Abstract
In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.
Highlights
Rice plays a significantly important role in people’s daily life, detection of the rice quality has received progressively increasing attention
A voltammetric electronic tongue, based on the combination of metallic sensors, was researched in order to create a suitable tool for discrimination and prediction of different varieties of rice
The sensor array coupled with data compression method, statistical method, and pattern recognition method, namely, fast Fourier transform (FFT), Discriminant Factorial Analysis (DFA), and radial basis function (RBF) artificial neural network, respectively, were employed to discriminate and predict different types of rice
Summary
Rice plays a significantly important role in people’s daily life, detection of the rice quality has received progressively increasing attention. The assessment indexes of sensory quality of rice are mainly based on the color, appearance, smell, taste and other features which are identified by the examiner’s sense organs and practical experience. The main assessment indexes of eating quality of rice include the gelatinization temperature, amylose content, and gel consistency. Evaluation of the nutritional quality of rice is mainly embodied in the detection of the content of rice starch, fat, protein, vitamins, and microelements which are beneficial to the human body. Modern analytical techniques are applied for the discrimination and identification of damaged rice plants These techniques are used for the discrimination of rice varieties based on volatile compounds released by the plant, and for rice detection by gas chromatography-mass spectrometry (GC-MC) or electronic noses [1,2,3,4,5,6]
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