Li Limin, Chin Wee Shong
Department of Chemistry, Faculty of Science, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore.
Department of Chemistry, Faculty of Science, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore.
Food Chem. 2021 Sep 30;357:129717. doi: 10.1016/j.foodchem.2021.129717. Epub 2021 Apr 6.
In this study, a facile Ag nanocube (NC) array substrate was fabricated for rapid SERS detection of melamine in milk. This easily-prepared substrate exhibited high Raman enhancement factor (~1.02 × 10) and good reproducibility with ~10.75% spot-to-spot variation in Raman intensity. Our proposed method can detect melamine as low as 0.01 ppm in standard solutions and 0.5 ppm in real milk samples after a simple one-step solvent extraction. Two multivariate analysis tools including partial least squares and support vector machines (SVM) were explored to develop reliable regression models for quantitative SERS analysis of melamine. By comparison, SVM regression models exhibited better predictive performance, especially in liquid milk, with root mean square error (RMSE) of calibration = 5.5783, coefficient of determination (R) of calibration = 0.9807, RMSE of prediction = 1.9636, and R of prediction = 0.9736. Hence, this study offers a rapid and sensitive detection of adulterant melamine in milk samples.
在本研究中，制备了一种简便的银纳米立方体（NC）阵列基底，用于牛奶中三聚氰胺的快速表面增强拉曼光谱（SERS）检测。这种易于制备的基底表现出高拉曼增强因子（约1.02×10）和良好的重现性，拉曼强度的点与点之间的变化约为10.75%。我们提出的方法在经过简单的一步溶剂萃取后，能够检测标准溶液中低至0.01 ppm的三聚氰胺以及真实牛奶样品中低至0.5 ppm的三聚氰胺。探索了两种多变量分析工具，包括偏最小二乘法和支持向量机（SVM），以开发用于三聚氰胺定量SERS分析的可靠回归模型。相比之下，SVM回归模型表现出更好的预测性能，尤其是在液态奶中，校准的均方根误差（RMSE）=5.5783，校准的决定系数（R）=0.9807，预测的RMSE=1.9636，预测的R=0.9736。因此，本研究提供了一种快速且灵敏的检测牛奶样品中掺假三聚氰胺的方法。
