Developing an Excitation-Emission Matrix Fluorescence Spectroscopy Method Coupled with Multi-way Classification Algorithms for the Identification of the Adulteration of Shanxi Aged Vinegars

Abstract

In this paper, an excitation-emission matrix (EEM) fluorescence spectroscopy method coupled with two kinds of multi-way classification algorithms was developed for the identification of the adulteration of Shanxi aged vinegars. The first classification model was built from selective information provided by parallel factor analysis (PARAFAC) based on partial least squares-discriminant analysis (PLS-DA) algorithm and the other one was modeled based on strictly multi-way classification algorithm multi-way partial least squares-discriminant analysis (N-PLS-DA). Both classification models showed good clustering tendency for authentic and adulterated Shanxi aged vinegars. Compared with the PARAFAC-PLS-DA method, more accurate and reliable classification results were provided by the N-PLS-DA method because it is a strictly multi-way classification method and can make the utmost use of the EEM fluorescence information of the vinegar samples. The N-PLS-DA model constructed from the EEM spectra enabled the recognition of adulterated samples with accurate rate of 100% both in training and prediction sets and obtained maximum sensitivity and specificity. This study showed that EEM fluorescence spectroscopy combined with multi-way classification algorithm N-PLS-DA could be used as a good method for the identification of the adulteration of Shanxi aged vinegar samples.