Nondestructive Technique for Identifying Adulteration and Additives in Lemon Juice Based on Analyzing Volatile Organic Compounds (VOCs)

Abstract

In light of the frequent occurrence of counterfeit food sold in global commercial markets, it is necessary to verify the authenticity of tasty natural-plant-based products by checking their labels, as well as their pricing and quality control. Lemon juice has repeatedly been the victim of fraud attempts by manufacturers to lower the price of products. Electronic noses are used in many fields, including the beverage industry, for classification and quality control. This involves the detection and differentiation of volatile organic compounds (VOCs) released from food. This study evaluated pure lemon juice and 11 counterfeit samples (water, lemon pulp, and wheat straw) using an electronic nose equipped with 8 metal oxide sensors to detect fraud. Chemometric methods such as principal component analysis (PCA), linear and quadratic analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs) were used to analyze the response patterns of the sensors. The outputs of eight sensors were considered as the input of the model and the number of lemon juice groups, and its adulterations were also considered as the output of the model. Of the total data, 60% (for training), 20% (for validation), and 20% (for testing) were used. According to the results, all models had an accuracy of more than 95%, and the Nu-SVM linear function method had the highest accuracy among all models. Hence, it can be concluded that the electronic nose based on metal oxide semiconductor sensors combined with chemometric methods can be an effective tool with high efficiency for rapid and nondestructive classification of pure lemon juice and its counterfeits.