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使用表面增强拉曼散射金纳米间隙基底对从宠物液体食品（牛奶）中提取的三聚氰胺和氰尿酸进行痕量检测。

Trace level detection of melamine and cyanuric acid extracted from pet liquid food (milk) using a SERS Au nanogap substrate.

作者信息

Joshi Rahul, Adhikari Samir, Kim Minjun, Jang Yudong, Min Hyun Jung, Lee Donghan, Cho Byoung-Kwan

机构信息

Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon, 34134, South Korea.

Department of Physics, Chungnam National University, Daejeon, 34134, South Korea.

出版信息

Curr Res Food Sci. 2024 Mar 30;8:100726. doi: 10.1016/j.crfs.2024.100726. eCollection 2024.

DOI:10.1016/j.crfs.2024.100726
PMID:38590692
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10999514/
Abstract

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

摘要

本研究报道了金纳米间隙基底在表面增强拉曼散射（SERS）测量中的应用，结合波段选择方法（即投影变量重要性，VIP）对宠物液体食品（牛奶）中痕量的三聚氰胺及其衍生物进行定量分析。制备了六种不同浓度的三聚氰胺、氰尿酸以及三聚氰胺与氰尿酸的混合物，并采集了550至1620cm的SERS光谱。对于受三聚氰胺污染的样品，检测限可达200 pM，其他两组的浓度检测限为400 pM。VIP-PLSR模型在预测数据集上的相关系数（R）值分别为0.997、0.985和0.981，预测均方根误差（RMSEP）值分别为18.492 pM、19.777 pM和15.124 pM。此外，使用偏最小二乘判别分析（PLS-DA）对纯样品和不同浓度的加标样品进行分类。结果表明，三聚氰胺的最大分类准确率为100%，氰尿酸为96%，三聚氰胺与氰尿酸混合物为95%。所得结果清楚地表明，金纳米间隙基底能够对宠物食用的液体食品中的有害添加剂化学品进行低浓度、快速且高效的检测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/b032446582be/gr10.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/ac6c715fa3e5/gr1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/7e8686ed691d/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/d298fcaaa4a6/gr4.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/a599eb788de2/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/8f0f06be4e29/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/f1436890542e/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/2c35cfa8e7a7/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/b032446582be/gr10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/1360067084db/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/ac6c715fa3e5/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/992e673b9d84/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/7e8686ed691d/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/d298fcaaa4a6/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/cb0126cb2cd2/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/a599eb788de2/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/8f0f06be4e29/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/f1436890542e/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/2c35cfa8e7a7/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67aa/10999514/b032446582be/gr10.jpg

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