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Article(id=1153986647537476135, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986642063905290, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20240920003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1726761600000, receivedDateStr=2024-09-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753061456808, onlineDateStr=2025-07-21, pubDate=1739548800000, pubDateStr=2025-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753061456808, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753061456808, creator=13701087609, updateTime=1753061456808, updator=13701087609, issue=Issue{id=1153986642063905290, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='3', pageStart='1', pageEnd='316', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753061455502, creator=13701087609, updateTime=1760070725729, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1183385652272968023, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986642063905290, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1183385652272968024, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986642063905290, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=7, ext={EN=ArticleExt(id=1153986648166621739, articleId=1153986647537476135, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Influences of temperature on the detection model of aflatoxin B1 in peanut oil by laser induced fluorescence spectrometry, columnId=1151895321526759957, journalTitle=Journal of Food Safety & Quality, columnName=Special Topic: Detection and Prevention and Control of Biotoxins in Food, runingTitle=null, highlight=null, articleAbstract=

Objective To detect aflatoxin B1 (AFB1) in peanut oil by laser induced fluorescence and explore the effects of temperature on the detection results. Methods The spectral information of AFB1 contaminated peanut oil at temperatures of 10, 20, 30, 40 and 50 ℃ was collected, linear discriminant analysis (LDA) model was established for qualitative analysis, and partial least squares regression model was established for quantitative analysis. Results The accuracy of the single-temperature LDA detection model in predicting samples at this single temperature exceeded 84%. The global LDA model was lower prediction accuracy than the single-temperature LDA model. The partial least squares regression (PLSR) model could not achieve quantitative prediction at either single temperature or mixed temperature. The PLSR quantitative model developed for single-variety peanut oil demonstrated optimal stability at 20 °C, yielding the most accurate sample predictions. Conclusion This study proposes that a global model can be established during qualitative discriminant analysis to adapt to the impact of temperature on the detection process. In quantitative analysis, the single temperature model can achieve more accurate predictions than the global model.

, correspAuthors=Zhong-Jun YAN, 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=Ge-Fei YANG, Min CHEN, Xi-Rong HUANG, Fei SHEN, Zhong-Jun YAN, Ji-Rong JIA, Jian-Wei CHEN), CN=ArticleExt(id=1153986667707883568, articleId=1153986647537476135, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=温度对花生油中黄曲霉毒素B1激光诱导荧光检测模型的影响, columnId=1151895321669366295, journalTitle=食品安全质量检测学报, columnName=本期专题：食品中生物毒素检测与防控, runingTitle=null, highlight=null, articleAbstract=

目的 利用激光诱导的方式产生荧光检测花生油中的黄曲霉毒素B1 (aflatoxin B1, AFB1), 并探究温度对检测结果的影响。方法 通过采集10、20、30、40、50 ℃下污染AFB1花生油的光谱信息, 建立线性判别(linear discriminant analysis, LDA)模型对其进行定性分析, 建立偏最小二乘回归模型进行定量分析。结果 单温度LDA模型在预测该温度下样本正确率在84%以上, 全局LDA模型比单温度LDA模型预测结果的正确率低, 使用偏最小二乘回归(partial least squares regression, PLSR)模型在单温度和混合温度下均不能实现定量预测。对单一品种花生油建立PLSR定量模型的结果表明在20 ℃条件下模型表现最稳定, 样品预测结果最好。结论 本研究提出在定性判别分析时可建立全局模型以适应温度对检测过程的影响, 在定量分析时单温度模型比全局模型更能实现精准预测。

, correspAuthors=严忠军, authorNote=null, correspAuthorsNote=
* 严忠军(1979—), 男, 硕士, 高级工程师, 主要研究方向为粮食仓储与质量安全。E-mail:
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杨鸽飞(2001—), 女, 硕士研究生, 主要研究方向为食品质量安全检测技术。E-mail:

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杨鸽飞(2001—), 女, 硕士研究生, 主要研究方向为食品质量安全检测技术。E-mail:

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杨鸽飞(2001—), 女, 硕士研究生, 主要研究方向为食品质量安全检测技术。E-mail:

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Laser induced fluorescence detection of aflatoxin B1 in peanut oil[D]. 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注: 1. 暗箱; 2. 光谱仪; 3. 电脑; 4. 接收准直镜; 5. 滤光片; 6. 置物台; 7. 样品; 8. 激发准直镜; 9. 光纤; 10. 激光器; 11. 稳压电源; 12. 检测角度(θ)为90度。

, figureFileSmall=a9ob9OIDWv3qyxrBB/WKlQ==, figureFileBig=+iArE9quLzJLeBKyji0KWg==, tableContent=null), ArticleFig(id=1183428141180662699, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Fig.2, caption=Original laser induced fluorescence spectral curves of different samples at 20 °C, figureFileSmall=eDUbjm/3HMi9zR4fsgP8Sw==, figureFileBig=OT1KRcFgDLV8xPE9Xbd+Vg==, tableContent=null), ArticleFig(id=1183428141235188652, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=图2, caption=20 ℃时不同样品原始激光诱导荧光光谱曲线, figureFileSmall=eDUbjm/3HMi9zR4fsgP8Sw==, figureFileBig=OT1KRcFgDLV8xPE9Xbd+Vg==, tableContent=null), ArticleFig(id=1183428141302297517, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Fig.3, caption=Laser induced fluorescence spectral curves of different samples at different temperatures, figureFileSmall=g1cPozzH1VKsc25uMPdP8A==, figureFileBig=DwvIKNkEiQfYVJOrbWzQBw==, tableContent=null), ArticleFig(id=1183428141377794990, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=图3, caption=不同温度下不同样品的激光诱导荧光光谱曲线

注: (a)~(j)为样品1~样品10, 图4同。

, figureFileSmall=g1cPozzH1VKsc25uMPdP8A==, figureFileBig=DwvIKNkEiQfYVJOrbWzQBw==, tableContent=null), ArticleFig(id=1183428141432320943, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Fig.4, caption=Laser induced fluorescence spectral curves of samples with different AFB1 contamination levels at 20 ℃, figureFileSmall=bkXgAVLfFNOTR0zT4qCznA==, figureFileBig=KbvoOB16tFP9aP/E1r38kA==, tableContent=null), ArticleFig(id=1183428141499429808, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=图4, caption=不同AFB1污染水平的样品在20 ℃下的激光诱导荧光光谱曲线, figureFileSmall=bkXgAVLfFNOTR0zT4qCznA==, figureFileBig=KbvoOB16tFP9aP/E1r38kA==, tableContent=null), ArticleFig(id=1183428141553955761, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Table 1, caption=

Source of peanut oil

, figureFileSmall=null, figureFileBig=null, tableContent=
品牌 产地 生产等级
样品1 山东省菏泽市 一级
样品2 江苏省苏州市 一级
样品3 上海市浦东新区 一级
样品4 广东省广州市 一级
样品5 山东省青岛市 一级
样品6 广东省广州市 一级
样品7 北京市朝阳区 一级
样品8 江苏省南通市 一级
样品9 山东省烟台市 一级
样品10 山东省滨州市 一级
), ArticleFig(id=1183428141625258930, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=表1, caption=

花生油来源

, figureFileSmall=null, figureFileBig=null, tableContent=
品牌 产地 生产等级
样品1 山东省菏泽市 一级
样品2 江苏省苏州市 一级
样品3 上海市浦东新区 一级
样品4 广东省广州市 一级
样品5 山东省青岛市 一级
样品6 广东省广州市 一级
样品7 北京市朝阳区 一级
样品8 江苏省南通市 一级
样品9 山东省烟台市 一级
样品10 山东省滨州市 一级
), ArticleFig(id=1183428141696562099, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Table 2, caption=

LDA discrimination results of single temperature model and global model for AFB1 fluorescence detection

, figureFileSmall=null, figureFileBig=null, tableContent=
样本温度/℃ 建模集正确率/预测集正确率/%
10 ℃模型 20 ℃模型 30 ℃模型 40 ℃模型 50 ℃模型 全局模型
10 92/91 71 61 51 73 86/83
20 85 94/93 75 47 73 89/81
30 63 67 91/84 55 61 87/81
40 61 63 75 95/84 71 86/83
50 69 69 79 72 91/84 86/83
), ArticleFig(id=1183428141751088052, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=表2, caption=

AFB1荧光检测的单温度模型和全局模型LDA判别结果

, figureFileSmall=null, figureFileBig=null, tableContent=
样本温度/℃ 建模集正确率/预测集正确率/%
10 ℃模型 20 ℃模型 30 ℃模型 40 ℃模型 50 ℃模型 全局模型
10 92/91 71 61 51 73 86/83
20 85 94/93 75 47 73 89/81
30 63 67 91/84 55 61 87/81
40 61 63 75 95/84 71 86/83
50 69 69 79 72 91/84 86/83
), ArticleFig(id=1183428141809808309, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Table 3, caption=

Results of PLSR quantitative model for contaminated AFB1 samples

, figureFileSmall=null, figureFileBig=null, tableContent=
温度/℃ R2C RMSEC/(μg/kg) R2P RMSEP/(μg/kg) LODs/(μg/kg) LOQs/(μg/kg) RPD 斜率
10 0.36 8.94 0.36 8.28 73.41 220.25 1.23 0.32
20 0.67 6.14 0.65 6.32 29.73 89.20 1.69 0.64
30 0.49 7.67 0.20 9.85 89.37 268.12 1.07 0.33
40 0.55 7.04 0.26 9.31 78.39 235.18 1.41 0.55
50 0.56 6.93 0.11 10.26 154.30 462.9 1.02 0.20
混合 0.41 8.32 0.29 8.81 85.50 256.49 1.18 0.31
), ArticleFig(id=1183428141876917174, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=表3, caption=

污染AFB1样品PLSR定量模型结果

, figureFileSmall=null, figureFileBig=null, tableContent=
温度/℃ R2C RMSEC/(μg/kg) R2P RMSEP/(μg/kg) LODs/(μg/kg) LOQs/(μg/kg) RPD 斜率
10 0.36 8.94 0.36 8.28 73.41 220.25 1.23 0.32
20 0.67 6.14 0.65 6.32 29.73 89.20 1.69 0.64
30 0.49 7.67 0.20 9.85 89.37 268.12 1.07 0.33
40 0.55 7.04 0.26 9.31 78.39 235.18 1.41 0.55
50 0.56 6.93 0.11 10.26 154.30 462.9 1.02 0.20
混合 0.41 8.32 0.29 8.81 85.50 256.49 1.18 0.31
), ArticleFig(id=1183428141939831735, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=EN, label=Table 4, caption=

Results of PLSR quantitative model for sample No.1 contamination AFB1

, figureFileSmall=null, figureFileBig=null, tableContent=
样本温度/℃ R2C RMSEC/(μg/kg) R2P RMSEP/(μg/kg) LODs/(μg/kg) LOQs/(μg/kg) RPD 斜率
10 0.97 1.80 0.93 2.78 2.56 7.67 3.90 1.00
20 0.99 0.96 0.97 2.06 2.42 7.26 4.78 1.00
30 0.98 1.48 0.96 2.41 5.90 17.70 4.90 1.01
40 0.93 2.60 0.90 2.66 6.82 20.46 3.26 0.88
50 0.97 1.48 0.94 2.12 5.86 17.59 4.28 1.11
全局 0.95 2.16 0.92 3.03 9.51 28.52 3.40 0.95
), ArticleFig(id=1183428142002746296, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986647537476135, language=CN, label=表4, caption=

样品1污染AFB1的PLSR定量模型结果

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样本温度/℃ R2C RMSEC/(μg/kg) R2P RMSEP/(μg/kg) LODs/(μg/kg) LOQs/(μg/kg) RPD 斜率
10 0.97 1.80 0.93 2.78 2.56 7.67 3.90 1.00
20 0.99 0.96 0.97 2.06 2.42 7.26 4.78 1.00
30 0.98 1.48 0.96 2.41 5.90 17.70 4.90 1.01
40 0.93 2.60 0.90 2.66 6.82 20.46 3.26 0.88
50 0.97 1.48 0.94 2.12 5.86 17.59 4.28 1.11
全局 0.95 2.16 0.92 3.03 9.51 28.52 3.40 0.95
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温度对花生油中黄曲霉毒素B1激光诱导荧光检测模型的影响
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杨鸽飞 1 , 陈敏 1 , 黄熙荣 2 , 沈飞 1 , 严忠军 3, * , 贾继荣 4 , 陈建伟 4
食品安全质量检测学报 | 本期专题：食品中生物毒素检测与防控 2025,16(3): 1-7
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食品安全质量检测学报 | 本期专题：食品中生物毒素检测与防控 2025, 16(3): 1-7
温度对花生油中黄曲霉毒素B1激光诱导荧光检测模型的影响
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杨鸽飞1 , 陈敏1, 黄熙荣2, 沈飞1, 严忠军3, * , 贾继荣4, 陈建伟4
作者信息
  • 1.南京财经大学食品科学与工程学院, 南京 210023
  • 2.江苏省储备粮管理有限公司, 南京 215699
  • 3.中国储备粮管理集团有限公司浙江分公司, 杭州 311500
  • 4.江苏省粮油质量监测中心, 南京 210031

    • 杨鸽飞(2001—), 女, 硕士研究生, 主要研究方向为食品质量安全检测技术。E-mail:

通讯作者:

* 严忠军(1979—), 男, 硕士, 高级工程师, 主要研究方向为粮食仓储与质量安全。E-mail:
Influences of temperature on the detection model of aflatoxin B1 in peanut oil by laser induced fluorescence spectrometry
Ge-Fei YANG1 , Min CHEN1, Xi-Rong HUANG2, Fei SHEN1, Zhong-Jun YAN3, * , Ji-Rong JIA4, Jian-Wei CHEN4
Affiliations
  • 1. College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
  • 2. Jiangsu Province Reserve Grain Management Co., Ltd., Nanjing 215699, China
  • 3. China Reserve Grain Management Group Co., Ltd., Zhejiang Branch, Hangzhou 311500, China
  • 4. Jiangsu Grain and Oil Quality Monitoring Center, Nanjing 210031, China
出版时间: 2025-02-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20240920003
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摘要
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目的 利用激光诱导的方式产生荧光检测花生油中的黄曲霉毒素B1 (aflatoxin B1, AFB1), 并探究温度对检测结果的影响。方法 通过采集10、20、30、40、50 ℃下污染AFB1花生油的光谱信息, 建立线性判别(linear discriminant analysis, LDA)模型对其进行定性分析, 建立偏最小二乘回归模型进行定量分析。结果 单温度LDA模型在预测该温度下样本正确率在84%以上, 全局LDA模型比单温度LDA模型预测结果的正确率低, 使用偏最小二乘回归(partial least squares regression, PLSR)模型在单温度和混合温度下均不能实现定量预测。对单一品种花生油建立PLSR定量模型的结果表明在20 ℃条件下模型表现最稳定, 样品预测结果最好。结论 本研究提出在定性判别分析时可建立全局模型以适应温度对检测过程的影响, 在定量分析时单温度模型比全局模型更能实现精准预测。

激光诱导荧光  /  花生油  /  黄曲霉毒素B1  /  温度  /  全局判别模型

Objective To detect aflatoxin B1 (AFB1) in peanut oil by laser induced fluorescence and explore the effects of temperature on the detection results. Methods The spectral information of AFB1 contaminated peanut oil at temperatures of 10, 20, 30, 40 and 50 ℃ was collected, linear discriminant analysis (LDA) model was established for qualitative analysis, and partial least squares regression model was established for quantitative analysis. Results The accuracy of the single-temperature LDA detection model in predicting samples at this single temperature exceeded 84%. The global LDA model was lower prediction accuracy than the single-temperature LDA model. The partial least squares regression (PLSR) model could not achieve quantitative prediction at either single temperature or mixed temperature. The PLSR quantitative model developed for single-variety peanut oil demonstrated optimal stability at 20 °C, yielding the most accurate sample predictions. Conclusion This study proposes that a global model can be established during qualitative discriminant analysis to adapt to the impact of temperature on the detection process. In quantitative analysis, the single temperature model can achieve more accurate predictions than the global model.

laser induced fluorescence  /  peanut oil  /  aflatoxin B1  /  temperature  /  global discriminant model
杨鸽飞, 陈敏, 黄熙荣, 沈飞, 严忠军, 贾继荣, 陈建伟. 温度对花生油中黄曲霉毒素B1激光诱导荧光检测模型的影响. 食品安全质量检测学报, 2025 , 16 (3) : 1 -7 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20240920003
Ge-Fei YANG, Min CHEN, Xi-Rong HUANG, Fei SHEN, Zhong-Jun YAN, Ji-Rong JIA, Jian-Wei CHEN. Influences of temperature on the detection model of aflatoxin B1 in peanut oil by laser induced fluorescence spectrometry[J]. Journal of Food Safety & Quality, 2025 , 16 (3) : 1 -7 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20240920003
正文
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0 引言
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花生是人类和动物的主要粮食作物[1], 是仅次于大豆、油菜籽和棉花的世界第四大油料作物[2]。花生的最重要的加工产品之一是花生油, 花生油营养丰富, 富含对人体有益的不饱和脂肪酸及磷脂、甾醇等活性成分[3], 这些物质可以较好起到抗衰老的作用, 可降低血脂和胆固醇[4], 还可为人体提供必需营养素[5], 正是因为这些丰富的物质使得花生油成为了真菌天然的培养基。花生油中的黄曲霉毒素B1 (aflatoxin B1, AFB1)主要源自原产料花生, 花生生长环境为地下, 表面接触真菌的概率较大, 而且由于脂肪含量高[6](>50%), 在面对温度、湿度的变化、自身含水量[7]及贮藏方式等多重因素时, 极易遭受黄曲霉的侵袭[8-10], 为霉菌生长提供良好的物质基础, 一旦花生发霉, 霉菌在贮藏过程中会生成黄曲霉毒素, 并通过接触污染其他花生籽粒[11], 进而通过生产加工等环节进入花生油中。污染AFB1的花生油会对人畜健康构成严重威胁[12], 国际癌症中心将其判定为Ⅰa类人类致癌物[13-14], 微量摄入就会导致疾病的发生, 因此对花生油中AFB1含量的检测非常重要。
当前光学技术因其快捷的样品预处理、可靠、高效且无损的特点[15], 在农产品质量检测领域展现出巨大潜力, 也为检测AFB1提供了新的思路和方法[16]。荧光法因为极强的灵敏性被应用在农产品及加工制品中AFB1的检测[17-18], 并展现出高灵敏度、响应速度快、简单的样品前处理等显著优势。然而, 荧光技术在检测环节特别敏感于环境温度变化, 尤其在处理液体样本时, 温度波动会显著影响荧光强度, 这种环境温度的变化就会很大程度加大毒素信号准确采集的难度。当前的研究表明, 大部分物质的荧光强度随温度升高呈现递减趋势, 温度上升导致油的黏度普遍下降[19], 这一变化容易引发荧光猝灭现象, 最终导致溶液荧光强度的降低[20]影响信号采集。WALSH等[21]的探究揭示了温度变化对散射与非散射组织自发荧光的显著影响, 此研究组织样品从最高诱导温度冷却至室温的同时进行测量来检查荧光变化的可逆性, 结果荧光强度随组织温度升高而降低。梁颖等[22]研究不同温度培养三角褐指藻观察叶绿素荧光特性与温度的关系, 其发现揭示了叶绿素荧光参数因为温度不同而变化。之前在荧光光谱检测中, 普遍采用的是在恒温环境下进行荧光光谱分析的方法[23-25], 鉴于此, 本研究在实施荧光技术检测时, 将温度因素对系统检测的影响进行考量, 并在构建数学模型时也加入温度这一变量。探究温度变化对样本荧光信号采集的影响, 在构建模型时整合实际检测过程中所覆盖的温度范围数据。
近年来有少量报道激光诱导荧光用于植物油脂中真菌毒素的检测[26], 但温度因素对该检测系统准确性的影响少有涉及。本研究拟通过自主构建的激光诱导荧光(laser induce fluorescence, LIF)检测系统, 研究不同温度条件下花生油的荧光特性变化规律, 并建立可以适应温度变化的全局检测模型, 对比以往在恒温条件下检测考虑了温度波动这一现实因素, 使得该检测方法更具有普遍现实适用性。
1 材料与方法
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1.1 材料、试剂与仪器
为确保样本的典型性和全面性, 在山东省、江苏省、上海市等5个地区中, 重点筛选花生油的生产厂家, 并从它们之中选取了10家信誉良好的企业作为样本来源。这些厂家生产的花生油均为2019年出产的产品, 并且均达到一级花生油品质标准。将购买的这批花生油随机进行编号(样品1~10), 如表1所示。
AFB1标准品(纯度≥98%)、乙腈(色谱纯)(美国Sigma-Aldrich公司)。
MW-GX-375 nm激光器(长春镭仕有限公司); QE pro光谱仪(上海蔚海光学仪器有限公司); NMSG-12振荡器(泰州诺米医疗科技有限公司)。
1.2 实验方法
1.2.1 样品制备
用乙腈溶液配制浓度为10000 μg/kg的AFB1标准溶液, 放置于-20 ℃条件下备用。随后, 制备了一系列不同浓度的AFB1浓度标品溶液(分别为0、5、10、20、25、30 μg/kg), 每个梯度安排10个样本, 设置对照组(control)为纯花生油样品, 对于温度因素的探究设置了以下5个温度梯度: 10、20、30、40、50 ℃, 在每个温度条件下, 每种花生油一共有70个样品(共350个样品, 每个浓度梯度10个样本×6个浓度梯度、1个对照组×5个温度条件)。重要的是采集光谱的样品需要及时配制, 振荡5 min后, 在暗处常温静置2 h, 然后取20 mL样品于比色皿中进行光谱分析。
1.2.2 光谱采集
本研究使用的设备装置示意图如图1所示, 所有检测过程均在暗箱中进行以避免自然光的进入。本研究中使用激发波长为375 nm的可调节功率激发光源, 激光发射和接收探头的角度可调节, 经过试验为了提高测量的精度和准确性, 本研究采用直角位检测法, 即发射探头和接受探头的角度呈90°夹角, 入射光线垂直于样品从正上方射入, 接收探头位于样品侧方垂直于发射探头, 入射光垂直照射样品诱导样品发出荧光, 接收探头采集样本发出的荧光信号, 使用Ocean View软件采集400~1000 nm范围内的样本荧光光谱, 每个样本采集两次并取平均, 积分时间设置为2 s。
1.3 数据处理
样本荧光数据的处理采用Matlab 2015 b和UnscrambleX 10.4软件。在模型建立过程中, 使用Kennard-Stone (KS)算法分别划分建模集和预测集, 比例为2:1。随后, 使用线性判别分析(linear discriminant analysis, LDA)和偏最小二乘回归(partial least squares regression, PLSR)等方法进行定性分析。
2 结果与分析
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2.1 原始荧光光谱图分析
图2显示的是20 ℃时不同样品原始激光诱导荧光光谱曲线。从图2中可知样品的光谱差异明显, 但是其光谱形状趋势相似, 说明10个样品中所含有的荧光物质相似, 但是每种荧光物质的含量有很大的差异。有研究表示, 植物油受到375 nm处的紫外光激发时, 因为花生中荧光特性的氨基酸、多酚类物质和生育酚等成分存在, 从而在400~600 nm范围内观察到一个显著的荧光特征峰[27-28], 而在670 nm附近的荧光峰可能与叶绿素密切相关[29]。
图3为不同温度下的不同样品的激光诱导荧光光谱曲线, 可以看到, 单一种类的花生油在荧光光谱形态上并没有明显的变动, 但是随着温度逐渐上升, 各个样品的荧光强度都呈现出不同程度的线性下降。温度的微小变化不足以破坏和降解这些荧光物质, 还能保持其本身的结构形态, 因此花生油的荧光强度下降并不是由其本身的荧光基团变化引起的。而图3中结果可能是由于温度的升高, 溶液熵值增大、黏度下降, 温度升高分子从中获得更多的热能, 分子之间相互作用阻力减小, 分子之间的运动加强其碰撞更加剧烈, 引发荧光猝灭现象[30-33], 这个运动过程结果抑制了荧光的发射, 导致样品的荧光强度降低。
图4展示了不同AFB1污染水平的样品在20 ℃下的激光诱导荧光光谱曲线。结果显示, 与未受污染的花生油相比, 由于AFB1的存在, 受污染样本的荧光光谱形状在不同温度下均显著变化, 并且部分样本荧光发射波长变化使光谱出现轻微的红移。在采集波长范围内, 控制组与空白组的光谱并无明显差异, 说明本研究中使用的乙腈溶液对荧光信号采集影响不大, 而随着AFB1污染程度的增加, 花生油样本的荧光强度增强。根据前人研究, AFB1在紫外光激发下会发出荧光峰值位于425~450 nm位置的蓝绿色光[34]。但由于花生油本身含有丰富的荧光物质, AFB1的荧光信号可能被掩盖, 因此仅凭荧光强度难以准确区分污染和未污染的花生油,因此需要采用化学计量学方法进一步分析光谱信息。
2.2 污染不同水平AFB1花生油的LDA判别模型
按照国家标准以20 μg/kg为划分阈值, 高于20 μg/kg的为超标样本。不同的花生油采用Kennard-Stone (KS)算法分别将各个温度下的光谱数据按照2:1的比例划分为建模集和预测集, 随后构建LDA判别模型, 表2为基于不同温度的10种花生油LDA判别模型的平均正确率, 在每个特定温度下的建模正确率均超过90%, 这表明LDA模型能够有效地鉴别AFB1污染是否超标, 但是模型在预测集上的正确率在84%以上并不高, 说明无法准确预测AFB1污染程度。从表2中数据可知各温度下的模型对相应温度的样品有最好的判别结果, 从整体来看20 ℃下的LDA判别模型表现出最高的判别稳定性, 建模集准确率均为94%, 预测集的正确率可高达93%。
为了进一步探究LDA模型在不同温度条件下对污染不同AFB1水平样本的分类预测效能, 基于此模型的预测分析, 针对各个特定温度条件的预测集样本进行了详细评估。然而, 当在某一温度下对其他几个温度的样本进行预测判别和分类时, 可看出预测的精确度出现了明显的下滑, 特别是在处理那些与训练集中的样本在温度上有显著差异的数据点时, 结果仅有47%, 显著说明模型在跨温度预测上无法运作。由此可见温度条件的变化影响了所构建模型对样品中AFB1污染水平的判定。在不同温度样本温度的预测集中, 全局模型实现了稳定的预测能力, 建模集和预测集正确率分别在86%和81%以上, 虽然比单温度模型结果有所降低, 但从整体来看, 它对其他温度的预测发挥稳定, 有很强的普遍适用性。所以如果检测条件不能满足严格控制样品温度的标准环境, 那么构建能够适应不同温度样本的不同温度下的全局模型就显得尤为重要[35]。
2.3 不同温度下污染AFB1花生油的PLSR模型
为了深入研究温度对检测效果的影响, 探究LIF系统检测已污染AFB1样品的能力, 进一步构建了PLSR定量模型, 表3即为建立的PLSR定量模型结果。各单温度下及混合条件下的PLSR建模效果不佳, 不同温度下的模型结果更是差异比较大。其中相对较好的是20 ℃模型: RPD值: 1.69, R2C: 0.67, R2p: 0.65, 由这些数值可知即使是效果最好的20 ℃的模型也无法实现定量预测能力, 因此该模型缺乏定量预测能力, 不能实现定量预测的目的。本次建模是多个品种花生油混合去构建模型, 而本身不同企业生产的花生油产品之间由于工艺、设备等因素, 其所含有的荧光物质含量必然会不同, 特别是在花生油品类繁多的情况下, 模型就难以实现定量预测。因此, 为了消除不同种类花生油荧光差异大的干扰, 同时更深度探究温度对检测的影响, 本研究选取10种花生油中的一种对其进行进一步的建模分析, 对其构建PLSR定量模型。
选取样品1进行进一步的建模分析。根据表4的分析结果可知, 对单个品种的花生油建立不同温度的PLSR模型可以进行预测, 在20 ℃下的PLSR模型有最好的结果, 具体数值如下: R2C: 0.99, R2P: 0.97, 最接近于1, 同时建模RMSEC和RMSEP分别为0.96 μg/kg和2.06 μg/kg, LOD为2.42 μg/kg, 达到最佳效果。其他温度下模型的R2C和R2P的最低值分别为0.93和0.90, RMSEP的值最高为2.78 μg/kg, LOD最高值6.82 μg/kg, RPD的值均大于3。从这些数据中可以得知, 单一品种的花生油在各温度下的PLSR模型和全局模型可以实现定量预测, 但是全局模型的预测结果不如单温度下的模型, 单温度下建模结果尤其是20 ℃下的花生油样品模型预测结果最好。
3 结论
收起
本研究深入探讨了在不同温度条件下, LIF系统对花生油中AFB1荧光检测的适应性和准确性, 以及对构建不同温度模型的判别正确率影响。通过运用化学计量学手段, 建立了不同温度条件下花生油AFB1污染的LDA定性和PLSR定量分析模型, 进一步对比了单温度和全局温度模型的影响。实验结果显示温度的升高, 引发了荧光淬灭而导致荧光强度逐渐减弱, 明确温度确实对样品的荧光特性产生影响后, 进一步对样品建立了单温度LDA模型和全局判别模型, 在样品温度下的单温度模型能进行准确定性, 而其他温度的结果就非常差, 此时全局模型展现了它在此情况下较强的稳定性和适应性, 但是预测结果的正确率不如单温度LDA模型高。通过对比得知, 20 ℃条件下所得模型的正确率最高, 而采用全局温度模型时, 正确率略有降低, 但均在80%以上, 有一定的实际应用潜力。在使用PLSR模型进行定量预测时, 单温度模型比全局模型有更好的准确度, 且同样在20 ℃下的模型预测结果最好。
基金
收起
  • 国家重点研发计划项目(2022YFF1102600)
  • 国家自然科学基金项目(32172306)
  • 江苏省重点研发计划项目(BE2023347)
  • 中储粮集团公司科技项目(2024-01)
文献
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2025年第16卷第3期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20240920003
  • 接收时间：2024-09-20
  • 首发时间：2025-07-21
  • 出版时间：2025-02-15
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  • 收稿日期：2024-09-20
基金
国家重点研发计划项目(2022YFF1102600)
国家自然科学基金项目(32172306)
江苏省重点研发计划项目(BE2023347)
中储粮集团公司科技项目(2024-01)
作者信息
    1.南京财经大学食品科学与工程学院, 南京 210023
    2.江苏省储备粮管理有限公司, 南京 215699
    3.中国储备粮管理集团有限公司浙江分公司, 杭州 311500
    4.江苏省粮油质量监测中心, 南京 210031

通讯作者:

* 严忠军(1979—), 男, 硕士, 高级工程师, 主要研究方向为粮食仓储与质量安全。E-mail:
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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