The Evaluation of Lime Juice Adulteration by Comparing Cyclic Voltammetry and Electronic Tongue Methods

Articles in Press

Document Type : Research Article-en

Authors

• Gh. Bahrami ¹
• M. H. Aghkhani ¹
• M. R. Golzarian ²
• B. Deiminiat ³

¹ Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

² Centre for Artificial Intelligence and Machine Learning, Edith Cowan University, Australia

³ Department of Chemistry, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran

10.22067/jam.2023.83040.1173

Abstract

The present study investigated the use of the cyclic voltammetric electrochemical method and the electronic tongue (e-tongue) method for detecting adulteration in lime juice. Since the measurement of citric acid content in lime juice is an accepted indicator of lime juice adulteration in laboratories, at first, attempts were made to determine its concentration using a potentiostat device and the cyclic voltammetry method, which involved various electrodes including glassy carbon, graphite, gold, and carbon nanotube and gold nanoparticle-modified glassy carbon electrodes. Different conditions were considered by testing citric acid at multiple concentrations in buffers with different pH levels. The results showed that the electrochemical behavior of citric acid was weak, so conventional electrochemical methods could not be used to check its behavior. In the second part, a portable electronic tongue system (e-tongue) was evaluated. Eight samples of adulteration levels (from 5% up to 95%) were created in lemon juice (0, 5, 10, 20, 40, 70, 95, and 100% impurity). Unsupervised models including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and supervised models including Multilayer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were used. Based on the results, the PCA fingerprint showed good discrimination between different levels of adulteration, and HCA further confirmed this. The results of the analysis of supervised methods showed that the MLP model outperformed the SVM model in predicting fraud levels with a success rate of 99.33% and high correlation coefficients (R² = 0.9973, RMSE = 0.09). These results show that the proposed system can separate different levels of adulteration in lemon juice and can be used as a taste quality control system.

Keywords

• Classification algorithms
• Galvanostat
• Large amplitude
• Potentiostat
• Voltammetric pulse

Main Subjects

• Design and Construction

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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