Coumarin is harmful to health but still used in cosmetics, tobacco, or illegally added into food as a spice in trace amounts so that it is exceedingly difficult to be determined accurately. Thus, it is important to develop a reliable method to qualitatively and quantitatively determine coumarin. Herein, we report a coumarin detection method using surface-enhanced Raman spectroscopy (SERS) coupled with an intelligent multivariate analysis. First, a flower-like silver-based substrate was fabricated and characterized by XRD, TEM, and EDS. Subsequently, coumarin with different concentrations was detected using this flower-like silver as the SERS substrate. The Raman vibration assignments reflect the information about the structure of the coumarin molecule efficiently. The limits of detection (LOD) for coumarin using the flower-like silver substrate can reach 10-8 M. It means the detection limit of coumarin by this method is less than 1.46 μg kg-1, which is much more sensitive than the previously reported one. Based on the Raman characteristic peaks of coumarin, various methods like linear regression, binary linear regression, and PCA were used to quantitatively analyze coumarin. These analysis results show that with the binary linear regression model, a strong linear relationship between lg I (I is the Raman peak intensity) and -lg C (C is the concentration of coumarin) can be observed and the correlation coefficient R2 was close to 1. This method provides a high sensitivity and rapid method to detect the additives in food and cosmetics, etc.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)