Abstract

The study presented the application of a voltammetric electronic tongue to discriminate and identify vegetable oil. Concretely, it aimed to research the discrimination of different oil and the prediction of unknown oil. Seven oil samples from different varieties and geographical origins were measured by a voltammetric platinum electrode as the sensing part. The electrochemical response current signals of samples which were the original data information were obtained with cyclic voltammetric measurement. Principal Component Analysis (PCA) and Cluster Analysis (CA) algorithms were used as the modeling tools to discriminate different vegetable oil respectively. Discriminant Factorial Analysis (DFA) and RBF Neural Network (RBFNN) were used as the prediction models for unknown oil. Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) were applied as feature extraction method for data input set of the prediction models. Different combinations of prediction strategies with feature extraction methods were compared. It was found the samples with different varieties or origins were clearly discriminated with using PCA and CA. The best prediction results were obtained with a 90.48% of identification accuracy by employing FFT-RBFNN. The implementation of this study suggests the electronic tongue may be a useful tool for oil quality evaluation and control.

Keywords:

Cluster analysis, discriminant factorial analysis, principal component analysis, RBF neural network, vegetable oil, voltammetric electronic tongue,

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The authors have no competing interests.

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