Lime juice adulteration detection by spectroscopy and machine learning

Article Type:
Research/Original Article (دارای رتبه معتبر)
Fruit juices, and especially lime juice, belong to the most targeted food commodities for fraud. Therefore, reliableand cost-effective analytical methodologies need to be developed to guarantee lime juice authenticity and quality.The manifestation of machine learning techniques (MLT) has paved the way for fast and reliable processing andanalysis of food and juice data for more effective use of inexpensive, readily available, and easy-to-use equipmentsuch as UV/Vis spectrometers for quality control. The study aimed to investigate UV/Vis spectrometry and MLT todetect at least 10% of water, acid, and sugar added to lime juice. For this purpose, 26 lime samples, includingMexican and Persian lime, were collected from the orchards of four main lime-cultivated areas in Iran to preparepure lime juice samples (as authentic samples). To investigate adulterated lime juice, four types of treatment weredefined by adding acid, sugar, a mix of acid and sugar solution, and water at different volume proportions (10, 20,30, 40, and 50 % v/v) to pure lime juice samples. Each treatment was repeated eight times. The absorption rate ofdifferent adulterated and pure lime juice samples was measured at different wavelengths in the 210–550 nm range.The evaluation results of different MLTs showed that the accuracy of separating samples using absorption data bydecision tree (DT), k-nearest neighbor (k-NN), random forest (RF), multilayer perceptron (MLP), and support vectormachine (SVM) were 75%, 79%, 80%, 87%, and 92%, respectively. SVM had the highest level of accuracy inseparating adulterated lime juice samples. Also, this model’s performance criteria (sensitivity and F-score) werehigher than other models for identifying adulterated samples using absorption data. This is the first time that thecommon adulterations in lime juice were identified by rapid and accessible screening methods using UV/Visspectroscopy and MLT with high accuracy, precision, and sensitivity.
Journal of Food and Bioprocess Engineering, Volume:6 Issue: 2, Summer-Autumn 2023
56 to 62  
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