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Article(id=1217529307065401883, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217529305693864468, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250324009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1742745600000, receivedDateStr=2025-03-24, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1768211207405, onlineDateStr=2026-01-12, pubDate=1752508800000, pubDateStr=2025-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768211207405, onlineIssueDateStr=2026-01-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768211207405, creator=13701087609, updateTime=1768211207405, 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=8, endPage=17, ext={EN=ArticleExt(id=1217529307547746851, articleId=1217529307065401883, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Real-time monitoring of freshness of Litopenaeus vannamei by colorimetric-surface-enhanced Raman scattering dual signal output indicator labels, columnId=1217529307430306336, journalTitle=Journal of Food Safety & Quality, columnName=Highlight: Processing and Quality Safety of Aquatic Products, runingTitle=null, highlight=null, articleAbstract=

Objective To develop a sensitive and stable dual signal output indicator label and realize real-time monitoring of the freshness of Litopenaeus vannamei. Methods Based on polyvinyl alcohol (PVA)/agar composite matrix combining anthocyanins (ACNs), silver coated gold nanoparticles (Au@AgNPs) and the metal organic framework material UiO-66, the dual signal output indicator labels were prepared by casting method. Next, the stabilizing effect of UiO-66 on the labels was investigated by storage stability, ultraviolet stability and anti-migration experiments. Ammonia, dimethylamine and trimethylamine were used as characteristic volatile markers to evaluate the gas sensitive response performance of the labels. Ultimately, the label was applied to real-time monitoring of the freshness of Litopenaeus vannamei. Results Compared to labels without UiO-66, the addition of UiO-66 effectively immobilized the anthocyanins and improved the UV stability and storage stability of the label. In addition, the label showed excellent responsiveness to ammonia and amine gases. In the application of freshness monitoring of Litopenaeus vannamei, the color of the indicator label gradually changed from purplish-red to bluish-green with the change of freshness of Litopenaeus vannamei. The partial least squares regression (PLSR) model based on the surface-enhanced Raman scattering (SERS) signals showed excellent prediction performance for the total volatile basic nitrogen (TVB-N) values, with a correction set coefficient of calibrated coefficient of determination (R2C) of 0.951, cross-validated coefficient of determination (R2CV) of 0.899, predicted coefficient of determination (R2P) of 0.966, and residual prediction deviation (RPD) of 4.765. Conclusion A colorimetric-SERS dual-signal output indicator label is developed in this study. The label enable simultaneous visual identification of the freshness of Litopenaeus vannamei and high-sensitivity prediction of TVB-N values, providing a reference for the design of novel intelligent food packaging systems.

, correspAuthors=Kai-Qiang WANG, 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=Hui-Wen LIU, Kai-Qiang WANG), CN=ArticleExt(id=1217529311209374364, articleId=1217529307065401883, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=比色-表面增强拉曼散射双信号输出指示标签对凡纳滨对虾新鲜度的实时监测, columnId=1217529307631632938, journalTitle=食品安全质量检测学报, columnName=本期重点：水产品加工与质量安全, runingTitle=null, highlight=null, articleAbstract=

目的 开发一种灵敏且稳定的双信号输出指示标签, 并实现对凡纳滨对虾(Litopenaeus vannamei)新鲜度的实时监测。方法 基于聚乙烯醇(polyvinyl alcohol, PVA)/琼脂复合基质, 结合花青素(anthocyanins, ACNs)、银包金纳米颗粒(Au@AgNPs)与金属有机框架材料UiO-66, 通过浇铸法制备双信号输出指示标签。其次, 通过储藏稳定性、紫外稳定性以及防迁移性实验考察UiO-66对标签的增稳作用。以氨气、二甲胺和三甲胺为特征挥发性标志物, 评估标签的气敏响应性能。最终将其应用于凡纳滨对虾的新鲜度实时监测。结果 相对于未添加UiO-66的标签, UiO-66的加入有效固定了ACNs, 提升了标签的紫外稳定性和储藏稳定性。此外, 该标签对氨及胺类气体表现出优异的响应能力。在凡纳滨对虾新鲜度监测应用中, 指示标签的颜色随凡纳滨对虾新鲜度变化从紫红色逐渐变为蓝绿色。基于表面增强拉曼散射(surface-enhanced Raman scattering, SERS)信号建立的偏最小二乘回归(partial least square regression, PLSR)模型对挥发性盐基氮(total volatile base nitrogen, TVB-N)值的预测性能优异, 其中校正集决定系数(calibrated coefficient of determination, R2C)为0.951, 交叉验证集决定系数(cross-validated coefficient of determination, R2CV)为0.899, 预测集决定系数(predicted coefficient of determination, R2P)为0.966, 残差预测偏差(residual prediction deviation, RPD)为4.765。结论 本研究构建了一种比色-SERS双信号输出指示标签, 可同步实现凡纳滨对虾新鲜度可视化鉴别与TVB-N值的高灵敏度预测, 为新型的食品智能包装的设计提供了参考。

, correspAuthors=王凯强, authorNote=null, correspAuthorsNote=
*王凯强(1991—), 男, 博士, 副教授, 主要研究方向为食品光谱学分析方法、人工智能与食品安全。E-mail:
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刘慧文(2000—), 女, 硕士研究生, 主要研究方向为食品质量安全检测。E-mail:

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language=EN, label=Fig.5, caption=Storage stabilities without UiO-66 and with UiO-66 indicating labels, figureFileSmall=dfzZ3l1EO8r6DO7UQ0rALQ==, figureFileBig=W9x7ZQTA8wqmmCDkDaoHfQ==, tableContent=null), ArticleFig(id=1217901260955632369, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图5, caption=未添加UiO-66和添加UiO-66指示标签的储藏稳定性

注: A. 未添加UiO-66的标签颜色信息稳定性; B. 未添加UiO-66的标签SERS信号稳定性; C. 未添加UiO-66的标签中ACNs特征SERS信号; D. 添加UiO-66的标签颜色信息稳定性; E. 添加UiO-66的标签SERS信号稳定性; F. 添加UiO-66的标签中ACNs特征SERS信号。图A、D中的小写字母不同表示差异显著。

, figureFileSmall=dfzZ3l1EO8r6DO7UQ0rALQ==, figureFileBig=W9x7ZQTA8wqmmCDkDaoHfQ==, tableContent=null), ArticleFig(id=1217901261106627323, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.6, caption=UV stabilities of UiO-66 unadded (A, B) and UiO-66 added (C, D) indicator labels, figureFileSmall=vSRo9QVxs5RbZJRXPA+zRg==, figureFileBig=SqqIaERPUFe0wWF+f2VRQg==, tableContent=null), ArticleFig(id=1217901261228262145, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图6, caption=未添加UiO-66 (A, B)和添加UiO-66 (C, D)指示标签的紫外稳定性, figureFileSmall=vSRo9QVxs5RbZJRXPA+zRg==, figureFileBig=SqqIaERPUFe0wWF+f2VRQg==, tableContent=null), ArticleFig(id=1217901261345702667, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.7, caption=Migration of anthocyanins in the label before and after addition of UiO-66, figureFileSmall=n/t33+vhK9iBNC+nSGHNyQ==, figureFileBig=d2cX2vg6+SSFG32ms1mEiA==, tableContent=null), ArticleFig(id=1217901261475726099, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图7, caption=添加UiO-66前后标签中ACNs的迁移情况

注: A. 未添加UiO-66; B. 添加UiO-66。图中左上角插图从左至右分别为标签置于3%乙酸、水、10%乙醇和95%乙醇中1 d后溶液的状态。

, figureFileSmall=n/t33+vhK9iBNC+nSGHNyQ==, figureFileBig=d2cX2vg6+SSFG32ms1mEiA==, tableContent=null), ArticleFig(id=1217901262830486298, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.8, caption=Smart indicator labels with the addition of UiO-66 responds to ammonia (A, D), dimethylamine (B, E) gases, trimethylamine (C, F) gas response, figureFileSmall=ewrhx29UQWL5Vg1F9uXLvQ==, figureFileBig=28/UdXSHcEnJ63NQliU2SQ==, tableContent=null), ArticleFig(id=1217901262960509731, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图8, caption=添加UiO-66的智能指示标签对氨气(A, D)、二甲胺(B, E)、三甲胺(C, F)气体的响应, figureFileSmall=ewrhx29UQWL5Vg1F9uXLvQ==, figureFileBig=28/UdXSHcEnJ63NQliU2SQ==, tableContent=null), ArticleFig(id=1217901263090533165, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.9, caption=Changes in TVB-N values of Litopenaeus vannamei during storage at 4 ℃ (0-7 d), figureFileSmall=vJnXpFjVpt2rTVQsHwDl/Q==, figureFileBig=vIf9gozHVwiirnH/W+D2Zg==, tableContent=null), ArticleFig(id=1217901263199585075, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图9, caption=凡纳滨对虾在4 ℃储存期间(0~7 d) TVB-N值的变化, figureFileSmall=vJnXpFjVpt2rTVQsHwDl/Q==, figureFileBig=vIf9gozHVwiirnH/W+D2Zg==, tableContent=null), ArticleFig(id=1217901263346385726, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.10, caption=Color change of Litopenaeus vannamei during storage at 4 ℃ (0-7 d) with the addition of UiO-66 indicator labels, figureFileSmall=AzHltjy4Q6xC2dDb+hGb1A==, figureFileBig=rdAQh6ktIUjWjucdAbiOYA==, tableContent=null), ArticleFig(id=1217901263497380681, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图10, caption=凡纳滨对虾在4 ℃储存期间(0~7 d)指示标签的颜色变化, figureFileSmall=AzHltjy4Q6xC2dDb+hGb1A==, figureFileBig=rdAQh6ktIUjWjucdAbiOYA==, tableContent=null), ArticleFig(id=1217901263627404112, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.11, caption=Changes in SERS signals in Litopenaeus vannamei during storage at 4 ℃ (0-7 d) with the addition of UiO-66 indicator labels, figureFileSmall=EzzNAfYLUu2ySQJUZ7zTpA==, figureFileBig=Bjcn9TEirOoLGJp5UrkAig==, tableContent=null), ArticleFig(id=1217901263774204762, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图11, caption=凡纳滨对虾在4 ℃储存期间(0~7 d)指示标签SERS信号变化, figureFileSmall=EzzNAfYLUu2ySQJUZ7zTpA==, figureFileBig=Bjcn9TEirOoLGJp5UrkAig==, tableContent=null), ArticleFig(id=1217901263895839583, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=EN, label=Fig.12, caption=PLSR quantitative modeling: Scatter plot of measured and predicted TVB-N values based on SERS spectra, figureFileSmall=3i1Co4uG/gx281kiQ4o3qQ==, figureFileBig=Lu0UJmIUg/blcU5BjanAjA==, tableContent=null), ArticleFig(id=1217901264013280102, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217529307065401883, language=CN, label=图12, caption=PLSR定量建模: 基于SERS光谱的TVB-N值测量值与预测值的散点图

注: Slope指校正曲线的斜率。

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比色-表面增强拉曼散射双信号输出指示标签对凡纳滨对虾新鲜度的实时监测
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食品安全质量检测学报 | 本期重点：水产品加工与质量安全 2025,16(13): 8-17
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食品安全质量检测学报 | 本期重点：水产品加工与质量安全 2025, 16(13): 8-17
比色-表面增强拉曼散射双信号输出指示标签对凡纳滨对虾新鲜度的实时监测
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刘慧文1 , 王凯强1, 2, *
作者信息
  • 1 中国海洋大学食品科学与工程学院, 青岛 266000
  • 2 海洋食品加工与安全控制全国重点实验室, 青岛 266000

    • 刘慧文(2000—), 女, 硕士研究生, 主要研究方向为食品质量安全检测。E-mail:

通讯作者:

*王凯强(1991—), 男, 博士, 副教授, 主要研究方向为食品光谱学分析方法、人工智能与食品安全。E-mail:
Real-time monitoring of freshness of Litopenaeus vannamei by colorimetric-surface-enhanced Raman scattering dual signal output indicator labels
Hui-Wen LIU1 , Kai-Qiang WANG1, 2, *
Affiliations
  • 1 College of Food Science and Engineering, Ocean University of China, Qingdao 266000, China
  • 2 State Key Laboratory of Marine Food Processing and Safety Control, Qingdao 266000, China
出版时间: 2025-07-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250324009
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摘要
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目的 开发一种灵敏且稳定的双信号输出指示标签, 并实现对凡纳滨对虾(Litopenaeus vannamei)新鲜度的实时监测。方法 基于聚乙烯醇(polyvinyl alcohol, PVA)/琼脂复合基质, 结合花青素(anthocyanins, ACNs)、银包金纳米颗粒(Au@AgNPs)与金属有机框架材料UiO-66, 通过浇铸法制备双信号输出指示标签。其次, 通过储藏稳定性、紫外稳定性以及防迁移性实验考察UiO-66对标签的增稳作用。以氨气、二甲胺和三甲胺为特征挥发性标志物, 评估标签的气敏响应性能。最终将其应用于凡纳滨对虾的新鲜度实时监测。结果 相对于未添加UiO-66的标签, UiO-66的加入有效固定了ACNs, 提升了标签的紫外稳定性和储藏稳定性。此外, 该标签对氨及胺类气体表现出优异的响应能力。在凡纳滨对虾新鲜度监测应用中, 指示标签的颜色随凡纳滨对虾新鲜度变化从紫红色逐渐变为蓝绿色。基于表面增强拉曼散射(surface-enhanced Raman scattering, SERS)信号建立的偏最小二乘回归(partial least square regression, PLSR)模型对挥发性盐基氮(total volatile base nitrogen, TVB-N)值的预测性能优异, 其中校正集决定系数(calibrated coefficient of determination, R2C)为0.951, 交叉验证集决定系数(cross-validated coefficient of determination, R2CV)为0.899, 预测集决定系数(predicted coefficient of determination, R2P)为0.966, 残差预测偏差(residual prediction deviation, RPD)为4.765。结论 本研究构建了一种比色-SERS双信号输出指示标签, 可同步实现凡纳滨对虾新鲜度可视化鉴别与TVB-N值的高灵敏度预测, 为新型的食品智能包装的设计提供了参考。

比色  /  表面增强拉曼散射  /  花青素  /  UiO-66  /  新鲜度  /  水产品

Objective To develop a sensitive and stable dual signal output indicator label and realize real-time monitoring of the freshness of Litopenaeus vannamei. Methods Based on polyvinyl alcohol (PVA)/agar composite matrix combining anthocyanins (ACNs), silver coated gold nanoparticles (Au@AgNPs) and the metal organic framework material UiO-66, the dual signal output indicator labels were prepared by casting method. Next, the stabilizing effect of UiO-66 on the labels was investigated by storage stability, ultraviolet stability and anti-migration experiments. Ammonia, dimethylamine and trimethylamine were used as characteristic volatile markers to evaluate the gas sensitive response performance of the labels. Ultimately, the label was applied to real-time monitoring of the freshness of Litopenaeus vannamei. Results Compared to labels without UiO-66, the addition of UiO-66 effectively immobilized the anthocyanins and improved the UV stability and storage stability of the label. In addition, the label showed excellent responsiveness to ammonia and amine gases. In the application of freshness monitoring of Litopenaeus vannamei, the color of the indicator label gradually changed from purplish-red to bluish-green with the change of freshness of Litopenaeus vannamei. The partial least squares regression (PLSR) model based on the surface-enhanced Raman scattering (SERS) signals showed excellent prediction performance for the total volatile basic nitrogen (TVB-N) values, with a correction set coefficient of calibrated coefficient of determination (R2C) of 0.951, cross-validated coefficient of determination (R2CV) of 0.899, predicted coefficient of determination (R2P) of 0.966, and residual prediction deviation (RPD) of 4.765. Conclusion A colorimetric-SERS dual-signal output indicator label is developed in this study. The label enable simultaneous visual identification of the freshness of Litopenaeus vannamei and high-sensitivity prediction of TVB-N values, providing a reference for the design of novel intelligent food packaging systems.

colorimetric  /  surface-enhanced Raman scattering  /  anthocyanins  /  UiO-66  /  freshness  /  seafood
刘慧文, 王凯强. 比色-表面增强拉曼散射双信号输出指示标签对凡纳滨对虾新鲜度的实时监测. 食品安全质量检测学报, 2025 , 16 (13) : 8 -17 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250324009
Hui-Wen LIU, Kai-Qiang WANG. Real-time monitoring of freshness of Litopenaeus vannamei by colorimetric-surface-enhanced Raman scattering dual signal output indicator labels[J]. Journal of Food Safety & Quality, 2025 , 16 (13) : 8 -17 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250324009
正文
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0 引言
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近年来, 随着水产品需求持续攀升, 其供给量也相应增长[1]。然而, 水产品因富含蛋白质和水分等营养成分, 极易发生腐败变质[2]。这不仅导致严重的食物浪费, 还可能对消费者健康构成威胁。因此, 需要在运输、销售、交易、储藏过程中对水产品新鲜度进行快速即时检测, 为后续的加工和售卖提供依据。近年来, 随着智能监测技术的需求不断提升[3], 食品新鲜度指示标签受到了广泛关注。食品新鲜度指示标签[4]是指印刷或黏附在包装内含有特定试剂的标签, 与生鲜食品在贮藏过程中释放的不同气体或产物作用产生不同的颜色变化, 其无需复杂作, 即可直接、科学地指示食品的新鲜度。
花青素(anthocyanins, ACNs)是一种多酚类色素, 也是广泛存在于植物中的水溶性pH敏感型色素[5], 它会随着pH的变化而出现颜色的变化, 常常作为新鲜度指示剂使用。基于ACNs的新鲜度指示标签已被广泛研究[6-7]。它具有制备工艺简单、可视化以及可持续监测的优点。但也存在着响应灵敏度不足、ACNs易受温度、湿度、光照等环境因素干扰[8], 或是在高湿度环境中水蒸气会导致ACNs迁移[9]等问题, 进而使得检测结果的稳定性降低。
表面增强拉曼散射(surface-enhanced Raman scattering, SERS)是一种近年来快速发展的振动光谱技术, 凭借其极高的灵敏度, 在食品安全和生物传感等领域得到了广泛应用[10-12]。该技术的检测原理基于金属纳米粒子的局部表面等离子体共振效应[13]。当待测分子靠近这些金属纳米粒子时, 会在拉曼热点区域产生显著的信号增强效应。这种增强机制使得SERS能够对微弱的分子变化产生明显的信号响应, 从而有效解决了传统拉曼检测方法中灵敏度不足的问题。金属有机框架(metal-organic framework, MOF)材料是由金属阳离子或金属簇与有机配体通过配位键结合形成的具有多孔结构的纳米材料[14]。得益于其高孔隙率和优异的稳定性, MOF材料在指示标签领域得到了广泛应用, 尤其在保护ACNs方面表现出显著优势。UiO-66作为一种典型的MOF材料, 具备出色的热稳定性、化学稳定性以及水稳定性[15], 并且UiO-66作为潜在的药物递送纳米材料具有良好的安全性[16]。通过UiO-66对ACNs进行封装, 不仅能够有效抑制ACNs的迁移, 还能为其提供一个稳定的微环境, 从而显著提升ACNs的稳定性[17-18]。这一特性进一步增强了基于ACNs的指示标签的可靠性。
本研究通过UiO-66吸附花青素-银包金纳米颗粒(ACNs-Au@AgNPs)聚集体得到UiO-66@ACNs-Au@AgNPs材料, 并掺入到聚乙烯醇(polyvinyl alcohol, PVA)/琼脂基质中, 制备了比色-SERS双信号输出指示标签。随着pH的升高, 标签中的ACNs会发生结构变化, 这种变化一方面引起颜色改变, 从而实现标签的可视化监测; 另一方面导致标签中ACNs的特征SERS信号变化, 进一步提升监测灵敏度, 最终实现双信号输出。通过引入MOF材料UiO-66, 提高标签的稳定性, 为可视化、高灵敏度和高稳定性的新鲜度指示标签的设计提供参考。
1 材料与方法
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1.1 材料与试剂
凡纳滨对虾(Litopenaeus vannamei, 青岛西海岸大哨头批发市场); 紫甘蓝(山东寿光美邻惠享家旗舰店)。
无水乙醇(分析纯, 上海麦克林生化科技股份有限公司); 硝酸银、柠檬酸钠、抗坏血酸(分析纯, 上海阿拉丁生化科技股份有限公司); PVA(纯度30%)、氯金酸、琼脂粉、氯化锆、对苯二甲酸、N,N-二甲基甲酰胺(分析纯)(国药集团化学试剂有限公司)。
1.2 仪器与设备
RMS1000便携式拉曼光谱仪(中国上海如海光电科技有限公司); MS3 control振荡器(德国IKA集团); Hei-VAP旋转蒸发仪(德国Heidolph公司); GZX-9070 MBE数显鼓风干燥箱(上海博讯实业有限公司医疗设备厂); 101OBS真空干燥箱(上海力辰邦西仪器科技有限公司); IS 10傅里叶红外光谱(Fourier transform infrared spectroscopy, FTIR)仪(美国赛默飞世尔科技有限公司); SU8100扫描电子显微镜(scanning electron microscope, SEM)(日本日立高科株式会社); JEM-1200 EX透射电子显微镜(transmission electron microscope, TEM)(日本电子株式会社); UV-2550紫外分光光度计(中国苏州岛津仪器公司)。
1.3 实验方法
1.3.1 紫甘蓝ACNs的提取
采用溶剂萃取法从紫甘蓝中提取ACNs[19]。具体步骤如下: 首先, 将新鲜紫甘蓝置于40 ℃烘箱中进行脱水处理, 获得干燥紫甘蓝样品。随后, 使用破壁机将干燥紫甘蓝粉碎成细粉。将30 g紫甘蓝粉末与300 mL 60%乙醇混合, 在室温条件下搅拌提取3 h。提取完成后, 使用Whatman滤纸对混合液进行过滤, 去除固体残渣。接着, 利用旋转蒸发仪去除滤液中的乙醇, 得到浓缩提取物, 其浓度(H)为721.89 mg/L。最后将其避光保存在4 ℃, 直至使用。
1.3.2 Au@AgNPs材料的制备
采用FRENS[20]报道的柠檬酸盐还原法制备金纳米颗粒(AuNPs), 在干净锥形瓶中加入100 mL超纯水, 然后加入1 mL 1%的HAuCl4·4H2O水溶液, 将锥形瓶置于磁力搅拌器上, 设置转速1000 r/min, 温度280 ℃, 当沸腾2 min后, 快速加入1.4 mL 1%的柠檬酸三钠溶液, 锥形瓶中的溶液逐渐由淡黄色变为紫黑色再转变为紫红色, 待颜色稳定后继续加热5 min, 然后冷却至室温备用。其次通过种子生长法合成银包金纳米颗粒(Au@AgNPs)[21], 具体步骤如下: 取3 mL上述制备的AuNPs溶液于10 mL无菌离心管中, 加入60 μL质量分数1%的柠檬酸钠, 再加入125 μL 100 mmol/L抗坏血酸, 置于振荡器450 r/min振荡混匀, 然后以30 s一滴的速度加入10 mmol/L硝酸银并加够125 μL, 加入结束后提升转速至1000 r/min继续振摇45 min, 最后得到橙黄色液体即为Au@AgNPs。
1.3.3 UiO-66材料的制备
UiO-66的制备主要参考LIN等[22]的方法。采用溶剂热合成法, 具体步骤如下: 称取2.9129 g氯化锆(ZrCl4)和2.0761 g对苯二甲酸溶于100 mL N,N-二甲基甲酰胺中, 再将上述溶液与25 mL醋酸溶液(醋酸:水=23:2, V:V)混合, 倒入反应釜中, 置于真空干燥箱120 ℃反应24 h。反应结束后用乙醇多次清洗, 最后烘干除去乙醇, 得到白色粉末, 即为UiO-66。
1.3.4 双信号输出指示标签的制备
首先, 制备ACNs-Au@AgNPs聚集体, 将0.2 mL ACNs与6 mL Au@AgNPs离心浓缩液混合, 涡旋孵育30 min, 得到ACNs-Au@AgNPs聚集体。后将上述聚集体与15 mg UiO-66混合, 室温下振荡吸附5 h, 吸附结束后离心并清洗, 去除未吸附的ACNs-Au@AgNPs聚集体。然后将其分散到0.4 mL超纯水中, 得到UiO-66@ACNs-Au@AgNPs。其次, 制备凝胶成膜液, 称取0.8 g琼脂粉和0.2 g PVA溶于50 mL超纯水中, 于磁力搅拌器上100 ℃搅拌1 h, 后将温度降至50 ℃左右, 从而制备得到凝胶成膜液。将上述UiO-66@ACNs-Au@AgNPs与3 mL的凝胶成膜液中混合均匀, 后将含材料的凝胶成膜液2.5 mL倒入60 mm一次性培养皿中, 待其冷却即可得到双信号输出指示标签。
1.4 双信号输出指示标签的表征
1.4.1 傅里叶变换红外光谱分析
采用FTIR仪对制备的UiO-66、UiO-66@ACNs和UiO-66@ACNs-Au@AgNPs进行了表征, 扫描范围为4000至500 cm−1, 获得了各材料的红外光谱。
1.4.2 电镜分析
通过SEM对制备的UiO-66进行了形貌分析; 同时, 利用TEM对UiO-66@ACNs-Au@AgNPs进行了表征, 以研究UiO-66对ACNs-Au@AgNPs的吸附行为。
1.4.3 储藏稳定性
储藏稳定性是衡量标签性能的关键指标之一。为评估其稳定性, 将添加与未添加UiO-66的指示标签分别封装于自封袋中, 置于4 ℃环境下避光保存。每日记录指示标签的颜色变化并采集其SERS信号, 以研究标签的颜色稳定性及SERS信号的稳定性。
1.4.4 紫外稳定性
指示标签的紫外稳定性是考察指示标签综合稳定性的重要指标。将未添加UiO-66的指示标签以及添加UiO-66的指示标签同时置于紫外灯下照射, 每隔一段时间取出采集标签的颜色信息和SERS信号, 对比两种标签在紫外光下的稳定性。
1.4.5 防迁移性能
天然染料的迁移不仅会影响食品的品质和外观, 还可能影响消费者的选择行为。因此, 为了探究添加UiO-66后是否能够有效抑制ACNs的迁移, 本研究对其防迁移性能进行了评价。将未添加UiO-66的指示标签与添加UiO-66的指示标签分别浸泡于4种不同的模拟食品液中, 持续1 d。这些模拟食品液分别代表不同类型的食品: 3%乙酸模拟酸性食品, 去离子水模拟中性食品, 10%乙醇溶液模拟酒精类食品, 95%乙醇溶液模拟脂肪类食品。最后通过紫外-可见光谱(ultraviolet-visible spectroscopy, UV-vis)分析, 评估ACNs在模拟食品液中的迁移情况。
1.4.6 指示标签的氨敏感性
选择氨气、二甲胺和三甲胺作为检测的特征气体。将50 mL浓度分别为0、2、5、10、20、40、80 mmol/L的氨水、二甲胺和三甲胺溶液分别倒入100 mL烧杯中, 然后将指示标签置于烧杯上方, 3 h后采集指示标签的颜色信息及SERS信号。
1.5 双信号输出指示标签在实际样品中的检测应用
1.5.1 标签颜色信息及SERS信号的采集
为了验证指示标签在实际样品中的应用, 本研究选择凡纳滨对虾作为研究对象。将新鲜的凡纳滨对虾放入培养皿中, 将指示标签置于培养皿的顶盖上, 然后用封口膜密封。装有样品和指示标签的培养皿在4 ℃下储藏7 d, 每天用智能手机拍照监测指示标签的颜色, 照片参数为焦距: 5.58 mm, 光圈: f/1.6, ISO: 50; 用便携式拉曼光谱仪采集指示标签的SERS信号, 采集激光功率为60 mW, 积分时间为2000 ms。
1.5.2 总挥发性盐基氮值的测定
根据GB 5009.228—2016《食品安全国家标准 食品中挥发性盐基氮的测定》方法, 使用自动凯氏定氮仪测定凡纳滨对虾的总挥发性盐基氮(total volatile base nitrogen, TVB-N)值。
1.5.3 PLSR定量模型构建
为了快速获得样品的TVB-N值, 本研究结合化学计量学方法和拉曼光谱建立定量模型。使用The Unscrambler X软件对样品数据进行建模和分析, 并使用OriginPro 2021进行绘图。首先, 将130个样品的数据按4:1的比例分为校准集和预测集。其次, 将校准集的光谱数据和TVB-N值输入The Unscrambler X软件, 并优化预处理方法, 以建立与TVB-N值相对应的定量模型。定量模型通过校正集决定系数(calibrated coefficient of determination, R2C)、交叉验证集决定系数(cross-validated coefficient of determination, R2CV)、校正集均方根误差(root mean square error of calibration, RMSEC)、交叉验证集均方根误差(root mean square error of cross validation, RMSECV)进行评价。最后, 将预测集的数据导入利用校准集数据建立的定量模型中, 进行模型验证。预测效果通过预测集决定系数(predicted coefficient of determination, R2P)、预测集均方根误差(root mean square error of prediction, RMSEP)和残差预测偏差(residual prediction deviation, RPD)进行评估[23]。当R2值最接近1且RMSE值越小时, 模型的性能最佳。根据RPD值的高低, 可以反映模型的预测精度。当RPD值低于1.5时, 表示模型不合格; 当RPD值在1.5和2.0之间时, 表示模型有可能区分高值和低值; 当RPD值在2.0和2.5之间时, 表示模型可以进行近似的定量预测。当RPD值在2.5和3.0之间以及大于3.0时, 预测效果分别为良好和优秀[24]。
1.6 数据处理
实验数据在Microsoft Office 365软件中进行处理, 数据绘图均在Origin 2021软件中进行。利用IBM SPSS Statistics 26进行显著性分析, 单因素方差分析中不同字母表示显著性差异(P<0.05)。指示标签颜色信息提取使用Adobe Photoshop 2023软件。定量模型数据归一化在MATLAB R2023b软件中进行, 定量模型建立及预测使用The Unscrambler X 10.4 (64-bit)软件。
2 结果与分析
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2.1 双信号输出指示标签工作机制
图1展示了比色-SERS双信号输出指示标签的工作原理。在水产品储存过程中, 微生物和内源性酶会分解蛋白质和氨基酸, 产生挥发性含氮化合物。这些化合物与智能标签接触后, 会改变标签微环境的pH, 进而引发标签内pH敏感型色素ACNs的结构变化。ACNs结构的变化会同时体现在颜色变化和SERS信号的变化上[25], 从而实现双信号输出。通过颜色变化, 可以对水产品的新鲜度进行定性分析, 判断其处于新鲜、次新鲜还是腐败状态; 而通过SERS信号的变化, 结合化学计量学方法, 可以对水产品的TVB-N值进行预测, 并且SERS信号相较于颜色变化更加灵敏, 有望实现水产品更早期的监测。
图2展示了ACNs的特征拉曼信号, 1287 cm-1(标黑)和1642 cm-1(标蓝)处的峰归因于羰基C=O的伸缩振动[v(CO)][26], 1320 cm-1(标红)处的峰与ACNs碳结构中环间键的拉伸振动[v(CC)]和平面内环的弯曲振动[δ(CH)][27]。随着pH增加, ACNs结构逐渐转变产生C=O, 1287 cm-1和1642 cm-1处的峰是C=O的伸缩振动, 这两个峰的强度会逐渐增强。1320 cm-1处的峰值对应于ACNs环之间的键振动, 该峰值的强度随着ACNs结构转变呈逐渐减弱的趋势[25]。因此, 预估标签对碱性挥发性气体具有SERS信号响应。
2.2 FT-IR分析
图3展示了UiO-66、UiO-66@ACNs以及UiO-66@ACNs- Au@AgNPs的红外光谱图。在红外光谱中, 3400 cm-1处的宽峰可归因于晶间水和空腔中物理吸附水的存在[28], 或ACNs中O-H键的伸缩振动。1583 cm-1和1392 cm-1处的吸收峰分别对应于-COOH中C-O键的振动以及芳环中C-C键的振动[29]。1100 cm-1附近的吸收峰则与Zr-O键的伸缩振动相关。在较低频率范围内, 由于OH和CH振动模式与Zr-O模式的耦合, 形成了742 cm-1和651 cm-1处的谱带。其中, 742 cm-1和651 cm-1处的峰分别归属于Zr-O2的纵向和横向振动峰[28], 这表明UiO-66的成功合成。此外, -O-H振动峰向低波数方向的移动表明UiO-66与ACNs之间形成了分子间氢键。同时, 添加ACNs和Au@AgNPs后, UiO-66的特征峰未发生明显变化, 说明其结构在添加后保持稳定。
2.3 电镜分析
通过扫描电镜(图4A)可以看出合成的UiO-66是粒径50 nm左右的球形, 呈团簇状态, 且与文献[22]中合成的形貌相同。通过透射电镜(图4C)可以看出, 合成的UiO-66@ACNs-Au@AgNPs中UiO-66的表面存在Au@AgNPs, 进而说明UiO-66@ACNs- Au@AgNPs成功制备(图4)。
2.4 储藏稳定性
将标签于4 ℃下密封避光储藏, 以检验其稳定性。如图5A所示, 在18 d的储存过程中, 未添加UiO-66的指示标签的颜色保持紫红色, 没有明显变化, 与最初制备的指示标签相比, 指示标签的ΔEmax=3.66±0.46<5, 表明指示标签在储存期间的颜色稳定性良好[30]。此外, 指示标签的SERS信号如图5B、5C所示, 计算了ACNs在1287、1320和1642 cm-1拉曼特征峰强的相对标准偏差(relative standard deviation, RSD), 发现RSD均小于8%。如图5D所示, 在36 d的储存过程中, 添加UiO-66的指示标签的颜色没有明显变化, 且指示标签的ΔEmax=3.23±0.92<5, 表明指示标签在储存期间的颜色稳定性良好。此外, 指示标签的SERS信号如图5E、5F所示, 计算了ACNs在1287、1320和1642 cm-1拉曼特征峰强度的RSD, 发现RSD均小于6%, 表明标签的SERS信号也具有良好的储存稳定性。与未添加UiO-66的标签相比, 引入UiO-66的标签在储藏稳定性方面显著提升, 储藏时间从18 d延长至36 d, 稳定性提高了一倍。此外, 其SERS信号的均方根误差从8%降至6%, 表明信号稳定性进一步增强。这一现象归因于UiO-66的高配位数、无机金属与连接体之间的强键合作用以及Zr-O键的高强度, 使其具备优异的热稳定性、化学稳定性和机械稳定性[31]。一方面, UiO-66能够将ACNs分子封装在其孔隙中, 这种物理屏障作用有效隔绝了外界环境中的光、热、氧气和湿度等因素, 从而减缓了ACNs的降解过程。另一方面, UiO-66的化学性质稳定, 能够在较宽的温度和pH范围内保持结构完整性, 为封装的ACNs提供了稳定的微环境, 进一步防止其化学降解。因此, UiO-66的添加显著提升了智能标签的储藏稳定性。
2.5 紫外稳定性
分别将添加和未添加UiO-66的智能标签置于紫外灯下照射, 并每隔一段时间进行拍照以及SERS信号的采集。图6A、6B为未添加UiO-66的标签, 120 min的紫外照射对颜色信息几乎不产生影响, 但对于SERS信号来说, 会使得信号不断降低, 1287、1320和1642 cm-1处拉曼特征峰强度的RSD分别为8.13%、35.38%、2.99%。图6C、6D为添加UiO-66的标签, 120 min的紫外照射对颜色信息也几乎没有影响, 对于SERS信号来说, 1287、1320和1642 cm-1处拉曼特征峰强度的RSD分别为2.75%、8.54%和1.51%。通过对比两种标签SERS信号的RSD值可以发现, UiO-66的加入显著增强了标签抵御紫外干扰的能力, 从而提高了标签的稳定性。这归因于UiO-66本身具有一定的紫外吸收能力, 其多孔结构能够增加紫外线的传输路径, 从而降低紫外线强度或重新吸收未被吸收的紫外线[32], 有效屏蔽紫外线对ACNs的直接破坏, 进而延长ACNs的稳定性。
2.6 防迁移性
图7展示了未添加和添加UiO-66的标签在各种模拟食品流体中1 d内的染料迁移性能。利用UV-vis光谱对ACNs在模拟食品液中的迁移进行了分析, 发现添加UiO-66后标签在酸性溶液中迁移情况有明显改善, 且在中性、酒精类、脂肪类中未发生迁移。由于UiO-66有机配体的苯环与酚类化合物之间的静电吸引和π-π相互作用, UiO-66对酚类化合物表现出很高的吸附性能[33]。因此, ACNs可以通过静电吸引和π-π相互作用固定在UiO-66框架载体上, 从而减少其迁移。综上所述, UiO-66对ACNs的吸附性能可以有效地防止ACNs的迁移。
2.7 指示标签的氨敏感性
为考察UiO-66材料对氨及胺类气体的传感性能, 分别测试了其对不同浓度氨气、二甲胺和三甲胺的响应特性。指示标签对氨、二甲胺和三甲胺的比色和SERS响应见下图8。在颜色变化方面, 由于ACNs结构转变, 指示标签从紫红色变为蓝绿色, 然后变为绿色, 最后变为黄绿色(图8A~C)。此外, 指示标签对氨、二甲胺和三甲胺都有良好的SERS响应。如图8D、8E、8F, 随着胺气体浓度的增加, ACNs在1320 cm-1处的拉曼峰逐渐减小。这是因为氨和胺等碱性气体会增加指示标签微环境的pH, 这种增加会导致ACNs结构转变, 从而产生颜色和SERS响应。由此可以看出, 标签对氨及胺类气体表现出良好的响应能力。
2.8 指示标签在实际样品中的应用
为了探索添加UiO-66的指示标签在实际样品中的应用情况, 本研究将其用于监测凡纳滨对虾在低温贮藏期间的新鲜度变化。据相关研究[34-35], 当TVB-N含量低于20 mg N/ 100 g时, 可判定样品为新鲜状态; 当TVB-N值超过30 mg N/ 100 g时, 则表明样品已发生变质。基于此标准, 本研究将TVB-N值介于20~30 mg N/100 g的样品界定为次新鲜状态。图9所示, 在4 ℃储藏期间, 凡纳滨对虾的TVB-N值在4 ℃的储存过程中呈上升趋势, 在3~4 d之间凡纳滨对虾由新鲜状态转变为次新鲜状态, 大约在第5 d时由次新鲜状态转变为腐败变质状态。
随着凡纳滨对虾从新鲜到变质, 指示标签出现了明显的颜色变化, 即从紫红色变为深紫色, 然后变为深蓝色, 最后变为蓝绿色(图10)。当指示标签的颜色由紫红色到深蓝色转变时, 凡纳滨对虾处于新鲜状态。当指示标签的颜色变由深蓝色向蓝绿色转变时, 凡纳滨对虾处于次新鲜状态。当指示标签的颜色变为蓝绿色时, 凡纳滨对虾处于变质状态。因此, 根据指示标签颜色反应的变化, 可以对凡纳滨对虾的新鲜度进行定性分析。进一步说明该标签可应用到水产品新鲜度的定性检测。
此外, 指示标签的SERS信号如图11所示, 随着凡纳滨对虾储存时间的延长, 1287 cm-1和1642 cm-1处的峰值逐渐增大, 1320 cm-1处的峰值逐渐减小。说明双信号输出指示标签会随着凡纳滨对虾新鲜度的降低产生规律性的SERS信号响应。
本研究采用SERS光谱技术凡纳滨对虾样品中的TVB-N含量进行定量分析。为了建立可靠的TVB-N含量预测模型, 本研究对比了不同预处理方法下的偏最小二乘回归(partial least square regression, PLSR)和主成分回归(principal component regression, PCR)模型的预测性能。如图12所示, 经过标准正态变量法(standard normal variate, SNV)预处理的PLSR模型表现出最优的预测性能, 其R2C达到0.951, R2CV为0.899, R2P高达0.966。此外, 该模型的RPD值为4.765, 根据RPD评价标准(RPD>3表示模型具有优秀的预测能力), 证实了该PLSR模型具有良好的预测准确性和稳定性。
3 讨论与结论
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本研究在传统ACNs基指示标签的基础上, 创新性地引入了SERS信号响应机制。通过利用ACNs随pH变化而产生的结构转变特性, 成功开发了一种兼具比色检测和高灵敏度SERS信号检测功能的双信号输出指示标签。为进一步提升标签稳定性能, 研究引入了UiO-66金属有机框架材料, 增强了指示标签的稳定性, 最终制备出具有可视化、高稳定性和高灵敏度的双信号输出型食品新鲜度指示标签。然而, 目前该技术的验证仅限于单一水产品体系。为拓展该技术的应用范围并验证其普适性, 建议后续研究可扩大应用对象范围, 将多种水产品(如鱼类、贝类等)纳入研究体系, 以期开发出具有广泛适用性的食品新鲜度指示标签。
基金
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  • 国家自然科学基金项目(32102065)
  • 国家海水鱼产业技术体系项目(CARS-47)
文献
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2025年第16卷第13期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250324009
  • 接收时间：2025-03-24
  • 首发时间：2026-01-12
  • 出版时间：2025-07-15
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  • 收稿日期：2025-03-24
基金
国家自然科学基金项目(32102065)
国家海水鱼产业技术体系项目(CARS-47)
作者信息
    1 中国海洋大学食品科学与工程学院, 青岛 266000
    2 海洋食品加工与安全控制全国重点实验室, 青岛 266000

通讯作者:

*王凯强(1991—), 男, 博士, 副教授, 主要研究方向为食品光谱学分析方法、人工智能与食品安全。E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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