Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis
Quantitative TLC-SERS detection of histamine in seafood with support vector machine analysis
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DOI：
10.1016/j.foodcont.2019.03.032
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发表时间：
2019-09-01
期刊：
影响因子：
6
通讯作者：
Wang, Alan X.
中科院分区：
文献类型：
--
作者：
Tan, Ailing;Zhao, Yong;Wang, Alan X.
在美国，由组胺中毒引起的鲭鱼中毒是与海鲜消费相关的最普遍的过敏之一。典型的症状范围从轻微的瘙痒到致命的心血管衰竭，可见于过敏反应。在本文中，我们展示了使用薄层色谱和表面增强拉曼散射（TLC-SERS）传感方法，在主成分分析（PCA）特征提取后基于支持向量回归（SVR）的机器学习分析，快速、敏感和定量地检测人工变质金枪鱼溶液和真正变质金枪鱼样品中的组胺。本文使用的薄层色谱板由商业食品级硅藻土制成，同时作为固定相分离混合金枪鱼肉中的组胺，并作为超灵敏的SERS底物以提高检测限。用简单的滴铸法将金胶体纳米颗粒滴在硅藻土板上，我们能够直接检测到人工变质金枪鱼溶液中组胺浓度低至10ppm。基于室温变质0 ~ 48 h的真实金枪鱼样品的TLC-SERS光谱数据，采用PCA-SVR定量模型取得了优于传统偏最小二乘回归（PLSR）方法的预测性能。本研究证明基于硅藻土的TLC-SERS技术结合机器学习分析是一种经济、可靠、准确的海鲜过敏原现场检测和定量方法，可提高食品安全。
Scombroid fish poisoning caused by histamine intoxication is one of the most prevalent allergies associated with seafood consumption in the United States. Typical symptoms range from mild itching up to fatal cardiovascular collapse seen in anaphylaxis. In this paper, we demonstrate rapid, sensitive, and quantitative detection of histamine in both artificially spoiled tuna solution and real spoiled tuna samples using thin layer chromatography in tandem with surface-enhanced Raman scattering (TLC-SERS) sensing methods, enabled by machine learning analysis based on support vector regression (SVR) after feature extraction with principal component analysis (PCA). The TLC plates used herein, which were made from commercial food-grade diatomaceous earth, served simultaneously as the stationary phase to separate histamine from the blended tuna meat and as ultra-sensitive SERS substrates to enhance the detection limit. Using a simple drop cast method to dispense gold colloidal nanoparticles onto the diatomaceous earth plate, we were able to directly detect histamine concentration in artificially spoiled tuna solution down to 10 ppm. Based on the TLC-SERS spectral data of real tuna samples spoiled at room temperature for 0-48 h, we used the PCA-SVR quantitative model to achieve superior predictive performance exceling traditional partial least squares regression (PLSR) method. This work proves that diatomaceous earth based TLC-SERS technique combined with machine-learning analysis is a cost-effective, reliable, and accurate approach for on-site detection and quantification of seafood allergen to enhance food safety.