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基于 SERS 传感器结合化学计量模型的虾中苯并(b)荧蒽(BbF)的快速定性和定量分析。

Rapid qualitative and quantitative analysis of benzo(b)fluoranthene (BbF) in shrimp using SERS-based sensor coupled with chemometric models.

机构信息

College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China; Department of Nutrition and Dietetics, Ho Teaching Hospital, P. O. Box MA 374, Ho, Ghana; Centre for Agribusiness Development and Mechanization in Africa (CADMA AgriSolutions), Ho 00233, Ghana.

School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.

出版信息

Food Chem. 2024 Oct 1;454:139836. doi: 10.1016/j.foodchem.2024.139836. Epub 2024 May 25.

DOI:10.1016/j.foodchem.2024.139836
PMID:38810447
Abstract

Benzo(b)fluoranthene (BbF), a polycyclic aromatic hydrocarbon (PAH), is a carcinogenic contaminant of concern in seafood. This study developed a simple, rapid, sensitive, and cost-effective surface-enhanced Raman scattering (SERS) sensor (AuNPs) coupled with chemometric models for detecting BbF in shrimp samples. Partial least squares (PLS) regression models were optimized using uninformative variable elimination (UVE), bootstrapping soft shrinkage (BOSS), and competitive adaptive reweighted sampling (CARS). Qualitative analysis was performed using principal component analysis (PCA), linear discriminant analysis (LDA), and k-nearest neighbors (KNN) to differentiate between BbF-contaminated and uncontaminated shrimp samples. The SERS-sensor exhibited excellent sensitivity (LOD = 0.12 ng/mL), repeatability (RSD = 6.21%), and anti-interference performance. CARS-PLS model demonstrated superior predictive ability (R = 0.9944), and qualitative analysis discriminated between contaminated and uncontaminated samples. The sensor's accuracy was validated using HPLC, demonstrating the ability of the SERS-sensor coupled with chemometrics to rapidly and reliably detect BbF in shrimp samples.

摘要

苯并(b)荧蒽(BbF)是一种多环芳烃(PAH),是海鲜中一种令人关注的致癌污染物。本研究开发了一种简单、快速、灵敏且具有成本效益的表面增强拉曼散射(SERS)传感器(AuNPs),并结合化学计量学模型用于检测虾样品中的 BbF。偏最小二乘(PLS)回归模型通过无信息变量消除(UVE)、引导软收缩(BOSS)和竞争自适应重加权采样(CARS)进行优化。使用主成分分析(PCA)、线性判别分析(LDA)和 K-最近邻(KNN)进行定性分析,以区分 BbF 污染和未污染的虾样品。SERS 传感器表现出优异的灵敏度(LOD=0.12ng/mL)、重复性(RSD=6.21%)和抗干扰性能。CARS-PLS 模型表现出优异的预测能力(R=0.9944),定性分析可区分污染和未污染的样品。通过 HPLC 验证了传感器的准确性,表明 SERS 传感器与化学计量学相结合能够快速可靠地检测虾样品中的 BbF。

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