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This study (here – open access) used an electronic nose based on an array of metal oxide semiconductor sensors, of the type routinely used in the beverage industry for quality control, to characterise lemon juice based upon its Volatile Organic Compound (VOC) profile. The authors prepared their own provenant lemon juice and their own samples adulterated with lemon pulp, water, citric acid, sugar and wheat straw. They then used chemometric methods such as principal component analysis (PCA), linear and quadratic analysis (LDA), support vector machines (SVMs), and artificial neural networks (ANNs) to analyze the response patterns of the sensors. Of the total data, 60% (for training), 20% (for validation), and 20% (for testing) were used. All models could classify the adulterated samples with an accuracy of more than 95%. The Nu-SVM linear function method had the highest accuracy among all models. The authors concluded that the use of 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.