foods Article Spectroscopic and Thermal Characterization of Extra Virgin Olive Oil Adulterated with Edible Oils Emigdio Chavez-Angel 1, * , Blanca Puertas 2 , Martin Kreuzer 3 , Robert Soliva Fortuny 4 , Ryan C. Ng 1 , Alejandro Castro-Alvarez 5, * and Clivia M. Sotomayor Torres 1,6 1 Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Spain; ryan.ng@icn2.cat (R.C.N.); clivia.sotomayor@icn2.cat (C.M.S.T.) 2 Departamento de Calidad, Döehler Fraga, Member of Döehler Group, Collidors S/N, 22520 Fraga, Spain; blanca.puertaspujadas@doehler.com 3 ALBA Synchrotron Light Source Experiment Division—MIRAS Beamline Cerdanyola del Valles, 08290 Barcelona, Spain; mkreuzer@cells.es 4 Agrotecnio-CeRCA Center, Department of Food Technology, University of Lleida, 25198 Lleida, Spain; robert.soliva@udl.cat 5 Centro de Excelencia en Medicina Traslacional, Laboratorio de Bioproductos Farmacéuticos y Cosméticos, Facultad de Medicina, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4780000, Chile 6 ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain * Correspondence: emigdio.chavez@icn2.cat (E.C.-A.); alejandro.castro.a@ufrontera.cl (A.C.-A.) Abstract: The substitution of extra virgin olive oil with other edible oils is the primary method for fraud in the olive-oil industry. Developing inexpensive analytical methods for confirming the quality and authenticity of olive oils is a major strategy towards combatting food fraud. Current methods used to detect such adulterations require complicated time- and resource-intensive preparation steps. In this work, a comparative study incorporating Raman and infrared spectroscopies, photolumines- Citation: Chavez-Angel, E.; Puertas, cence, and thermal-conductivity measurements of different sets of adulterated olive oils is presented. B.; Kreuzer, M.; Soliva Fortuny, R.; The potential of each characterization technique to detect traces of adulteration in extra virgin olive Ng, R.C.; Castro-Alvarez, A.; oils is evaluated. Concentrations of adulterant on the order of 5% can be detected in the Raman, Sotomayor Torres, C.M. infrared, and photoluminescence spectra. Small changes in thermal conductivity were also found Spectroscopic and Thermal for varying amounts of adulterants. While each of these techniques may individually be unable Characterization of Extra Virgin to identify impurity adulterants, the combination of these techniques together provides a holistic Olive Oil Adulterated with Edible approach to validate the purity and authenticity of olive oils. Oils. Foods 2022, 11, 1304. https:// doi.org/10.3390/foods11091304 Keywords: edible oils; Raman; photoluminescence; FTIR; thermal conductivity; PCA; 2DCOS Academic Editor: Daniel Cozzolino Received: 14 April 2022 Accepted: 28 April 2022 1. Introduction Published: 29 April 2022 Olive oil is considered to be one of the best edible oils and an essential component in the Publisher’s Note: MDPI stays neutral Mediterranean diet due to extraordinary organoleptic qualities and a large number of health with regard to jurisdictional claims in benefits. According to the Codex alimentarius of the Food and Agriculture Organization published maps and institutional affil- of the United Nations (FAO) [1], olive oils are classified in three categories: virgin olive, iations. refined olive, and refined olive-pomace oils. These, in turn, are divided into different grades depending on their organoleptic qualities, median defects, and color, among other attributes. From the hierarchy list of grades among these categories, extra virgin olive oil Copyright: © 2022 by the authors. (EVOO) is considered to have the highest nutritional value with various health benefits. Licensee MDPI, Basel, Switzerland. Among its nutritional properties, EVOO possesses high antioxidant activity [2,3], exhibits This article is an open access article anti-inflammatory effects [3,4], improves the metabolism of carbohydrates in patients distributed under the terms and with type-2 diabetes [5–8], reduces blood pressure and the risk of hypertension [7,9], and conditions of the Creative Commons improves vasodilation [10,11], to name a few. These many health benefits have boosted the Attribution (CC BY) license (https:// popularity of olive oil in recent decades [12], although this popularity has also brought creativecommons.org/licenses/by/ about other problems associated with the adulteration and/or deliberate mislabeling of 4.0/). EVOO [13,14]. One of the principal motivations for olive-oil fraud is the large price gap Foods 2022, 11, 1304. https://doi.org/10.3390/foods11091304 https://www.mdpi.com/journal/foods Foods 2022, 11, 1304 2 of 13 between EVOO and other non-olive oils or even between EVOO and other types of olive oils. Due to its relative scarcity and high production/selling price, unscrupulous processors have been fined for adulterating EVOO with large amounts of cheaper oils. EVOO itself is a vegetal fat with high levels of monounsaturated fatty acids (e.g., 78%) and low levels of saturated acids (e.g., 14%), in contrast to cheap seed oils (e.g., sunflower, corn, and soybean), which have high levels of polyunsaturated fats [1]. Consequently, adulteration with other oils results in the loss of many of the healthy properties of EVOO. There is a long list of properties that can be tested to ensure the quality of EVOO [15–17]. The standard and official methods to characterize EVOO are gas chromatography (GC) and high-performance liquid-chromatography (HPLC). GC is mainly used to determine the composition of the saponifiable fraction, which contains fatty acids and their derivatives, as well as the unsaponifiable fraction, which contains waxes, aliphatic alcohols, tocopherols, and phenolic compounds, among others. On the other hand, HPLC is mainly used to determine the structure of triglycerides, the quantity of pigments such as chlorophylls and carotenes, and other quality parameters (other than purity). Apart from these offi- cial methods, there are a number of alternative and complementary methods that have been suggested over the past decade. Among them, infrared and Raman spectroscopy are gaining attention [18–22]. This work aims at evaluating the ability to detect traces of adulteration in EVOO with three spectroscopic techniques: Raman, photoluminescence (PL), and Fourier-transform infrared (FTIR) spectroscopies. In addition, we explore the use of thermal conductivity as a potential new parameter to be used as a detection tool. Despite its relative measurement simplicity, thermal conductivity has, to date, been overlooked as a figure of merit to determine food purity. The combination of all of these techniques provides an easy method, free of sample pre-processing, to ascertain the quality and authenticity of food. 2. Materials and Methods Twenty samples of EVOO were intentionally adulterated using five different types of edible oils: sunflower (La Masia, “masiasol”, Sevilla, Spain), high oleic (HO) sunflower (Carrefour, “Aceite refinado de girasol”, Madrid, Spain), 95–5% soybean–nut blend (La española, “Soy plus”, Jaen, Spain), corn (Coosol, “Maiz”, Jaen, Spain), and olive-pomace (Carrefour, “Aceite refinado de orujo de oliva”, Madrid, Spain), in volume concentrations of 5%, 10%, 20%, and 50%. A single type of EVOO (Salvatge “Les Garrigues”, Lleida, Spain) was adulterated, and the oil was provided directly from the factory to guarantee its purity. All samples were stored in a dry place protected from light to preserve their quality (see Figure S1a, Supplementary Materials). The Raman and photoluminescence spectra were recorded using the same equipment (a T64000 Raman spectrometer using a liquid-nitrogen-cooled Symphony CCD manufac- tured by HORIBA Jobin Yvon, Chilly-Mazarin, France) optimized in the visible regime (400–800 nm). It was used in single-grating mode with 2400 and 300 lines per mm and a spectral resolution of at least 0.4 cm−1 and 0.2 nm for Raman and photoluminescence, respectively. The use of 2400 lines for Raman measurements provides a very high frequency resolution at the cost of a small frequency window. On the other hand, 300 lines allow for a larger spectroscopic window which is ideal for the broad PL signal. The measurements were performed by focusing a diode laser (532 nm) onto a transparent quartz cuvette with a 10× long working distance microscope objective (see Figure S1b,c, Supplementary Mate- rials). The power of the laser was kept as low as possible (<0.5 mW) to avoid any possible damage from self heating of the samples. For the photoluminescence measurements (also known as fluorescence spectroscopy), all samples were measured using 3 accumulations with the same integration time of 0.3 s with a fixed focal plane, to allow for direct compari- son between each sample. For each sample, 5 to 10 spectra were recorded at positions on the sample. FTIR spectra were recorded (64 co-added scans) by a Hyperion 3000 infrared (IR) microscope coupled to a Vertex 70 spectrometer manufactured by Bruker (Billerica, MA, Foods 2022, 11, 1304 3 of 13 USA) at the infrared beamline MIRAS of the ALBA synchrotron [23]. Data was recorded with a liquid-nitrogen-cooled MCT detector. A 2–5 µL drop of oil was placed on the center of a piece of ZnSe glass and pressured with a second slide to create a homogenous oil film. The setup was used in the transmission configuration with a spectral resolution of 4 cm−1 with a Globar as the infrared light source. The IR light was focused onto the ZnSe slide using a 30× Schwarzschild condenser and collected with a matching objective. Principal component analysis (PCA) was used to treat the FTIR spectra using the soft- ware Orange Data Mining [24]. For each sample, 50 spectra at different sample positions were recorded and concatenated in a large matrix, as displayed in Table S1 in the supple- mentary information. Prior to the calculation, baseline corrections, spectral normalization, and Savitzky–Golay filters (for smoothing) and derivatives (to reduce scatter effects), were applied to process the spectra (see Figure S2, Supplementary Materials). The thermal conductivity (k) was determined by the bidirectional three-omega (3ω) method [25,26] over the temperature range T = 298–400 K. The bidirectional 3ω method is based on the measurement of a rise in temperature that is produced by an alternating current (AC) passing through a metallic strip via the Joule heating effect. The metal line is composed of four rectangular pads connected by pins to a narrow wire that is used simultaneously as both a heater and temperature sensor (see Figure S1d, Supplementary Materials). The two outer pads are used to apply the AC current while the inner pads are used to measure the third component voltage (3ω-voltage), which contains the information regarding the temperature rise ∆T. Metal heaters (Cr:Au, 5:95 nm) were deposited by physical vapor deposition onto quartz substrates (5 × 5 × 0.5 mm3 ). For the measurement, a drop of oil (~10 µL) was placed on top of the 3ω heater. First, an empty 3ω cell was measured (reference). Then, a second measurement took place using the same cell after the sample to be studied was placed on top of the heater. Assuming that heat transfer occurs only across sample-heater-substrate interfaces, the total measured temperature change of the heater (∆TTotal ) is given by [25]: 1 1 1 = + (1) ∆TTotal ∆TSample ∆TSubstrate where ∆TSample and ∆TSustrate correspond to the temperature fluctuations induced by the sample (oil) and substrate (reference) located at the top and the bottom of the heater, respec- tively. Lubner et al. [26] showed that the error associated with this interface assumption (Equation (1)) can be as small as 1% if three experimental conditions are fulfilled: (i) the ratio of the thermal diffusivities of the sample (αoil ) and the substrate (αSub ), αoil /αSub > 10−1 ; (ii) the ratio of the thermal conductivities is in the range 10−2 < koil /kSubstrate < 1; and (iii) the excitation frequencies are <100 Hz (low frequency limit). In our case, the room-temperature thermal diffusivity of the oils fluctuated within the range of (0.5–0.8) × 10−7 m2 s−1 [27,28], while that of the quartz fluctuated within the range of (0.8–1) × 10−7 m2 s−1 [29], i.e., 1 > αoil /αSub > 0.5. The k of quartz is ~1.2–1.4 W m−1 K−1 [29,30], and the k of oils was ~0.15–0.17 W m−1 K−1 [31,32], i.e., koil /kSubstrat e < 1. The frequency range used here was (5–100) Hz, which falls within the low frequency limit. The relationship between the temperature rise and the heat generation rate can be expressed as [33,34]: Z ∞ P sin2 ( xb) ∆T = dx (2) lπk 0 ( xb)2 x2 + iq2 p p p q = 4π f /α= 4π f CV /k (3) ∆T = ∆TX + i∆TY (4) where ∆T is the complex temperature rise oscillation; b and l are the heater’s half width (5 µm) and length (1 mm), respectively;√ q is the inverse of thermal penetration depth; CV is the volumetric heat capacity; i = −1 is the imaginary number; f is the excitation Foods 2022, 11, 1304 4 of 13 Foods 2022, 11, 1304 4 of 13 Δ𝑇 = Δ𝑇𝑋 + 𝑖Δ𝑇𝑌 (4) where ΔT is the complex temperature rise oscillation; b and l are the heater’s half width (5 frequency; µm) andand P is(1the length ACrespectively; mm), power. Theqreal and is the the imaginary inverse of thermalparts are proportional penetration depth; CV isto the in-phase (‘X’) and heat the volumetric quadrature capacity; 𝑖 =components (‘Y’) of three-omega √−1 is the imaginary voltage. number; f is the excitation frequency; andthermal Finally, the P is theconductivity AC power. The of real and was the oils the imaginary found byparts leastare proportional square fitting oftothe in- thesignal phase in-phase (‘X’)kand using andquadrature (‘Y’)variables. CV as fitting components of three-omega A detailed voltage.of the bidirectional description techniqueFinally, and the thethermal conductivity of full development of the theoils was found equations by be can least squareinfitting found of the the supporting in-phase signal using k and C V as fitting variables. A detailed description of the information of our previous works [35,36]. bidirectional technique and the full development of the equations can be found in the supporting information of our previous works [35,36]. 3. Results 3.1. Photoluminescence 3. Results The 3.1. photoluminescence (PL) spectra acquired from pure and adulterated EVOO with Photoluminescence different amounts of olive-pomace oil adulterant is depicted in Figure 1a. PL spectra for all The photoluminescence (PL) spectra acquired from pure and adulterated EVOO with of the different different adulterated amounts EVOO oil of olive-pomace samples thatiswere adulterant adulterated depicted in Figurewith 1a. PLdifferent oils are spectra for depicted in Figure S3a–e, Supplementary Materials. Pure adulterant all of the different adulterated EVOO samples that were adulterated with different oils oils (HO sunflower, sunflower, corn, and are depicted soy–nut) in Figure do not S3a–e, present anyMaterials. Supplementary PL activityPure under this 532 oils adulterant nm excitation. (HO In the case of pure sunflower, olive-pomace sunflower, corn, andoil, whichdo soy–nut) is anot common present adulterant oil, the any PL activity PL this under is very 532 weak withnma excitation. clear blueInshift in itsofPL the case peak pure relative tooil,that olive-pomace of pure which EVOO.adulterant is a common Despite the oil, fact the that bothPLEVOO is very and weakolive-pomace with a clear blueoilshift areinderived its PL peak from relative olives,to that theirofPL pure EVOO.present spectra Despite large the fact that differences frombothoneEVOO another and olive-pomace due oil are derived of to the low concentration from olives, their compounds PL luminescent with spectra present large properties, suchdifferences as pigments from onechlorophyll, (e.g., another due to the low concentration carotenes, and derivatives),of compounds phenols, and with luminescent tocopherols properties, in the adulterant oils such as pigments [37]. For EVOO, the (e.g., strongchlorophyll, luminescence carotenes, aroundand 670 nm derivatives), phenols, and tocopherols in the adulterant oils [37]. For EVOO, the strong and 720 nm is mainly associated to the photosystem of chlorophyll [38]. The first peak luminescence around 670 nm and 720 nm is mainly associated to the photosystem of is attributed to photosystem I (PSI) and the second peak is due to the combination of chlorophyll [38]. The first peak is attributed to photosystem I (PSI) and the second peak is photosystems I and II [38]. The strong photoluminescence can be seen even by naked eye due to the combination of photosystems I and II [38].The strong photoluminescence can (Figure S1b,c, Supplementary be seen even by naked eye (Figure Materials). S1b,c, Supplementary Materials). Olive-pomace oil EVOO 48 674.5 Integrated spectra [a.u.×10 ] Peak position [nm] 16 4 5 vol % 674.4 10 vol % 44 Intensity [a.u.×10 ] 674.3 3 20 vol % 12 40 674.2 50 vol % Pure Pomance 36 674.1 0 20 40 60 8 Oil concentration vol [%] 32 28 Fit 95% confidence 4 EVOO Pomace 24 Corn HO Sunflower Soy-nut Sunflower 0 20 640 680 720 760 0 10 20 30 40 50 60 Wavelength [nm] (a) Oil concentration [%] (b) Figure Figure 1. (a) 1. (a) Photoluminescencespectra Photoluminescence spectra ofofEVOO EVOOadulterated adulteratedwith different with concentrations different of olive- concentrations of olive- pomace oil. (b) Integrated photoluminescence spectra of the different oil mixtures as a function of pomace oil. (b) Integrated photoluminescence spectra of the different oil mixtures as a function of the the adulterant-oil concentration. The light-grey shaded region represents the 95% range of adulterant-oil concentration. confidence region around theThe light-grey best-fit shaded line. (Inset) region peak represents position of the PLthe 95% as spectra range of confidence a function of region around the adulterant-oil best-fit line. (Inset) peak position of the PL spectra as a function of adulterant- concentration. oil concentration. The numerically integrated PL intensity of all of the spectra as a function of the adulterant-oil concentration of different adulterant oils is shown in Figure 1b. The light- grey shaded region represents the 95% range of confidence region around the best-fit line. We note that the best-fit line passes through pure EVOO, but a clear linear decrease and vari- ation in the integrated intensity is observed due to the negligible luminescent activity of the adulterant oils. Additionally, we observe a small blue shift of the chlorophyll/pheophytin peak as the concentration of adulterant oil increases (see inset Figure 1b). The origin of this The numerically integrated PL intensity of all of the spectra as a function of the adulterant-oil concentration of different adulterant oils is shown in Figure 1b. The light- grey shaded region represents the 95% range of confidence region around the best-fit line. We note that the best-fit line passes through pure EVOO, but a clear linear decrease and Foods 2022, 11, 1304 variation in the integrated intensity is observed due to the negligible luminescent activity 5 of 13 of the adulterant oils. Additionally, we observe a small blue shift of the chlorophyll/pheophytin peak as the concentration of adulterant oil increases (see inset Figure 1b). The origin of this blue shift is even more pronounced when comparing the PL blue shift is even more pronounced when comparing the PL spectra of pure EVOO with spectra of pure EVOO with pure adulterant oil, since the PL peak in pure adulterant oil pure adulterant oil, since the PL peak in pure adulterant oil sits at lower wavelengths (see sits at lower wavelengths (see Figure S3, Supplementary Materials). Figure S3, Supplementary Materials). 3.2. 3.2. Raman Raman Spectroscopy Spectroscopy The The Raman Raman spectra spectra of of pure pure EVOO, EVOO, olive-pomace, olive-pomace, HO HO sunflower, sunflower, corn,corn, and and soy–nut soy–nut blend blend oils are depicted in Figure 2. Four common bands can be observed in all ofthe oils are depicted in Figure 2. Four common bands can be observed in all of theoils, oils, located at ~ 1265, 1305, 1440 and 1656 cm −−11, which correspond to the common Raman located at ~1265, 1305, 1440 and 1656 cm , which correspond to the common Raman modes modes of of unsaturated unsaturated fatty fatty acids acids such such as:as: oleic oleic (OA, (OA, C18:1), C18:1), linoleic linoleic (LA, (LA, C18:2), C18:2), andand linolenic linolenic (ALA, C18:3) acid [39]. These molecules are all 18-carbon carboxylic acids (ALA, C18:3) acid [39]. These molecules are all 18-carbon carboxylic acids with with one, one, two, two, and and three three cis-double cis-double bonds, bonds, respectively. respectively. Each Each of of the the oils oils under under study study hashas aa comparable fatty-acidcomposition comparable fatty-acid composition(see (see Table Table S2the S2 in insupplementary the supplementary information), information), which which leads toleads theseto these common common carboxyliccarboxylic acid peaksacid peaks in their Ramanin their spectra.Raman These spectra. These characteristic characteristic Raman bands have already been previously studied Raman bands have already been previously studied by El-Abassy et al. and Lv et al. [39,40]. by El-Abassy et al. and Lv et al. [39,40]. The attribution of each of the observed peaks The attribution of each of the observed peaks to their associated vibrational mode for all to their associated vibrational mode for spectra in Figure 2 isall spectra in Figure summarized in Table 2 is1.summarized The remaining in Table 1. The remaining two Raman bands located two Raman at ~1155 bands 1523 cmat−1~,1155 and located whichandare1523 cm−1, to unique which EVOO,are unique to EVOO, have have previously been previously associated been associated with C–C and C=C with C-C andvibrations stretching C=C stretching vibrations of the main polyeneof chain the main polyene chain of carotenoids of [41,42]. carotenoids These additional [41,42].twoThese bands additional two bands are not detected in are anynot detected of the in anyoils, adulterant of the adulterant including the oils, includingoil. olive-pomace theAsolive-pomace was the caseoil. with Asthe was the case with the photoluminescence, photoluminescence, Raman spectroscopyRaman clearly distinguishes clearly spectroscopy a spectroscopic distinguishesdifference between EVOO a spectroscopic and between difference all of theEVOO other edible and alloils, of including the olive-pomace other edible oil, which oils, including shares a common olive-pomace oil, which derivation from olives. shares a common The absence derivation from of carotenoids olives. The absence in refined oils results of carotenoids in from refined theoils degradation results from that thethey suffer during degradation that food they processing, suffer duringstorage, and thermal food processing, treatment. storage, and thermalThermal treatment treatment. during Thermal the refinement treatment during process the leads toprocess refinement the isomerization leads to theofisomerization the carotenoids andcarotenoids of the a consequent andchange in their a consequent molecular structure [43]. change in their molecular structure [43]. 1050 1200 1350 1500 1650 1155 1523 1656 Assignment of the common Raman bands 1440 among the edibles oils Frequency [cm-1] Vibrational mode EVOO Intensity [a.u] 1265 =C-H bending (scissoring) Olive-pomace 1305 C-H bending (twisting) 1440 C-H bending(scissoring) 1656 C=C stretching HO Sunflower Corn 1265 1305 Soy-nut 1050 1200 1350 1500 1650 -1 Raman Shift [cm ] Raman spectrum Figure 2. Raman spectrum of pure EVOO, olive-pomace, sunflower, corn, and soy–nut soy–nut oils. oils. Foods 2022, 11, 1304 6 of 13 Foods 2022, 11, 1304 6 of 13 Table 1. Assignment of the Raman bands of the edible oils. Table 1. Assignment of the Raman bands of the edible oils. Frequency [cm−1] Vibrational Mode Frequency [cm−1 ] Vibrational Mode 1155 C-C stretching (carotenoid) 1155 1265 C–Cbending =C-H stretching (carotenoid) scissoring 1265 =C–H bending scissoring 1305 1305 C-HC–Hbending bendingtwisting twisting 1440 1440 C-HC–Hbending scissoring bending scissoring 1523 1523 C=CC=C stretching (carotenoid) stretching (carotenoid) 1656 1656 C=C C=C stretching stretching Figure Figure3a 3a showsshows thethe normalized normalized Raman Raman spectrum spectrum of of pure pure EVOOEVOO adulterated adulterated with with different differentamounts amounts of of HO HO sunflower. sunflower. AsAs thetheHO HO sunflower sunflower concentration concentration increases, increases, thethe intensity intensity at at 15231523 cmcm −1 (carotenoid −1 (carotenoid peak)peak) decreases. decreases. Similar Similar results results were were also also found found forfor thethe restrest of of thethe adulterant adulterant oilsoils (see (see Figure Figure S4,S4, Supplementary Supplementary Materials). Materials). A summary A summary of of these these results are shown in Figure 3b, which presents the ratio of the numerical integration of thethe results are shown in Figure 3b, which presents the ratio of the numerical integration of −1 and 1656 −1 areas areas of of thethe Raman Raman peaks peaks at at 15231523cmcm −1 and 1656 cm cm . A−1clear . A clear decrease decrease in/II1656 in I1523 /I1656can 1523ratio ratio be can be observed observed as the adulterant as the adulterant oil content oil increases. content increases. This effect This comeseffect fromcomes fromRaman the zero the zero Raman activity foractivity for carotenoids carotenoids peaks shown peaksbyshown by all adulterant all adulterant oils studiedoils studied here. Notably, here. Notably, the the Raman Raman spectra spectra can be can be directly directly measured measured fromfrom as-packaged as-packaged oil oil without without opening opening and and manipulating the oil, allowing for non-invasive verification even from an unopened oiloil manipulating the oil, allowing for non-invasive verification even from an unopened bottle bottle (seeFigure (see Figure S5, S5, Supplementary SupplementaryMaterials). Materials). Similar to the Similar to PL, thethe PL,addition of adulterant the addition of oils leads to a decrease adulterant oils leads to a decrease in the I /I in the 1523 1656 integral ratio. Qui et al. recently observed I1523/I1656 integral ratio. Qui et al. recently observed a similar result using a similar resultthe usingI1523the /I1656 I1523ratio /I1656 to determine ratio to determinethe free-fatty-acid (FFA) content the free-fatty-acid (FFA) contentof oliveofoils andoils olive found and that found this intensity that ratio decreases this intensity linearly ratio decreases with FFA linearly withcontent, although FFA content, the FFA although the FFA content was obtained from the nutrition label of each of the oils [21]. Thus, /I content was obtained from the nutrition label of each of the oils [21]. Thus, the I1523 the1656 integral ratio is an additional useful figure of merit to quantify I1523/I1656 integral ratio is an additional useful figure of merit to quantify EVOO purity. EVOO purity. 1500 1550 1600 1650 1700 HO Sunflower oil Corn 1.0 Olive pomace HO Sunflower 0.8 Soy-nut EVOO I1523/I1656 Pure EVOO 5 vol % 0.6 10 vol % 20 vol % 0.4 50 vol % Pure HO Sunflower 0.2 1500 1550 1600 1650 1700 0 20 40 60 -1 Raman shift [cm ] (a) Oil concentration [%] (b) Figure Figure 3. 3.(a)(a)Raman Raman spectra of ofEVOO EVOO adulterated adulterated with with different different concentrations concentrations of highofoleic high oleic sunflower sunflower oil. (b) Intensity oil. (b) Intensity ratio ratio of the of the carotenoid carotenoid cm−(1523 peak (1523peak 1, I cm 1523 ) −1, I1523) to the C=C stretching peak −1 to the C=C stretching peak (1656 cm , (1656 I cm ) as, aI1656 −1 ) as a function function of the adulterant of the adulterant oil concentration. oil concentration. 1656 3.3.3.3. Fourier-Transform Fourier-Transform Infrared Infrared Spectroscopy Spectroscopy TheThe IR spectra IR spectra of pure of pure EVOO,EVOO, corn,and corn, soy, soy, and olive-pomace olive-pomace oils are depicted oils are depicted in Figure in Figure 4. For ease of visualization, the spectra were separated in two wavenumber 4. For ease of visualization, the spectra were separated in two wavenumber ranges: (3150– ranges: (3150–2800) cm −1 and (1500–1000) cm −1 . The first window shows the characteristic IR 2800) cm and (1500–1000) cm . The first window shows the characteristic IR peaks −1 −1 peaks resulting resulting from hydrogen from hydrogen stretchingstretching functionalfunctional groups, groups, while thewhile secondthe second shows window window shows other bond deformations and bending that are primarily associated with vibrations Foods 2022, 11, 1304 7 of 13 Foods 2022, 11, 1304 7 of 13 other bond deformations and bending that are primarily associated with vibrations of CH i groups (with i = 1, 2, 3) and C-O bonds [44]. Unlike the PL and Raman results, the FTIR spectra shows remarkable similarities between the spectra of the studied samples, making of CHi groups (with i = 1, 2, 3) and C–O bonds [44]. Unlike the PL and Raman results, the them difficult to differentiate. Therefore, a deep analysis using principal component FTIR spectra shows remarkable similarities between the spectra of the studied samples, analysis (PCA), a technique that allows for patterns and variations within a dataset to be making them difficult to differentiate. Therefore, a deep analysis using principal component readily visualized, was performed to allow for facile differentiation of each of the analysis (PCA), a technique that allows for patterns and variations within a dataset to be spectrum from one another. PCA analysis is relatively common in food chemistry, as readily visualized, was performed to allow for facile differentiation of each of the spectrum optical spectra tend to be very similar within particular foods and their associated from one another. PCA analysis is relatively common in food chemistry, as optical spectra derivatives. tend to be veryThesimilar resultswithin of this analysisfoods particular are displayed in the inset and their associated of Figure The derivatives. 4a,b. Our results results showed are of this analysis thatdisplayed EVOO and in olive-pomace oils could the inset of Figure not be 4a,b. Our differentiated results from showed that EVOOone another in FTIR spectroscopy, as the PCA scores were almost identical. However, and olive-pomace oils could not be differentiated from one another in FTIR spectroscopy, the PCA scores of corn, as the PCA soy–nut scores oil, andidentical. were almost sunflower oils showed However, clear the PCA differences scores of corn,when compared soy–nut oil, and with EVOO. sunflower oils showed clear differences when compared with EVOO. Normalized Absorbance [a.u.] Normalized Absorbance [a.u.] 0.04 2925 0.10 1164 PC-2 (16%) PC-2 (17%) 0.05 0.00 0.00 -0.04 2854 -0.05 -0.08 -0.04 0.00 0.04 0.08 -0.10 -0.05 0.00 0.05 0.10 0.15 PC-1 (78%) PC-1 (67%) 1464 1099 EVOO 2956 1377 Pomace 3009 1240 1118 Corn 2872 Soy 3150 3100 3050 3000 2950 2900 2850 2800 1500 1400 1300 1200 1100 1000 -1 -1 Wavenumber [cm ] (a) Wavenumber [cm ] (b) Figure 4. Figure FTIRspectra 4. FTIR spectraofoffour four oils:oils: EVOO EVOO (pale (pale green), green), olive-pomace olive-pomace (blue), (blue), corncorn (orange) (orange) and and soy (dark green) soy (dark overover green) twotwo different different wavenumber wavenumber ranges rangesfocusing focusingononthethe(a) (a) hydrogen hydrogen stretching stretching functional functional groups groups and and (b) (b) CH CHii (i(i ==1, 1,2, 2,3) 3)functional functional groups groups present present in in each each oil. oil. At At the the most most superficial superficial level, level, aa quick quick differentiation differentiation of of the the IR IR spectra spectra of of the the oils oils was was established via PCA, though established though aa deeper deeper analysis analysis ofof the the PCA PCA scores scores ofof the the adulterated adulterated EVOO EVOO is possible. is possible. Figure Figure5 5shows shows thethe subsequent subsequentPCA PCAanalysis of IRof analysis spectra of adulterated IR spectra EVOO. of adulterated This rapid EVOO. Thisand simple rapid andPCA analysis simple highlights PCA analysis the impurities highlights added toadded the impurities EVOOtobyEVOOshowingby a shift in the scores of adulterated samples as the adulterant oil increases. showing a shift in the scores of adulterated samples as the adulterant oil increases. The The shift is observed even with less than 5% of added adulterant (Figure 5a,b,d,e). shift is observed even with less than 5% of added adulterant (Figure 5a,b,d,e). Similar Similar results have been observed results have beenby Vanstone observed et byal. [45], whoetdemonstrated Vanstone the potential ofthe al. [45], who demonstrated a combination potential of ofa near-infrared of combination spectroscopy near-infrared withspectroscopy PCA to detectwith EVOO PCA adulteration to detect at EVOOlevelsadulteration as low as 2.7%,at given an levels as unadulterated low as 2.7%, given reference an sample (i.e., pure unadulterated EVOO).sample reference However, wepure (i.e., demonstrate EVOO). similar conclusions with FTIR in the mid-IR spectral range, which However, we demonstrate similar conclusions with FTIR in the mid-IR spectral range, is advantageous as molecular fundamental vibrational modes lie in the mid-IR, while which is advantageous as molecular fundamental vibrational modes lie in the mid-IR,spectral measurements in the spectral while near-IR are measurements measurements in of themolecular near-IR arevibrational overtones. measurements While thevibrational of molecular PCA alone exhibits potential in its ability to discriminate similar spectra, the addition overtones. While the PCA alone exhibits potential in its ability to discriminate similar of a multivariable regression spectra, themodel willofbea necessary addition to obtain multivariable true quantification regression model will beofnecessary EVOO adulteration. to obtain true quantification of EVOO adulteration. Foods Foods 2022, 2022, 11, 11, 1304 1304 8 8of of 13 13 0.06 10% (a) (b) (c) 0.04 5% 20% EVOO Soy-nut EVOO 50% 10% EVOO 0.02 5% PC-2 0.00 20% 20% -0.02 Sunflower 10% 50% 50% -0.04 Corn -0.06 -0.06 -0.03 0.00 0.03 0.06 0.09 -0.09 -0.06 -0.03 0.00 0.03 0.06 0.09 -0.06 -0.03 0.00 0.03 0.06 0.09 PC-1 PC-1 PC-1 (d) (e) 0.015 (f) 0.10 EVOO EVOO EVOO 50% Soy-nut PC-2 0.010 5 % 10 % 50 % 5% 10% 0.05 0.005 20% Sunflower PC-2 0 20 % 10 % 0 0.005 0.010 0.015 PC-1 0.00 Sunflower 20 % -0.05 Corn 50 % -0.10 -0.05 0.00 0.05 0.10 0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 -0.10 -0.05 0.00 PC-1 PC-1 PC-1 PCA score −1 −1 Figure 5. Figure 5. PCA score plots plots of ofoil oilmixtures mixturesatatthe the3000 3000cmcm−1and and1300 1300cmcm−1 window: window: (a,d) corn–EVOO, (a) and (d) corn– (b,e) soy–nut–EVOO, EVOO, and (c,f) (b,e) soy–nut–EVOO, sunflower–EVOO. and The inset (c,f) sunflower–EVOO. Thein (f) shows inset a zoom in (f) shows around a zoom the PCA around the PCA scoresscores of theofsunflower-based the sunflower-based sample. sample. The color The color gradient gradient infigure in each each figure indicates indicates the evolution the evolution of the of PCsthefrom PCspure fromadulterant pure adulterant (darker(darker colors)colors) to smallest to smallest amount amount of adulterant of adulterant (lighter(lighter colors).colors). As As the thePCA PCAofofFTIRFTIRspectra spectra did didnotnot showshow significant differences significant differencesbetween betweenEVOO and EVOO and olive-pomace, olive-pomace, we applied we applied two-dimensional two-dimensional correlation correlation spectroscopy(2DCOS) spectroscopy (2DCOS)to to gain gain greater insight greater insightinto intothe theFTIR FTIRspectra. spectra.The The 2DCOS 2DCOS technique technique is aismathematical a mathematical method method for for analyzing analyzing changes changes in ainsignal a signal produced produced byby ananexternal externalperturbation perturbation(e.g., (e.g.,aa change change in temperature, pressure, temperature, pressure, pH,pH, concentration concentration of of mixtures, mixtures, etc.). etc.). To To calculate the 2DCOS2DCOS map map we used the concentration of olive-pomace oil as an external perturbation perturbation and the the spectra spectra dataset was dataset was ordered ordered from from pure pure EVOO EVOO (0% of oil adulterant) adulterant) to pure pomace pomace (100% (100% of of oil oil adulterant), i.e., adulterant), i.e., 0, 0, 5, 10, 20, 50, 100%. The The rawraw spectra spectra were were baselined baselined and and normalized normalized using the using themost-intense most-intenseband bandfor for eacheach frequency frequencywindow in Figure window in 4. The average Figure 4. The spectrum average was used was spectrum as a used reference as a spectrum following following reference spectrum the same procedure as reference the same procedure [46]. The as reference 2DCOS [46]. Theanalysis 2DCOS was performed analysis with the with was performed Mat2dcorr Matlab toolbox the Mat2dcorr Matlab [47]. toolboxFigure [47].6 Figure shows the synchronous 2DCOS map at the 3000 cm −1 (Figure 6a) and 1300 cm−1 (Figure 6b) FTIR 6 shows the synchronous 2DCOS map at the 3000 cm (Figure 6a) and 1300 cm (Figure −1 −1 frequency 6b) windows.windows. FTIR frequency The respective autocorrelation The respective and FTIR-averaged autocorrelation spectra arespectra and FTIR-averaged shown above each frequency window. Autocorrelation spectra are are shown above each frequency window. Autocorrelation spectra are defined by defined by a diagonal line a along the line diagonal 2DCOS alongmap and the their bands 2DCOS map areandknown as autopeaks. their bands are known The autopeaks as autopeaks.represent The real changes autopeaks betweenreal represent thechanges FTIR spectra thatthe between areFTIR produced spectra by that the external are producedperturbation by the (such as the addition of olive-pomace oil, in this external perturbation (such as the addition of olive-pomace case). The autocorrelation oil, in spectra this case). show The three autopeaks located at 2844, 2900, and 2974 cm −1 in the 3000 cm−1 window and autocorrelation spectra show three autopeaks located at 2844, 2900, and 2974 cm −1 in the only cm 3000 one−1 autopeak window and around only1330 cm−1 for around one autopeak the 13001330 cm−cm 1 window. The comparison of −1 for the 1300 cm−1 window. the auto correlation and FTIR spectra show that the larger changes among the spectra The comparison of the auto correlation and FTIR spectra show that the larger changes occur at wavelengths where the FTIR spectra is very weak, which indicates why the PCA among the spectra occur at wavelengths where the FTIR spectra is very weak, which analysis was not able to find significant differences between the olive-pomace oil and indicates why the PCA analysis was not able to find significant differences between the EVOO. Furthermore, a 2DCOS analysis of pure EVOO oils was also performed using the olive-pomace oil and EVOO. Furthermore, a 2DCOS analysis of pure EVOO oils was also same data treatment (Figure S6, Supplementary Materials) to verify that the observed performed using the same data treatment (Figure S6, Supplementary Materials) to verify variations are not artificial variations resulting from the data treatment such as background that the observed variations are not artificial variations resulting from the data treatment subtraction and/or normalization. In this analysis, the same large variation in autopeaks are such as background subtraction and/or normalization. In this analysis, the same large Foods 2022, 11, 1304 9 of 13 Foods 2022, 11, 1304 9 of 13 variation in autopeaks are in fact observed around 2844 and 2900 cm−1, indicating that such variations in fact observedare dependent around not2900 2844 and onlycmon−real 1 , indicating significant differences that in oil concentration, such variations are dependent but not also onlyononexperimental fluctuations. real significant differencesNotably, autopeaks around in oil concentration, but also2974oncm −1 are only experimental present in theNotably, fluctuations. EVOO/olive-pomace 2DCOS autopeaks around 2974 map. − 1 cm Consequently, while are only present in this type of data the EVOO/olive- pomace 2DCOS processing enablesmap. evenConsequently, while this to such small fluctuations type of data be used as processing identifiers forenables even such authentication small fluctuations between to be origin, oils of similar used asadditional identifiers processing for authentication between oilsofof2DCOS and identification similar peaks origin, additional processing may first be required. and identification of 2DCOS peaks may first be required. FTIR Auto correlation 3050 3000 2950 2900 2850 2800 1450 1350 1250 1150 1050 2800 1050 Wavenumber [cm ] Wavenumber [cm ] -1 -1 2850 1150 ×10-6 2.19 ×10-4 2900 1250 4.7 1.29 2950 2.9 1350 -0.260 3000 0.0 1450 3050 -1.60 -1.5 3050 3000 2950 2900 2850 2800 1450 1350 1250 1150 1050 -1 -1 Wavenumber [cm ] (a) Wavenumber [cm ] (b) Figure Figure 66.Contour Contourmapmapofofthethesynchronous synchronous2D 2DFTIR FTIRcorrelation correlationspectra spectraofof the the EVOO–pomace EVOO–pomace mixtures at the (a) 3000 mixtures at the (a) 3000 cm − −1 1 and (b) 1300 cm −1−frequency 1 and (b) 1300 cm frequency windows. The spectra abovethe windows. The spectra above the2D 2Dplots plots provide provide the auto correlation spectrum (black solid lines) of each 2DCOS map. The averageof the auto correlation spectrum (black solid lines) of each 2DCOS map. The average ofthe the FTIR FTIRspectra spectrainineach eachwindow windowisisalsoalsoincluded includedforforcomparison comparison(grey (greydashed dashedlines). lines). 3.4.Thermal 3.4. ThermalConductivities Conductivities Thetemperature The temperaturedependence dependence of of thethe thermal thermal conductivity conductivity (k) of(k) of pure pure oilsofand oils and threeof three adulterated adulterated mixtures mixtures are shown are shown in Figure in Figure 7a,b, respectively. 7a,b, respectively. A monotonic A monotonic decrease decrease in the in the thermal thermal conductivity conductivity as the temperature as the temperature increasesincreases can be observed can be observed in all in all of the of the studied studied samples. A similar temperature dependence was also reported samples. A similar temperature dependence was also reported by Turgut et al. [31]. by Turgut et al. [31]. Interestingly, the temperature dependence of the k of the EVOO–soy–nut Interestingly, the temperature dependence of the k of the EVOO–soy–nut oil mixture oil mixture followsthe follows thesame sametemperature temperaturedependence dependenceas asthe thepure pureEVOO, EVOO,evenevenatat50%50%of ofsoy–nut soy–nutoil oil concentration.AAcomparative concentration. comparativeanalysis analysisofofthe thekkvalues valuesatatroom roomtemperature temperature(Figure (Figure7c)7c)and and at 400 at 400 KK(Figure (Figure7d) demonstrate 7d) demonstratenoticeable differences noticeable between differences each pure between eachoilpure and oil between and EVOO and its adulterated mixtures. This highlights the ability of the between EVOO and its adulterated mixtures. This highlights the ability of the k—which, k—which, to date, has to tended date, to be overlooked has tended as a useful to be overlooked as afigure usefuloffigure meritofinmerit food in authentication—to food authentication—toprovide information that enables the distinction of pure and adulterated oils, provide information that enables the distinction of pure and adulterated oils, or, more or, more generally, other foodother generally, products food as well. as well. products Foods 2022, 11, Foods 2022, 11, 1304 1304 1010of of 13 13 0.19 0.18 0.18 k [W K m ] -1 0.17 0.17 k [W K m ] -1 -1 -1 0.16 Pure oils 0.16 Soy-Nut EVOO Corn 0.15 0.15 Soy-Nut 10% HO Sunflower Pomace Soy-Nut 50% EVOO Corn 50% 0.14 0.14 280 300 320 340 360 380 400 280 300 320 340 360 380 400 Temperature [K] (a) Temperature [K] (b) 0.178 0.17 Room temperature T = 400 K Soy Nut pure 0.176 0.16 Pomace k [W K m ] k [W K m ] 0.174 -1 -1 Corn 50% Soy-nut 50% 0.15 EVOO -1 -1 0.172 Soy Nut 10% Sunflower Corn 0.170 EVOO Corn Pomace 0.14 HO sunflower 0.168 Soy-nut 0.13 0 10 20 30 50 40 1 2 3 4 5 6 7 8 Oil concentration [%] (c) Samples (d) Figure 7. (a) Thermal conductivity of pure oils as a function of temperature. (b) Thermal conductivity Figure 7. (a) Thermal conductivity of pure oils as a function of temperature. (b) Thermal of adulterated oils as a function of temperature. (c) Room-temperature thermal conductivity as a conductivity of adulterated oils as a function of temperature. (c) Room-temperature thermal function of adulterant conductivity oil concentration. as a function (d) oil of adulterant Comparison of the (d) concentration. thermal conductivity Comparison at 400 of the K for thermal different pureatoils conductivity 400orKadulterated for differentEVOO. pure oils or adulterated EVOO. 4. Discussion 4. Discussion In this work, the potential of four different characterization techniques to detect In this work, the potential of four different characterization techniques to detect traces of adulteration in EVOO were analyzed. Photoluminescence, Raman, and Fourier- traces of adulteration in EVOO were analyzed. Photoluminescence, Raman, and Fourier- transform infrared spectroscopies demonstrated the successful detection of small traces transform infrared spectroscopies demonstrated the successful detection of small traces of adulteration on the order of 5%, while the thermal conductivity analysis showed small of adulteration on the order of 5%, while the thermal conductivity analysis showed small but constant fluctuations as a function of adulterant oil concentration. Notably, we demon- but constant strated fluctuations four different as a function characterization of adulterant methods that are able oil toconcentration. rapidly assessNotably, the puritywe of demonstrated four different characterization methods that are able EVOO. Photoluminescence showed a linear decrease in the peak intensity and position to rapidly assess theas purity of EVOO. the adulterant oilPhotoluminescence showed adue concentration was increased linear to adecrease decreaseininthe thepeak amountintensity and of chloro- position phyll andaspheophytin, the adulterant oil are which concentration was increased naturally present in EVOOdue but to a decrease absent in the amount in the adulterant oils. of chlorophyll and pheophytin, which are naturally present Raman spectroscopy also presented a clear difference between the spectra of in EVOO but absent EVOO in and the adulterant oils. Raman spectroscopy also presented a clear difference between adulterant oils (even in olive-pomace oil, which is also derived from olives) was also found. the spectra of EVOO two Notably, and peaks adulterant oils (even at ~1155 cm−1 in andolive-pomace 1523 cm−1 wereoil, which is alsoonly detectable derived from olives) in EVOO. These was also found. Notably, two peaks at ~1155 cm −1 and 1523 cm−1 were detectable only in modes are associated with the polyene chain of the carotenoids that are naturally presented EVOO. in EVOO These modesinare but absent theassociated adulterants. with the polyene A clear decreasechainin theof the carotenoids intensity that are ratio between the naturally presented peaks at 1523 in EVOO cm−1 (only presentedbut in absent EVOO) in and the 1656 adulterants. A clear decrease cm−1 (a common in the mode presented intensity ratio between the peaks at 1523 cm−1 (only presented in EVOO) and 1656 cm−1 (a Foods 2022, 11, 1304 11 of 13 in all the studied oils) was observed as a function of the adulterant-oil concentration. While a rough comparison between the IR spectra did not show appreciable differences, a statis- tical analysis showed grouping of the spectra and distinguished a remarkable difference in the PCA scores between pure EVOO and adulterated oils, demonstrating detection of as low as 5% adulterant concentration via FTIR spectroscopy. It is important to note that, while even PCA did not show significant differences between EVOO and EVOO–pomace mixtures, a deeper analysis using a two-dimensional correlation treatment was sensitive to small fluctuations around 2900 cm−1 . This result is a nascent effort that demonstrates the potential of 2DCOS analysis for the detection of EVOO adulterated with oils of very similar origin. Finally, an appreciable fluctuation in the thermal conductivity of EVOO was ob- served for different amounts of adulterant oils. Thermal conductivity has previously been overlooked as a simple but useful figure of merit for assessing food authenticity, but is also a useful manner in which purity can be ascertained. These results highlight the potential of these techniques to detect adulteration, and indicate that the results of the current study can be used as a starting point for the development of spectroscopic methods that allow for the effective and efficient detection of adulteration in olive oils by aiding in identification and classification. While each technique independently may fail to reliably capture small amounts of adulteration in EVOO given the complexity and chemical variability in the oils, a combination of all of them together provides a more holistic base for authentication. For example, as was observed in the case of the FTIR spectra, it is difficult to differentiate EVOO from olive-pomace oils, due to their common origin, though other techniques such as Raman can clearly distinguish the two. Future subsequent development of multiple sensors incorporating and combining these techniques will allow for the acquisition of complete spectral data sets that are critical for precise EVOO authentication. Beyond the authentication of EVOO, the combination of spectroscopic and thermal techniques has the potential to facilitate simplified authentication throughout the food industry. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/foods11091304/s1, Figure S1: Sample preparation; Figure S2: IR spectra second derivative and pc loading of EVOO, pomace, corn, and soy-nut oils; Figure S3: Photoluminescence of EVOO adulterated with different concentrations of: (a) corn, (b) soy-nut blend, (c) high oleic sunflower, (d) sunflower oils, and (e) olive-pomace oils; Figure S4: Normalized Raman spectra of EVOO adulterated with different concentrations of: (a) ol-ive-pomace, (b) soy-nut blend, and (c) corn oils; Figure S5: Normalized Raman spectra of EVOO measured directly from its package; Figure S6: 2DCOS map of pure EVOO; Table S1: Schematic representation of FTIR dataset for PCA; Table S2: Acid content of the studied edible oils [48]. Author Contributions: Conceptualization, E.C.-A., C.M.S.T. and A.C.-A.; Data curation, E.C.-A., B.P., M.K. and R.C.N.; Formal analysis, E.C.-A., B.P. and M.K.; Funding acquisition, C.M.S.T. and A.C.-A.; Investigation, E.C.-A., B.P. and R.C.N.; Methodology, E.C.-A. and A.C.-A.; Software, E.C.-A. and A.C.-A.; Supervision and validation R.S.F. and C.M.S.T.; Writing—original draft, E.C.-A., R.S.F. and R.C.N.; Writing—review and editing, E.C.-A., R.C.N. and R.S.F. All authors have read and agreed to the published version of the manuscript. Funding: ICN2 is supported by the Severo Ochoa program from the Spanish Research Agency (AEI, grant no. SEV-2017-0706) and by the CERCA Programme/Generalitat de Catalunya. ICN2 authors acknowledge the support from the Spanish MICINN project SIP (PGC2018-101743-B-I00). A.C.-A. acknowledges the support from Fondecyt Iniciación 11200620. R.C.N. acknowledges funding from the EU-H2020 research and innovation program under the Marie Sklodowska Curie Fellowship (Grant No. 897148). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Raw data can be provided by the corresponding author (ECA) on reasonable request. Foods 2022, 11, 1304 12 of 13 Acknowledgments: The infrared-spectroscopy measurements were performed at MIRAS beamline at ALBA Synchrotron with the collaboration of ALBA staff. Conflicts of Interest: The authors declare no conflict of interest. References 1. FAO Codex Standars for Fats and Oils from Vegetable Sources. 1999. Available online: http://www.fao.org/docrep/004/y2774e/ y2774e04.htm (accessed on 18 October 2021). 2. Dairi, S.; Carbonneau, M.-A.; Galeano-Diaz, T.; Remini, H.; Dahmoune, F.; Aoun, O.; Belbahi, A.; Lauret, C.; Cristol, J.-P.; Madani, K. 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