## 1 Introduction
In addition, the members of a taste panel can only evaluate a limited number of olive oil samples every day due to sensory fatigue, rendering the method unsuitable for on-line evaluation during the extraction process and the distribution and marketing of olive oil. For these reasons, recent analytical advances are needed to overcome the drawbacks and limitations of the official methods to evaluate olive oil quality and to rapidly differentiate EVOO from other cheaper olive oil categories. Thus, there is a need for analytical methodologies that are more robust, efficient, sensitive, user-friendly, easy to implement by non-specialized personnel, rapid, and cost-effective to guarantee the quality, authenticity, and geographic and varied origin traceability based on recent technological progress in the analytical field. Among the most recent and effective tools to ensure the authenticity and traceability of olive oils and their declared blends with other vegetable oils is the use of nuclear magnetic resonance (1H NMR) fingerprinting in combination with chemometrics [10]. The discrimination between EVOOs and virgin olive oils (VOOs) according to their geographical origin was also achieved using flash gas chromatography (FGC) for volatile compound analysis combined with untargeted chemometric data analysis, Partial Least Squares-Discriminant Analysis (PLS-DA), and Artificial Neural Networks (ANNs) [11]. The same technique has proven to be a reliable approach for the rapid screening of quality grades [EVOO, VOO, and lampante olive oil (LOO)] and a valid solution for supporting sensory panels, reducing the number of samples that must be assessed by panels or at least prioritizing their assessment [12]. In the quest for rapid analytical techniques that mimic the human olfactory system and provide an overall assessment of the volatile fingerprint of foods, the electronic nose (E-nose) has recently been explored as a complementary tool for organoleptic analysis for olive oil classification [13]. In this study, the authors investigated the potential of an electronic olfactory system (EOS) to classify virgin olive oil into quality classes, as previously defined by three sensory panels recognized by the IOC, and concluded that EOS could effectively face inconsistent classifications of olive oil samples derived from inherent human factor variability. In the food industry, near-infrared (NIR) and mid-infrared (MIR) spectroscopy have become quick and effective tools for tasks such as process monitoring, quality control, and shelf-life estimation [14]. They can be used directly and simply in olive oils, and their application could be a useful tool for the industry and producers to guarantee the quality and authenticity of olive oil [15; 16]. Fourier transform infrared spectroscopy (FTIR) has been used to detect adulteration of olive oil with other edible oils, such as hazelnut oil, canola oil, and safflower oil, by focusing on the entire range of the FTIR spectrum (3.100-650 cm\({}^{-1}\)) [17]. Examination of specific regions of the mid-infrared spectrum (3.600-2.500 cm\({}^{-1}\), 1.900-1.600 cm\({}^{-1}\), and 1.500-900 cm\({}^{-1}\)) can differentiate edible and non-edible oils [18]. Moreover, the quality defects of olive oil (fusty, winey, musty, and rancid), previously assessed by a trained sensory panel, were investigated using mid-infrared spectra (4000-600 cm\({}^{-1}\)) in tandem with data analytics (PLS-DA) [19]. The authors reported that the collected spectra could effectively identify the musty defect in the samples with a predictive ability of 87% and the winey, fusty, and rancid defects with correct classification rate of approximately 77%, providing promising perspectives for the advancement of the instrumental determination of sensory defects in olive oil that are perceived today only by a human taste panel.