Determination of Olive Maturity Stage and Optimal Harvest Interval of ‘Kalinjot’ Cultivar Using Destructive and Non-Destructive Methods

Abstract

This study investigated the maturity and optimal harvest interval of the ‘Kalinjot’ olive cultivar in the Vlora region. Fruit samples were collected from six randomly selected trees over nine harvest dates at 10-day intervals from September to December 2024. Physical, chemical, and instrumental analyses were conducted to evaluate parameters related to olive ripening and oil quality. Destructive methods measured the fruit diameter, fresh weight, maturity index, flesh firmness, and detachment index, while non-destructive techniques assessed the color and absorbance indices using portable Vis/NIR devices. Chemical analyses determined the fruit moisture, oil content, and total polyphenols. The results showed that the fruit diameter, fresh weight, and oil content increased with ripening, whereas the flesh firmness and detachment index decreased significantly. A negative correlation between the maturity index and color index was observed, along with strong positive correlations between the Kiwi-Meter’s IAD values, maturity index, and oil content. The optimal harvest interval was identified when olives reached up to 25.42% oil content and 1820.89 mg GAE/kg FW total polyphenols, ensuring both the technological and nutritional quality of the oil.

1. Introduction

The olive tree (Olea european L.) has been a cornerstone of Mediterranean agriculture for millennia, valued not only for its ecological adaptability but also for its substantial economic importance. It is the primary source of olive oil, a key component of the Mediterranean diet, which is recognized for its health benefits and nutritional properties. Olive cultivation plays a vital role in agricultural production and contributes significantly to the cultural and social identity of the Mediterranean [1]. Among the various olive cultivars, the Kalinjot cultivar is gaining recognition for its unique characteristics and suitability for high-quality oil production. This cultivar is particularly noted for its mid-to-late fruit ripening and its distinct profile in terms of fruit size, firmness, and oil content [2]. The proper determination of the optimal harvesting time for this cultivar is crucial for ensuring the highest quality of olive oil, which requires the precise and timely assessment of fruit maturity.

Determining the optimal harvest stage of olives is a challenging task, as it directly impacts both the yield and quality of olive oil and strongly influences the difference between costs and revenues [3]. Traditionally, this assessment has been based on destructive methods that provide reliable and accurate data but necessitate harvesting and laboratory analysis of the fruit. Destructive methods include evaluating key parameters such as fruit size, flesh firmness, oil content, and the maturity index, all of which are indicators of fruit ripeness and oil quality [4]. However, these methods have limitations, such as being time-consuming, requiring specialized laboratory equipment, and destroying the fruit samples, which prevents continuous monitoring throughout the ripening process [5].

In contrast, there is increasing interest in applying non-destructive techniques for assessing olive fruit maturity. According to Tarquini et al. (2022) [6], combining non-destructive sensors with classical maturity indices significantly improves the accuracy of harvest timing decisions and supports precision agriculture approaches in olive groves.

These methods, which do not require fruit destruction, offer several advantages, including the ability to continuously monitor fruit characteristics over time without damaging the samples. Non-destructive methods can measure parameters such as the fruit color and moisture content, providing valuable insights into the fruit ripeness and harvest suitability.

Vis/NIR devices, being portable and relatively cheaper, are worthy of investigation, because they could be very useful for a preliminary, quick quality assessment of olive drupes [7]. Instrumentation advances promise lower cost and easier use of Vis-NIRS within postharvest applications [8]. Techniques such as the Color Index (CI) measured using a colorimeter, as well as the use of the Index of Absorbance Difference (IAD) with instruments such as the Kiwi-Meter^® and standard DA-Meter^®, have been explored as potential tools for assessing olive fruit maturity directly in the field [9,10,11]. These non-destructive techniques provide real-time in situ measurements, which could greatly improve the efficiency and accuracy of harvest timing, leading to better-quality oil production and reducing waste.

The objective of this study is to determine the optimal maturity stage and harvest interval for the ‘Kalinjot’ cultivar by using both destructive and non-destructive methods. While traditional destructive methods such as the maturity index, flesh firmness, and oil content analysis are commonly used, this study introduces for the first time the application of IAD-based non-destructive techniques—specifically the Kiwi-Meter^® and DA-Meter^®—to monitor olive ripening in the ‘Kalinjot’ cultivar.

Importantly, this is the first study to apply such portable Vis/NIR instruments for in-field maturity assessment in this cultivar within the Vlora region of Albania. This novelty contributes to precision harvesting practices and offers a practical alternative for growers lacking laboratory infrastructure. By combining both destructive and non-destructive approaches, this study aims to provide a comprehensive understanding of the optimal harvesting interval for the ‘Kalinjot’ cultivar. It seeks to bridge the gap between traditional methods and innovative technologies, offering a practical solution for olive growers in the region. Additionally, this research will contribute to enhancing olive oil quality by identifying the most accurate and efficient methods for determining fruit maturity. The results of this research have the potential to modernize the olive harvest, providing valuable tools for both large-scale and small-scale producers, while promoting sustainability in olive cultivation and oil production practices.

2. Materials and Methods

In this study, olive fruit samples from the ‘Kalinjot’ cultivar were collected at the Germplasm Bank of Agricultural Technology Transfer Center (ATTC) of Vlora during the 2024 season. The sampling followed the methodology established by Rodríguez et al. (1955) [12], where fruits were harvested at the operator’s height from four different sides of the tree, ensuring that fruits from the interior were avoided. Approximately 3 kg of samples were collected from each tree during each harvest. Sampling occurred from the beginning of September, at 10-day intervals, with measurements taken from six randomly selected trees of the cultivar under study (H1–H9).

The following analytical determinations were performed:

1. Maturity index (MI). For the MI measurements, 100 fruits were taken and categorized according to the color scale. The number of fruits in each color category was multiplied by the corresponding category number. Pigmentation was determined using the MI, which ranges from 0 to 7, with 0 representing green olives and 7 indicating olives with pigmentation up to 100% in both the epicarp and mesocarp [13]. The maturity index was obtained by applying the following formula.

$$\mathrm{M}\mathrm{I}={\displaystyle \frac{\mathrm{a}\mathrm{ }\times \mathrm{ }0+\mathrm{b}\mathrm{ }\times \mathrm{ }1+\mathrm{c}\times \mathrm{ }2+\mathrm{d}\mathrm{ }\times \mathrm{ }3+\mathrm{e}\mathrm{ }\times 4+\mathrm{f}\mathrm{ }\times \mathrm{ }5+\mathrm{g}\mathrm{ }\times 6+\mathrm{h}\mathrm{ }\times \mathrm{ }7}{100}}$$

(1)

where a–h represents the number of fruits in ripening categories 0–7, respectively.

2. Olive Fruit Diameter. The diameter of olive fruits was measured using a Vernier caliper accurate to ±0.1 mm, following the standardized procedure from previous research. This method ensures precise and reliable measurements of fruit dimensions.

3. Fresh weight (FW). The average fruit weight was determined by weighing 100 randomly selected fruits using a scale that has a sensitivity of ±0.01 g [14].

4. Color index (CI). This is a non-destructive method for assessing fruit color. The CIE L*, a*, and b* values of olives were measured using a colorimeter (Model CR-400, Konica-Minolta, Osaka, Japan). Measurements were taken from both sides of the fruit at the axis area [15,16,17]. The Color Index (CI) is calculated using a formula that directly relates to the ripening of olive cultivars.

$$\mathrm{C}\mathrm{I}=\mathrm{L}\mathrm{ }\times \mathrm{ }{\displaystyle \frac{\mathrm{b}-\mathrm{a}}{100}}$$

(2)

where

–

L is the lightness component (ranging from 0 = black to 100 = white),

–

a is the red–green component (positive values indicate red, negative values indicate green),

–

b is the yellow–blue component (positive values indicate yellow, negative values indicate blue).

5. Flesh firmness (FF). The fruit flesh firmness was measured using a hand-held Push-Pull Dynamometer (Model FD 101. TR Turoni, Forlì, Italy), which has a measurement range of 0 to 1000 g and a resolution of 10 g. The plunger is made of steel and has a diameter of 1.5 mm. Firmness is expressed in Newton (N) [11,18].

6. Detachment Index (DI). The Detachment Index is calculated by dividing the applied force by the fresh weight of the selected olive fruits [15,19]. The traction force was measured using a hand-held Push–Pull Dynamometer (Model FD 101, TR Turoni, Forlì, Italy) with a range of 0 to 1000 g and a resolution of 10 g. The Detachment Index is expressed in Newton per gram (N/g).

$$\mathrm{D}\mathrm{I}\mathrm{ }(\mathrm{N}/\mathrm{g})=\mathrm{ }{\displaystyle \frac{\mathrm{F}}{\mathrm{F}\mathrm{W}}}$$

(3)

where

–

DI is the Detachment Index, expressed in Newton per gram (N/g);

–

F is the detachment force measured in Newtons (N);

–

FW is the fresh weight of the fruit in grams (g).

7. Index of Absorbance Difference (IAD): A non-destructive method. Measurements were performed on 25 fruits per tree from six trees using the Kiwi-Meter^® (Model FRM04–AP, TR Turoni, Forlì, Italy) and DA-Meter^® (Model FRM04–F, TR Turoni, Forlì, Italy). The instruments were set to the standard mode for fruit maturity assessment with automatic averaging of readings per fruit. Measurements were performed under consistent ambient temperature (20 ± 2 °C) and relative humidity (60 ± 5%) conditions to minimize the variability. The DA-Meter^® is a portable visible and near-infrared (Vis/NIR) device designed to measure IAD by detecting absorbance differences at specific wavelengths related to chlorophyll and anthocyanin content. Specifically, the DA-Meter^® calculates the IAD as the difference in absorbance between 670 nm and 720 nm, which corresponds to chlorophyll levels, whereas the Kiwi-Meter^® measures the anthocyanin absorbance between 545 nm and 640 nm, using 750 nm as a reference wavelength. Both devices allow direct, rapid, and non-destructive monitoring of fruit ripening in the field on the tree [11,18].

8. Moisture Content Determination. Moisture content was evaluated using the gravimetric oven-drying method [14]. Approximately 5–10 g of homogenized olive fruit paste was placed in aluminum dishes and dried at 105 ± 2 °C until a constant weight was achieved (approximately 16 h). The moisture percentage was calculated based on the weight loss before and after drying.

9. Oil Content Determination (Soxhlet Method). Oil content was determined using the Soxhlet extraction method [14]. Approximately 5–10 g of dried and ground olive sample was placed in a cellulose extraction thimble and extracted with petroleum ether (boiling point 40–60 °C) for 6–8 h. After extraction, the solvent was evaporated, and the remaining oil was weighed. The oil percentage was calculated based on the initial dry weight of the sample.

10. Total phenolic contents. The total polyphenol content was determined using the Folin–Ciocalteu colorimetric method [20], with slight modifications. Briefly, 1 mL of each extract (in the concentration of 1 mg mL⁻¹) was mixed with 5 mL of Folin–Ciocalteu reagent (previously diluted tenfold with distilled water) and allowed to stand at room temperature for 10 min. Then, a 4 mL sodium bicarbonate solution (75 g/L) was added. The mixture was allowed to stand for a further 30 min in the dark at room temperature, after which absorbance was measured at 765 nm using a UV/VIS spectrophotometer. The total phenolic contents were quantified using a calibration curve obtained from measuring the absorbance of four known concentrations of gallic acid (GA) standard (25–50–70–100–200 µg/L). The concentrations are expressed as milligrams of gallic acid equivalents (GAE) per gram of dry extract.

11. Statistical Analysis

Statistical analyses were performed using Statistics 10 (Analytical Software, Tallahassee, FL, USA) and The Unscrambler^® X version 10.4 (CAMO Software AS, Oslo, Norway). One-way analysis of variance (ANOVA) was applied to assess the effect of the harvest date on the physicochemical parameters of the olive fruits. When significant differences were found (p < 0.05), Tukey’s Honest Significant Difference (HSD) test was used for post hoc comparisons among group means. Pearson correlation coefficients (r) were calculated to evaluate the linear relationships between ripening indicators. Additionally, multivariate analysis—specifically principal component analysis (PCA)—was conducted using The Unscrambler^® X software to visualize sample clustering and identify associations between maturity indices and harvest stages.

3. Results and Discussion

3.1. Physicochemical Evolution of Olive Fruit During Ripening

Understanding the physicochemical evolution of olive fruits throughout the ripening period is crucial for determining the optimal harvest time, especially when the goal is to produce high-quality olive oil. This study evaluates key indicators of ripeness, firmness, pigmentation, and internal composition across nine different harvest dates (H1–H9). The findings provide insights into the dynamic changes in fruit characteristics, supporting evidence-based decisions for harvest scheduling.

Table 1. Assessment of olive fruit characteristics (destructive and non-destructive) from the Kalinjot cultivar at different harvest stages.

Harvest	Diameter (mm)	Fresh Weight (g)	Maturity Index (MI)	Color Index (CI)	Flesh Firmness (N)	Detachment Index (DI) (N/g)	IAD (Kiwi-Meter®)	IAD (DA-Meter®)
H1	15.96 ± 0.8 c	2.82 ± 0.3 d	0.0 ± 0.0 d	29.90 ± 2.8 abc	5.73 ± 0.4 a	2.09 ± 0.3 a	0.83 ± 0.3 d	2.99 ± 0.7 bc
H2	17.31 ± 0.9 b	3.06 ± 0.4 c	0.14 ± 0.3 d	31.73 ± 2.9 ab	4.06 ± 0.3 b	1.75 ± 0.3 b	1.16 ± 0.2 c	3.42 ± 0.2 a
H3	17.31 ± 1.1 b	3.16 ± 0.3 bc	0.25 ± 0.4 d	30.17 ± 3.1 abc	3.77 ± 0.3 b	1.69 ± 0.3 b	1.19 ± 0.2 c	3.08 ± 0.3 abc
H4	14.22 ± 0.8 d	3.36 ± 0.4 b	1.55 ± 0.9 c	23.23 ±16.3 c	2.87 ± 0.4 c	2.16 ± 0.6 a	1.21 ± 0.2 c	3.17 ± 0.4 ab
H5	17.98 ± 0.7 ab	3.57 ± 0.2 ab	1.75 ± 0.6 bc	34.47 ± 6.3 ab	2.76 ± 0.2 cd	1.17 ± 0.2 c	1.26 ± 0.2 bc	3.17 ± 0.2 ab
H6	18.25 ± 1.1 ab	3.59 ± 0.6 ab	1.80 ± 0.6 bc	36.43 ± 5.5 a	2.60 ± 0.3 cde	1.20 ± 0.2 c	1.26 ± 0.1 bc	3.13 ± 0.3 ab
H7	18.25 ± 1.1 ab	3.57 ± 0.4 ab	1.80 ± 0.6 bc	36.84 ± 4.7 a	2.51 ± 0.4 de	1.20 ± 0.2 c	1.27 ± 0.1 bc	3.13 ± 0.3 ab
H8	18.54 ± 1.0 a	3.70 ± 0.4 a	2.15 ± 0.5 b	26.80 ± 14.1 bc	2.44 ± 0.3 e	1.08 ± 0.1 c	1.42 ± 0.1 b	2.74 ± 0.4 c
H9	18.52 ± 1.1 a	3.64 ± 0.4 a	3.25 ± 0.4 a	−0.85 ± 5.6 d	2.03 ± 0.2 f	0.99 ± 0.2 c	1.62 ± 0.3 a	1.83 ± 0.3 d

Note: Data are presented as the mean ± standard deviation (n = 100). Within each column, values followed by different lowercase letters (a–f) are significantly different according to Tukey’s HSD test (p < 0.05).

presents the data obtained from both the destructive and non-destructive measurements, including the fruit diameter, fresh weight, maturity index (MI), color index (CI), flesh firmness, detachment index (DI), and IAD of a standard DA-Meter for green and DA-Kiwi-Meter for red pigment. These parameters are used for monitoring fruit development and assessing the ripening stage without compromising the sample integrity.

Throughout the nine harvest stages, the fruit diameter and fresh weight progressively increased until H8 reached 18.54 mm and 3.70 g, respectively, due to ongoing tissue growth and potential oil accumulation. These findings are consistent with observations in other olive cultivars [21].

The Maturity Index (MI) remained very low during the early stages (0.00–0.25) but steadily increased at harvest stage 9 (H9). This reflects the typical change in skin color from green to dark purple/black [22].

The Color Index (CI) showed a slight increase during mid-ripening (H6–H7) but sharply declined at H9, likely due to color changes or over-ripening effects [23].

The flesh firmness and the Detachment Index (DI) exhibited a consistent decreasing trend, indicating fruit softening as maturity and ripening progressed. This is a physiological change linked to cell wall degradation [22,24].

The Index of Absorbance Difference (IAD) measured by the standard DA-Meter declined, indicating internal changes and chlorophyll breakdown, while IAD measured by the Kiwi-Meter increased due to anthocyanin formation. These results confirm that non-destructive DA-Meter measurements can serve as reliable maturity indicators [7,25,26].

Among all parameters, the Maturity Index showed the highest variability (C.V. = 81.9%), followed by the Color Index (48.5%) and Flesh Firmness (35.3%), reflecting the heterogeneity of the transitional ripening stages. In contrast, the IAD values from the DA-Kiwi and DA-Standard had lower coefficients of variation (21.9% and 19.1%, respectively), supporting their use for more consistent maturity assessment.

Overall, the data suggest that the harvest interval H5–H6 corresponding to the period from mid-October to early November, provides the optimal balance between maturity and technological quality for oil production (Figure 1). Conversely, later harvests (H8–H9) may lead to over-ripening, which is associated with excessive softening and pigment loss. Study results support the use of destructive and non-destructive methods, particularly IAD and flesh firmness, as reliable olive maturity indices, in line with findings in both table and oil olives.

3.2. Chemical Composition Changes During Ripening

The chemical composition changes during olive fruit ripening, emphasizing the oil content, water content, and total phenolic compounds (expressed as mg GAE/kg) across nine harvest stages are summarized in

Table 2. Dynamics of oil, water, and total phenol content in olive fruit affected by harvest stage.

Harvest	Oil Content (%)	Water Content (%)	Total Phenols (mg GAE/kg)
H1	16.17 ± 0.6 f	50.83 ± 0.5 h	1175.20 ± 1.6 d
H2	16.77 ± 0.0 ef	48.48 ± 0.1 i	1134.08 ± 1.2 f
H3	17.78 ± 0.0 e	56.20 ± 0.0 g	1027.66 ± 0.0 h
H4	17.88 ± 0.1 de	58.59 ± 0.3 d	1433.10 ± 0.9 c
H5	18.88 ± 0.2 d	61.08 ± 0.1 b	1722.31 ± 1.1 b
H6	21.63 ± 0.6 c	59.24 ± 0.2 c	1820.89 ± 1.6 a
H7	23.38 ± 0.1 b	62.82 ± 0.0 a	1095.48 ± 3.5 g
H8	23.52 ± 0.0 b	57.17 ± 0.1 e	1161.75 ± 0.7 e
H9	25.42 ± 0.1 a	57.73 ± 0.1 f	1171.49 ± 0.9 d

Note: Different letters within the same column indicate significant differences according to Tukey’s HSD test (p < 0.05). Data are expressed as the mean ± standard deviation (n = 3, descriptive analysis).

.

The oil content steadily increased from 16.17% at harvest stage H1 to 25.42% at harvest stage H9, with the most significant increase occurring between H5 and H9. This suggests a heightened lipid biosynthesis during the final stages of ripening. These results are consistent with those reported by Beltrán et al. (2004) [27] and Ranalli et al. (2003) [28], confirming that peak oil accumulation occurs in fully ripe olives. However, delaying the harvest for extended periods can adversely affect the oil quality due to fruit softening and oxidative degradation.

The water content exhibited a less consistent trend, ranging from 48.48% (H2) to 62.82% (H7). The variability (C.V. = 7.66%) may be attributed to environmental factors such as rainfall or temperature changes during the sampling period, as noted by [13]. Water content affects both the oil extraction efficiency and the stability of the final product.

The total phenolic compounds followed a bell-shaped curve, peaking at 1820.89 mg/kg in H6, and then sharply declining in H7. The high variability (C.V. = 20.02%) highlights the sensitivity of phenolic accumulation to the ripening stage.

These results are in line with prior studies by Servili and Montedoro (2002) [29] and Aparicio and Luna (2002) [30], which showed that phenolic levels reach their maximum before full ripeness and then decline due to enzymatic activity and tissue dilution.

From a technological perspective, harvest stage H6 offers the most favorable balance between oil yield and phenolic richness. While the oil content continues to increase in H7–H9, the decline in phenolic content may lead to the reduced oxidative stability and nutraceutical value of the oil. Therefore, harvesting around H5–H6 (Figure 1a) is recommended to achieve both high oil content and optimal antioxidant capacity. This result is consistent with reports by Lu et al. [31].

3.3. Statistical Interpretation of Ripening Parameters

A General Linear Model ANOVA (GLM ANOVA) demonstrated that all the assessed parameters varied significantly across the nine harvest dates (H1–H9), with high statistical significance (p < 0.0001). This indicates a strong temporal effect on both the physical and chemical attributes of ‘Kalinjot’ olives during the ripening period, supporting the reliability of using time-based indicators for harvest decision-making.

Tukey HSD Post Hoc Results

Post hoc analysis using Tukey’s Honest Significant Difference (HSD) test further clarified the nature of these differences by grouping statistically similar means:

The Maturity Index (MI) consistently increased, while the Color Index (CI) decreased over time. Harvest dates H1–H3 clustered together, reflecting early maturity, whereas H7–H9 formed distinct homogeneous groups that represented late maturity.

The firmness parameter significantly decreased with ripening, showing strong group separation after H5, which suggests advanced softening in the later stages.

The IAD values (DA-Kiwi-Meter values increased, while DA-Standard values decreased) were strongly correlated with the progression of maturity and formed distinct groups that paralleled changes observed in destructive indices.

The oil content increased significantly until H6, after which the differences either stabilized or slightly declined. This suggests that there is an optimal oil accumulation plateau around mid-late October (H5–H6).

Pearson Correlation Results

Pearson correlation analysis (r) revealed several strong and biologically meaningful associations:

The Maturity Index (MI) was positively and significantly correlated with the IAD index as determined by the DA-Kiwi-Meter (r = 0.874), indicating their concurrent increase as the fruits mature and ripen. Conversely, MI was negatively correlated with the IAD of the standard DA-Meter (r = −0.697).

The Maturity Index (MI) showed a negative correlation (r = −0.566) with the Color Index (CI) due to a delayed color change over time.

A significant positive correlation was observed between the IAD and oil content, with a correlation coefficient (r) of 0.841 for the DA-Kiwi-Meter and − 0.690 for the DA-Standard. This reinforces their validity as non-destructive maturity indicators.

The flesh firmness and Detachment indexes were decreased and negatively correlated with the MI, IAD (DA-Kiwi), and oil content, highlighting an inverse relationship with maturity progression.

These statistical findings confirm that both destructive and non-destructive measurements evolve in coherent and predictable patterns throughout the harvest season. They can reliably inform the optimal harvest window for the ‘Kalinjot’ cultivar in the Vlora region.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) was conducted to investigate the variability among olive fruit samples collected at different harvest stages, based on both destructive and non-destructive parameters. The analysis aimed to reduce the dimensionality of the dataset while retaining the most relevant variation related to fruit ripening.

Variance Explained

The results revealed that the first principal component (PC1) accounted for 99.74% of the total variance during the calibration phase and 99.68% during the validation phase, indicating that this component captures the majority of the dataset’s structure. The second principal component (PC2) explained an additional 0.23% of the variance in calibration and 0.24% in validation. The third component (PC3) contributed minimally (<0.03%), confirming that the first two components sufficiently describe the variation among samples (Figure 2a,b). Together, PC1 and PC2 explain more than 99.9% of the total variability.

Loading Plot Interpretation

The loading plot analysis provided insights into the contribution of individual variables to each principal component:

PC1 was strongly and positively influenced by the Color Index (CI), total polyphenol content, and the IAD values measured by both the DA-Meter^® and Kiwi-Meter^®. These variables are closely related to fruit ripening, particularly pigment accumulation and phenolic compound development. Hence, PC1 can be interpreted as a component that reflects biochemical maturity and ripeness progression.

PC2 was mainly associated with the fresh weight, fruit diameter, and oil content, indicating its relationship to physical growth and lipid accumulation. This component is thus more indicative of size and oil-related traits during maturation.

Sample Distribution

The scores plot (Figure 2b) showed clear distribution patterns among samples:

Sample H6 exhibited high scores along PC1, suggesting advanced ripening status with elevated phenolic content and pigment development.

Sample H9 showed a stronger influence on PC2, indicating distinct characteristics in terms of larger fruit size and higher oil accumulation compared to other harvest stages.

Overall, PCA effectively reduced the complexity of the dataset while preserving the key sources of variation. The strong explanatory power of the first two components supports their utility in distinguishing olive fruit samples according to ripening stage and quality traits. These findings demonstrate the potential of combining PCA with both traditional (destructive) and field-applicable (non-destructive) indicators to optimize harvest timing and enhance quality monitoring in olive production.

4. Conclusions

The study findings confirm that both methodological approaches, when applied together, can provide a comprehensive and practical framework for harvest decision-making.

The evaluation of fruit quality parameters across multiple harvest stages revealed that the period between H5 and H6 represents the optimal window for harvest.

Non-destructive tools, particularly the Kiwi-Meter^® and DA-Meter^®, proved to be reliable alternatives for in-field maturity assessments. These tools, when validated against destructive measures such as oil content and firmness, demonstrated high consistency and strong correlations.

Significant correlations among key maturity indices (e.g., MI, CI, IAD, oil content, and firmness) reinforce the usefulness of a combined indicator approach. The maturity index and IAD emerged as effective indicators for predicting the optimal maturity stage for harvest.