024, 44(3): 722-730., articleTitle=紫外-可见吸收光谱结合化学计量学算法的水体总有机碳浓度快速检测, refAbstract=null), Reference(id=1184566956318409314, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=3, pageStart=722, pageEnd=730, url=null, language=null, rfNumber=[33], rfOrder=48, authorNames=LI Y, BI WH, SUN JC, journalName=Spectroscopy and Spectral Analysis, refType=null, unstructuredReference=LI Y, BI WH, SUN JC, et al. Rapid detection of total organic carbon concentration in water using UV-Vis absorption spectra combined with chemometric algorithms[J]. Spectroscopy and Spectral Analysis, 2024, 44(3): 722-730., articleTitle=Rapid detection of total organic carbon concentration in water using UV-Vis absorption spectra combined with chemometric algorithms, refAbstract=null)], funds=[Fund(id=1184566951406879232, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, awardId=2018YFD0101004-03, language=CN, fundingSource=国家重点研发计划项目(2018YFD0101004-03), fundOrder=null, country=null), Fund(id=1184566951486571009, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, awardId=61807001, language=CN, fundingSource=国家自然科学基金项目(61807001), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1184566947728474572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, xref=null, ext=[AuthorCompanyExt(id=1184566947736863181, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China), AuthorCompanyExt(id=1184566947749446094, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, companyId=1184566947728474572, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048)])], figs=[ArticleFig(id=1184566950333137394, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Fig.1, caption=Schematic diagram of HSI, figureFileSmall=RANO95qphakPeSTn1wxVvA==, figureFileBig=BfpNu2YyUQYvhQS3NJQ+Jw==, tableContent=null), ArticleFig(id=1184566950391857651, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=图1, caption=HSI示意图, figureFileSmall=RANO95qphakPeSTn1wxVvA==, figureFileBig=BfpNu2YyUQYvhQS3NJQ+Jw==, tableContent=null), ArticleFig(id=1184566950479938036, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Fig.2, caption=Average spectral curves of high and low maturity Zea mays L. seeds, figureFileSmall=kAaKVax5oLmPtxrU9ImLmg==, figureFileBig=Id5rRfSb+EoGGn3dirLAfg==, tableContent=null), ArticleFig(id=1184566950572212725, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=图2, caption=高、低成熟度玉米种子平均光谱曲线, figureFileSmall=kAaKVax5oLmPtxrU9ImLmg==, figureFileBig=Id5rRfSb+EoGGn3dirLAfg==, tableContent=null), ArticleFig(id=1184566950639321590, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Fig.3, caption=Effective wavelength selection process of BOSS algorithm, figureFileSmall=aYyPqq0ed+t/7GTNJwAj/w==, figureFileBig=Gog/IwtKX7FY2RdnRHQbIw==, tableContent=null), ArticleFig(id=1184566950681264631, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=图3, caption=BOSS算法的有效波长选择过程, figureFileSmall=aYyPqq0ed+t/7GTNJwAj/w==, figureFileBig=Gog/IwtKX7FY2RdnRHQbIw==, tableContent=null), ArticleFig(id=1184566950756762104, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Fig.4, caption=Process of selecting effective wavelength by SPA algorithm, figureFileSmall=oXaxck0DF/OozXNPRWFSLg==, figureFileBig=oUVBapNCiIoOBw4OfEPZKA==, tableContent=null), ArticleFig(id=1184566950819676665, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=图4, caption=SPA算法选择有效波长过程, figureFileSmall=oXaxck0DF/OozXNPRWFSLg==, figureFileBig=oUVBapNCiIoOBw4OfEPZKA==, tableContent=null), ArticleFig(id=1184566950903562746, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Fig.5, caption=BOSS-SPA feature wavelength extraction results, figureFileSmall=fCqkOtIlOP9tPtikB31TzQ==, figureFileBig=G2ITHUbCbo6WbXpq/MJmrQ==, tableContent=null), ArticleFig(id=1184566950979060219, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=图5, caption=BOSS-SPA特征波长提取结果, figureFileSmall=fCqkOtIlOP9tPtikB31TzQ==, figureFileBig=G2ITHUbCbo6WbXpq/MJmrQ==, tableContent=null), ArticleFig(id=1184566951046169084, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Table 1, caption=

Results of different pre-treatment methods

, figureFileSmall=null, figureFileBig=null, tableContent=
预处理方法 Lvs 预测集/%
低成熟度 高成熟度 总体
无 10 87 85 86.0
SG 7 89 90 89.5
SNV 9 91 93 92.0
1Der 8 85 80 82.5
SG-SNV 8 92 93 92.5
SG-1Der 7 90 88 89.0
), ArticleFig(id=1184566951142638077, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=表1, caption=

不同预处理方法结果

, figureFileSmall=null, figureFileBig=null, tableContent=
预处理方法 Lvs 预测集/%
低成熟度 高成熟度 总体
无 10 87 85 86.0
SG 7 89 90 89.5
SNV 9 91 93 92.0
1Der 8 85 80 82.5
SG-SNV 8 92 93 92.5
SG-1Der 7 90 88 89.0
), ArticleFig(id=1184566951218135550, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=EN, label=Table 2, caption=

Zea mays L. seed maturity classification model results

, figureFileSmall=null, figureFileBig=null, tableContent=
建模算法 模型输入 测试集/%
低成熟度 高成熟度 总体
PLS-DA 光谱特征 91.4 95.6 93.5
图像特征 88.7 90.2 89.5
光谱图像融合特征 94.5 97.9 96.2
LS-SVM 光谱特征 91.9 93.8 92.9
图像特征 87.2 87.7 87.5
光谱图像融合特征 95.1 96.4 95.8
), ArticleFig(id=1184566951281050111, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986713824256485, language=CN, label=表2, caption=

玉米种子成熟度分类模型结果

, figureFileSmall=null, figureFileBig=null, tableContent=
建模算法 模型输入 测试集/%
低成熟度 高成熟度 总体
PLS-DA 光谱特征 91.4 95.6 93.5
图像特征 88.7 90.2 89.5
光谱图像融合特征 94.5 97.9 96.2
LS-SVM 光谱特征 91.9 93.8 92.9
图像特征 87.2 87.7 87.5
光谱图像融合特征 95.1 96.4 95.8
)], attaches=null, journal=Journal(id=1146438185598431260, delFlag=0, nameCn=食品安全质量检测学报, nameEn=Journal of Food Safety & Quality, nameHistory1=null, nameHistory2=null, issn=2095-0381, eissn=null, cn=11-5956/TS, coden=null, periodic=3, language=CN, oaType=0, ccby=CC BY-NC-ND, superviseOffice=null, ownerOffice=null, pubOffice=null, editorOffice=null, officeType=null, aims=null, clcCode=null, officeProv=null, officeCity=null, officeAddr=null, officeZip=null, officeEmail=null, officePhone=null, editDirector=null, officeDirector=null, officeDirectorPhone=null, officeStaffNum=null, officeEmpNum=null, coverPicUrl=4kwA5ri4VfzavOn19fwc2g==, journalPrice=null, startedYear=null, abbrevIsoEn=J Food Saf Qual, journalRemark=null, publicationField=null, createdTime=1751261763241, updatedTime=1754445151803, createdBy=18614031015, updatedBy=13701087609, firstLetterCn=J, firstLetterEn=J, subjectCode=Engineering, subjectName=工程, subjectCodeEn=Engineering, subjectNameEn=null, picCn=4kwA5ri4VfzavOn19fwc2g==, picEn=eHhRQNEus+t1f6yOGnBJ3w==, jcr=null, cjcr=null, exts=[JournalExt(id=1159790285120618579, language=CN, name=食品安全质量检测学报, nameHistory1=null, nameHistory2=null, managedBy=, sponsoredBy=, publishedBy=, editorOffice=, officeProv=null, officeCity=null, officeAddr=, officeZip=, editDirector=null, officeDirector=null, officePhone=null, coverPicUrl=null, journalRemark=, submitArticleUrl=null, websiteUrl=, createdTime=1754445151834, updatedTime=1754445151834, createdBy=13701087609, updatedBy=13701087609, submissionGuidelinesUrl=http://chinafoodj.ijournals.cn/ch/first_menu.aspx?parent_id=20160111022419868, submissionAuthorUrl=http://chinafoodj.ijournals.cn/ch/author/login.aspx, submissionEditorUrl=http://chinafoodj.ijournals.cn/ch/login.aspx, submissionReviewUrl=http://chinafoodj.ijournals.cn/ch/auditor/login.aspx, submissionCeEditorUrl=, submissionAeEditorUrl=, option={"copyright":""}), JournalExt(id=1159790285187727444, language=EN, name=Journal of Food Safety & Quality, nameHistory1=null, nameHistory2=null, managedBy=, sponsoredBy=, publishedBy=, editorOffice=, officeProv=null, officeCity=null, officeAddr=, officeZip=, editDirector=null, officeDirector=null, officePhone=null, coverPicUrl=null, journalRemark=, submitArticleUrl=null, websiteUrl=, createdTime=1754445151850, updatedTime=1754445151850, createdBy=13701087609, updatedBy=13701087609, submissionGuidelinesUrl=http://chinafoodj.ijournals.cn/ch/first_menu.aspx?parent_id=20160111022419868, submissionAuthorUrl=http://chinafoodj.ijournals.cn/ch/author/login.aspx, submissionEditorUrl=http://chinafoodj.ijournals.cn/ch/login.aspx, submissionReviewUrl=http://chinafoodj.ijournals.cn/ch/auditor/login.aspx, submissionCeEditorUrl=, submissionAeEditorUrl=, option={"copyright":""})], databaseList=null, tenantJournalId=1149652044408987649, websiteList=[Website(id=1151872930754474249, webName=null, webTitle=null, webDomain=null, webCopyrigh=null, webIpcNo=null, seoTitle=null, seoKeywords=null, seoDescription=null, tenantJournalId=null, journalId=1149652044408987649, journalNameCn=null, journalNameEn=null, grayFlag=null, tenantId=1146029695717560320, platformId=null, journalGroupId=null, journalGroupNameCn=null, journalGroupNameEn=null, type=1, domain=https://castjournals.cast.org.cn/joweb/spaq/CN, language=CN, createTime=1752557507456, createBy=18614031015, updateTime=1752558523388, updateBy=18614031015, name=食品安全质量检测学报, tplId=1146099689490845704, title=食品安全质量检测学报, delFlag=0, indexPage=/home, props=[WebsiteProps(id=1151887749801407249, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930754474249, code=articleTextType, value=kx, createTime=1752561040592, updateTime=1752561040592, creator=18614031015, updator=18614031015), WebsiteProps(id=1151887749776241422, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930754474249, code=banner, value=null, createTime=1752561040586, updateTime=1752561040586, creator=18614031015, updator=18614031015), WebsiteProps(id=1151887749767852813, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930754474249, code=logo, value=https://castjournals.cast.org.cn/joweb/kjdb/CN/file/pic?fileId=RhDdm4lBDQfNHl8cX0659g==, createTime=1752561040584, updateTime=1752561040584, creator=18614031015, updator=18614031015), WebsiteProps(id=1151887749793018640, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930754474249, code=picServerUrl, value=https://castjournals.cast.org.cn/joweb/kjdb/CN/file/pic, createTime=1752561040590, updateTime=1752561040590, creator=18614031015, updator=18614031015), WebsiteProps(id=1151887749784630031, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930754474249, code=staticResourcePath, value=https://castjournals.cast.org.cn/joweb/cast_kjdb_cn_619/, createTime=1752561040588, updateTime=1752561040588, creator=18614031015, updator=18614031015)]), Website(id=1151872930855137548, webName=null, webTitle=null, webDomain=null, webCopyrigh=null, webIpcNo=null, seoTitle=null, seoKeywords=null, seoDescription=null, tenantJournalId=null, journalId=1149652044408987649, journalNameCn=null, journalNameEn=null, grayFlag=null, tenantId=1146029695717560320, platformId=null, journalGroupId=null, journalGroupNameCn=null, journalGroupNameEn=null, type=1, domain=https://castjournals.cast.org.cn/joweb/spaq/EN, language=EN, createTime=1752557507480, createBy=18614031015, updateTime=1752558528290, updateBy=18614031015, name=食品安全质量检测学报, tplId=1146101810881728533, title=食品安全质量检测学报, delFlag=0, indexPage=/home, props=[WebsiteProps(id=1155904094024884374, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930855137548, code=articleTextType, value=kx, createTime=1753518611675, updateTime=1753518611675, creator=18614031015, updator=18614031015), WebsiteProps(id=1155904094008107155, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930855137548, code=banner, value=null, createTime=1753518611671, updateTime=1753518611671, creator=18614031015, updator=18614031015), WebsiteProps(id=1155904094003912850, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930855137548, code=logo, value=https://castjournals.cast.org.cn/joweb/kjdb/EN/file/pic?fileId=RhDdm4lBDQfNHl8cX0659g==, createTime=1753518611670, updateTime=1753518611670, creator=18614031015, updator=18614031015), WebsiteProps(id=1155904094020690069, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930855137548, code=picServerUrl, value=https://castjournals.cast.org.cn/joweb/kjdb/EN/file/pic, createTime=1753518611674, updateTime=1753518611674, creator=18614031015, updator=18614031015), WebsiteProps(id=1155904094016495764, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1151872930855137548, code=staticResourcePath, value=https://castjournals.cast.org.cn/joweb/cast_kjdb_en_623/, createTime=1753518611673, updateTime=1753518611673, creator=18614031015, updator=18614031015)])], journalTitle=食品安全质量检测学报, weixinUrl=null, journalUrl=null, iacademicId=null, status=0, seqNo=null, journalTitleEn=Journal of Food Safety & Quality, journalPhotoCn=4kwA5ri4VfzavOn19fwc2g==, journalPhotoEn=eHhRQNEus+t1f6yOGnBJ3w==, journalFirstLetter=J, journalRecommend=null, journalNew=null, journalCollection=null, jcrJf=null, cjcrJf=null, jcrJfStr=null, cjcrJfStr=null, submissionFirstDecision=null, sciSubjectClassification=null, casSubjectClassification=null, citeScore=null, totalCitationFrequency=null, icpCode=null, psCode=null, advertisingLicenseCode=null, copyrightInformation=null, country=null, option=null, provinceCode=null, provinceName=null, collectFlag=false), detailUrlCn=https://castjournals.cast.org.cn/joweb/spaq/CN/10.19812/j.cnki.jfsq11-5956/ts.20241111012, detailUrlEn=https://castjournals.cast.org.cn/joweb/spaq/EN/10.19812/j.cnki.jfsq11-5956/ts.20241111012, pdfUrlCn=https://castjournals.cast.org.cn/joweb/spaq/CN/PDF/10.19812/j.cnki.jfsq11-5956/ts.20241111012, pdfUrlEn=https://castjournals.cast.org.cn/joweb/spaq/EN/PDF/10.19812/j.cnki.jfsq11-5956/ts.20241111012, aliStartDate=null, aliEndDate=null, collectionFlag=false, citedCount=null, citedUrl=null, reference=null)
收藏切换
基于多模态融合的玉米种子成熟度的无损检测
收藏切换
PDF下载
曾柯宜 , 刘禹彤 , 张倩 , 陈媛媛 * , 吴静珠
食品安全质量检测学报 | 本期专题：现代分析仪器在食品检测中的应用 2025,16(2): 171-177
收起
收藏切换
食品安全质量检测学报 | 本期专题：现代分析仪器在食品检测中的应用 2025, 16(2): 171-177
基于多模态融合的玉米种子成熟度的无损检测
全屏
曾柯宜 , 刘禹彤, 张倩, 陈媛媛* , 吴静珠
作者信息
  • 北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048

    • 曾柯宜(2004—), 女, 主要研究方向为机器学习。E-mail:

通讯作者:

* 陈媛媛(1979—), 女, 博士, 副教授, 主要研究方向为光电无损检测。E-mail:
Non-destructive detection of Zea mays L. seed maturity based on multimodal fusion
Ke-Yi ZENG , Yu-Tong LIU, Qian ZHANG, Yuan-Yuan CHEN* , Jing-Zhu WU
Affiliations
  • Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
出版时间: 2025-01-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241111012
文章导航
收藏切换
摘要
收起

目的 应用高光谱成像技术, 结合多模态融合方法, 实现对玉米种子成熟度精准、无损检测。方法 获取高、低成熟度玉米种子高光谱图像, 采用自举软收缩算法与连续投影算法的级联算法(bootstrapping soft shrinkage-successive projections algorithm, BOSS-SPA)进行特征波长提取, 采用灰度共生矩阵法(gray-level co-occurrence matrix, GLCM)进行图像纹理特征提取, 选择能量、熵、相关性、逆方差和对比度5个特征参数, 将光谱与图像数据进行特征级融合, 利用偏最小二乘判别(partial least squares-discriminant analysis, PLS-DA)和最小二乘支持向量机(least squares support vector machine, LS-SVM)建立玉米种子成熟度分类模型。结果 确定使用SG卷积平滑-标准正态变量变换(Savitzky-Golay convolution smoothing-standard normal variable, SG-SNV)作为最佳光谱预处理方法, 采用BOSS-SPA方法提取的11个波长表现出良好建模性能, 基于光谱图像融合数据的模型测试集总体识别准确率均达到95%以上。结论 高光谱技术结合多模态特征融合方法有望成为玉米种子成熟度的无损检测提供切实可行的参考方法。

高光谱成像  /  玉米种子成熟度  /  多模态融合  /  特征波长提取  /  纹理特征提取

Objective To achieve accurate and non-destructive detection of Zea mays L. seed maturity by applying hyperspectral imaging technology combined with multimodal fusion methods. Methods Hyperspectral images of high and low maturity Zea mays L. seeds were acquired. The cascade algorithm of bootstrapping soft shrinkage and successive projections algorithm (BOSS-SPA) was used for feature wavelength extraction, while the gray-level co-occurrence matrix method (GLCM) was used for image texture feature extraction. Five feature parameters—energy, entropy, correlation, homogeneity and contrast were selected to integrate the spectra with the image data in a feature level fusion. Results The partial least squares-discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) were used to establish a Zea mays L. seed maturity classification model. The use of Savitzky-Golay convolution smoothing-standard normal variable transformation (SG-SNV) was identified as the best spectral preprocessing method, and the 11 wavelengths extracted using the BOSS-SPA method showed good modelling performance, and the overall recognition accuracies of the model test set based on the fused data of the spectral images all reached over 95%. Conclusion Hyperspectral technology combined with multimodal feature fusion method is expected to provide a practical reference method for non-destructive detection of Zea mays L. seed maturity.

hyperspectral imaging  /  Zea mays L. seed maturity  /  multimodal fusion  /  characteristic wavelength extraction  /  texture feature extraction
曾柯宜, 刘禹彤, 张倩, 陈媛媛, 吴静珠. 基于多模态融合的玉米种子成熟度的无损检测. 食品安全质量检测学报, 2025 , 16 (2) : 171 -177 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241111012
Ke-Yi ZENG, Yu-Tong LIU, Qian ZHANG, Yuan-Yuan CHEN, Jing-Zhu WU. Non-destructive detection of Zea mays L. seed maturity based on multimodal fusion[J]. Journal of Food Safety & Quality, 2025 , 16 (2) : 171 -177 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241111012
正文
收起
0 引言
收起
玉米具有高产稳产、抗逆性强和适应性广等特点, 是世界上最主要的粮食作物之一, 同时也是重要的饲料作物以及工业原料作物[1-2]。我国是玉米生产大国, 玉米作为我国第一大粮食作物, 对我国的粮食供求形势影响巨大, 尤其是在保障国家粮食安全和农产品供给方面至关重要[3-4]。玉米种子的品质是决定玉米面积产量的重要因素, 而成熟度是决定玉米品质的关键因素, 对整个玉米生产都具有一定的影响, 进而影响到玉米的口感和营养价值, 以及市场竞争力和消费者的接受程度[5-6]。因此, 如何快速、无损、便捷的评价玉米的成熟度是当前研究的热点问题。
目前, 判断玉米种子成熟度并没有一个行业标准, 常采用的方法有人工剔除、显微镜检测、实验室化学分析等方法, 这些传统方法存在效率低下、破坏性高、成本高以及准确性和灵敏度有限等缺陷, 无法满足种业专业化生产的需求。近年来, 现代光谱分析技术因其快速、高效和非破坏性的特点, 以及其作为现场快速或在线实时分析工具的能力, 在农产品检测领域得到广泛应用, 并日益发挥着至关重要的作用[7-9]。高光谱成像(hyperspectral imaging, HSI)技术作为一项新兴的检测技术, 在现代光谱分析技术中脱颖而出, 克服了机器视觉和近红外光谱技术的局限性, 集光谱技术和成像技术优点于一身, 可以同时获得不同模态的信息数据, 实现“图谱合一”[10-11]。目前, HSI技术已广泛应用于食品[12]、医学[13]、工业[14]和农业[15]等多个领域。
HSI技术既可以通过光谱技术分析待测种子的内部成分信息, 又可以通过成像技术检测种子的外部形态特征, 以解决传统检测方式的专业性强、人工成本高、耗时、精确度低以及对玉米种子造成破坏等缺点问题。ZHANG等[16]利用HSI技术检测单粒玉米种子水分含量, 对玉米种子两面的图像都分别进行采集, 建立单粒种子水分模型, 基于胚胎侧建立的模型最佳。杨欢等[17]基于HSI技术, 通过选出特征波长建立支持向量机(support vector machines, SVM)和线性判别分析(linear discriminant analysis, LDA)等纯度识别模型, 实现玉米种子纯度无损、快速识别。AMBROSE等[18]利用400~2500 nm范围的高光谱数据建立偏最小二乘判别(partial least squares-discriminant analysis, PLS-DA)模型, 实现了对有活力(未处理)和非有活力(热处理)玉米种子的准确分类。WANG等[19]研究了HSI技术检测单粒玉米种子成熟度的方法, 利用主成分分析进行特征波长的选择, 建立偏最小二乘判别和决策树分类模型。杨小玲等[20]通过HSI技术研究不同成熟度玉米种子光谱特性, 通过采用图像分析方法处理特征波段图像, 实现了成熟度较低的玉米种子的识别。
以上研究表明HSI技术可应用于玉米种子的水分含量、品种纯度、活力以及成熟度检测等多方面, 但目前在玉米种子检测上, 大多利用从所获高光谱数据的感兴趣区域中提取出的光谱信息进行分析建模, 而对图像空间信息挖掘和联合利用较少。因此, 本研究重点探索高光谱图像数据中图像与光谱特征有效提取和多模态融合方法, 构建高性能机器学习模型用于精确、无损判别玉米成熟度。
1 材料与方法
收起
1.1 材料与仪器
本研究所用玉米种子由种子公司购入, 品种为仙玉335, 由于玉米种子在成熟过程中, 会有较明显的外观变化, 即种子胚乳面的乳线由冠部逐渐向基部移动, 所以定义乳线占比为乳线以下区域与整粒种子区域的面积比, 通过观察乳线占比来初步判定种子成熟度, 以获得不同成熟度的玉米种子。本研究所用种子共800粒, 其中低成熟度400粒, 高成熟度400粒。样本按3:1比例随机划分训练集和测试集。其中训练集有600粒种子, 测试集有200粒种子。为了减少样本划分对模型的影响, 提升模型的稳定性, 将样本随机划分20次, 分别建立模型, 将20次模型的平均准确率作为最终模型的结果。
实验仪器采用SisuCHEMA高光谱成像系统(芬兰Specim公司): 由型号为SWIR的相机、光源、载物台以及计算机软件和硬件组成。
1.2 高光谱图像采集
采用SisuCHEMA高光谱成像系统获得样本的高光谱图像, 成像光谱仪的曝光时间和帧频分别设置为1.89 ms和39.06 Hz。为提高数据采集效率, 每一次扫描时都将50粒种子10行5列排列在载物台上, 以同时获取高光谱图像。扫描样本获得一个3D超立方体图像, 包括384×782个像素点, 每个像素在968.05~2575.05 nm波长范围内具有288个波段, 即同时含有一维的光谱信息和二维空间的物体信息, 如图1所示。采用黑白板校正将原始高光谱图像转换为伪吸光度高光谱图像[21]。
1.3 数据处理与建模方法
1.3.1 光谱预处理
为了消除原始光谱中的信号偏移和光散射, 提高光谱数据质量, 采用多种方法对光谱进行预处理, 包括SG卷积平滑、标准正态变量变换(standard normal variate, SNV)、一阶求导光谱预处理方法(derivative, Der)及其组合策略, 其中, SG在光谱分析中通过局部多项式拟合的方式, 有效地平滑光谱数据、去除噪声, 并保留重要的光谱特征; SNV通过标准化处理, 能够有效地消除光谱数据中的系统性偏移和缩放效应, 提高数据质量和分析的准确性; 一阶求导通过计算光谱数据的变化率, 有助于提取样品的细微特征信息, 增强光谱特征[22-23]。基于此, 建立PLS-DA判别模型, 比较模型结果, 从而选出最优的光谱预处理方法。
1.3.2 特征波长提取
在获取的光谱数据中, 含有较多的波长变量, 其中存在诸多无关的波长变量, 影响模型的检测精度, 应当找到最佳特征波长子集, 提高分类性能, 降低模型复杂性。自举软收缩算法(bootstrapping soft shrinkage, BOSS)可以有效解决由于变量中存在共线性信息而导致的模型预测精度较低的问题[24]。它依据回归系数的绝对值确定变量权重, 并运用加权自举抽样法(weighted bootstrap sampling, WBS)生成子模型, 采用模型总体分析法更新变量权重。算法通过优化及迭代, 最终选取交互均方根误差(root mean square error cross validation, RMSECV)最小的最优变量集。连续投影算法(successive projections algorithm, SPA)通过前向循环方法选定特征波长, 选择的是含有最少冗余信息及最小共线性的变量组合。然而, 单独使用SPA效果不是很理想, 因此将SPA算法与BOSS算法组合使用, 对预处理后光谱数据进行有效波长选择[25]。
1.3.3 纹理特征提取
纹理分析是一种常见的图像分析技术, 对相邻像素及其强度进行分析或建模, 以获得有关图像表面特性的深入信息, 目前已有一些研究证明了使用图像纹理来检测种子质量的潜力[26]。本研究选用灰度共生矩阵(grey-level co-occurrence matrix, GLCM)方法来进行图像纹理分析, 其元素值表示像素对的联合条件概率密度分布, 可以从分布中提取最佳特征, 用于进一步分析特征, 以表征图像纹理。结合玉米种子图像特性, 选取了能量(energy)、熵(entropy)、相关性(correlation)、逆方差(homogeneity)和对比度(contrast)这5种属性来定量描述纹理特征。
1.3.4 特征融合
特征级融合是使用变量选择方法从单个数据块中提取特征, 利用低维数据来有效表达高维数据的信息, 然后整合处理后的特征矩阵。本研究所选择的波长和纹理参数基于它们与所研究问题的相关性进行串行组合, 形成一组特征向量, 特征融合过程结合了不同数据的最主要特征, 以实现可能的最佳分类精度。
1.3.5 检测模型建立
最小二乘支持向量机(least squares support vector machine, LS-SVM)是一种改进的支持向量机算法[27], 以径向基函数(radial basis function, RBF)为核函数, 可以同时处理线性和非线性信息, 用于定性鉴别和定量预测。PLS是一种经典的多元线性分析方法[28]。PLS-DA是一种基于PLS算法的判别分析方法, 通过投影将自变量和因变量映射到低维空间, 在消除与类标签无关的信息后, 尽可能地对每类数据的投影点进行聚类。本研究基于高光谱图像光谱特征、图像特征以及光谱图像融合特征, 采用LS-SVM和PLS-DA构建玉米种子成熟度检测模型。
2 结果与分析
收起
2.1 光谱预处理方法筛选
从单个种子高光谱图像中提取出种子样本所有像素点的平均光谱。图2为两类样本的平均光谱(实线)与标准差(误差带), 可以观察到在960、1200、1450、1750、1950、2100和2300 nm附近存在吸收峰, 这些峰值可能来源玉米种子中含有的水、淀粉、脂质、蛋白质和碳水化合物等, 其中960和1450 nm附近的吸收峰与O-H键的一级倍频和二级倍频相关, 1200、1750和2300 nm处的峰值则可能与C-H键的二级倍频和三级倍频组合有关, 2100 nm处的峰值可能是C-O键的二级倍频吸收[29-30]。玉米种子的原始光谱由于这些丰富的物质含量和形态结构而复杂, 并且由于环境和设备等影响, 光谱信息还会包含一些噪声和无用信息。因此, 本研究比较了5种不同的预处理方法提高光谱质量, 并建立PLS-DA判别模型。
表1总结了基于经过5种不同预处理后的光谱数据所建判别模型的检测结果。从表1可以看出, 经过一阶求导预处理后的模型效果最差, 其检测精度甚至低于未经过任何预处理的原始光谱模型。经过SG-SNV预处理后的模型将检测精度由原始的86.0%提升到了92.5%, 为这5种方法中效果最佳的预处理方法。因此, 选用SG-SNV对所有光谱数据进行预处理, 并用于后续研究。
2.2 特征波长提取
在本研究中, 采用BOSS-SPA级联算法对预处理后光谱数据进行有效波长选择。图3为对SG-SNV预处理后的全波段光谱数据采用BOSS算法的有效波长选择过程。基于最佳模型的回归系数绝对值确定的变量权重, 通过WBS方法生成新的子集, 剔除重复变量建立PLS子模型, 如此迭代运行21次至变量数为1时停止。图3(a)和(b)分别显示了每次迭代运行时, 子模型的变量个数及其RMSECV值的变化趋势。由图3(b)可以看出, 在第6次迭代时, RMSECV达到最小值, 因此, 在第6次迭代时获得了最佳的建模变量个数, 此时图3(a)中第6次迭代对应的变量数为58。图3(c)显示了每次迭代时各波长的权重变化, 权重越大的变量越容易被选择。最终, BOSS变量选择算法选择了58个变量。
由于后续需提取特征波长下图像进行图像特征提取, 为减少计算量, 利用SPA进一步进行波段选择, 使用BOSS选择的58个有效波长信息作为SPA的输入, 图4为选择不同波长数所建立的多元线性回归(mixed logistic regression, MLR)模型的预测均方根误差(root mean square error of prediction, RMSEP), 当模型所包含的变量数为11时, RMSEP达到最佳为0.22, 并且随着所选变量数的进一步增加, RMSEP几乎不再降低, 因此这11个变量作为SPA选定变量数。
图5所示为BOSS-SPA特征变量提取结果, 筛选出的11个特征波长分布见图5, 其中996和1469 nm是O-H第二泛音和第一泛音的拉伸, 这与玉米种子的水分含量密切相关[31], 1700~1800 nm之间的波段与脂肪中甲基或亚甲基的C-H延伸的第一和第二泛音相关, 这与种子中的脂肪和油的含量有关[32], 1800与2400 nm之间的特征波段主要对应于蛋白质, 纤维素和糖中存在的C-H键、C-O键和N-H键[33]。这些化学键都与玉米种子成熟过程中的蛋白质、淀粉和脂质等密切相关。
2.3 纹理特征提取
基于上述11个特征波长下的图像进行纹理特征提取, 纹理特征参数分别为能量、熵、相关性、逆方差和对比度。使用4个独立方向(θ=0°、45°、90°、135°)应用GLCM, 并计算4个方向上的平均值, 设定距离d为1, 灰度等级N为64。因此, 在本研究中从每个波长的单个图像获得总共5个纹理特征用于进一步分析。在968.05~2575.05 nm的光谱范围中, 总共288个波段, 也就是每个样本的高光谱图像包含288张灰度图像, 单个样本的纹理特征为5×288(纹理特征×波长)。
2.4 模型建立
本研究从光谱和纹理参数数据中选择携带玉米种子成熟度信息的特征, 然后将其组合成新的数据向量, 以构建特征级模型。以光谱特征、图像特征以及二者融合特征作为模型输入, 建立了LS-SVM和PLS-DA分类模型, 模型结果如表2所示。
从表2可以看出, 融合数据建立的模型取得了较好的结果。对于单独光谱特征, PLS-DA和LS-SVM分类器的测试集平均准确率分别为93.5%和92.9%, 这表明基于BOSS-SPA选择的11个特征波长可识别玉米种子成熟度, 可见该级联算法可准确选出与玉米种子成熟度信息有关的特征波长, 并在保证模型精度的前提下, 减少变量数量。对于单独图像特征, PLS-DA和LS-SVM平均准确率分别为89.5%和87.5%, 尽管基于纹理特征的模型在单独使用时并没有在成熟度分类中取得良好的结果, 但纹理特征可能为使用HSI技术确定种子成熟度提供了额外的信息。对于融合图像光谱特征与纹理特征的模型, PLS-DA和LS-SVM平均准确率分别为96.2%与95.8%, 与单独使用光谱或图像相比, 整合二者特征, 在不同程度上改善了成熟度分类性能。
对于PLS-DA模型, 基于多模态融合数据的准确率比基于单独光谱数据和单独图像数据的准确率提升了2.7%和6.7%; 对于LS-SVM模型, 基于多模态融合数据的准确率比基于单独光谱数据和单独图像数据的准确率提升了2.9%和8.3%。在基于光谱图像融合特征确定种子成熟度方面, 线性PLS-DA和非线性LS-SVM都表现良好, 表明PLS-DA和LS-SVM是通过分别从两个不同特征中提取有用信息来整合这两个不同特征的有效方法, 其中, PLS-DA在高成熟度种子检测中具有更高的准确率为97.9%, 而LS-SVM在低成熟度种子检测中具有更高的准确率为95.1%。
通过比较以上模型的性能, 当使用光谱和图像的组合时, 实现了最佳的成熟度识别分类, 表明机器学习结合多模态融合可较好地实现玉米种子成熟度检测任务。
3 结论
收起
采用HSI技术, 结合多模态数据融合方法, 实现对玉米种子成熟度的精确、无损检测。SG-SNV组合方法是消除原始光谱数据的噪声干扰的最佳预处理方法。BOSS-SPA组合算法被证明是一种高效的特征选择算法, 最终筛选出了11个波段作为光谱数据特征波长, 利用GLCM方法提取单粒玉米种子的图像特征, 并将光谱特征和图像特征进行融合, 分别采用PLS-DA和LS-SVM 2种方法进行建模预测。结果表明, 基于光谱图像特征融合方法建立的模型分类精度更高, 2个分类器均取得良好的建模效果, 准确分别可达到96.2%和95.8%。
本研究综合利用HSI技术提供的丰富波段的图像与光谱数据进行多模态信息融合, 对研究玉米种子成熟度的无损检测具有重要意义。后续可进一步深入对玉米种子光谱与图像特征的研究, 考虑更加智能的、无监督的特征搜索算法, 以优化特征参数选择, 从而达到更好的建模效果。
基金
收起
  • 国家重点研发计划项目(2018YFD0101004-03)
  • 国家自然科学基金项目(61807001)
文献
收起
参考文献 引证文献
排序方式：
[1]
崔爱民, 张久刚, 张虎, 等. 我国玉米生产现状及发展变革[J]. 中国农业科技导报, 2020, 22(7): 10-19.
CUI AIM, ZHANG JG, ZHANG H, et al. Preliminary exploration on current situation and development of maize production in China[J]. Journal of Agricultural Science and Technology, 2020, 22(7): 10-19.
[2]
刘彬, 陈柳, 高艳琼, 等. 三大粮食作物基础研究:中国开始引领世界[J]. 中国农业大学学报, 2019, 24(3): 219-238.
LIU B, CHEN L, GAO YQ, et al. Basic research on the three grain crops: China is beginning to lead the world[J]. Journal of China Agricultural University, 2019, 24(3): 219-238.
[3]
余欣悦. 中国玉米产业高质量发展现状及驱动因素分析[J]. 现代营销(下旬刊), 2022(9): 53-55.
YU XY. High-quality development status and driving factors analysis of China's corn industry[J]. Marketing Management Review, 2022(9): 53-55.
[4]
杨小康, 王瑞华. 玉米栽培新技术及病虫害防治对策探究[J]. 农业开发与装备, 2022(11): 222-224.
YANG XK, WANG RH. Exploration of new maize cultivation techniques and pest and disease control strategies[J]. Agricultural Development & Equipments, 2022(11): 222-224.
[5]
韦小了, 何腾兵. 玉米品质的形成及其调控[J]. 植物学研究, 2022, 11(4): 541-551.
WEI XL, HE TB. The formation and regulation of maize quality[J]. Plant Science Journal, 2022, 11(4): 541-551.
[6]
陈龙江, MICHAEL RR. 种子质量对中国玉米产出的影响[J]. 华南农业大学学报(社会科学版), 2016, 15(3): 19-27.
CHEN LJ, MICHAEL RR. The effect of seeds quality improvement on maize yield[J]. Journal of South China Agricultural University (Social Science Edition), 2016, 15(3): 19-27.
[7]
李静, 柴岳, 程志斌, 等. 浅析果蔬农产品快速检测技术的应用及优化研究[J]. 中国食品工业, 2023(1): 60-62, 65.
LI J, CHAI Y, CHENG ZB, et al. A brief analysis of the application and optimization research of rapid detection technology in fruits and vegetables agricultural products[J]. China Food Industry, 2023(1): 60-62, 65.
[8]
刘妍, 周新奇, 俞晓峰, 等. 无损检测技术在果蔬品质检测中的应用研究进展[J]. 浙江大学学报(农业与生命科学版), 2020, 46(1): 27-37.
LIU Y, ZHOU XQ, YU XF, et al. Research progress of nondestructive testing techniques for fruit and vegetable quality[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2020, 46(1): 27-37.
[9]
李世诚. 光谱检测技术在农产品质量安全检测中的应用[J]. 农业工程技术, 2024, 44(1): 109-110.
LI SC. Application of spectroscopic detection technology in the quality and safety detection of agricultural products[J]. Agricultural Engineering Technology, 2024, 44(1): 109-110.
[10]
倪淳宇, 蒙庆华, 吴哲锋, 等. 基于高光谱成像技术对番石榴可溶性固形物的快速测定[J]. 食品安全质量检测学报, 2024, 15(11): 116-124.
NI CY, MENG QH, WU ZF, et al. Rapid determination of soluble solids content in Psidium guava fruit based on hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2024, 15(11): 116-124.
[11]
李威, 黄云峰, 代作晓, 等. 基于高光谱成像技术无损检测芒果轻微损伤[J]. 食品安全质量检测学报, 2023, 14(1): 98-104.
LI W, HUANG YF, DAI ZX, et al. Non-destructive detection of minor damage in mangoes based on hyperspectral imaging technology[J]. Journal of Food Safety & Quality, 2023, 14(1): 98-104.
[12]
KANG ZL, ZHANG YC, CHEN L, et al. Advances in machine learning and hyperspectral imaging in the food supply chain[J]. Food Engineering Reviews, 2022, 14(4): 596-616.
[13]
MELIT DEVASSY B, GEORGE S. Forensic analysis of beverage stains using hyperspectral imaging[J]. Scientific Reports, 2021, 11(1): 6512.
[14]
DAIKOS O, SCHERZER T. Monitoring of the residual moisture content in finished textiles during converting by NIR hyperspectral imaging[J]. Talanta, 2021, 221: 121567.
[15]
GU X, WANG Y, SUN Q, et al. Hyperspectral inversion of soil organic matter content in cultivated land based on wavelet transform[J]. Computers and Electronics in Agriculture, 2019, 167: 105053.
[16]
ZHANG L, ZHANG Q, WU J, et al. Moisture detection of single corn seed based on hyperspectral imaging and deep learning[J]. Infrared Physics and Technology, 2022, 125: 104279.
[17]
杨欢, 罗斌, 张晗, 等. 基于高光谱成像技术和IRIV算法的玉米种子品种纯度识别[J]. 江苏大学学报(自然科学版), 2023, 44(2): 159-165.
YANG H, LUO B, ZHANG H, et al. Recognition of maize seed variety purity based on hyperspectral imaging technology and IRIV algorithm[J]. Journal of Jiangsu University (Natural Science Edition), 2023, 44(2): 159-165.
[18]
AMBROSE A, KANDPAL ML, KIM SM, et al. High speed measurement of corn seed viability using hyperspectral imaging[J]. Infrared Physics and Technology, 2016, 75: 173-179.
[19]
WANG Z, TIAN X, FAN S, et al. Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models[J]. Infrared Physics and Technology, 2021, 112: 103596.
[20]
杨小玲, 由昭红, 成芳. 高光谱成像技术检测玉米种子成熟度[J]. 光谱学与光谱分析, 2016, 36(12): 4028-4033.
YANG XL, YOU ZH, CHENG F. Study on identification of immature corn seed using hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2016, 36(12): 4028-4033.
[21]
苏红军. 高光谱遥感影像降维: 进展、挑战与展望[J]. 遥感学报, 2022, 26(8): 1504-1529.
SU HJ. Dimensionality reduction for hyperspectral remote sensing: advances, challenges, and prospects[J]. National Remote Sensing Bulletin, 2022, 26(8): 1504-1529.
[22]
XIAO Q, TANG W, ZHANG C, et al. Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves[J]. Plant Phenomics, 2022, 2022: 9813841.
[23]
BARNES RJ, DHANOA MS, SUSAN, et al. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra[J]. Applied Spectroscopy Reviews, 1989, 45(5): 772-777.
[24]
DENG BC, YUN YH, CAO DS, et al. A bootstrapping soft shrinkage approach for variable selection in chemical modeling[J]. Analytica Chimica Acta, 2016, 908: 63-74.
[25]
ZHANG Q, HUANG W, WANG Q, et al. Detection of pears with moldy core using online full-transmittance spectroscopy combined with supervised classifier comparison and variable optimization[J]. Computers and Electronics in Agriculture, 2022, 200: 107231.
[26]
KINI MGR, BHANDARKAR R. Quality assessment of seed using supervised machine learning technique[J]. Journal of the Institution of Engineers (India): Series B, 2023, 104(4): 901-909.
[27]
SUYKENS JA, VANDEWALLE J. Least squares support vector machine classifers[J]. Neural Processing Letters, 1999, 9(3): 293-300.
[28]
MOINDJIÉ IA, DABO-NIANG S, PREDA C. Classification of multivariate functional data on different domains with partial least squares approaches[J]. Statistics and Computing, 2023, 34(1): 5-12.
[29]
夏宇, 孟静武, 罗斌, 等. 高光谱结合理化参数跨品种识别玉米种子贮藏年份[J]. 农业工程学报, 2024, 40(16): 261-268.
XIA Y, MENG JW, LUO B, et al. Cross-species identification of maize seed storage year by hyperspectral combination of physicochemical parameters[J]. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(16): 261-268.
[30]
LI J, YU XN, GE WZ, et al. Qualitative analysis of maize haploid kernels based on calibration transfer by near-infrared spectroscopy[J]. Analytical Letters, 2019, 52(2): 249-267.
[31]
DARFOUR B, AYEH EA, ODOI KM, et al. Physical characteristics of maize grain as influenced by varietal and moisture differences[J]. International Journal of Food Properties, 2022, 25(1): 1351-1364.
[32]
MA LJ, PENG YF, PEI YL, et al. Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds[J]. Scientific Reports, 2019, 9(1): 1-17.
[33]
李煜, 毕卫红, 孙建成, 等. 紫外-可见吸收光谱结合化学计量学算法的水体总有机碳浓度快速检测[J]. 光谱学与光谱分析, 2024, 44(3): 722-730.
LI Y, BI WH, SUN JC, et al. Rapid detection of total organic carbon concentration in water using UV-Vis absorption spectra combined with chemometric algorithms[J]. Spectroscopy and Spectral Analysis, 2024, 44(3): 722-730.
2025年第16卷第2期
PDF下载
232
112
引用本文
BibTeX
文章信息
doi: 10.19812/j.cnki.jfsq11-5956/ts.20241111012
  • 接收时间：2024-11-11
  • 首发时间：2025-07-21
  • 出版时间：2025-01-25
补充材料
相关文章
文章信息
作者
出版历史
  • 收稿日期：2024-11-11
基金
国家重点研发计划项目(2018YFD0101004-03)
国家自然科学基金项目(61807001)
作者信息
    北京工商大学食品安全大数据技术北京市重点实验室, 北京 100048

通讯作者:

* 陈媛媛(1979—), 女, 博士, 副教授, 主要研究方向为光电无损检测。E-mail:
参考文献
分享链接
https://castjournals.cast.org.cn/joweb/spaq/CN/10.19812/j.cnki.jfsq11-5956/ts.20241111012
分享至
全文二维码

扫描看全文

引用本文
BibTeX
本文的引用情况
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
关闭全屏
相关链接
论文
最新文章
热读文章
热点专题
内参
蓝皮书
产业发展报告
传播力报告
期刊分级目录
资讯
学术新闻
行业新闻
学术会议
行业会议
关于
关于我们
联系我们
北京市海淀区学院南路86号
100081
010-62199257
qkjq@cast.org.cn

中国科学技术协会版权所有 京ICP 备10216604号-4 海淀分局备案 1101084647
科技导报社主办 中国科协信息中心技术支持