Laser diode induced excitation of PDO extra virgin olive oils for cognitive authentication and fraud detection

Abstract

Extra virgin olive oil (EVOO), the star of the Mediterranean diet, is a valuable product within the food sector. In certain cases, due to specific and special olive varietals and blends, as well as organoleptic properties, EVOOs are even authenticated with protected designation of origin (PDO) labels. Because of this, false labeling and even adulterations have been reported in this sector. During this research, a laser diode has been used to gather 254 fluorescence spectra from that many artificially prepared samples of fresh PDO EVOO mixed with small amounts of old olive oils (adulterants). The database was then treated and employed to train and optimize an intelligent model based on a supervised artificial neural network known as multilayer perceptron (MLP). The goal of this non-linear model was dual: to identify the PDO of every sample and to quantify the amount of fresh EVOO. After meticulous validation using blinded samples to assess the performance of the MLP, the outcome was successful. The algorithm could perfectly distinguish the PDOs and determine the amount of fresh EVOO with a mean absolute error of around 1.5% (w/w). Therefore, the combination of a laser diode and cognitive modeling leads to a fast and cost-effective tool able to authenticate PDO labels of EVOOs and estimate the amount of potential adulterating agents such as olive oils from old harvests.