Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China.
Pharmacognosy Group, Department of Pharmaceutical Biosciences, BMC, Uppsala University, Box 591, SE 751 24, Uppsala, Sweden; International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang 212013, China.
Food Chem. 2022 Nov 15;394:133481. doi: 10.1016/j.foodchem.2022.133481. Epub 2022 Jun 22.
Cadmium (Cd) causes pervasive harm on human health as a poisonous heavy metal. This study proposed a surface-enhanced Raman spectroscopy (SERS) approach using sodium alginate (SA) as green reductant in combination with edge enrichment and chemometrics to build label-free Cd quantitative models. The silver nanoparticles synthesized by SA had good dispersion and enhancement factor (3.48 × 10). The optimal detection system was established by optimizing the concentration of specific molecules (trimercaptotriazine) and the droplet volume of measured liquid. Partial least squares models based on preprocessing methods and selection algorithms were compared. The results indicated that the model combined with first-order derivative preprocessing and competitive adaptive reweighted sampling algorithms achieved the best performance (R = 0.9989, RMSEP = 1.6225) with the limit of detection of 2.36 × 10 μg L in food. The SERS approach combined with edge enrichment and chemometrics holds promise for rapid and label-free determination of Cd in food.
镉（Cd）作为一种有毒重金属，对人类健康造成广泛危害。本研究提出了一种基于表面增强拉曼光谱（SERS）的方法，使用海藻酸钠（SA）作为绿色还原剂，结合边缘富集和化学计量学，构建无标记 Cd 定量模型。SA 合成的银纳米粒子具有良好的分散性和增强因子（3.48×10）。通过优化特定分子（三嗪三巯基）的浓度和测量液体的液滴体积，建立了最佳检测系统。比较了基于预处理方法和选择算法的偏最小二乘模型。结果表明，结合一阶导数预处理和竞争自适应重加权采样算法的模型表现最佳（R=0.9989，RMSEP=1.6225），在食品中检测限为 2.36×10μg L。该 SERS 方法结合边缘富集和化学计量学，有望实现食品中 Cd 的快速、无标记测定。
