Article(id=1217529306796970897, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250110002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1736438400000, receivedDateStr=2025-01-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1768211207340, onlineDateStr=2026-01-12, pubDate=1752508800000, pubDateStr=2025-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768211207340, onlineIssueDateStr=2026-01-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768211207340, creator=13701087609, updateTime=1768211207340, updator=13701087609, issue=Issue{id=1217529305693864468, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='13', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1768211207077, creator=13701087609, updateTime=1768212057891, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217532874337730593, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217532874337730594, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=205, endPage=213, ext={EN=ArticleExt(id=1217529309405828001, articleId=1217529306796970897, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Identification of varietal and years of wines based on elemental characteristics, columnId=1151895321388347923, journalTitle=Journal of Food Safety & Quality, columnName=Food Analysis and Detection, runingTitle=null, highlight=null, articleAbstract=

Objective To detect and analyze the content of mineral elements in wines of different varieties and years from the Helan Mountain East Slope region, and construct a model for identifying wine varieties and years based on mineral elements using multivariate statistical analysis methods. Methods Grapes of 5 varieties, namely, Zinfandel, Merlot, Malbec, Cabernet Sauvignon and Marselan, were collected from a winery in Qingtongxia, Ningxia, and were processed into wines using the same method. Wines of the Cabernet Sauvignon variety from 6 years (2018—2023) were collected from a winery in Yongning, Ningxia. The content of 54 kinds of mineral elements including B, Bi, Be, etc. in the wines was determined by inductively coupled plasma mass spectrometry. A model for identifying wine varieties and years was constructed based on the differences in mineral element content among different varieties and years of wines. Results A total of 53 mineral elements were detected in wines of different varieties, and 47 kinds of mineral elements including As and B showed extremely significant differences among varieties (P<0.01). All 54 kinds of mineral elements were detected in wines of different years, and all showed extremely significant differences among years (P<0.01). The correct discrimination rate of the model for identifying wine varieties and years based on Fisher discriminant analysis was 100%. The 9 kinds of mineral elements, namely, As, Cs, Dy, Mo, Na, Sb, U, Yb and Fe, were identified as the differential elements for different varieties of wines, and 11 kinds of mineral elements, namely, Cu, Li, Na, Sb, Tl, U, V, Y, Yb, Ag and Pt, were identified as the differential elements for wines of the same variety from different years. The correct discrimination of wines from the same origin but of different varieties and years was achieved based on mineral elements. Conclusion Mineral elements show significant differences among wines of different varieties and years, and can be used for the identification of wine varieties and years from the same region.

, correspAuthors=Cai-Yan WANG, Qian GE, 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=Jian-Rong KAI, Jia-Yan WU, Wei ZHANG, Cai-Hong LI, Dan-Qing ZHAO, Ting-Ting MA, Xiang-Yu SUN, Cai-Yan WANG, Qian GE), CN=ArticleExt(id=1217529310991274963, articleId=1217529306796970897, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=基于元素特征的葡萄酒品种和年份鉴别, columnId=1151895321958773274, journalTitle=食品安全质量检测学报, columnName=食品分析与检测, runingTitle=null, highlight=null, articleAbstract=

目的 检测分析贺兰山东麓产区不同品种及年份葡萄酒中矿物元素含量, 结合多元统计分析方法, 构建基于矿物元素的品种和年份葡萄酒鉴别模型。方法 采集了宁夏青铜峡产区一酒庄紫大夫、美乐、马尔贝克、赤霞珠、马瑟兰葡萄, 采用同一方法酿制成葡萄酒; 采集宁夏永宁产区一酒庄2018—2023年6个年份的赤霞珠单品葡萄酒, 采用电感耦合等离子体质谱仪测定葡萄酒中B、Bi、Be等54种矿物元素含量, 基于不同品种和不同年份葡萄酒中矿物元素含量差异构建葡萄酒品种和年份鉴别模型。结果 不同品种葡萄酒中共检出53种矿物元素, As、B等47种矿物元素在品种间具有极显著差异(P<0.01), 不同年份葡萄酒中54种矿物元素均有检出, 且均在年份葡萄酒间具有极显著差异(P<0.01); 基于Fisher判别分析方法建立的葡萄酒品种和年份鉴别模型的正确判别率为均为100%, As、Cs、Dy、Mo、Na、Sb、U、Yb、Fe 9种矿物元素为不同品种葡萄酒的差异元素, Cu、Li、Na、Sb、Tl、U、V、Y、Yb、Ag、Pt 11种矿物元素为同一品种不同年份葡萄酒的差异元素, 基于矿物元素实现了同一产地不同品种和不同年份葡萄酒的正确判别。结论 矿物元素在不同品种和年份葡萄酒中存在显著差异, 可用于同一产区品种葡萄酒和年份葡萄酒的鉴别。

, correspAuthors=王彩艳, 葛谦, authorNote=null, correspAuthorsNote=
*王彩艳(1975—), 女, 副研究员, 主要研究方向为农产品产地识别研究。E-mail: ;
葛谦(1988—), 女, 副研究员, 主要研究方向为葡萄酒微生物选育与葡萄酒风格评价。E-mail:
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开建荣(1988—), 女, 博士, 主要研究方向为农产品质量评价及产地识别研究。E-mail:

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开建荣(1988—), 女, 博士, 主要研究方向为农产品质量评价及产地识别研究。E-mail:

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开建荣(1988—), 女, 博士, 主要研究方向为农产品质量评价及产地识别研究。E-mail:

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University, Shanghai 201600, China), AuthorCompanyExt(id=1217901250964799555, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, companyId=1217901250948022336, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 东华大学机械工程学院, 上海 201600)]), AuthorCompany(id=1217901251099017297, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, xref=4, ext=[AuthorCompanyExt(id=1217901251107405906, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, companyId=1217901251099017297, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 Institute of Agricultural Resources and Environment, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China), AuthorCompanyExt(id=1217901251115794517, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, companyId=1217901251099017297, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 宁夏农林科学院农业资源与环境研究所, 银川 750002)])], figs=[ArticleFig(id=1217901258690708049, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Fig 1, caption=Scatter plot of different wine samples based on mineral element content, figureFileSmall=UumHO6KBGS6LtDXo2sk1QA==, figureFileBig=yHtOB9CzS7NXdzYBP7DweA==, tableContent=null), ArticleFig(id=1217901258837508703, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=图1, caption=基于矿物元素含量的不同品种葡萄酒样品分布散点图, figureFileSmall=UumHO6KBGS6LtDXo2sk1QA==, figureFileBig=yHtOB9CzS7NXdzYBP7DweA==, tableContent=null), ArticleFig(id=1217901259001086572, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Fig 2, caption=Scatter plot of wine samples distribution in different years based on mineral elements, figureFileSmall=W2TcQwv8u1giImWM7i4fkQ==, figureFileBig=jhB+mc6nFxLQ71GG584bFw==, tableContent=null), ArticleFig(id=1217901259126915698, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=图2, caption=基于矿物元素的不同年份葡萄酒样品分布散点图, figureFileSmall=W2TcQwv8u1giImWM7i4fkQ==, figureFileBig=jhB+mc6nFxLQ71GG584bFw==, tableContent=null), ArticleFig(id=1217901259235967615, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Table 1, caption=

Mineral element content in different varieties of wine

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 紫大夫 美乐 马尔贝克 赤霞珠 马瑟兰 P
Al 11.80±0.34a 9.87±0.56b 11.45±1.08a 9.57±0.66b 11.03±0.46a 0.000
P 124.62±6.30c 179.38±10.83a 141.88±7.86b 93.25±8.75d 75.56±5.02e 0.000
Ca 98.11±9.47a 103.44±7.85a 96.79±4.80a 99.66±10.24a 112.27±6.78a 0.143
Mg 126.25±8.85b 127.50±6.74b 118.50±7.38b 118.81±8.15b 142.50±6.85a 0.000
Mn 0.57±0.04b 0.84±0.06a 0.48±0.03c 0.60±0.05b 0.62±0.04b 0.000
Na 1.24±0.12c 3.79±0.32a 1.74±0.09b 1.25±0.11c 1.81±0.12b 0.000
Fe 1.13±0.12a 0.84±0.08c 0.53±0.03e 1.02±0.06b 0.75±0.06d 0.000
Cu 0.042±0.004c 0.022±0.001e 0.033±0.001d 0.046±0.003b 0.059±0.005a 0.000
B 5.20±0.33e 9.42±0.35c 10.89±0.85b 7.92±0.28d 13.39±0.82a 0.000
As 0.030±0.002a 0.011±0.001c 0.009±0.002d 0.008±0.001d 0.022±0.002b 0.000
Cd* 0.00±0.00b 0.00±0.00b 0.00±0.00b 0.002±0.002a 0.00±0.00b 0.005
Cr* 0.58±0.61b 1.90±0.36b 0.64±0.30b 5.90±3.60a 1.80±0.18b 0.000
Hg* 0.004±0.000b 0.020±0.022b 0.002±0.002b 0.030±0.010b 0.093±0.039a 0.000
Ni* 3.42±0.21d 5.91±0.39c 6.08±0.69c 9.72±0.91b 12.02±1.51a 0.000
Pb* 18.23±1.81a 4.32±0.29c 3.31±0.44c 5.50±0.29b 6.21±0.41b 0.000
Ba 0.089±0.006a 0.082±0.006b 0.056±0.005c 0.025±0.003d 0.059±0.006c 0.000
Ce* 0.13±0.09b 0.21±0.10a 0.00±0.00c 0.00±0.00c 0.00±0.00c 0.000
Co* 0.74±0.10c 1.00±0.07b 0.27±0.06d 0.77±0.06c 1.50±0.14a 0.000
Cs* 0.48±0.06e 6.81±0.31a 1.91±0.10c 2.21±0.21b 0.10±0.09d 0.000
Dy* 0.09±0.03b 0.07±0.04b 0.09±0.02b 0.14±0.01a 0.12±0.03a 0.000
Eu* 0.070±0.013b 0.059±0.006c 0.043±0.030d 0.038±0.075d 0.082±0.007a 0.000
Gd* 0.17±0.04a 0.10±0.02b 0.10±0.00b 0.11±0.04b 0.09±0.02b 0.008
Ho* 0.019±0.008b 0.025±0.006ab 0.008±0.006c 0.031±0.013ab 0.033±0.006a 0.000
Li 0.014±0.001e 0.051±0.003c 0.035±0.003d 0.079±0.002a 0.066±0.005b 0.000
Lu* 0.018±0.003b 0.019±0.000b 0.016±0.002bc 0.013±0.005c 0.028±0.002a 0.000
Mo* 3.40±0.23b 0.15±0.03d 0.38±0.16d 2.80±0.10c 4.31±0.37a 0.000
Nb* 0.47±0.03a 0.18±0.03c 0.34±0.08b 0.36±0.06b 0.49±0.08a 0.000
Nd* 0.38±0.29a 0.30±0.03ab 0.10±0.07b 0.12±0.07b 0.21±0.11ab 0.017
Pr* 0.17±0.02a 0.11±0.02b 0.08±0.02c 0.11±0.02b 0.09±0.02c 0.000
Rb 0.38±0.03c 0.86±0.04a 0.43±0.03b 0.45±0.05b 0.33±0.02d 0.000
Sb* 11.12±0.43a 1.63±0.16d 2.44±0.17b 1.14±0.07e 2.14±0.21c 0.000
Sc* 0.99±0.08ab 0.80±0.24ab 1.43±1.10a 0.39±0.27b 0.30±0.33b 0.100
Se* 2.61±0.20ab 2.93±0.25a 3.06±0.15a 2.33±0.32b 2.61±0.38ab 0.030
Sm* 0.08±0.05ab 0.08±0.03ab 0.05±0.01b 0.07±0.01ab 0.12±0.04a 0.100
Sn* 1.43±0.13a 1.42±0.07a 1.52±0.10a 1.40±0.45a 1.73±0.16a 0.157
Sr 2.10±0.14a 1.69±0.15c 1.55±0.08c 1.66±0.11c 1.86±0.16b 0.000
Tb* 0.014±0.004c 0.028±0.012b 0.025±0.006b 0.031±0.003b 0.050±0.008a 0.000
Ti 0.36±0.02a 0.32±0.10b 0.20±0.01d 0.31±0.02b 0.23±0.03c 0.000
Th* 0.20±0.09b 0.08±0.02c 0.05±0.01c 0.07±0.01c 0.26±0.02a 0.000
Tl* 0.21±0.02b 0.20±0.02b 0.18±0.04b 0.14±0.05b 0.45±0.06a 0.000
Tm* 0.020±0.003b 0.020±0.003b 0.016±0.008b 0.023±0.003b 0.039±0.004a 0.000
U* 0.14±0.013c 0.51±0.04a 0.00±0.00d 0.22±0.05b 0.00±0.00d 0.000
V 0.025±0.001a 0.026±0.002a 0.013±0.000b 0.027±0.000a 0.011±0.003b 0.000
Y* 0.69±0.15b 0.64±0.06bc 0.55±0.09cd 0.82±0.07a 0.50±0.05d 0.000
Yb* 0.078±0.005b 0.12±0.01a 0.022±0.01c 0.14±0.04a 0.073±0.010b 0.000
Ag* 0.20±0.02a 0.22±0.01a 0.17±0.00b 0.22±0.00a 0.21±0.01a 0.001
Ta* 0.080±0.025bc 0.067±0.012c 0.074±0.007bc 0.090±0.004b 0.13±0.01a 0.000
Pt* 0.23±0.01c 0.43±0.09a 0.20±0.023c 0.20±0.04c 0.29±0.02b 0.000
W* 1.22±0.22c 1.13±0.24c 1.23±0.04c 1.60±0.12b 2.33±0.22a 0.000
Hf* 0.033±0.026a 0.000±0.000c 0.000±0.000c 0.002±0.002b 0.012±0.006b 0.000
Pd* 0.23±0.06a 0.11±0.04b 0.08±0.04b 0.08±0.03b 0.11±0.01b 0.000
Ga* 0.52±0.16a 0.61±0.10a 0.13±0.04c 0.00±0.00d 0.28±0.04b 0.000
Zr* 0.94±0.40a 0.00±0.00b 0.00±0.00b 0.00±0.00b 0.00±0.00b 0.000
), ArticleFig(id=1217901259395351176, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=表1, caption=

不同品种葡萄酒中矿物元素含量

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 紫大夫 美乐 马尔贝克 赤霞珠 马瑟兰 P
Al 11.80±0.34a 9.87±0.56b 11.45±1.08a 9.57±0.66b 11.03±0.46a 0.000
P 124.62±6.30c 179.38±10.83a 141.88±7.86b 93.25±8.75d 75.56±5.02e 0.000
Ca 98.11±9.47a 103.44±7.85a 96.79±4.80a 99.66±10.24a 112.27±6.78a 0.143
Mg 126.25±8.85b 127.50±6.74b 118.50±7.38b 118.81±8.15b 142.50±6.85a 0.000
Mn 0.57±0.04b 0.84±0.06a 0.48±0.03c 0.60±0.05b 0.62±0.04b 0.000
Na 1.24±0.12c 3.79±0.32a 1.74±0.09b 1.25±0.11c 1.81±0.12b 0.000
Fe 1.13±0.12a 0.84±0.08c 0.53±0.03e 1.02±0.06b 0.75±0.06d 0.000
Cu 0.042±0.004c 0.022±0.001e 0.033±0.001d 0.046±0.003b 0.059±0.005a 0.000
B 5.20±0.33e 9.42±0.35c 10.89±0.85b 7.92±0.28d 13.39±0.82a 0.000
As 0.030±0.002a 0.011±0.001c 0.009±0.002d 0.008±0.001d 0.022±0.002b 0.000
Cd* 0.00±0.00b 0.00±0.00b 0.00±0.00b 0.002±0.002a 0.00±0.00b 0.005
Cr* 0.58±0.61b 1.90±0.36b 0.64±0.30b 5.90±3.60a 1.80±0.18b 0.000
Hg* 0.004±0.000b 0.020±0.022b 0.002±0.002b 0.030±0.010b 0.093±0.039a 0.000
Ni* 3.42±0.21d 5.91±0.39c 6.08±0.69c 9.72±0.91b 12.02±1.51a 0.000
Pb* 18.23±1.81a 4.32±0.29c 3.31±0.44c 5.50±0.29b 6.21±0.41b 0.000
Ba 0.089±0.006a 0.082±0.006b 0.056±0.005c 0.025±0.003d 0.059±0.006c 0.000
Ce* 0.13±0.09b 0.21±0.10a 0.00±0.00c 0.00±0.00c 0.00±0.00c 0.000
Co* 0.74±0.10c 1.00±0.07b 0.27±0.06d 0.77±0.06c 1.50±0.14a 0.000
Cs* 0.48±0.06e 6.81±0.31a 1.91±0.10c 2.21±0.21b 0.10±0.09d 0.000
Dy* 0.09±0.03b 0.07±0.04b 0.09±0.02b 0.14±0.01a 0.12±0.03a 0.000
Eu* 0.070±0.013b 0.059±0.006c 0.043±0.030d 0.038±0.075d 0.082±0.007a 0.000
Gd* 0.17±0.04a 0.10±0.02b 0.10±0.00b 0.11±0.04b 0.09±0.02b 0.008
Ho* 0.019±0.008b 0.025±0.006ab 0.008±0.006c 0.031±0.013ab 0.033±0.006a 0.000
Li 0.014±0.001e 0.051±0.003c 0.035±0.003d 0.079±0.002a 0.066±0.005b 0.000
Lu* 0.018±0.003b 0.019±0.000b 0.016±0.002bc 0.013±0.005c 0.028±0.002a 0.000
Mo* 3.40±0.23b 0.15±0.03d 0.38±0.16d 2.80±0.10c 4.31±0.37a 0.000
Nb* 0.47±0.03a 0.18±0.03c 0.34±0.08b 0.36±0.06b 0.49±0.08a 0.000
Nd* 0.38±0.29a 0.30±0.03ab 0.10±0.07b 0.12±0.07b 0.21±0.11ab 0.017
Pr* 0.17±0.02a 0.11±0.02b 0.08±0.02c 0.11±0.02b 0.09±0.02c 0.000
Rb 0.38±0.03c 0.86±0.04a 0.43±0.03b 0.45±0.05b 0.33±0.02d 0.000
Sb* 11.12±0.43a 1.63±0.16d 2.44±0.17b 1.14±0.07e 2.14±0.21c 0.000
Sc* 0.99±0.08ab 0.80±0.24ab 1.43±1.10a 0.39±0.27b 0.30±0.33b 0.100
Se* 2.61±0.20ab 2.93±0.25a 3.06±0.15a 2.33±0.32b 2.61±0.38ab 0.030
Sm* 0.08±0.05ab 0.08±0.03ab 0.05±0.01b 0.07±0.01ab 0.12±0.04a 0.100
Sn* 1.43±0.13a 1.42±0.07a 1.52±0.10a 1.40±0.45a 1.73±0.16a 0.157
Sr 2.10±0.14a 1.69±0.15c 1.55±0.08c 1.66±0.11c 1.86±0.16b 0.000
Tb* 0.014±0.004c 0.028±0.012b 0.025±0.006b 0.031±0.003b 0.050±0.008a 0.000
Ti 0.36±0.02a 0.32±0.10b 0.20±0.01d 0.31±0.02b 0.23±0.03c 0.000
Th* 0.20±0.09b 0.08±0.02c 0.05±0.01c 0.07±0.01c 0.26±0.02a 0.000
Tl* 0.21±0.02b 0.20±0.02b 0.18±0.04b 0.14±0.05b 0.45±0.06a 0.000
Tm* 0.020±0.003b 0.020±0.003b 0.016±0.008b 0.023±0.003b 0.039±0.004a 0.000
U* 0.14±0.013c 0.51±0.04a 0.00±0.00d 0.22±0.05b 0.00±0.00d 0.000
V 0.025±0.001a 0.026±0.002a 0.013±0.000b 0.027±0.000a 0.011±0.003b 0.000
Y* 0.69±0.15b 0.64±0.06bc 0.55±0.09cd 0.82±0.07a 0.50±0.05d 0.000
Yb* 0.078±0.005b 0.12±0.01a 0.022±0.01c 0.14±0.04a 0.073±0.010b 0.000
Ag* 0.20±0.02a 0.22±0.01a 0.17±0.00b 0.22±0.00a 0.21±0.01a 0.001
Ta* 0.080±0.025bc 0.067±0.012c 0.074±0.007bc 0.090±0.004b 0.13±0.01a 0.000
Pt* 0.23±0.01c 0.43±0.09a 0.20±0.023c 0.20±0.04c 0.29±0.02b 0.000
W* 1.22±0.22c 1.13±0.24c 1.23±0.04c 1.60±0.12b 2.33±0.22a 0.000
Hf* 0.033±0.026a 0.000±0.000c 0.000±0.000c 0.002±0.002b 0.012±0.006b 0.000
Pd* 0.23±0.06a 0.11±0.04b 0.08±0.04b 0.08±0.03b 0.11±0.01b 0.000
Ga* 0.52±0.16a 0.61±0.10a 0.13±0.04c 0.00±0.00d 0.28±0.04b 0.000
Zr* 0.94±0.40a 0.00±0.00b 0.00±0.00b 0.00±0.00b 0.00±0.00b 0.000
), ArticleFig(id=1217901259542151826, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Table 2, caption=

Classification function coefficients based on mineral elements

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 品种
紫大夫 美乐 马尔贝克 赤霞珠 马瑟兰
As 15376.081 6125.754 9623.569 4055.748 25931.711
Cs 67605.130 439070.059 110905.461 142626.153 137635.529
Dy 74809.502 870839.869 -263585.592 1816325.084 775259.410
Mo 218489.840 202103.938 65861.389 236925.906 289393.590
Na -297.856 165.868 125.597 -214.697 119.621
Sb 579886.672 97302.978 119433.131 130246.911 101149.528
U 1647492.287 2023525.383 -422882.740 2476681.967 107204.116
Yb 1383223.071 641779.855 -1248017.104 2906462.701 -23079.620
Fe 1881.527 245.629 343.946 637.727 348.031
(常量) -4832.407 -2623.402 -447.859 -1350.225 -1371.017
), ArticleFig(id=1217901259684758170, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=表2, caption=

基于矿物元素的品种分类函数系数

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 品种
紫大夫 美乐 马尔贝克 赤霞珠 马瑟兰
As 15376.081 6125.754 9623.569 4055.748 25931.711
Cs 67605.130 439070.059 110905.461 142626.153 137635.529
Dy 74809.502 870839.869 -263585.592 1816325.084 775259.410
Mo 218489.840 202103.938 65861.389 236925.906 289393.590
Na -297.856 165.868 125.597 -214.697 119.621
Sb 579886.672 97302.978 119433.131 130246.911 101149.528
U 1647492.287 2023525.383 -422882.740 2476681.967 107204.116
Yb 1383223.071 641779.855 -1248017.104 2906462.701 -23079.620
Fe 1881.527 245.629 343.946 637.727 348.031
(常量) -4832.407 -2623.402 -447.859 -1350.225 -1371.017
), ArticleFig(id=1217901259844141734, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Table 3, caption=

Results of Fisher discriminant analysis based on mineral elements for different wine varieties

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 品种 预测组成员 整体正确判别率/%
紫大夫 美乐 马尔
贝克
赤霞珠 马瑟兰
回代
检验/
交叉
检验
紫大夫
(n=6)
6 0 0 0 0 100
美乐
(n=6)
0 6 0 0 0
马尔贝克(n=6) 0 0 6 0 0
赤霞珠(n=6) 0 0 0 6 0
马瑟兰(n=6) 0 0 0 0 6
正确
判别率/%
100 100 100 100 100
), ArticleFig(id=1217901259965776558, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=表3, caption=

基于矿物元素的不同品种葡萄酒的Fisher判别分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 品种 预测组成员 整体正确判别率/%
紫大夫 美乐 马尔
贝克
赤霞珠 马瑟兰
回代
检验/
交叉
检验
紫大夫
(n=6)
6 0 0 0 0 100
美乐
(n=6)
0 6 0 0 0
马尔贝克(n=6) 0 0 6 0 0
赤霞珠(n=6) 0 0 0 6 0
马瑟兰(n=6) 0 0 0 0 6
正确
判别率/%
100 100 100 100 100
), ArticleFig(id=1217901260095799991, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Table 4, caption=

Content of mineral elements in Cabernet Sauvignon wines from different years

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 2018年 2019年 2020年 2021年 2022年 2023年 P
Al 10.15±0.81a 9.77±0.43a 6.79±0.48c 10.45±1.00a 9.54±0.60a 8.03±0.40b 0.000
P 207.2±19.8c 244.8±21.3c 239.2±12.4c 416.8±38.2a 377.6±7.6a 289.6±20.3b 0.000
Ca 87.11±6.97c 102.24±8.85c 90.79±4.80c 135.53±8.24a 118.29±7.20b 132.89±4.60a 0.000
Mg 258.18±2.58c 256.36±2.56c 266.36±12.21c 464.55±52.76a 410.91±29.63b 366.36±28.61b 0.000
Mn 4.78±0.22b 4.40±0.16bc 3.72±0.32d 5.30±0.42a 4.35±0.26bc 4.06±0.18cd 0.000
Na 16.15±1.12c 48.00±1.27a 18.31±0.37c 17.10±1.53c 8.04±0.81d 40.25±3.19b 0.000
Fe 8.33±0.87ab 8.16±0.67ab 6.67±0.83b 7.46±0.52b 9.34±0.49a 8.16±0.36ab 0.006
Zn 0.62±0.13b 0.92±0.06a 0.52±0.04c 0.49±0.03c 0.52±0.05c 0.36±0.02d 0.000
Cu 0.20±0.01c 0.14±0.01d 0.53±0.03b 0.13±0.01d 0.73±0.06a 0.08±0.00d 0.000
B 9.41±0.47c 11.40±1.42c 10.44±0.64c 25.07±1.57a 20.15±1.92b 19.12±0.76b 0.000
As* 4.70±0.37c 6.91±0.25a 6.50±0.30a 7.20±0.47a 5.71±0.30b 3.83±0.15d 0.000
Cd* 0.006±0.000d 0.030±0.003b 0.023±0.002c 0.020±0.002c 0.040±0.004a 0.018±0.001c 0.000
Cr 0.046±0.004c 0.053±0.004ab 0.050±0.003bc 0.057±0.006a 0.043±0.003c 0.047±0.002bc 0.007
Hg* 0.069±0.006b 0.036±0.003d 0.010±0.009a 0.036±0.003d 0.036±0.003d 0.052±0.003c 0.000
Ni 0.032±0.003c 0.034±0.035bc 0.031±0.011c 0.039±0.007b 0.065±0.005a 0.038±0.004b 0.000
Pb* 7.05±0.56d 10.61±0.80b 9.32±0.73c 14.55±0.88a 11.74±0.62b 15.61±0.31a 0.000
Ba 0.11±0.01b 0.11±0.01b 0.10±0.01b 0.14±0.00a 0.16±0.01a 0.15±0.01a 0.000
Ce* 0.33±0.02a 0.17±0.01c 0.18±0.01 0.35±0.03a 0.27±0.02b 0.15±0.01c 0.000
Co* 5.60±0.17b 5.34±0.19bc 4.44±0.52c 6.64±0.63a 5.22±0.14bc 4.78±0.35c 0.000
Cs* 5.70±0.20d 6.03±0.54d 5.33±0.19d 13.82±0.60a 10.56±0.53c 12.06±0.60b 0.000
Dy* 0.13±0.00a 0.11±0.01b 0.06±0.00d 0.07±0.00c 0.04±0.00e 0.03±0.00f 0.000
Eu* 0.066±0.003a 0.054±0.005b 0.040±0.001d 0.043±0.003cd 0.043±0.002cd 0.048±0.002c 0.000
Gd* 0.14±0.01a 0.12±0.00b 0.10±0.01c 0.10±0.01c 0.10±0.01c 0.07±0.00d 0.000
Ho* 0.031±0.001a 0.019±0.002b 0.013±0.001c 0.017±0.002b 0.005±0.000e 0.010±0.001d 0.000
Li 0.11±0.00d 0.25±0.02a 0.16±0.00b 0.23±0.01a 0.15±0.01bc 0.13±0.01c 0.000
Lu* 0.0097±0.0006a 0.0084±0.0009b 0.0071±0.0002c 0.0041±0.0003e 0.0055±0.0004d 0.0033±0.0003e 0.000
Mo* 7.9±0.44b 5.57±0.15c 8.81±0.75a 3.74±0.33e 2.97±0.11e 4.63±0.35d 0.000
Nb* 1.03±0.06b 1.21±0.04a 0.84±0.07c 0.64±0.06d 0.51±0.06e 0.47±0.02e 0.000
Nd* 2.48±0.02a 2.42±0.02a 1.30±0.03b 2.52±0.05a 2.25±0.01a 1.21±0.01b 0.000
Pr* 0.22±0.02a 0.16±0.01b 0.15±0.02b 0.12±0.02c 0.11±0.01c 0.10±0.01c 0.000
Rb 1.72±0.13b 1.22±0.10c 1.31±0.04c 1.88±0.15b 2.22±0.07a 2.22±0.25a 0.000
Sb* 0.60±0.00b 0.34±0.02d 0.56±0.02b 0.39±0.02c 0.32±0.02d 0.83±0.052 0.000
Sc* 3.35±0.23a 2.75±0.26b 2.69±0.16b 3.54±0.25a 3.38±0.25a 3.15±0.30ab 0.005
Se* 2.92±0.15c 3.68±0.33b 4.08±0.16b 4.56±0.27a 4.02±0.25b 3.90±0.039b 0.000
Sm* 0.098±0.009a 0.098±0.005a 0.048±0.004c 0.053±0.006c 0.085±0.008ab 0.082±0.004b 0.000
Sn* 0.99±0.05e 1.21±0.07d 1.75±0.05a 1.57±0.12bc 1.44±0.04c 1.66±0.11ab 0.000
Sr 2.12±0.16c 5.04±0.23a 3.49±0.27b 5.00±0.22a 3.14±0.31b 3.26±0.13b 0.000
Tb* 0.023±0.000a 0.021±0.001b 0.016±0.001c 0.010±0.001e 0.011±0.001d 0.005±0.000f 0.000
Ti 0.27±0.01b 0.27±0.02b 0.34±0.03a 0.25±0.02b 0.24±0.02b 0.25±0.11b 0.000
Th* 0.50±0.02a 0.43±0.02b 0.30±0.02c 0.14±0.09d 0.15±0.01d 0.13±0.01d 0.000
Tl* 0.50±0.03a 0.50±0.02a 0.40±0.03b 0.21±0.01c 0.15±0.01d 0.12±0.00d 0.000
Tm* 0.051±0.004a 0.038±0.002b 0.041±0.002b 0.038±0.002b 0.028±0.001c 0.020±0.002d 0.000
U* 0.57±0.02b 1.38±0.04a 0.54±0.03b 0.05±0.03b 1.42±0.1a 0.50±0.01b 0.000
V 0.038±0.001a 0.013±0.002b 0.010±0.000c 0.012±0.000b 0.010±0.001c 0.007±0.000d 0.000
Y* 1.11±0.10a 0.97±0.01b 0.55±0.06c 0.60±0.01c 0.52±0.04c 0.48±0.05c 0.000
Yb* 0.11±0.01b 0.13±0.01a 0.06±0.00c 0.03±0.00e 0.05±0.01d 0.01±0.00f 0.000
Ag* 0.11±0.01a 0.05±0.00b 0.03±0.00c 0.02±0.00d 0.01±0.00d 0.01±0.00d 0.000
Ta* 0.78±0.01b 1.01±0.01a 0.64±0.04c 0.64±0.04c 0.44±0.04d 0.43±0.04d 0.000
Pt* 0.49±0.02b 0.36±0.04c 0.72±0.02a 0.76±0.04a 0.76±0.06a 0.66±0.04a 0.000
W* 1.96±0.17a 1.53±0.12b 1.54±0.13b 1.31±0.10b 0.92±0.03c 0.92±0.06c 0.000
Hf* 0.46±0.02a 0.40±0.02b 0.23±0.02c 0.21±0.01c 0.23±0.02c 0.10±0.00d 0.000
Pd* 2.41±0.02a 2.16±0.09b 1.26±0.08c 0.89±0.07d 0.66±0.05e 0.60±0.06e 0.000
Ga* 3.51±0.15b 3.29±0.26b 2.82±0.12c 5.00±0.31a 5.45±0.36a 5.25±0.24a 0.000
Zr* 7.45±0.76b 9.30±0.61a 2.78±0.03c 1.88±0.02d 1.52±0.05d 1.14±0.10d 0.000
), ArticleFig(id=1217901260246794943, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=表4, caption=

不同年份单品赤霞珠葡萄酒中矿物元素含量

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 2018年 2019年 2020年 2021年 2022年 2023年 P
Al 10.15±0.81a 9.77±0.43a 6.79±0.48c 10.45±1.00a 9.54±0.60a 8.03±0.40b 0.000
P 207.2±19.8c 244.8±21.3c 239.2±12.4c 416.8±38.2a 377.6±7.6a 289.6±20.3b 0.000
Ca 87.11±6.97c 102.24±8.85c 90.79±4.80c 135.53±8.24a 118.29±7.20b 132.89±4.60a 0.000
Mg 258.18±2.58c 256.36±2.56c 266.36±12.21c 464.55±52.76a 410.91±29.63b 366.36±28.61b 0.000
Mn 4.78±0.22b 4.40±0.16bc 3.72±0.32d 5.30±0.42a 4.35±0.26bc 4.06±0.18cd 0.000
Na 16.15±1.12c 48.00±1.27a 18.31±0.37c 17.10±1.53c 8.04±0.81d 40.25±3.19b 0.000
Fe 8.33±0.87ab 8.16±0.67ab 6.67±0.83b 7.46±0.52b 9.34±0.49a 8.16±0.36ab 0.006
Zn 0.62±0.13b 0.92±0.06a 0.52±0.04c 0.49±0.03c 0.52±0.05c 0.36±0.02d 0.000
Cu 0.20±0.01c 0.14±0.01d 0.53±0.03b 0.13±0.01d 0.73±0.06a 0.08±0.00d 0.000
B 9.41±0.47c 11.40±1.42c 10.44±0.64c 25.07±1.57a 20.15±1.92b 19.12±0.76b 0.000
As* 4.70±0.37c 6.91±0.25a 6.50±0.30a 7.20±0.47a 5.71±0.30b 3.83±0.15d 0.000
Cd* 0.006±0.000d 0.030±0.003b 0.023±0.002c 0.020±0.002c 0.040±0.004a 0.018±0.001c 0.000
Cr 0.046±0.004c 0.053±0.004ab 0.050±0.003bc 0.057±0.006a 0.043±0.003c 0.047±0.002bc 0.007
Hg* 0.069±0.006b 0.036±0.003d 0.010±0.009a 0.036±0.003d 0.036±0.003d 0.052±0.003c 0.000
Ni 0.032±0.003c 0.034±0.035bc 0.031±0.011c 0.039±0.007b 0.065±0.005a 0.038±0.004b 0.000
Pb* 7.05±0.56d 10.61±0.80b 9.32±0.73c 14.55±0.88a 11.74±0.62b 15.61±0.31a 0.000
Ba 0.11±0.01b 0.11±0.01b 0.10±0.01b 0.14±0.00a 0.16±0.01a 0.15±0.01a 0.000
Ce* 0.33±0.02a 0.17±0.01c 0.18±0.01 0.35±0.03a 0.27±0.02b 0.15±0.01c 0.000
Co* 5.60±0.17b 5.34±0.19bc 4.44±0.52c 6.64±0.63a 5.22±0.14bc 4.78±0.35c 0.000
Cs* 5.70±0.20d 6.03±0.54d 5.33±0.19d 13.82±0.60a 10.56±0.53c 12.06±0.60b 0.000
Dy* 0.13±0.00a 0.11±0.01b 0.06±0.00d 0.07±0.00c 0.04±0.00e 0.03±0.00f 0.000
Eu* 0.066±0.003a 0.054±0.005b 0.040±0.001d 0.043±0.003cd 0.043±0.002cd 0.048±0.002c 0.000
Gd* 0.14±0.01a 0.12±0.00b 0.10±0.01c 0.10±0.01c 0.10±0.01c 0.07±0.00d 0.000
Ho* 0.031±0.001a 0.019±0.002b 0.013±0.001c 0.017±0.002b 0.005±0.000e 0.010±0.001d 0.000
Li 0.11±0.00d 0.25±0.02a 0.16±0.00b 0.23±0.01a 0.15±0.01bc 0.13±0.01c 0.000
Lu* 0.0097±0.0006a 0.0084±0.0009b 0.0071±0.0002c 0.0041±0.0003e 0.0055±0.0004d 0.0033±0.0003e 0.000
Mo* 7.9±0.44b 5.57±0.15c 8.81±0.75a 3.74±0.33e 2.97±0.11e 4.63±0.35d 0.000
Nb* 1.03±0.06b 1.21±0.04a 0.84±0.07c 0.64±0.06d 0.51±0.06e 0.47±0.02e 0.000
Nd* 2.48±0.02a 2.42±0.02a 1.30±0.03b 2.52±0.05a 2.25±0.01a 1.21±0.01b 0.000
Pr* 0.22±0.02a 0.16±0.01b 0.15±0.02b 0.12±0.02c 0.11±0.01c 0.10±0.01c 0.000
Rb 1.72±0.13b 1.22±0.10c 1.31±0.04c 1.88±0.15b 2.22±0.07a 2.22±0.25a 0.000
Sb* 0.60±0.00b 0.34±0.02d 0.56±0.02b 0.39±0.02c 0.32±0.02d 0.83±0.052 0.000
Sc* 3.35±0.23a 2.75±0.26b 2.69±0.16b 3.54±0.25a 3.38±0.25a 3.15±0.30ab 0.005
Se* 2.92±0.15c 3.68±0.33b 4.08±0.16b 4.56±0.27a 4.02±0.25b 3.90±0.039b 0.000
Sm* 0.098±0.009a 0.098±0.005a 0.048±0.004c 0.053±0.006c 0.085±0.008ab 0.082±0.004b 0.000
Sn* 0.99±0.05e 1.21±0.07d 1.75±0.05a 1.57±0.12bc 1.44±0.04c 1.66±0.11ab 0.000
Sr 2.12±0.16c 5.04±0.23a 3.49±0.27b 5.00±0.22a 3.14±0.31b 3.26±0.13b 0.000
Tb* 0.023±0.000a 0.021±0.001b 0.016±0.001c 0.010±0.001e 0.011±0.001d 0.005±0.000f 0.000
Ti 0.27±0.01b 0.27±0.02b 0.34±0.03a 0.25±0.02b 0.24±0.02b 0.25±0.11b 0.000
Th* 0.50±0.02a 0.43±0.02b 0.30±0.02c 0.14±0.09d 0.15±0.01d 0.13±0.01d 0.000
Tl* 0.50±0.03a 0.50±0.02a 0.40±0.03b 0.21±0.01c 0.15±0.01d 0.12±0.00d 0.000
Tm* 0.051±0.004a 0.038±0.002b 0.041±0.002b 0.038±0.002b 0.028±0.001c 0.020±0.002d 0.000
U* 0.57±0.02b 1.38±0.04a 0.54±0.03b 0.05±0.03b 1.42±0.1a 0.50±0.01b 0.000
V 0.038±0.001a 0.013±0.002b 0.010±0.000c 0.012±0.000b 0.010±0.001c 0.007±0.000d 0.000
Y* 1.11±0.10a 0.97±0.01b 0.55±0.06c 0.60±0.01c 0.52±0.04c 0.48±0.05c 0.000
Yb* 0.11±0.01b 0.13±0.01a 0.06±0.00c 0.03±0.00e 0.05±0.01d 0.01±0.00f 0.000
Ag* 0.11±0.01a 0.05±0.00b 0.03±0.00c 0.02±0.00d 0.01±0.00d 0.01±0.00d 0.000
Ta* 0.78±0.01b 1.01±0.01a 0.64±0.04c 0.64±0.04c 0.44±0.04d 0.43±0.04d 0.000
Pt* 0.49±0.02b 0.36±0.04c 0.72±0.02a 0.76±0.04a 0.76±0.06a 0.66±0.04a 0.000
W* 1.96±0.17a 1.53±0.12b 1.54±0.13b 1.31±0.10b 0.92±0.03c 0.92±0.06c 0.000
Hf* 0.46±0.02a 0.40±0.02b 0.23±0.02c 0.21±0.01c 0.23±0.02c 0.10±0.00d 0.000
Pd* 2.41±0.02a 2.16±0.09b 1.26±0.08c 0.89±0.07d 0.66±0.05e 0.60±0.06e 0.000
Ga* 3.51±0.15b 3.29±0.26b 2.82±0.12c 5.00±0.31a 5.45±0.36a 5.25±0.24a 0.000
Zr* 7.45±0.76b 9.30±0.61a 2.78±0.03c 1.88±0.02d 1.52±0.05d 1.14±0.10d 0.000
), ArticleFig(id=1217901260376818376, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Table 5, caption=

Year classification function coefficients based on mineral elements

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 2018年 2019年 2020年 2021年 2022年 2023年
Cu -415611.716 -338770.959 -170978.928 -124753.525 -141694.308 -45686.007
Li -1043503.101 -827062.251 -383137.593 -245878.411 -398549.384 109091.933
Na -4682.713 -3075.169 -537.598 -118.434 -2214.353 4074.076
Sb -319983960.641 -201917882.924 -27032750.901 -3416442.874 -149505669.185 294649404.554
Tl 1805024526.879 1602292234.934 817261247.514 487112610.228 752613148.339 -766368.371
U 445283173.744 391316332.544 189691021.143 111457439.022 190435256.785 -33896290.423
V 9818793.351 5576430.112 1640563.911 1830956.402 2701601.423 -2621559.302
Y 177408457.806 102213852.443 23528019.498 24354529.289 57285072.997 -80409681.638
Yb 5340229167.110 5139552778.490 2846219489.699 1621816268.650 2338375292.725 632927697.979
Ag 4459430818.263 4406995131.052 2585106102.715 1526650145.998 1881597624.696 1148751272.136
Pt 223580568.404 201663164.786 112944253.860 72141859.452 85975176.882 42648828.562
(常量) -1230651.538 -1009331.839 -316646.509 -123981.689 -204021.840 -196891.658
), ArticleFig(id=1217901260494258893, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=表5, caption=

基于矿物元素的年份分类函数系数

, figureFileSmall=null, figureFileBig=null, tableContent=
元素 2018年 2019年 2020年 2021年 2022年 2023年
Cu -415611.716 -338770.959 -170978.928 -124753.525 -141694.308 -45686.007
Li -1043503.101 -827062.251 -383137.593 -245878.411 -398549.384 109091.933
Na -4682.713 -3075.169 -537.598 -118.434 -2214.353 4074.076
Sb -319983960.641 -201917882.924 -27032750.901 -3416442.874 -149505669.185 294649404.554
Tl 1805024526.879 1602292234.934 817261247.514 487112610.228 752613148.339 -766368.371
U 445283173.744 391316332.544 189691021.143 111457439.022 190435256.785 -33896290.423
V 9818793.351 5576430.112 1640563.911 1830956.402 2701601.423 -2621559.302
Y 177408457.806 102213852.443 23528019.498 24354529.289 57285072.997 -80409681.638
Yb 5340229167.110 5139552778.490 2846219489.699 1621816268.650 2338375292.725 632927697.979
Ag 4459430818.263 4406995131.052 2585106102.715 1526650145.998 1881597624.696 1148751272.136
Pt 223580568.404 201663164.786 112944253.860 72141859.452 85975176.882 42648828.562
(常量) -1230651.538 -1009331.839 -316646.509 -123981.689 -204021.840 -196891.658
), ArticleFig(id=1217901260641059543, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=EN, label=Table 6, caption=

Results of Fisher discriminant analysis of wines from different years based on mineral elements

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 年份 预测组成员 正确判别率/%
2018年 2019年 2020年 2021年 2022年 2023年
回代
检验/交叉检验
2018年(n=3) 3 0 0 0 0 0 100
2019年(n=3) 0 3 0 0 0 0
2020年(n=3) 0 0 3 0 0 0
2021年(n=3) 0 0 0 3 0 0
2022年(n=3) 0 0 0 0 3 0
2023年(n=3) 0 0 0 0 0 3
正确
判别率/%
100 100 100 100 100 100
), ArticleFig(id=1217901260792054501, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529306796970897, language=CN, label=表6, caption=

基于矿物元素的不同年份葡萄酒的Fisher判别分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 年份 预测组成员 正确判别率/%
2018年 2019年 2020年 2021年 2022年 2023年
回代
检验/交叉检验
2018年(n=3) 3 0 0 0 0 0 100
2019年(n=3) 0 3 0 0 0 0
2020年(n=3) 0 0 3 0 0 0
2021年(n=3) 0 0 0 3 0 0
2022年(n=3) 0 0 0 0 3 0
2023年(n=3) 0 0 0 0 0 3
正确
判别率/%
100 100 100 100 100 100
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基于元素特征的葡萄酒品种和年份鉴别
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开建荣 1, 2 , 吴嘉言 3 , 张伟 4 , 李彩虹 1 , 赵丹青 1 , 马婷婷 2 , 孙翔宇 2 , 王彩艳 1, * , 葛谦 1, *
食品安全质量检测学报 | 食品分析与检测 2025,16(13): 205-213
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食品安全质量检测学报 | 食品分析与检测 2025, 16(13): 205-213
基于元素特征的葡萄酒品种和年份鉴别
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开建荣1, 2 , 吴嘉言3, 张伟4, 李彩虹1, 赵丹青1, 马婷婷2, 孙翔宇2, 王彩艳1, * , 葛谦1, *
作者信息
  • 1 宁夏农产品质量标准与检测技术研究所, 银川 750002
  • 2 西北农林科技大学葡萄酒学院, 杨凌 712100
  • 3 东华大学机械工程学院, 上海 201600
  • 4 宁夏农林科学院农业资源与环境研究所, 银川 750002
  • 开建荣(1988—), 女, 博士, 主要研究方向为农产品质量评价及产地识别研究。E-mail:

通讯作者:

*王彩艳(1975—), 女, 副研究员, 主要研究方向为农产品产地识别研究。E-mail: ;
葛谦(1988—), 女, 副研究员, 主要研究方向为葡萄酒微生物选育与葡萄酒风格评价。E-mail:
Identification of varietal and years of wines based on elemental characteristics
Jian-Rong KAI1, 2 , Jia-Yan WU3, Wei ZHANG4, Cai-Hong LI1, Dan-Qing ZHAO1, Ting-Ting MA2, Xiang-Yu SUN2, Cai-Yan WANG1, * , Qian GE1, *
Affiliations
  • 1 Institute of Quality Standard and Testing Technology for Agro-products of Ningxia, Yinchuan 750002, China
  • 2 College of Enology, Northwest A&F University, Yangling 712100, China
  • 3 School of Mechanical Engineering, Donghua University, Shanghai 201600, China
  • 4 Institute of Agricultural Resources and Environment, Ningxia Academy of Agricultural and Forestry Sciences, Yinchuan 750002, China
出版时间: 2025-07-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250110002
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目的 检测分析贺兰山东麓产区不同品种及年份葡萄酒中矿物元素含量, 结合多元统计分析方法, 构建基于矿物元素的品种和年份葡萄酒鉴别模型。方法 采集了宁夏青铜峡产区一酒庄紫大夫、美乐、马尔贝克、赤霞珠、马瑟兰葡萄, 采用同一方法酿制成葡萄酒; 采集宁夏永宁产区一酒庄2018—2023年6个年份的赤霞珠单品葡萄酒, 采用电感耦合等离子体质谱仪测定葡萄酒中B、Bi、Be等54种矿物元素含量, 基于不同品种和不同年份葡萄酒中矿物元素含量差异构建葡萄酒品种和年份鉴别模型。结果 不同品种葡萄酒中共检出53种矿物元素, As、B等47种矿物元素在品种间具有极显著差异(P<0.01), 不同年份葡萄酒中54种矿物元素均有检出, 且均在年份葡萄酒间具有极显著差异(P<0.01); 基于Fisher判别分析方法建立的葡萄酒品种和年份鉴别模型的正确判别率为均为100%, As、Cs、Dy、Mo、Na、Sb、U、Yb、Fe 9种矿物元素为不同品种葡萄酒的差异元素, Cu、Li、Na、Sb、Tl、U、V、Y、Yb、Ag、Pt 11种矿物元素为同一品种不同年份葡萄酒的差异元素, 基于矿物元素实现了同一产地不同品种和不同年份葡萄酒的正确判别。结论 矿物元素在不同品种和年份葡萄酒中存在显著差异, 可用于同一产区品种葡萄酒和年份葡萄酒的鉴别。

矿物元素  /  葡萄酒  /  品种  /  年份  /  鉴别

Objective To detect and analyze the content of mineral elements in wines of different varieties and years from the Helan Mountain East Slope region, and construct a model for identifying wine varieties and years based on mineral elements using multivariate statistical analysis methods. Methods Grapes of 5 varieties, namely, Zinfandel, Merlot, Malbec, Cabernet Sauvignon and Marselan, were collected from a winery in Qingtongxia, Ningxia, and were processed into wines using the same method. Wines of the Cabernet Sauvignon variety from 6 years (2018—2023) were collected from a winery in Yongning, Ningxia. The content of 54 kinds of mineral elements including B, Bi, Be, etc. in the wines was determined by inductively coupled plasma mass spectrometry. A model for identifying wine varieties and years was constructed based on the differences in mineral element content among different varieties and years of wines. Results A total of 53 mineral elements were detected in wines of different varieties, and 47 kinds of mineral elements including As and B showed extremely significant differences among varieties (P<0.01). All 54 kinds of mineral elements were detected in wines of different years, and all showed extremely significant differences among years (P<0.01). The correct discrimination rate of the model for identifying wine varieties and years based on Fisher discriminant analysis was 100%. The 9 kinds of mineral elements, namely, As, Cs, Dy, Mo, Na, Sb, U, Yb and Fe, were identified as the differential elements for different varieties of wines, and 11 kinds of mineral elements, namely, Cu, Li, Na, Sb, Tl, U, V, Y, Yb, Ag and Pt, were identified as the differential elements for wines of the same variety from different years. The correct discrimination of wines from the same origin but of different varieties and years was achieved based on mineral elements. Conclusion Mineral elements show significant differences among wines of different varieties and years, and can be used for the identification of wine varieties and years from the same region.

mineral elements  /  wine  /  variety  /  year  /  identification
开建荣, 吴嘉言, 张伟, 李彩虹, 赵丹青, 马婷婷, 孙翔宇, 王彩艳, 葛谦. 基于元素特征的葡萄酒品种和年份鉴别. 食品安全质量检测学报, 2025 , 16 (13) : 205 -213 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250110002
Jian-Rong KAI, Jia-Yan WU, Wei ZHANG, Cai-Hong LI, Dan-Qing ZHAO, Ting-Ting MA, Xiang-Yu SUN, Cai-Yan WANG, Qian GE. Identification of varietal and years of wines based on elemental characteristics[J]. Journal of Food Safety & Quality, 2025 , 16 (13) : 205 -213 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250110002
葡萄酒作为一种低酒精度、高营养、益脑健身的保健饮品。GB/T 15037—2006《葡萄酒》中对“年份葡萄酒”“品种葡萄酒”和“产地葡萄酒”进行了标准化的定义, 市场上也相应地衍生出年份葡萄酒、品种葡萄酒和产地葡萄酒3种重要的商品形式, 但不同年份、品种、产地酒缺乏特征识别因子和鉴别技术体系, 因而存在产地、品种和年份酒的保护和监测监督管理缺乏力度的实际问题。随着人民对高品质生活的追求, 葡萄酒的真实性与溯源越来越受到消费者的关注, 因此, 产地葡萄酒、品种葡萄酒和年份葡萄酒的准确鉴别对规范市场具有非常重要的意义。
矿物元素是植物生长所需要的一类无机元素, 对植物的生长发育有着重要的影响, 参与植物的营养吸收、光合作用、细胞分裂、蛋白质合成等关键的生理过程, 是植物体内各种生物化学反应和代谢过程的组成部分。目前, 矿物元素在不同地域和不同品种生物体内都存在各自独特的矿物元素指纹特征, 已被认为是一种有效的、可用于食品及农产品鉴别的分析技术[1-3], 在植源性农产品的产地识别研究中广泛应用, 如枸杞[4-5]、葡萄酒[6-7]、茶叶[8-9]等。CHEAJESADAGUL等[10]基于泰国、中国的31份茉莉花米中矿物元素含量差异实现了不同产地大米样品的正确归类, 整体正确判别率为90.32%; ANDERSSON等[11]通过将矿物元素与多元统计分析相结合, 发现Cs能够用于鉴别高海拔区域的绵羊品种; 矿物元素指纹技术在葡萄酒产地、品种、年份鉴别[12-14]中也有相关研究, 但这些研究所采用的样品受产地、年份和品种多个因素的共同影响, 不能反映单一因素对葡萄酒中矿物元素的影响。李彩虹等[12]研究发现不同品种葡萄酒中有35种矿物元素含量存在显著差异, 但基于矿物元素的差异性不能有效鉴别不同品种的葡萄酒; 钱丽丽等[15]通过分析品种、年份和产地及其交互作用对大米矿物元素含量的影响, 结果表明Mg、Ca、Cr、Mn、Zn、As、Rb、Sr、Ag、Cd、Ba、La、Sm、Dy、Ho、Er、Pb、U受产地影响较大, Na、Mg、Al、Ca、Pb、U、V受年份影响较大; Na、Cr、Co、Ni、Tl、U、Mg、Al、La、Ho受品种影响较大。PASVANKA等[16]通过测定希腊6个产区的90个葡萄酒中的44种矿物元素含量, 实现了葡萄酒品种和产区的成功区分, 正确率判别率达到了98%; 李彩虹等[17]采集了我国6个葡萄酒产区78个酒样, 基于矿物元素分析技术实现了葡萄酒产区的准确区分, 初始验证和交叉验证的正确判别率分别为98.6%和84.7%; ORELLANA等[18]基于矿物元素含量, 采用差异显著性、线性判别、主成分对美国4个主产区的葡萄酒进行分析, 整体正确判别率达到96.2%; 开建荣等[19]基于葡萄酒中25种矿物元素含量差异, 实现了贺兰山东麓红寺堡、青铜峡、永宁、贺兰和镇北堡5个小产区马瑟兰葡萄酒的正确判别, 正确判别率达95.6%。
由于影响农产品中矿物元素含量和组成的因素很多, 例如品种、年份、产地等, 目前已有研究证实矿物元素在产地葡萄酒中具有显著差异, 但葡萄酒中矿物元素是否受品种和年份的影响还需要进一步研究。因此, 本研究以贺兰山东麓同一酒庄不同品种酿酒葡萄自酿的葡萄酒和同一酒庄不同年份的赤霞珠单品葡萄酒为实验材料, 基于矿物元素分析技术, 检测分析不同品种和不同年份葡萄酒中矿物元素差异, 筛选出能有效区分不同年份品种和不同年份葡萄酒的特征元素, 初步建立一种基于矿物元素的葡萄酒品种和年份鉴别方法, 以期为品种和年份葡萄酒的鉴别提供研究思路和数据支撑。
不同品种葡萄酒: 采集贺兰山东麓青铜峡产区同一酒庄的紫大夫、美乐、马尔贝克、赤霞珠、马瑟兰5个品种的酿酒葡萄各3份, 采用相同的酿造工艺进行小罐发酵实验, 每个品种每份样品各2个平行样, 共计30个酒样。
不同年份葡萄酒: 2024年采集贺兰山东麓永宁产区一酒庄2018—2023年连续6个年份的赤霞珠单品葡萄酒, 每个年份采集3个葡萄酒样品, 共计18个样品。
标准溶液为4组53种元素混合标液, 包括Al、P、Ca、Mg、Mn、Na、Fe、Zn、Cu、B、As、Cd、Cr、Ni、Pb、Ba、Ce、Co、Cs、Dy、Eu、Gd、Ho、Li、Lu、Mo、Nb、Nd、Pr、Rb、Sb、Sc、Se、Sm、Sn、Sr、Tb、Ti、Th、Tl、Tm、U、V、Y、Yb、Ag、Ta、Pt、W、Hf、Pd、Ga、Zr(质量浓度为10 mg/L, 美国Perkin Elmer公司); Hg单元素标准溶液(质量浓度为1000 mg/L, 中国计量科学研究院); 质控标准物质——胡萝卜标准物质(GBW10047)(中国地质科学院地球物理地球化学勘查研究所); 硝酸(优级纯, 德国Merck公司); 水为实验室一级用水。
ELAN DRC-e型电感耦合等离子体质谱仪(美国Perkin Elmer公司); Mars 6 Xpress微波消解仪(美国CEM公司); EHD-24赶酸仪(24位)(莱伯泰科公司); PL202-L型百分之一电子天平(瑞士梅特勒-托利多公司)。
矿物元素含量测定参照开建荣等[20]的方法, 并加以改进, 葡萄酒样品称样量为5 g(精确至0.01 g), 微波消解管置于赶酸仪上130 ℃加热使酒精和水挥发近干后加酸进行消解。
电感耦合等离子体质谱仪测定元素采用标准模式。优化后的工作条件为: 发生器功率: 1250 W; 检测器模拟阶电压: -2250 V; 离子透镜电压: 6.00 V; 雾化器流量: 0.93 L/min; 等离子炬冷却气流量: 16.0 L/min; 辅助器流量: 1.20 L/min。上机测定葡萄酒样品及质控品中54种矿物元素。
使用Excel 2010对数据进行汇总, 所有数据均使用IBM SPSS Statistics 25.0软件进行单因素方差分析和Tukey检验, 采用Fisher线性判别分析方法(步进法)进行判别分析。
检测分析不同品种葡萄酒中矿物元素含量, 由表1可知, 5个品种单品葡萄酒中矿物元素种类基本一致, Zn元素未检出, 共检出53种元素, 且元素含量差异较大, 葡萄酒中Mg、P、Ca含量较高, 接近或大于100 mg/kg, Al、B、Na、Sr等含量介于1~20 mg/kg, 其他元素含量较低。
由单因素方差分析结果可知, 同一产地不同品种酿酒葡萄酿制的葡萄酒有其各自的元素特征, 这与李彩虹等[12]的研究结果一致。由于生物自身无法进行矿物元素的合成, 必须从水、土壤、空气等周边环境中获得, 但不同生物体对矿物元素的吸收特性不同, 因此造成矿物元素在不同品种之间具有较大的差异[21]。如MCKENZIE等[22]测定了白茶、绿茶、红茶、乌龙茶和普洱茶5个品类中14种矿物元素含量, 结果显示, 普洱茶中Na、Mg、Zn、Ca、Fe和S含量与其他4个品种差异显著, 红茶中Mg和Zn含量与白茶、乌龙茶和普洱茶存在差异, 红茶与绿茶的K含量存在显著差异。
本研究发现, 53种矿物元素中, 仅Ca、Sc、Sm、Sn在不同品种葡萄酒间无显著差异(P>0.05), Nd、Se在不同品种葡萄酒间具有显著差异(P<0.05), 其他47种矿物元素在品种间具有极显著差异(P<0.01)。在5个品种间均具有显著差异的元素有P、Fe、Cu、B、Cs、Li、Sb 7种元素。
紫大夫对Fe、As、Pb、Ba、Gd、Pr、Sb、Sr、Ti、Hf、Pd和Zr元素具有较强的富集作用; 美乐对P、Mn、Na、Ce、Cs、Rb、U和Pt具有较强的富集作用; 赤霞珠对Cd、Cr、Li和Y元素的富集作用较强; 马瑟兰对Mg、Cu、B、Hg、Ni、Co、Eu、Ho、Lu、Mo、Tb、Th、Tl、Tm、Ta和W的富集作用较强; 马尔贝克对矿物元素的富集能力较弱。
基于53种矿物元素含量, 采用Fisher线性判别分析方法中的步进法对紫大夫、美乐、马尔贝克、赤霞珠和马瑟兰单品葡萄酒进行品种判别分析。As、Cs、Dy、Mo、Na、Sb、U、Yb、Fe 9种矿物元素被引入到了判别模型中, 以9种品种间差异明显的矿物元素指标作为判别分析的自变量, 建立了不同品种葡萄酒的判别模型, 分类函数系数见表2, 提取模型前4个典型判别函数, Willks’ Lambda检验结果进一步证实, 在α=0.05的显著性性水平下, 4个函数对分类效果均为显著, 表明判别模型拟合率可接受, 其中判别函数1和判别函数2累积解释判别模型能力为83.2%, 且相关系数均大于0.999, 表明判别函数1和判别函数2对5个品种分类占主要贡献作用, 利用判别函数1和判别函数2的得分值作散点图, 见图1。由图1可知, 紫大夫、美乐、马尔贝克、赤霞珠和马瑟兰单品葡萄酒可明显地区分开来, 5个品种葡萄酒样品无重叠现象。
利用所建立的判别模型对5个品种的葡萄酒样品进行归类, 并对所建模型的有效性进行验证。由表3可知, 不同品种葡萄酒样本的回代检验和交叉检验的整体正确判别率均为100%, 矿物元素可以有效区分同一产地不同品种的单品葡萄酒。
检测分析不同年份葡萄酒中54种矿物元素含量, 由表4可知, 54种矿物元素均有检出, 54种矿物元素在年份葡萄酒间均具有显著差异(P<0.05), 说明年际间的环境差异对矿物元素吸收具有显著影响。2018年葡萄酒中Dy、Eu、Gd、Ho、Lu、Pr、Sm、Tb、Th、Tm、Y、Ag、W、Hf、Pd元素含量显著高于其他年份; 2019年葡萄酒中Na、Zn、Nb、Yb、Ta、Zr含量显著高于其他年份; 2020年葡萄酒中Mo、Hg和Ti含量显著高于其他年份, 2021年葡萄酒中Mg、Mn、B、Co、Cs、Se含量显著高于其他年份; 2022年葡萄酒中Cu、Cd、Ni、含量高于其他年份; 2023年葡萄酒中Sb含量显著高于其他年份。赤霞珠单品葡萄酒在不同年份间存在显著差异, 可能受种植年降雨量、日照时长等自然因素的影响造成的[23]
基于54种矿物元素含量, 采用Fisher判别分析方法中的步进法对2018—2023年6个年份的的单品赤霞珠葡萄酒进行判别分析。Cu、Li、Na、Sb、Tl、U、V、Y、Yb、Ag、Pt 11种矿物元素被引入到了判别模型中, 以这11种矿物元素指标作为判别分析的自变量, 建立了葡萄酒的年份鉴别模型, 分类函数系数见表5, 提取模型前4个典型判别函数, Willks’ Lambda检验结果进一步证实, 在α=0.05的显著性性水平下, 4个函数对分类效果均为显著, 表明判别模型拟合率可接受, 其中判别函数1和判别函数2累积解释判别模型能力为96.8%, 且相关系数为1.000, 表明判别函数1和判别函数2对6个年份分类占主要贡献作用, 利用判别函数1和判别函数2的得分值作散点图, 见图2。由图2可知, 2018—2023年6个年份葡萄酒样品可有效区分开, 每个年份的葡萄酒样品聚合度较高, 且年份间的葡萄酒样品均有一定距离。
利用所建立的判别模型对6个年份的葡萄酒样品进行归类, 并对所建模型的有效性进行验证。由表6可知, 不同年份葡萄酒样本的回代检验和交叉检验的整体正确判别率均为100%, 矿物元素可以有效区分同一产地不同年份的单品葡萄酒。
有研究表明, 植物中的矿物元素吸收积累主要受环境条件[24-25]、栽培方式[26-27]的影响。矿物元素通过岩石风化后的土壤进入葡萄植株, 再经酿酒工艺进入葡萄酒, 显示产地、年份及工艺特征性指纹信息[28], 这些元素在葡萄植株生长过程中起着重要作用, 但田间管理(化肥、营养补充剂及杀菌剂等)和酿酒工艺(加酸、减酸、SO2及澄清剂等)会影响其自然分布[29]。本研究通过分析宁夏贺兰山东麓同一产区紫大夫、美乐、马尔贝克、赤霞珠和马瑟兰5个品种酿酒葡萄所酿制的葡萄酒中矿物元素含量, 结果发现同一产地不同品种酿酒葡萄酿制的葡萄酒有其各自的元素特征,Ca、Sc、Sm、Sn元素在不同品种葡萄酒间无显著差异(P>0.05), Nd、Se在不同品种葡萄酒间具有显著差异(P<0.05), 其他47种矿物元素在品种间具有极显著差异(P<0.01)。本研究中的5个品种酿酒葡萄实验材料采自同一葡萄园, 可规避掉栽培管理、地理环境因素差异的影响, 5个品种葡萄酒中矿物物质的差异主要源于品种差异, 致使其对矿物元素的吸收累积的能力不同。通过Fisher线性判别分析方法, 最终构建了包含As、Cs、Dy、Mo、Na、Sb、U、Yb、Fe 9种矿物元素的品种葡萄酒的鉴别模型, 模型回代检验和交叉检验的整体正确判别率均为100%, 说明矿物元素可以有效区分同一产地不同品种的单品葡萄酒。
采用同样的方法对同一产地不同年份的赤霞珠葡萄酒中的矿物元素进行分析, 结果表明不同年份葡萄酒中54种矿物元素在年份葡萄酒间均具有显著差异(P<0.05), 可能是因为不同年份的气象条件、施肥等环境因素差异导致葡萄果实吸收累积的矿物元素含量有所不同, 同时, 每年的酿造条件也有所差异, 致使葡萄酒中矿物元素含量不同。通过Fisher线性判别分析方法构建了基于Cu、Li、Na、Sb、Tl、U、V、Y、Yb、Ag、Pt 11种矿物元素的赤霞珠年份葡萄酒的鉴别模型, 模型正确判别率为100%, 矿物元素可以有效区分同一产地同一品种不同年份的葡萄酒。矿物元素技术结合多元统计方法是鉴别同一产区不同品种葡萄酒或者同一产区同一品种不同年份的有效方法。
矿物元素与酒中有机化合物成分不同, 对酒风味的影响很小, 且能在较长时间保持稳定, 是研究植物源食品真实性中最常用、且最有效的工具之一[30]。本研究基于矿物元素对品种、年份单一影响因素下的葡萄酒的品种和年份鉴别模型的正确判别率均可达到100%, 这对品种葡萄酒和年份葡萄酒的鉴别提供了一定的理论依据和数据支撑, 但市售的葡萄酒很难做到在单一因素影响下进行鉴别, 目前品质因子的多变量分析也逐渐应用到农产品的判别分析中, 因此, 为了鉴别模型更具有市场应用价值, 在后续的研究工作中, 将加大样本量对建立的判别模型的准确性进行修正和验证, 同时探究品种、产地、年份等共同影响下, 矿物元素指纹技术在葡萄酒品种、产地和年份鉴别中的可行性, 为宁夏贺兰山东麓葡萄酒溯源提供理论支撑以及数据支持。
  • “十四五”农业高质量发展和生态保护科技创新示范课题项目(NGSB-2021-5)
  • 宁夏回族自治区自然科学基金项目(2023AAC03419)
  • 国家自然科学基金项目(32260628)
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2025年第16卷第13期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250110002
  • 接收时间:2025-01-10
  • 首发时间:2026-01-12
  • 出版时间:2025-07-15
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  • 收稿日期:2025-01-10
基金
“十四五”农业高质量发展和生态保护科技创新示范课题项目(NGSB-2021-5)
宁夏回族自治区自然科学基金项目(2023AAC03419)
国家自然科学基金项目(32260628)
作者信息
    1 宁夏农产品质量标准与检测技术研究所, 银川 750002
    2 西北农林科技大学葡萄酒学院, 杨凌 712100
    3 东华大学机械工程学院, 上海 201600
    4 宁夏农林科学院农业资源与环境研究所, 银川 750002

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*王彩艳(1975—), 女, 副研究员, 主要研究方向为农产品产地识别研究。E-mail: ;
葛谦(1988—), 女, 副研究员, 主要研究方向为葡萄酒微生物选育与葡萄酒风格评价。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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