Rapid Detection of Adulteration in Extra-Virgin Olive Oil using Three-Dimensional Fluorescence Spectra Technology with Selected Multivariate Calibrations

To rapidly and efficiently detect the presence of adulterants in extra-virgin olive oil, 3D fluorescence spectra technology was employed with the help of multivariate calibration. Parallel factor analysis and characteristic parameters method were comparatively employed to compress and extract the data of 3D fluorescence spectra. Then, three different non-linear and linear classification tools (i.e., back-propagation artificial neural network, least-square support vector machine and k-nearest neighbor) were systemically studied and compared in developing the model. The number of principle components and parameters of models were optimized by cross-validation. Compared with parallel factor analysis, characteristic parameters method, in this article, has its own superiority. Experimental results also showed that the performance of least-square support vector machine model is the best among the three models. The optimal least-square support vector machine model was achieved when seven principle components were used, with the discrimination rate of 98.96% in calibration set and 96.88% in prediction set, respectively. The misclassified samples are adulterated extra-virgin olive oil, and their adulterated concentrations were lower than 2.5% (wt/wt). The overall results sufficiently demonstrated that 3D fluorescence spectroscopy technology coupled with characteristic parameters method and least-square support vector machine classification tool has the potential to detect adulterated extra-virgin olive oil products when their adulterant concentrations are more than 2.5% (wt/wt).