# Ultrasound and microwave assisted extraction of acorn oil (Quercus brantii): Optimization and characterization

Samaneh Niari, Nasser Hamdami, Mohsen Dalvi-Isfahan, Alain Le-Bail

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Samaneh Niari, Nasser Hamdami, Mohsen Dalvi-Isfahan, Alain Le-Bail. Ultrasound and microwave assisted extraction of acorn oil (Quercus brantii): Optimization and characterization. Applied Food Research, 2025, 5 (1), pp.100706. 10.1016/j.afres.2025.100706. hal-05366254

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# Ultrasound and microwave assisted extraction of acorn oil (*Quercus brantii*): Optimization and characterization

Samaneh Niari<sup>a</sup>, Nasser Hamdami<sup>c, *,</sup> ●, Mohsen Dalvi-Isfahan<sup>b, *,</sup> Alain Le-Bail<sup>c</sup>

## ARTICLE INFO

### Keywords:

Extraction optimization

Non-conventional extraction methods

Quercus brantii

Plant-based oils

## ABSTRACT

This study aimed to optimize the extraction parameters and increase the oil yield, by comparing both microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE) methods of extracting acorn oil (*Quercus brantii*). The study demonstrates that UAE and MAE are efficient, rapid, and resource-saving techniques for extracting acorn oil compared to conventional methods like Soxhlet extraction and long-duration maceration. UAE achieved a significantly higher oil yield (7.44 %) than MAE (5.45 %), reducing the extraction time from hours to just 33 min, and lowering solvent consumption. This efficiency makes UAE particularly advantageous for industrial-scale operations seeking to minimize environmental impact and operational costs. The findings further emphasize that UAE not only provides superior oil yield but also enhances the total phenolic content of the extracted oil, achieving a value 2.5 times higher than that obtained with MAE. Moreover, UAE demonstrated no significant increase in free fatty acids (FFA), maintaining the oil's quality, while MAE conditions showed a negative effect by increasing FFA levels. In addition to yield and quality, UAE also produced oil with superior color parameters, which could enhance consumer acceptability. These results underscore UAE's potential as a sustainable alternative, capable of reducing the environmental footprint by minimizing energy use, extraction time, and solvent waste.

## 1. Introduction

Persian oak trees (*Quercus brantii var. persica*) dominate the Zagros forests, which span approximately six million hectares in western Iran (Shahamati et al., 2024). Acorns, the fruit of these trees, are rich in carbohydrates, fiber, and minerals, with a moderate protein content. Their oil content varies widely among species, ranging from 2% to 30% (Taib et al., 2020). While climatic variations in the Zagros forests influence the crude fat content of acorns, the fatty acid composition and physicochemical properties of acorn oil closely resemble those of olive oil (Özcan, 2007). Acorn oil holds significant potential for the food industry due to its nutritional and functional properties. It is a renewable, cost-effective resource derived from *Quercus brantii* nuts, which are abundant in the Zagros region but remain underutilized as a food source (Mahdavi et al., 2020). Nutritionally, acorn oil is rich in bioactive compounds, including tocopherols, phytosterols, and flavonoids. It contains 75%–90% unsaturated fatty acids, primarily oleic acid and essential linoleic acid (Shahamati et al., 2024), making it appealing to

health-conscious consumers. Additionally, acorns contain triterpenes, which show promise as anti-diabetic agents and in preventing liver fibrosis. The high levels of essential minerals such as iron, zinc, copper, and calcium further enhance their nutritional profile. The vast availability of acorns in the extensive Zagros oak forests underscores their potential as a sustainable, valuable raw material for both the food and pharmaceutical industries (Szablowska & Tańska, 2024).

However, conventional extraction methods, such as boiling, crushing, or pressing, may compromise acorn oil's quality and yield due to exposure to high temperatures and oxidative conditions (Özdikicierler & Akcan, 2023). Advanced extraction techniques can better preserve its beneficial properties, including high levels of monounsaturated fatty acids, sterols, phenolic compounds, and tocopherols, all of which exhibit strong antioxidant activity. Green extraction technologies, such as microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE), have emerged as sustainable alternatives to conventional techniques, addressing the food industry's growing need for eco-friendly methods that avoid harmful solvents and reduce energy consumption

<footnote><sup>a</sup> Department of Food Science and Technology, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran</footnote>

<footnote><sup>b</sup> Department of Food Science and Technology, Faculty of Agriculture, Jahrom University, Jahrom, Fars, PO Box 74137-66171, Iran</footnote>

<footnote><sup>c</sup> ONIRIS - GEPEA (UMR CNRS 6144), Site de la Géraudière CS 82225, 44322 Nantes cedex 3, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: nasser.hamdami@oniris-nantes.fr (N. Hamdami), Dalvi@jahromu.ac.ir (M. Dalvi-Isfahan).</footnote>
(Mwaurah et al., 2020). These innovative techniques aim to enhance both the yield and quality of bioactive compounds while aligning with sustainability goals (García-Ortíz et al., 2023). MAE works by utilizing microwave radiation to heat solvents through molecular friction and rotation. This creates high pressure and heat within plant cells, breaking down cell walls and facilitating solvent penetration. Factors such as microwave intensity, solvent dielectric properties, and extraction conditions significantly influence its efficiency (Bagade & Patil, 2021). MAE has been shown to reduce extraction times, energy consumption, and solvent use while preserving bioactive compounds and ensuring oil quality (Majid et al., 2023). UAE employs high-frequency sound waves to generate cavitation bubbles in the extraction medium. These bubbles create shear forces, erosion, and sonoporation effects, which physically break down plant materials, enhancing solvent access to intracellular components (Isopencu et al., 2019). UAE operates at low temperatures with minimal energy and solvent requirements, making it effective at preserving sensitive compounds such as polyphenols, flavonoids, and essential fatty acids. Its effectiveness is influenced by ultrasound frequency, amplitude, duration, and solvent properties (Shen et al., 2023). Both MAE and UAE are recognized as eco-friendly oil extraction methods, offering significant advantages over conventional techniques, including reduced extraction times, lower energy consumption, and higher retention of oils' nutritional and functional properties. For instance, UAE has demonstrated success in enhancing the yield of pumpkin seed oil (Singh & Kumar, 2023) and *Moringa peregrina* oil (Mohammadpour et al., 2019), while MAE has proven effective in extracting olive pomace oil (Chanioti & Tzia, 2017).

Response Surface Methodology (RSM) is a structured statistical tool widely used to optimize extraction parameters, such as solvent-to-solid ratio, temperature, and extraction duration. It provides a reliable approach for maximizing efficiency and yield while minimizing resource use and processing time (Kumar et al., 2021; Mandal et al., 2023). Central Composite Design (CCD), a common form of RSM, is particularly useful for elucidating factor interactions and supporting process design and product development (Sai-Ut et al., 2024). Studies have highlighted the versatility of RSM in optimizing the extraction of bioactive compounds, including oil from various sources such as sea buckthorn seeds (Isopencu et al., 2019), *Moringa peregrina* oil (Mohammadpour et al., 2019), pumpkin seed oil (Singh & Kumar, 2023), and olive pomace (Chanioti & Tzia, 2017). No previous studies have reported the use of MAE or UAE for acorn oil extraction. This study, therefore, employs RSM to determine the influence of process parameters and their interactions on acorn oil extraction using MAE and UAE. The physical and chemical characteristics of acorn oil extracted by both methods were examined and compared to advance understanding of their potential application in the food and pharmaceutical industries.

## 2. Materials and methods

### 2.1. Raw material

The oak acorns (Q. brantii) were collected in November 2023 from forests near Khorramabad city, Lorestan province, Iran. The acorns were carefully selected based on ripeness, ensuring they were free from physical damage, pest infestations, or mold. The acorns were washed, peeled with a knife, and then dried in a Memmert oven (Germany) at 45 °C for 24 h to reduce their moisture content to the final level of 10 %. The testa was removed from the kernels, which were then ground into a fine powder using a Moulinex blender (France). The powder was sieved through a 0.250 mm to ensure a uniform particle size. It was then stored in sealed bags at -18 °C until further use (Erfan et al., 2023).

### 2.2. Soxhlet extraction

A thimble containing 5 g of acorn powder was placed in a Soxhlet extractor, and 200 mL of *n*-hexane was used as the solvent to extract the

oil over a 12-hour period. *n*-hexane was selected due to its complete miscibility with oils, low boiling point (approximately 68 °C), ease of recyclability, and relatively low cost. The 12-hour extraction time was chosen because studies have shown that no significant additional extraction occurs beyond this duration during Soxhlet operation, ensuring an efficient and thorough extraction process (Cravotto et al., 2022). After the extraction, the solvent was evaporated, and the vapors were recovered using rotary evaporation. The resulting oil was then dried in a vacuum dryer until it reached a constant weight.

### 2.3. Conventional solvent extraction (CE)

10 g of acorn powder was mixed with 200 mL of *n*-hexane in a 1:20 ratio in an Erlenmeyer flask. The mixture was stirred using a mechanical stirrer (IKA Laboratory Technology Co., Ltd., Guangdong) at approximately 600 rpm for 24 h. To maintain solvent purity and prevent sample degradation, gentle stirring was employed to minimize air exposure. Additionally, the flask was sealed with Parafilm to prevent solvent evaporation and contamination during stirring.

After stirring, the supernatant was separated from the residue using a vacuum filtration system to avoid exposure to external contaminants. The extracted liquid was then evaporated at 40 °C under vacuum using a rotary evaporator (Lab Tech EV311, LabTech Inc.) to ensure efficient solvent removal while avoiding thermal decomposition of the oil. Finally, the oil was dried in a vacuum dryer until a constant weight was achieved, ensuring purity and stability.

### 2.4. UAE

In order to extract seed oil using UAE, 20 g of acorn powder was weighed and placed in a 400 mL glass beaker, and then according to design of experiments, the required amount of solvent was added to the beaker. An aluminum cap was used to prevent solvent evaporation. The ultrasonic probe (Top sonics-UP400-A, Iran) was inserted into the mixture, so that 2 cm of the probe was submerged in the mix. The samples were subjected to continuous ultrasonic waves at 20 kHz, with different power levels. After the extraction process, the separation of extract and powder was carried out with filtration of the extracts, using a Whatman No. 1 filter paper. The collected solution was evaporated with a rotary evaporator (Lab Tech EV311, LabTech Inc.) under vacuum conditions. Finally, the obtained oil was weighed after reaching a constant weight by drying in a vacuum dryer. The resulting oil was collected in plastic containers, and stored in the freezer at -18 °C. The oil extraction with similar conditions of UAE, without applying power ultrasound, is the control and used to compare the recovery and efficiency of UAE.

### 2.5. Pre-treatment by microwave

The following procedure was used to obtain acorn oil by MAE. A 400 mL glass beaker containing 20 g of powder and the solvent amount determined by design of experiments was sealed with para film and put in a domestic microwave oven (Panasonic-NN-SM330 W, China). The microwave frequency was 2450 MHz and the power and time levels were 200, 100 and 300 W and 4, 2 and 6 min, respectively, as per the experimental design. After every 30 s of pretreatment, the sample was taken out of the oven and stirred for 90 s. A water bath at room temperature between 26 and 27 °C was prepared by the side of microwave oven to lower the temperature of the extraction mixture after each extraction cycle. The provision of the water bath also allowed for that the condensation of trapped vapour within the bottle to prevent any loss of solvent during the extraction process. Next, the extract and powder were separated by filtering the extracts with a Whatman No. 1 filter paper, as in the UAE method. The solution was then reduced using a rotary evaporator under vacuum. The oil was then dried in a vacuum dryer until it had a constant weight. The oil was stored in plastic
<caption>**Table 1**</caption>

Variables and design levels of the central composite design (CCD) for the response surface analysis of extraction methods for acorn powder.

<table><thead><tr><th rowspan="2">No.</th><th rowspan="2">Factors</th><th rowspan="2">Extractive methods</th><th colspan="3">Level</th></tr><tr><th>-1</th><th>0</th><th>1</th></tr></thead><tbody><tr><td>1</td><td>Ultrasonic extraction time (min)</td><td>UAE</td><td>15</td><td>30</td><td>45</td></tr><tr><td>2</td><td>Microwave extraction time (min)</td><td>MAE</td><td>2</td><td>4</td><td>6</td></tr><tr><td>3</td><td>Power (W)</td><td>UAE, MAE</td><td>100</td><td>200</td><td>300</td></tr><tr><td>4</td><td>Solvent to solid ratio (V/w)</td><td>UAE</td><td>4</td><td>8</td><td>12</td></tr></tbody></table>

containers in the freezer at -18 °C.

## 2.6. Statistical design and data analyses

This study utilized response surface methodology (RSM) with Design-Expert® software version 11 for experimental design and statistical analysis. A central composite design (CCD) was employed for both UAE and MAE to investigate the effects of key process variables on extraction efficiency. For UAE, the CCD included three factors at three levels (-1, 0, 1): extraction time (min), power level (W), and solvent-to-solid ratio (mL/g), with ten center points for better precision. The temperature was maintained at 30 °C by placing the samples in water-ice containers to ensure consistency. For MAE, the CCD considered two factors at three levels: extraction time (min) and power level (W), with seven center points. Similarly, the temperature during MAE experiments was kept constant at ambient conditions (30 °C). The ranges for these parameters were determined based on preliminary experiments and literature review (Çavdar et al., 2017; Ibrahim et al., 2019). The UAE and MAE experiments involved 20 and 13 runs, respectively, with the detailed parameters shown in Table 1 and results presented in Tables 2 and 3. The experimental data were fitted to quadratic regression models to describe the relationships between the independent variables and the responses, as shown in Eq. (1):

$$Y = \beta_0 + \sum_{i=1}^{j} \beta_i x_i + \sum_{i=1}^{j} \beta_{ii} x_i^2 + \sum_{i=1}^{k-1} \sum_{j=i+1}^{k} \beta_{ij} x_i x_j + \varepsilon \quad (1)$$

where Y is the response, i and j are the linear and quadratic coefficients, respectively; xᵢ and xⱼ are the uncoded independent parameters; k

represents the number of studied and optimized variables in this study; β₀ is the constant coefficient; βᵢ, βⱼ, and βᵢⱼ are the interaction coefficients of linear, quadratic and second-order terms, respectively; and lastly, ε is the error that occurred (Mahmoodi-Eshkaftaki & Dalvi-Isfahan, 2024). Model validation was performed using statistical tests, including the coefficient of determination (R²), adjusted R², and predicted R², to assess the model's fit and predictive capability. A high R² value (close to 1) indicated that the model effectively explained the variability in the response. The adjusted R² accounted for the number of predictors in the model to prevent overfitting, while the predicted R² evaluated the model's ability to predict new data accurately. Analysis of Variance (ANOVA) and Fisher's F-test were conducted to assess the significance of the model and its terms. Terms with p-values < 0.05 were considered significant contributors to the response. A lack-of-fit test ensured that the model adequately represented the data. Diagnostic checks, such as residual plots, were applied to confirm normality and homoscedasticity of residuals.

Three-dimensional response surface plots were generated to visualize interactions among variables and their effects on extraction efficiency. To validate the model further, cross-validation techniques were employed, using experimental runs excluded from model fitting to evaluate predictive performance. The quadratic models were confirmed

<caption>**Table 3**</caption>

Response variables for acorn oil extraction using MAE.

<table><thead><tr><th>Run</th><th>Time (min) x<sub>1</sub></th><th>Power (W) x<sub>2</sub></th><th>Yield (%) Observed</th><th>Yield (%) Calculated</th><th>*Free fatty acid (%) Observed</th><th>Free fatty acid (%) Calculated</th></tr></thead><tbody><tr><td>1</td><td>4</td><td>200</td><td>5.15</td><td>5.19</td><td>1.814</td><td>1.81</td></tr><tr><td>2</td><td>4</td><td>200</td><td>5.21</td><td>5.19</td><td>1.814</td><td>1.81</td></tr><tr><td>3</td><td>6</td><td>200</td><td>5.95</td><td>5.92</td><td>1.842</td><td>1.84</td></tr><tr><td>4</td><td>4</td><td>100</td><td>4.51</td><td>4.58</td><td>1.818</td><td>1.81</td></tr><tr><td>5</td><td>6</td><td>100</td><td>5.45</td><td>5.42</td><td>1.831</td><td>1.83</td></tr><tr><td>6</td><td>6</td><td>300</td><td>6.22</td><td>6.28</td><td>1.875</td><td>1.87</td></tr><tr><td>7</td><td>4</td><td>200</td><td>5.18</td><td>5.19</td><td>1.821</td><td>1.81</td></tr><tr><td>8</td><td>2</td><td>300</td><td>5.38</td><td>5.43</td><td>1.814</td><td>1.82</td></tr><tr><td>9</td><td>4</td><td>300</td><td>5.78</td><td>5.66</td><td>1.837</td><td>1.84</td></tr><tr><td>10</td><td>2</td><td>100</td><td>4.17</td><td>4.13</td><td>1.802</td><td>1.81</td></tr><tr><td>11</td><td>4</td><td>200</td><td>5.12</td><td>5.19</td><td>1.808</td><td>1.81</td></tr><tr><td>12</td><td>2</td><td>200</td><td>4.86</td><td>4.85</td><td>1.806</td><td>1.80</td></tr><tr><td>13</td><td>4</td><td>200</td><td>5.25</td><td>5.19</td><td>1.808</td><td>1.81</td></tr></tbody></table>

<caption>**Table 2**</caption>

Response variables for acorn oil extraction using UAE.

<table><thead><tr><th>Run</th><th>Time (min) x<sub>1</sub></th><th>Power (W) x<sub>2</sub></th><th>Solvent to solid ratio (V/w) x<sub>3</sub></th><th>Yield (%) Observed</th><th>Yield (%) Calculated</th><th>*Free fatty acid (%) Observed</th><th>Free fatty acid (%) Calculated</th></tr></thead><tbody><tr><td>1</td><td>45</td><td>200</td><td>8</td><td>6.48</td><td>6.20</td><td>1.84</td><td>1.83</td></tr><tr><td>2</td><td>15</td><td>200</td><td>8</td><td>5.01</td><td>5.04</td><td>1.84</td><td>1.82</td></tr><tr><td>3</td><td>30</td><td>200</td><td>8</td><td>5.40</td><td>5.55</td><td>1.79</td><td>1.80</td></tr><tr><td>4</td><td>15</td><td>300</td><td>4</td><td>4.41</td><td>4.40</td><td>1.82</td><td>1.82</td></tr><tr><td>5</td><td>30</td><td>200</td><td>8</td><td>5.71</td><td>5.55</td><td>1.79</td><td>1.80</td></tr><tr><td>6</td><td>30</td><td>300</td><td>8</td><td>5.89</td><td>5.82</td><td>1.84</td><td>1.82</td></tr><tr><td>7</td><td>30</td><td>200</td><td>4</td><td>4.70</td><td>4.65</td><td>1.79</td><td>1.79</td></tr><tr><td>8</td><td>30</td><td>200</td><td>12</td><td>7.25</td><td>7.04</td><td>1.81</td><td>1.79</td></tr><tr><td>9</td><td>45</td><td>100</td><td>12</td><td>7.11</td><td>7.18</td><td>1.83</td><td>1.84</td></tr><tr><td>10</td><td>30</td><td>200</td><td>8</td><td>5.54</td><td>5.55</td><td>1.79</td><td>1.80</td></tr><tr><td>11</td><td>45</td><td>100</td><td>4</td><td>4.65</td><td>4.75</td><td>1.82</td><td>1.82</td></tr><tr><td>12</td><td>30</td><td>200</td><td>8</td><td>5.35</td><td>5.55</td><td>1.79</td><td>1.80</td></tr><tr><td>13</td><td>30</td><td>200</td><td>8</td><td>5.47</td><td>5.55</td><td>1.79</td><td>1.80</td></tr><tr><td>14</td><td>45</td><td>300</td><td>4</td><td>5.26</td><td>5.26</td><td>1.83</td><td>1.83</td></tr><tr><td>15</td><td>30</td><td>100</td><td>8</td><td>5.22</td><td>5.04</td><td>1.84</td><td>1.83</td></tr><tr><td>16</td><td>15</td><td>100</td><td>4</td><td>4.07</td><td>4.02</td><td>1.82</td><td>1.83</td></tr><tr><td>17</td><td>15</td><td>100</td><td>12</td><td>5.66</td><td>5.72</td><td>1.82</td><td>1.83</td></tr><tr><td>18</td><td>30</td><td>200</td><td>8</td><td>5.30</td><td>5.55</td><td>1.79</td><td>1.80</td></tr><tr><td>19</td><td>15</td><td>300</td><td>12</td><td>6.79</td><td>6.75</td><td>1.81</td><td>1.81</td></tr><tr><td>20</td><td>45</td><td>300</td><td>12</td><td>8.23</td><td>8.34</td><td>1.83</td><td>1.84</td></tr></tbody></table>

<footnote>* FFA concentration was calculated based on acid values and the oleic acid molar mass value.</footnote>

<footnote>* FFA concentration was calculated based on acid values and the oleic acid molar mass value.</footnote>
<table><caption>**Table 4**
ANOVA results (p-values and F-values) and regression coefficients (βn) for the response surface quadratic model optimizing UAE from acorn.</caption><thead><tr><th rowspan="2">Source <sup>l</sup><sup>*</sup></th><th rowspan="2">Degree of freedom</th><th colspan="2">F-value</th><th colspan="2">p-value <sup>l</sup><sup>*</sup></th><th colspan="2">Coefficients of regression function (β<sub>n</sub>)</th></tr><tr><th>Yield %</th><th>FFA%</th><th>Yield %</th><th>FFA%</th><th>Yield %</th><th>FFA%</th></tr></thead><tbody><tr><td>Model</td><td>3</td><td>81.13</td><td>1.97</td><td>&lt;0.0001*</td><td>0.1527</td><td>1.350</td><td>1.80</td></tr><tr><td>X<sub>1</sub></td><td>1</td><td>42.65</td><td>0.6031</td><td>&lt;0.0001*</td><td>0.4554</td><td>0.0386</td><td>0.0038</td></tr><tr><td>X<sub>2</sub></td><td>1</td><td>19.06</td><td>0.0418</td><td>&lt;0.0001*</td><td>0.8422</td><td>0.00387</td><td>-0.0010</td></tr><tr><td>X<sub>3</sub></td><td>1</td><td>181.69</td><td>0.1207</td><td>&lt;0.0001*</td><td>0.7355</td><td>0.29875</td><td>0.0017</td></tr><tr><td>Residual</td><td>16</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Lack of fit (LOF)</td><td>11</td><td>—</td><td>4.73</td><td>0.495<sup>n.s</sup></td><td>—</td><td>—</td><td>—</td></tr><tr><td>Pure error</td><td>5</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Total error</td><td>19</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>R<sup>2</sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.938</td><td>0.6395</td></tr><tr><td>R<sup>2</sup><sub>Adj</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.926</td><td>0.3150</td></tr><tr><td>R<sup>2</sup><sub>Pre</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.891</td><td>-0.6171</td></tr><tr><td>C.V. %</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>4.94</td><td>0.8520</td></tr></tbody></table>

(*l*) The parameters of X₁, X₂ and X₃ related to independent variables of time (X₁), power (X₂) and solvent to solid ratio (X₃), respectively. *l*<sup>*</sup> Different superscript letters in each column show the significant ([*] for *p* < 0.05, respectively) differences between the parameters. As well, the subscribe letter of [n.s.] indicates the non-significant differences between the parameters.

to be suitable for describing the relationships between independent variables, such as extraction time, power level, and solvent-to-solid ratio, and the responses.

The response variable was the extraction efficiency (yield) calculated using Eq. (2) and free fatty acids content.

$$ \text{Yield}(\%) = \frac{W_o}{W_t} \times 100 \qquad (2) $$

Where W_o is the weight of extracted acorn oil (g) and W_t is the weight of ground acorn (g). The total free fatty acids level was determined by titration according to the standard method (AOCS, 2017) and expressed as the percentage of free fatty acids content (% FFA).

## 2.7. Physicochemical and qualitative properties of oil

The physicochemical properties of acorn oils were measured in triplicate according to the following AOAC methods: iodine value (920.158, Hanus method), acid value (Cd 3d-63), and saponification number (920.160). The specific gravity (using a 10 mL pycnometer at 25 °C), refractive index (using digital refractometer at 25 °C), and peroxide value (Cd 8b-90) of the samples were determined according to the AOCS methods. The color of acorn oil was determined by using a colorimetric device (ZE-6000 Nippon Denshoku, Kogyo Co., Tokyo, Japan) that was calibrated against a white plate in the CIELAB color space. The chromaticity coordinates of L* (lightness), a* (redness), and b* (yellowness) were measured for each sample.

## 2.8. Total phenol content

The total phenol content of acorn oil extracted from Quercus brantii was measured according to the method of Capannesi et al. (2000) with some modifications. Different concentration of gallic acid (10, 50, 100 and 250 ppm) was prepared to draw a calibration curve. The phenolic compounds were extracted three times by adding 5 mL of 80 % methanol to the test tube, vortexing for 5 min, and centrifuging at 5000 rpm for 5 min using a centrifuge. After each centrifugation, 4 mL of the supernatant was transferred to a new test tube and finally, 15 mL of methanolic extract of phenolic compounds was obtained.

The solutions for the spectrophotometric analysis were prepared as follows: 40 µL of standard solution (0.1 mg/mL) or prepared extract was taken in a 10 mL glass test tube and 200 µL of Folin-Ciocâlteu reagent (2 N) and 3.2 mL of distilled water were added to it and after mixing well by vortexing, it was incubated for 8 min at room temperature (25 °C). Then 600 µL of 20 % Na₂CO₃ (prepared by dissolving 20 g of Na₂CO₃ in 100 mL of distilled water) was added to the test tube and stored in the dark at 25 °C for 2 h to form a blue color complex. Finally, the

absorbance of the samples was measured at 765 nm using a Shimadzu UV-1800 spectrophotometer. The concentration of phenolic compounds was determined according to the calibration curve using Microsoft Excel.

## 2.9. Gas-chromatographic analysis

The fatty acids composition of extracted acorn oil was determined using gas-chromatography (GC) instrument (Agilent Technologies 6890 N, USA). 50 µL of the oil was taken in a test tube and 100 µL sodium sulfite and 1 mL hexane were added to it, respectively. Sodium sulfite was used to reduce the free fatty acids to fatty acid sulfonates. The resulting solution was mixed on a vortex shaker (Vortex Genie 2, Scientific Industries, USA) for 15 min and the upper phase was centrifuged and transferred for injection into the GC. The fatty acids were separated and identified on a DB-23, 60 m × 0.25 mm × 0.25 µm capillary column (Agilent Technologies, USA), equipped with a flame ionization detector (FID) (Agilent Technologies, USA). Injection (1 µL) was performed in the splitless, and nitrogen was the carrier gas with a constant flow rate of 1.1 mL/min. The detector and injector temperature were 270 °C and 220 °C, respectively. The temperature program was as follows: the injection temperature was 50 °C, the oven temperature was increased to 150 °C after 1 min, then the oven temperature increased at a rate of 30 °C/min and held for 2 min at 250 °C, then for 5 min at 300 °C. Fatty acids were identified by comparison with the standard fatty acid mixture (FAME Mix, Sigma, USA) and quantified and quantified as relative percentages of the total fatty acids peak areas (Tang et al., 2021).

# 3. Results and discussion

## 3.1. Extraction efficiency of acorn oil by RSM

The UAE and MAE methods for acorn oil extraction are compared in Tables 2 and 3, which show the calculated and observed yield, respectively. The UAE method obtained a significantly higher mean oil yield (5.67±0.45 %) than the MAE method (5.24±0.53 %). Therefore, the UAE method is more efficient than the MAE method in extracting oil from acorn in terms of oil yield and free fatty acid content. Regression models for acorn oil extraction, based on the second-order polynomial equation (Eq. (1)), were determined for UAE and MAE methods and are presented as Eqs. (3) and (4), respectively:

$$ Y_1 = 3.766 - 0.009X_1 + 0.0046X_2 - 0.176X_3 + 0.000022X_1X_2 \\ + 0.00304X_1X_3 + 0.000406X_1X_2 + 0.000323X_1^2 - 0.000012X_1^2 \\ + 0.0189X_3^2 \qquad (3) $$
<table><caption>**Table 5**
ANOVA results (*p*-values and *F*-values) and regression coefficients (β<sub>n</sub>) for the response surface quadratic model optimizing MAE from acorn.</caption><thead><tr><th rowspan="2">Source</th><th rowspan="2">Degree of freedom</th><th colspan="2">F-value</th><th colspan="2">p-value</th><th colspan="2">Coefficients of regression function (β<sub>n</sub>)</th></tr><tr><th>Yield %</th><th>FFA%</th><th>Yield %</th><th>FFA%</th><th>Yield%</th><th>FFA%</th></tr></thead><tbody><tr><td>Model</td><td>2</td><td>91.33</td><td>15.09</td><td>&lt; 0.0001</td><td>0.0001</td><td>3.095</td><td>1.755</td></tr><tr><td>x<sub>1</sub></td><td>1</td><td>90.20</td><td>22.29</td><td>&lt; 0.0001</td><td>&lt; 0.0001</td><td>0.2675</td><td>0.0105</td></tr><tr><td>x<sub>2</sub></td><td>1</td><td>92.46</td><td>7.90</td><td>&lt; 0.0001</td><td>0.0002</td><td>0.0054</td><td>0.00125</td></tr><tr><td>Residual</td><td>10</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Lack of fit (LOF)</td><td>6</td><td>11.68</td><td>6.16</td><td>0.163<sup>n.s</sup></td><td>0.0501<sup>n.s</sup></td><td>—</td><td>—</td></tr><tr><td>Pure error</td><td>4</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Total error</td><td>12</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>R<sup>2</sup></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.9481</td><td>0.7511</td></tr><tr><td>R<sup>2</sup><sub>Adj</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.9377</td><td>0.7014</td></tr><tr><td>R<sup>2</sup><sub>Pre</sub></td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.892</td><td>0.5529</td></tr><tr><td>C.V. %</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>2.63</td><td>0.5979</td></tr></tbody></table>

l̂<sup>*</sup> The parameters of X₁, X₂ and X₃ related to independent variables of time (X₁), power (X₂) and solvent to solid ratio (X₃), respectively. l̂<sup>*</sup> Different superscript letters in each column show the significant ([*] for *p* < 0.05, respectively) differences between the parameters. As well, the subscribe letter of [n.s.] indicates the non-significant differences between the parameters.

<table><caption>**Table 6**
Regression coefficients for the coded factors, along with associated standard errors, 95 % confidence intervals (CI), and variance inflation factors (VIF) for the response surface model assessing the effects of time (X₁), power (X₂), and solvent-to-solid ratio (X₃) on the extraction yield of oil using UAE.</caption><thead><tr><th>Factor</th><th>Coefficient Estimate</th><th>df</th><th>Standard Error</th><th>95 % CI Low</th><th>95 % CI High</th><th>VIF</th></tr></thead><tbody><tr><td>Intercept</td><td>5.55</td><td>1</td><td>0.0643</td><td>5.40</td><td>5.69</td><td></td></tr><tr><td>X<sub>1</sub>-time</td><td>0.5790</td><td>1</td><td>0.0592</td><td>0.4472</td><td>0.7108</td><td>1.0000</td></tr><tr><td>X<sub>2</sub>-power</td><td>0.3870</td><td>1</td><td>0.0592</td><td>0.2552</td><td>0.5188</td><td>1.0000</td></tr><tr><td>X<sub>3</sub>-rate</td><td>1.20</td><td>1</td><td>0.0592</td><td>1.06</td><td>1.33</td><td>1.0000</td></tr><tr><td>X<sub>1</sub>×<sub>2</sub></td><td>0.0325</td><td>1</td><td>0.0661</td><td>-0.1149</td><td>0.1799</td><td>1.0000</td></tr><tr><td>X<sub>1</sub>×<sub>3</sub></td><td>0.1825</td><td>1</td><td>0.0661</td><td>0.0351</td><td>0.3299</td><td>1.0000</td></tr><tr><td>X<sub>2</sub>×<sub>3</sub></td><td>0.1625</td><td>1</td><td>0.0661</td><td>0.0151</td><td>0.3099</td><td>1.0000</td></tr><tr><td>X<sub>1</sub><sup>2</sup></td><td>0.0727</td><td>1</td><td>0.1128</td><td>-0.1787</td><td>0.3241</td><td>1.82</td></tr><tr><td>X<sub>2</sub><sup>2</sup></td><td>-0.1173</td><td>1</td><td>0.1128</td><td>-0.3687</td><td>0.1341</td><td>1.82</td></tr><tr><td>X<sub>3</sub><sup>2</sup></td><td>0.3027</td><td>1</td><td>0.1128</td><td>0.0513</td><td>0.5541</td><td>1.82</td></tr></tbody></table>

<table><caption>**Table 7**
Regression coefficients for the coded factors, along with associated standard errors, 95 % confidence intervals (CI), and variance inflation factors (VIF) for the response surface model assessing the effects of time (x₁) and power (x₂) on the extraction yield of oil using MAE.</caption><thead><tr><th>Factor</th><th>Coefficient Estimate</th><th>df</th><th>Standard Error</th><th>95 % CI Low</th><th>95 % CI High</th><th>VIF</th></tr></thead><tbody><tr><td>Intercept</td><td>5.19</td><td>1</td><td>0.0312</td><td>5.12</td><td>5.26</td><td></td></tr><tr><td>x<sub>1</sub>-time</td><td>0.5350</td><td>1</td><td>0.0306</td><td>0.4625</td><td>0.6075</td><td>1.0000</td></tr><tr><td>x<sub>2</sub>-power</td><td>0.5417</td><td>1</td><td>0.0306</td><td>0.4692</td><td>0.6141</td><td>1.0000</td></tr><tr><td>x<sub>1</sub>×<sub>2</sub></td><td>-0.1100</td><td>1</td><td>0.0375</td><td>-0.1988</td><td>-0.0212</td><td>1.0000</td></tr><tr><td>x<sub>1</sub><sup>2</sup></td><td>0.1926</td><td>1</td><td>0.0452</td><td>0.0858</td><td>0.2994</td><td>1.17</td></tr><tr><td>x<sub>2</sub><sup>2</sup></td><td>-0.0674</td><td>1</td><td>0.0452</td><td>-0.1742</td><td>0.0394</td><td>1.17</td></tr></tbody></table>

$$Y_1 = 3.0980 - 0.00767x_1 + 0.01031x_2 - 0.000550x_1x_2 + 0.0481x_1^2 - 0.00000674x_2^2 \quad (4)$$

In Eq. (3), X₁, X₂, and X₃ represent time (min), power (W), and solvent-to-solid ratio (V/w) for the UAE method, respectively. In Eq. (4), x₁ and x₂ represent time (min) and power (W) for the MAE method, respectively. In both equations, Y₁ indicates the yield (%) of extracted oil.

The statistical significance of the regression model was checked by an *F*-test and a *p*-value, and the analysis of variance (ANOVA) for the response surface second order model was performed, as shown in Tables 4 and 5, respectively. We observed that the quadratic model is highly significant, with a significant *F*-value and a *p*-value <0.0001. The

high values obtained indicate that the model has the ability to describe the relationship between the extraction yields and the operating variables accurately. The lack of fit values (2.17 and 3.78 %) and *p*-values (0.207 and 0.115) for UAE and MAE extraction methods imply that the lack of fit is not significant. This means that the models are adequate for the experimental data. The linear effects of the extraction parameters in UAE and MAE were significant (*p* < 0.05). The model had an excellent goodness of fit, as indicated by the high values of the determination coefficients (R²) (98.2 % and 98.9 %). Tables 6 and 7 present the coefficient estimates, standard errors, 95 % confidence intervals (CI), and variance inflation factors (VIF), providing a robust statistical evaluation of the models. Notably, all VIF values are below 10, indicating no significant multicollinearity among the predictors.

## 3.2. Effect of assisted extraction parameters on yield

UAE relies on the cavitation phenomenon, where ultrasonic waves generate microscopic bubbles in the solvent. These bubbles collapse under high pressure and temperature, creating intense shear forces that disrupt plant cell structures and facilitate the release of intracellular oil. This cavitation effect intensifies mass transfer between the solvent and plant matrix, thereby enhancing oil extraction. Among the parameters influencing UAE, power has the most significant impact on extraction yield, as evidenced by the steep gradients in the response surface plots (Fig. 1). Increasing power from 100 W to 300 W resulted in a 13 % increase in oil yield, attributed to the formation and collapse of more vigorous bubbles, generating intense cavitation. Prolonged sonication also significantly improved oil yield, particularly between 30 and 60 min. However, the increase became less pronounced beyond this duration due to system saturation or depletion of extractable oil, with yields plateauing at 8.27 % after 90 min. A higher solvent-to-solid ratio, such as 12:1, improved solubility and enhanced mass transfer, though its effect was less pronounced compared to power and time. Unlike conventional thermal methods, UAE maintains a controlled temperature environment due to the localized nature of heat generation during cavitation, preventing oil degradation and preserving quality parameters such as lipid profile and oxidative stability. The maximum oil yield of 8.27 %, achieved under optimal conditions, underscores UAE's capability to deliver high extraction efficiency, making it especially suited for scalable applications that require a balance of yield and quality. Supporting studies corroborate UAE's efficiency. For instance, Mohammadpour et al. (2019) reported a 53.10 % oil yield from Moringa peregrina seeds, emphasizing the role of the solvent-to-solid ratio in enhancing mass transfer, while de Mello et al. (2017) demonstrated that solvent-to-seed ratio and temperature optimization were critical in chia seed oil extraction, where UAE excelled under controlled conditions.

In contrast, MAE employs microwave energy to rapidly heat the
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Response surface plots depicting the interaction effects on extraction yield (Y₁, %) in the UAE method. (a) Interaction between time (X₁, min) and ultrasonic power (X₂, W); (b) Interaction between time (X₁, min) and solvent-solid ratio (X₃, V/w); (c) Interaction between ultrasonic power (X₂, W) and solvent-solid ratio (X₃, V/w).</figcaption></figure>

solvent and sample matrix, reducing solvent viscosity, enhancing penetration, and facilitating solubilization of the oil. This method relies predominantly on thermal effects for efficient mass transfer. The interaction of time and power is crucial for MAE. At moderate power levels of 200 W, a maximum yield of 6.51 % was achieved within 8 min. However, higher power levels (300 W) or extended durations caused declines in yield due to thermal degradation and oxidation. Short extraction times, typically between 5 and 8 min, proved optimal, as prolonged irradiation exceeding 10 min induced adverse thermal effects that reduced oil yield and potentially compromised quality. Unlike UAE, which benefits from localized heat generation, MAE's reliance on thermal mechanisms makes it particularly sensitive to overexposure, necessitating precise control of power and duration to prevent lipid oxidation or hydrolysis. While MAE yields are slightly lower compared to UAE, the method offers a significant advantage in processing speed, completing effective extraction within minutes. For time-sensitive applications, this rapid heating mechanism is advantageous. Evidence from the literature supports these observations. Ibrahim et al. (2019) demonstrated that MAE achieved maximum oil yields within 5 min, while Zhang et al. (2009) noted declining yields with prolonged durations, underscoring the importance of parameter optimization in MAE. This result aligns with findings reported by Isopencu et al. (2019), who investigated the extraction of sea buckthorn oil. Their study demonstrated that UAE optimized yields under conditions of 13.77 W/cm² ultrasonic intensity, 40 °C, and a 10-minute extraction time, whereas MAE required 225 W microwave power, a 15 mL/g solid-to-liquid ratio, and 20 min to achieve a comparable yield of 87.4 % ± 0.44 %. These findings highlight the efficiency of UAE in achieving maximum yields

within a significantly shorter time compared to MAE, supporting our observations for acorn oil extraction.

The comparative dynamics of UAE and MAE highlight distinct advantages for each method (Fig. 3). UAE yields increase steadily with longer extraction times, particularly between 30 and 60 min, driven by sustained cavitation effects, culminating in a maximum yield of 8.27 % under 300 W power and a 12:1 solvent-to-solid ratio over 90 min. In contrast, MAE achieves rapid initial extraction, with yields peaking at 6.51 % under moderate conditions (200 W and 8 min). However, at higher power levels or extended durations, yields decline due to thermal degradation and oxidation, emphasizing the thermal sensitivity of MAE. UAE's ability to maintain temperature stability ensures the preservation of oil quality and oxidative stability, whereas MAE, while faster, requires precise control to prevent degradation. UAE is particularly effective in achieving high yields and maintaining oil quality, making it ideal for large-scale, quality-focused applications, while MAE is better suited for scenarios prioritizing speed and efficiency.

These findings emphasize the importance of tailoring extraction parameters to exploit the strengths of each method. For UAE, optimizing power and solvent-to-solid ratio ensures maximum yield and quality, while for MAE, controlling power and time balances speed and quality preservation. UAE is recommended for high-yield extractions that require stringent quality standards and scalable industrial processes, while MAE is valuable for rapid extractions with moderate yield requirements in time-sensitive settings. By understanding these dynamics, researchers and industries can select the most suitable technique based on specific extraction goals, with UAE excelling in high-efficiency applications and MAE offering speed-focused solutions.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Response surface plots illustrating the interaction effects on extraction yield (Y₁, %) and free fatty acid levels (Y₂, %) in the MAE method. (a) Interaction between time (x₁, min) and microwave power (x₂, W) for extraction yield (Y₁); (b) Interaction between time (x₁, min) and microwave power (x₂, W) for free fatty acid levels (Y₂).</figcaption></figure>

### 3.3. Effect of assisted extraction techniques on FFA

FFA are produced by the hydrolysis of oils and fats. They are more prone to oxidation and to turning rancid than triglycerides. Thus, high levels of FFA indicate poor quality and low commercial value of oils and fats, as they affect the thermal performance of the oil, rancidity, off-flavors, and reduced shelf life. Therefore, FFA levels were selected to estimate the hydrolytic degradation of oil extracted by different extraction methods (Musa Ozcan et al., 2019). The FFA content obtained through MAE was 1.822 %, representing a 2.14 % increase compared to the FFA levels in acorn oil extracted by UAE. Hernández-Santos et al. (2016) reported that ultrasound parameters, such as amplitude and extraction time, significantly influenced the FFA content of pumpkin seed oil, with longer ultrasound exposure increasing FFA levels. However, in the current study, the FFA content of acorn oil extracted via UAE was not significantly affected by the ultrasound conditions. This discrepancy can be attributed to effective temperature control during the ultrasound process, which minimized thermal degradation and hydrolysis reactions that typically elevate FFA levels. Maintaining controlled temperature conditions during UAE is crucial for preserving oil quality, as noted in previous studies (Clodoveo et al., 2013; Tang et al., 2021).

ANOVA of a quadratic regression model showed no significance effect of the ultrasound parameters on the FFA, and the coefficient of determination (R²) of the model was also low (0.639), which indicated a poor fit of the data (Table 4). This difference could be due to the range of ultrasonic conditions selected in this study, which had a low influence on FFA. In addition, increase in FFA of pumpkin seed during UAE could be attributed to the increase in temperature of the medium, which promoted the oxidation of conjugated double bonds with increased ultrasound time. But in our experiment, the temperature was carried out at ambient temperature by circulating the ice water and a temperature controller was used throughout the experiments, and hence the FFA was not influenced by the UAE. Isopencu et al. (2019) also reported no significant change in acidity and free fatty acids content of sea buckthorn seeds oil extracted by UAE at 50 °C and amplitude of 40 % ultrasound. Due to the non-significance of the free fatty acids model (p > 0.05) and low R² value (R² < 0.63), the equation and its surface plot are not reported in this study.

The second order model that describes the increase in FFA content (%) of acorn oil (Y₂) as a function of microwave power (x₁), and extraction time (x₂), is:

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparison of oil yield (%) over time (min) and as a function of power (W) for (a) UAE and (b) MAE methods, with a fixed solvent-to-sample ratio of 8:1.</figcaption></figure>
<table><caption>**Table 8**
Comparison of optimal conditions of acorn oil obtained by UAE and MAE.</caption><thead><tr><th>Extraction method</th><th>Optimal conditions</th><th>Extraction yield (%)</th><th>FFA (%)</th><th>Desirability</th></tr></thead><tbody><tr><td>UAE</td><td>Time= 33.52 min<br/>Power= 231.175 W<br/>Solvent to sample ratio (V/w) = 12</td><td>7.389</td><td>-</td><td>0.893</td></tr><tr><td>MAE</td><td>Time= 2 min<br/>Power= 297.218 W</td><td>5.418</td><td>1.815</td><td>0.709</td></tr></tbody></table>

$$Y_2 = 1.8502 - 0.01177x_1 - 0.00046x_2 + 0.00004x_1x_2 + 0.001784x_1^2 + 0.00000106x_2^2 \quad (5)$$

The quadratic model is highly significant, as evidenced by the very high F-value of 29.79 and the low p-value <0.0001 (Table 5). The lack of fit value of 1.13 and p-value of 0.437 implies that the lack of fit is not significant. The goodness of fit of the model was also checked using the values of R² (0.9551) and of Adj R² (0.923). These values are close to 1, indicating that the model accounts for most of the variation in the FFA. Hence, the model can accurately capture the relationship between the FFA and the operating variables for acorn oil MAE (Table 5). The effects of microwave extraction parameters on the FFA were investigated using the response surface plots (Fig. 2b). As shown, the highest FFA are produced in the harshest operating conditions, when both the power and microwave irradiation are at their maximum. These results are in agreement with other findings from microwave pretreatment, which showed a partial increase in the amount of free fatty acids in microwave heating, possibly by promoting the occurrence of hydrolysis in the samples (Musa Ozcan et al., 2019; Taghvaei et al., 2014; Uquiche et al., 2008).

### 3.4. Optimization

The optimization in this study aimed to achieve the highest possible extraction efficiency while minimizing the production of FFA, a key indicator of oil quality. To balance these objectives, a desirability function was employed, as recommended by Isopencu et al. (2019), to identify the optimal conditions for UAE and MAE. These conditions, detailed in Table 8, demonstrate the trade-offs and practical implications of optimizing extraction techniques.

The performance of UAE and MAE was compared with conventional extraction methods to evaluate improvements in efficiency. For the control sample, the oil yield achieved using the conventional method was 2.31±0.26 % over 33 min with a solvent-to-sample ratio of 12:1. In contrast, UAE under optimal conditions yielded 7.44±0.18 %, representing a substantial increase of 5.31 %. Oil recovery—defined as the ratio of extracted oil to the initial oil content—reached 84.44 % after 45 min at 300 W, a significant improvement compared to the Soxhlet method (93.41 %) and conventional stirring (96.74 %). Under the most intense ultrasonic conditions (300 W, 45 min), UAE achieved a remarkable 93.41 % oil recovery compared to the Soxhlet method and 7 % higher than conventional methods, underscoring its efficiency and scalability for industrial applications.

Similarly, MAE demonstrated its effectiveness with a dramatic increase in extraction yield. The control sample yielded 0.65±0.11 % in 2 min, whereas optimal MAE conditions produced 5.455±0.185 %, making MAE-treated samples 7.38 times more efficient than the control. The oil recovery under these conditions reached 61.86 % compared to the Soxhlet method and 70.87 % compared to conventional stirring. At maximum microwave power (300 W) and 6 min of treatment, oil recovery increased to 70.60 % for the Soxhlet method and 80.88 % for the conventional method.

The optimization results not only highlight the remarkable extraction efficiencies achieved by UAE and MAE but also reveal the inherent trade-offs.

<table><caption>**Table 9**
Results of physicochemical properties of acorn oil extracted by UAE, MAE and conventional methods.</caption><thead><tr><th>Quality attributes</th><th>Conventional</th><th>UAE</th><th>MAE</th></tr></thead><tbody><tr><td>Peroxide value</td><td>2.89±0.12<sup>a</sup></td><td>2.48±0.15<sup>a</sup></td><td>2.65±0.18<sup>a</sup></td></tr><tr><td>Acidity</td><td>1.78±0.011<sup>a</sup></td><td>1.80±0.010<sup>a</sup></td><td>1.81±0.013<sup>a</sup></td></tr><tr><td>Total phenols</td><td>24.28±3.2<sup>c</sup></td><td>102.14±4.29<sup>a</sup></td><td>39.53±1.95<sup>b</sup></td></tr><tr><td>Iodine value</td><td>80.97±0.48<sup>a</sup></td><td>82.01±0.69<sup>a</sup></td><td>82.53±0.44<sup>a</sup></td></tr><tr><td>Saponification number</td><td>184.33±0.90<sup>a</sup></td><td>183.73±0.84<sup>a</sup></td><td>185.25±0.43<sup>a</sup></td></tr><tr><td>Specific gravity</td><td>0.914±0.015</td><td>0.914±0.011</td><td>0.913±0.016</td></tr><tr><td>Refractive index</td><td>1.469</td><td>1.469</td><td>1.469</td></tr><tr><td>L*</td><td>69.12±0.20<sup>b</sup></td><td>73.59±0.65<sup>a</sup></td><td>68.08±0.45<sup>b</sup></td></tr><tr><td>a*</td><td>36.28±0.27<sup>ab</sup></td><td>37.06±0.53<sup>a</sup></td><td>34.95±0.32<sup>b</sup></td></tr><tr><td>b*</td><td>118.88±0.35<sup>a</sup></td><td>117.16±0.73<sup>ab</sup></td><td>115.34±0.27<sup>b</sup></td></tr></tbody></table>

UAE's extended processing time and higher power requirements may lead to increased energy consumption and operational costs, although the enhanced oil recovery and quality justify these inputs for applications requiring premium output. Conversely, MAE offers rapid extraction with lower energy demands over short durations, but its dependence on thermal effects necessitates precise control to avoid oil degradation.

These findings suggest that the choice of extraction method must align with specific priorities. UAE is advantageous for maximizing yield and quality, making it suitable for large-scale operations where resource use can be offset by high efficiency. MAE, on the other hand, excels in time-sensitive scenarios, balancing moderate efficiency with lower resource demands. Ultimately, optimizing extraction efficiency involves navigating these trade-offs, emphasizing the importance of tailoring techniques to specific industrial or research objectives to achieve sustainable and cost-effective oil recovery.

### 3.5. Quality characterization of acorn oil

The oil quality of acorns was evaluated by measuring eight physicochemical properties, as shown in Table 9. The UAE and MAE methods had higher phenolic compounds content than the conventional sample by 4.2 and 1.28 times, respectively. These results agreed with those reported by Dzah et al. (2020) and Taghvaei et al. (2014), who both found that the MAE and UAE methods increased the levels of phenolic compounds and phytosterols, enhancing the oxidative stability of the oil obtained.

Makhlouf et al. (2018) conducted a study on the amount of antioxidants and phenolic compounds of three oak species and reported that the phenolic compounds content of three species of Algerian Quercus oils ranged from 121.32 to 299.29 (mg kg⁻¹ oil), indicating high phenolic content and antioxidant activity. Similarly, Ajo et al. (2020) conducted a study on three Mediterranean oak species and found that the phenolic compounds content ranged from 84 to 109 mg/kg. The phenolic compounds content observed in these two studies was higher than that obtained in the present study, which could be attributed to the differences in factors such as species, season, and geographical region of the acorn fruits.

The oil obtained by UAE showed significantly higher L* and a* values than the other treatments, indicating a lighter and greener color. This could be due to the reduced turbidity and increased transparency of the oil, as reported by Taticchi et al. (2019). No significant differences were detected in the refractive index, specific gravity, saponification number, acid value, and iodine value of the oil among the treatments (p > 0.05). The oil obtained by UAE and MAE showed a slightly lower peroxide values than the oil obtained by the conventional method, suggesting higher oxidation stability and longer shelf life. In conclusion, the oil extracted from Q. brantii acorns by UAE and MAE had good quality and high potential as a novel edible oil.
<table><caption>**Table 10**
Results of major fatty acid profiles (%) of acorn oil from UAE, MAE and conventional methods.</caption><thead><tr><th>No.</th><th>Fatty acid</th><th>Conventional</th><th>UAE</th><th>MAE</th></tr></thead><tbody><tr><td>1</td><td>Myristic acid</td><td>0.09</td><td>0.13</td><td>0.12</td></tr><tr><td>2</td><td>Palmitic acid</td><td>15.33</td><td>15.53</td><td>15.37</td></tr><tr><td>3</td><td>Stearic acid</td><td>2.02</td><td>2.33</td><td>2.09</td></tr><tr><td>4</td><td>Oleic acid</td><td>63.79</td><td>63.68</td><td>63.15</td></tr><tr><td>5</td><td>Linoleic acid</td><td>17.71</td><td>17.13</td><td>18.06</td></tr><tr><td>6</td><td>Linolenic acid</td><td>1.03</td><td>1.17</td><td>1.18</td></tr><tr><td></td><td>Total saturated fatty acids</td><td>17.44</td><td>17.99</td><td>17.58</td></tr><tr><td></td><td>Total monounsaturated fatty acids</td><td>63.79</td><td>63.68</td><td>63.15</td></tr><tr><td></td><td>Total polyunsaturated fatty acids</td><td>18.74</td><td>18.3</td><td>19.24</td></tr></tbody></table>

### 3.6. Fatty acid composition of acorn oil

Gas chromatography-mass spectrometry (GC-MS) was used to determine the fatty acids composition of acorn oil extracted by UAE, MAE, and conventional methods under optimal conditions. Table 10 presents the results, which indicate that six fatty acids were detected in the acorn oil and that there was no significant difference in the main fatty acid composition among the methods. Oleic acid (63–64 %), linoleic acid (17–18 %), palmitic acid (15–16 %), and stearic acid (2–2.5 %) were the major fatty acids in oil. The unsaturated fatty acids constituted 81.98 % and 82.39 % of the total acids for UAE and MAE, respectively. These findings agreed with those reported by Hu et al. (2021) and Tang et al. (2021) for the effect of ultrasound and microwave on the fatty acids composition of other oilseeds, such as *Sapindus mukorossi* seed oil and *Osmanthus fragrans* fruit oil, where the authors found no significant differences in fatty acid composition among the different extraction techniques. In addition, previous studies revealed lower oleic acid (54.3 %) and higher linoleic acid (24.2 %) contents in *Quercus brantii* cultivated in Turkey (Ozcan, 2007). This finding could be attributed to the differences in species origins, cultivation seasons, and geographical regions of acorn fruits. The high content of unsaturated fatty acids in acorn oil suggested that it could be a useful source of edible oil.

### 4. Conclusion

In this study, UAE and MAE methods were compared for oil extraction from acorn powder. UAE yielded 7.44 %, significantly higher than MAE’s 5.45 %. While MAE conditions influenced FFA, MAE parameters did not, indicating its superiority in terms of oil yield and FFA content. Both methods’ efficiency was influenced by power and irradiation time. However, UAE resulted in a brighter and more intense color (higher L* and a* values) and a 2.5-fold increase in TPC compared to MAE and the conventional method. This highlights UAE’s efficiency in reducing solvent consumption and enhancing extraction yield. These findings suggest that UAE could be a promising technique for the efficient extraction of high-quality acorn oil, with potential applications in the food and pharmaceutical industries. Future studies will explore a wider range of process parameters to further optimize extraction efficiency.

#### CRediT authorship contribution statement

**Samaneh Niari:** Validation, Methodology, Formal analysis, Data curation. **Nasser Hamdami:** Writing – review & editing, Validation, Supervision, Project administration, Conceptualization. **Mohsen Dalvi-Isfahan:** Writing – review & editing, Writing – original draft, Validation, Software, Methodology, Formal analysis. **Alain Le-Bail:** Writing – review & editing, Supervision.

#### Declaration of competing interest

The authors declare that they have no known competing financial

interests or personal relationships that could have appeared to influence the work reported in this paper.

#### Acknowledgments

The authors would thank the Isfahan University of Technology for their financial support.

#### Funding information

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

#### Ethical approval

This study does not involve any human or animal testing.

#### Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the author(s) used ChatGPT-4 to improve language and readability. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

#### Data availability

The data that support the findings of this study are available on reasonable request from the corresponding author.

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