Rapid and sensitive SERS detection of melamine in milk using Ag nanocube array substrate coupled with multivariate analysis

Limin Li; Wee Shong Chin

doi:10.1016/j.foodchem.2021.129717

Rapid and sensitive SERS detection of melamine in milk using Ag nanocube array substrate coupled with multivariate analysis

Food Chem. 2021 Apr 6:357:129717. doi: 10.1016/j.foodchem.2021.129717. Online ahead of print.

Authors

Limin Li¹, Wee Shong Chin²

Affiliations

¹ Department of Chemistry, Faculty of Science, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore.
² Department of Chemistry, Faculty of Science, National University of Singapore, 3 Science Drive 3, Singapore 117543, Singapore. Electronic address: chmcws@nus.edu.sg.

PMID: 33964627
DOI: 10.1016/j.foodchem.2021.129717

Abstract

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 × 10⁵) 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 (R²) of calibration = 0.9807, RMSE of prediction = 1.9636, and R² of prediction = 0.9736. Hence, this study offers a rapid and sensitive detection of adulterant melamine in milk samples.

Keywords: Ag nanocube array; Multivariate analysis; Partial least squares (PLS); Poly(dimethylsiloxane) (PDMS); Support vector machines (SVM); Surface-enhanced Raman spectroscopy (SERS).