**1. Introduction**

Counterfeiters target high-value products, including those with a strong brand name, deceiving consumers by substituting a high-value product with a less expensive or lower quality alternative. Although most food fraud concerns do not result in a public health or food safety crisis, these acts can lead to severe health hazards, as evidenced by oil fraudulently sold as olive oil that caused an outbreak of a condition known as the toxic oil syndrome, affecting 20,000 people, of which more than 300 died in Spain (1981) due to the ingestion of a food-grade rapeseed oil containing aniline derivatives sold for human consumption by street vendors [1]. To prevent olive oil adulteration, global governmental agencies (e.g., European Commission, United States Department of Agriculture, International Olive Council, Codex Alimentarius, German/Australian Standard, North American Olive Oil Association) have developed different standards to regulate olive oil by establishing a set of physical, chemical, and organoleptic characteristics [2]. A 2013 report by the U.S. International Trade Commission (USITC) indicated that current standards for extra virgin olive oil (EVOO) are widely unenforced leading to adulterated and mislabeled products in the market [3]. Common adulterants of EVOO include lower quality olive oils (refined, pomace, or lampante) or seed oils [4].

Numerous analytical techniques have been proposed to detect and control olive oil adulteration, including Ultraviolet-visible (UV–vis) absorption [5,6], front-face total fluorescence spectroscopy [7],

vibrational spectroscopy [8–11], mass spectrometry [12–14], nuclear magnetic resonance [15–20], and techniques such as DNA-based methods [21] and electronic noses [22]. Most methods to detect olive oil adulteration have focused on targeted approaches, providing great selectivity and sensitivity for identification and quantification of pre-defined compounds or classes of compounds, but fail to detect emerging risks from unexpected adulterants [23]. On the other hand, non-targeted screening, which is currently at the heart of metabolomics, focuses on the detection of all compounds in a sample without any prior knowledge of chemical entities which can then be compared with the fingerprint profile of pure reference sample [24].

Advancements in semiconductors have allowed miniaturization and cost reduction of spectrometer components, leading to commercially available portable, handheld, compact, and micro-devices in the industry. Key enabling technologies leading to miniaturized structures have been fostered by developments in Micro Electro Mechanical Systems (MEMS), thin-film filters, solid-state lasers, light-emitting devices (LEDs) and alternative light sources, fiber optic assemblies, and high-performance detector arrays [25]. These devices have been at the forefront of cutting-edge technologies and have become progressively smaller and easier to use. Miniaturized devices can be taken to or placed at/in/on-line points of vulnerability along with complex food supply networks and moved from the confines of the relatively stable and controlled laboratory environment into the potentially more challenging and dynamic environs of the food supply chain (point-and-shoot) [26].

Limited information is reported in the literature regarding the detection of olive oil adulteration using non-targeted classification approaches. Mossoba et al. (2017) evaluated FT-NIR in conjunction with a partial least square analysis to predict EVOO authenticity of 93 samples collected from online and local grocery stores [27]. The authors developed an FT-NIR index based on two carbonyl overtone (5280 cm−<sup>1</sup> and 5180 cm−<sup>1</sup> ) absorptions and generated partial least squares regression (PLSR) models for four specific oils (refined, high oleic, high linoleic, and palm olein) based on the different fatty acid composition of the potential adulterants in EVOO [27]. FT-IR equipped with an attenuated total reflectance (ATR) accessory and combined with supervised pattern recognition techniques (soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) have detected adulteration of EVOO with vegetable oils at levels above 10% [28,29]. Jimenez-Carvelo and others (2017) evaluated the use of FTIR-ATR and Raman spectroscopy (785 nm excitation laser) with different chemometric classification methods to detect adulteration of olive oil in blends with vegetable oils [30]. They successfully discriminated olive oils from blends containing over 10% vegetable oils by using PLS-DA and support vector machine-classification (SVM-C) for FT-IR and Raman analysis, respectively. Georgouli and others (2017) assessed the capabilities of a compact FTIR-ATR and a bench-top 1064 nm Raman spectrometers on the detection of EVOO adulteration with hazelnut oil (1–90%) mixtures by using a novel continuous locality preserving projections (CLPP) technique accompanied by a k-nearest neighbors (kNN) algorithm, reporting a classification rate ≥69% [31]. Although these studies have shown the capabilities of vibrational spectroscopy to detect EVOO adulteration with vegetable oils, they have not included lower quality olive oil (refined, lampante, or pomace), and most have been developed using a limited number of olive oil samples coming from restricted varietal origins and geographical areas, which limits their use as global methods to detect adulteration of olive oil (independently of the cultivars) with any edible vegetable oil [2,30].

This study aimed to develop an authentication program for EVOO using vibrational spectroscopy signatures combined with pattern recognition analysis for non-targeted screening of commercial EVOO samples and to generate prediction models for monitoring olive oil quality parameters.

#### **2. Materials and Methods**

A total of 151 olive oil samples were used in this study. Samples from Turkey (*n* = 91) were obtained from Aydin Commodity Exchange Laboratories in Aydin, Turkey, which monitors EVOOs for exportation to different countries. In addition, we included EVOO samples that were kindly provided by the California Olive Oil Council (*n* = 20) and samples purchased from grocery stores that included origins from Italy, Spain, Greece, Turkey, Tunisia, Portugal, and Peru (*n* = 40). Oils were placed in amber glass vials and stored at −18 ◦C until further analysis to minimize oxidation and any compositional changes.
