Influence of Genotype and Environment on Fruit Phenolic Composition of Olive

Abstract

Extra virgin olive oil (EVOO) is renowned for its numerous minor compounds, particularly phenols, which contribute to its health-promoting properties and commercial quality. The phenolic composition of EVOO largely relies on the initial phenolic content of the olive fruits, which represents, therefore, an important trait to be considered in olive breeding programs. However, only limited studies have been conducted so far to compare the relative influence of genotype and environment in the variability of this trait. For that reason, this study aims to assess the influence of genotype (cultivars ‘Arbequina’, ‘Coratina’, ‘Hojiblanca’, ‘Koroneiki’, ‘Martina’, and ‘Picual’) and environment (harvest date, season, and location) on the fruit phenolic content and composition. A wide general variability was obtained in the whole dataset, with total phenols ranging from 6019 to 38,380 μg/g. A stronger effect of the genotype than the environment was observed for total phenolic content (representing 76–90% of total sums of squares) and the different groups of compounds. Notably, significant variations were found among cultivars not only in the total phenol content but also in the percentage of individual compounds within each main phenolic group. Overall, taking into account the entire dataset available, a clear grouping of samples according to genotypes was observed. The results obtained highlight the prominent influence of the genotype (cultivar) over the environment and genotype–environment interactions on the phenolic content and composition in olive fruits; even when considering very contrasting environments as the Mediterranean and subtropical in the present work. These findings suggest the feasibility of breeding selection of new cultivars with distinctive phenolic content and composition.

1. Introduction

Extra virgin olive oil (EVOO), the cornerstone of the Mediterranean diet, has been found to have numerous benefits for human health. The chemical composition of EVOO defines its health-related properties and distinguishes it from other vegetable oils. It is mainly composed of triglycerides, glycerol esters, and fatty acids accompanied by a unsaponificable fraction of a myriad of compounds in low proportion [1]. The high EVOO content in monounsaturated fatty acids helps to lower bad cholesterol levels and reduce the risk of heart disease [2]. Among the unsaponificable fraction components, phenols are considered one of the most important ones due to their anti-inflammatory properties and help in protecting the body against free radicals and oxidative stress [2]. In addition, EVOO consumption is considered to be linked to a lower risk of stroke, type 2 diabetes, and certain types of cancer [3].

The most abundant group of phenolic compounds found in EVOO are the secoiridoids, which include the phenolic alcohol tyrosol or its hydroxyl derivative hydroxytyrosol in their structure. These compounds play an important role in the nutritional and sensory properties of olive oil [4]. Secoiridoids are the phenolic compounds most transferred from the olive fruit to the oil [5]. The final phenolic compound composition in the oil depends not only on the fruit content but also on the activity of hydrolytic and oxidative enzymes occurring during the malaxation process [6], especially the presence of olive β-glucosidase as a key enzyme [7]. Still, a high correlation has been found between fruit and oil for total phenolic content as well as for some individual phenols [8]. This relationship between the phenolic composition of the final EVOO product and the initial content of the fruit represents a potential advantage for quality evaluation in olive breeding programs, where a high number of samples must be tested [8].

Previous studies have evidenced that phenol content and composition in both olive fruits and oil are significantly influenced by environmental factors as irrigation [9], harvest date [10], and climatic conditions [8]. A strong genotypic effect has also been reported, with high segregation and variability between genotypes even from the same cross combination [11]. However, only one study has considered the interaction between environmental factors and genotype, and specifically on the phenolic composition of the fruit [8]. In addition, this study was performed under Mediterranean conditions and using a narrow genetic base (‘Picual’ × ‘Arbequina’ breeding selections). Obtaining consistent estimations of the genotype × environment interaction for important traits as the phenol content and composition would be compulsory for breeding programs aimed at developing cultivars with improved EVOO composition [12].

For that reason, the main objective of this work was to estimate the relative influence of genotype, environment, and their interaction on the phenol content and composition of olive fruit. For that purpose, six cultivars with diverse genetic bases were evaluated at two different harvest dates in two years and in two locations, under Mediterranean (Cordoba) and non-Mediterranean (subtropical, Tenerife) climates. We aimed to gain knowledge on both the potential of fruit phenolic profiling on the breeding program and the behavior of the cultivars studied in new locations outside the classical distribution area.

2. Materials and Methods

2.1. Plant Materials and Locations

This study included some of the most widespread cultivars at an international level, i.e., ‘Arbequina’, ‘Coratina’, ‘Hojiblanca’, ‘Koroneiki’, and ‘Picual’ that were propagated from the World Olive Germplasm Bank of Cordoba, Spain [13]. Also, the new cultivar ‘Martina’ obtained from a breeding program in Cordoba was used. Experimental trials were established in two locations characterized by very different climates: a typically Mediterranean climate in Cordoba (37°53′29″ N, 4°46′21″ W) and a subtropical climate condition in Tenerife, Canary Islands (28°16′07″ N, 16°36′20″ W). The Mediterranean climate is distinguished by its colder winters and hotter summers in comparison to the subtropical climate (

Table 1. Mean temperatures (maximum, minimum, and average) and monthly rainfall during 2021 in the two locations studied: Tenerife and Cordoba.

	Average Temp (°C)		Max Temp (°C)		Min Temp (°C)		Rainfall (mm)
	Tenerife	Cordoba	Tenerife	Cordoba	Tenerife	Cordoba	Tenerife	Cordoba
January	14.69	7.91	19.29	14.10	11.46	2.95	135.30	70.00
February	14.61	12.10	19.21	18.24	11.10	7.56	85.55	45.80
March	15.32	13.52	20.54	21.83	11.54	6.36	5.20	9.40
April	16.38	16.54	21.10	23.33	12.96	10.97	18.30	83.80
May	19.01	20.65	24.52	29.35	14.47	11.80	0.10	9.40
June	19.69	24.61	25.19	33.32	15.45	15.83	1.00	12.80
July	24.04	28.57	29.42	37.60	19.33	18.87	0.00	0.00
August	23.45	28.75	28.84	37.60	19.27	19.83	1.10	0.00
September	21.28	23.94	26.25	31.72	17.86	17.20	0.20	40.60
November	16.45	10.94	21.15	18.43	13.79	4.96	0.00	27.20
October	20.67	19.34	26.25	27.86	16.55	12.46	54.20	19.40
December	15.66	10.45	20.31	17.16	12.81	4.76	13.90	122.80
Average	18.47	18.17	23.54	25.96	14.74	11.16	314.85 *	441.20 *

* Accumulated rainfall.

). Experimental plots were in typical clay-loam and sandy-loam soils in Córdoba and Tenerife, respectively. In each experimental trial, cultivars were arranged in a randomized complete block design with two blocks and three four trees per experimental plot.

Sampling was carried out when trees were 5 years old by randomly and representatively collecting approximately one kilogram of olive fruits from the four trees of each genotype and block as is routinely performed in olive breeding programs [8]. Comparisons were carried out between two harvest dates in Cordoba (mid-October vs. mid-November) in 2021 and between Cordoba and Tenerife on the same harvest date (mid-November). Additionally, the experiment in Cordoba was also sampled in 2022 to test the effect of the harvest year.

2.2. Analysis of Fruit Phenolic Compounds

Fruit phenolic compounds were extracted from each sample utilizing a well-established previously developed protocol [14]. For the analysis of fruit phenolic compounds, longitudinal mesocarp pieces with a thickness of 1 mm were precisely sectioned from the pulp of around 20 fruits per genotype (approximately 1 g in total). These samples were subsequently immersed in a dimethyl sulfoxide (DMSO) solution (6 mL/g fruit) containing syringic acid (24 mg/mL) as an internal standard and stored at 4 °C for a period of 72 h. The obtained extracts were subjected to filtration using a 0.45 µm nylon mesh and following that were stored at −20 °C until further analysis using High-Performance Liquid Chromatography (HPLC) [8]. The analysis of phenolic extracts from the fruits was carried out using an HPLC system comprised of a Beckman Coulter liquid chromatography system equipped with a System Gold 168 UV-Vis detector selected at 280 and 335 nm, an autosampler module 508, a Waters column heater module at constant 35 °C, and a solvent module 126. A Superspher RP 18 column (4.6 mm i.d. × 250 mm, particle size 4 μm: Dr Maisch GmbH, Germany) at flow rate 1 mL/min of 0.5% phosphoric acid (solvent A) and acetonitrile:methanol (1:1) (solvent B) was used under the following program: 0–25 min, 5–30%B; 25–35 min, 30–38%B; 35–40 min, 38% B; 40–45 min, 38–45% B; 45–50 min, 45–100% B; 50–55 min, 100% B; 55–57 min, 100–5% B; 57–60 min, 5% B.

A total of 10 phenolic compounds were identified in the fruit phenolic extracts and were used for subsequent analysis, according to the three main groups of compounds: hydroxytyrosol derivatives [DERHT: hydroxytyrosol-1-O-glucoside (HT1G), oleuropein (OLEU), demethyloleuropein (DMOLEU), verbascoside (VERBAS)], tyrosol derivatives [DERTY: ligstroside (LIGS), demethylligstroside (DMLIGS, tyrosol-1-O-glucoside (TY1G)], and flavonoids [FLV: rutin (RUT), luteolin-7-O-glucoside (LUT7G), apigenin-7-O-glucoside (API7G)].

The tentative identification of compounds by their UV-Vis spectra was confirmed by using HPLC/ESI-qTOF-HRMS. The liquid chromatograph system used was the Dionex Ultimate 3000 RS UHPLC liquid chromatograph system (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a similar Superspher RP 18 column but with formic acid (1%) instead of phosphoric acid (0.5%) in the mobile phase. A split post-column of 0.4 mL/min was introduced directly on the mass spectrometer electrospray ion source. The HPLC/ESI-qTOF operated for mass analysis using a micrOTOF-QII High Resolution Time-of-Flight mass spectrometer (UHRTOF) with qQ-TOF geometry (Bruker Daltonics, Bremen, Germany) equipped with an electrospray ionization (ESI) interface. Mass spectra were acquired in MS fullscan mode and data were processed using TargetAnalysis 1.2 software (Bruker Daltonics, Bremen, Germany) (Figure S1 in Supplementary Material).

2.3. Data Analysis

The study involved the evaluation of three subsets of data: (i) two different harvest dates at the Cordoba location, (ii) a comparison between the Cordoba and Tenerife locations in the same year, and (iii) data from two consecutive years at the Cordoba location. The data obtained were subjected to statistical analysis using R software (version 4.3.0.) [15]. Analysis of variance (ANOVA) was performed for each subset of data to examine the effect of different sources of variation, cultivar, environment, and their interaction on individual and total phenolic compound content expressed in μg per g of fruit. Principal Component Analysis (PCA) was utilized to assess the patterns of association among the phenolic compounds across different cultivars, locations, and harvest dates. The original variables were standardized to ensure that they all have equal influence on the principal components.

3. Results

The phenolic compounds were divided into three main groups: hydroxytyrosol derivatives (DERHT), tyrosol derivatives (DERTY), and flavonoids (FLV). The average total phenol content was measured at 16,577 μg/g with a range of variation spanning from 6019.7 μg/g to 38,380 μg/g. Hydroxytyrosol derivatives were the most abundant compounds, accounting on average for 88.7% of this content. The FLV group exhibited the highest variation among the main groups (50.1%), whereas in terms of individual components, hydroxytyrosol-1-O-glucoside showed the highest variation (145.4%) (

Table 2. Descriptive statistics of total phenols and fruit phenolic components in the whole dataset (all cultivars, locations, harvest dates, and replicates) evaluated. The quantification of phenolic compound groups is expressed as a percentage (%) relative to the total phenol content, while the individual components are expressed as a percentage of their corresponding group.

	Average	Coefficient of Variation (%)	Min	Max
Total phenols (μg/g)	16,577	48.53	6019	38,380
DERHT (%)	88.72	3.19	80.80	94.06
HT1G	8.82	145.42	0.09	60.08
DMOLEU	29.59	119.11	0.01	86.61
OLEU	48.52	70.87	0.31	97.73
VERBAS	10.14	78.89	0.69	33.13
DERTY (%)	5.55	49.85	2.02	14.34
LIGS	48.83	69.20	0.73	96.67
DMLIGS	32.21	106.66	0.22	97.65
TY1G	16.23	89.41	0.03	70.16
FLV (%)	5.73	50.07	1.15	15.79
RUT	57.65	19.29	36.99	78.04
LUT7G	37.44	26.56	18.74	56.32
API7G	4.91	41.55	0.23	8.57

DERHT: Hydroxytyrosol derivatives, DERTY: Tyrosol derivatives, FLV: Flavonoids, HT1G: Hydroxytyrosol-1-O-glucoside, OLEU: Oleuropein, DMOLEU: Demethyloleuropein, VERBAS: Verbascoside, LIGS: Ligstroside, DMLIGS: Demethylligstroside, TY1G: Tyrosol-1-O-glucoside, RUT: Rutin, LUT7G: Luteolin-7-O-glucoside, API7G: Apigenin-7-O-glucoside.

).

Separate ANOVA analyses were conducted for three subsets of data: two different harvest dates at the Cordoba location, the comparison between Cordoba and Tenerife locations at the same harvest date, and the two consecutive years of data from Cordoba. To evaluate the contribution of each factor to the total variance, the sum of squares (%) was calculated for each of the main groups and for each individual component within groups. In all subsets of data, genotype contributed the highest proportion to the total variance for total phenols and all groups and individual compounds, except for the group of DERHT (

Table 3. Percentage of sum of square (%) for genotype (G), harvest date (HD), and their interaction (G × HD) related to the variability of total phenols and fruit phenolic components. Data is expressed as a percentage of total sums of squares and signification level of the F test in ANOVA analysis.

	Sum of Squares (%)
	G		HD		G × HD		Error
Total phenols	90	***	7	***	1	ns	3
DERHT	20	ns	2	ns	15	ns	63
HT1G	72	***	8	***	19	***	1
DMOLEU	99	***	0	**	1	***	0
OLEU	94	***	2	***	4	***	0
VERBAS	93	***	0	ns	2	ns	4
DERTY	89	***	5	***	4	*	2
LIGS	88	***	3	***	7	***	1
DMLIGS	98	***	0	ns	1	ns	1
TY1G	42	***	10	**	40	***	7
FLV	52	*	6	ns	6	ns	36
RUT	94	***	0	ns	2	ns	3
LUT7G	94	***	0	ns	2	ns	4
API7G	90	***	2	*	2	ns	6

p ≤ 0.001 ***; p ≤ 0.01 **; p ≤ 0.05 *; ns: non-significant. DERHT: Hydroxytyrosol derivatives, DERTY: Tyrosol derivatives, FLV: Flavonoids, HT1G: Hydroxytyrosol-1-O-glucoside, OLEU: Oleuropein, DMOLEU: Demethyloleuropein, VERBAS: Verbascoside, LIGS: Ligstroside, DMLIGS: Demethylligstroside, TY1G: Tyrosol-1-O-glucoside, RUT: Rutin, LUT7G: Luteolin-7-O-glucoside, API7G: Apigenin-7-O-glucoside.

,

Table 4. Percentage of sum of square (%) for genotype (G), location (L), and their interaction (G × L) related to the variability of total phenols and fruit phenolic components. Data is expressed as a percentage of total sums of squares and signification level of the F test in ANOVA analysis.

	Sum of Squares (%)
	G		L		G × L		Error
Total phenols	76	***	14	***	6	ns	4
DERHT	8	ns	5	ns	29	ns	57
HT1G	79	***	5	***	14	***	1
DMOLEU	94	***	2	***	3	**	1
OLEU	84	***	7	***	8	***	1
VERBAS	69	***	1	ns	29	***	2
DERTY	80	***	1	ns	17	***	2
LIGS	82	***	8	***	8	**	2
DMLIGS	90	***	4	***	5	**	1
TY1G	77	***	4	ns	9	ns	9
FLV	47	ns	6	ns	11	ns	36
RUT	89	***	1	ns	6	ns	4
LUT7G	90	***	1	ns	5	ns	4
API7G	74	***	2	ns	18	**	6

p ≤ 0.001 ***; p ≤ 0.01 **; ns: non-significative. DERHT: Hydroxytyrosol derivatives, DERTY: Tyrosol derivatives, FLV: Flavonoids, HT1G: Hydroxytyrosol-1-O-glucoside, OLEU: Oleuropein, DMOLEU: Demethyloleuropein, VERBAS: Verbascoside, LIGS: Ligstroside, DMLIGS: Demethylligstroside, TY1G: Tyrosol-1-O-glucoside, RUT: Rutin, LUT7G: Luteolin-7-O-glucoside, API7G: Apigenin-7-O-glucoside.

and

Table 5. Percentage of sum of square (%) for genotype (G), year (Y), and their interaction (G × Y) related to the variability of total phenols and fruit phenolic components. Data are expressed as a percentage of total sums of squares and signification level of the F test in ANOVA analysis.

	Sum of Squares (%)
	G		Y		G × Y		Error
Total phenols	88	***	1	ns	8	**	3
DERHT	16	ns	2	ns	17	ns	65
HT1G	96	***	2	**	1	ns	1
DMOLEU	93	***	2	**	3	*	2
OLEU	90	***	2	*	6	*	3
VERBAS	82	***	7	***	7	*	4
DERTY	92	***	1	ns	3	ns	4
LIGS	83	***	1	ns	11	*	5
DMLIGS	87	***	4	***	7	**	2
TY1G	81	***	2	ns	6	ns	12
FLV	45	ns	4	ns	7	ns	45
RUT	93	***	1	ns	1	ns	4
LUT7G	93	***	1	ns	1	ns	5
API7G	82	***	0	ns	4	ns	14

p ≤ 0.001 ***; p ≤ 0.01 **; p ≤ 0.05 *; ns: non-significative. DERHT: Hydroxytyrosol derivatives, DERTY: Tyrosol derivatives, FLV: Flavonoids, HT1G: Hydroxytyrosol-1-O-glucoside, OLEU: Oleuropein, DMOLEU: Demethyloleuropein, VERBAS: Verbascoside, LIGS: Ligstroside, DMLIGS: Demethylligstroside, TY1G: Tyrosol-1-O-glucoside, RUT: Rutin, LUT7G: Luteolin-7-O-glucoside, API7G: Apigenin-7-O-glucoside.

).

Comparison of genotypes on two harvest dates at the Cordoba location in 2021 showed significant differences between genotypes for total phenols and the percentage of all the components analyzed, excluding the percentage of the main group DERHT. The difference between the two harvest dates was significant for total phenols, the percentage of main group DERTY, and the percentage of individual components withing groups HT1G, DMOLEU, OLEU, LIGUS, TY1G, and API7G. Similarly, the interaction between genotype and harvest date was significant for HT1G, DMOLEU, OLEU, LIGUS, and TY1G (Table 3).

Comparison of genotypes at two locations, Cordoba and Tenerife, in 2021 showed also significant differences between genotypes for total phenols and the percentage of all the components analyzed, excluding the percentage of the main groups DERHT and FLV (Table 4). The influence of the location was significant for total phenols, HT1G, DMOLEU, OLEU, LIGS, and DMLIGS. Furthermore, significant interaction genotype × location was obtained for HT1G, DMOLEU, OLEU, VERBAS, the main group of tyrosol derivatives (DERTY), LIGS, DMLIGS, and API7G.

Finally, the analysis of two consecutive years in the Cordoba location also showed significant differences between genotypes for total phenols and the percentage of all the components analyzed, excluding the percentage of the main groups hydroxytyrosol derivatives and flavones (Table 5). Between years, differences were significant for HT1G, DMOLEU, VERBAS, and DMLIGS, while the interaction between genotype and year was significant for DMOLEU, OLEU, VERBAS, LIGS, and DMLIGS.

Therefore, the main differences in all experiments tested were obtained between genotypes for both the total phenol content and the percentage of individual components within the three main groups of phenols. The average results for the whole dataset are presented in Figure 1. ‘Coratina’ stood out as having the highest total phenol content, more than double the other genotypes tested, while ‘Hojiblanca’ showed the lowest content. ‘Koroneiki’ and ‘Picual’ were characterized by high content of oleuropein within the hydroxytyrosol derivatives and ligstroside among the tyrosol derivatives. ‘Martina’, coming from a cross ‘Picual’ × ‘Arbequina’, resembled much more its male parental ‘Arbequina’ than the female parental ‘Picual’.

PCA analysis was applied to three subsets of data and the entire dataset. In the separate PCA analyses performed on the sub-data sets, it was evident that the effect of genotype remained prominent, with no discernible grouping based on different locations, years, or harvest dates. Upon conducting PCA analysis using the entire dataset, an even more pronounced grouping of samples according to genotypes was observed, with PC1 and PC2 accounting for 65.3% of the total variance (Figure 2). PC1 was mainly positively associated with DERTY, RUT, and total phenols, and negatively with LUT7G, API7G, and FLV. PC2 was positively related to OLEU and LIGS and negatively related to DMLIGS and DMOLEU.

4. Discussion

Consistent with previous research [4,8,16], the hydroxytyrosol derivatives, namely oleuropein, demethyloleuropein, and verbascoside, displayed noteworthy prevalence as the most prominent fruit phenolic compounds, ranked in descending order of abundance.

The variability of most of the fruit phenolic compounds evaluated was mainly due to the genotype effect. As a result, not only the total phenol content but also its composition was very different from one cultivar to another. As different phenol compounds could have different antioxidant properties, the measurement of the total phenol content does not seem sufficient to really determine the antioxidant capacity of the EVOO of a cultivar. The relative amount of the different phenolic compounds would greatly influence the oil stability and other phenol-related properties of the EVOO.

This predominant genotype effect has also been reported in previous studies in which one of these environmental factors (harvest date) was tested [5], although major effects of the environment (location) have also been reported [8]. According to the variance of components, the highest genotypic effect was obtained for Demethyloleuropein and Demethylligstroside in the sub-datasets related to the comparison of two harvest dates in the Cordoba location and the comparison of the Cordoba and Tenerife locations. In the third sub-dataset, which consisted of two-year data from the Cordoba location, the highest genotypic effect was found for Hydroxytyrosol-1-O-glucoside.

‘Coratina’, a cultivar well known for its high phenol content in the oil [17], stood out also in this work compared to the other cultivars tested. Regarding individual compounds, demethyloleuropein was detected in trace amounts in ‘Hojiblanca’, ‘Koroneiki’, and ‘Picual’ varieties, at moderate levels in ‘Coratina’, and at high levels in ‘Arbequina’ and ‘Martina’. These findings are consistent with the results of a similar investigation conducted by [4], where Demethyloleuropein was identified in ‘Arbequina’ but not in ‘Picual’. In the present study, the phenol profile of the cultivar ‘Martina’ resembled more its male parent ‘Arbequina’ than to the female one ‘Picual’. Similar results were obtained in a previous study including six breeding selections derived from the same cross combination ‘Picual’ × ‘Arbequina’ [8].

The contribution of harvest date, location, and year to the total variance, although statistically significant, remained quite low compared to genotype effect according to the results obtained from three sub-datasets. However, a marked reduction in the total phenol content of all the genotypes was observed at the second harvest date. This suggests a decline of total phenol content throughout the ripening process, as previously reported [10,16,18], even though only two dates were evaluated in the present work. Comparing two locations, in Tenerife, the total phenol content of each cultivar was found to be higher compared to Cordoba. This could be attributed to the higher rainfall in this latter region, as reported in previous studies [10,19]. Significant influence of the location of the fruit phenol content was previously reported [18,20], with a concomitant variation of the antioxidant activity. In the second year at the Cordoba location, even though total rainfall was quite similar, 446 mm in 2022 and 441 mm in 2021, all cultivars exhibited an increase in total phenol content, except for ‘Coratina’ which showed a decrease of 22% (Table S1). Therefore, it is not possible to associate phenol variations between seasons with simple climate factors.

The interaction between genotype and the environmental factors was also significant for many phenol components. For example, ‘Picual’ showed the most significant decline in phenol content with the harvest date (44%), while ‘Coratina’ exhibited the least reduction (9%) (Table S1). Total phenol content of the ‘Picual’ variety was approximately double in the subtropical climate of Tenerife compared to the Mediterranean climate of Cordoba, whereas ‘Martina’ exhibited the smallest difference between environments with a 9% difference. This significant effect of the genotype x environment interaction was previously reported for other traits [12,21,22].

The distribution of samples on the PCA biplot supports the results of the analysis of variance, highlighting the dominant impact of genotype in elucidating the observed variability. In the PCA analysis, oleuropein and ligstroside were positioned in the first quadrant together with the ‘Koroneiki’ and ‘Picual’ cultivars. Similar results obtained for the cultivar ‘Picual’ in previous studies [4,5,8]. ‘Hojiblanca’ was located away from the other genotypes in the second quadrant along with apigenin-7-O-glucoside, hydroxytyrosol-1-O-glucoside, tyrosol-1-O-glucoside, luteolin-7-O-glucoside, and the total value of the flavones group. On the other hand, the ‘Arbequina’ and ‘Martina’ cultivars exhibited a clear separation from the other genotypes and were positioned in the third quadrant along with demethyloleuropein and demethylligstroside. Previously, it has been reported that ‘Sikitita’ [23] and other breeding selections [8], all of them derived from the ‘Picual’ × ’Arbequina’ cross, were classified together with its parent variety ‘Arbequina’ in the PCA analysis. Finally, ‘Coratina’ was positioned in the fourth quadrant along with rutin, verbascoside, and total phenol components.

5. Conclusions

In summary, genotype seems to be the main factor affecting the variability of olive fruit phenol content and composition, compared to several environmental factors (harvest date, season, and location). Even the genotype–environment interaction seems to have a lesser effect on that variability. In this genotype effect, the predominance of one of the parents on the phenolic composition observed in previous studies and confirmed here could be of great interest when designing the crosses in olive breeding programs and should be investigated in further studies. The wide genetic variability found indicates the need to evaluate not only the total phenol content but also composition to really determine the antioxidant and other phenol-related properties of EVOO. This wide genetic variability, with respect to the environmental variability, also underlines the possibility of promoting new breeding programs aimed at obtaining new cultivars with distinctive phenolic content and composition.