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In this research, the detection and quantification of adulterants in one of the most common varieties of extra virgin olive oil (EVOO) have been successfully carried out. Visible absorption information was collected from binary mixtures of Picual EVOO with one of four adulterants: refined olive oil, orujo olive oil, sunflower oil, and corn oil. The data gathered from the absorption spectra were used as input to create an artificial neural network (ANN) model. The designed mathematical tool was able to detect the type of adulterant with an identification rate of 96% and to quantify the volume percentage of EVOO in the samples with a low mean prediction error of 1.2%. These significant results make ANNs coupled with visible spectroscopy a reliable, inexpensive, user-friendly, and real-time method for difficult tasks, given that the matrices of the different adulterated oils are practically alike.

A wide variety of olive oil samples from different origins and olive types has been chemically analyzed as well as evaluated by trained sensory panelists. Six chemical parameters have been obtained for each sample (free fatty acids, peroxide value, two UV absorption parameters (K232 and K268), 1,2-diacylglycerol content, and pyropheophytins) and linked to their quality using an artificial neural network-based model. Herein, the nonlinear algorithms were used to distinguish olive oil quality. Two different methods were defined to assess the statistical performance of the model (a K-fold cross-validation (K = 6) and three different blind tests), and both of them showed around a 95-96% correct classification rate. These results support that a relationship between the chemical and the sensory analyses exists and that the mathematical tool can potentially be implemented into a device that could be employed for various useful applications.