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Article

Quantification of Phenolic Compounds in Olive Oils by Near-Infrared Spectroscopy and Multiple Regression: Effects of Cultivar, Hydroxytyrosol Supplementation, and Deep-Frying

by
Taha Mehany
,
José M. González-Sáiz
and
Consuelo Pizarro
*
Department of Chemistry, University of La Rioja, 26006 Logroño, Spain
*
Author to whom correspondence should be addressed.
Antioxidants 2025, 14(6), 672; https://doi.org/10.3390/antiox14060672
Submission received: 6 May 2025 / Revised: 25 May 2025 / Accepted: 29 May 2025 / Published: 31 May 2025

Abstract

:
Near-infrared (NIR) spectroscopy, combined with multivariate calibration techniques such as stepwise decorrelation of variables (SELECT) and ordinary least squares (OLS) regression, was used to develop robust, reduced-spectrum regression models for quantifying key phenolic compound markers in various olive oils. These oils included nine extra virgin olive oil (EVOO) varieties, refined olive oil (ROO) blended with virgin olive oil (VOO) or EVOO, and pomace olive oil, both with and without hydroxytyrosol (HTyr) supplementation. Olive oils were analyzed before and after deep frying. The results show that HTyr ranged from 7.28 mg/kg in Manzanilla (lowest) to 21.43 mg/kg in Royuela (highest). Tyrosol (Tyr) varied from 5.87 mg/kg in Royuela (lowest) to 14.86 mg/kg in Hojiblanca (highest). Similar trends were observed in all phenolic fractions across olive oil cultivars before and after deep-frying. HTyr supplementation significantly increased both HTyr and Tyr levels in non-fried and fried supplemented oils, with HTyr rising from single digits in some controls (around 0 mg/kg) to over 300 mg/kg in most of the supplemented samples. SELECT efficiently reduced redundancy by selecting the most vital wavelengths and thus significantly improved the regression models for key phenolic compounds, including HTyr, Tyr, caffeic acid, decarboxymethyl ligstroside aglycone in dialdehyde form (oleocanthal), decarboxymethyl oleuropein aglycone in dialdehyde form (oleacein), homovanillic acid, pinoresinol, oleuropein aglycone in oxidized aldehyde and hydroxylic form (OAOAH), ligstroside aglycone in oxidized aldehyde and hydroxylic form (LAOAH), and total phenolic content (TPC), achieving correlation coefficients (R) of 0.91–0.98. The SELECT-OLS method generated highly predictive models with minimal complexity, using at most 30 wavelengths out of 700. The number of decorrelated predictors varied, at 12, 14, 15, 30, 30, 21, 30, 30, 30, and 18 for HTyr, Tyr, caffeic acid, oleocanthal, oleacein, homovanillic acid, pinoresinol, OAOAH, LAOAH, and TPC, respectively, demonstrating the adaptability of the SELECT-OLS approach to different spectral patterns. These reliable calibration models enabled online and routine quantification of phenolic compounds in EVOO, VOO, ROO, including both non-fried and fried as well as supplemented and non-supplemented samples. They performed well across eight deep-frying conditions (3–6 h at 170–210 °C). Implementing an NIR instrument with optimized variable selection would simplify spectral analysis and reduce costs. The developed models all demonstrated strong predictive performance, with low leave-one-out mean prediction errors (LOOMPEs) with values of 15.69, 8.47, 3.64, 9.18, 16.71, 3.26, 8.57, 13.56, 56.36, and 82.38 mg/kg for HTyr, Tyr, caffeic acid, oleocanthal, oleacein, homovanillic acid, pinoresinol, OAOAH, LAOAH, and TPC, respectively. These results confirm that NIR spectroscopy combined with SELECT-OLS is a feasible, rapid, non-destructive, and eco-friendly tool for the reliable evaluation and quantification of phenolic content in edible oils.

Graphical Abstract

1. Introduction

Virgin olive oil (VOO) is a key component of the Mediterranean diet (MD), renowned for its nutritional value and health benefits. The MD, rich in protective nutrients and bioactive compounds, has been linked to the prevention of various diseases, including obesity and cancer [1]. In addition, VOO is more resistant to oxidation than most edible oils, offering a relatively long shelf life (12–18 months). This stability is due to its high antioxidant content, particularly phenolic compounds, and its low levels of polyunsaturated fatty acids [2]. Among the different categories of olive oil, extra virgin olive oil (EVOO) stands out for its superior composition and sensory attributes, evaluated by recognized panels [3]. Regular EVOO consumption is associated with a lower risk of chronic diseases such as cardiovascular disease, stroke, type 2 diabetes, metabolic syndrome, cognitive decline, and certain cancers, including breast and colorectal cancer. Additionally, it has been shown to reduce obesity risk, prevent weight gain, and improve overall longevity, underscoring its importance in dietary recommendations [4].
Olive oil is primarily composed of triacylglycerols, with a minor fraction (0.5–1.0%) consisting of non-glyceridic compounds, including over 30 phenolic compounds that enhance oxidative stability. A strong correlation exists between phenolic content and EVOO’s resistance to rancidity, with extra virgin olive oils consistently exhibiting higher phenol levels than refined oils [5,6,7]. EVOO’s health benefits are largely attributed to its phenolic content and its fatty acid composition [8]. The type and concentration of phenols depend on factors such as olive variety, cultivation, harvesting, and processing methods [9]. In addition to their biological activity, these compounds contribute to EVOO’s distinctive sensory characteristics [10,11]. This minor fraction also contains free fatty acids, tocopherols, sterols, phospholipids, waxes, squalene, hydrocarbons, and volatile compounds, some of which influence both flavor and health properties. Phenolic compounds, in particular, are responsible for EVOO’s characteristic bitterness and pungency while enhancing its antioxidant capacity. They also inhibit low-density lipoprotein (LDL) oxidation, a key factor in atherosclerosis, and provide various other protective health effects [12,13]. Besides phenolic compounds, the unsaturated fatty acids (UFAs) in VOO, particularly oleic and linoleic acids, have been shown to impact cancer development and metastasis by suppressing the overexpression of the oncogene HER2 [14].
Phenolic compound extraction from olive oil using traditional methods such as liquid–liquid extraction (LLE) and solid-phase extraction (SPE) has several disadvantages. LLE often requires large volumes of toxic organic solvents, is time-consuming, and can lead to emulsion formation, complicating phase separation. SPE, while reducing solvent use, is associated with high operational costs, limited sample capacity, and potential analyte loss during extraction. In contrast, newer extraction techniques—such as ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and miniaturized solvent techniques—offer several advantages, including reduced solvent consumption, shorter extraction times, improved extraction efficiency, and lower environmental impact. These modern approaches can enhance reproducibility and are more suitable for routine analysis and high-throughput applications. Moreover, numerous methods for determining phenolic compounds in olive oil have been reported, including high-performance liquid chromatography (HPLC), LC-MS, HPLC coupled with fluorescence detection (FLD) or photodiode array (PDA), nuclear magnetic resonance (NMR), mass spectrometry (MS), and, more recently, time-of-flight mass spectrometry (TOF-MS), gas chromatography–mass spectrometry (GC-MS), colorimetric assay (Folin–Ciocalteu method), and capillary electrophoresis (CE) [15,16,17,18,19,20]. While these techniques are effective and offer highly sensitive and specific tools for identifying and quantifying phenolic compounds in complex matrices like olive oil, they have several drawbacks. They are often time-consuming due to extensive sample preparation and long analysis times, making them inefficient for rapid screening. Additionally, they are costly, requiring specialized equipment, solvents, and reagents. Complex sample preparation, solvent usage, and environmental concerns further add to their limitations. Some methods struggle with detecting low-concentration phenolic compounds, while high-temperature techniques like GC can lead to compound degradation. Moreover, these techniques require skilled operators for precise instrument handling and data interpretation.
To address these limitations, non-destructive vibrational spectroscopic techniques such as near infrared (NIR), mid infrared (MIR), and Raman spectroscopy have emerged as efficient alternatives for analyzing olive oils. These methods enable the rapid and accurate identification of key bioactive compounds, including unsaturated fatty acids, phenolic compounds, and antioxidants, surpassing traditional approaches [20,21,22,23]. Advances in spectroscopy, data processing, and chemometric techniques have further enhanced detection accuracy. Vibrational spectroscopy provides precise measurement of compound concentrations and structural variations, offering a robust scientific foundation for quality certification and functional assessment of olive oil [20,24]. Among spectroscopic techniques, near-infrared (NIR) spectroscopy stands out for its speed, environmental friendliness, and non-destructive nature, making it highly suitable for both qualitative and quantitative analysis of olive oil. However, it does have some limitations, including lower sensitivity and specificity compared with traditional chromatographic methods. The accuracy of NIR spectroscopy relies heavily on the development of robust calibration models, as overlapping spectral bands can complicate the precise quantification of chemically similar compounds within complex matrices. Therefore, the development of a reliable and robust NIR calibration model is of significant importance for improving the analytical performance and applicability of this technique in olive oil evaluation [25,26,27,28]. NIR is a spectroscopic technique that operates on the principle of molecular vibrations. When NIR light, typically within the 780–2500 nm wavelength range, interacts with a sample, specific molecules absorb light energy at characteristic wavelengths. This absorption induces transitions in vibrational energy levels, corresponding to the vibrational modes of chemical bonds such as C–H, N–H, and O–H. By analyzing the intensity of these absorption peaks, valuable insights into the molecular composition of the sample can be obtained [29].
Artificial intelligence, chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data, due to their ability to model complex datasets and classify unknown samples [30]. However, the accuracy of these models can be affected by redundant variables, irrelevant information, and instrumental noise [31,32]. NIR spectroscopy, widely used across various industries, benefits from variable selection to enhance precision and interpretability. Identifying relevant wavelengths is crucial for improving predictive models [33]. Variable selection plays a crucial role in multivariate regression and has become an essential tool across various research fields [34].
This study employed SELECT as a chemometric tool for variable selection on NIR spectra of various olive oils to extract a minimal yet highly informative set of predictors [35,36,37]. By iteratively reducing collinearity through the SELECT algorithms, SELECT enhances model performance and interpretability. This approach facilitates the use of linear regression models, which, despite their simplicity and ease of interpretation, are particularly sensitive to collinearity among regressors. Furthermore, ordinary least squares (OLS) regression is a commonly used statistical technique for estimating relationships between variables by minimizing errors in a least-squares sense. It plays a key role in multivariate calibration and predictive modeling, operating under assumptions of linearity, independence, and normality of residuals [38,39].
This study aims to develop a novel, unified model for quantifying phenolic compounds in various olive oil types. This study evaluated phenolic compounds in nine EVOO cultivars and three olive oil blends (virgin or EVOO mixed with refined oils) under various treatments, including HTyr supplementation and deep-frying. Both fried and non-fried samples were analyzed. Phenolic quantification was performed using HPLC, and predictive modeling was developed using NIR spectroscopy combined with SELECT and OLS regression methods.

2. Materials and Methods

2.1. Materials

Olive fruit extract (OFE) enriched mainly with hydroxytyrosol (HTyr) and its derivatives was obtained from Natac BioTech, Madrid, Spain. The chemicals and reagents used in this study included phosphoric acid (H3PO4) (49–51%), purchased from Sigma-Aldrich (Saint Louis, MO, USA). HPLC-grade syringic acid (≥97% purity) and tyrosol (≥98% purity) were obtained from Sigma-Aldrich Chemie GmbH (Steinheim, Germany). LC-MS-grade methanol (≥99.9% purity) and acetonitrile (100% purity) were supplied by Fisher Scientific Ltd. (Loughborough, UK). Ultrapure water was sourced from a Milli-Q system (Millipore, Bedford, MA, USA).

2.2. Olive Oil Sampling and Experimental Design

The present study examined four categories of olive oils. The olive oils included nine EVOOs from different Spanish varieties, i.e., Picual, Cornicabra, Empeltre, Arbequina, Hojiblanca, Manzanilla Cacereña, Royuela/Arróniz, Koroneiki, and Arbosana, sourced from various Spanish producers. Additionally, an EVOO blended with refined olive oil (ROO) and labeled olive oil 1° (maximum acidity 1% as oleic acid), a refined olive oil mixed with EVOO, known as pomace olive oil or as Orujo oil in Spain, and a virgin olive oil blended with ROO, labeled olive oil 0.4° (maximum acidity 0.4% as oleic acid). Table 1 shows the experimental design (23) methodology including various olive oil types supplemented with olive fruit extract under different deep-frying conditions. Eleven samples were analyzed for each olive oil category, including (1) Control 1 (original, non-fried olive oil), (2) Control 2 (a mixture of non-fried original olive oil and olive oil supplemented with HTyr), (3) supplemented non-fried olive oil, and (4) eight deep-fried (D-F) samples under full factorial experimental design (Table 1) subjected to different conditions such as time, temperature, and polyphenol addition. In total, 132 samples were evaluated, covering both control oils and those processed under deep-frying experimental conditions. In addition, Table 2 represents the sampling design for the various olive oil categories used in this study.

2.3. Supplementation Procedure of Various Olive Oils with HTyr Extract

Olive oils were supplemented with HTyr-rich OFE to evaluate the effect of supplementation during deep-frying. The process involved enriching base oils (EVOO or other olive oil types) to create a polyphenol-rich supplemented oil, which was then blended with the original oil to form Control 2.
In detail, 40 g of OFE extract was added to 400 g of H2O (10% w/v). Then, the solution was stirred using a magnetic stirrer (IKA-WERKE, Staufen, Germany) at room temperature (RT) for 30 min. Subsequently, 200 g of this aqueous extract solution was mixed with 500 g of olive oil (2 OFE aqueous solution: 5 olive oil w/w), and the mixture was stirred mechanically at RT for 60 min. The prepared solution was then centrifuged (9961× g/20 min using a Sorvall RC-6 Plus device, Thermo Scientific, Dreieich, Germany) [40]. The supplemented oil (supernatant phase) was stored at 7 °C ± 2 in an amber container for further analysis. In this regard, Figure 1 illustrates the supplementation process of olive oil with olive fruit extract, which enriches it with HTyr and its derivatives.

2.4. Deep-Frying Experiments

Various categories of olive oils (supplemented or not with HTyr extract) were heated using a Soxhlet heating apparatus (SELECTA, Barcelona, Spain). In each deep-frying experiment, 400 mL of oil was placed in the fryer and continuously heated at 170 ± 10 °C for 3 or 6 h or at 210 ± 10 °C for 3 or 6 h. After frying under different conditions including varying the oil type, duration, temperature, and exogenous polyphenol addition, 400 mL of oil was collected in standard amber glass containers for further analysis of phenolic compounds using HPLC as a reference method, and spectra were recorded using NIR spectroscopy (132 samples in triplicates). The samples were stored at 5 °C in the dark to prevent further oxidation before analysis.

2.5. Extraction of Phenolic Compounds from Olive Oil Samples and Their Quantification by HPLC Analysis

For extraction and analyses of phenolic compounds from olive oil samples, the protocol of the International Olive Council [41] was followed. About 2.0 g of each deep-fried olive oil sample and non-fried samples (controls) were placed in 10 mL screw-cap test tubes. Then, 1 mL of the internal standard solution (syringic acid) was added to the previously weighed sample. The tubes were sealed with screw caps and shaken vigorously for exactly 30 s at RT using a shaker (Heidolph, D-91126, Schwabach, Germany). Next, 5 mL of the methanol/water (80/20, v/v) extraction solution was added to each tube. Then, the samples were shaken robustly again for 1 min. Additionally, the samples were sonicated in an ultrasonic bath for 15 min at 30 °C (Ultrasons 00-A, 50/60 Hz, 360 W, J. P. SELECTA, Barcelona, Spain) with the use of a shaker (Heidolph, RZR1, Schwabach, Germany) and a water bath to adjust the extraction temperature to 30 °C (Julabo, F25, Seelbach, Germany). Finally, the ultrasonicated and extracted samples were centrifuged (Eppendorf 5403 Refrigerated Centrifuge, Hamburg, Germany) at 4193× g/25 min. Then, an aliquot from the supernatant phase was filtered through a 5 mL plastic syringe, with a 0.45 µm PVDF filter (Millex HV, Merck Millipore Ltd., Cork, Ireland), for further injection for HPLC analysis.
HPLC analysis was performed using a Hewlett Packard 1100 series system (Agilent Technologies, Waldbronn, Germany), equipped with a high-pressure gradient pump, a photodiode array detector (DAD), an autosampler, and a degasser. Separation was carried out on a Spherisorb octadecyl silyl (ODS) column (250 mm × 4.6 mm ID, 5.0 μm particle size, Waters, Dublin, Ireland). The mobile phase consisted of a mixture of 0.2% H3PO4 in water (v/v), methanol, and acetonitrile (96/2/2, v/v/v), using gradient elution. To determine response factors (RF), 20 µL of the external calibration standard solution containing tyrosol and syringic acid was injected, allowing the calculation of the relative response factor (RRF) for syringic acid and tyrosol. Following calibration, a 20 µL aliquot of each sample was injected into the HPLC system and analyzed in triplicate. The chromatograms were recorded at 280 nm. The polyphenol content, expressed in mg/kg, was calculated using the following equation:
Polyphenols content mg kg = Σ Area × 1.000 × RRFsyr tyr × Weight of syringic acid Area syringic acid × Weight of sample
where RRF is the multiplication coefficient of (Syr) syringic acid/(Tyr) tyrosol.
A standard stock solution of tyrosol (1 mg/mL) was prepared by dissolving 10 mg of tyrosol in 10 mL of an 80:20 (v/v) methanol/water mixture. This solution was then used to generate a series of standard concentrations ranging from 0.030 to 0.090 mg/mL. To construct the HPLC calibration curve, the ratio of the tyrosol peak area to its corresponding concentration was plotted (Figure 2). A linear relationship was observed between the peak area and the tyrosol concentration within this range, described by the regression equation y = 10915x + 611.2 with an R2 value of 0.9986, where x denotes tyrosol concentration (mg/mL) and y represents the peak area. The calculated limits of detection (LOD) and quantification (LOQ) were 0.0098 mg/kg and 0.0298 mg/kg, respectively. For assessment of accuracy (trueness and precision), each oil sample was analyzed in triplicate. Standard deviation values were used to evaluate the consistency of measurements across repeated samplings of the same homogeneous sample, confirming the method’s precision, reproducibility, repeatability, and overall reliability.

2.6. Near Infrared (NIR) Spectroscopy

A total of 396 spectra were recorded from 132 olive oil samples, each analyzed in triplicate using NIR spectroscopy. Before spectral acquisition, samples were centrifuged (Sorvall RC-6 Plus, Dreieich, Germany) at 20,000 rpm for 30 min to remove particles and dispersed water droplets, minimizing light-scattering effects. NIR spectra were collected using a Foss NIRSystems 5000 spectrophotometer (Foss NIRSystems, Silver Spring, MD, USA), equipped with a thermostated liquid analyzer module and a Suprasil quartz flow cell. The spectral acquisition parameters included an optical path length of 10 mm; 700 wavelengths ranging from 1100 to 2498 nm were recorded at spectral resolution of 2 nm and 32 scans per spectrum.

2.7. Feature Variables Selection by SELECT and OLS Regression Model Development for Phenolic Compounds Quantification

To enhance data quality, preprocessing techniques such as column autoscaling were applied. The content of phenolic compounds in extra virgin and refined oil blends with virgin or extra virgin olive oils was quantified using OLS regression after employing SELECT algorithms for feature variables selection. Selection of variables and development of the linear regression model were conducted using V-PARVUS software (PARVUS2011, Michele Forina, Genoa, Italy), with the SELECT algorithm identifying a subset of decorrelated, significant variables to optimize the regression models.
The SELECT algorithm iteratively selects the most influential wavelength while minimizing redundancy and collinearity, improving predictive accuracy. SELECT was employed to identify the most relevant predictors for NIR spectroscopy. SELECT generates a set of decorrelated variables. It identifies the variable with the highest weight, selects it, and then removes its correlation with the remaining variables. This process continues, selecting variables with the highest weights until a predefined number of variables is reached or the weight falls below a specified threshold [37]. Eliminating unnecessary predictors is crucial, as redundant variables can weaken the predictive performance of the regression model [36].
OLS regression models were evaluated using leave-one-out (LOO) cross-validation metrics, including residual variance, residual standard deviation, explained variance, and mean prediction error. The most robust model was identified as the one with the lowest LOO mean prediction error. LOO residual variance and standard deviation were used as error metrics, while LOO explained variance reflected the proportion of variance captured. LOO mean prediction error represented the model’s average prediction error during cross-validation. To prevent overfitting, the optimal number of decorrelated predictors in the SELECT-OLS model was determined through complete validation, ensuring unbiased predictive accuracy. By ranking variables based on weights and selection frequency, the SELECT algorithm further refined the regression models, improving both interpretability and predictive performance.

3. Results and Discussion

3.1. Changes in Phenolic Compounds Content by HPLC Across Various Olive Oil Cultivars, Hydroxytyrosol Supplementation, and Deep-Frying

As shown in the results presented in Table 3, the changes in phenolic compounds across nine different EVOO cultivars—Picual, Cornicabra, Empeltre, Arbequina, Hojiblanca, Manzanilla Cacereña, Royuela/Arróniz, Koroneiki, and Arbosana—were studied. Additionally, one EVOO mixed with refined olive oil (1° acidity), one pomace olive oil mixed with EVOO, and one virgin olive oil mixed with refined olive oil (0.4° acidity) were included, along with hydroxytyrosol supplementation and deep-frying treatments.
Regarding the cultivars, there were clear variations in hydroxytyrosol content across the different cultivars. Royuela showed the highest level (21.43 mg/kg), while Manzanilla showed the lowest (7.28 mg/kg). For tyrosol, Hojiblanca had the highest value (14.86 mg/kg), whereas Royuela showed the lowest (5.87 mg/kg).
As presented in Table 2, the results of the full factorial experimental design demonstrate clear quantitative differences in phenolic compound concentrations among the differently treated samples. Deep-frying significantly affected the levels of specific phenols, with a general trend of degradation observed. Moreover, distinct EVOO cultivars showed notable variations in their phenolic profiles. For example, original samples from Royuela, Arbosana, and Empeltre cultivars showed high total phenolic contents of 400.63, 393.00, and 337.00 mg/kg, respectively, whereas refined samples such as Orujo and 0.4° olive oil contained significantly lower levels, at 3.89 and 26.50 mg/kg, respectively, confirming both the impact of thermal processing, refining, and cultivar differences.
These trends were also observed in all phenolic fractions in EVOO cultivars before deep frying, probably due to differences in environmental conditions, agricultural practices, and the specific cultivar characteristics, which significantly affect phenolic content in terms of both quantity and antioxidant quality.
From the same table, it can be observed that exogenous hydroxytyrosol supplementation dramatically increased the phenolic content in the supplemented oils and Control 2 samples, as well as in the deep-fried samples supplemented with hydroxytyrosol extract, especially increasing the content of hydroxytyrosol and tyrosol. Moreover, HTyr and Tyr decreased gradually with the progress of deep-frying. This extract also played a significant role in improving the stability and antioxidant potential of olive oil before and after deep frying.

3.2. NIR Spectra Interpretation of Oil Samples

In this study, 396 spectra from 132 different olive oil types were recorded using NIR spectroscopy to quantify phenolic compounds, covering hydroxytyrosol supplementation and deep-frying as well as non-fried and non-supplemented oils. The findings included prominent peaks at 1204, 1208, 1210, 1212, 1214, and 1216 nm, which are useful for evaluating quality parameters, including free fatty acid content. Additionally, significant absorption at 1388, 1390, 1392, 1394, and 1396 nm was observed; these spectral regions are linked to O-H combination bands and are valuable for quality assessment (Figure 3).
The spectral range of 1350–1570 nm proved particularly effective for distinguishing different olive oils, aiding in authentication and quality control by identifying original olive oil and detecting primary oxidation compounds. Higher absorbance was recorded at 1408, 1410, 1412, 1414, 1416, and 1418 nm, with particularly strong signals at 1414 and 1416 nm, corresponding to overtones of C-H and O-H bonds (Figure 3).
Furthermore, the NIR spectra of various olive oils exhibited significant absorption around 1724 nm due to the first overtone of C-H vibrations. Similarly, absorption at 1760 nm was linked to the first overtone of C-H vibrations and lipid oxidation, enabling the detection of primary oxidation products. Higher absorbance was also detected at 1860 and 1862 nm, particularly at 1860 nm. Additionally, strong absorbance was noted at 1890, 1892, 1894, 1896, 1898, 1900, 1902, and 1904 nm, with a prominent peak at 1900 nm, indicating oxidation and degradation.
High absorbance around 2144 nm corresponded to C=O stretching (carbonyl compounds), indicating the formation of aldehydes and ketones from lipid degradation. Similarly, a distinct absorption peak at approximately 2226 nm was linked to O-H and C-H combination bands, making it valuable for assessing hydrolysis and secondary oxidation. Significant absorption at 2250, 2252, 2254, and particularly at 2256 nm varied among the samples and was useful for distinguishing oxidation stability while also associated with O-H and C-H combination bands.
The 1700–2500 nm region was strongly correlated with lipid oxidation and hydrolytic degradation (Figure 1). NIR devices also demonstrated effectiveness in classifying EVOO based on spectral variations. These findings confirm the broad applicability of NIR spectroscopy for ensuring the authenticity, freshness, and overall quality of EVOO [42,43,44].
These findings highlight the importance of selecting stable olive oils for frying and employing NIR spectroscopy for real-time monitoring to ensure the safety and quality of fried extra virgin olive oils, virgin olive oils, and their blends. NIR not only serves as a valuable tool for tracking EVOO oxidation during deep frying, as specific wavelength regions correspond to compounds formed during oil degradation, but it can also be used for rapid quantification of phenolic compounds in olive oils instead of conventional methods like Folin–Ciocalteu and HPLC approaches. This enables effective assessment of oil quality and safety. As shown in Table 4, these critical wavelengths play a key role in monitoring oil degradation and quality indices. Oxidation results in the formation of peroxides, aldehydes, and other degradation products that significantly impact the oil’s quality, taste, and safety during deep frying.

3.3. SELECT-OLS Models for Quantification of Phenolic Compounds

The findings reveal that the SELECT approach identified only 12 key infrared (IR) wavelengths out of 700 for quantifying hydroxytyrosol (HTyr) across various olive oil categories (Table 5). Among these, 1962 nm ranked first, followed by 1856 nm, which ranked second. The 1962 nm wavelength exhibited strong absorbance in most EVOO varieties supplemented with HTyr and in non-fried olive oil. Comparing its absorption intensity with pure EVOO spectra can help detect dilution with olive fruit extract-supplemented oils. This suggests that NIR and SELECT can effectively distinguish between supplemented/non-fried olive oil and deep-fried samples.
EVOO samples subjected to continuous deep frying at 210 °C for 6 h showed significantly lower HTyr levels than those fried at 170 °C for 3 h. Higher HTyr content was observed in HTyr-supplemented samples, emphasizing the protective effect of lower frying temperatures against oxidation [51]. Moreover, HTyr in supplemented oils maintained a more stable phenolic composition during prolonged high-temperature frying than other phenolic fractions.
The SELECT algorithm identified distinct wavelengths, highlighting that spectral variations and oxidation features depended on EVOO variety, supplementation, frying conditions (temperature/duration), and HTyr stability. The optimization process achieved remarkable data compression—selecting only 12 wavelengths from 700 predictors—to model HTyr content in EVOO, refined VOO, and mixed EVOO, whether supplemented or not or deep-fried or not.
The high reliability and robustness of the resulting OLS models underscore the effectiveness of SELECT-OLS for feature selection and correction. The progressive reduction in residual variance, tracked from the initial stage through successive decorrelation cycles, led to an optimal model with negligible residual variance (Figure 4A–D), using hydroxytyrosol, tyrosol, caffeic acid, and oleocanthal as response variables, respectively. The SELECT algorithms output the retained original variables, demonstrating their efficiency in refining predictive models [36,37].
Regarding tyrosol, the findings indicate that the SELECT approach identified 14 key spectral variables out of 700 for predicting tyrosol content across various olive oil categories, considering hydroxytyrosol supplementation and different deep-frying conditions (Table 6). Among these, 1962 nm was most significant, followed by 1856 nm. The results show that the 1962 nm wavelength exhibited high absorbance in most EVOO varieties supplemented with HTyr and in blends of original olive oil with HTyr-supplemented olive oil. This suggests that NIR and SELECT can effectively distinguish between supplemented and non-supplemented olive oils, as well as between non-fried and deep-fried samples.
Furthermore, EVOO samples subjected to continuous deep frying at 210 °C for 6 h exhibited significantly lower tyrosol levels compared with those fried at 170 °C for 3 h, indicating the impact of frying temperature and duration on tyrosol degradation. A similar trend was observed in HTyr-supplemented samples, highlighting the protective effect of HTyr extract in preserving tyrosol content in olive oil. Additionally, pomace olive oil and olive oil with 0.4° acidity contained zero tyrosol, indicating their low phenolic compound content compared with EVOO varieties.
In addition, the SELECT approach identified 15 significant wavelengths out of 700 for quantifying caffeic acid (Table 7), with the 1968 nm wavelength selected as the first-order variable. The 1968 nm wavelength is associated with overtones and combination bands of molecular vibrations, primarily related to C-H, O-H, and aromatic functional groups found in phenolic compounds, including caffeic acid in olive oil. Caffeic acid, a key phenolic compound, contributes to the antioxidant properties, bitterness, and stability of olive oil [52,53]. The 1968 nm absorption can provide insights into phenolic content, oxidation status, and potential degradation due to thermal processing or prolonged storage. Additionally, it helps differentiate natural phenolic profiles across various EVOO varieties and can be used to assess adulteration or dilution with lower-quality oils. Comparing 1968 nm spectral data with reference EVOO spectra enables quality control, authenticity verification, and monitoring of phenolic stability under different processing and storage conditions.
The SELECT procedure efficiently narrowed down the relevant variables, selecting only a subset of wavelengths from the full NIR spectral range. The final model, which uses just these 15 wavelengths, has been optimized for prediction while avoiding overfitting and unnecessary complexity. This procedure provides a more streamlined model with better predictive performance than using the full 700-variable NIR dataset. By focusing on a relatively small number of key wavelengths, the model becomes both efficient and effective for predicting caffeic acid content in various olive oil categories. This method allows rapid, non-destructive analysis of olive oil quality and can be easily implemented in quality control and certification processes within the olive oil industry.
Secoiridoids, though rare in most plants, are abundant in Olea europaea leaves and fruits. However, due to their oil insolubility, only a small portion transfer to EVOO during extraction [54]. Despite this, they are key micronutrients, contributing to EVOO’s sensory properties and health benefits. The most common secoiridoids in EVOO include oleuropein and ligstroside aglycones [7,54,55].
The SELECT approach identified 30 significant wavelengths out of 700 for quantifying decarboxymethyl ligstroside aglycone in dialdehyde form (oleocanthal) (Table 8), with the 2084 nm having the first order of selection, followed by 1220 nm. In the first order of selection, the results revealed that lower-quality olive oils, such as pomace olive oil, olive oil with °1 and °0.4 acidity, as well as Hojiblanca, exhibited higher absorbance at 2084 nm, indicating increased oxidation and lower stability compared with EVOO varieties like Manzanilla, Picual, Koroneiki, Arbosana, and Royuela. This suggests that EVOOs may differ in stability due to their higher phenolic content, which contributes to both oxidative resistance and unique sensorial attributes, as reported earlier by Mehany et al. [40].
The wavelength around 2084 nm is also associated with the –COOR and C–H stretching vibrations, along with C=O stretching, which are sensitive to oxidative changes and degradation in olive oil [20,46]. This further reinforces the role of 2084 nm in detecting oxidative stress and quality degradation, which is particularly relevant when monitoring the stability of olive oils under varying processing conditions.
By incorporating this wavelength into the chemometric model, the SELECT-OLS method enables reliable prediction of oleocanthal in various olive oil types, providing an efficient and accurate way to monitor this compound under different processing conditions, such as supplemented and non-supplemented oils and deep-fried versus non-fried samples. This demonstrates the potential of NIR spectroscopy combined with variable selection for quantifying specific phenolic compounds like oleocanthal in olive oils.
Moreover, the current results demonstrate that the SELECT approach successfully identified 30 spectral markers out of 700 variables for quantifying oleacein content in various olive oil categories (Table 9). The first-order selection was at 2006 nm, followed by 2216 nm. The results indicated that lower-quality olive oils, such as pomace olive oil, olive oils with °1 and °0.4, and Hojiblanca, exhibited higher absorbance at 2006 nm, suggesting increased oxidation and lower stability compared with EVOO varieties like Cornicabra, Picual, Manzanilla, Arbequina, and Royuela. These findings imply that EVOOs may vary in stability due to their higher phenolic content, which contributes to both oxidative resistance and distinctive sensorial attributes. Indeed, oleacein, a key secoiridoid compound in EVOO, has been shown to enhance mitochondrial function by increasing mitochondrial mass, DNA content, respiration, and ATP production in colorectal cancer cells. It triggers a protective cellular response involving antioxidant pathways mediated by AMPK, NRF2, and PGC-1α. Oleacein acts as a partial agonist of PPARγ, a receptor involved in regulating mitochondrial metabolism. Its beneficial effects on mitochondrial pathways and antioxidant defense are significantly mediated through PPARγ activation [56].
The wavelength around 2006 nm is associated with –COOR and C–H stretching vibrations, as well as C=O stretching, which are sensitive to oxidative changes and degradation in olive oil. Additionally, the same trend of high oxidation was observed in oil samples fried at a high temperature (210 °C) for an extended time (6 h). This reinforces the role of 2006 nm in detecting oxidative stress and quality degradation, which is particularly relevant for monitoring the stability of olive oils under different processing conditions.
Moreover, the continuous reduction in residual variance, observed from the initial stage before variable selection through each decorrelation cycle until the optimal model complexity was reached, further validates the method’s effectiveness in minimizing residual variance (Figure 5A–D), using oleacein, homovanillic acid, pinoresinol, and OAOAH as response variables, respectively.
Additionally, SELECT identified 21 key variables out of 700 wavelengths for quantifying homovanillic acid across various olive oil categories (Table 10). Among these, the 1962 nm wavelength had the first order of selection, followed by 1862 nm. The 1962 nm wavelength in NIR is significant due to its association with the O–H (hydroxyl) group and C=O (carbonyl) stretching vibrations, which are sensitive to secondary oxidation products such as aldehydes and ketones. These functional groups are key indicators of oxidative degradation in olive oil, and their presence can be used for assessing the oil’s quality [45]. Thus, the 1962 nm wavelength plays a critical role in monitoring secondary oxidation, especially in detecting aldehydes and ketones that form during the oxidative process. These oxidation products contribute to the rancidity and degradation of olive oils, making this wavelength valuable for quality control.
The 1962 nm wavelength showed strong absorbance, especially in HTyr-supplemented and non-fried EVOO, making it effective for distinguishing between pure and supplemented oils. Variations in absorbance at this wavelength can indicate dilution effects from mixing non-virgin oils with fruit extract, which may affect quality. Comparing absorption intensities across pure, supplemented, fried, and non-fried samples confirmed the utility of NIR spectroscopy combined with SELECT for differentiating treatment conditions. This wavelength is also sensitive to oxidation products, aiding in the detection of oxidative degradation. Most EVOO varieties, including Picual, Cornicabra, Koroneiki, Royuela, Arbequina, and Manzanilla, showed lower oxidation values, reflecting higher stability that is likely to have been due to their rich phenolic profiles and distinct sensory characteristics [40].
Regarding pinoresinol, the SELECT method identified 30 key wavelengths out of 700 for its quantification under varying supplementation and frying conditions. As detailed in Table 11, the most significant wavelengths were 1932 nm and 1922 nm. The 1932 nm wavelength played a dominant role, presumably due to its sensitivity to molecular vibrations associated with phenolic compounds like pinoresinol. The 1922 nm wavelength also contributed by capturing complementary spectral features, refining the model’s accuracy. These wavelengths, along with the others selected, form the foundation of the SELECT-OLS model for pinoresinol prediction, demonstrating high accuracy with minimal input variables. The method’s efficiency is further validated by the consistent decline in residual variance during variable selection and decorrelation cycles, confirming the model’s robustness and utility for precise olive oil quality assessment. In addition, our quantitative analysis (Table 3) confirmed that pinoresinol content was significantly higher in the EVOO compared with the refined and blended olive oils. For instance, pomace (orujo) oil contained no detectable pinoresinol, while olive oil 0.4° showed 2.58 mg/kg. These results align with previous findings by Cecchi et al. [57], who reported that pinoresinol is relatively stable and less susceptible to degradation during the refining process.
From the 700 NIR-recorded variables, the SELECT method identified 30 key wavelengths for quantifying OAOAH content in olive oils under different supplementation and frying conditions (Table 12). The most critical wavelength was 1854 nm, followed by 2028 nm. The 1854 nm wavelength is particularly significant, reflecting molecular interactions linked to oxidative degradation and phenolic content. Its high absorbance in the HTyr-supplemented oils suggests that it detected externally added antioxidants rather than those intrinsic to EVOO. This confirms the capability of NIR combined with SELECT to differentiate between supplemented and non-supplemented oils. The efficiency of this approach is further supported by the continuous reduction in residual variance through the decorrelation cycle. The SELECT-OLS model, using only the most relevant variables, delivers high predictive accuracy while remaining cost-effective and suitable for routine quality assessment. This method is especially valuable for predicting OAOAH levels across extra virgin, virgin, and refined olive oils, serving as a reliable marker of oxidative stability and the oil’s authenticity. Its rapid, non-destructive nature supports online and in-process quality control, with minimal preparation and high accuracy.
Furthermore, from the 700 variables recorded in the NIR system, the SELECT method identified 30 key variables for quantifying LAOAH in olive oils under different supplementation and frying conditions (Table 13). The results showed that 1860 nm was the most significant wavelength, followed by 1110 nm. Particularly high absorbance at 1860 nm was observed in olive oils supplemented with HTyr, suggesting that the extract was added during processing rather than being part of the original EVOO content. This underscores NIR and SELECT’s ability to differentiate between oils supplemented with external antioxidants and those that are not. The method’s efficiency is further validated by the continuous reduction in residual variance throughout the decorrelation cycle. This consistent decrease, observed from the initial stage before variable selection through each successive cycle until optimal model complexity was achieved, demonstrates the method’s effectiveness in minimizing residual variance (Figure 6A,B), using LAOAH and TPC as response variables, respectively.
In the quantification of total phenolic content, the SELECT method identified 18 key wavelengths from the 700 NIR-recorded variables across various olive oil supplementation and frying conditions (Table 14). The most significant wavelength was 1518 nm, followed by 2016 nm. The 1518 nm band is linked to C-H stretching vibrations in lipids and water and is commonly used in food oil analysis. In olive oil, this wavelength provides information about fatty acid composition and oxidative degradation, particularly the presence of oxidized lipids such as aldehydes and ketones. It is also sensitive to hydrogen bonding and can detect changes due to storage or heat exposure. High absorbance at 1518 nm was observed in HTyr-supplemented oils, reflecting their elevated phenolic content and allowing clear differentiation from pure EVOO. This highlights the effectiveness of this wavelength in identifying supplementation effects and oxidative changes. Moreover, pomace olive oil and Hojiblanca, when fried at high temperatures for extended durations, showed the highest levels of aldehydes and ketones, confirming their lower oxidative stability. In contrast, EVOOs, especially those with HTyr supplementation, maintained better stability due to their natural antioxidant content.

3.4. SELECT-OLS Models Validation Assessment

The statistical characteristics of the developed NIR and SELCT-OLS models for quantifying phenolic compounds in various olive oils, including HTyr supplementation and deep frying, are illustrated in Table 15. The standard deviation of the error (SDE) reflects the average deviation between observed and predicted values in the model, with a lower SDE indicating better model fit and more accurate predictions. For instance, hydroxytyrosol has an SDE of 19.51, attributed to the high HTyr content in the analyzed samples, particularly in olive oils supplemented with olive fruit extract, a rich source of HTyr and its derivatives. In contrast, compounds like homovanillic acid show better precision with an SDE of 3.89. The mean absolute error (MAE), which measures the average magnitude of prediction errors, similarly indicates better accuracy when smaller. Caffeic acid, with the smallest MAE of 3.19, demonstrated more accurate predictions compared with compounds like LAOAH (MAE = 69.87). This difference is likely to have been due to variations in the content of phenolic compounds and their dynamics under deep-frying conditions. The multiple correlation coefficient (R) assesses the strength and direction of the linear relationship between observed and predicted values. An R value close to 1 indicates a strong correlation; the results for most of the compounds, including hydroxytyrosol, tyrosol, and caffeic acid (R = 0.98), demonstrated excellent prediction accuracy. Pinoresinol, with a slightly lower R value of 0.91, still demonstrated a good prediction model. Leave-one-out residual variance (LOORV) measured the model’s stability when one data point was left out at a time, with lower values indicating better generalizability. Caffeic acid had the lowest LOORV of 4.57%, indicating high stability. Similarly, the leave-one-out residual standard deviation (LOORSD), measured in standard deviation units, for caffeic acid was relatively low at 4.87%, suggesting consistent predictions. The leave-one-out explained variance (LOOEV) shows how much of the data’s total variance the model explains, with most compounds showing high values (above 79%) and hydroxytyrosol reaching 95%. The leave-one-out mean prediction error (LOOMPE) measured the average error when one data point was excluded, with caffeic acid again performing best with a LOOMPE of 3.64. In conclusion, most phenolic compounds were predicted excellently by the models, as evidenced by their high correlation coefficients (R = 0.91–0.98) and low prediction errors. These compounds were quantified with high precision and low residual variance, making the models ideal for reliable quantification of olive oil. Total phenolic content (TPC), with an R value of 0.96, showed a high explained variance (90.14%) and low mean prediction error, suggesting a strong and accurate model. Overall, the models for quantifying phenolic compounds in olive oils are robust, particularly for hydroxytyrosol, tyrosol, and caffeic acid, for which these models are highly reliable.
Overall, the results demonstrate the robustness and flexibility of SELECT-OLS as an effective feature selection and correction method for quantifying phenolic content across various olive oil types, including both fried and non-fried oils, and those supplemented with exogenous phenolic compounds or without supplementation. By systematically reducing dimensionality while preserving high predictive performance, SELECT-OLS optimizes model complexity, minimizes residual variance, and improves the accuracy of calibration models for phenolic substances in complex oil matrices. The consistency observed across different phenolic compounds further highlights the reliability of SELECT-OLS for spectral data analysis and quantitative modeling.
The goal of this study was achieved by developing regression models to quantify the phenolic content of different olive oil samples based on measured spectral data. In SELECT-OLS, predictions are made by transforming inter-correlated variables into a set of independent factors, known as latent variables (LVs), which capture the maximum covariance between spectral data and response variables such as HTyr, Tyr, caffeic acid, oleocanthal, oleacein, homovanillic acid, pinoresinol, OAOAH, LAOAH, and TPC. Each SELECT-OLS model’s LVs are statistically independent (uncorrelated) and contain all relevant information necessary for stable predictions. As noted in recent studies, only the first few LVs account for the majority of variation in the original variables, while the remaining LVs primarily represent random noise or linear dependencies [58,59]. This also aids in understanding multivariate data analytics algorithms [60,61].

4. Conclusions

In the current study, NIR spectroscopy combined with selection of variables using SELECT and OLS regression proved to be an effective approach for developing robust, reduced-spectrum regression models to quantify key phenolic compounds in various olive oils. These models successfully identified and quantified compounds such as HTyr, Tyr, caffeic acid, oleocanthal, oleacein, homovanillic acid, pinoresinol, OAOAH, LAOAH, and TPC across different olive oil types, both supplemented and non-supplemented with HTyr, before and after deep frying under various thermal stress conditions. The SELECT-OLS models demonstrated high predictive accuracy, with correlation coefficients (R) ranging from 0.91 to 0.98, using at most 30 wavelengths from a total of 700, ensuring minimal model complexity. These models performed reliably across diverse phenolic supplementation and deep-frying conditions, offering dependable tools for routine, online prediction and quantification of phenolic compounds. Furthermore, the optimized variable selection in NIR spectroscopy simplifies spectral analysis, reduces costs, and highlights the feasibility of NIR as a rapid, non-destructive, and eco-friendly tool for evaluating and quantifying edible oils.

Author Contributions

Conceptualization, T.M., J.M.G.-S. and C.P.; methodology, T.M. and J.M.G.-S.; software, T.M., J.M.G.-S. and C.P.; validation, C.P. and T.M.; formal analysis, T.M., J.M.G.-S. and C.P.; investigation, T.M., J.M.G.-S. and C.P.; resources, J.M.G.-S. and C.P.; data curation, T.M. and C.P.; writing—original draft preparation, T.M.; writing—review and editing, T.M., J.M.G.-S. and C.P.; visualization, T.M., J.M.G.-S. and C.P.; supervision, J.M.G.-S. and C.P.; project administration, C.P.; funding acquisition, T.M., J.M.G.-S. and C.P. All authors have read and agreed to the published version of the manuscript.

Funding

The European Union’s H2020 research and innovation program supported this study under Marie Sklodowska-Curie Grant No 801586.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The supplementation process of olive oil with olive fruit extract enriches it with hydroxytyrosol (HTyr) and its derivatives.
Figure 1. The supplementation process of olive oil with olive fruit extract enriches it with hydroxytyrosol (HTyr) and its derivatives.
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Figure 2. Tyrosol calibration curve used for validation of the HPLC method.
Figure 2. Tyrosol calibration curve used for validation of the HPLC method.
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Figure 3. NIR spectra of different categories of supplemented olive oils with HTyr under frying conditions, compared with non-fried and non-supplemented olive oils with HTyr. NS-NF: Non-supplemented, non-fried; S-NF: Supplemented, non-fried; NS-F: Non-supplemented, fried; S-F: Supplemented, fried.
Figure 3. NIR spectra of different categories of supplemented olive oils with HTyr under frying conditions, compared with non-fried and non-supplemented olive oils with HTyr. NS-NF: Non-supplemented, non-fried; S-NF: Supplemented, non-fried; NS-F: Non-supplemented, fried; S-F: Supplemented, fried.
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Figure 4. Residual variance of the variables decorrelated by SELECT after 1, 2, 3, and 4 (blue, red, turquoise, green, respectively) selections obtained when working on auto-scaled NIR spectra, using hydroxytyrosol (A), tyrosol (B), caffeic acid (C), and oleocanthal (D) as response variables.
Figure 4. Residual variance of the variables decorrelated by SELECT after 1, 2, 3, and 4 (blue, red, turquoise, green, respectively) selections obtained when working on auto-scaled NIR spectra, using hydroxytyrosol (A), tyrosol (B), caffeic acid (C), and oleocanthal (D) as response variables.
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Figure 5. Residual variance of the variables decorrelated by SELECT after 1, 2, 3, and 4 (blue, red, turquoise, green, respectively) selections obtained when working on auto-scaled NIR spectra, using oleacein (A), homovanillic acid (B), pinoresinol (C), and OAOAH (D) as response variables.
Figure 5. Residual variance of the variables decorrelated by SELECT after 1, 2, 3, and 4 (blue, red, turquoise, green, respectively) selections obtained when working on auto-scaled NIR spectra, using oleacein (A), homovanillic acid (B), pinoresinol (C), and OAOAH (D) as response variables.
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Figure 6. Residual variance of the variables decorrelated by SELECT after 1, 2, 3, and 4 (blue, red, turquoise, green, respectively) selections obtained when working on auto-scaled NIR spectra, using LAOAH (A) and TPC (B) as response variables.
Figure 6. Residual variance of the variables decorrelated by SELECT after 1, 2, 3, and 4 (blue, red, turquoise, green, respectively) selections obtained when working on auto-scaled NIR spectra, using LAOAH (A) and TPC (B) as response variables.
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Table 1. Experimental design (23 factorial) of several olive oil types supplemented with olive fruit extract under various deep-frying conditions, including 8 treatments and 3 controls before frying.
Table 1. Experimental design (23 factorial) of several olive oil types supplemented with olive fruit extract under various deep-frying conditions, including 8 treatments and 3 controls before frying.
ExperimentDesign MatrixIndependent VariablesResponse
X1X2X3Time (h)Temp. (°C)Polyphenols (mg/kg)
1−1−1−13170-Phenolic compounds
2+1−1−16170-
3−1+1−13210-
4+1+1−16210-
5−1−1+13170650
6+1−1+16170650
7−1+1+13210650
8+1+1+16210650
LevelTime (h)Temp. (°C)Polyphenols (mg/kg)
−13170Original concentration (0 addition)
+16210650
Table 2. Representative sampling design of various olive oil categories used in this study.
Table 2. Representative sampling design of various olive oil categories used in this study.
Olive Oil TypeCultivar/CategoryType/BlendSource/BrandLocationNotes
Extra Virgin Olive Oil (EVOO)PicualSingle variety EVOOLa casa del aceite, S.L. Cascante, NavarraSpainSpanish variety
EVOOCornicabraSingle variety EVOOAceite del Sur, COOSUR, Vilches, JaénSpainSpanish variety
EVOOEmpeltreSingle variety EVOOLa casa del aceite, NavarraSpainSpanish variety
EVOOArbequinaSingle variety EVOOAceite del Sur, COOSUR, Vilches, JaénSpainSpanish variety
EVOOHojiblancaSingle variety EVOOAceite del Sur, COOSUR, Vilches, JaénSpainSpanish variety
EVOOManzanilla CacereñaSingle variety EVOOAceite Artajo, Finca Los Llanos s/n, Fontellas, NavarraSpainSpanish variety
EVOORoyuela/ArrónizSingle variety EVOOAceite Artajo, Finca Los Llanos s/n, Fontellas, NavarraSpainSpanish variety
EVOOKoroneikiSingle variety EVOOAceite Artajo, Finca Los Llanos s/n, Fontellas, NavarraSpainGreek-origin variety cultivated in Spain
EVOOArbosanaSingle variety EVOOAceite Artajo, Finca Los Llanos s/n, Fontellas, NavarraSpainSpanish variety
EVOO and refined olive oil Olive oil 1%BlendLa Masia, Oleo Masia, S.A. SevillaSpainSpanish variety
Refined olive oil and virgin olive oil (VOO)Olive oil 0.4%Blend La Española oils, SevilleSpainSpanish variety
Orujo olive oil (pomace) and EVOO OrujoBlendSimplySpainSpanish variety
Table 3. Evolution and changes in phenolic compounds in olive oils as affected by cultivar, hydroxytyrosol supplementation, and deep-frying. Data are presented as the mean of three replicates, prior to use in regression models.
Table 3. Evolution and changes in phenolic compounds in olive oils as affected by cultivar, hydroxytyrosol supplementation, and deep-frying. Data are presented as the mean of three replicates, prior to use in regression models.
SamplesHTyrTyr Caffeic AcidOleocanthalOleaceinHomovanillic AcidPinoresinolOAOAHLAOAHTPC
PC_C111.897.510.0031.1273.260.0019.874.146.97307.89
PC_S359.91195.5990.99110.3633.9946.9229.827.4623.731524.32
PC_C2125.2870.4546.1914.4454.7718.7444.534.926.47658.60
PC_19.896.620.0045.71131.460.0015.7865.42184.37589.94
PC_29.545.310.0041.97105.290.0022.5494.53347.34819.73
PC_310.666.440.0053.31130.410.0017.0536.72185.68537.89
PC_42.655.400.0037.9438.610.0016.9151.60356.54696.80
PC_5124.5071.8642.0363.78153.1018.0848.0078.34223.281107.15
PC_6102.5159.7329.9659.32124.0915.3346.5996.99329.231159.89
PC_7120.0172.0734.2039.88151.4116.2534.1424.56207.78969.29
PC_862.7847.0027.8628.0493.046.6920.4332.05298.56944.61
CC_C118.1312.000.0034.9131.910.0017.3618.167.25275.67
CC_S389.49215.5083.3662.49153.3760.2171.307.365.581683.03
CC_C2128.5779.6336.1528.5624.9721.3951.246.1411.31658.14
CC_117.1111.960.0088.2877.600.0015.49128.09360.85847.25
CC_210.9510.280.0054.7759.650.0013.99125.54399.34836.85
CC_313.5310.490.0060.6088.960.0013.7338.06276.31588.02
CC_48.858.830.0051.2862.950.0016.1735.85347.69679.57
CC_5122.8680.1233.3352.7344.8417.6348.04149.29411.571276.62
CC_679.1967.7631.0245.2626.9315.8750.14149.04616.281376.11
CC_7115.7673.5829.1066.2774.4317.1250.0470.08325.43997.90
CC_876.3347.6930.3161.4367.4711.9145.4468.69462.201126.87
EP_C117.807.990.0042.1179.230.009.4525.205.81337.91
EP_S363.50193.0469.5722.50140.5821.0247.9515.2012.811468.88
EP_C2122.4465.0824.9736.9773.8315.8243.9511.7312.01647.23
EP_116.657.560.0057.43136.380.0010.6696.76304.95821.61
EP_211.556.920.0058.54120.450.0013.7992.61368.61895.81
EP_310.857.900.0064.69118.540.007.2753.27330.76775.75
EP_49.857.000.0056.8684.570.005.6648.93339.26741.32
EP_591.1456.9022.7753.05115.5813.9641.4591.75303.271062.90
EP_682.5354.5721.1756.88116.6011.4243.5589.95346.851104.52
EP_764.4441.7817.3665.79116.955.6532.1335.58276.04858.28
EP_850.5934.9315.1162.3897.244.8327.8328.90226.27734.49
AQ_C18.849.560.0024.4424.260.0034.223.627.37227.71
AQ_S332.69177.9862.5817.8114.6520.7441.9413.464.511384.96
AQ_C2139.2989.1732.3215.0514.9118.2757.7511.1211.53663.96
AQ_15.467.360.0034.7433.390.0025.62138.03513.57940.33
AQ_22.596.550.0034.3131.960.0027.82118.61534.12945.26
AQ_35.566.840.0045.0743.420.0029.1674.61428.93781.58
AQ_44.386.790.0043.0840.600.0029.2263.83400.00715.60
AQ_5122.1987.1235.7825.6516.0121.0654.29144.73456.381279.66
AQ_696.4577.4528.8026.3518.3317.4154.92176.65686.591534.98
AQ_799.9971.2628.4730.6720.3415.0852.04134.05559.031335.45
AQ_865.3351.5726.6331.7228.589.4446.7773.09514.761133.19
HB_C113.5314.860.0022.8315.720.0020.685.468.55209.51
HB_S320.65174.4968.554.0619.9944.4635.3410.864.341208.67
HB_C2186.0398.6732.5718.9510.3017.3051.498.169.35655.73
HB_19.0513.880.0031.7117.620.0019.88107.68278.19598.94
HB_22.7312.660.0024.559.490.0022.13116.35405.57807.24
HB_39.7912.490.0038.2332.850.0019.5028.48178.72377.85
HB_42.649.740.0033.5216.970.0019.9940.38275.02553.77
HB_5132.3181.9030.0527.9824.1617.1248.16105.33312.311044.73
HB_692.9766.1927.6224.3517.8019.0042.70107.12400.141093.86
HB_7112.2763.6231.8331.2426.7418.8845.6614.38106.19639.49
HB_865.1039.6126.9930.9627.0314.4738.8121.46154.96637.05
MZ_C17.289.360.9529.0135.060.0050.223.667.64309.04
MZ_S347.51181.1670.0829.4022.7439.62109.1124.7914.461255.28
MZ_C2156.6778.0429.9317.2217.2019.1750.8314.1010.06661.28
MZ_14.448.930.0066.6085.930.0021.2740.23125.85428.46
MZ_24.138.450.0062.2364.930.0022.8951.60216.92604.60
MZ_34.5411.100.0072.9673.230.0020.976.6743.47255.57
MZ_42.719.960.0062.4138.280.0019.3511.9892.00338.04
MZ_5128.9979.3330.7956.9659.0517.2252.0043.72147.92870.67
MZ_6117.1576.6528.4145.4050.6515.8048.7454.22206.21945.18
MZ_7138.4379.4228.9473.2381.4213.1949.619.7871.34723.96
MZ_890.0557.3427.5259.9455.308.6142.7619.05125.95727.40
RY_C121.435.870.0023.45116.260.0065.196.3021.80400.63
RY_S290.17166.0267.7726.9330.4347.8637.6011.097.431211.16
RY_C2120.0062.4926.3822.6961.1513.1542.3813.219.22662.38
RY_120.055.880.0057.46199.340.0022.1338.34126.15539.16
RY_215.435.170.0047.22132.340.0022.1864.32274.21691.69
RY_315.636.030.0055.55156.570.0021.4615.28100.20415.14
RY_411.665.240.0049.8999.760.0020.6517.26144.43428.41
RY_5110.1462.0422.7851.90166.9212.0540.7638.73145.25854.57
RY_690.6552.7220.4546.13134.4011.1839.1957.27256.38937.29
RY_797.8658.3121.7350.87149.0714.3235.3111.5279.56688.53
RY_872.8044.5218.8849.58125.459.8930.8820.75146.43688.71
OJ_C10.000.000.000.002.650.000.000.000.003.89
OJ_S338.49197.8286.16112.016.2252.8048.455.395.601259.06
OJ_C2190.0995.1848.2649.3011.1621.0953.060.000.00652.25
OJ_10.000.000.000.000.000.000.007.4825.5649.11
OJ_20.000.000.000.000.000.000.0037.11168.70298.40
OJ_30.000.000.000.000.000.000.0012.7275.98119.62
OJ_40.000.000.000.000.000.000.0044.40281.70426.64
OJ_5163.0083.8839.688.260.0017.9750.0515.6271.60657.61
OJ_6140.7376.7139.446.440.0016.8350.8936.04175.42803.54
OJ_7130.5164.6236.4426.040.0013.6149.759.1465.47559.38
OJ_884.6044.3232.405.700.0015.7741.5325.67167.59609.17
KN_C115.1412.660.0040.5045.180.0068.800.000.00327.77
KN_S275.46154.9968.3037.9919.4949.2248.8037.160.001230.89
KN_C2113.7465.8625.7120.0834.3016.9444.648.1112.32663.93
KN_111.628.920.0010.86113.930.0039.84135.1929.87564.96
KN_210.208.760.0073.0187.370.0011.3854.92202.13642.58
KN_311.969.080.0085.25107.270.008.2525.65134.52473.15
KN_48.718.150.0076.6860.390.008.8428.05217.57588.67
KN_5111.3765.1825.5673.5698.7616.8346.7446.71127.38876.73
KN_6101.3263.7426.8072.6794.1715.0148.3961.08208.02986.50
KN_785.5760.8520.1079.4684.198.1134.2813.08129.50697.12
KN_853.1441.260.0069.4564.503.5928.7316.60162.42662.45
AS_C113.8711.260.0047.7457.620.0056.9117.295.24393.00
AS_S301.93153.9978.2944.6126.6055.8561.9717.625.881434.58
AS_C297.6554.6020.5751.9830.3912.5946.8215.025.41666.95
AS_110.267.110.0085.80130.840.0057.2557.47160.79631.38
AS_210.777.820.0082.86112.850.0059.1963.76238.83749.95
AS_37.788.660.0090.0385.280.0056.578.48128.73451.35
AS_47.657.020.0075.2161.660.0051.2612.60159.54486.19
AS_596.6955.4725.3780.77122.8613.3742.3961.64167.09956.98
AS_689.0655.2921.5576.57105.9911.5644.9868.30248.221032.78
AS_784.4656.9417.9385.03111.7712.1034.9936.97182.61818.89
AS_865.0444.7115.4776.6088.648.8730.8835.94234.29839.94
1ºO_C121.9111.420.0016.1820.710.0046.047.774.08181.88
1ºO_S283.72148.3263.3515.4416.5841.06102.1318.9510.611152.24
1ºO_C2130.2782.3732.1219.1413.6021.9255.484.512.72653.11
1ºO_119.2311.510.0028.2027.160.0036.7487.19239.75568.77
1ºO_210.4610.910.0020.9816.820.0030.2594.33358.21746.04
1ºO_314.0810.140.0030.0438.050.0015.1146.75227.41454.87
1ºO_48.888.420.0026.2223.840.0011.1535.42263.41525.17
1ºO_5121.4273.0127.8326.1424.2516.6047.8093.18258.61968.57
1ºO_699.7364.5624.5823.4718.5515.6646.65101.27337.551057.58
1ºO_798.0158.1725.6423.8827.4313.4343.3230.05204.18713.34
1ºO_862.0337.7021.1124.7825.899.5139.2218.54181.50623.20
0.4ºO_C10.000.000.004.954.050.002.580.000.0026.50
0.4ºO_S315.35156.6171.3615.275.3046.91104.4220.1610.651113.40
0.4ºO_C2180.7597.6642.939.484.8226.8463.120.000.00650.87
0.4ºO_10.000.000.006.333.600.001.6719.6071.28144.45
0.4ºO_20.000.000.006.262.060.001.5245.41206.83392.82
0.4ºO_30.000.000.005.784.130.001.1612.6469.86131.76
0.4ºO_40.000.000.005.862.360.001.1017.02176.36333.24
0.4ºO_5161.4693.1939.614.644.7024.1361.2226.27103.13776.85
0.4ºO_6120.0776.5634.764.113.9919.7053.4149.82225.78886.43
0.4ºO_7144.9778.4840.637.870.0018.6661.7011.87107.97721.89
0.4ºO_876.3245.7031.456.410.009.6649.0020.41175.28650.79
PC_C1: Picual_Control 1; PC_S: Picual_Supplemented; PC_C2: Picual_Control 2; PC_1: Picual_Exp 1; PC_2: Picual_Exp 2; PC_3: Picual_Exp 3; PC_4: Picual_Exp 4; PC_5: Picual_Exp 5; PC_6: Picual_Exp 6; PC_7: Picual_Exp 7; PC_8: Picual_Exp 8; CC_C1: Cornicabra_Control 1; CC_S: Cornicabra_Supplemented; CC_C2: Cornicabra_Control 2; CC_1: Cornicabra_Exp 1; CC_2: Cornicabra_Exp 2; CC_3: Cornicabra_Exp 3; CC_4: Cornicabra_Exp 4; CC_5: Cornicabra_Exp 5; CC_6: Cornicabra_Exp 6; CC_7: Cornicabra_Exp 7; CC_8: Cornicabra_Exp 8; EP_C1: Empeltre_Control 1; EP_S: Empeltre_Supplemented; EP_C2: Empeltre_ Control 2; EP_1: Empeltre_Exp 1; EP_2: Empeltre_Exp 2; EP_3: Empeltre_Exp 3; EP_4: Empeltre_Exp 4; EP_5: Empeltre_Exp 5; EP_6: Empeltre_Exp 6; EP_7: Empeltre_Exp 7; EP_8: Empeltre_Exp 8; AQ_C1: Arbequina_Control 1; AQ_S: Arbequina_Supplemented; AQ_C2: Arbequina_ Control 2; AQ_1: Arbequina_Exp 1; AQ_2: Arbequina_Exp 2; AQ_3: Arbequina_Exp 3; AQ_4: Arbequina_Exp 4; AQ_5: Arbequina_Exp 5; AQ_6: Arbequina_Exp 6; AQ_7: Arbequina_Exp 7; AQ_8: Arbequina_Exp 8; HB_C1: Hojiblanca_Control 1; Hojiblanca_Supplemented; Hojiblanca_Control 2; HB_1: Hojiblanca_Exp 1; HB_2: Hojiblanca_Exp 2; HB_3: Hojiblanca_Exp 3; HB_4: Hojiblanca_Exp 4; HB_5: Hojiblanca_Exp 5; HB_6: Hojiblanca_Exp 6; HB_7: Hojiblanca_Exp 7; HB_8: Hojiblanca_Exp 8; MZ_C1: Manzanilla_ Control 1; MZ_S: Manzanilla_Supplemented; MZ_C2: Manzanilla_Control 2; MZ_1: Manzanilla_Exp 1; MZ_2: Manzanilla_Exp 2; MZ_3: Manzanilla_Exp 3; MZ_4: Manzanilla_Exp 4; MZ_5: Manzanilla_Exp 5; MZ_6: Manzanilla_Exp 6; MZ_7: Manzanilla_Exp 7; MZ_8: Manzanilla_Exp 8; RY_C1: Royuela_ Control 1; RY_S: Royuela_Supplemented; RY_C2: Royuela_ Control 2; RY_1: Royuela_Exp 1; RY_2: Royuela_Exp 2; RY_3: Royuela_Exp 3; RY_4: Royuela_Exp 4; RY_5: Royuela_Exp 5; RY_6: Royuela_Exp 6; RY_7: Royuela_Exp 7; RY_8: Royuela_Exp 8; OJ_C1: Pomace_Control 1; OJ_S: Pomace_ Supplemented; OJ_C2: Pomace_ Control 2; OJ_1: Pomace_Exp 1; OJ_2: Pomace_Exp 2; OJ_3: Pomace_Exp 3; OJ_4: Pomace_Exp 4; OJ_5: Pomace_Exp 5; OJ_6: Pomace_Exp 6; OJ_7: Pomace_Exp 7; OJ_8: Pomace_Exp 8; KN_C1: Koroneiki_Control 1; KN_S: Koroneiki_Supplemented; KN_C2: Koroneiki_Control 2; KN_1: Koroneiki_Exp 1; KN_2: Koroneiki_Exp 2; KN_3: Koroneiki_Exp 3; KN_4: Koroneiki_Exp 4; KN_5: Koroneiki_Exp 5; KN_6: Koroneiki_Exp 6; KN_7: Koroneiki_Exp 7; KN_8: Koroneiki_Exp 8; AS_C1: Arbosana_Control 1; AS_S: Arbosana_Supplemented; AS_C2: Arbosana_Control 2; AS_1: Arbosana_Exp 1; AS_2: Arbosana_Exp 2; AS_3: Arbosana_Exp 3; AS_4: Arbosana_Exp 4; AS_5: Arbosana_Exp 5; AS_6: Arbosana_Exp 6; AS_7: Arbosana_Exp 7; AS_8: Arbosana_Exp 8; 1°O_C1: Olive 1°_ Control 1; 1°O_S: Olive 1°_Supplemented; 1°O_C2: Olive 1°_ Control 2; 1°O_1: Olive 1°_Exp 1; 1°O_2: Olive 1°_Exp 2; 1°O_3: Olive 1°_Exp 3; 1°O_4: Olive 1°_Exp 4; 1°O_5: Olive 1°_Exp 5; 1°O_6: Olive 1°_Exp 6; 1°O_7: Olive 1°_Exp 7; 1°O_8: Olive 1°_Exp 8; 0.4°O_C1: Olive 0.4°_ Control 1; 0.4°O_S: Olive 0.4°_Supplemented; 0.4°O_C2: Olive 0.4°_ Control 2; 0.4°O_1: Olive 0.4°_Exp 1; 0.4°O_2: Olive 0.4°_Exp 2; 0.4°O_3: Olive 0.4°_Exp 3; 0.4°O_4: Olive 0.4°_Exp 4; 0.4°O_5: Olive 0.4°_Exp 5; 0.4°O_6: Olive 0.4°_Exp 6; 0.4°O_7: Olive 0.4°_Exp 7; 0.4°O_8: Olive 0.4°_Exp 8. Where, Control 1 (used as the control for Experiments 1–4) refers to original, non-deep-fried olive oil. Supplemented oil refers to non-deep-fried olive oil that had been enriched with olive fruit extract, which was also used in the preparation of Control 2. Control 2 (used as the control for Experiments 5–8) was a mixture of Control 1 and the supplemented oil, resulting in a total polyphenol content of up to 650 mg/kg. Exp.1 was olive oil deep-fried at 170 °C for 3 h without polyphenol supplementation, Exp.2 was olive oil deep-fried at 170 °C for 6 h without polyphenol supplementation, Exp.3 was olive oil deep-fried at 210 °C for 3 h without polyphenol supplementation, Exp.4 was olive oil deep-fried at 210 °C for 6 h without polyphenol supplementation, Exp.5 was olive oil deep-fried at 170 °C for 3 h with polyphenol supplementation, Exp.6 was olive oil deep-fried at 170 °C for 6 h with polyphenol supplementation, Exp.7 was olive oil deep-fried at 210 °C for 3 h with polyphenol supplementation, and Exp.8 was olive oil deep-fried at 210 °C for 6 h with polyphenol supplementation. Where, OAOAH: oleuropein aglycone, oxidized aldehyde and hydroxylic form; LAOAH: ligstroside aglycone, oxidized aldehyde and hydroxylic form; TPC: total phenolic content.
Table 4. Wavelength range (nm), functional groups, associated compounds, and their significance in olive oil analysis and oxidation detection.
Table 4. Wavelength range (nm), functional groups, associated compounds, and their significance in olive oil analysis and oxidation detection.
Wavelength (nm)Functional GroupsAssignmentSignificance in Oil Analysis and Oxidation DetectionSource
1100–1150–CH3C–H stretching in lipidsDecetion of triglycerides and oil purity[42,43,44,45]
1167–CH3C–H stretch 2nd overtoneDetection of triglycerides and oil purity[20,46]
1208–CH2C–H stretch 2nd overtoneFree fatty acid estimation[20,47]
1220HC=CH–C–H stretch 2nd overtoneAssessment of unsaturation levels, critical for nutritional value and oxidative stability[48]
1392–CH32C–H stretch + C–H deformationOils differentiation and fatty acid characterization[20,46]
1414–OHO–H stretchOils differentiation and monitoring oil degradation[20,49]
1724–CH2, –CH3, =CH2C–H 1st overtoneDetection of primary oxidation productsDegradation and oxidation assessment[20,46]
1760–CH2, –CH3, =CH2C–H 1st overtoneDetection of primary oxidation productsDegradation and oxidation assessment[50]
1900O–H (Hydroxyl) groupO–H stretchingDeformation of hydroperoxides (ROOH), and assessing oxidative stability and quality control[42,43,44,45]
1930–1950O–H (Hydroxyl) groupC=O stretchingSecondary oxidation monitoring, assessing aldehydes and ketones, and useful for quality assessment of the oil[42,43,44,45]
2022–COORC–H stretch + C=O stretchDetection of oxidation and rancidity and olive oil quality assessment[20,46]
2049–COORC–H stretch + C=O stretchDetectionb of oxidative changes and degradation[20,46]
2144HC=CH–C–H stretch + C=C stretchAldehydes and ketones formation from lipid degradation[20,46]
2200–2300C=O, –CH2–, and –CH3O–H and C–H combination bandsHydrolysis and secondary oxidation assessment[42,43,44,45]
2350–2500–CH2– and
–CH=CH–
C–H and O–H overtonesDetection of advanced lipid degradation and rancidity[42,43,44,45]
Table 5. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the hydroxytyrosol content of extra virgin, refined, and virgin olive oils.
Table 5. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the hydroxytyrosol content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor IndexWavelength (nm)Correlation Coefficient
1432196213,381.71346
23791856−19,674.36170
34712040−2295.36323
4172144233,590.38074
51451388−67,177.54010
6641226−30,164.76968
71591416−40,448.03140
81931484−40,430.36614
93561810−7959.20002
10143138448,504.25880
111731444121,733.59762
1268724721178.58109
Intercept83.02092
Table 6. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the tyrosol content of extra virgin, refined, and virgin olive oils.
Table 6. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the tyrosol content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
143219626857.74742
23791856−11,765.70594
34712040−1448.50769
4501210018,388.42902
54692036147,906.20886
6171144012,127.19843
7671232−17,562.75333
81621422−33,122.62349
934117802544.81899
101821462−11,396.59050
113521802−4174.90923
125642226−3601.86826
134772052−20,359.73093
14166143039,544.90655
Intercept49.18834
Table 7. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the caffeic acid content of extra virgin olive oils, refined, and virgin olive oils.
Table 7. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the caffeic acid content of extra virgin olive oils, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
143519683835.45630
23781854−3831.74460
34592016−1019.82007
417114407922.85146
51461390−20,396.29076
62791656−2858.14468
753021582408.24660
85432184−3324.87634
93721842−6005.49065
101611420−6219.91960
11167143228,476.44287
12386187012,525.95903
1336618308481.03903
144632024−10,510.69378
1553521689185.45452
Intercept19.66402
Table 8. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to predict the oleocanthal content of extra virgin, refined, and virgin olive oils.
Table 8. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to predict the oleocanthal content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
14932084−973.10870
26112203179.34659
3601218−35,914.37957
4321162−5062.88544
528115425,979.19369
63541806−2969.11419
752621502468.63699
81221342−16,987.85359
9124134660,171.73968
10486207012,653.54897
114652028−10,701.89750
12499209613,800.27621
135072112−13,875.11363
1451721328589.20041
15502210266,639.48526
16621222−53,120.18264
176392376757.05104
185782254−2359.59280
1957522482747.00328
206022302−2004.92531
215702238−3813.96484
226462390−1727.15655
236372372−3925.10362
2459522884414.91786
252991696−3080.57288
2640117818,876.78924
27421182−54,827.86908
2857222425307.74630
294952088−42,817.13491
3052021388912.73045
Intercept40.60485
Table 9. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the oleacein content of extra virgin, refined, and virgin olive oils.
Table 9. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the oleacein content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
14542006−5680.06361
255922164967.28419
361122011,218.27851
4601218−67,297.61086
51971492−14,963.85598
63111720−1926.68819
7322174210,172.90761
8172144212,251.43432
92001498183,886.86250
1054921968976.99565
113201738−9690.73672
125472192−13,332.89685
1332317448936.87708
1453621707626.86144
1534017788774.22502
16554220638,724.02968
17310171813,885.03698
185462190−28,739.20861
19591216−86,830.86445
201741446−6,6198.23886
215632224−14,392.59184
2256822346843.62945
233301758−10,526.22840
243191736−10,684.44448
25552220252,941.22023
2650721127329.05989
27211152043,106.58213
28177145268,510.88996
295142126−10,193.90995
30143138410,586.05871
Intercept56.93204
Table 10. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the homovanillic acid content of extra virgin, refined, and virgin olive oil.
Table 10. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the homovanillic acid content of extra virgin, refined, and virgin olive oil.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
143219621803.82264
23821862−3185.60381
341519281214.22297
4441198015,247.41441
534217821293.83673
65762250−656.30091
73431784−9270.34062
85092116815.81331
92981694−1630.11451
1028916768402.71210
115462190−1198.27546
123721842−14,972.89837
13491196−3369.44202
1453021584207.78595
153081714−1218.14237
1633417661457.10606
17431196012,734.88053
183461790−4338.50419
194912080−516.84851
20414192610,753.56018
216992496232.21421
Intercept10.76000
Table 11. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the pinoresinol content of extra virgin, refined, and virgin olive oils.
Table 11. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the pinoresinol content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
14171932940.26055
24121922−7229.33948
3361170−2222.22699
413413666513.29833
53681834−2646.51335
635017987312.16815
7365182810,722.64540
85112120798.70996
94742046−2723.24544
10172144213,752.73636
11388187410,525.32507
124181934−33,274.88057
135672232−4748.97864
1465224021195.20392
156392376−1197.03181
163511800−10,461.43040
17510211811,310.22065
18359181627,253.61862
19425194813,948.01071
2056922365891.40474
21372184216,657.38826
223611820−22,317.84085
233431784−3832.32465
2439117627,581.13642
25346179013,282.46818
26381174−50,538.23421
271371372−37,104.25827
283861870−31,285.16992
29701238−14,403.49116
3071124037,693.61572
Intercept36.18394
Table 12. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to predict the OAOAH content of extra virgin, refined, and virgin olive oils.
Table 12. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to predict the OAOAH content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
13781854−7664.67224
24652028−778.56914
3479205617,208.69174
44682034−43,612.82562
551121203675.37981
65532204−2352.47170
74592016−33,349.05465
84732044−45,325.72438
95012100−13,752.79497
104782054103,414.74005
11451200029,388.31732
1229416863802.10521
135202138−12,363.94991
141421382−9717.46283
15173144418,557.58366
16565222810,119.49859
1754221825334.01003
186452388−2111.64565
1964923964593.83527
20461202081,335.20840
215232144−10,767.74041
22519213615,190.96859
23168143434,017.42804
244351968−18,648.15455
25401178−27,541.37822
26545218827,163.66433
2739117684,555.71808
282961690−30,092.64793
295462190−26,058.31194
301701438−74,892.22478
Intercept43.56524
Table 13. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the LOAOAH content of extra virgin, refined, and virgin olive oils.
Table 13. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to quantify the LOAOAH content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
13811860−35,235.15684
26111021,773.36900
391116−453,292.11841
430517085975.73021
56932484−2724.40990
664023786900.81223
7690247811,176.88662
85762250−10,842.84480
959122806993.52346
106212340−23,881.16167
11371172−41,980.32481
125822262−27,614.03977
13635236812,590.34322
1466624306433.66654
155932284−21,368.79201
16603230433,900.17635
17597229224,159.88427
186152328−25,504.11354
1964923968973.45220
206472392−15,458.81667
21111120−202,417.68592
22584226620,866.93140
2367024388404.83943
24605230817,921.74157
256272352−10,725.60514
26629235622,890.11148
276422382−14,941.76960
285852268−18,343.33486
296302358−22,761.90145
306612420−9882.55575
Intercept178.61028
Table 14. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to predict the TPC content of extra virgin, refined, and virgin olive oils.
Table 14. Chemometric details of the variable selection/decorrelation procedure carried out by SELECT, corresponding to the optimal OLS regression model developed from column auto-scaled NIR spectra. This model is proposed to predict the TPC content of extra virgin, refined, and virgin olive oils.
Order of SelectionPredictor (Original) IndexWavelength (nm)Correlation Coefficient
1210151891,715.98370
24592016−19,771.41183
3504210675,503.16798
42791656−136,266.77356
5411180140,228.33992
64942086−78,991.92309
75222142−31,354.37443
8171144070,302.98905
91781454−541,936.28613
10529215634,012.60207
111801458−587,689.37666
125462190−81,746.44220
134972092−451,381.06447
1465023987408.86801
155332164172,347.35998
16671232114,433.12526
175262150−89,944.76714
18401178−420,629.27826
Intercept739.44489
Table 15. Statistical characteristics of the developed NIR and SELECT-OLS models for quantifying phenolic compounds in various olive oils with HTyr supplementation and deep frying.
Table 15. Statistical characteristics of the developed NIR and SELECT-OLS models for quantifying phenolic compounds in various olive oils with HTyr supplementation and deep frying.
Response (Phenolic Compound)SDEMAERLOORV%LOORSDLOOEV%LOOMPE
Hydroxytyrosol19.5113.970.985.0021.1095.0015.69
Tyrosol10.287.460.984.7511.0795.258.47
Caffeic acid4.583.190.984.574.8795.433.64
Oleocanthal9.666.820.9420.9311.6679.079.18
Oleacein18.5912.740.9418.9721.1381.0316.71
Homovanillic acid3.892.670.9610.264.3689.743.26
Pinoresinol9.666.510.9127.9811.0572.028.57
OAOAH14.9710.300.9517.9617.1182.0413.56
LAOAH62.2643.370.9420.0770.0779.9356.36
TPC97.8169.870.969.86107.5690.1482.38
Concentration of Response (mg/kg)Min Max
Hydroxytyrosol0 389.49
Tyrosol0 215.50
Caffeic acid0 90.99
Oleocanthal0 112.01
Oleacein0 199.34
Homovanillic acid0 60.21
Pinoresinol0 109.11
OAOAH0 176.65
LAOAH0 686.59
TPC3.89 1683.03
SDE: Standard deviation of error; MAE: Mean absolute error; Multiple correlation coefficient (R); LOORV: LOO Residual variance; LOORSD: LOO Residual standard deviation; LOOEV: LOO Explained variance; LOOMPE: LOO mean prediction error. Oleocanthal: decarboxymethyl ligstroside aglycone, dialdehyde form; oleacein: decarboxymethyl oleuropein aglycone, dialdehyde form; OAOAH: oleuropein aglycone, oxidized aldehyde and hydroxylic form; LAOAH: ligstroside aglycone, oxidized aldehyde and hydroxylic form.
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Mehany, T.; González-Sáiz, J.M.; Pizarro, C. Quantification of Phenolic Compounds in Olive Oils by Near-Infrared Spectroscopy and Multiple Regression: Effects of Cultivar, Hydroxytyrosol Supplementation, and Deep-Frying. Antioxidants 2025, 14, 672. https://doi.org/10.3390/antiox14060672

AMA Style

Mehany T, González-Sáiz JM, Pizarro C. Quantification of Phenolic Compounds in Olive Oils by Near-Infrared Spectroscopy and Multiple Regression: Effects of Cultivar, Hydroxytyrosol Supplementation, and Deep-Frying. Antioxidants. 2025; 14(6):672. https://doi.org/10.3390/antiox14060672

Chicago/Turabian Style

Mehany, Taha, José M. González-Sáiz, and Consuelo Pizarro. 2025. "Quantification of Phenolic Compounds in Olive Oils by Near-Infrared Spectroscopy and Multiple Regression: Effects of Cultivar, Hydroxytyrosol Supplementation, and Deep-Frying" Antioxidants 14, no. 6: 672. https://doi.org/10.3390/antiox14060672

APA Style

Mehany, T., González-Sáiz, J. M., & Pizarro, C. (2025). Quantification of Phenolic Compounds in Olive Oils by Near-Infrared Spectroscopy and Multiple Regression: Effects of Cultivar, Hydroxytyrosol Supplementation, and Deep-Frying. Antioxidants, 14(6), 672. https://doi.org/10.3390/antiox14060672

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