Red Mullet (Mullus barbatus) Collected from North and South Euboean Gulf, Greece: Fishing Location Effect on Nutritive Quality

Abstract

Red mullet (Mullus barbatus), a prominent fish species in the Mediterranean Sea, is a fish with a particular abundance of unsaturated fatty acids and other nutrients, including a substantial quantity of minerals. The nutritive quality parameters (lipid quality indices, fatty acid profiles, and mineral content, along with proximate composition) of 75 red mullet samples collected from five distinct locations (L1–L5) in the North and South Euboean Gulf, Euboea Island (Evia), Greece, were examined. It was hypothesized that the different habitats may have an impact on each fish’s chemical composition. Proximate composition (protein, ash, moisture, fat, and minerals) and bioactive compound determination (total carotenoids, and vitamins A, E, and C) were conducted on the lyophilized fish samples. The protein and lipid content of the wet fillet varied substantially from 10.8 to 14.3 and 13.2 to 16.8% w/w, respectively. The samples exhibited statistically non-significant variation in the total SFAs and MUFAs (p > 0.05). The level of total PUFAs was above 30% in all the samples and no significant differences were observed between them. However, arachidonic acid (20:4 ω-6) was only detected in fish samples from two locations (i.e., L1 and L3). The concentrations of Fe, Na, Mg, K, Ca, Ag, Sr, Li, and Zn varied significantly (p < 0.05) in relation to the size of the fish samples. The highest concentrations of heavy metals were detected at the northern location (L5), indicating a possible negative correlation between size and arsenic concentration. The varied mineral composition and fatty acid content of the samples can be attributed to their distinctive biological characteristics (i.e., length and weight) and dietary environments.

1. Introduction

Nutrients are substances that nourish the body, facilitate growth, and sustain and repair cells and tissues and are categorized into micronutrients and macronutrients [1]. Fish contain macronutrients, including proteins, lipids, ash, and carbohydrates. Micronutrients, including vitamins and minerals, are vital dietary components required in minimal amounts and must be obtained from external sources for the body. Fish serves a crucial role in supplying nutrients to several animals and humans. Fish is a valuable dietary product with significant nutritional benefits that enhance health. Regular fish consumption contributes to the prevention of cardiovascular diseases [1]. Seafood is the primary source of omega-3 polyunsaturated fatty acids (PUFAs), which have been linked to health benefits and protection against chronic, inflammatory, and cardiovascular diseases. High content of vitamins, minerals, and proteins can also be found in fish [2]. Fish consumption is essential for a well-balanced diet, with various fish species recommended to be consumed twice per week [3].

Mullus barbatus, commonly known as red mullet, is a crucial species for the fishing sector in the Mediterranean region. Red mullet is a demersal fish species of the Mullidae family, inhabiting the Mediterranean, Black Sea, Northwest Africa, and Northern European coastlines. Red mullet, a fish with moderate fat content, is regarded as a source abundant in fatty acids [4]. This species is heavily caught due to its economic and nutritional importance. As a demersal species, it serves as a significant indicator for assessing metal pollution levels [5]. Currently, urbanization and industrialization adversely affect the environment, necessitating a thorough examination of this vital issue. Pollutants in marine habitats may detrimentally impact organisms and jeopardize human health via the food chain [6]. As a result, the feeding preferences of red mullets differ according to their habitat. Red mullets may forage deeper on soft bottoms, such as those with crinoid beds or muddy bottoms. Contrary to common belief, it prefers hard substrates such as maerl beds, where it may catch a broader variety of crustaceans. Indicator species like red mullet in the Mediterranean Sea accurately reflect the concentration and bioavailability of contaminants in the marine environment [7]. It is commonly utilized as a biomarker for pollution monitoring [8]. Ecological traits and geographic origin can have an impact on the chemical composition of fish, particularly its lipid profile. The dietary preferences of red mullets might vary depending on the availability of their prey [9].

Although studies on red mullet chemical composition have been conducted in the past, this study aims to fill that gap by focusing on samples collected from the yet unexplored Euboean Gulf. Specifically, multiple-sized red mullets were gathered from several locations in the Euboean Gulf and tested for proximate composition (i.e., protein, fat, moisture, ash, and minerals). Bioactive compounds such as carotenoids and vitamins A, C, and E were also measured. The study also examined the impact of fish size (length and weight) and fishing location (natural habitat) in correspondence with proximate composition, fatty acid profile, bioactive compounds, and toxic substances (such as arsenic) through correlation analyses.

2. Materials and Methods

2.1. Chemicals and Reagents

The chromatography solvents were all of HPLC grade. Formic acid (99% v/v) and acetonitrile were purchased from Carlo Erba Co. (Val de Reuil, France). Tocopherol standards (catalog no. 613424) containing all four homologs (i.e., α-, β-, γ-, and δ-) were acquired from Merck Ltd. (Darmstadt, Germany). Folin–Ciocalteu reagent (catalog no. 251567), trichloroacetic acid, sodium sulfate, and 65% w/w nitric acid (part no. 133255.1612) were obtained from Panreac (Barcelona, Spain). Potassium hydroxide and chloroform (stabilized with ~1% ethanol) were obtained from Penta Co. (Prague, Czech Republic). FAME Mix C₈–C₂₄ reference standard (catalog no. CRM18918), retinol (Vitamin A) (catalog no. R7632), Bradford reagent (catalog no. B6916), bovine serum albumin, Tris base, hydrochloric acid, ascorbic acid, methanol, and n-hexane were all purchased from Sigma-Aldrich (Darmstadt, Germany). An ICP Multi-Element Calibration Standard (catalog no. REICPCAL26T) was purchased from Reagecon (Lismacleane, Ireland). The experiments utilized ultrapure water with a resistivity of 0.055 µS/cm.

2.2. Instrumentation

The weighing process of the red mullet samples was conducted on an EWJ 600-2M precision scale from Kern (Frankfurt, Germany). In a Biobase BK-FD10P lyophilizer (Jinan, China), fish fillets from each location were mixed and freeze-dried for 24 h at −54 °C. After lyophilization, the dried fish were ground to a fine powder using a WSG30 electrical mill from Waring Commercial (Torrington, CT, USA). The centrifugation process was conducted on a NEYA 16R from Remi Elektrotechnik Ltd. (Palghar, India) at 4500 rpm (3600× g) for 5 min to isolate the supernatant solution and discard the solid residue. A Raypa HM-9 muffle furnace (Barcelona, Spain) was used to generate red mullet ash. The obtained ash was further analyzed for its mineral content through an Agilent 7700 inductively coupled plasma mass spectrometry (ICP–MS) instrument from Agilent Technologies (Santa Clara, CA, USA). A Heidolph magnetic stirring hotplate from Heidolph Instruments GmbH & Co. KG (Schwabach, Germany) was utilized to perform the stirring procedure. An Elmasonic P70H ultrasonication bath from Elma Schmidbauer GmbH (Singen, Germany) was used for ultrasound pretreatment. A Shimadzu UV-1900i PharmaSpec double-beam spectrophotometer (Kyoto, Japan) was used for all the spectrophotometric analyses. For chromatographic analyses, instruments from Shimadzu Europa GmbH (Duisburg, Germany) and Agilent Technologies (Santa Clara, CA, USA) were employed. Initially, a Shimadzu CBM-20A high-performance liquid chromatograph (HPLC) with a Shimadzu RF-10AXL fluorescence detector (FLD), and a Shimadzu SPD-M20A diode array detector (DAD) were all from the same company. The liquid chromatograph was utilized for analyzing vitamins A and E. The separation was achieved using a Phenomenex Luna C18(2) column (100 Å, 5 μm, 4.6 mm × 250 mm; Phenomenex Inc., Torrance, CA, USA) at 40 °C, and a Waters μ-Porasil column (125 Å, 10 μm, 3.9 mm × 300 mm; Waters Corp., Milford, MA, USA) at 25 °C, respectively. In addition, an Agilent model 7890A gas chromatograph connected to a flame ionization detector (GC–FID), which was equipped with an Omegawax capillary column (30 m × 320 μm × 0.25 μm) from Supelco (Bellefonte, PA, USA) and was used to quantify fatty acids. Finally, a Human Corporation NEX Power 1000 device (Seoul, Republic of Korea) was used to generate ultrapure water.

2.3. Sampling and Handling of Red Mullets

In May 2024, seventy-five red mullet fish were obtained directly from local fishermen in the North and South Euboean Gulf (Euboea Island, Greece) in five distinct locations (each containing fifteen fish). Figure 1 shows the map of the different fishing locations created by Google Earth (ver. 10.59.0.2). The fish samples were delivered to the lab for further examination within 8 h after being promptly stored in ice in polyethylene bags. The fish were cleaned and paper-dried upon delivery. The average weight and length were measured by means of analytical measurements using a weighing precision scale and ruler. The liver, gills, and bones from each fish were removed, and two fillets were separated. Both light and dark muscles were analyzed together in this study to provide a comprehensive assessment of the nutritional composition of the fish fillets. Fifteen fish samples from each location (i.e., L1–L5) were divided into three groups, with each group consisting of a pooled sample from five individuals, making the total number of samples (n = 3). Figure 2 illustrates the assays conducted on the red mullet fillets.

2.4. Proximate Composition of Red Mullet Fillets

2.4.1. Moisture Content

The proximate composition of the red mullet fillets was conducted. The moisture content was calculated by means of weighing the red mullet fillets before and after the lyophilization process.

2.4.2. Ash Content

A quantity of 1 g of freeze-dried fillets was weighed in porcelain crucibles to proceed with the dry-ashing technique. Then, they were placed in a muffle furnace and incinerated at 650 °C until completion, with the ash content being calculated by means of weighing the porcelain crucibles right after.

2.4.3. Fat Content

A slight modification of the widely known Folch method [10] was used to adequately isolate lipids from the fish fillets. Briefly, 5 g of the dry fish fillets were mixed with 50 mL of 2:1 v/v chloroform/methanol mixture, vortexed, and centrifuged for 10 min at 3000 rpm (1600× g). A volume of 5 mL of water was added right after to the supernatant and the new mixture was centrifuged again at 3000 rpm (1600× g) for 10 min. The aqueous phase (upper) was discarded. The organic phase (lower) consisting of lipids dissolved in chloroform was mixed with anhydrous sodium sulfate (to eliminate moisture), filtered, and finally removed in vacuo. The fat content was finally measured by means of a gravimetric analysis after the chloroform was totally evaporated. Further analysis including fats was conducted within 3 h of extraction, further minimizing the risk of oxidation and ensuring the integrity of the extracted lipids.

2.4.4. Protein Content

Proteins were extracted from the fish samples using a Tris-HCl buffer following the protocol adapted from Delima and Trio [11], and were quantified using a method originally introduced by Bradford [12]. This established method has been cited as widely used, is automated, and offers the chance of multiple sampling of a large number of samples [12]. First, we used chloroform as the sole solvent to extract lipids from the dried fish sample. A quantity of 0.1 g of the lyophilized and defatted fish sample powder was solubilized with 5 mL of the extraction solvent. The mixture underwent sonication for 15 min at room temperature to prevent coagulation, followed by vortex mixing for 1 min. This sonication–vortex process was repeated three times, then the mixtures were incubated at 56 °C for 1 h. Subsequently, the mixtures were sonicated again for 15 min at room temperature, centrifuged for 5 min at 4500 rpm (3600× g), and the supernatant was collected into another vial. To ensure complete protein extraction, the extraction–isolation step was performed two additional times, and the supernatants were combined. The protein content in the pooled sample was determined using the Bradford method, where 900 μL of Bradford reagent was mixed with 100 μL of the sample extract and reacted for 10 min in darkness. Finally, the absorbance of the samples was measured at 595 nm with a spectrophotometer. A standard calibration curve using bovine serum albumin was prepared to quantify the protein amount.

2.4.5. Mineral Composition

After determining the ash content in the red mullet fillets using a slightly modified methodology from Crosby et al. [13], prior to the specific analysis, the fish ash samples were treated with 2% v/v nitric acid at a concentration of 1 mg/mL. Following a vigorous 5 min vortexing, the samples underwent a 10 min ultrasonication at 37 kHz. Subsequently, the samples were syringe-filtered using a 0.45 µm PTFE filter.

The ICP–MS instrument was operated with a plasma power of 1550 W, an argon plasma gas flow of 15 L/min, an auxiliary gas flow of 0.90 L/min, a carrier gas flow of 1.03 L/min, and a sample intake rate of 0.2 mL/min. All the minerals were measured in Helium [He] mode by ICP-MS. The analytical method parameters for mineral determination, such as the limit of detection (LOD), mass to charge ratio (m/z), recoveries, precision, and calibration curve range, are shown in

Table A1. Analytical method parameters for mineral determination.

Mineral	m/z Ratio 1	LOD (µg/L) 2	LOD (µg/100 g)	Recoveries (%)	Precision (%)	Calibration Curve Range (µg/L)
Aluminum (Al)	27	2.42	24.2	95	2.5	0.1–10
Arsenic (As)	75	0.52	5.20	98	3	0.05–5
Barium (Ba)	137	0.1	1.00	97	2	0.01–1
Beryllium (Be)	9	0.05	0.500	96	2.8	0.005–0.5
Boron (B)	11	0.045	0.450	99	1.5	0.01–1
Cadmium (Cd)	111	0.15	1.50	97	2.5	0.01–1
Calcium (Ca)	44	0.2	2.00	98	2	0.05–5
Chromium (Cr)	52	0.91	9.10	96	2.5	0.1–10
Cobalt (Co)	59	0.1	1.00	97	2	0.01–1
Copper (Cu)	63	4.55	45.5	95	3	0.1–10
Iron (Fe)	56	4.55	45.5	96	2.5	0.1–10
Lead (Pb)	206	0.88	8.80	95	3	0.1–10
Lithium (Li)	7	0.05	0.500	98	2	0.01–1
Magnesium (Mg)	24	0.2	2.00	97	2.5	0.05–5
Manganese (Mn)	55	1.52	15.2	96	2	0.1–10
Molybdenum (Mo)	95	0.1	1.00	97	2.5	0.01–1
Nickel (Ni)	60	1.52	15.2	95	3	0.1–10
Potassium (K)	39	0.2	2.00	98	2	0.05–5
Selenium (Se)	82	0.89	8.90	96	2.5	0.1–10
Silver (Ag)	107	0.05	0.500	97	2	0.01–1
Sodium (Na)	23	0.2	2.00	98	2.5	0.05–5
Strontium (Sr)	88	0.1	1.00	97	2	0.01–1
Thallium (Tl)	205	0.05	0.500	96	2.5	0.005–0.5
Uranium (U)	238	0.1	1.00	97	2	0.01–1
Vanadium (V)	51	0.1	1.00	96	2.5	0.01–1
Zinc (Zn)	66	0.1	1.00	98	2	0.01–1

¹ m/z means mass to charge ratio; ² LOD means limit of detection.

. The LOD values in the analytical solution were measured according to the following Equation (1). Calibration curves, including a blank solution and five points, were generated for each element quantification (Ag, Al, As, B, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Rb, Se, Sr, Te, Tl, U, V, and Zn). A standard calibration solution was assessed before and after the sample analysis to ensure accuracy, along with a blank solution to check for carryover effects. The ICP–MS MassHunter Workstation software (version A.01.02) was used for data analysis.

LOD = 3.3 × (standard deviation of the reference curve)/Slope (S)

(1)

2.5. Fatty Acid Composition

The preparation of fatty acid methyl esters (FAMEs) from oils was carried out using a slightly modified method (A) described in Commission Regulation (EC) No 796/2002 (Annex XB) [14]. Briefly, 0.1 g of the fish oil sample was dissolved in 2 mL of n-hexane in a glass vial with a screw cap. After thorough mixing, 0.2 mL of 2 N methanolic potassium hydroxide solution was added to react and convert the fatty acids to their corresponding methylated esters, and the mixture was vortexed for 1 min. Finally, the mixture was left until the phases were separated while their quantification was performed employing a gas chromatography with flame ionization detection (GC–FID) method as previously outlined by Athanasiadis et al. [15]. Split mode (1:100) was used to inject 1.0 μL samples. The column temperature program was determined to be isothermic for 5 min at 70 °C, ramped up to 160 °C at a rate of 20 °C/min, then raised to 200 °C at a rate of 4 °C/min, and finally raised to 240 °C at a rate of 5 °C/min. The temperatures for the injector and FID were kept at 240 °C and 250 °C, respectively. At a flow rate of 1.4 mL/min (i.e., 27 cm/sec velocity), helium was used as the carrier gas. Hydrogen, air, and helium flows were set at 50, 450, and 50 mL/min, respectively. Identification was achieved by comparing the individual peaks to FAME Mix C₈–C₂₄ reference standards, which were appropriately diluted with n-hexane. The percentage of each component was calculated based on the average of three GC–FID analyzes. The results were expressed as the percentage of total fatty acids.

The Atherogenic Index (AI) and Thrombogenic Index (TI) were calculated following the method of Ulbricht and Southgate [16], as shown in Equations (2) and (3), respectively. The Hypocholesterolemic/Hypercholesterolemic (HH) ratio (Equation (4)) calculation was based on the study by Santos-Silva et al. [17]. Lastly, the Health-Promoting Index (HPI) (Equation (5)), which focuses on the effects of fatty acid composition on cardiovascular diseases, was proposed by Chen et al. [18].

$$\mathrm{AI}={\displaystyle \frac{\left[\mathrm{C}12:0+\left(4 \times \mathrm{C}14:0\right)+\mathrm{C}16:0\right]}{\sum \mathrm{UFA}}}$$

(2)

$$\mathrm{TI}={\displaystyle \frac{\left(\mathrm{C}14:0+\mathrm{C}16:0+\mathrm{C}18:0\right)}{\left[\right(0.5 \times \sum \mathrm{MUFA})+(0.5 \times \sum \mathsf{\omega }-6)+(3 \times \sum \mathsf{\omega }-3)+(\mathsf{\omega }-3/\mathsf{\omega }-6\left)\right]}}$$

(3)

$$\mathrm{HH}={\displaystyle \frac{(\mathrm{C}18:1+\sum \mathrm{PUFA})}{(\mathrm{C}12:0+\mathrm{C}14:0+\mathrm{C}16:0)}}$$

(4)

$$\mathrm{HPI}={\displaystyle \frac{\sum \mathrm{UFA}}{\left[\mathrm{C}12:0+\left(4 \times \mathrm{C}14:0\right)+\mathrm{C}16:0\right]}}$$

(5)

2.6. Bioactive Compounds Determination

The parameters of the analytical method for determining bioactive compounds, including limit of detection (LOD), recoveries, calibration curve range, linearity (coefficient of determination, R²), and repeatability (%RSD), are displayed in

Table A2. Analytical method parameters for bioactive compounds determination.

Parameter	LOD (µg/L)	LOD (µg/100 g)	Recoveries (%)	Calibration Curve Range (mg/L)	Linearity (R2)	Repeatability (%RSD)
Retinol	0.44	2.2	95	0–5	0.998	2.5
α-Tocopherol	4.14	4.14	97	0–1	0.996	2.8
β-Tocopherol	3.95	3.95	96	0–1	0.995	2.7
γ-Tocopherol	3.59	3.59	95	0–1	0.994	2.6
δ-Tocopherol	2.87	2.87	94	0–1	0.993	2.5
Total Carotenoids	4.09	40.9	98	Extinction coefficient for astaxanthin	0.999	2
L-Ascorbic acid	6.97	13.9	99	0–100	0.997	1.5

.

2.6.1. Vitamin A (Retinol) Content

For the specific analysis, 0.1 g of the extracted oil was weighed and diluted in 5 mL of n-hexane. The separation of compounds occurred with the chromatographic column being maintained at 40 °C. The mobile phase was composed of 0.5% aqueous formic acid (A) and 0.5% formic acid in acetonitrile (B). The gradient program was adjusted from a modified method from Palma-Duran et al. [19], and was set to increase from 5% B to 95% B over 40 min, maintain for 5 min, and then return to 5% B within 15 min. The flow rate of the mobile phase was maintained at 1 mL/min. Retention times and absorbance spectra at 325 nm were matched with pure retinol for compound identification and quantified using calibration curves ranging from 0–5 mg/L.

2.6.2. Vitamin E (Tocopherol) Content

The established method by Athanasiadis et al. [15] was followed in order to quantify tocopherol. A quantity of 0.5 g of fish oil was mixed with n-hexane in a 5 mL volumetric flask; the excitation and emission wavelengths were adjusted at 294 nm and 329 nm, respectively. The tocopherol content was determined by HPLC–FLD as mg tocopherol per 100 g wet fillet weight.

2.6.3. Total Carotenoid Content (TCC) Determination

The determination of TCC was performed as reported previously [15]. We used the established method from Sharayei et al. [20] to quantify astaxanthin, the main carotenoid of the red mullet. A quantity of fish oil sample (0.05 g) was mixed with hexane in a 5 mL volumetric flask. The absorbance was recorded spectrophotometrically at 474 nm using a 1 cm quartz cell. The total carotenoid concentration (C_TCn) was evaluated using the following equation:

$$C_{\mathrm{TCn}} (\mathrm{mg}/\mathrm{L} \mathrm{Oil})={\displaystyle \frac{A \times C^{1\%}}{A^{1\%}}}$$

(6)

where A is the measured absorbance at 474 nm, extinction coefficient A^1% = 2100, and C^1% = 10,000 mg/L. TCC, represented as astaxanthin equivalents (AxE), was then determined as follows:

$$\mathrm{TCC} (\mathrm{mg} \mathrm{A}\mathrm{x}\mathrm{E}/\mathrm{g} \mathrm{Oil})={\displaystyle \frac{C_{\mathrm{TCn}} \times V}{w}}$$

(7)

where V is the volume of the hexane phase (in L) and w is the amount of oil (in g). Finally, the TCC was calculated as mg AxE per 100 g of wet fillet.

2.6.4. L-Ascorbic Acid Content

The ascorbic acid content (AAC) in the sample was determined using a modified colorimetric method outlined by Jagota and Dani [21]. Exactly 1 g of lyophilized and defatted fish sample powder was measured and transferred into a 50 mL polypropylene centrifuge tube. In this step, we used only chloroform as a solvent to extract lipids from the fish sample. Subsequently, 18 mL of a water/methanol mixture (60:40 v/v) and 2 mL of a 10% w/v trichloroacetic acid solution were added. The mixture was vigorously stirred for 1 min, followed by continuous stirring at 500 rpm for 30 min at room temperature to facilitate extraction. The mixture was centrifuged at 4500 rpm (3600× g) for 5 min post-stirring to separate the aqueous phase. A total of 1 mL of the aqueous phase was then transferred to a 2 mL polypropylene micro-centrifuge tube, to which 0.5 mL of Folin–Ciocalteu reagent (10% v/v) was added, and the reaction was allowed to proceed for 10 min at room temperature. Absorbance was measured at 760 nm using a double-beam spectrophotometer, whereas quantification was performed using an ascorbic acid standard curve.

2.7. Statistical Analysis

All the analyses were conducted in triplicate, with each of the three groups from each location analyzed three times, and the results are presented appropriately (n = 3). The Shapiro–Wilk test was utilized to assess data normality. The statistical significance of differences between mean values was determined using a one-way analysis of variance (ANOVA) test, with p-values less than 0.05 deemed statistically significant. This was followed by a post hoc Tukey HSD (Honestly Significant Difference) test employing the Tukey–Kramer method. All statistical analyzes, including multiple factor analysis (MFA) and multivariate correlation analysis (MCA), were carried out using JMP™ Pro 16 software (SAS, Cary, NC, USA).

3. Results and Discussion

3.1. Proximate Composition of Red Mullet

The weight of a fish is intrinsically correlated with its length and age. Therefore, in order to utilize weight as a health indicator, it is imperative to normalize weight data to either age or length. Shephard et al. [22] recommended the utilization of weight-at-age data to assess anomalies in the average weight of fish concerning the availability and limitation of sustenance in the ecosystem. Nevertheless, the process of fish maturation, which is complex and susceptible to human error, is necessary to determine weight at age. Additionally, fast-growing fish in the Mediterranean exhibit notable weight fluctuations within the early age classes [23]. The measurements regarding the length and weight of the fish fillets are displayed in

Table 1. Average weights and lengths along with minimum and maximum values of red mullet in different fishing locations (n = 3).

Parameter	L1	L2	L3	L4	L5
Length (cm)	17.4 ± 0.9 a	14.2 ± 0.6 b,c	14.5 ± 0.7 b	14.4 ± 1 b	13.5 ± 0.8 c
Min–Max (cm)	15.5–19.3	13.2–15.3	12.5–15.3	13.1–16.2	12.1–15.2
Weight (g)	59.7 ± 10.1 a	30.8 ± 4.5 b	33.3 ± 5 b	33.1 ± 7.5 b	27.5 ± 5.5 b
Min–Max (g)	43.2–82.3	23.2–37.7	20.5–39.2	22.4–44.8	16.7–40

The values represent the mean ± standard deviation (SD) of three independent replicates (n = 3) for each location. Different superscript letters (e.g., a–c) indicate statistically significant differences (p < 0.05) within each line.

. The L1 sample had a wider variety of lengths when compared to the L2, L3, and L4 samples. Regarding weights, the L1 sample had the highest weight, with values ranging from 43.2 to 82.3 g.

Parameters including moisture, ash, protein, and fat of the red mullet obtained at locations L1–L5 are displayed in

Table 2. Proximate composition (% w/w wet fillet) of red mullet in different fishing locations (n = 3).

Parameter	L1	L2	L3	L4	L5
Moisture	74.4 ± 2.7 a	69.3 ± 2.1 a	73.2 ± 5.1 a	70.5 ± 4.7 a	70 ± 4.1 a
Ash	0.849 ± 0.017 c	0.969 ± 0.021 b,c	1.08 ± 0.05 b	1.38 ± 0.05 a	1.38 ± 0.07 a
Protein	14.3 ± 0.9 a	12 ± 0.8 b,c	12.2 ± 0.7 b,c	12.8 ± 0.4 a,b	10.8 ± 0.3 c
Fat	13.2 ± 0.9 c	14.2 ± 0.3 b,c	13.3 ± 0.3 c	16.8 ± 1 a	15.4 ± 0.3 a,b

The values represent the mean ± standard deviation (SD) of three independent replicates (n = 3) for each location. Different superscript letters (e.g., a–c) indicate statistically significant differences (p < 0.05) within each line.

. To start with, it was observed that the protein content of the red mullet in all the sites was high and consistent, averaging ~12 g/100 g of wet fillet. The difference between L1 (14.4%) and L5 (10.8%) is most likely due to the difference in mass and length, as indicated in Table 1. Future studies including other methods of protein determination (i.e., the Kjeldahl method) could also be conducted to ensure the protein content of the fish samples. The moisture content of the fish samples remained consistent and non-statistically significant (p > 0.05), and ranged from 69.3 to 74.4%. The total fat and ash contents did not show a wide range between the fish samples. The red mullet at L1 and L3 had the lowest total fat content, whereas L4 and L5 had considerably greater lipid levels. However, the fat content of L5 was not significantly different from that of L2. The geographical origin showed a significant effect on the proximate composition of red mullet in all the locations, as demonstrated by comparing the L1, L2, L3, L4, and L5 samples. The measured values in L1 and L3 had the lowest content in fat content of any other location. Polat et al. [24] measured moisture (~75%), ash (~1%), protein (~18%), and fat (~5%) in red mullet fillets from Mediterranean countries (i.e., from Iskenderun Bay, Turkey) with comparable values to our study. It was reported that red mullet fat content ranged from ~3–7% throughout the season, with the value of 6.12% being measured in spring time in a study from Durmuş et al. [6], who examined fish samples from the Black Sea. Despite the differences in proteins and lipids, the chemical composition of various fish species will vary based on seasonal variation, migratory behavior, sexual maturation, and dietary cycles. These factors are noticed in free-living fish inhabiting the open sea and inland waters [25]. A study by Roncarati et al. [26] that examined red mullet proximate composition from the Adriatic, Tyrrhenian, and Ionian Seas (i.e., those close to the Aegean Sea) showed statistically significant differences (p < 0.05) only in lipid content from 1.82 to 7.54% and moisture content from 72.2 to 78.3%. The other parameters did not show such a wide range, as protein averaged ~19% and ash ~2%. Regarding our results, methanol was avoided for the defatting process in protein determination (Folch method), as it is an organic solvent that can denature proteins by disrupting their structure and function. Only chloroform was used. In our study, we aimed to minimize variability by selecting fish with similar age, weight, and length for each pool. This approach was taken to ensure more consistent results, and we acknowledge that this might not fully capture the natural variability in the proximate composition.

3.2. Mineral Composition in Red Mullet

Examining the concentrations of minerals, classified as complementary nutritional variables, is crucial for public health and the nutritional quality of seafood [6]. The mineral composition was chosen to be shown separately from other proximate analyses in

Table 3. Mineral composition (μg/100 g wet fillet) of red mullet from different fishing locations (n = 3).

Mineral	L1	L2	L3	L4	L5
Aluminum (Al)	86.4 ± 3.5 b	33.4 ± 0.8 d	62.2 ± 3.5 c	96.4 ± 7.1 a,b	107 ± 8 a
Arsenic (As)	12.6 ± 0.5 c	6.9 ± 0.3 d	23.2 ± 1.5 b	8.33 ± 0.24 d	33 ± 1.1 a
Barium (Ba)	5.93 ± 0.3 b	3.12 ± 0.23 c	3.53 ± 0.08 c	5.9 ± 0.34 b	6.95 ± 0.28 a
Beryllium (Be)	<LOD	<LOD	<LOD	<LOD	<LOD
Boron (B)	17.7 ± 1 a	6.4 ± 0.28 b	18.4 ± 0.5 a	17.5 ± 1 a	16.9 ± 0.8 a
Cadmium (Cd)	<LOD	<LOD	<LOD	<LOD	<LOD
Calcium (Ca)	18,023 ± 667 d	24,611 ± 1624 c	25,444 ± 1018 c	45,631 ± 1734 a	36,462 ± 1057 b
Chromium (Cr)	<LOD	<LOD	<LOD	<LOD	<LOD
Cobalt (Co)	<LOD	<LOD	<LOD	<LOD	<LOD
Copper (Cu)	<LOD	<LOD	<LOD	<LOD	<LOD
Iron (Fe)	425 ± 31 d	534 ± 40 c	411 ± 24 d	660 ± 15 b	782 ± 16 a
Lead (Pb)	<LOD	<LOD	<LOD	<LOD	<LOD
Lithium (Li)	102 ± 5 b	66.7 ± 2.2 c	36.5 ± 2.3 d	108 ± 5 b	145 ± 5 a
Magnesium (Mg)	3324 ± 113 b	3610 ± 245 b	3462 ± 145 b	6045 ± 151 a	5917 ± 379 a
Manganese (Mn)	<LOD	<LOD	<LOD	20.5 ± 1.1 a	19.6 ± 0.7 a
Molybdenum (Mo)	<LOD	<LOD	1.34 ± 0.05 b	1.85 ± 0.06 a	<LOD
Nickel (Ni)	<LOD	<LOD	<LOD	<LOD	<LOD
Potassium (K)	485 ± 14 c	615 ± 22 b	478 ± 29 c	837 ± 22 a	812 ± 27 a
Selenium (Se)	<LOD	<LOD	<LOD	<LOD	<LOD
Silver (Ag)	<LOD	<LOD	<LOD	<LOD	<LOD
Sodium (Na)	19.5 ± 0.5 c	20.9 ± 0.7 b,c	22.9 ± 1.7 b	36.2 ± 1.3 a	38.4 ± 1.6 a
Strontium (Sr)	101 ± 6 d	154 ± 4 c	145 ± 10 c	254 ± 12 a	213 ± 9 b
Thallium (Tl)	<LOD	<LOD	<LOD	<LOD	<LOD
Uranium (U)	<LOD	<LOD	<LOD	<LOD	<LOD
Vanadium (V)	1.28 ± 0.08 c	<LOD	<LOD	1.62 ± 0.1 b	3.09 ± 0.08 a
Zinc (Zn)	213 ± 13 d	262 ± 5 c	207 ± 14 d	340 ± 12 b	405 ± 16 a

The values represent the mean ± standard deviation (SD) of three independent replicates (n = 3) for each location. Different superscript letters (e.g., a–d) indicate statistically significant differences (p < 0.05) within each line. LOD means limit of detection.

, which shows that mineral concentrations varied significantly among the five sites. The red mullet levels were substantially greater in both L5 and L4 (p < 0.05). All the mineral concentrations were obtained using a wet-weight basis. L5 had the greatest quantities of Al, As, Ba, Fe, Li, Mg, Mn, K, Na, U, V, and Zn of any fish sample tested (p < 0.05). The lowest values were found mostly in the South Euboean Gulf. The L3 sample had considerably lower (p < 0.05) mean mineral levels compared to the other regions. Elevations or decreases in mineral concentrations within a species may be correlated with biological traits (i.e., age, sex, and maturity), food sources (position in the food chain), and natural reserves [27]. Fish species that live in the same habitat might differ in terms of their trophic levels, ionic physiology and absorption rates, growth, lifespan, eating and feeding behaviors, and other factors, all of which can affect the quantities of heavy metals in their bodies [28].

The principal food source of red mullet is often composed of polychaetes, bivalves, and crustaceans [29]. Mollusks may bioaccumulate metals in greater quantities from their diets [30] and have been regarded as promising bioindicator species because of this capacity [30,31,32]. All metal concentrations in red mullet were found to be greater in L5 when the seasonal fluctuations in metal levels were analyzed. Metal bioaccumulation in marine species may be influenced by factors such as pH, salinity, temperature, and dissolved oxygen [33]. For instance, metal solubility increases at elevated temperatures (>25 °C) and decreases at slightly basic pH levels. Fish physiological characteristics and metal uptake are positively correlated. Almost all the metal levels showed significant interactions between fillets and location (p < 0.05).

3.2.1. Essential and Non-Essential Minerals

The results from the concentration of essential minerals or macro elements (i.e., Ca, Mg, K, and Na) revealed that Ca was the most abundant. In addition, the concentration of non-essential minerals significantly varied (p < 0.05), as indicated by the measured values of Al (33.4–107 μg/100 g), Li (36.5–145 μg/100 g), and Sr (101–254 μg/100 g). Fish flesh accumulates small levels of metals due to low metabolic activity [34]. This finding is consistent with prior studies [28,35,36] and is based on each tissue’s functions, which significantly dictate metal bioaccumulation. However, a current study [37] indicates a correlation between metal accumulation and the moisture percentage of fish flesh. Via gill filtration, food, and sediment consumption, fish can absorb large amounts of minerals from the water. It should be mentioned that the length of exposure and the concentration of minerals in the water affect how quickly minerals accumulate. Therefore, it can be assumed that the mineral concentrations in the fish flesh and lipids may be influenced by variables related to the bioavailability of these minerals for fish consumption [38]. Studies have indicated that meals high in fat have an impact on the concentration of minerals in fish bodies. In light of these suppositions, it is suggested that there is a negative relationship between lipids and mineral accumulation in fillet tissue [38].

It was observed that some metals (i.e., Fe, Na, Mg, K, Ca, Sr, Li, and Zn) exhibited higher concentrations in fish samples with smaller lengths and weights (i.e., L3 and L5). According to Sofoulaki et al. [39], bigger fish have higher moisture levels, which causes metals to be diluted and calculated at lower concentrations in their tissues. Smaller red mullets often inhabit shallower waters [40], where the contaminants that end up in the water and sediment cause ongoing changes in the water’s biochemical characteristics. Consequently, a larger quantity of metals may be obtained. It is also possible that fish achieve a stable age point in the accumulation of metals because they have more tissue control (bioregulation), as proposed by Douben et al. [41]. On the other hand, if the concentration of metals in the water is higher than the organisms’ capability, there is a chance that the size of the fish and the metal accumulation will positively correlate. Under these circumstances, the metals will continue to accumulate, and it may not be possible to detect the concentration decrease brought on by dilution with growing size and low metabolic rate [36,42,43,44].

3.2.2. Essential Trace Minerals

The results revealed that some essential trace metals were not detected in some fish samples. For instance, Mo which has a Recommended Dietary Allowance of 45 μg/day [45] was not detected in three sites (L1, L2, and L5), and Mn (with a tolerable upper intake level of 7 mg/day [46]) was only detected in sites L4 and L5. An established tolerable upper intake level of 255 μg/day is reported for Se [47], which was not detected in any sample of the sites. The concentration of most of the essential trace metals was similar to that reported in other studies. In a study by Korkmaz et al. [48], the authors examined several metals from 24 red mullet samples from Mersin, Turkey. Regarding the specific minerals, the authors quantified them in wide ranges: Cr (23–280 μg/100 g), Mn (8–60 μg/100 g), Fe (93–1958 μg/100 g), Ni (3–21 μg/100 g), Cu (9–196 μg/100 g), and Zn (405–3579 μg/100 g) of wet weight. The Cr concentrations differed significantly across locations (p < 0.05). The reported permissible tolerable daily intake (in μg/kg/day) as declared by several agencies is as follows for Cr⁺³ (1500) [49], Mn (140) [50], Ni (20) [51], and Zn (300) [52]. The results of the measured concentration of the essential trace minerals revealed a comparable order to the study from Durmuş et al. [6] who examined the specific fish species (Mullus barbatus Linnaeus 1758) from the Black Sea. In our results, the Ni concentration in red mullet fillets was not quantified (<LOD). Our findings were in accordance with Fındık and Çiçek [53] who examined the metal concentration of red mullet on the West Black Sea Coast (Bartin) in Turkey. We measured lower concentrations expressed in μg/100 g than the authors in Mn (77), B (673), Zn (1603), and Fe (2120) which would require further investigation to identify the reason.

3.2.3. Toxic Minerals

The dry ashing method is advantageous for digesting larger samples when elements are present at extremely low levels. A safe and straightforward dry-ashing technique was used for the quantification of elements. However, it is reported that minerals like As, Se, and Cd are volatile in extremely high temperatures, whereas Pb could show poor results due to contamination during the ashing step [54]. The preparation of ash through the dry-ash method could have affected those elements. The Pb levels in the red mullet samples collected from all the sites were <LOD. The corresponding levels of Pb were also non-detected for most of the samples or were as low as 0.01 mg/kg in the study by Panagiotouna [36]. Regarding Cd, it was also not quantified (<LOD) in this study. Research by Giannakopoulou and Neofitou [55] found 200–400 μg/kg of Cd in the fillets of red mullet and Pagellus erythrinus in Greece’s Pagasitikos Gulf. In the previously mentioned study by Korkmaz et al. [48], the authors quantified As (508–2526 μg/100 g), Cd (<0.04 μg/100 g), and Pb (2–42 μg/100 g) in wet weight. The permissible tolerable daily intakes (in μg) for an individual with 70 kg of body weight are 21 for As [56], 70 for Cd [57], and 170 for Pb [58]. The corresponding tolerable daily intake for adults (in μg/kg of body weight/day) should not exceed 0.3 for As, 1 for Cd, and 2.43 for Pb. Regarding heavy metal concentrations in red mullet fillets, our results were in line with Aissioui et al. [59] who investigated the specific species concentrations of toxic heavy metals Cd, Pb, and Hg. The authors measured Pb (0.25 μg/g of wet weight), Cd (0.28 μg/g of wet weight), and Hg (0.12 μg/g of wet weight) in samples from the Algerian coast. Among the heavy metals that pose the greatest threat to human health and the environment is As. Arsenic pollution has been caused by both human actions and natural geological processes [60]. It is necessary to highlight that larger fish from the L1 region did not present elevated higher As levels compared to the other four samples, contrary to expectations. Their concentration was 12.6 μg/100 g of wet fillet. These results may indicate that the As levels are more attributable to the metal’s concentration in the region rather than to bioaccumulation.

3.2.4. Radioactive Minerals

The concentrations of Tl and U were either not detected or extremely low (<LOD), which could indicate safe levels when consuming this fish.

3.3. Bioactive Compounds Found in Red Mullet

Bioactive compounds were quantified to shed more light on the red mullet fillet composition, the results of which are depicted in

Table 4. Bioactive compounds measured in red mullet fillets (mg/100 g wet fillet) (n = 3).

Parameter	L1	L2	L3	L4	L5
Vitamin A	0.436 ± 0.022 a	0.0793 ± 0.003 d	0.102 ± 0.002 c,d	0.181 ± 0.006 b	0.111 ± 0.006 c
δ-Tocopherol	4.14 ± 0.26 b	3.95 ± 0.26 b	3.59 ± 0.15 b	2.87 ± 0.07 c	6.09 ± 0.4 a
∑ Tocopherols (Vitamin E)	4.14 ± 0.26 b	3.95 ± 0.26 b	3.59 ± 0.15 b	2.87 ± 0.07 c	6.09 ± 0.4 a
Total Carotenoids	4.09 ± 0.15 a	1.76 ± 0.05 c	2.28 ± 0.15 b	2.49 ± 0.14 b	2.19 ± 0.16 b
Ascorbic acid	6.97 ± 0.36 c	9.19 ± 0.63 a,b	10.6 ± 0.5 a	8.38 ± 0.52 b,c	9.2 ± 0.66 a,b

The values represent the mean ± standard deviation (SD) of three independent replicates (n = 3) for each location. Different superscript letters (e.g., a–d) indicate statistically significant differences (p < 0.05) within each line.

. The vitamin A levels showed a statistically significant variation as they ranged from 0.0793 to 0.436 mg/100 g, with the L1 sample having the highest value. Regarding tocopherols, the α-, β-, and γ- homologs were not quantified in any sample. However, the L5 sample had considerably greater amounts of δ-tocopherol (p < 0.05) than any other sample. A finding of high interest is the study by Polat et al. [61] where the authors measured a comparable amount of total tocopherols (i.e., 2.39 mg/100 g of raw red mullet); however, the most abundant homolog (>99%) was found to be δ-tocopherol. According to Saldeen et al. [62], δ-tocopherol has a stronger antioxidant potency than α-tocopherol. In a study conducted by Di Lena et al. [63], seasonal differentiation in red mullet bioactive compounds from two sites was examined. δ-Tocopherol homologs ranged from 0.05 to 0.16 mg/100 g of wet fillet; however, the authors also quantified α-tocopherol (0.67–2.2 mg/100 g wet fillet). The authors stated that their findings could have significant consequences for incorporating red mullet into human dietary formulas. As a rule, red mullet is considered a medium-fat fish when looking at food composition tables. However, they also state that this species is considered a lean fish in late spring and a fatty fish in October. To that end, in another study, Passi et al. [64], the authors, measured 0.74 mg/100 g of red mullet muscles from the central Tyrrhenian Sea.

Because of the high concentration of photosynthetic microorganisms in the marine environment and carotenoids, which are naturally occurring pigments generated by algae, Mullidae have reddish skin. The presence of carotenoids in the red mullet samples, as in other fish species, is due to the ingestion of crustaceans and mollusks because they are unable to synthesize these biomolecules. Astaxanthin is the primary carotenoid in the diet of these fish, known to provide a reddish hue to the flesh of red mullet [65,66,67]. The measured carotenoids from red mullet, which were expressed as astaxanthin equivalents, were found in the lipid fraction from fish fillets, despite the fact that the skin had been removed. The quantities of the total carotenoids measured varied from 1.76 to 4.09 mg/100 g wet fillet, with sample L1 having the highest value. In the previously mentioned study by Di Lena et al. [63], the authors quantified total carotenoids in lower quantities than ours. Specifically, total carotenoids from fish in the Central Tyrrhenian Sea had a range of 0.291–0.468 mg/100 g of wet fillet, whereas fish from the Central Adriatic Sea had 0.396–0.508 mg/100 g of wet fillet. As there is a lack of data on the carotenoid content in red mullet, a future study including this analysis would be useful, and if possible, quantification has to be performed by chromatographic techniques.

The data indicate variations in ascorbic acid content, with sample L3 showing the highest concentration and sample L1 the lowest. Specifically, sample L3 contained the most ascorbic acid (10.6 mg/100 g wet fillet), making it the richest in the group. In contrast, sample L1 had the lowest ascorbic acid (6.97 mg/100 g wet fillet). Intermediate concentrations were found in samples L2, L4, and L5, with L2 and L5 having similar levels and close to L3, while L4’s concentration is closer to that of L1. In the defatting process for the determination of L-ascorbic acid, methanol (Folch method) was avoided due to its polarity and the solubility of L-ascorbic acid in polar solvents. Only chloroform was used. Additionally, L-ascorbic acid is more stable at lower pH, which helps prevent its oxidation. For this reason, the trichloroacetic acid solution was used to extract it from the fish sample.

3.4. Fatty Acid Profile

The total fatty acid quantification is shown in

Table 5. Fatty acid profile, expressed as a percentage (%) of total fatty acids, along with health parameters derived from them for red mullets in different fishing locations (n = 3).

FAME/Parameter	L1	L2	L3	L4	L5
C8:0	0.0894 ± 0.0049 b	0.11 ± 0.005 a	0.106 ± 0.006 a	0.0958 ± 0.006 a,b	0.0813 ± 0.0056 b
C10:0	0.111 ± 0.006 a	0.0327 ± 0.0008 c	0.0206 ± 0.0007 d	0.0185 ± 0.0006 d	0.0419 ± 0.001 b
C11:0	0.11 ± 0.007 a	0.0156 ± 0.0008 c	n.d.	n.d.	0.0321 ± 0.0008 b
C12:0	0.198 ± 0.005 a	0.097 ± 0.0057 b	0.0988 ± 0.0052 b	0.104 ± 0.002 b	0.106 ± 0.008 b
C13:0	0.0451 ± 0.0014 a	0.0294 ± 0.0009 c	0.0334 ± 0.0009 b	0.0318 ± 0.0017 b,c	0.0459 ± 0.0021 a
C14:0	2.59 ± 0.18 a	2.39 ± 0.17 a	2.61 ± 0.15 a	2.49 ± 0.13 a	2.39 ± 0.16 a
C15:0	1.32 ± 0.09 a	0.864 ± 0.022 b	0.981 ± 0.042 b	0.911 ± 0.031 b	0.914 ± 0.028 b
C16:0	24.8 ± 0.6 a	25.4 ± 0.97 a	24.9 ± 1.6 a	25.1 ± 1.7 a	25.9 ± 1.2 a
C18:0	2.95 ± 0.22 a	1.97 ± 0.07 b	2.18 ± 0.06 b	2.1 ± 0.09 b	1.94 ± 0.11 b
C20:0	0.194 ± 0.015 c	1.71 ± 0.04 b	0.0887 ± 0.0059 d	1.89 ± 0.04 a	1.81 ± 0.04 a
C24:0	0.00509 ± 0.00033 a	0.00529 ± 0.0002 a	n.d.	n.d.	0.00522 ± 0.00037 a
∑ SFA	32.4 ± 1.2 a	32.6 ± 1.3 a	31 ± 1.9 a	32.8 ± 2 a	33.3 ± 1.5 a
C14:1	0.668 ± 0.031 a	0.353 ± 0.008 c,d	0.417 ± 0.013 b	0.395 ± 0.025 b,c	0.319 ± 0.007 d
C16:1	2.73 ± 0.19 a	1.44 ± 0.03 b,c	1.62 ± 0.1 b	1.52 ± 0.09 b	1.18 ± 0.05 c
C18:1 ω-9 cis	27.5 ± 2 b	30.9 ± 0.7 a,b	29.1 ± 1.5 a,b	30 ± 0.9 a,b	32.2 ± 2.3 a
C18:1 ω-9 trans	2.64 ± 0.1 a	2.41 ± 0.06 a	2.58 ± 0.12 a	2.51 ± 0.14 a	2.73 ± 0.2 a
C20:1 ω-9	0.218 ± 0.009 a	n.d.	n.d.	n.d.	n.d.
C24:1 ω-9	0.609 ± 0.004 d	0.861 ± 0.004 c	0.971 ± 0.003 a	0.911 ± 0.006 b	0.536 ± 0.004 e
∑ MUFA	34.4 ± 2.4 a	36 ± 0.8 a	34.7 ± 1.7 a	35.3 ± 1.2 a	36.9 ± 2.5 a
C18:2 ω-6	7.11 ± 0.16 a	3.72 ± 0.27 b	4 ± 0.09 b	3.92 ± 0.25 b	3.73 ± 0.19 b
C18:3 ω-6	3.76 ± 0.09 a	1.89 ± 0.06 d	2.19 ± 0.08 c	2.12 ± 0.15 c,d	2.55 ± 0.07 b
C18:3 ω-3	7.73 ± 0.35 a	6.41 ± 0.36 b	6.8 ± 0.33 b	6.59 ± 0.13 b	7.2 ± 0.3 a,b
C20:2 ω-6	2.45 ± 0.17 a	0.464 ± 0.021 c	1.87 ± 0.04 b	0.562 ± 0.015 c	0.583 ± 0.015 c
C20:4 ω-6	1.67 ± 0.08 a	n.d.	0.668 ± 0.027 b	n.d.	n.d.
C20:5 ω-3 (EPA)	1.22 ± 0.03 a	0.946 ± 0.031 b	1.02 ± 0.05 b	1.03 ± 0.04 b	0.939 ± 0.024 b
C22:6 ω-3 (DHA)	9.25 ± 0.53 c	18 ± 1.2 a	17.7 ± 0.5 a	17.7 ± 0.5 a	14.8 ± 0.7 b
∑ PUFA	33.2 ± 1.4 a,b	31.4 ± 2 a,b	34.3 ± 1.1 a	31.9 ± 1.1 a,b	29.8 ± 1.3 b
∑ UFA	67.6 ± 3.8 a	67.4 ± 2.8 a	69 ± 2.8 a	67.2 ± 2.3 a	66.7 ± 3.8 a
∑ ω-3	18.2 ± 0.91 b	25.3 ± 1.6 a	25.6 ± 0.9 a	25.3 ± 0.7 a	22.9 ± 1 a
∑ ω-6	15 ± 0.5 a	6.1 ± 0.3 c	8.7 ± 0.2 b	6.6 ± 0.4 c	6.9 ± 0.3 c
∑ ω-9	31 ± 2.1 a	34.2 ± 0.7 a	32.6 ± 1.6 a	33.4 ± 1.1 a	35.4 ± 2.5 a
∑ EPA + DHA	10.5 ± 0.6 c	18.9 ± 1.3 a	18.8 ± 0.5 a	18.7 ± 0.6 a	15.7 ± 0.7 b
ω-3:ω-6	1.21 ± 0.02 e	4.17 ± 0.03 a	2.93 ± 0.02 d	3.83 ± 0.14 b	3.34 ± 0.01 c
(SFA + MUFA):PUFA	2.01 ± 0.02 c	2.19 ± 0.07 b	1.92 ± 0.04 c	2.13 ± 0.03 b	2.36 ± 0.04 a
AI	0.524 ± 0.009 a	0.521 ± 0.003 a	0.514 ± 0.012 a	0.524 ± 0.016 a	0.534 ± 0.002 a
TI	0.377 ± 0.007 a	0.294 ± 0.004 c	0.293 ± 0.008 c	0.295 ± 0.011 c	0.322 ± 0.001 b
HH	2.29 ± 0.06 a	2.32 ± 0 a	2.39 ± 0.06 a	2.32 ± 0.08 a	2.28 ± 0.02 a
HPI	1.91 ± 0.03 a	1.92 ± 0.01 a	1.94 ± 0.04 a	1.91 ± 0.06 a	1.87 ± 0.01 a

The values represent the mean ± standard deviation (SD) of three independent replicates (n = 3) for each location. Different superscript letters (e.g., a–e) indicate statistically significant differences (p < 0.05) within each line. n.d. means not detected.

. When comparing the red mullet from L1 to L2, there was no statistically significant difference in the amounts of total and PUFA (p > 0.05) or MUFA proportions, nonetheless. These differences might suggest that during the season, prey with a high MUFA content, such as polychaetes, and a low PUFA content, namely crustaceans, were not as readily available in the two separate sites.

The results revealed that C8–C13 SFAs were measured in low quantities (i.e., from 0.0294 to 0.198%). C16:0 was the most prevalent SFA, averaging ~25% of the total lipids and marking ~75% of the total SFA in all the samples. Regarding MUFAs, oleic acid was found to be the most prevalent in most cases, with its concentration significantly (p < 0.05) ranging from 27.5 to 32.2% of the total fatty acids. Sample L1 showed significantly (p < 0.05) lower amounts of ω-3 fatty acids than the other samples. This result was attributed to the excessively high amount of ω-6 fatty acids, which could possibly occur due to their nutrition compared to other fish samples. Finally, concerning PUFAs, it was revealed that DHA had the highest proportion in all the samples, wherein the total sum of PUFAs ranged from 31.4 to 34.3%, signaling a similar figure to MUFAs and SFAs. It was also observed that different sites of the study had little to no effect on the variance in fatty acids. Comparable amounts of fatty acids have been reported by Di Lena et al. [63], with slight modifications in the MUFA and PUFA ratios. The authors measured total fatty acids in a ratio of 34.1:18.4:42.4 SFA:MUFA:PUFA. It should also be noted that arachidonic acid was measured only in two samples (i.e., L1 and L3). The observed changes in the overall lipid composition might suggest the mobilization of reserve lipids for the gonad’s maturation throughout the spring season. It is well known that fish use lipids as energy storage, especially when they are not eating or reproducing, and that when stress or reproduction occurs, lipids are the first to be mobilized. Fish lipid content in fillets can also be influenced by the water temperature, availability of nutrients, fish origin, and habitat quality. Seasonal variations in fish fat content are mainly related to the reproduction period.

Regarding health indices, the red mullets had low AI and TI, with only small statistical differences in AI. These results might be explained by the relatively high PUFA content. AI and TI are lipid indicators of quality that can predict if a diet or just one meal is likely to cause cardiovascular diseases and platelet aggregation. Since no established reference index is provided for these indices [68], an example of high UFA-containing olive oil (AI: 0.16; TI: 0.39) has significantly lower values than palm kernel oil (AI: 2.62; TI: 2.62) [69], which has high content in SFAs. AI and TI values of white needle (0.26 and 0.44), black needle (0.26 and 0.21), sardines (0.60 and 0.20), and mackerel indices (0.48 and 0.24) were reported by Fernandes et al. [70]. As a result, the lowest possible values for such indices are preferable to avoid possible coronary artery disease [40]. Our results were in line with Di Lena et al. [63] who found that the AI ranged from 0.52 to 0.77 and TI from 0.26 to 0.51. Concerning the HH and HPI values, statistically non-significant differences (p > 0.05) were observed throughout the examined samples since they had comparable amounts of SFAs and UFAs. In the previously mentioned study by Roncarati et al. [26], the authors measured comparable SFAs to our study (~33%). MUFAs significantly ranged from 28.9 to 42.1%; however, samples from the Ionian Sea (i.e., the closest to the Aegean Sea) had comparable values to those in our study (34%). PUFAs also widely ranged from 17.5 to 30.3%.

3.5. Correlation Analyzes

The consensus map in our study serves as a robust tool for visualizing and comparing fish samples from five distinct locations (L1, L2, L3, L4, and L5) based on a range of measured parameters (Figure 3). This map facilitates an effortless visual comparison of the fish samples across these locations. Plotting the samples on a unified scale underscores the similarities and disparities in their measured parameters. The axes (Dim1 and Dim2) indicate the percentage of variance each dimension explains (i.e., 51.4% for Dim1 and 30.2% for Dim2), aiding in discerning which parameters are pivotal in differentiating between locations. The map can uncover correlations between locations and specific parameters, assisting in the analysis of how environmental or geographical factors may affect the fish samples. The “inertia” value was used to interpret this result. One measure of cluster coherence is “inertia”, which is another name for the within-cluster sum of squares. If the inertia value is high, then the data points in that cluster are substantially different from each other [71]. Inertia values are also depicted with gold arrows (with a dashed line) to shed more light on the obtained results. For example, L2 and L3 lengths were not observed to vary significantly (p > 0.05); thus, their corresponding red lines are in proximity to one another. On the contrary, the total mineral content from L1 and L5 samples significantly varied in a two-fold manner, and that resulted in the high inertia value between their corresponding brown lines.

The RV coefficient serves as a multivariate extension of the Pearson correlation coefficient, evaluating the linear association between two sets of matrices. Within the scope of

Table 6. RV correlations measure the similarity between two sets of parameters.

Parameters	Size	Composition	Bioactive Compounds	Minerals	Fatty Acids	Health Indices	Centroid
Size	–	0.753	0.821	0.297	0.857	0.244	0.825
Composition		–	0.487	0.744	0.752	0.298	0.839
Bioactive compounds			–	0.280	0.822	0.506	0.815
Minerals				–	0.488	0.585	0.708
Fatty acids					–	0.600	0.941
Health indices						–	0.675
Centroid							–

, a centroid denotes the central location within a data point cluster, signifying the mean position of all the points within that cluster. In MFA, the centroid aids in discerning the central tendency of data points associated with a specific group or location. Several interesting findings were observed. Initially, the study confirmed a low correlation between fish size and metal concentration. Concurrently, it was reaffirmed that metal concentrations are more likely linked to the fish’s moisture composition, as indicated by the high correlation (>0.7). In terms of health indices, bioactive compounds were found to have a significant contribution (>0.5), aligning with expectations, while minerals, particularly macro elements, were deemed crucial for fish nutrition. Notably, no negative correlations were detected. The data table offers insightful revelations about the interrelations among various parameters. The pronounced correlations between certain parameters denote strong mutual dependencies, which are instrumental in deciphering the interactions and mutual influences of these parameters. For instance, the marked correlation between size and fatty acids suggests that larger fish may possess greater fatty acid content, a factor of significance for nutritional and health research. Likewise, the connection between bioactive compounds and health indices underscores the prospective health benefits of bioactive compounds.

The block partial contributions plot is an essential tool in our study as it assists in identifying which blocks, such as size, bioactive compounds, composition, fatty acids, and health indices, contribute most to the variance explained by the principal dimensions (Dim1 and Dim2). Figure 4 facilitates the interpretation of the MFA results by illustrating the contribution of each block to the principal components. The correlations between the variables and the components are shown by the variable loadings. Different-sized dots represent the relative value of the data pieces. With bigger dots signifying more significance and smaller ones less so, the size of the dots represents the relative importance of the values. The proximity of some variables could also be a matter of high correlation. For example, size and composition variables were positioned together and far from health indices since low correlation was previously revealed in RV correlations (0.2–0.3).

Our study provides a comprehensive analysis of fish composition, offering a multi-faceted approach to understanding the environmental impact on marine life. By utilizing tools like the consensus map and block partial contributions plot via MFA, we pinpointed the correlation between different fishing locations in Euboean gulf and the quality of fish populations. Moreover, the data derived from this study can guide public health recommendations and support sustainable fishing practices, ensuring that future generations have access to high-quality seafood. The implications of this research extend beyond immediate commercial benefits, potentially influencing policy-making and promoting further scientific inquiry into the factors affecting marine ecosystems. Overall, the findings underscore the importance of location-specific data in enhancing our understanding of the environment’s role in shaping the health and nutritional value of fish, which is essential for ongoing efforts in aquaculture and fisheries management.

4. Conclusions

The findings of this research may be utilized to evaluate the potential hazards associated with consuming fish and to comprehend the chemical composition of fish. This study examined the fatty acid profile of five locations in the Euboean Gulf of a frequently consumed food by the local people. Variations in the fatty acid content among fish from different locations can be attributed to their unique biological traits and dietary environments. Given that DHA is the most prevalent fatty acid across all the PUFA groups, fish species contain a comparatively high proportion of ω-3, highly PUFA. The study highlights the better nutritional quality of fish as well as the biochemical distinctions among the five locations. The study also reveals that the distribution of metals varies throughout red mullet fillets. There were significant and noteworthy discrepancies in the accumulation of the chosen metals in diverse fillets and aquatic environments. Our results suggest that metal accumulation and size appear to be inversely related. The L5 sample had the highest sum of metals and could imply possible pollution in the specific site. This contamination may be a result of recent advancements in technology and industry in the area (i.e., mining and construction). Thus, to have a clearer picture of the North and South Euboean Gulf, more research should be conducted to determine the elemental concentrations of various marine species. In addition, these assays could be replicated in a future study to ensure that the experimental results are reproducible.