Deep-Frying of Orange-Spotted Grouper Fillets in Olive, Corn, and Grape Seed Oils: Effects on nutritional quality indices, Lipid Oxidation, Minerals, and Vitamins composition

Deep-Frying of Orange-Spotted Grouper Fillets in Olive, Corn, and Grape Seed Oils: Effects on nutritional quality indices, Lipid Oxidation, Minerals, and Vitamins composition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep-Frying of Orange-Spotted Grouper Fillets in Olive, Corn, and Grape Seed Oils: Effects on nutritional quality indices, Lipid Oxidation, Minerals, and Vitamins composition Zahra Momenzadeh, Ainaz Khodanazary, Kamal Ghanemi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8251357/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study was to determine the nutritional quality indices , lipid oxidation, minerals, and vitamins composition of deep-fried orange-spotted grouper. Different vegetable oils (olive oil, grapeseed oil, and corn oil) were used for deep-frying. The results indicated that deep-frying significantly increased fat content and decreased moisture. The fatty acid profile of the fillets was markedly influenced by oil absorption. Corn oil-fried fillets exhibited the most favorable nutritional lipid indices, including the highest polyunsaturated-to-saturated fatty acid (PUFA/SFA) ratio (2.03) and hypocholesterolemic/hypercholesterolemic (HH) ratio, alongside the lowest atherogenic (AI) and thrombogenic (TI) indices. However, this was accompanied by a substantial reduction in beneficial long-chain omega-3 fatty acids (EPA+DHA), which decreased from 6.06% in raw fillets to 1.98%. Grape seed oil best preserved the EPA+DHA content among fried samples. Olive oil frying led to a significant increase in the arachidonic-to-eicosapentaenoic acid (ARA/EPA) ratio, indicating a potential negative impact on this sensitive nutritional index. Lipid oxidation analysis revealed no significant change in TBA values post-frying, possibly due to the volatility of malondialdehyde (MDA) or antioxidant effects from the oils. FFA content decreased significantly after frying, with olive oil-fried fillets showing the lowest value (1.42% oleic acid). Regarding vitamins, frying caused significant degradation of heat-sensitive vitamins B1 and B3, while vitamin A content increased, highest in corn oil-fried samples (8.36 mg/100g). Vitamin D levels varied, with olive oil-fried fillets retaining the highest content. Most minerals remained stable during frying, except for Cu, which decreased significantly. In summary, selecting a frying oil involves balancing distinct advantages and drawbacks: corn oil improves heart-healthy fatty acid ratios yet significantly reduces omega-3 content; olive oil better controls lipid hydrolysis and maintains higher vitamin D levels, but it negatively alters the ARA/EPA balance. Grape seed oil, meanwhile, is superior in preserving omega-3 fatty acids. Consequently, the optimal oil should be chosen based on the specific nutritional priorities for the end product. Biological sciences/Biochemistry Health sciences/Health care Deep-frying Vegetable oils Nutritional quality indices Lipid oxidation Sensory analysis Figures Figure 1 Introduction Deep frying is a globally renowned cooking technique with ancient origins, employed to enhance both the sensory qualities and the shelf life of food (Manzoor et al., 2022 ). Deep Frying is a ubiquitous cooking technique that involves submerging food in hot oil at high temperatures, typically between 150°C and 200°C, creating a dynamic interaction between the oil, air, and the food (Chen et al., 2014 ). This process is driven by simultaneous heat and mass transfer, which transforms the food into a final product with a distinctive sensory profile. The resulting desirable qualities, such as a characteristic fried flavor, a golden-brown color, and a crisp texture, are highly favored by consumers (Rahimi et al., 2017 ). The primary drawback of this method is the high level of oil uptake by the food during cooking (Mahmud et al., 2023 ). Azahrani et al. ( 2019 ) observed a substantial rise in the fat content of fish due to deep-fat frying, with whiting fillets increasing from 1% to 10.5% fat. This cooking method simultaneously exposes both the food and frying oil to multiple physicochemical processes. These reactions, including protein denaturation, lipid oxidation, and maillard reactions, generate numerous harmful compounds such as free fatty acids, small molecular alcohol, aldehyde, ketone, acid, lactone and hydrocarbon (Pokorný, 1989 ), and may affect the final nutritional quality of the product. The selection of frying oil is crucial, as its composition directly impacts heat transfer efficiency, resistance to oxidation, and the extent of lipid absorption by the food. There is a notable variation in the composition of frying oils, particularly in their fatty acid profiles, which include saturated, monounsaturated, and polyunsaturated fats, as well as their content of fat-soluble micronutrients such as carotenoids, tocols, and sterols. Research indicates that the quality of frying oil is significantly influenced by several factors, including the number of times the oil is used, the composition of the food being fried, and the type of oil selected (Koh & Surh, 2015 ; Li et al., 2019 ; Naz et al., 2005 ). Although vegetable oils such as olive, grapeseed, and corn oil are common choices, their distinct fatty acid profiles, differing in saturated, monounsaturated, and polyunsaturated ratios, can have varying effects on the nutritional quality and safety of the final fried fish product. The study's oil selection, olive, grapeseed, and corn oil, was guided by their distinct fatty acid compositions, which contrast with those of common alternatives like sunflower or canola oil. The high monounsaturated fatty acid (MUFA) content of olive oil and the high polyunsaturated fatty acid (PUFA) levels in corn and grapeseed oils were expected to be key factors causing divergent outcomes in the final fried product. Lipid oxidation in fried seafood is highly dependent on the frying oil's composition. According to Chiou et al. (2017), oils with high PUFA content (e.g., corn oil) lead to a faster accumulation of primary and secondary oxidation products compared to stable oils like palm olein. This oxidative degradation significantly compromises the nutritional quality of fish fillets. Research by Choo et al. (2018) shows that prolonged frying at 180°C can deplete endogenous omega-3 fatty acids (EPA and DHA) by 25% to 40%. The choice of frying medium critically influences this process; using vegetable oils high in polyunsaturated fatty acids, such as sunflower oil, results in the formation of 2–3 times more peroxide and malondialdehyde (MDA) than when using more stable oils like palm olein (Abrante-Pascual et al., 2024). This study investigates how deep-frying grass carp fillets in vegetable oils with distinct fatty acid profiles affects their final nutritional composition. We hypothesize that oils rich in monounsaturated fats (e.g., olive oil) will better preserve heat-sensitive nutrients compared to those high in polyunsaturated fats (e.g., corn oil). To the best of our knowledge, limited research has been conducted to date on the influence of oil type during the deep-frying process. Consequently, this research aims to evaluate the impact of different frying oils on the fatty acid profile, vitamin, and mineral content of orange-spotted grouper. Material and methods Samples preparation Orange-spotted grouper ( Epinephelus coioides ) were purchased fresh from local market in Khorramshahr, Khuzestan province, Iran, and transported to the laboratory. Upon arrival, the fish were immediately stored in polystyrene boxes containing ice (with a fish-to-ice ratio of 1:2 w/w) and transported within less than 2 hours to the fisheries laboratory at Khorramshahr University of Marine Science and Technology. The average weight and length of the common grouper were 1656 grams and 36.5 cm, respectively. Upon arrival at the laboratory, the fish were washed with cold water, beheaded, and eviscerated. Two fillets were prepared from each fish without removing the bones. A 100-gram sample from the upper part of the fish fillet's lateral line section was used for cooking. Deep-frying procedure The samples were cooked according to the AOAC 976.16 method (the procedure for cooking seafood) (Larsen et al., 2010 ), and a portion of the samples was analyzed as the raw (control) sample. For deep-frying, the fish fillet samples were placed in a wire mesh basket and then immersed in a pan containing oil at 180°C for 5 minutes until fried. After frying, the basket was shaken, and the samples were placed on absorbent paper. For this frying method, olive oil, corn oil, and grapeseed oil were used. Upon completion of the frying process, the samples were cooled to room temperature. The cooled samples were stored in freezer bags at -80°C until analysis. Prior to analysis, the skin and bones were separated from the cooked fillets. All samples for each cooking method were homogenized using a kitchen blender. Proximate composition Proximate composition analyses of both cooked and uncooked fish samples were performed in triplicate according to AOAC (2002) standards. Moisture content was measured by drying samples in an oven at 105°C until a constant weight was achieved. Crude protein content was determined using the Kjeldahl method, applying a nitrogen-to-protein conversion factor of 6.25. Total lipid content was obtained via Soxhlet extraction, and ash content was quantified gravimetrically after combustion in a muffle furnace at 525°C for 24 hours. Analytical procedures Determination of fatty acid profile Lipid extraction was performed according to the method of Folch et al. (1957) (22) with slight modifications, using a chloroform:methanol solvent mixture (2:1, v/v) containing 0.01% butylated hydroxytoluene (BHT) as an antioxidant. Fatty acid methyl esters (FAMEs) were analyzed using a Phillips GC-PU4400 gas chromatography system (Phillips Scientific, Cambridge, UK) equipped with a BPX70 capillary column (60 m × 0.32 mm ID, 0.25-µm film thickness; SGM, Victoria, Australia) and a flame ionization detector (FID). Helium was used as the carrier gas, and an injection volume of 0.2 µL was applied with a 1:20 split ratio. The oven temperature program was as follows: Initial temperature: 160°C; Ramp 1: 160°C to 180°C at 20°C/min; Ramp 2: 180°C to 210°C at 1.5°C/min; Ramp 3: 210°C to 230°C at 20°C/min; Hold times were 5 min at 160°C, 10 min at 180°C, 3 min at 210°C, and 5 min at the final temperature (230°C). The injector and detector temperatures were maintained at 240°C and 280°C, respectively. Fatty acid identification and quantification were performed by comparing peak retention times with known FAME standards and using ClarityTM DataApex software (Prague, Czech Republic). Determination of TBA and FFA The Thiobarbituric Acid (TBA) value was determined according to the method of Siripatrawan and Noipha (2012). Briefly, 10 g of homogenized fish sample was mixed with 97.5 mL of distilled water and 2.5 mL of 4 N hydrochloric acid. The mixture was then distilled, and 5 mL of the resulting distillate was reacted with 5 mL of thiobarbituric acid reagent. The solution was heated at 100°C for 35 minutes, cooled, and the absorbance of the resulting pink pigment was measured at 538 nm using a spectrophotometer. The TBA value was calculated by multiplying the measured absorbance by the factor 7.8 and expressed as mg of malondialdehyde equivalent per kg of sample. The free fatty acid (FFA) content was determined by extracting lipids from 10 g of meat sample using chloroform/methanol according to Woyewoda et al. (1986). The extracted lipids were titrated with sodium hydroxide to quantify free carboxylic acid groups. A mixture of chloroform, methanol, and 2-propanol (2:1:2 ratio) along with metacresol purple indicator was added to the lipid extract. Titration was continued until the endpoint color change from yellow to blue was observed. The FFA value was expressed as percentage oleic acid. Determination of vitamins B1 and B3 vitamins The water-soluble vitamins (B1 and B3) were quantified following the methodology described by Erosy and Özeren (2009) using high-performance liquid chromatography (HPLC). The HPLC analysis was performed under the following conditions: detection wavelength of 245 nm, flow rate of 1 mL/min, injection volume of 20 µL, mobile phase composition of 1000 mL phosphate buffer and 360 mL methanol, operating pressure of 150–160 bar, and a total run time of 22 minutes. Vitamins A and D Fat-soluble vitamins (A and D) were analyzed using high-performance liquid chromatography (HPLC) according to the method described by Stancheva and Dobreva (2013). The analysis employed an HPLC system equipped with a Eurosphere™ RP C18 analytical column (250×4.6 mm, 5 µm; Knauer, Germany). Vitamin A and D were detected using UV detection at wavelengths of 325 nm and 265 nm, respectively. Determination of mineral Mineral content analysis was conducted according to AOAC (1999) method 923.03 using a GBC Savant AA flame atomic absorption spectrometer (Italy). For mineral determination, 15 g of dried samples were powdered and asked in a muffle furnace using a graded temperature program: starting at 100°C for 1 hour, then increasing 50°C hourly until reaching 450°C, followed by 8 hours of holding at this temperature. After cooling, 1–3 mL distilled water was added to the asked samples and evaporated on a hot plate. The samples were then returned to the furnace at < 200°C for 1–2 hours, followed by gradual temperature increase (50–100°C/hour) to 450°C with final holding for 2 hours. For ash dissolution, 5 mL of 6M HCl was added to porcelain crucibles and evaporated on a hot plate. Digestion was completed with 10–30 mL of 0.1M nitric acid, followed by covering and room temperature incubation for 1–2 hours. The solutions were filtered into plastic containers, and 1 mL aliquots were diluted to 100 mL in volumetric flasks with distilled water for AAS analysis of Na, K, Ca, Mg, Mn, Cu, Zn, and Fe concentrations. Phosphorus content was determined spectrophotometrically using Barton's reagent after color development (Uran and Gokoglu, 2014 ). Statistical analyses Data were analyzed using SPSS 18.0 for Windows. Significant differences between treatment means were determined by one-way analysis of variance (ANOVA), followed by Duncan's multiple range test for post-hoc comparisons. A probability level of P < 0.05 was considered statistically significant for all analyses. Results and discussion Changes in proximate composition The proximate composition of raw and deep fried orange-spotted grouper fillets, cooked using different vegetable oils, is shown in Table 1 . The fat and moisture content of raw grouper were 4.58% and 76.45%, respectively. A significant reduction (p < 0.05) in moisture content was observed in all fried fillets compared to their raw counterparts. This decrease is likely due to water evaporation at high frying temperatures coupled with the thermal denaturation of myofibrillar proteins (García-Arias et al., 2003; Gladyshev et al., 2006). Conversely, a significant increase in fat content was observed in the fried fillets, resulting from oil uptake as moisture was driven off during the high-temperature frying process (Erosy and Özeren, 2009; Koubaa et al., 2012; Rosa et al., 2007). No significant difference was found in the moisture and fat contents of fillets fried with different oils (p > 0.05). The protein content of raw grouper fillets (14.05%) increased significantly after frying in various oils. This apparent increase is primarily due to the concentration of protein mass resulting from moisture loss during the frying process (García-Arias et al., 2003; Ersoy and Özeren). The ash content of the raw samples (2.08%) increased significantly following deep-frying in various vegetable oils. This increase is likely due to the moisture loss during the frying process (Ersoy and Özeren). Table 1 Proximate composition of raw and deep-fried orange-spotted grouper with different vegetable oils. Treatment Moisture Fat Protein Ash Raw 76.45 ± 0.14 a 4.58 ± 0.22 b 14.05 ± 0.97 b 2.08 ± 0.03 b Fried (Olive Oil) 56.99 ± 0.94 b 6.27 ± 0.58 a 22.41 ± 0.61 a 3.24 ± 0.09 a Fried (Grape Seed Oil) 54.57 ± 0.56 b 7.26 ± 0.55 a 23.07 ± 0.36 a 3.46 ± 0.28 a Fried (Corn Oil) 54.59 ± 1.65 b 7.25 ± 0.32 a 22.24 ± 0.53 a 3.66 ± 0.28 a Results are mean ± standard error of triplicates. Means within the same column having different superscripts are significantly different (P < 0.05). Fatty acid composition The results of changes in the fatty acid composition of grouper fish prepared by deep-frying with different vegetable oils are presented in Table 2 . Deep-frying significantly altered the fatty acid profile of the fish, affecting the levels of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids (p < 0.05). These changes may be attributed to oil absorption, thermal degradation, and oxidative reactions during the frying process. In the raw fillet (containing 4% total fat), the fatty acids were distributed in the following order of abundance: SFA (45.16%) > MUFA (23.97%) > PUFA (12.97%). These results are consistent with those reported by Koubaa et al. (2012) for red mullet. In contrast, the largest proportion of total fatty acids in Brazilian sardines is composed of polyunsaturated fatty acids (Saldanha et al., 2008). Palmitic acid (C16:0), palmitoleic acid (C16:1), oleic acid (C18:1), and EPA (C20:5) were the most abundant fatty acids identified in the raw fillet. Frying grouper fillets in various vegetable oils led to a significant decrease in the level of SFAs compared to the raw fish fillets (p < 0.05). The reduction in SFA content observed in the fried fillets can be attributed to several mechanisms: (1) Lipid exchange with the frying medium (Dobarganes and Márquez-Ruiz, 2015); (2) Thermal degradation of short-chain SFAs (e.g., C12:0, C14:0) at the high frying temperatures (160°C–190°C), which generates volatile compounds (Choe and Min, 2007); (3) Maillard reaction-mediated binding of SFAs to proteins, thereby reducing the extractable lipid fractions (Zamora and Hidalgo, 2015); (4) Steam distillation effects, where volatilized water facilitates the removal of SFAs (Nawar, 1996). The amounts of MUFAs and PUFAs in the fried fillets increased a significant difference compared to the raw sample (p < 0.05). Moisture loss during deep-frying leads to an increase in the total MUFAs and PUFA content of the fried samples (Sioen et al., 2006). However, the amount of PUFAs decreased in the fish fillets fried with olive oil. Weber et al. (2008) demonstrated that fillets fried in canola oil had a higher MUFA content and lower PUFA and SFA contents than the raw fillets. The fried fish exhibited a higher level of oleic acid (C18:1) compared to the raw fillets. This increase can be attributed to the absorption of olive oil during the deep-frying process. Since olive oil, a popular frying oil in Iran, is rich in oleic acid (Portarena et al., 2015; Sioen et al., 2006; Jalarma Reddy et al., 2015), its fatty acid profile directly influenced the final composition of the fillets. In the raw grouper fillets, the content of n-6 PUFA was similar to that of n-3 PUFA. Although the n-6 PUFA content of fried grouper fillets was significantly higher than the n-3 PUFA content, this was attributed to oil absorption during the frying process. α- linolenic acid (C18:3 n-3, ALA), eicosapentaenoic acid (C20:5n-3, EPA) and docosahexanoicacid (C22:6 n-3, DHA) are the most abundant n-3 fatty acids. The frying process using various oils resulted in a statistically significant decrease (p < 0.05) in the n-3 fatty acid profile of the fish compared to the raw fillets. It was observed that EPA was identified as the predominant n-3 fatty acid. The primary mechanism for the reduction of total n-3 content during fish frying is the absorption of cooking oil (Hosseini et al., 2014). This is supported by Weber et al. (2008), who observed a decrease in n-3 fatty acids when frying was conducted in hydrogenated vegetable oil. Consistent findings have been reported in other species, including kutum (Hosseini et al., 2014), red mullet (Koubaa et al., 2012), and seabass (Türkkan et al., 2008). Furthermore, the extent of this reduction is influenced by the fatty acid profile of the fillet and its inherent susceptibility to oxidation. The highest levels of n-6 fatty acids in grouper fillets are linoleic acid (C18:2) and arachidonic acid (C20:4). The frying process resulted in a significant increase in linoleic acid content, particularly in fillets fried with corn oil. This can be attributed to the high linoleic acid composition of corn oil, which typically contains 58–62% of this fatty acid, leading to its absorption during frying (). Table 2 Fatty acid composition of raw and fried orange-spotted grouper fillets. Fatty Acid Crude Olive Oil Grapeseed Oil Corn Oil C14 (Myristic) 0.46 ± 42.6a 0.32 ± 2.87c 4.62 ± 0.08b 1.55 ± 0.11d C16 (Palmitic) 0.32 ± 30.06a 1.01 ± 20.17c 23.15 ± 0.05b 16.34 ± 0.05d C18 (Stearic) 0.31 ± 8.09a 0.35 ± 6.59b 5.79 ± 0.09c 3.35 ± 0.09d C24 (Lignoceric) 0.01 ± 0.59b 0.03 ± 0.88a 0.63 ± 0.05b 0.23 ± 0.06c ∑ SFA 0.47 ± 45.16a 0.40 ± 30.52c 34.21 ± 0.08b 21.49 ± 0.03d C16:1 (Palmitoleic) 0.48 ± 15.39a 0.36 ± 6.43c 11.96 ± 0.14b 4.46 ± 0.98d C18:1 (Oleic) 0.40 ± 8.57d 1.01 ± 45.10a 14.54 ± 0.12c 24.26 ± 1.24b ∑ MUFA 0.88 ± 23.97c 1.15 ± 51.53a 27.51 ± 0.16b 28.73 ± 1.25b C18:2 n6 (Linoleic) 0.27 ± 2.79d 0.57 ± 4.38c 22.55 ± 0.44b 40.76 ± 0.31a C20:4 n6 (Arachidonic) 0.30 ± 3.27ab 0.27 ± 2.69b 1.74 ± 0.05c 0.82 ± 0.05d ∑ n6 0.48 ± 6.06c 0.30 ± 7.08c 24.30 ± 0.50b 41.59 ± 0.34a C18:3 n3 (α-Linolenic) 0.07 ± 0.84a 0.01 ± 0.09c 0.08 ± 0.37b 0.01 ± 0.18c C20:5 n3 (EPA) 0.63 ± 4.23a 0.16 ± 1.49c 0.83 ± 2.81b 0.17 ± 1.34c C22:6 n3 (DHA) 0.31 ± 1.82ab 0.06 ± 1.28ab 0.16 ± 1.73a 0.04 ± 0.64c ∑ n3 0.96 ± 6.91a 0.21 ± 2.88c 0.10 ± 4.92b 0.21 ± 2.17c ∑ PUFA 1.15 ± 12.97c 0.46 ± 9.96d 0.60 ± 29.22b 0.50 ± 43.77a Note: Different superscript letters (a, b, c, d) in each row indicate significant differences (p < 0.05). Nutritional quality indices (NQI) The nutritional quality indices of raw and fried grouper fish samples fried with different vegetable oils are presented in Table 3 . The nutritional quality indices include PUFA/SFA, UFA/SFA, n-3/n-6, ARA/EPA, EPA + DHA, HH (Hypocholesterolemic/Hypercholesterolemic ratio), AI (Atherogenic Index), and TI (Thrombogenic Index). PUFA/SFA and UFA/SFA The key indicators for evaluating seafood's nutritional quality are the ratios of polyunsaturated to saturated fatty acids (PUFA/SFA) and unsaturated to saturated fatty acids (UFA/SFA) (Larsen et al., 2010 ). For a healthy diet, the World Health Organization recommends a minimum PUFA/SFA ratio of 0.4 (WHO, 2003). This study found that while raw grouper fillets, with a PUFA/SFA ratio of 0.28, did not meet this recommendation, the frying process markedly improved this value. Specifically, frying in corn oil raised the ratio to 2.03, aligning it with WHO guidelines. A similar trend was observed for the UFA/SFA ratio, which increased significantly (p < 0.05) from 0.81 in raw fillets to a maximum of 3.37 in corn-oil-fried samples, demonstrating a substantial enhancement in the overall fatty acid profile. According to Fehily et al. (1994), a diet high in PUFA/SFA and UFA/SFA ratios significantly reduces the risk of atherogenesis and thrombogenesis. The markedly increased ratios observed in the corn-oil-fried fillets in this study therefore suggest a potential enhancement of their cardioprotective properties. Our results confirm that the choice of frying oil profoundly impacts the final fatty acid profile, a phenomenon consistently documented in the literature. For instance, Weber et al. (2008) also reported significant differences (p < 0.05) in the PUFA/SFA and UFA/SFA ratios of deep-fried silver catfish when different oils, soybean, canola, and hydrogenated vegetable oil, were used. This aligns with our observations where corn oil, rich in linoleic acid, led to the most substantial improvement in these nutritional indices compared to grape seed oil. n-3/n-6 and ARA/EPA The n-3/n-6 ratio is a critical biomedical index, as it influences inflammatory responses and cardiovascular health (Simopoulos, 2002 ). In the present study, the n-3/n-6 ratio in raw grouper was 1.14, indicating a favorable balance. However, frying with various vegetable oils led to a significant decrease (p < 0.05) in this ratio, shifting the fatty acid profile towards a higher proportion of n-6 fatty acids. In human nutrition, an n-3/n-6 fatty acid ratio between 1:1 and 1:1.5 is considered indicative of a healthy dietary pattern (Osman et al., 2001). In this study, the n-3/n-6 ratio in the raw fillets fell within this recommended range. However, frying with olive oil, corn oil, or grapeseed oil significantly reduced the n-3/n-6 ratio to levels below the recommended limit for a healthy human diet. This phenomenon can be attributed to two primary mechanisms: (1) Thermal degradation of omega-3 fatty acids during the frying process, and (2) The dilution effect caused by the transfer of omega-6 fatty acids - particularly linoleic acid (LA, 18:2n-6) - from the frying medium into the fish fillets (Sioen et al., 2006). The ARA/EPA ratio in raw samples was 0.82. The raw samples showed no significant difference in the ARA/EPA ratio with fried fillets, except for those fried with olive oil (P > 0.05). The ARA/EPA ratio is considered a superior nutritional quality index compared to the n-3/n-6 ratio due to its greater sensitivity to lipid oxidation (Hosseini et al., 2014). This is supported by Larsen et al. (2011), who demonstrated that higher ARA/EPA ratios correlate with reduced nutritional quality in fish oil. In the present study, deep-frying fillets with olive oil significantly increased the ARA/EPA ratio (p < 0.05). However, fillets fried with grape seed oil and corn oil showed no significant difference in this ratio compared to the raw fillet. Among the oils tested, olive oil had the most detrimental effect on this specific, sensitive nutritional index. EPA + DHA The combined level of EPA + DHA represents one of the most crucial nutritional quality indices for seafood (Hosseini et al., 2014). To reduce the risk of coronary heart disease mortality, the American Heart Association recommends a daily intake of 500–1000 mg of EPA + DHA. This intake can be achieved by consuming at least two weekly servings of fatty fish (Hosseini et al., 2014; Larsen et al., 2011; Neff et al., 2014). The EPA + DHA index in the raw sample was 6.06%. The EPA + DHA content in fish fillets fried with corn oil (1.98%) and olive oil (2.78%) showed a significant difference compared to the raw sample (p < 0.05). The fillet fried with grape seed oil had the highest EPA + DHA content. The present study demonstrates that frying, particularly with corn and olive oil, led to a significant reduction in these beneficial fatty acids, decreasing the EPA + DHA content from 6.06% in raw fillets to as low as 1.98%. HH The hypocholesterolemic/hypercholesterolemic (HH) ratio serves as a key indicator for assessing the collective impact of dietary fatty acids on cholesterol metabolism, quantifying the balance between cholesterol-lowering and cholesterol-elevating fatty acids (Santos-Silva et al., 2002). The HH value in raw grouper was measured at 0.59. A significant increase (p < 0.05) in the HH index was observed in the fried samples compared to the raw fillet. Hosseini et al. (2014) and Testi et al. (2006) reported that HH values for different fish species range from 0.25 to 3.23. In the present study, deep-fried samples prepared with corn oil exhibited a statistically higher HH ratio compared to fillets fried with other vegetable oils. AI and TI The Atherogenic Index (AI) and Thrombogenic Index (TI), initially proposed by Ulbricht and Southgate (1991), are pivotal metrics for assessing the health impact of dietary lipids. The nutritional value of beneficial lipids such as EPA and DHA is inversely related to these indices; lower AI and TI values signify a higher-quality, more cardioprotective lipid profile (Ulbricht and Southgate, 1991; Hosseini et al., 2014). Consequently, diets characterized by low AI and TI values are associated with a reduced risk of coronary heart disease (Hosseini et al., 2014; Rosa et al., 2007). The AI and TI in raw grouper samples were 1.51 and 1.22, respectively. A significant decrease (p < 0.05) was observed in both AI and TI values in the fried samples. This reduction indicates a more favorable health profile as these indices are associated with cardiovascular risk. The most favorable (lowest) Atherogenic Index (AI) and Thrombogenic Index (TI) were associated with the fish fillets fried in corn oil. Determination of TBA and FFA The thiobarbituric acid (TBA) test is a standard method for assessing secondary lipid oxidation. This assay quantifies malondialdehyde (MDA), a key dialdehyde produced from the degradation of hydroperoxides derived from PUFAs (Man & Tan, 1999 ; Raeisi et al., 2021 ). Changes in the TBA value of fried grouper fish resulting from different vegetable oils are shown in Fig. 1 . The TBA value of the raw sample was 1.46 mg MDA/ kg of meat. The TBA values of the fried fillets showed no significant difference compared to the raw sample (P > 0.05). The mechanisms this phenomenon is: (1) Volatility of Oxidation Products: The intense heat of frying causes the newly formed MDA to evaporate and escape from the fillet's surface into the surrounding air. Therefore, the measured TBA value in the fish tissue remains low because the compounds are driven off almost as quickly as they are formed (Zhao et al., 2023; Liu et al., 2022 ); (2) Protective effect of olive oil: The antioxidants activity (phenolic compounds) of vegetable oils can slow down the chain reaction of lipid oxidation, potentially reducing the net formation rate of MDA within the fillet itself, counteracting the pro-oxidant effect of high heat (Abeyrathne et al., 2021 ; Liu et al., 2023 ); (3) Formation of Complex Compounds: Under high heat, MDA can react with other food components (like proteins or amino acids) to form complex compounds that are no longer reactive in the TBA test. This means the oxidation occurred, but the end-products are not measured by the specific TBARS assay, leading to an underestimation of the actual oxidation level ( De Leon & Borges, 2020 ; Jeronimo & Alves, 2022; M. Zhao et al., 2025 ). The Free Fatty Acid (FFA) content, which serves as a quantitative indicator of the acid value in oily foods, directly reflects the extent of triglyceride hydrolysis (Zula & Teferra, 2022 ). Changes in the amount of free fatty acids in deep-fried grouper fish with different vegetable oils are shown in Fig. 1 . The initial free fatty acid index in the raw sample was 6.12% oleic acid. The free fatty acid content of the raw sample decreased significantly after frying (P < 0.05). The free fatty acid levels in fried fillet with olive, grape seed, and corn oils were 1.42, 2.86, and 2.19% oleic acid, respectively. It may be attributed to (1) Rapid Enzyme Inactivation: The high heat of the oil quickly denatures the lipolytic enzymes in the fish, shutting down enzymatic lipolysis (Weber et al., 2008); (2) Barrier Effect and Limited Hydrolysis: The process of frying can quickly seal the surface of the fillet, creating a crust. This barrier might limit the release of internal lipids and their subsequent breakdown into free fatty acids, effectively "trapping" them inside; (3) Lipid Exchange: During frying, there is an exchange of fats. The fish may lose some of its more unstable lipids into the cooking oil while absorbing the more stable monounsaturated fats from the vegetable oils; (4) The release of volatile free fatty acids during cooking may lead to a reduction in free fatty acid content (Weber et al., 2008). The lowest free fatty acid content belonged to the frying filet with olive oil, which showed a significant difference with other samples (P < 0.05). Olive oil, especially extra virgin olive oil, contains phenolic compounds with antioxidant properties. The antioxidants may help suppress the oxidative reactions that can lead to free fatty acid formation. Vitamin contents Table 4 shows the content of vitamin A (retinol), vitamin D (calciferol), vitamin B1 (thiamine), and vitamin B3 (niacin) in raw and fried orange-spotted grouper fish, expressed in mg/ 100 g of dry weight. According to the obtained results, the vitamin A content of the raw sample was 0.41 ± 0.09 mg/100 g. The vitamin A content in the orange-spotted grouper fillets increased after cooking by deep-frying methods, except for the samples fried with grape seed oil (P < 0.05). The highest vitamin A content was observed in the samples fried with corn oil, with a value of 8.36 ± 0.79 mg/ 100 g. The vitamin D content of the raw sample was 0.36 ± 0.05 mg/100 g. The vitamin D content showed significant difference between raw and fried fillets (P < 0.05). The fillets fried with olive oil demonstrated the highest vitamin D content among all fried samples. While fat-soluble vitamins generally demonstrate lower sensitivity to heat (Badiani et al., 2013), this thermal stability may not apply uniformly to all such vitamins. Contrary to this general trend, studies on specific fish species have reported a reduction in vitamin D content due to frying. For instance, Hosseini et al. (2014) observed a decrease in vitamin D in kutum roach fillets, and similarly, Rezaei et al. (2014) documented a declining trend in vitamin D levels in tigertooth croaker after thermal processing. Table 4 Vitamin content (mg/kg) of raw and deep-fried orange-spotted grouper fillets. Treatment A D B1 B3 Raw 0.09 ± 0.41c 0.05 ± 0.36a 0.04 ± 0.30a 0.17 ± 1.56a Frying with Olive Oil 0.19 ± 2.18b 0.03 ± 0.17b 0.02 ± 0.12b 0.26 ± 0.82b Frying with Grapeseed Oil 0.29 ± 0.84bc 0.00 ± 0.01c 0.03 ± 0.11b 0.17 ± 0.87b Frying with Corn Oil 0.79 ± 8.36a 0.00 ± 0.06c 0.03 ± 0.08b 0.06 ± 0.76b Note: Different superscript letters (a, b, c) within each column indicate significant differences (p < 0.05). Table 5 Mineral content (mg/ kg) of raw and deep-fried grass carp fillets. Mineral Raw Frying with Olive Oil Frying with Grapeseed Oil Frying with Corn Oil Na 93.41 ± 800.00a 93.41 ± 774.80a 68.93 ± 798.80a 21.65 ± 491.90b K 3.87 ± 956.88b 27.25 ± 1179.14a 8.08 ± 590.68c 13.56 ± 926.23b Mg 12.55 ± 172.56a 12.07 ± 172.38a 7.39 ± 163.26ab 6.42 ± 130.78b P 91.91 ± 2336.91ab 45.65 ± 2422.65a 32.63 ± 2398.51ab 45.65 ± 2422.65a Mn 0.11 ± 0.62a 0.09 ± 0.58a 1.58 ± 0.698a 0.15 ± 0.99a Cu 0.16 ± 0.46a 0.01 ± 0.06b 0.01 ± 0.08b 0.00 ± 0.14b Zn 0.60 ± 13.37ab 0.31 ± 12.62b 0.31 ± 13.73ab 0.15 ± 14.13ab Ca 0.57 ± 272.40a 177.01 ± 536.20a 8.02 ± 259.300a 31.86 ± 353.200a Fe 0.28 ± 1.52a 0.06 ± 1.00a 0.50 ± 1.97a 0.21 ± 1.33a Results are mean ± standard error of triplicates. Means within the same row having different superscripts are significantly different (P < 0.05). The vitamin B1 content of the raw sample was 0.30 ± 0.04 mg/100 g. Vitamin B1 decreased significantly in all cooking methods (P < 0.05). The vitamin B3 content of the raw sample was 1.56 ± 0.17 mg/100 g. Vitamin B3 showed a significant reduction in the fried samples (P < 0.05). Consistent with the findings of Hosseini et al. (2014) and Erosy and Özeren (2009), a significant reduction in vitamin B1 content was observed. This loss is attributable to the pronounced heat sensitivity of vitamin B1, which is the least stable among the B vitamins and prone to thermal degradation during cooking processes. Furthermore, Erosy and Özeren (2009) also established a direct correlation between alterations in the moisture content of the fillets and the levels of water-soluble vitamins. Mineral contents The mean concentrations of minerals (sodium (Na), potassium (K), magnesium (Mg), phosphorus (P), manganese (Mn), copper (Cu), zinc (Zn), calcium (Ca), and iron (Fa)) in raw and fried orange-spotted grouper fillets are presented in Table 4 , expressed in mg/kg of dry weight. The Na content of the raw sample was 800 ± 93.41 mg/kg. No significant difference (P > 0.05) in Na content was observed between raw and fried samples, except for those fried in corn oil. The K content in the raw sample was measured at 956.88 ± 3.87 mg/kg. A significant difference (P 0.05) was observed in the Mg content of orange-spotted grouper samples after deep-frying with olive oil (172.38 ± 12.07 mg/kg) or grape seed oil (163.26 ± 7.39 mg/kg). The mean P content of the raw samples was 2336.91 ± 91.91 mg/kg. No significant difference was observed in P levels between the raw and fried samples. The mean Mn content in the raw treatment was measured at 0.62 ± 0.11 mg/kg. Throughout the cooking methods, no significant difference (P > 0.05) in Mn content was observed, except in the samples fried with grape seed oil. The mean Cu content in the raw treatment was measured at 0.46 ± 0.16 mg/kg. Cu content decreased significantly during cooking methods (P 0.05) was observed in Zn content across the various cooking methods compared to the raw sample. The mean Ca content in the raw treatment was measured at 272.40 ± 0.57 mg/kg. Ca content in samples fried with olive oil showed a significant difference compared to the raw sample (P 0.05). The Fe content of the raw treatment was measured at 1.52 ± 0.28 mg/kg. None of the cooking methods showed a significant effect on the Fe content of the orange-spotted grouper (P > 0.05). Conclusions This study demonstrates that deep-frying significantly alters the physicochemical and nutritional composition of orange-spotted grouper fillets, with the choice of cooking oil being a critical determinant of the final product's quality. The use of different vegetable oils led to distinct fatty acid profiles in the fried fillets. Corn oil, rich in PUFAs, produced fillets with the most favorable nutritional lipid indices, including the highest PUFA/SFA and HH ratios, and the lowest AI and TI. Conversely, this benefit was counterbalanced by a significant reduction in beneficial long-chain omega-3 fatty acids (EPA+DHA). Olive oil, high in MUFAs, was effectively absorbed by the fish but led to an unfavorable increase in the ARA/EPA ratio. Grape seed oil showed an intermediate performance, best preserving the EPA+DHA content among the fried samples. Oxidation indicators revealed complex interactions: the TBA value was not significantly affected, potentially due to the volatility of MDA or antioxidant effects from the oils. The FFA content decreased post-frying, with olive oil yielding the lowest value, suggesting superior control over hydrolytic rancidity. Regarding vitamins, deep-frying induced significant degradation of heat-sensitive water-soluble vitamins (B1 and B3). In contrast, the content of the fat-soluble vitamin A increased, likely due to absorption from the oil and concentration effects, with corn oil-fried samples showing the highest levels. Vitamin D exhibited variable stability, with olive oil-fried fillets retaining the highest content. The mineral composition was largely stable during frying, with most elements (P, Mn, Zn, Fe) showing no significant changes. Notable exceptions were copper, which decreased significantly, and specific minerals like sodium and potassium, which were affected only by corn oil frying. In summary, no single oil was superior in all aspects. Corn oil enhanced beneficial fatty acid ratios but degraded omega-3s. Olive oil better preserved vitamin D and minimized FFA formation but negatively impacted the ARA/EPA ratio. Grape seed oil was most effective at preserving omega-3 content. Therefore, the selection of a frying oil represents a trade-off, and olive oil may be considered a generally favorable choice for its balanced performance in preserving key vitamins and mitigating lipid hydrolysis. Zahra Momenzadeh: Investigation, methodology, Writing the primary manuscript, Ainaz Khodanazary: Conceptualization, Supervision, Review & Editing, Project administration, Resources, Kamal Ghanemi: Methodology. Declarations Funding The paper has not been published or submitted for publication elsewhere. The paper has not been published or submitted for publication elsewhere. All authors are in agreement with the content of the manuscript. Funding: This study was funded by Gonbad Kavous University of Iran. Availability of data The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Abeyrathne EDNS, Nam K, Ahn DU. 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(2022). Effect of apple pomace- based antioxidants on the stability of mustard oil during deep frying of French fries. LWT, 163, 113576. Naz, S., Siddiqi, R., Sheikh, H., & Sayeed, S. A. (2005). Deterioration of olive, corn and soybean oils due to air, light, heat and deep-frying. Food Research International, 38 (2), 127–134. Pokorný, J., 1989. Flavor chemistry of deep fat frying in oil. In: Min, D.B., Smouse, T.H., Zhang, S.S. (Eds.), Flavor Chemistry of Lipid Foods. American Oil Chemists Society, Champaign, pp. 113–154. Rahimi, J., Adewale, P., Ngadi, M., & Agyare, K. (2017). Food and Bioproducts Processing Changes in the textural and thermal properties of batter coated fried potato strips during post frying holding. Food and Bioproducts Processing, 102, 136–143. Simopoulos, A. P. (2002). The importance of the ratio of omega-6/omega-3 essential fatty acids. Biomedicine & Pharmacotherapy, 56 (8), 365-379. Uran H, Gokoglu N (2014) Effects of cooking methods and temperatures on nutritional and quality characteristics of anchovy ( Engraulis encrasicholus ). J Food Sci Technol51: 722- 728 Zula, T. A., & Teferra, F. K. (2022). Effect of frying oil stability over repeated reuse cycles on the quality and safety of deep-fried nile tilapia fish (Oreochromis niloticus): A response surface modeling approach. International Journal of Food Properties, 25(1) Raeisi, S., Ojagh, M. S., Pourashouri, P., Salaün, F., & Quek, Y. S. (2021). Shelf-life and quality of chicken nuggets fortified with encapsulated fish oil and garlic essential oil during refrigerated storage. Journal of Food Science & Technology, 58(1). Zhao, M., Liu, Z., Zhang, W., Xia, G., Li, C., Rakariyatham, K., & Zhou, D. (2025). Advance in aldehydes derived from lipid oxidation: A review of the formation mechanism, attributable food thermal processing technology, analytical method and toxicological effect. Food Research International, 203, 115811. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":50367,"visible":true,"origin":"","legend":"\u003cp\u003eTBA and FFA of raw and fried orange-spotted grouper fillets.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8251357/v1/44de12911054d446a07fc36e.png"},{"id":104779237,"identity":"9b12e3ef-a8fd-4c85-9826-96b9b6d15ea0","added_by":"auto","created_at":"2026-03-17 07:37:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8251357/v1/be382371-3beb-4eff-8a0e-d3a369de23d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep-Frying of Orange-Spotted Grouper Fillets in Olive, Corn, and Grape Seed Oils: Effects on nutritional quality indices, Lipid Oxidation, Minerals, and Vitamins composition","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeep frying is a globally renowned cooking technique with ancient origins, employed to enhance both the sensory qualities and the shelf life of food (Manzoor et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Deep Frying is a ubiquitous cooking technique that involves submerging food in hot oil at high temperatures, typically between 150\u0026deg;C and 200\u0026deg;C, creating a dynamic interaction between the oil, air, and the food (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This process is driven by simultaneous heat and mass transfer, which transforms the food into a final product with a distinctive sensory profile. The resulting desirable qualities, such as a characteristic fried flavor, a golden-brown color, and a crisp texture, are highly favored by consumers (Rahimi et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The primary drawback of this method is the high level of oil uptake by the food during cooking (Mahmud et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Azahrani et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed a substantial rise in the fat content of fish due to deep-fat frying, with whiting fillets increasing from 1% to 10.5% fat. This cooking method simultaneously exposes both the food and frying oil to multiple physicochemical processes. These reactions, including protein denaturation, lipid oxidation, and maillard reactions, generate numerous harmful compounds such as free fatty acids, small molecular alcohol, aldehyde, ketone, acid, lactone and hydrocarbon (Pokorn\u0026yacute;, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), and may affect the final nutritional quality of the product. The selection of frying oil is crucial, as its composition directly impacts heat transfer efficiency, resistance to oxidation, and the extent of lipid absorption by the food. There is a notable variation in the composition of frying oils, particularly in their fatty acid profiles, which include saturated, monounsaturated, and polyunsaturated fats, as well as their content of fat-soluble micronutrients such as carotenoids, tocols, and sterols. Research indicates that the quality of frying oil is significantly influenced by several factors, including the number of times the oil is used, the composition of the food being fried, and the type of oil selected (Koh \u0026amp; Surh, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Although vegetable oils such as olive, grapeseed, and corn oil are common choices, their distinct fatty acid profiles, differing in saturated, monounsaturated, and polyunsaturated ratios, can have varying effects on the nutritional quality and safety of the final fried fish product. The study's oil selection, olive, grapeseed, and corn oil, was guided by their distinct fatty acid compositions, which contrast with those of common alternatives like sunflower or canola oil. The high monounsaturated fatty acid (MUFA) content of olive oil and the high polyunsaturated fatty acid (PUFA) levels in corn and grapeseed oils were expected to be key factors causing divergent outcomes in the final fried product. Lipid oxidation in fried seafood is highly dependent on the frying oil's composition. According to Chiou et al. (2017), oils with high PUFA content (e.g., corn oil) lead to a faster accumulation of primary and secondary oxidation products compared to stable oils like palm olein. This oxidative degradation significantly compromises the nutritional quality of fish fillets. Research by Choo et al. (2018) shows that prolonged frying at 180\u0026deg;C can deplete endogenous omega-3 fatty acids (EPA and DHA) by 25% to 40%. The choice of frying medium critically influences this process; using vegetable oils high in polyunsaturated fatty acids, such as sunflower oil, results in the formation of 2\u0026ndash;3 times more peroxide and malondialdehyde (MDA) than when using more stable oils like palm olein (Abrante-Pascual et al., 2024). This study investigates how deep-frying grass carp fillets in vegetable oils with distinct fatty acid profiles affects their final nutritional composition. We hypothesize that oils rich in monounsaturated fats (e.g., olive oil) will better preserve heat-sensitive nutrients compared to those high in polyunsaturated fats (e.g., corn oil). To the best of our knowledge, limited research has been conducted to date on the influence of oil type during the deep-frying process. Consequently, this research aims to evaluate the impact of different frying oils on the fatty acid profile, vitamin, and mineral content of orange-spotted grouper.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eSamples preparation\u003c/p\u003e \u003cp\u003eOrange-spotted grouper (\u003cem\u003eEpinephelus coioides\u003c/em\u003e) were purchased fresh from local market in Khorramshahr, Khuzestan province, Iran, and transported to the laboratory. Upon arrival, the fish were immediately stored in polystyrene boxes containing ice (with a fish-to-ice ratio of 1:2 w/w) and transported within less than 2 hours to the fisheries laboratory at Khorramshahr University of Marine Science and Technology. The average weight and length of the common grouper were 1656 grams and 36.5 cm, respectively. Upon arrival at the laboratory, the fish were washed with cold water, beheaded, and eviscerated. Two fillets were prepared from each fish without removing the bones. A 100-gram sample from the upper part of the fish fillet's lateral line section was used for cooking.\u003c/p\u003e \u003cp\u003eDeep-frying procedure\u003c/p\u003e \u003cp\u003eThe samples were cooked according to the AOAC 976.16 method (the procedure for cooking seafood) (Larsen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and a portion of the samples was analyzed as the raw (control) sample. For deep-frying, the fish fillet samples were placed in a wire mesh basket and then immersed in a pan containing oil at 180\u0026deg;C for 5 minutes until fried. After frying, the basket was shaken, and the samples were placed on absorbent paper. For this frying method, olive oil, corn oil, and grapeseed oil were used. Upon completion of the frying process, the samples were cooled to room temperature. The cooled samples were stored in freezer bags at -80\u0026deg;C until analysis. Prior to analysis, the skin and bones were separated from the cooked fillets. All samples for each cooking method were homogenized using a kitchen blender.\u003c/p\u003e \u003cp\u003eProximate composition\u003c/p\u003e \u003cp\u003e Proximate composition analyses of both cooked and uncooked fish samples were performed in triplicate according to AOAC (2002) standards. Moisture content was measured by drying samples in an oven at 105\u0026deg;C until a constant weight was achieved. Crude protein content was determined using the Kjeldahl method, applying a nitrogen-to-protein conversion factor of 6.25. Total lipid content was obtained via Soxhlet extraction, and ash content was quantified gravimetrically after combustion in a muffle furnace at 525\u0026deg;C for 24 hours.\u003c/p\u003e \u003cp\u003eAnalytical procedures\u003c/p\u003e \u003cp\u003eDetermination of fatty acid profile\u003c/p\u003e \u003cp\u003eLipid extraction was performed according to the method of Folch et al. (1957) (22) with slight modifications, using a chloroform:methanol solvent mixture (2:1, v/v) containing 0.01% butylated hydroxytoluene (BHT) as an antioxidant. Fatty acid methyl esters (FAMEs) were analyzed using a Phillips GC-PU4400 gas chromatography system (Phillips Scientific, Cambridge, UK) equipped with a BPX70 capillary column (60 m \u0026times; 0.32 mm ID, 0.25-\u0026micro;m film thickness; SGM, Victoria, Australia) and a flame ionization detector (FID). Helium was used as the carrier gas, and an injection volume of 0.2 \u0026micro;L was applied with a 1:20 split ratio. The oven temperature program was as follows: Initial temperature: 160\u0026deg;C; Ramp 1: 160\u0026deg;C to 180\u0026deg;C at 20\u0026deg;C/min; Ramp 2: 180\u0026deg;C to 210\u0026deg;C at 1.5\u0026deg;C/min; Ramp 3: 210\u0026deg;C to 230\u0026deg;C at 20\u0026deg;C/min; Hold times were 5 min at 160\u0026deg;C, 10 min at 180\u0026deg;C, 3 min at 210\u0026deg;C, and 5 min at the final temperature (230\u0026deg;C). The injector and detector temperatures were maintained at 240\u0026deg;C and 280\u0026deg;C, respectively. Fatty acid identification and quantification were performed by comparing peak retention times with known FAME standards and using ClarityTM DataApex software (Prague, Czech Republic).\u003c/p\u003e \u003cp\u003eDetermination of TBA and FFA\u003c/p\u003e \u003cp\u003eThe Thiobarbituric Acid (TBA) value was determined according to the method of Siripatrawan and Noipha (2012). Briefly, 10 g of homogenized fish sample was mixed with 97.5 mL of distilled water and 2.5 mL of 4 N hydrochloric acid. The mixture was then distilled, and 5 mL of the resulting distillate was reacted with 5 mL of thiobarbituric acid reagent. The solution was heated at 100\u0026deg;C for 35 minutes, cooled, and the absorbance of the resulting pink pigment was measured at 538 nm using a spectrophotometer. The TBA value was calculated by multiplying the measured absorbance by the factor 7.8 and expressed as mg of malondialdehyde equivalent per kg of sample.\u003c/p\u003e \u003cp\u003e The free fatty acid (FFA) content was determined by extracting lipids from 10 g of meat sample using chloroform/methanol according to Woyewoda et al. (1986). The extracted lipids were titrated with sodium hydroxide to quantify free carboxylic acid groups. A mixture of chloroform, methanol, and 2-propanol (2:1:2 ratio) along with metacresol purple indicator was added to the lipid extract. Titration was continued until the endpoint color change from yellow to blue was observed. The FFA value was expressed as percentage oleic acid.\u003c/p\u003e \u003cp\u003eDetermination of vitamins\u003c/p\u003e \u003cp\u003eB1 and B3 vitamins\u003c/p\u003e \u003cp\u003eThe water-soluble vitamins (B1 and B3) were quantified following the methodology described by Erosy and \u0026Ouml;zeren (2009) using high-performance liquid chromatography (HPLC). The HPLC analysis was performed under the following conditions: detection wavelength of 245 nm, flow rate of 1 mL/min, injection volume of 20 \u0026micro;L, mobile phase composition of 1000 mL phosphate buffer and 360 mL methanol, operating pressure of 150\u0026ndash;160 bar, and a total run time of 22 minutes.\u003c/p\u003e \u003cp\u003eVitamins A and D\u003c/p\u003e \u003cp\u003eFat-soluble vitamins (A and D) were analyzed using high-performance liquid chromatography (HPLC) according to the method described by Stancheva and Dobreva (2013). The analysis employed an HPLC system equipped with a Eurosphere\u0026trade; RP C18 analytical column (250\u0026times;4.6 mm, 5 \u0026micro;m; Knauer, Germany). Vitamin A and D were detected using UV detection at wavelengths of 325 nm and 265 nm, respectively.\u003c/p\u003e \u003cp\u003eDetermination of mineral\u003c/p\u003e \u003cp\u003eMineral content analysis was conducted according to AOAC (1999) method 923.03 using a GBC Savant AA flame atomic absorption spectrometer (Italy). For mineral determination, 15 g of dried samples were powdered and asked in a muffle furnace using a graded temperature program: starting at 100\u0026deg;C for 1 hour, then increasing 50\u0026deg;C hourly until reaching 450\u0026deg;C, followed by 8 hours of holding at this temperature. After cooling, 1\u0026ndash;3 mL distilled water was added to the asked samples and evaporated on a hot plate. The samples were then returned to the furnace at \u0026lt;\u0026thinsp;200\u0026deg;C for 1\u0026ndash;2 hours, followed by gradual temperature increase (50\u0026ndash;100\u0026deg;C/hour) to 450\u0026deg;C with final holding for 2 hours. For ash dissolution, 5 mL of 6M HCl was added to porcelain crucibles and evaporated on a hot plate. Digestion was completed with 10\u0026ndash;30 mL of 0.1M nitric acid, followed by covering and room temperature incubation for 1\u0026ndash;2 hours. The solutions were filtered into plastic containers, and 1 mL aliquots were diluted to 100 mL in volumetric flasks with distilled water for AAS analysis of Na, K, Ca, Mg, Mn, Cu, Zn, and Fe concentrations. Phosphorus content was determined spectrophotometrically using Barton's reagent after color development (Uran and Gokoglu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eData were analyzed using SPSS 18.0 for Windows. Significant differences between treatment means were determined by one-way analysis of variance (ANOVA), followed by Duncan's multiple range test for post-hoc comparisons. A probability level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eChanges in proximate composition\u003c/p\u003e\n\u003cp\u003eThe proximate composition of raw and deep fried orange-spotted grouper fillets, cooked using different vegetable oils, is shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The fat and moisture content of raw grouper were 4.58% and 76.45%, respectively. A significant reduction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in moisture content was observed in all fried fillets compared to their raw counterparts. This decrease is likely due to water evaporation at high frying temperatures coupled with the thermal denaturation of myofibrillar proteins (Garc\u0026iacute;a-Arias et al., 2003; Gladyshev et al., 2006). Conversely, a significant increase in fat content was observed in the fried fillets, resulting from oil uptake as moisture was driven off during the high-temperature frying process (Erosy and \u0026Ouml;zeren, 2009; Koubaa et al., 2012; Rosa et al., 2007). No significant difference was found in the moisture and fat contents of fillets fried with different oils (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The protein content of raw grouper fillets (14.05%) increased significantly after frying in various oils. This apparent increase is primarily due to the concentration of protein mass resulting from moisture loss during the frying process (Garc\u0026iacute;a-Arias et al., 2003; Ersoy and \u0026Ouml;zeren). The ash content of the raw samples (2.08%) increased significantly following deep-frying in various vegetable oils. This increase is likely due to the moisture loss during the frying process (Ersoy and \u0026Ouml;zeren).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProximate composition of raw and deep-fried orange-spotted grouper with different vegetable oils.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMoisture\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAsh\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRaw\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFried (Olive Oil)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFried (Grape Seed Oil)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFried (Corn Oil)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eResults are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of triplicates.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eMeans within the same column having different superscripts are significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFatty acid composition\u003c/p\u003e\n\u003cp\u003eThe results of changes in the fatty acid composition of grouper fish prepared by deep-frying with different vegetable oils are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Deep-frying significantly altered the fatty acid profile of the fish, affecting the levels of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These changes may be attributed to oil absorption, thermal degradation, and oxidative reactions during the frying process. In the raw fillet (containing 4% total fat), the fatty acids were distributed in the following order of abundance: SFA (45.16%)\u0026thinsp;\u0026gt;\u0026thinsp;MUFA (23.97%)\u0026thinsp;\u0026gt;\u0026thinsp;PUFA (12.97%). These results are consistent with those reported by Koubaa et al. (2012) for red mullet. In contrast, the largest proportion of total fatty acids in Brazilian sardines is composed of polyunsaturated fatty acids (Saldanha et al., 2008). Palmitic acid (C16:0), palmitoleic acid (C16:1), oleic acid (C18:1), and EPA (C20:5) were the most abundant fatty acids identified in the raw fillet. Frying grouper fillets in various vegetable oils led to a significant decrease in the level of SFAs compared to the raw fish fillets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The reduction in SFA content observed in the fried fillets can be attributed to several mechanisms: (1) Lipid exchange with the frying medium (Dobarganes and M\u0026aacute;rquez-Ruiz, 2015); (2) Thermal degradation of short-chain SFAs (e.g., C12:0, C14:0) at the high frying temperatures (160\u0026deg;C\u0026ndash;190\u0026deg;C), which generates volatile compounds (Choe and Min, 2007); (3) Maillard reaction-mediated binding of SFAs to proteins, thereby reducing the extractable lipid fractions (Zamora and Hidalgo, 2015); (4) Steam distillation effects, where volatilized water facilitates the removal of SFAs (Nawar, 1996). The amounts of MUFAs and PUFAs in the fried fillets increased a significant difference compared to the raw sample (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moisture loss during deep-frying leads to an increase in the total MUFAs and PUFA content of the fried samples (Sioen et al., 2006). However, the amount of PUFAs decreased in the fish fillets fried with olive oil. Weber et al. (2008) demonstrated that fillets fried in canola oil had a higher MUFA content and lower PUFA and SFA contents than the raw fillets. The fried fish exhibited a higher level of oleic acid (C18:1) compared to the raw fillets. This increase can be attributed to the absorption of olive oil during the deep-frying process. Since olive oil, a popular frying oil in Iran, is rich in oleic acid (Portarena et al., 2015; Sioen et al., 2006; Jalarma Reddy et al., 2015), its fatty acid profile directly influenced the final composition of the fillets. In the raw grouper fillets, the content of n-6 PUFA was similar to that of n-3 PUFA. Although the n-6 PUFA content of fried grouper fillets was significantly higher than the n-3 PUFA content, this was attributed to oil absorption during the frying process. \u0026alpha;- linolenic acid (C18:3 n-3, ALA), eicosapentaenoic acid (C20:5n-3, EPA) and docosahexanoicacid (C22:6 n-3, DHA) are the most abundant n-3 fatty acids. The frying process using various oils resulted in a statistically significant decrease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the n-3 fatty acid profile of the fish compared to the raw fillets. It was observed that EPA was identified as the predominant n-3 fatty acid. The primary mechanism for the reduction of total n-3 content during fish frying is the absorption of cooking oil (Hosseini et al., 2014). This is supported by Weber et al. (2008), who observed a decrease in n-3 fatty acids when frying was conducted in hydrogenated vegetable oil. Consistent findings have been reported in other species, including kutum (Hosseini et al., 2014), red mullet (Koubaa et al., 2012), and seabass (T\u0026uuml;rkkan et al., 2008). Furthermore, the extent of this reduction is influenced by the fatty acid profile of the fillet and its inherent susceptibility to oxidation. The highest levels of n-6 fatty acids in grouper fillets are linoleic acid (C18:2) and arachidonic acid (C20:4). The frying process resulted in a significant increase in linoleic acid content, particularly in fillets fried with corn oil. This can be attributed to the high linoleic acid composition of corn oil, which typically contains 58\u0026ndash;62% of this fatty acid, leading to its absorption during frying ().\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFatty acid composition of raw and fried orange-spotted grouper fillets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFatty Acid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOlive Oil\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrapeseed Oil\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorn Oil\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC14 (Myristic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;42.6a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16 (Palmitic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;30.06a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;20.17c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18 (Stearic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC24 (Lignoceric)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sum; SFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;45.16a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;30.52c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e34.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC16:1 (Palmitoleic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;15.39a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:1 (Oleic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.57d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;45.10a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sum; MUFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;23.97c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;51.53a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e27.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e28.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:2 n6 (Linoleic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:4 n6 (Arachidonic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sum; n6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e41.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC18:3 n3 (\u0026alpha;-Linolenic)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC20:5 n3 (EPA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC22:6 n3 (DHA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sum; n3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;6.91a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.92b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sum; PUFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.97c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;29.22b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;43.77a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Different superscript letters (a, b, c, d) in each row indicate significant differences (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eNutritional quality indices (NQI)\u003c/p\u003e\n\u003cp\u003eThe nutritional quality indices of raw and fried grouper fish samples fried with different vegetable oils are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The nutritional quality indices include PUFA/SFA, UFA/SFA, n-3/n-6, ARA/EPA, EPA\u0026thinsp;+\u0026thinsp;DHA, HH (Hypocholesterolemic/Hypercholesterolemic ratio), AI (Atherogenic Index), and TI (Thrombogenic Index).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003cp\u003ePUFA/SFA and UFA/SFA\u003c/p\u003e\n\u003cp\u003eThe key indicators for evaluating seafood\u0026apos;s nutritional quality are the ratios of polyunsaturated to saturated fatty acids (PUFA/SFA) and unsaturated to saturated fatty acids (UFA/SFA) (Larsen et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). For a healthy diet, the World Health Organization recommends a minimum PUFA/SFA ratio of 0.4 (WHO, 2003). This study found that while raw grouper fillets, with a PUFA/SFA ratio of 0.28, did not meet this recommendation, the frying process markedly improved this value. Specifically, frying in corn oil raised the ratio to 2.03, aligning it with WHO guidelines. A similar trend was observed for the UFA/SFA ratio, which increased significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from 0.81 in raw fillets to a maximum of 3.37 in corn-oil-fried samples, demonstrating a substantial enhancement in the overall fatty acid profile. According to Fehily et al. (1994), a diet high in PUFA/SFA and UFA/SFA ratios significantly reduces the risk of atherogenesis and thrombogenesis. The markedly increased ratios observed in the corn-oil-fried fillets in this study therefore suggest a potential enhancement of their cardioprotective properties. Our results confirm that the choice of frying oil profoundly impacts the final fatty acid profile, a phenomenon consistently documented in the literature. For instance, Weber et al. (2008) also reported significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the PUFA/SFA and UFA/SFA ratios of deep-fried silver catfish when different oils, soybean, canola, and hydrogenated vegetable oil, were used. This aligns with our observations where corn oil, rich in linoleic acid, led to the most substantial improvement in these nutritional indices compared to grape seed oil.\u003c/p\u003e\n\u003cp\u003en-3/n-6 and ARA/EPA\u003c/p\u003e\n\u003cp\u003eThe n-3/n-6 ratio is a critical biomedical index, as it influences inflammatory responses and cardiovascular health (Simopoulos, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). In the present study, the n-3/n-6 ratio in raw grouper was 1.14, indicating a favorable balance. However, frying with various vegetable oils led to a significant decrease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in this ratio, shifting the fatty acid profile towards a higher proportion of n-6 fatty acids. In human nutrition, an n-3/n-6 fatty acid ratio between 1:1 and 1:1.5 is considered indicative of a healthy dietary pattern (Osman et al., 2001). In this study, the n-3/n-6 ratio in the raw fillets fell within this recommended range. However, frying with olive oil, corn oil, or grapeseed oil significantly reduced the n-3/n-6 ratio to levels below the recommended limit for a healthy human diet. This phenomenon can be attributed to two primary mechanisms: (1) Thermal degradation of omega-3 fatty acids during the frying process, and (2) The dilution effect caused by the transfer of omega-6 fatty acids - particularly linoleic acid (LA, 18:2n-6) - from the frying medium into the fish fillets (Sioen et al., 2006).\u003c/p\u003e\n\u003cp\u003eThe ARA/EPA ratio in raw samples was 0.82. The raw samples showed no significant difference in the ARA/EPA ratio with fried fillets, except for those fried with olive oil (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The ARA/EPA ratio is considered a superior nutritional quality index compared to the n-3/n-6 ratio due to its greater sensitivity to lipid oxidation (Hosseini et al., 2014). This is supported by Larsen et al. (2011), who demonstrated that higher ARA/EPA ratios correlate with reduced nutritional quality in fish oil. In the present study, deep-frying fillets with olive oil significantly increased the ARA/EPA ratio (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, fillets fried with grape seed oil and corn oil showed no significant difference in this ratio compared to the raw fillet. Among the oils tested, olive oil had the most detrimental effect on this specific, sensitive nutritional index.\u003c/p\u003e\n\u003ch3\u003eEPA\u0026thinsp;+\u0026thinsp;DHA\u003c/h3\u003e\n\u003cp\u003eThe combined level of EPA\u0026thinsp;+\u0026thinsp;DHA represents one of the most crucial nutritional quality indices for seafood (Hosseini et al., 2014). To reduce the risk of coronary heart disease mortality, the American Heart Association recommends a daily intake of 500\u0026ndash;1000 mg of EPA\u0026thinsp;+\u0026thinsp;DHA. This intake can be achieved by consuming at least two weekly servings of fatty fish (Hosseini et al., 2014; Larsen et al., 2011; Neff et al., 2014). The EPA\u0026thinsp;+\u0026thinsp;DHA index in the raw sample was 6.06%. The EPA\u0026thinsp;+\u0026thinsp;DHA content in fish fillets fried with corn oil (1.98%) and olive oil (2.78%) showed a significant difference compared to the raw sample (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The fillet fried with grape seed oil had the highest EPA\u0026thinsp;+\u0026thinsp;DHA content. The present study demonstrates that frying, particularly with corn and olive oil, led to a significant reduction in these beneficial fatty acids, decreasing the EPA\u0026thinsp;+\u0026thinsp;DHA content from 6.06% in raw fillets to as low as 1.98%.\u003c/p\u003e\n\u003ch3\u003eHH\u003c/h3\u003e\n\u003cp\u003eThe hypocholesterolemic/hypercholesterolemic (HH) ratio serves as a key indicator for assessing the collective impact of dietary fatty acids on cholesterol metabolism, quantifying the balance between cholesterol-lowering and cholesterol-elevating fatty acids (Santos-Silva et al., 2002). The HH value in raw grouper was measured at 0.59. A significant increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the HH index was observed in the fried samples compared to the raw fillet. Hosseini et al. (2014) and Testi et al. (2006) reported that HH values for different fish species range from 0.25 to 3.23. In the present study, deep-fried samples prepared with corn oil exhibited a statistically higher HH ratio compared to fillets fried with other vegetable oils.\u003c/p\u003e\n\u003cp\u003eAI and TI\u003c/p\u003e\n\u003cp\u003eThe Atherogenic Index (AI) and Thrombogenic Index (TI), initially proposed by Ulbricht and Southgate (1991), are pivotal metrics for assessing the health impact of dietary lipids. The nutritional value of beneficial lipids such as EPA and DHA is inversely related to these indices; lower AI and TI values signify a higher-quality, more cardioprotective lipid profile (Ulbricht and Southgate, 1991; Hosseini et al., 2014). Consequently, diets characterized by low AI and TI values are associated with a reduced risk of coronary heart disease (Hosseini et al., 2014; Rosa et al., 2007). The AI and TI in raw grouper samples were 1.51 and 1.22, respectively. A significant decrease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed in both AI and TI values in the fried samples. This reduction indicates a more favorable health profile as these indices are associated with cardiovascular risk. The most favorable (lowest) Atherogenic Index (AI) and Thrombogenic Index (TI) were associated with the fish fillets fried in corn oil.\u003c/p\u003e\n\u003cp\u003eDetermination of TBA and FFA\u003c/p\u003e\n\u003cp\u003eThe thiobarbituric acid (TBA) test is a standard method for assessing secondary lipid oxidation. This assay quantifies malondialdehyde (MDA), a key dialdehyde produced from the degradation of hydroperoxides derived from PUFAs (Man \u0026amp; Tan, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Raeisi et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Changes in the TBA value of fried grouper fish resulting from different vegetable oils are shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The TBA value of the raw sample was 1.46 mg MDA/ kg of meat. The TBA values of the fried fillets showed no significant difference compared to the raw sample (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mechanisms this phenomenon is: (1) Volatility of Oxidation Products: The intense heat of frying causes the newly formed MDA to evaporate and escape from the fillet\u0026apos;s surface into the surrounding air. Therefore, the measured TBA value in the fish tissue remains low because the compounds are driven off almost as quickly as they are formed (Zhao et al., 2023; Liu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); (2) Protective effect of olive oil: The antioxidants activity (phenolic compounds) of vegetable oils can slow down the chain reaction of lipid oxidation, potentially reducing the net formation rate of MDA within the fillet itself, counteracting the pro-oxidant effect of high heat (Abeyrathne et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); (3) Formation of Complex Compounds: Under high heat, MDA can react with other food components (like proteins or amino acids) to form complex compounds that are no longer reactive in the TBA test. This means the oxidation occurred, but the end-products are not measured by the specific TBARS assay, leading to an underestimation of the actual oxidation level \u003cstrong\u003e(\u003c/strong\u003eDe Leon \u0026amp; Borges, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jeronimo \u0026amp; Alves, 2022; M. Zhao et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe Free Fatty Acid (FFA) content, which serves as a quantitative indicator of the acid value in oily foods, directly reflects the extent of triglyceride hydrolysis (Zula \u0026amp; Teferra, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Changes in the amount of free fatty acids in deep-fried grouper fish with different vegetable oils are shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The initial free fatty acid index in the raw sample was 6.12% oleic acid. The free fatty acid content of the raw sample decreased significantly after frying (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The free fatty acid levels in fried fillet with olive, grape seed, and corn oils were 1.42, 2.86, and 2.19% oleic acid, respectively. It may be attributed to (1) Rapid Enzyme Inactivation: The high heat of the oil quickly denatures the lipolytic enzymes in the fish, shutting down enzymatic lipolysis (Weber et al., 2008); (2) Barrier Effect and Limited Hydrolysis: The process of frying can quickly seal the surface of the fillet, creating a crust. This barrier might limit the release of internal lipids and their subsequent breakdown into free fatty acids, effectively \u0026quot;trapping\u0026quot; them inside; (3) Lipid Exchange: During frying, there is an exchange of fats. The fish may lose some of its more unstable lipids into the cooking oil while absorbing the more stable monounsaturated fats from the vegetable oils; (4) The release of volatile free fatty acids during cooking may lead to a reduction in free fatty acid content (Weber et al., 2008). The lowest free fatty acid content belonged to the frying filet with olive oil, which showed a significant difference with other samples (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Olive oil, especially extra virgin olive oil, contains phenolic compounds with antioxidant properties. The antioxidants may help suppress the oxidative reactions that can lead to free fatty acid formation.\u003c/p\u003e\n\u003cp\u003eVitamin contents\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the content of vitamin A (retinol), vitamin D (calciferol), vitamin B1 (thiamine), and vitamin B3 (niacin) in raw and fried orange-spotted grouper fish, expressed in mg/ 100 g of dry weight. According to the obtained results, the vitamin A content of the raw sample was 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 mg/100 g. The vitamin A content in the orange-spotted grouper fillets increased after cooking by deep-frying methods, except for the samples fried with grape seed oil (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The highest vitamin A content was observed in the samples fried with corn oil, with a value of 8.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 mg/ 100 g. The vitamin D content of the raw sample was 0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/100 g. The vitamin D content showed significant difference between raw and fried fillets (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The fillets fried with olive oil demonstrated the highest vitamin D content among all fried samples. While fat-soluble vitamins generally demonstrate lower sensitivity to heat (Badiani et al., 2013), this thermal stability may not apply uniformly to all such vitamins. Contrary to this general trend, studies on specific fish species have reported a reduction in vitamin D content due to frying. For instance, Hosseini et al. (2014) observed a decrease in vitamin D in kutum roach fillets, and similarly, Rezaei et al. (2014) documented a declining trend in vitamin D levels in tigertooth croaker after thermal processing.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVitamin content (mg/kg) of raw and deep-fried orange-spotted grouper fillets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRaw\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrying with Olive Oil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrying with Grapeseed Oil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrying with Corn Oil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e Different superscript letters (a, b, c) within each column indicate significant differences (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMineral content (mg/ kg) of raw and deep-fried grass carp fillets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMineral\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRaw\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrying with Olive Oil\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrying with Grapeseed Oil\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrying with Corn Oil\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.41\u0026thinsp;\u0026plusmn;\u0026thinsp;800.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.41\u0026thinsp;\u0026plusmn;\u0026thinsp;774.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.93\u0026thinsp;\u0026plusmn;\u0026thinsp;798.80a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.65\u0026thinsp;\u0026plusmn;\u0026thinsp;491.90b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;956.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1179.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;590.68c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.56\u0026thinsp;\u0026plusmn;\u0026thinsp;926.23b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.55\u0026thinsp;\u0026plusmn;\u0026thinsp;172.56a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.07\u0026thinsp;\u0026plusmn;\u0026thinsp;172.38a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.39\u0026thinsp;\u0026plusmn;\u0026thinsp;163.26ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;130.78b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2336.91ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2422.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2398.51ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2422.65a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.698a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;13.37ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.62b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;13.73ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13ab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;272.40a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177.01\u0026thinsp;\u0026plusmn;\u0026thinsp;536.20a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.02\u0026thinsp;\u0026plusmn;\u0026thinsp;259.300a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.86\u0026thinsp;\u0026plusmn;\u0026thinsp;353.200a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eResults are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of triplicates.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eMeans within the same row having different superscripts are significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe vitamin B1 content of the raw sample was 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg/100 g. Vitamin B1 decreased significantly in all cooking methods (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The vitamin B3 content of the raw sample was 1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 mg/100 g. Vitamin B3 showed a significant reduction in the fried samples (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Consistent with the findings of Hosseini et al. (2014) and Erosy and \u0026Ouml;zeren (2009), a significant reduction in vitamin B1 content was observed. This loss is attributable to the pronounced heat sensitivity of vitamin B1, which is the least stable among the B vitamins and prone to thermal degradation during cooking processes. Furthermore, Erosy and \u0026Ouml;zeren (2009) also established a direct correlation between alterations in the moisture content of the fillets and the levels of water-soluble vitamins.\u003c/p\u003e\n\u003cp\u003eMineral contents\u003c/p\u003e\n\u003cp\u003eThe mean concentrations of minerals (sodium (Na), potassium (K), magnesium (Mg), phosphorus (P), manganese (Mn), copper (Cu), zinc (Zn), calcium (Ca), and iron (Fa)) in raw and fried orange-spotted grouper fillets are presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, expressed in mg/kg of dry weight. The Na content of the raw sample was 800\u0026thinsp;\u0026plusmn;\u0026thinsp;93.41 mg/kg. No significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in Na content was observed between raw and fried samples, except for those fried in corn oil. The K content in the raw sample was measured at 956.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87 mg/kg. A significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in K content was observed in all processing methods, except for the samples fried in corn oil. The Mg content in the raw sample was 172.56\u0026thinsp;\u0026plusmn;\u0026thinsp;12.55 mg/kg. No significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was observed in the Mg content of orange-spotted grouper samples after deep-frying with olive oil (172.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07 mg/kg) or grape seed oil (163.26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.39 mg/kg). The mean P content of the raw samples was 2336.91\u0026thinsp;\u0026plusmn;\u0026thinsp;91.91 mg/kg. No significant difference was observed in P levels between the raw and fried samples. The mean Mn content in the raw treatment was measured at 0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 mg/kg. Throughout the cooking methods, no significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in Mn content was observed, except in the samples fried with grape seed oil. The mean Cu content in the raw treatment was measured at 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 mg/kg. Cu content decreased significantly during cooking methods (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the lowest Cu level observed in samples deep-fried with olive oil (0.06 mg/kg). The Zn content in the raw treatment was measured at 13.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 mg/kg. No significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was observed in Zn content across the various cooking methods compared to the raw sample. The mean Ca content in the raw treatment was measured at 272.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 mg/kg. Ca content in samples fried with olive oil showed a significant difference compared to the raw sample (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while other treatments showed no significant difference from the raw sample (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The Fe content of the raw treatment was measured at 1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 mg/kg. None of the cooking methods showed a significant effect on the Fe content of the orange-spotted grouper (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that deep-frying significantly alters the physicochemical and nutritional composition of orange-spotted grouper fillets, with the choice of cooking oil being a critical determinant of the final product's quality. The use of different vegetable oils led to distinct fatty acid profiles in the fried fillets. Corn oil, rich in PUFAs, produced fillets with the most favorable nutritional lipid indices, including the highest PUFA/SFA and HH ratios, and the lowest AI and TI. Conversely, this benefit was counterbalanced by a significant reduction in beneficial long-chain omega-3 fatty acids (EPA+DHA). Olive oil, high in MUFAs, was effectively absorbed by the fish but led to an unfavorable increase in the ARA/EPA ratio. Grape seed oil showed an intermediate performance, best preserving the EPA+DHA content among the fried samples. Oxidation indicators revealed complex interactions: the TBA value was not significantly affected, potentially due to the volatility of MDA or antioxidant effects from the oils. The FFA content decreased post-frying, with olive oil yielding the lowest value, suggesting superior control over hydrolytic rancidity. Regarding vitamins, deep-frying induced significant degradation of heat-sensitive water-soluble vitamins (B1 and B3). In contrast, the content of the fat-soluble vitamin A increased, likely due to absorption from the oil and concentration effects, with corn oil-fried samples showing the highest levels. Vitamin D exhibited variable stability, with olive oil-fried fillets retaining the highest content. The mineral composition was largely stable during frying, with most elements (P, Mn, Zn, Fe) showing no significant changes. Notable exceptions were copper, which decreased significantly, and specific minerals like sodium and potassium, which were affected only by corn oil frying. In summary, no single oil was superior in all aspects. \u003cstrong\u003eCorn oil\u003c/strong\u003eenhanced beneficial fatty acid ratios but degraded omega-3s. \u003cstrong\u003eOlive oil\u003c/strong\u003e better preserved vitamin D and minimized FFA formation but negatively impacted the ARA/EPA ratio. \u003cstrong\u003eGrape seed oil\u003c/strong\u003e was most effective at preserving omega-3 content. Therefore, the selection of a frying oil represents a trade-off, and olive oil may be considered a generally favorable choice for its balanced performance in preserving key vitamins and mitigating lipid hydrolysis.\u003c/p\u003e\n\u003cp\u003eZahra Momenzadeh: Investigation, methodology, Writing the primary manuscript, Ainaz Khodanazary: Conceptualization, Supervision, Review \u0026amp; Editing, Project administration, Resources, Kamal Ghanemi: Methodology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003cstrong\u003e\u0026nbsp;The paper has not been published or submitted for publication elsewhere.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe paper has not been published or submitted for publication elsewhere.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll authors are in agreement with the content of the manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by\u0026nbsp;Gonbad Kavous University\u0026nbsp;of Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbeyrathne EDNS, Nam K, Ahn DU. 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Effect of cooking method on the fatty acid profile of New Zealand King Salmon (\u003cem\u003eOncorhynchus tshawytscha\u003c/em\u003e). Food Chemistry, 119(2), 785-790.\u003c/li\u003e\n\u003cli\u003eLi, Xu, Wu, G., Yang, F., Meng, L., Huang, J., Zhang, H., J, et al. (2019). Influence of fried food and oil type on the distribution of polar compounds in discarded oil during restaurant deep frying. Food Chemistry, 272(2018), 12\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eLiu X, Wang S, Tamogami S, Chen J, Zhang H. An Evaluation Model for the Quality of Frying Oil Using Key Aldehyde Detected by HS-GC/MS. Foods. 2022 Aug 11;11(16):2413. doi: 10.3390/foods11162413. PMID: 36010412; PMCID: PMC9407462.\u003c/li\u003e\n\u003cli\u003eLiu, W., Luo, X., Huang, Y., Zhao, M., Liu, T., Wang, J., \u0026amp; Feng, F. (2023). Influence of cooking techniques on food quality, digestibility, and health risks regarding lipid oxidation. \u003cem\u003eFood Research International, 167,\u003c/em\u003e 112685. https://doi.org/10.1016/j.foodres.2023.112685\u003c/li\u003e\n\u003cli\u003eMahmud, N., Islam, J., Oyom, W., Adrah, K., Adegoke, S. C., \u0026amp; Tahergorabi, R. (2023). A review of different frying oils and oleogels as alternative frying media for fat-uptake reduction in deep-fat fried foods. \u003cem\u003eHeliyon\u003c/em\u003e, 9 (11), e21500.\u003c/li\u003e\n\u003cli\u003eMan, Y. B. C., \u0026amp; Tan, C. P. (1999). Effects of natural and synthetic antioxidants on changes in refined, bleached, and deodorized palm olein during deep-fat frying of potato chips. Journal of the American Oil Chemists\u0026rsquo; Society, 76(3), 331\u0026ndash;339.\u003c/li\u003e\n\u003cli\u003eManzoor, S., Masoodi, F. A., Rashid, R., \u0026amp; Dar, M. M. (2022). Effect of apple pomace- based antioxidants on the stability of mustard oil during deep frying of French fries. LWT, 163, 113576.\u003c/li\u003e\n\u003cli\u003eNaz, S., Siddiqi, R., Sheikh, H., \u0026amp; Sayeed, S. A. (2005). Deterioration of olive, corn and soybean oils due to air, light, heat and deep-frying. Food Research International, 38 (2), 127\u0026ndash;134.\u003c/li\u003e\n\u003cli\u003ePokorn\u0026yacute;, J., 1989. Flavor chemistry of deep fat frying in oil. In: Min, D.B., Smouse, T.H., Zhang, S.S. (Eds.), Flavor Chemistry of Lipid Foods. American Oil Chemists Society, Champaign, pp. 113\u0026ndash;154.\u003c/li\u003e\n\u003cli\u003eRahimi, J., Adewale, P., Ngadi, M., \u0026amp; Agyare, K. (2017). Food and Bioproducts Processing Changes in the textural and thermal properties of batter coated fried potato strips during post frying holding. Food and Bioproducts Processing, 102, 136\u0026ndash;143.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSimopoulos, A. P. (2002). The importance of the ratio of omega-6/omega-3 essential fatty acids. \u003c/strong\u003e\u003cem\u003eBiomedicine \u0026amp; Pharmacotherapy, 56\u003c/em\u003e\u003cstrong\u003e(8), 365-379.\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eUran H, Gokoglu N (2014) Effects of cooking methods and temperatures on nutritional and quality characteristics of anchovy (\u003cem\u003eEngraulis encrasicholus\u003c/em\u003e). J Food Sci Technol51: 722- 728 \u003c/li\u003e\n\u003cli\u003eZula, T. A., \u0026amp; Teferra, F. K. (2022). Effect of frying oil stability over repeated reuse cycles on the quality and safety of deep-fried nile tilapia fish (Oreochromis niloticus): A response surface modeling approach. International Journal of Food Properties, 25(1)\u003c/li\u003e\n\u003cli\u003eRaeisi, S., Ojagh, M. S., Pourashouri, P., Sala\u0026uuml;n, F., \u0026amp; Quek, Y. S. (2021). Shelf-life and quality of chicken nuggets fortified with encapsulated fish oil and garlic essential oil during refrigerated storage. Journal of Food Science \u0026amp; Technology, 58(1).\u003c/li\u003e\n\u003cli\u003eZhao, M., Liu, Z., Zhang, W., Xia, G., Li, C., Rakariyatham, K., \u0026amp; Zhou, D. (2025). Advance in aldehydes derived from lipid oxidation: A review of the formation mechanism, attributable food thermal processing technology, analytical method and toxicological effect. \u003cem\u003eFood Research International, 203,\u003c/em\u003e 115811.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deep-frying, Vegetable oils, Nutritional quality indices, Lipid oxidation, Sensory analysis","lastPublishedDoi":"10.21203/rs.3.rs-8251357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8251357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study was to determine the nutritional quality indices\u003cstrong\u003e, lipid oxidation, minerals, and vitamins composition \u003c/strong\u003eof deep-fried orange-spotted grouper. Different vegetable oils (olive oil, grapeseed oil, and corn oil) were used for deep-frying. The results indicated that deep-frying significantly increased fat content and decreased moisture. The fatty acid profile of the fillets was markedly influenced by oil absorption. Corn oil-fried fillets exhibited the most favorable nutritional lipid indices, including the highest polyunsaturated-to-saturated fatty acid (PUFA/SFA) ratio (2.03) and hypocholesterolemic/hypercholesterolemic (HH) ratio, alongside the lowest atherogenic (AI) and thrombogenic (TI) indices. However, this was accompanied by a substantial reduction in beneficial long-chain omega-3 fatty acids (EPA+DHA), which decreased from 6.06% in raw fillets to 1.98%. Grape seed oil best preserved the EPA+DHA content among fried samples. Olive oil frying led to a significant increase in the arachidonic-to-eicosapentaenoic acid (ARA/EPA) ratio, indicating a potential negative impact on this sensitive nutritional index. Lipid oxidation analysis revealed no significant change in TBA values post-frying, possibly due to the volatility of malondialdehyde (MDA) or antioxidant effects from the oils. FFA content decreased significantly after frying, with olive oil-fried fillets showing the lowest value (1.42% oleic acid). Regarding vitamins, frying caused significant degradation of heat-sensitive vitamins B1 and B3, while vitamin A content increased, highest in corn oil-fried samples (8.36 mg/100g). Vitamin D levels varied, with olive oil-fried fillets retaining the highest content. Most minerals remained stable during frying, except for Cu, which decreased significantly. In summary, selecting a frying oil involves balancing distinct advantages and drawbacks: corn oil improves heart-healthy fatty acid ratios yet significantly reduces omega-3 content; olive oil better controls lipid hydrolysis and maintains higher vitamin D levels, but it negatively alters the ARA/EPA balance. Grape seed oil, meanwhile, is superior in preserving omega-3 fatty acids. Consequently, the optimal oil should be chosen based on the specific nutritional priorities for the end product.\u003c/p\u003e","manuscriptTitle":"Deep-Frying of Orange-Spotted Grouper Fillets in Olive, Corn, and Grape Seed Oils: Effects on nutritional quality indices, Lipid Oxidation, Minerals, and Vitamins composition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 09:29:12","doi":"10.21203/rs.3.rs-8251357/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b9fbed5-900f-40c6-9fcf-c2757ad507dc","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59804679,"name":"Biological sciences/Biochemistry"},{"id":59804680,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-03-04T02:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 09:29:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8251357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8251357","identity":"rs-8251357","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}