SERS spectrum recognition for Malachite Green real-time detection in aquaculture used seawater

Abstract

Surface enhanced Raman spectroscopy (SERS) is a useful chemical analysis technique for its high sensitivity, which was used for Malachite Green qualitative analysis in real cases in the present article. Automatic recognition algorithms were put forward, which is a combination of three modules, including a robust Fourier transform for background rejection, a principal component analysis based character extraction method and artificial neural networks for classifying. Low-frequency background was rejected by iterative Fourier transform in order to eliminate the effect of variable background. The best principal component combination was obtained according to the Euclidean distances between-class and within-class in the sample space. And a three-layer back-propagating neural network was constructed for classifying. As it was shown, it would both minimize the network and reduce the classifying mistakes from variable baseline and Raman characters of other substances in seawater with best principal component combination. Malachite Green real-time detection in aquaculture used seawater was realized with a lower density limit of 0. 1 microg L-1. Moreover, the method proposed in this article could be extended for other sol analysis based on SERS technique.