Physicochemical characteristics of safflower oil to expand its production in Egypt

Physicochemical characteristics of safflower oil to expand its production in Egypt | 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 Research Article Physicochemical characteristics of safflower oil to expand its production in Egypt Walid S. Abd El-Baset, Rania I.M. Almoselhy, Susan M.M. Abd-Elmageed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5159596/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2024 Read the published version in The North African Journal of Food and Nutrition Research → Version 1 posted You are reading this latest preprint version Abstract Background: Increasing demand for sustainable and economical non-traditional edible oils as alternatives to common oils is pivotal to bridge the edible oils gap, accompanied by negative impacts of climate change on the agroecological settings for common oilseed crop productivity. Safflower is one of the fast-growing medicinal oilseed crops rich in polyunsaturated fatty acids, known as the “king of linoleic acid”, with capability for growing under high temperatures, drought, salinity, and marginal environments. Aims: The current research aimed to study in-depth the physicochemical characteristics along with the lipid nutritional indices of safflower oil to validate its potential for expansion in production in Egypt. Materials and Methods: Safflower oils extracted from seeds of two spineless varieties of Egypt were subjected to proximate composition, physicochemical, fatty acid composition, and α-tocopherol analyses. A frying stability test was carried out for safflower oil and its blends with soybean oil in different ratios, monitored by analyses of free fatty acid, peroxide value, and total polar compounds. Lipid nutritional indices were calculated to explore their health-related applications. Results: Safflower oil revealed similar proximate composition as sunflower oil with similar physicochemical characteristics. The fatty acid composition of safflower oil was greatly similar to sunflower oil, with smaller oleic acid and greater linoleic acid contents, along with recognized stability in the frying process. Lipid nutritional indices calculated from the fatty acid composition supported the medicinal uses of safflower oil as a valuable source of ω-6 fatty acids and revealed optimum indices of atherogenicity (IA), thrombogenicity (IT), and hypocholesterolemic/hypercholesterolemic (HH) with the health-promoting index (HPI) along with the powerful antioxidant effect of the high content of α-tocopherol. Conclusions: Safflower oil successfully demonstrated its potential as a promising non-traditional edible oil qualified for expansion in production in Egypt. Food Science & Technology Carthamus tinctorius L. safflower oil edible oil gap lipid nutritional indices frying stability non-traditional edible oils Figures Figure 1 1 Introduction Food insecurity in the edible oils sector has led researchers to seek out new sources of oilseed crops with immense research to adjust their physical, chemical, and functional properties (Khan et al. , 2024). In light of the increasing consumption of edible oils without self-sufficiency and increasing prices of imported edible oils, the utilization of novel cultivars, which differ from traditional ones in their complex traits and practical qualities, has gained popularity recently. From the standpoint of plant food resources, safflower plays a significant role and has the potential to challenge the well-known oil-bearing crops in the future (Mursalykova et al. , 2023). Safflower ( Carthamus tinctorius L.), a well-known member of the Asteraceae family rich in flavonoids, phenols, alkaloids, polysaccharides, fatty acids, polyacetylene, and other bioactive components, is effective for treating cardiovascular, neurodegenerative, and respiratory diseases (Cheng et al. , 2024). Safflower has been frequently prescribed to treat metabolic diseases in Persia, China, Korea, Japan, and other East Asian countries. In patients with Metabolic Syndrome (MetS), safflower oil consumption without lifestyle changes improved blood pressure, insulin resistance, and abdominal obesity. These improvements included decreased waist circumference, waist to hip ratio, and systolic and diastolic blood pressure (Ruyvaran et al. , 2022). Cold-pressed safflower seed oil (SSO) exhibited strong antioxidant and antibacterial properties against a range of opportunistic skin infections. It appeared to have a potent antifungal, growth inhibition mechanism in addition to bacteriostatic and bactericidal pathways (Khémiri et al. , 2020). Because it can stop UVB-induced matrix metalloproteinase-1 (MMP-1) expression, which causes skin photoaging, SSO and its active ingredient acacetin may be used therapeutically as an anti-wrinkle treatment to improve skin health (Jeong et al. , 2020). Rich concentrations of vital unsaturated fatty acids, including oleic and linoleic acids, greatly add to its nutritional value and health-promoting effects, as well as boosting immunity with the management and prevention of a number of illnesses. Safflower seeds are rich in proteins, lipids, and carbohydrates with vitamins, minerals, and macro- and microelements (Iskakov et al. , 2023). Safflower seed oil, besides being recognized as the “king of linoleic acid,” is rich in omega 6, or the unsaturated fatty acid, linoleic acid. Safflower seed oil is considered an excellent raw material for the preparation of the conjugated linoleic acid with significant physiological functions, bearing the ability to reduce cholesterol and monounsaturated fatty acids in the body, with powerful antioxidant effects (Wang et al. , 2024). A high diet of animal fat with a high saturated fatty acid content can lead to a variety of life-threatening disorders. Therefore, many official health organizations and governmental agencies have performed operations to promote concepts aiming at reducing the saturated fat content in foodstuffs, stimulating the food industry companies to begin working on developing foods with minor fat or a different fatty acid composition. Essentially, the best strategy to substitute saturated fat is to use structured vegetable oils. Pre-emulsification, microencapsulation, the formation of gelled emulsions, and the development of oleogels are the four basic oil structuring procedures (Botella-Martínez et al. , 2023). The future perspective for safflower seed oil is promising as it presents avenues for sustainable and economical innovation, enhanced functionality, and broader applications in the food industry by manufacturing oleogels and solid fats to replace animal fat in order to offer healthier vegetable fat compared with the risky animal fat (Almeida et al., 2022; Badem & Baştürk, 2023; Kang et al. , 2023; Potdar et al. , 2022). Grown all over the world, safflower is a multipurpose crop that offers farmers a number of advantages. The plant can withstand long dry spells by drawing deep water till reaching the depth of 4 m through its long, strong, and wide roots. Additionally, Safflower is a versatile crop that can tolerate some salinity, making it potentially advantageous for cultivation in saline soils. Safflower can be effectively cultivated with minimal maintenance on farmlands where pest animals and birds pose a problem for other crops due to its spines, which discourage them. Furthermore, planting safflower between two different crops, can disrupt the life cycles of the microorganisms responsible for various grain diseases (Shahid et al. , 2020). Due to its low production and market capitalization, safflower may hold the key to bridging the vegetable oil gap for the expanding global population's needs both now and in the future. Not only does the final product exceed quality standards, but it also increases oil production. Also, safflower oil needs to be recommended to be in accordance with the European Pharmacopoeia if it is used, or at least advertised, with health claims (with or without pharmaceutical usage), to establish the proper quality criteria (Deliorman Orhan et al. , 2022). The current research aimed to study in-depth the physicochemical characteristics and lipid nutritional indices of safflower oil extracted from two different varieties of Egypt, to establish their validity for expansion in production in Egypt to meet the increasing oil demands and bridge the large edible oils gap. 2 Materials and Methods 2.1 Materials 2.1.1. Raw materials Safflower seeds of two different spineless varieties (Giza1, Kharga1) and sunflower seeds of one variety (Giza120) were delivered from the Experimental Farm of Shandaweel Research Station, Sohag Governorate, Upper Egypt, via the Oil Crops Research Department, Field Crops Research Institute (FCRI), Agricultural Research Center (ARC), Egypt. Whereas, refined, bleached, and deodorized (RBD) soybean oil was obtained from Arma Company for Edible Oils, Egypt. 2.1.2. Chemicals and reagents All solvents and chemicals used in the study were of analytical and HPLC grade obtained from Sigma-Aldrich, USA. 2.2 Methods 2.2.1 Extraction of safflower and sunflower crude oils Spineless safflower and sunflower seeds were harvested from flower heads of plants grown in Upper Egypt. Safflower seed oil (SSO) and sunflower seed oil (SFO) were extracted by cold pressing mechanical procedure using screw press configuration (Fig. 1 ) after cleaning the seeds from impurities and dust. The extracted oils were kept in the dark in hermetically sealed containers till analysis. 2.2.2 Analytical Methods 2.2.2.1 Proximate composition analysis of safflower and sunflower seeds Proximate composition analysis of safflower and sunflower seeds, including moisture, ash, lipid, and protein, was determined according to the AOAC Official Methods: 925.10, 942.05, 923.05, and 992.23, respectively, with total carbohydrates by difference and energy in calories per 100 g of seeds. 2.2.2.2 Physicochemical characteristics of safflower, sunflower, and soybean oils The physicochemical characteristics of the oils under study were determined by the official methods of analysis as: refractive index (AOCS Cc 7–25), specific gravity (AOCS Cc 10b-25), color measurement (AOCS Cc 13e-92), UV spectroscopic characteristics at 232 and 270 nm (ISO 3656:2011/Amd 1:2017), saponification value (AOCS Tl 1a-64), unsaponifiable matter (AOCS Ca 6a-40), acidity (ISO 660:2020), peroxide value (AOAC 965.33), oxidative stability as induction period by Rancimat Method (AOCS Cd 12b-92), α-tocopherol content (Wong et al. , 1988), fatty acid composition (ISO 12966-2:2017). 2.2.2.3 Lipid nutritional indices from the fatty acid composition Lipid nutritional indices were calculated from mathematical relations of the fatty acid composition. Polyunsaturated fatty acids/saturated fatty acids (PUFA/SFA), index of atherogenicity (IA), index of thrombogenicity (IT), hypocholsterolemic/hypercholesterolemic (HH), health-promoting index (HPI), and unsaturation index (UI) were calculated according to the method mentioned by Chen & Liu (2020). Iodine value (IV calculated) was determined by the formula assigned by Kyriakidis & Katsiloulis (2000). Whereas, the peroxidability index (PI) was calculated according to the method mentioned by Yun & Surh (2012), the allylic position equivalent (APE) and the bis-allylic position equivalent (BAPE) were calculated according to the method mentioned by Stoyanova & Romova (2024). The oxidative stability index was calculated according to the method mentioned by Pinto et al. (2021), and the oxidizability value (COX) was calculated according to the method mentioned by Fatemi & Hammond (1980). All the abovementioned lipid indices with their mathematical formulas were tabulated in Table 1 . 2.2.3 Deep Frying Test The deep frying procedure was performed according to the method described by Benmeziane et al. (2024) with some modifications, using a 4L capacity local electrical fryer, where the frying oil blends were classified into 2 categories as follows: (1) Frying blends from the mixture of the 2 varieties of safflower oil (SSO) and soybean oil (SBO) in different ratios of SSO:SBO (100:0–20:80–40:60–60:40–80:20–50:50). (2) Frying blends of sunflower oil (SFO) and soybean oil (SBO) in different ratios of SSO:SBO (100:0–20:80–40:60–60:40–80:20–50:50). Then the frying cycles were performed uninterruptedly for a total of 6 hours in a day in a time interval of 1 hour for a complete frying cycle for 125 grams of fresh potato as French fries in 1 L of oil at 180 ℃ in a 1/8 ratio of potatoes/frying oil. The starting amount of frying oil is constant, and no additional fresh oil was added during the frying procedure in order to assess the quality and safety of the same used oil throughout the frying period. Alternatively, the amount of fresh potatoes was regularly reduced with development in the frying cycles to comply with the decreasing amount of the used frying oil to maintain a constant ratio of 1/8 between the amount of fresh potatoes to the amount of the remaining frying oil (w/w). The frying oil samples were collected at the end of each complete frying cycle for an hour (after: 1, 2, 3, 4, 5, 6 hours) and stored in hermetically sealed containers till analysis of free fatty acid (FFA), peroxide value (PV), and total polar compounds (TPC) in the fried oil samples. 2.2.4 Statistical analysis Measurements were performed in three replicates for the proximate composition and physicochemical analyses, whereas the determinations of fatty acid composition and analysis of FFA, PV, and TPC in the fried oils were carried out once for each oil sample. Data were shown as mean ± standard deviation (SD). Analysis of variance (ANOVA) was conducted with the SPSS software at P < 0.05. 3 Results and Discussion 3.1 Proximate composition of safflower and sunflower seeds Proximate compositions per 100 g of safflower and sunflower seeds were explored to help better understand their nutritional values. The obtained results are tabulated in Table 2 , and there were significance differences (P < 0.05) among the proximate composition parameters of the three kinds of the oilseeds, from which there were two varieties of safflower (Giza1-Kharga1) with sunflower (Giza120) seeds. Table 2 Proximate composition of safflower, sunflower, and soybean seeds per 100 g Proximate composition Parameter Safflower seeds Giza1 Safflower seeds Kharga1 Sunflower seeds Giza120 Oil content (Lipid) 28.47 ± 0.33 a 26.60 ± 0.43 c 27.58 ± 0.48 b Moisture 2.27 ± 0.02 c 2.34 ± 0.02 b 2.61 ± 0.03 a Ash 2.64 ± 0.01 c 2.93 ± 0.01 a 2.88 ± 0.01 b Protein 14.88 ± 0.05 c 15.36 ± 0.06 b 15.4 ± 0.06 a Carbohydrates 51.74 ± 0.13 b 52.77 ± 0.15 a 51.53 ± 0.18 c Energy (Calories) 522.71 ± 1.3 a 511.92 ± 1.2 c 515.94 ± 1.2 b Values are means of three replicates ± SD. Values in the same row followed by different superscripts are significantly different at P < 0.05. As shown in Table 2 ., the proximate composition parameters were recorded in the ascending order for oil content as safflower (Kharga1), sunflower (Giza120), and safflower (Giza1) seeds, respectively, moisture content as safflower (Giza1), safflower (Kharga1), and sunflower (Giza120) seeds, respectively, the ash content as safflower (Giza1), sunflower (Giza120), and safflower (Kharga1) seeds, respectively, the protein content as safflower (Giza1), safflower (Kharga1), and sunflower (Giza120) seeds, respectively, the carbohydrates content as sunflower (Giza120), safflower (Giza1), and safflower (Kharga1) seeds, respectively, and the energy in calories of 100 g seeds as safflower (Kharga1), sunflower (Giza120), and safflower (Giza1) seeds, respectively. Despite the significant differences (P < 0.05) among the proximate composition parameters of the abovementioned seeds (safflower and sunflower), they are still in close relationship because of their similar compositions, which revealed the high similarity between safflower and sunflower in their structure and proximate composition. Therefore, sunflower has been selected as the well-known common seeds to be compared with safflower as the non-traditional seeds, in order to simplify the concept of novel food (safflower seeds) with similar characteristics of sunflower seeds to be accepted by consumers as it bears already the highly similar structure with the difference involved in lower cost of safflower seeds, which is considered a good sustainable economic oilseed crop. 3.2 Physicochemical characteristics of safflower, sunflower, and soybean oils Physicochemical characteristics of safflower, sunflower, and soybean oil were explored to help better understand their nutritional values. The obtained results are tabulated in Table 3 , and there were significant differences (P < 0.05) among the physicochemical parameters of the four kinds of oils, from which there were two varieties of safflower (Giza1-Kharga1) and sunflower (Giza120) seeds with their oils extracted by cold pressing, and the soybean oil was refined, bleached, and deodorized (RBD) oil. All oils were subjected to physical analysis (refractive index, specific gravity, color, UV characteristics) and chemical analysis (saponification value, unsaponifiable matter, acidity, peroxide value, oxidative stability test as induction period, total polar compounds, α-tocopherol content with the fatty acid composition). Table 3 Physicochemical characteristics of safflower, sunflower, and soybean oils Physicochemical parameter Safflower oil Giza1 Safflower oil Kharga1 Sunflower oil Giza120 Soybean oil (RBD) Refractive index at 25 o C 1.4732 ± 0.0001 a 1.4726 ± 0.0001 b 1.4720 ± 0.0001 c 1.4700 ± 0.0001 d Specific gravity at 25 o C 0.919 ± 0.002 b 0.922 ± 0.003 a 0.917 ± 0.002 c 0.919 ± 0.002 b Color measurement - Yellow 35 ± 0 a 35 ± 0 a 35 ± 0 a 35 ± 0 a Color measurement - Red 3.8 ± 0.1 b 4 ± 0.1 a 2.9 ± 0.5 c 2.0 ± 0.1 d UV characteristics - K 232 0.49 ± 0.01 c 0.46 ± 0.01 d 1.483 ± 0.01 b 1.785 ± 0.01 a UV characteristics - K 270 0.038 ± 0.01 c 0.033 ± 0.01 d 0.231 ± 0.01 b 0.413 ± 0.01 a Saponification value 189 ± 1 d 190 ± 1 c 192 ± 2 b 196 ± 2 a Unsaponifiable matter (%) 0.92 ± 0.02 c 0.95 ± 0.04 b 1.02 ± 0.06 a 0.90 ± 0.04 d Acidity (%as oleic acid) 0.43 ± 0.002 b 0.23 ± 0.001 d 0.44 ± 0.004 a 0.35 ± 0.003 c Peroxide value(meq/kg) 3.22 ± 0.09 a 2.26 ± 0.07 d 2.65 ± 0.06 c 2.73 ± 0.006 b Induction period (hrs.) 6.77 ± 0.33 d 7.67 ± 0.44 c 9.87 ± 0.54 b 10.24 ± 0.67 a Total polar compounds (TPC) 11.4 ± 0.8 b 11.4 ± 0.7 b 12 ± 0.9 a 10.9 ± 0.7 c α-Tocopherol content (mg/kg) 170 ± 1.5 b 190 ± 1.7 a 150 ± 1.1 c 114 ± 1.0 d Values are means of three replicates ± SD. Values in the same row followed by different superscripts are significantly different at P < 0.05. Table 3 . summarizes the physicochemical characteristics of SSO, SFO, and SBO where they have been checked with the Codex standard for named vegetable oils (CODEX STAN 210–1999, 2019). A great number of research papers and routine works are devoted to discrimination of different oil types and detecting adulteration in valuable oils such as safflower seed oil (Han et al. , 2022; Zou et al. , 2024), as its promotion as a medicinal plant oil with great health benefits encouraged bad people for its adulteration with cheaper oils to gain more profit from selling adulterated oil, threatening human health and accompanied by huge economic losses. Thereby, the physicochemical characteristics should be examined thoroughly and matched with the standards assigned by the official organizations as Codex Alimentarius International Food Standards supported by the Food and Agriculture Organization of the United Nations and the World Health Organization (CODEX STAN 210–1999, 2019). Refractive index is related to the unsaturated fatty acids; a higher refractive index corresponds to more unsaturated fatty acids. Peroxide value (peroxides and hydroperoxides), acidity (measure of rancidity with formation of free fatty acids), and saponification value (measure of molecular weight of triacylglycerols and free fatty acids) are related to the quality of an edible oil. Tocopherols inhibit the oxidation of polyunsaturated fatty acids by reducing free radical reactions to improve oil stability (Hou et al. , 2024). As it will be shown in Table 4 , the order of increasing unsaturated fatty acids coincides with the order of increasing refractive index (SSO-Giza1 < SSO-Kharga1 < SFO < SBO). From Table 3 ., the specific gravity recorded for all oils is similar with negligible differences. There are significant differences among the color measurements, UV-K 232 /K 270 , the peroxide value, the oxidative stability as induction period (hrs.), the total polar compounds (TPC), and the α-tocopherol content of the studied oils. All values recorded for all tests were in the range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210–1999, 2019). Also, the recorded values were in good agreement when compared with measurements performed by different authors for soybean and sunflower oils (Almoselhy et al. , 2020; Almoselhy et al. , 2021, Ayouaz et al., 2022), and safflower oil (Ghiasy-Oskoee & AghaAlikhani, 2023; Song et al. , 2023; Stojanović et al. , 2023). 3.3 Fatty acid composition of SSO, SFO, and SBO The fatty acid compositions as very important indices to evaluate the nutritional values of the edible oils under study are tabulated in Table 4 , and there were significant differences among the fatty acid profiles of the four kinds of oils, from which there were two varieties of safflower (Giza1-Kharga1) with sunflower (Giza120) seeds with their oils extracted by cold pressing, and the soybean oil was refined, bleached, and deodorized (RBD) oil. Table 4 Fatty acid composition and lipid nutritional indices of SSO, SFO, and SBO Fatty acid% SSO Giza1 SSO Kharga1 SFO Giza120 SBO (RBD) C 12:0 ND ND ND ND C 14:0 0.096 0.10 0.061 0.07 C 16:0 6.58 6.90 6.35 10.17 C 16:1 0.08 0.09 0.09 0.10 C 17:0 0.026 0.031 0.04 0.088 C 17:1 0.013 0.016 0.03 0.06 C 18:0 2.31 2.49 4.02 4.80 C 18:1 11.43 11.56 16.48 22.24 C 18:2 Trans ND ND ND ND C 18:2 (ω-6) 78.46 77.93 71.47 54.17 C 18:3 (ω-3) 0.083 0.039 0.26 6.37 C 20:0 0.34 0.33 0.32 0.36 C 20:1 0.215 0.17 0.166 0.15 C 22:0 0.25 0.24 0.711 0.44 ΣSFA 9.602 10.091 11.502 15.928 ΣUSFA 90.281 89.805 88.496 83.09 ΣMUFA 11.738 11.836 16.766 22.55 ΣPUFA 78.543 77.969 71.73 60.54 ΣUSFA/ΣSFA 9.402 8.9 7.694 5.217 ND: non detectable – SBO: soybean oil – SFO: sunflower oil – SSO: safflower oil The main fatty acids in all oils with their ranges were palmitic (C 16:0 ) 6.35–10.17%; stearic (C 18:0 ) 2.31–4.80%; oleic (C 18:1 ) 11.43–22.24%; linoleic or ω-6 (C 18:2 ) 54.17–78.46%; linolenic or ω-3 (C 18:3 ) 0.039–6.37%. Saturated fatty acids (SFA) ranged 9.602–15.928%; unsaturated fatty acids (USFA) ranged 83.09-90.281%; monounsaturated fatty acids (MUFA) ranged 11.738–22.55%; polyunsaturated fatty acids (PUFA) ranged 60.54-78.543%; and the ratio USFA/SFA ranged 5.127–9.402. It is well-observed the high similarity in fatty acid composition between safflower and sunflower oils, with higher oleic acid (C 18:1 ) in sunflower oil and higher linoleic acid (C 18:2 ) in safflower oils. The fatty acid compositions were in the range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210–1999, 2019). Also, the recorded values were in good agreement when compared with measurements performed by different authors for soybean and sunflower oils (Almoselhy et al. , 2020; Almoselhy et al. , 2021, Ayouaz et al. , 2022), and safflower oil (Ghiasy-Oskoee & AghaAlikhani, 2023; Song et al. , 2023; Stojanović et al. , 2023). 3.4 Lipid nutritional indices of SSO, SFO, and SBO Lipid nutritional indices are calculated from the fatty acid composition of the oils under investigation for the possible assessment of health-related benefits of oils as shown in Table 5 . Table 5 Lipid nutritional indices of SSO, SFO, and SBO № Lipid nutritional indices SSO Giza1 SSO Kharga1 SFO Giza120 SBO (RBD) 1 PUFA/SFA 8.18 7.73 6.24 3.8 2 ω-6/ω-3 (C 18:2 / C 18:3 ) 945.3 1998.2 274.9 8.5 3 Index of atherogenicity (IA) 0.077 0.081 0.075 0.126 4 Index of thrombogenicity (IT) 0.198 0.211 0.232 0.261 5 Hypocholesterol./hypercholesterol. (HH) 13.477 12.789 13.759 8.084 6 Health-promoting index (HPI) 12.964 12.302 13.421 7.951 7 Unsaturation index (UI) 168.907 167.813 160.486 150 8 Iodine value (IV calculated) 129.65 128.83 130.96 115.08 9 Peroxidability index (PI) 78.92 78.34 72.67 73.84 10 Allylic Position equivalent (APE) 179.946 179.058 176.42 165.56 11 Bis-Allylic position equivalent (BAPE) 78.626 78.008 71.99 66.91 12 Oxidation Stability Index (OSI) 0.37183 0.39964 0.67045 0.89905 13 Oxidizability value (COX) 8.2136 8.1508 7.5824 7.1778 SBO: soybean oil – SFO: sunflower oil – SSO: safflower oil Considering the important ratio between polyunsaturated fatty acids and saturated fatty acids, or PUFA/SFA, it was ranged 3.8–8.18, with the highest values assigned for SSO, followed by SFO, then SBO, and it is considered an important index generally used to evaluate the effect of diet on the cardiovascular health (CVH), the higher PUFA/SFA, the healthier the effect on CVH besides the important ratio of ω-6/ω-3, or C 18:2 /C 18:3, which was ranged 8.5-1998.2 for the edible oils under investigation, with the highest values for SSO, followed by SFO, then SBO with the lowest value. The index of atherogenicity, or IA, of the oils under investigation ranged from 0.075–0.126, which is considered an excellent value for a safe fatty acid profile, as IA demonstrates the relationship between SFA (which are considered proatherogenic, increasing cholesterol in blood with deposition on walls of arteries) and USFA (as an antiatherogenic agent). The smaller the IA value, the healthier the edible oil, with the best minimum value assigned for SFO, followed by SSO, and SBO, with slight differences between SFO and SSO owing to the high similarity in fatty acid composition of the two oils. The index of thrombogenicity, or IT, of the studied oils ranged from 0.198–0.261, which is considered another excellent parameter confirming the healthy profile of fatty acids in the edible oils under examination, as IT exhibits the thrombogenic effect of fatty acids, with affinity to form accumulations or clots in blood vessels, with the best minimum value assigned for SSO, followed by SFO, and SBO. It is noteworthy to mention that both IA and IT can be used to evaluate the possible effects of fatty acid composition on CVH, where the fatty acid composition with lower values of IA and IT presents better nutritional quality, and its consumption can reduce the risk of coronary heart disease (CHD). The hypocholesterolemic/hypercholesterolemic or HH index ranged from 8.084–13.759, which is an indicator of a healthy fatty acid profile, as the higher ratio demonstrates the relationship between the hypocholesterolemic fatty acid ( cis -C 18:1 and PUFA) and the hypercholesterolemic fatty acids to evaluate the effect of the fatty acid composition on cholesterol. The evaluated HH index for all edible oils under study was higher than 1.0, suggesting the positive effect on CVDs (Stoyanova & Romova, 2024), with the highest values assigned for SFO, followed by SSO with small differences, and then SBO had the last order. Considering the health-promoting index, or HPI, it was ranged from 7.951 to 13.421, and it is simply the inverse of the IA with the same indication to ensure the safety of these consumed edible oils for health. Overall, the abovementioned indices (IA, IT, HH) are well-known calculated indices from the fatty acid composition to be used in the evaluation of the potential effects of fatty acids on cardiovascular diseases (Chen & Liu, 2020). The unsaturation index (UI) for the edible oils under investigation ranged from 150 to 168.907, with the highest value for SSO, followed by SFO, and SBO in the last order of decreasing unsaturation. This is the same order of USFA, which was highest in SSO, followed by SFO, and SBO when calculating USFA directly from the fatty acid composition analysis without the sophisticated mathematical equations of UI, which resulted in a similar trend as in the calculation of USFA. The iodine value (IV) ranged from 115.08 to 130.96, with the highest value for SFO followed by SSO, then SBO, which is greatly similar to the trend of the unsaturation index (UI), and the USFA percent with slight differences emerged from the variation in the source and variety of SFO, which made it preceding SSO in IV despite the superiority of SSO with higher USFA. Peroxidability index (PI) evaluation based on fatty acid composition was found to range between 72.67 and 78.92 which is considered a good indicator for the good stability of oils under study according to the review of literature mentioning the measured values from 7.10 (olive oils) to 111.87 (perilla oils), where malondialdehyde (MDA), as the secondary product in the lipid oxidation process, was produced more in oils with higher PI without induced oxidative stress (Yun & Surh, 2012). The rate of oxidation of fatty constituents depends on the double bond number and their relative positions per mole, as demonstrated by Stoyanova & Romova (2024), considering the allylic position equivalent, or APE (-H 2 C = CH-CH 2 -), and the bis-allylic position equivalent, or BAPE (R-CH = CH-CH 2 -CH = CH-R). The APE value for the oils under study ranged from 165.56 to 179.946 and the BAPE value ranged from 66.91 to 78.626, which are expected due to their high-unsaturated composition. The higher the results for these two indices are, the higher is the susceptibility of the oil to oxidation. The unsaturation index (UI), with its range ranging from 150 to 168.907, matches the PI, APE, and BAPE in the same tendencies. Oxidation Stability Index (OSI) can be used to predict the oil shelf life (Pinto et al. , 2021). Oxidizability value (COX) calculated according to the formula mentioned by Fatemi & Hammond (1980) of the studied oils ranged 7.1778–8.2136. The OSI of the studied oil ranged from 0.37183 to 0.89905, which was found to be inversely correlated to the APE, BAPE, and COX values. The recorded values were in good agreement when compared with measurements performed by different authors for safflower oils (Longoria-Sanchez et al. , 2019). 3.5 Monitoring changes in FFA, PV, and TPC of oil blends during frying procedure The effect of the deep frying procedure on the quality of oil blends was studied by performing two frying schemes with different ratios of two oils to be carried out for safflower with soybean oils and soybean with sunflower oils, in order to reach the best blends for deep frying through the exact monitoring of free fatty acid, peroxide value, and total polar compounds. During the deep frying procedure, the oil comes into contact with air, moisture, and foodstuffs at a high temperature (180 o C), where many changes occur, including oxidation, hydrolysis, polymerization, and thermal degradation for the oil through deteriorative reactions with the formation of many hazardous volatile and non-volatile components, which significantly reduce the nutritional value of the oil (Aşkın & Kaya, 2020; Kittipongpittaya et al. , 2020). Peroxide levels (PV) were found to peak during frying and then fall at the end of the frying procedure. Oxidation is more likely to occur in linoleic acids. TPC measures directly the level of the degraded components in an oil. The maximum value of TPC for commercial frying oils is accepted as 24% in several European countries. Table 6 Monitoring changes in FFA, PV, and TPC of SSO blends with SBO during frying Parameter Frying oil blend SSO + SBO Frying hours 1 2 3 4 5 6 FFA% SSO (Pure 100%) 0.39 0.47 0.50 0.58 0.63 0.78 SSO:SBO (20:80) 0.32 0.35 0.40 0.45 0.53 0.57 SSO:SBO (40:60) 0.34 0.40 0.43 0.48 0.55 0.59 SSO:SBO (60:40) 0.35 0.42 0.45 0.52 0.58 0.62 SSO:SBO (80:20) 0.37 0.45 0.47 0.55 0.60 0.64 SSO:SBO (50:50) 0.30 0.35 0.38 0.42 0.50 0.55 PV SSO (Pure 100%) 17.9 26.0 13.9 12.8 12.9 10.8 SSO:SBO (20:80) 16.7 24.7 11.5 12.6 11.6 9.5 SSO:SBO (40:60) 16.9 24.9 11.7 12.9 11.9 9.7 SSO:SBO (60:40) 17.3 25.3 12.2 13.2 12.3 10.1 SSO:SBO (80:20) 17.6 25.6 12.5 13.6 12.6 10.4 SSO:SBO (50:50) 15.7 23.3 10.2 11.1 10.3 8.1 TPC SSO (Pure 100%) 11.4 14.7 16.9 18.3 20.0 22.6 SSO:SBO (20:80) 9.9 12.0 13.6 14.8 15.8 17.3 SSO:SBO (40:60) 10.3 12.7 14.5 15.7 16.8 18.7 SSO:SBO (60:40) 10.6 13.4 15.3 16.5 17.9 19.9 SSO:SBO (80:20) 11.0 14.0 16.0 17.6 19.0 21.3 SSO:SBO (50:50) 9.5 11.3 12.8 13.9 14.7 15.9 FFA: free fatty acids – PV: peroxide value – SBO: soybean oil – SSO: safflower oil – TPC: total polar compounds Table 7 Monitoring changes in FFA, PV, and TPC of SFO blends with SBO during frying Parameter Frying oil blend SFO + SBO Frying hours 1 2 3 4 5 6 FFA% SFO (Pure 100%) 0.52 0.59 0.68 0.75 0.81 0.87 SFO:SBO (20:80) 0.33 0.36 0.41 0.45 0.51 0.55 SFO:SBO (40:60) 0.43 0.47 0.50 0.55 0.60 0.64 SFO:SBO (60:40) 0.53 0.59 0.62 0.67 0.70 0.74 SFO:SBO (80:20) 0.62 0.69 0.72 0.76 0.80 0.83 SFO:SBO (50:50) 0.44 0.49 0.55 0.61 0.65 0.68 PV SFO (Pure 100%) 12.88 18.95 24.51 12.70 12.06 11.75 SFO:SBO (20:80) 12.52 18.61 24.18 12.32 11.67 11.37 SFO:SBO (40:60) 12.59 18.66 24.22 12.42 11.76 11.46 SFO:SBO (60:40) 12.70 18.75 24.31 12.50 11.87 11.57 SFO:SBO (80:20) 12.78 18.88 24.43 12.60 11.96 11.65 SFO:SBO (50:50) 12.64 18.74 24.27 12.47 11.82 11.51 TPC SFO (Pure 100%) 15.40 17.60 19.10 20.80 21.50 23.90 SFO:SBO (20:80) 14.00 16.30 17.80 19.60 20.30 22.80 SFO:SBO (40:60) 14.30 16.50 18.00 19.80 20.50 23.00 SFO:SBO (60:40) 14.90 17.10 18.70 20.40 21.10 23.60 SFO:SBO (80:20) 15.10 17.40 18.90 20.70 21.40 23.90 SFO:SBO (50:50) 14.60 16.80 18.30 20.10 20.90 23.30 FFA: free fatty acids – PV: peroxide value – SBO: soybean oil – SFO: sunflower oil TPC: total polar compounds Monitoring changes in FFA, PV, and TPC of SSO blends with SBO during frying For SSO blends with SBO, during the repeated frying, the deterioration of the frying oil blend was detected after 2 hours, as indicated by the increase in peroxide values, which exceeded the permitted range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210–1999, 2019). Whereas, for SFO blends with SBO, during the repeated frying, the deterioration of the frying oil blend was detected after 3 hours, as indicated by the increase in peroxide values, which exceeded the permitted range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210–1999, 2019). Therefore, it is recommended to avoid the repeated usage of these frying oil blends in deep frying processes after the end of the determined period of their validity for human consumption to avoid the health risks resulting from consumption of the deteriorated used oil. Also, it is noteworthy to mention that the cold-pressed safflower oil is characterized by superior quality and safety, as it does not involve additional refining, bleaching, and deodorizing (RBD) processes as many vegetable and seed oils (Almoselhy et al. , 2020), as the refining processes at high temperatures are possibly accompanied by hazardous processing contaminants such as 3-MCPD (Almoselhy et al. , 2021). 4 Novelty impact statement The novelty of the current research can be summarized as a new approach to studying in-depth the physicochemical characteristics of safflower oil to validate its potential for expansion in production in Egypt. The significance of this study appears mainly in the sustainable economic utilization of safflower as a non-traditional oilseed crop capable of growing under high temperatures, drought, salinity, and marginal environments in order to bridge the edible oil gap accompanied by negative impacts of climate change on the agroecological settings for common oilseed crop productivity. A new innovative achievement is the detailed presentation of the lipid nutritional indices for the first time in this original research paper to reveal the great health benefits of safflower seed oil on a scientific basis, applying the evidence-based approach using all available information and mathematical equations to calculate the lipid nutritional indices from the fatty acids composition. Therefore, this work should be of special value to researchers requiring up-to-date information on safflower seed oil, including physicochemical characteristics with lipid nutritional indices, in an informative and concise way. 5 Conclusions Safflower seed oil, a valuable natural source of sustainable and economical non-traditional edible oil with capability for growing under high temperatures, drought, salinity, and marginal environments, needs to be expanded in production in Egypt to help in bridging the edible oil gap, which is considered a serious threat to food security in the edible oils sector. The physicochemical characteristics and fatty acid composition of safflower seed oil are highly similar to the common sunflower oil, with slight differences. The most innovative are the lipid nutritional indices calculated from the fatty acid composition of safflower seed oil, signifying its medicinal benefits. Being a novel non-traditional edible oil of plant origin rich in ω-6 fatty acids with optimum indices of atherogenicity (IA), thrombogenicity (IT), and hypocholesterolemic/hypercholesterolemic (HH) with the health-promoting index (HPI), along with the powerful antioxidant effect of a high content of α-tocopherol with superior health benefits compared with animal fat, safflower seed oil successfully demonstrated its potential as a promising non-traditional edible oil qualified for expansion in production in Egypt. 6. Recommendations for expansion in production of safflower oil It is highly recommended to support the expansion in production of safflower seed oil in different ways, such as by giving technical support and incentives to farmers for potential cultivation of safflower and highlighting the economic importance and health benefits of safflower. The association between farmers, stakeholders, experts, industry leaders, policymakers, representatives from regulatory bodies, production companies, and scientific research institutions, should play a crucial role in facilitating the cultivation of safflower and production of safflower oil in Egypt. 7. Future directions and challenges in harnessing medicinal potential of safflower 7.1. Future directions 7.1.1. Clinical trials and standardization: To realize the full potential of safflower, comprehensive clinical trials are necessary. It is necessary to develop standardized processes for dosage and administration in order to evaluate the medication's efficacy and safety across a range of patient populations. 7.1.2. Mechanistic understanding: Determining the precise molecular mechanisms underlying safflower's medicinal effects would shed insight on the plant's mode of action and make the development of targeted remedies for certain ailments easier. 7.1.3. Formulation development: Examining innovative formulations and techniques of administration, like liposomes, nanoparticles, and nanoemulsions, can improve the stability and bioavailability of bioactive compounds, therefore augmenting their therapeutic effectiveness. 7.1.4. Drug interactions and safety profile: It is essential to investigate potential drug interactions and evaluate the plant's long-term safety profile before incorporating safflower into traditional medicine and ensuring patient safety. 7.2. Challenges 7.2.1. Diversity in regulatory status: One problem is that different countries and regions have varied safflower regulatory statuses. Some nations accept it as traditional medicine or pharmaceutical preparation, while others ban its use because of safety and efficacy concerns. 7.2.2. Quality control and standardization: One major obstacle to maintaining consistent quality and potency of safflower-based products is the absence of standard operating procedures for safflower production, harvesting, and extraction. 7.2.3. Complex authentication and analytical techniques: Authentication procedures can be costly and intricate. It's always difficult to come up with easier, less expensive ways to confirm the legitimacy of safflower products. GC, HPLC, and other spectrometric techniques are just a few of the expensive and complex analytical techniques utilized in quality evaluation. Widespread adoption requires the development of more affordable, simple, and approachable procedures. 7.2.4. Geographical variation: The origins of safflower cultivars have a major influence on the end products. Developing consistent and trustworthy authentication techniques is hampered by the need to comprehend and account for this geographic heterogeneity. 7.2.5. Bioavailability issues: Safflower's therapeutic efficacy is limited by the low bioavailability of several of its bioactive components, which calls for creative methods to improve absorption and systemic administration. 7.2.6. Regulatory hurdles: In order to guarantee the quality, safety, and effectiveness of safflower-based products for consumer use, regulatory frameworks and norms must be established before they can be commercialized. 7.2.7. Global market dynamics: Complexity increases when one must satisfy international standards while responding to the demand of the worldwide market for verified food quality. The issue in realizing safflower's full potential lies in finding a balance between customs and modern market demands. 7.2.8. Global awareness and accessibility: Raising public knowledge of safflower's possible health advantages and making it available, particularly in areas with limited resources, continue to be major obstacles to its broad adoption and use. 7.2.9. Educational awareness: It is crucial to educate consumers, producers, and healthcare professionals about the changing role of safflower from a traditional treatment to a pharmaceutical preparation. Closing the information gap guarantees acceptance and well-informed choices. 7.2.10. Integration into pharmacopeia: Establishing precise administration guidelines, consistent dosages, and guaranteeing respect to pharmacological norms are obstacles in the process of including safflower in pharmacopeias. Its approval and usage in pharmacies depend on overcoming these obstacles. 7.2.11. Maintaining therapeutic integrity: One of the major challenges is making sure that safflower products' medicinal value is preserved in their pharmaceutical formulations. It is a hard task to balance standardization without sacrificing the variety of medicinal chemicals found in safflower. References Almeida, O.P., de Freitas Marques, M.B., de Oliveira, J.P., da Costa, J.M.G., Rodrigues, A.P. et al. (2022). Encapsulation of safflower oil in nanostructured lipid carriers for food application. J Food Sci Technol. ,59(2):805-814. https://doi.org/10.1007/s13197-021-05078-5 Almoselhy, R.I.M., Eid, M.M., Abd El-Baset, W.S. & Aboelhassan, A.F.A. (2021). Determination of 3-MCPD in Some Edible Oils using GC-MS/MS. Egypt. J. Chem. , 64(3):1639-1652. https://doi.org/10.21608/ejchem.2021.64084.3373 Almoselhy, R.I.M., Eid, M.M., Abd El-Mageed, S.M.M. & Youness, R.A. (2020). Using Nanotechnology in Bleaching Vegetable Oils. Egypt. J. Chem. , 63(7):2699-2706. https://doi.org/10.21608/ejchem.2020.23625.2407 Aşkın, B. & Kaya, Y. (2020). Effect of deep frying process on the quality of the refined oleic/linoleic sunflower seed oil and olive oil. J Food Sci Technol. , 57(12):4716-4725. https://doi.org/10.1007/s13197-020-04655-4 Ayouaz, S., Bensadia, D., Hamitri-Guerfi, F., Muhammad, D.R.A., Mouhoubi, K. et al. (2022). Impact of incorporating sesame oil ( Sesamum indicum L.) in an Algerian frying oil and margarine formulation. The North African Journal of Food and Nutrition Research , 6(14):165–177. https://doi.org/10.51745/najfnr.6.14.165-177 Badem, Ş. & Baştürk, A. (2023). Oxidative stability and characterization of oleogels obtained from safflower oil-based beeswax and rice bran wax and their effect on the quality of cake samples. J Am Oil Chem Soc ., 100(8):635–649. https://doi.org/10.1002/aocs.12694 Benmeziane, F., Araba, K. & Belahcene, A. (2024). The effect of deep-frying process on the physicochemical characteristics of two edible vegetable oils marketed in Algeria. The North African Journal of Food and Nutrition Research , 8(17):21 – 31. https://doi.org/10.51745/najfnr.8.17.21-31 Botella-Martínez, C., Pérez-Álvarez, J.Á., Sayas-Barberá, E., Navarro Rodríguez de Vera, C., Fernández-López, J. et al. (2023). Healthier Oils: A New Scope in the Development of Functional Meat and Dairy Products: A Review. Biomolecules , 13(5):778. https://doi.org/10.3390/biom13050778 Chen, J. & Liu, H. (2020). Nutritional Indices for Assessing Fatty Acids: A Mini-Review. Int J Mol Sci. , 21(16):5695. https://doi.org/10.3390/ijms21165695 Cheng, H., Yang, C., Ge, P., Liu, Y., Zafar, M.M. et al. (2024). Genetic diversity, clinical uses, and phytochemical and pharmacological properties of safflower ( Carthamus tinctorius L.): an important medicinal plant Front. Pharmacol. , Sec. Ethnopharmacology , 15 – 2024. https://doi.org/10.3389/fphar.2024.1374680 Deliorman Orhan, D., Pekacar, S., Ulutaş, O.K., Özüpek, B., Sümmeoğlu, D. et al. (2022). Assessment of Commercially Safflower Oils (Carthami Oleum Raffinatum) in Terms of European Pharmacopoeia Criteria and Their Weight Control Potentials. Turk J Pharm Sci. , 19(3):273-279. https://doi.org/10.4274/tjps.galenos.2021.84484 Fatemi, S.H. & Hammond, E.G. (1980), Analysis of oleate, linoleate and linolenate hydroperoxides in oxidized ester mixtures. Lipids , 15:379-385. https://doi.org/10.1007/BF02533555 Ghiasy-Oskoee, M. & AghaAlikhani, M. (2023). Towards utilizing Asteraceae alternative oilseed species on marginal lands: Agronomic performance, fatty acid composition, oil biocompounds, and oil physicochemical properties of Asteraceae species. Journal of Agriculture and Food Research , 14, 100799, https://doi.org/10.1016/j.jafr.2023.100799 Han, L., Chen, M., Li, Y., Wu, S., Zhang, L. et al. (2022). Discrimination of different oil types and adulterated safflower seed oil based on electronic nose combined with gas chromatography-ion mobility spectrometry. Journal of Food Composition and Analysis ,114, 104804. https://doi.org/10.1016/j.jfca.2022.104804 Hou, N.C., Gao, H.H., Qiu, Z.J., Deng, Y.H., Zhang, Y.T. et al. (2024). Quality and active constituents of safflower seed oil: A comparison of cold pressing, hot pressing, Soxhlet extraction and subcritical fluid extraction, LWT , 200, 116184. https://doi.org/10.1016/j.lwt.2024.116184 Iskakov, B., Kakimov, M., Kudelski, R., Mursalykova, M., Kassenov et al. (2023). Improving the Technology of Primary Purification of the Safflower Oil Using Secondary Products of Processing on a Biological Basis. Foods , 12(17):3275. https://doi.org/10.3390/foods12173275 Jeong, E.H., Yang, H., Kim, J.E. & Lee, K.W. (2020). Safflower Seed Oil and Its Active Compound Acacetin Inhibit UVB-Induced Skin Photoaging. J Microbiol Biotechnol. , 30(10):1567-1573. https://doi.org/10.4014/jmb.2003.03064 Kang, Z.L., Xie, J.J., Hu, Z.L., Li, Y.P. & Ma, H.J. (2023). Effect of safflower oil and magnetic field-modified soy 11S globulin on rheological and emulsifying properties of oil-in-water emulsions. Food Hydrocolloids , 142, 108775, https://doi.org/10.1016/j.foodhyd.2023.108775 Khan, A., Nadeem, M., Ullah, R., Gulzar, N., Al-Asmari, F. et al. (2024). Fatty Acid Composition, Phenolic Compounds, Phytosterols, and Lipid Oxidation of Single- and Double-Fractionated Olein of Safflower Oil Produced by Low-Temperature Crystallization. ACS Omega 9 (6):6787-6796. https://doi.org/10.1021/acsomega.3c08099 Khémiri, I., Essghaier, B., Sadfi-Zouaoui, N. & Bitri, L. (2020). Antioxidant and Antimicrobial Potentials of Seed Oil from Carthamus tinctorius L. in the Management of Skin Injuries. Oxid Med Cell Longev , 4103418. https://doi.org/10.1155/2020/4103418 Kittipongpittaya, K., Panya, A., Prasomsri, T. & Sueaphet, P. (2020). Tropical Oil Blending and Their Effects on Nutritional Content and Physicochemical Properties during Deep Fat Frying. J Nutr Sci Vitaminol (Tokyo), 66(Supplement):S206-S214. https://doi.org/10.3177/jnsv.66.s206 Kyriakidis, N.B. & Katsiloulis, T. (2000). Calculation of iodine value from measurements of fatty acid methyl esters of some oils: Comparison with the relevant American Oil Chemists Society method. J Am Oil Chem Soc , 77(12):1235-1238. https://doi.org/10.1007/s11746-000-0193-3 Longoria-Sanchez, A., Morales, M.V., Oomah, B.D., Ochoa-Espinoza, X.M., Góngora-Gómez, A.M. et al. (2019). Characteristics and Antioxidant Properties of Cold Pressed High Oleic and Linoleic Oils from Mexican Safflower Varieties. Emirates Journal of Food and Agriculture , 31(9):679-87. https://doi:10.9755/ejfa.2019.v31.i9.1999 Mursalykova, M., Kakimov, M., Kassenov, A., Iskakov, B., Sergibayeva, Z. et al. (2023). Mathematical Modeling of Screw Press Configuration for Processing Safflower Oil. Applied Sciences , 13(5):3057. https://doi.org/10.3390/app13053057 Pinto, T. I., Coelho, J. A., Pires, B. I., Neng, N. R., Nogueira, J. M. et al. (2021). Supercritical Carbon Dioxide Extraction, Antioxidant Activity, and Fatty Acid Composition of Bran Oil from Rice Varieties Cultivated in Portugal. Separations , 8(8):115. https://doi.org/10.3390/separations8080115 Potdar, S., Bagale, U., Potoroko, I., Hakke, V.S., Maralla, Y. et al. (2022). Sonochemical approach for the synthesis of safflower oil based low fat emulsion: Effect of ultrasonic parameters. Materials Today: Proceedings , 57(4):1619-1625, https://doi.org/10.1016/j.matpr.2021.12.232 Ruyvaran, M., Zamani, A., Mohamadian, A., Zarshenas, M.M., Eftekhari, M.H. et al. (2022). Safflower ( Carthamus tinctorius L.) oil could improve abdominal obesity, blood pressure, and insulin resistance in patients with metabolic syndrome: A randomized, double-blind, placebo-controlled clinical trial. Journal of Ethnopharmacology , 282, 114590. https://doi.org/10.1016/j.jep.2021.114590 Shahid, M., Jaradat, A. & Rao, N.K. (2020). Safflower: A Multipurpose Crop for the Marginal Lands. In: Hirich, A., Choukr-Allah, R., Ragab, R. (eds) Emerging Research in Alternative Crops. Environment & Policy , 58. Springer, Cham., Switzerland, 58, 279-294. https://doi.org/10.1007/978-3-319-90472-6_12 Song, L., Geng, S. & Liu, B (2023). Characterization of Wei Safflower Seed Oil Using Cold-Pressing and Solvent Extraction. Foods , 12(17):3228. https://doi.org/10.3390%2Ffoods12173228 Stojanović, Z.S., Uletilović, D.D., Kravić, S.Ž., Kevrešan, Ž.S., Grahovac, N.L. et al. (2023). Comparative Study of the Nutritional and Chemical Composition of New Oil Rape, Safflower and Mustard Seed Varieties Developed and Grown in Serbia. Plants , 12(11):2160. https://doi.org/10.3390/plants12112160 Stoyanova, L.S., & Romova, M.Y.A. (2024). Bioactive Compounds and Nutritive Composition of Waste Seeds from Nicotiana tobacum L. (Solanaceae). Current Research in Nutrition and Food Science, 12(1):374-383. http://dx.doi.org/10.12944/CRNFSJ.12.1.30 Wang, R., Wang, D., Yu, J., Wang, T., Yu, D. et al. (2024). Conjugated safflower seed oil and its oleogels stabilization properties: Thermodynamic properties, rheology, oxidative stability. Food Bioscience , 59, 104203, https://doi.org/10.1016/j.fbio.2024.104203 Wong, M.L., Timms, R.E. & Goh, E.M. (1988). Colorimetric determination of total tocopherols in plam oil, olein and stearin. J Am Oil Chem Soc , 65:258–261. https://doi.org/10.1007/BF02636412 Yun, J.M. & Surh, J. (2012). Fatty Acid Composition as a Predictor for the Oxidation Stability of Korean Vegetable Oils with or without Induced Oxidative Stress. Prev Nutr Food Sci. , 17(2):158-165. https://doi.org/10.3746/pnf.2012.17.2.158 Zou, Z., Wang, Q., Li, M., Zhen, J., Yuan, D. et al. (2024). Combination of gas chromatography-mass spectrometry and hyperspectral imaging for identification of adulterated Safflower seed oil. Journal of Food Composition and Analysis , 135, 106593, https://doi.org/10.1016/j.jfca.2024.106593 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2024 Read the published version in The North African Journal of Food and Nutrition Research → 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5159596","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359427175,"identity":"84e8797b-fb47-4b8e-a16f-d0a44e592874","order_by":0,"name":"Walid S. Abd El-Baset","email":"","orcid":"","institution":"Oils and Fats Research Department, Food Technology Research Institute, Agricultural Research Center, Giza, Egypt","correspondingAuthor":false,"prefix":"","firstName":"Walid","middleName":"S. Abd","lastName":"El-Baset","suffix":""},{"id":359427326,"identity":"ab45b5c1-71a7-458d-a7af-54193144af08","order_by":1,"name":"Rania I.M. Almoselhy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie3PMUvDQBjG8acUdDm59YYQP4FwEoiD4GfJEWiWi64BoRaE3GDFtf0WHR0Tbuhy3Ts2OLhkSLcOIh5BikhqHAXvT0Jewv3gXsDl+qMV9vXaqZrAt5+B/RP1EtJOYoIAGKKX4CuxTw+5UKuy2OGKnClVbcSzTp7YTVE02QhUPXQ6z1xH5RQxCY0JuDA6nc9ilDMjwcxq0UUYJC8IhiRcyyMmcp0u1jH0SZ6Bs7Sb0JqXb7izJHndWZLwXsIk1wTakiiEJdEnkT+QmmsPy3YXe7HkfD594XaXETm4C5XBtsatHy5V1Wzzy1N6LKpNk8U+VY+dpG3wvh/v9xM5ePxb498edLlcrn/UB+JbaCXfZELZAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6314-6144","institution":"Oils and Fats Research Department, Food Technology Research Institute, Agricultural Research Center, Giza, Egypt","correspondingAuthor":true,"prefix":"","firstName":"Rania","middleName":"I.M.","lastName":"Almoselhy","suffix":""},{"id":359427425,"identity":"69b6a2e0-22e4-4ecf-8723-ebe81843c7e9","order_by":2,"name":"Susan M.M. Abd-Elmageed","email":"","orcid":"","institution":"Oils and Fats Research Department, Food Technology Research Institute, Agricultural Research Center, Giza, Egypt","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"M.M.","lastName":"Abd-Elmageed","suffix":""}],"badges":[],"createdAt":"2024-09-26 14:51:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5159596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5159596/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.51745/najfnr.8.18.140-153","type":"published","date":"2024-10-16T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65434788,"identity":"0ce2fc19-7295-46a9-bad7-6ccbed80b66c","added_by":"auto","created_at":"2024-09-27 12:11:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":410080,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental design of extraction and physicochemical characteristics of SSO\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5159596/v1/f5bad18b2a06ce40a0ee8858.png"},{"id":68756828,"identity":"d73ec446-51e0-4dc9-b7f2-fde70c70f913","added_by":"auto","created_at":"2024-11-11 17:15:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2121088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5159596/v1/def523ac-73cb-4b25-8bdb-40616b67dfb0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePhysicochemical characteristics of safflower oil to expand its production in Egypt\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFood insecurity in the edible oils sector has led researchers to seek out new sources of oilseed crops with immense research to adjust their physical, chemical, and functional properties (Khan \u003cem\u003eet al.\u003c/em\u003e, 2024). In light of the increasing consumption of edible oils without self-sufficiency and increasing prices of imported edible oils, the utilization of novel cultivars, which differ from traditional ones in their complex traits and practical qualities, has gained popularity recently. From the standpoint of plant food resources, safflower plays a significant role and has the potential to challenge the well-known oil-bearing crops in the future (Mursalykova \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003eSafflower (\u003cem\u003eCarthamus tinctorius\u003c/em\u003e L.), a well-known member of the Asteraceae family rich in flavonoids, phenols, alkaloids, polysaccharides, fatty acids, polyacetylene, and other bioactive components, is effective for treating cardiovascular, neurodegenerative, and respiratory diseases (Cheng \u003cem\u003eet al.\u003c/em\u003e, 2024). Safflower has been frequently prescribed to treat metabolic diseases in Persia, China, Korea, Japan, and other East Asian countries. In patients with Metabolic Syndrome (MetS), safflower oil consumption without lifestyle changes improved blood pressure, insulin resistance, and abdominal obesity. These improvements included decreased waist circumference, waist to hip ratio, and systolic and diastolic blood pressure (Ruyvaran \u003cem\u003eet al.\u003c/em\u003e, 2022). Cold-pressed safflower seed oil (SSO) exhibited strong antioxidant and antibacterial properties against a range of opportunistic skin infections. It appeared to have a potent antifungal, growth inhibition mechanism in addition to bacteriostatic and bactericidal pathways (Kh\u0026eacute;miri \u003cem\u003eet al.\u003c/em\u003e, 2020). Because it can stop UVB-induced matrix metalloproteinase-1 (MMP-1) expression, which causes skin photoaging, SSO and its active ingredient acacetin may be used therapeutically as an anti-wrinkle treatment to improve skin health (Jeong \u003cem\u003eet al.\u003c/em\u003e, 2020).\u003c/p\u003e \u003cp\u003eRich concentrations of vital unsaturated fatty acids, including oleic and linoleic acids, greatly add to its nutritional value and health-promoting effects, as well as boosting immunity with the management and prevention of a number of illnesses. Safflower seeds are rich in proteins, lipids, and carbohydrates with vitamins, minerals, and macro- and microelements (Iskakov \u003cem\u003eet al.\u003c/em\u003e, 2023). Safflower seed oil, besides being recognized as the \u0026ldquo;king of linoleic acid,\u0026rdquo; is rich in omega 6, or the unsaturated fatty acid, linoleic acid. Safflower seed oil is considered an excellent raw material for the preparation of the conjugated linoleic acid with significant physiological functions, bearing the ability to reduce cholesterol and monounsaturated fatty acids in the body, with powerful antioxidant effects (Wang \u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e \u003cp\u003eA high diet of animal fat with a high saturated fatty acid content can lead to a variety of life-threatening disorders. Therefore, many official health organizations and governmental agencies have performed operations to promote concepts aiming at reducing the saturated fat content in foodstuffs, stimulating the food industry companies to begin working on developing foods with minor fat or a different fatty acid composition. Essentially, the best strategy to substitute saturated fat is to use structured vegetable oils. Pre-emulsification, microencapsulation, the formation of gelled emulsions, and the development of oleogels are the four basic oil structuring procedures (Botella-Mart\u0026iacute;nez \u003cem\u003eet al.\u003c/em\u003e, 2023). The future perspective for safflower seed oil is promising as it presents avenues for sustainable and economical innovation, enhanced functionality, and broader applications in the food industry by manufacturing oleogels and solid fats to replace animal fat in order to offer healthier vegetable fat compared with the risky animal fat (Almeida et al., 2022; Badem \u0026amp; Başt\u0026uuml;rk, 2023; Kang \u003cem\u003eet al.\u003c/em\u003e, 2023; Potdar \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003cp\u003eGrown all over the world, safflower is a multipurpose crop that offers farmers a number of advantages. The plant can withstand long dry spells by drawing deep water till reaching the depth of 4 m through its long, strong, and wide roots. Additionally, Safflower is a versatile crop that can tolerate some salinity, making it potentially advantageous for cultivation in saline soils. Safflower can be effectively cultivated with minimal maintenance on farmlands where pest animals and birds pose a problem for other crops due to its spines, which discourage them. Furthermore, planting safflower between two different crops, can disrupt the life cycles of the microorganisms responsible for various grain diseases (Shahid \u003cem\u003eet al.\u003c/em\u003e, 2020).\u003c/p\u003e \u003cp\u003eDue to its low production and market capitalization, safflower may hold the key to bridging the vegetable oil gap for the expanding global population's needs both now and in the future. Not only does the final product exceed quality standards, but it also increases oil production. Also, safflower oil needs to be recommended to be in accordance with the European Pharmacopoeia if it is used, or at least advertised, with health claims (with or without pharmaceutical usage), to establish the proper quality criteria (Deliorman Orhan \u003cem\u003eet al.\u003c/em\u003e, 2022).\u003c/p\u003e \u003cp\u003eThe current research aimed to study in-depth the physicochemical characteristics and lipid nutritional indices of safflower oil extracted from two different varieties of Egypt, to establish their validity for expansion in production in Egypt to meet the increasing oil demands and bridge the large edible oils gap.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Raw materials\u003c/h2\u003e \u003cp\u003eSafflower seeds of two different spineless varieties (Giza1, Kharga1) and sunflower seeds of one variety (Giza120) were delivered from the Experimental Farm of Shandaweel Research Station, Sohag Governorate, Upper Egypt, via the Oil Crops Research Department, Field Crops Research Institute (FCRI), Agricultural Research Center (ARC), Egypt. Whereas, refined, bleached, and deodorized (RBD) soybean oil was obtained from Arma Company for Edible Oils, Egypt.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Chemicals and reagents\u003c/h2\u003e \u003cp\u003eAll solvents and chemicals used in the study were of analytical and HPLC grade obtained from Sigma-Aldrich, USA.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Extraction of safflower and sunflower crude oils\u003c/h2\u003e \u003cp\u003eSpineless safflower and sunflower seeds were harvested from flower heads of plants grown in Upper Egypt. Safflower seed oil (SSO) and sunflower seed oil (SFO) were extracted by cold pressing mechanical procedure using screw press configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) after cleaning the seeds from impurities and dust. The extracted oils were kept in the dark in hermetically sealed containers till analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Analytical Methods\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.1 Proximate composition analysis of safflower and sunflower seeds\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e Proximate composition analysis of safflower and sunflower seeds, including moisture, ash, lipid, and protein, was determined according to the AOAC Official Methods: 925.10, 942.05, 923.05, and 992.23, respectively, with total carbohydrates by difference and energy in calories per 100 g of seeds.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.2 Physicochemical characteristics of safflower, sunflower, and soybean oils\u003c/h2\u003e \u003cp\u003eThe physicochemical characteristics of the oils under study were determined by the official methods of analysis as: refractive index (AOCS Cc 7\u0026ndash;25), specific gravity (AOCS Cc 10b-25), color measurement (AOCS Cc 13e-92), UV spectroscopic characteristics at 232 and 270 nm (ISO 3656:2011/Amd 1:2017), saponification value (AOCS Tl 1a-64), unsaponifiable matter (AOCS Ca 6a-40), acidity (ISO 660:2020), peroxide value (AOAC 965.33), oxidative stability as induction period by Rancimat Method (AOCS Cd 12b-92), α-tocopherol content (Wong \u003cem\u003eet al.\u003c/em\u003e, 1988), fatty acid composition (ISO 12966-2:2017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section4\"\u003e \u003ch2\u003e2.2.2.3 Lipid nutritional indices from the fatty acid composition\u003c/h2\u003e \u003cp\u003eLipid nutritional indices were calculated from mathematical relations of the fatty acid composition. Polyunsaturated fatty acids/saturated fatty acids (PUFA/SFA), index of atherogenicity (IA), index of thrombogenicity (IT), hypocholsterolemic/hypercholesterolemic (HH), health-promoting index (HPI), and unsaturation index (UI) were calculated according to the method mentioned by Chen \u0026amp; Liu (2020). Iodine value (IV calculated) was determined by the formula assigned by Kyriakidis \u0026amp; Katsiloulis (2000). Whereas, the peroxidability index (PI) was calculated according to the method mentioned by Yun \u0026amp; Surh (2012), the allylic position equivalent (APE) and the bis-allylic position equivalent (BAPE) were calculated according to the method mentioned by Stoyanova \u0026amp; Romova (2024). The oxidative stability index was calculated according to the method mentioned by Pinto \u003cem\u003eet al.\u003c/em\u003e (2021), and the oxidizability value (COX) was calculated according to the method mentioned by Fatemi \u0026amp; Hammond (1980). All the abovementioned lipid indices with their mathematical formulas were tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"719\" height=\"433\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Deep Frying Test\u003c/h2\u003e \u003cp\u003eThe deep frying procedure was performed according to the method described by Benmeziane \u003cem\u003eet al.\u003c/em\u003e (2024) with some modifications, using a 4L capacity local electrical fryer, where the frying oil blends were classified into 2 categories as follows:\u003c/p\u003e \u003cp\u003e(1) Frying blends from the mixture of the 2 varieties of safflower oil (SSO) and soybean oil (SBO) in different ratios of SSO:SBO (100:0\u0026ndash;20:80\u0026ndash;40:60\u0026ndash;60:40\u0026ndash;80:20\u0026ndash;50:50).\u003c/p\u003e \u003cp\u003e(2) Frying blends of sunflower oil (SFO) and soybean oil (SBO) in different ratios of SSO:SBO (100:0\u0026ndash;20:80\u0026ndash;40:60\u0026ndash;60:40\u0026ndash;80:20\u0026ndash;50:50).\u003c/p\u003e \u003cp\u003eThen the frying cycles were performed uninterruptedly for a total of 6 hours in a day in a time interval of 1 hour for a complete frying cycle for 125 grams of fresh potato as French fries in 1 L of oil at 180 ℃ in a 1/8 ratio of potatoes/frying oil. The starting amount of frying oil is constant, and no additional fresh oil was added during the frying procedure in order to assess the quality and safety of the same used oil throughout the frying period. Alternatively, the amount of fresh potatoes was regularly reduced with development in the frying cycles to comply with the decreasing amount of the used frying oil to maintain a constant ratio of 1/8 between the amount of fresh potatoes to the amount of the remaining frying oil (w/w). The frying oil samples were collected at the end of each complete frying cycle for an hour (after: 1, 2, 3, 4, 5, 6 hours) and stored in hermetically sealed containers till analysis of free fatty acid (FFA), peroxide value (PV), and total polar compounds (TPC) in the fried oil samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eMeasurements were performed in three replicates for the proximate composition and physicochemical analyses, whereas the determinations of fatty acid composition and analysis of FFA, PV, and TPC in the fried oils were carried out once for each oil sample. Data were shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Analysis of variance (ANOVA) was conducted with the SPSS software at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Proximate composition of safflower and sunflower seeds\u003c/h2\u003e \u003cp\u003eProximate compositions per 100 g of safflower and sunflower seeds were explored to help better understand their nutritional values. The obtained results are tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and there were significance differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among the proximate composition parameters of the three kinds of the oilseeds, from which there were two varieties of safflower (Giza1-Kharga1) with sunflower (Giza120) seeds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProximate composition of safflower, sunflower, and soybean seeds per 100 g\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximate composition\u003c/p\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSafflower seeds\u003c/p\u003e \u003cp\u003eGiza1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafflower seeds\u003c/p\u003e \u003cp\u003eKharga1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSunflower seeds\u003c/p\u003e \u003cp\u003eGiza120\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOil content (Lipid)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMoisture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProtein\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarbohydrates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy (Calories)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e522.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e511.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e515.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are means of three replicates\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003cp\u003eValues in the same row followed by different superscripts are significantly different at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e., the proximate composition parameters were recorded in the ascending order for oil content as safflower (Kharga1), sunflower (Giza120), and safflower (Giza1) seeds, respectively, moisture content as safflower (Giza1), safflower (Kharga1), and sunflower (Giza120) seeds, respectively, the ash content as safflower (Giza1), sunflower (Giza120), and safflower (Kharga1) seeds, respectively, the protein content as safflower (Giza1), safflower (Kharga1), and sunflower (Giza120) seeds, respectively, the carbohydrates content as sunflower (Giza120), safflower (Giza1), and safflower (Kharga1) seeds, respectively, and the energy in calories of 100 g seeds as safflower (Kharga1), sunflower (Giza120), and safflower (Giza1) seeds, respectively.\u003c/p\u003e \u003cp\u003eDespite the significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among the proximate composition parameters of the abovementioned seeds (safflower and sunflower), they are still in close relationship because of their similar compositions, which revealed the high similarity between safflower and sunflower in their structure and proximate composition. Therefore, sunflower has been selected as the well-known common seeds to be compared with safflower as the non-traditional seeds, in order to simplify the concept of novel food (safflower seeds) with similar characteristics of sunflower seeds to be accepted by consumers as it bears already the highly similar structure with the difference involved in lower cost of safflower seeds, which is considered a good sustainable economic oilseed crop.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Physicochemical characteristics of safflower, sunflower, and soybean oils\u003c/h2\u003e \u003cp\u003ePhysicochemical characteristics of safflower, sunflower, and soybean oil were explored to help better understand their nutritional values. The obtained results are tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and there were significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among the physicochemical parameters of the four kinds of oils, from which there were two varieties of safflower (Giza1-Kharga1) and sunflower (Giza120) seeds with their oils extracted by cold pressing, and the soybean oil was refined, bleached, and deodorized (RBD) oil. All oils were subjected to physical analysis (refractive index, specific gravity, color, UV characteristics) and chemical analysis (saponification value, unsaponifiable matter, acidity, peroxide value, oxidative stability test as induction period, total polar compounds, α-tocopherol content with the fatty acid composition).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical characteristics of safflower, sunflower, and soybean oils\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysicochemical\u003c/p\u003e \u003cp\u003eparameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSafflower oil\u003c/p\u003e \u003cp\u003eGiza1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafflower oil\u003c/p\u003e \u003cp\u003eKharga1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSunflower oil\u003c/p\u003e \u003cp\u003eGiza120\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoybean oil\u003c/p\u003e \u003cp\u003e(RBD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRefractive index at 25\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4732\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4726\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4720\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4700\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0001\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpecific gravity at 25\u003c/b\u003e\u003csup\u003e\u003cb\u003eo\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.919\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.922\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.919\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColor measurement - Yellow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColor measurement - Red\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUV characteristics - K\u003c/b\u003e\u003csub\u003e\u003cb\u003e232\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.483\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.785\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUV characteristics - K\u003c/b\u003e\u003csub\u003e\u003cb\u003e270\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.231\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.413\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSaponification value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnsaponifiable matter (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAcidity (%as oleic acid)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeroxide value(meq/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInduction period (hrs.)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal polar compounds (TPC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eα-Tocopherol content (mg/kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eValues are means of three replicates\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003cp\u003eValues in the same row followed by different superscripts are significantly different at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. summarizes the physicochemical characteristics of SSO, SFO, and SBO where they have been checked with the Codex standard for named vegetable oils (CODEX STAN 210\u0026ndash;1999, 2019). A great number of research papers and routine works are devoted to discrimination of different oil types and detecting adulteration in valuable oils such as safflower seed oil (Han \u003cem\u003eet al.\u003c/em\u003e, 2022; Zou \u003cem\u003eet al.\u003c/em\u003e, 2024), as its promotion as a medicinal plant oil with great health benefits encouraged bad people for its adulteration with cheaper oils to gain more profit from selling adulterated oil, threatening human health and accompanied by huge economic losses. Thereby, the physicochemical characteristics should be examined thoroughly and matched with the standards assigned by the official organizations as Codex Alimentarius International Food Standards supported by the Food and Agriculture Organization of the United Nations and the World Health Organization (CODEX STAN 210\u0026ndash;1999, 2019).\u003c/p\u003e \u003cp\u003eRefractive index is related to the unsaturated fatty acids; a higher refractive index corresponds to more unsaturated fatty acids. Peroxide value (peroxides and hydroperoxides), acidity (measure of rancidity with formation of free fatty acids), and saponification value (measure of molecular weight of triacylglycerols and free fatty acids) are related to the quality of an edible oil. Tocopherols inhibit the oxidation of polyunsaturated fatty acids by reducing free radical reactions to improve oil stability (Hou \u003cem\u003eet al.\u003c/em\u003e, 2024). As it will be shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the order of increasing unsaturated fatty acids coincides with the order of increasing refractive index (SSO-Giza1\u0026thinsp;\u0026lt;\u0026thinsp;SSO-Kharga1\u0026thinsp;\u0026lt;\u0026thinsp;SFO\u0026thinsp;\u0026lt;\u0026thinsp;SBO). From Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e., the specific gravity recorded for all oils is similar with negligible differences. There are significant differences among the color measurements, UV-K\u003csub\u003e232\u003c/sub\u003e/K\u003csub\u003e270\u003c/sub\u003e, the peroxide value, the oxidative stability as induction period (hrs.), the total polar compounds (TPC), and the α-tocopherol content of the studied oils. All values recorded for all tests were in the range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210\u0026ndash;1999, 2019). Also, the recorded values were in good agreement when compared with measurements performed by different authors for soybean and sunflower oils (Almoselhy \u003cem\u003eet al.\u003c/em\u003e, 2020; Almoselhy \u003cem\u003eet al.\u003c/em\u003e, 2021, Ayouaz et al., 2022), and safflower oil (Ghiasy-Oskoee \u0026amp; AghaAlikhani, 2023; Song \u003cem\u003eet al.\u003c/em\u003e, 2023; Stojanović \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Fatty acid composition of SSO, SFO, and SBO\u003c/h2\u003e \u003cp\u003eThe fatty acid compositions as very important indices to evaluate the nutritional values of the edible oils under study are tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and there were significant differences among the fatty acid profiles of the four kinds of oils, from which there were two varieties of safflower (Giza1-Kharga1) with sunflower (Giza120) seeds with their oils extracted by cold pressing, and the soybean oil was refined, bleached, and deodorized (RBD) oil.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFatty acid composition and lipid nutritional indices of SSO, SFO, and SBO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatty acid%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSO\u003c/p\u003e \u003cp\u003eGiza1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSO\u003c/p\u003e \u003cp\u003eKharga1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSFO\u003c/p\u003e \u003cp\u003eGiza120\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSBO\u003c/p\u003e \u003cp\u003e(RBD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e12:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e14:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e16:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e16:1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e17:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e17:1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:2\u003c/b\u003e \u003cb\u003eTrans\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(ω-6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:3\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(ω-3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e20:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e20:1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e\u003csub\u003e\u003cb\u003e22:0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΣSFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΣUSFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΣMUFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΣPUFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΣUSFA/ΣSFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eND: non detectable \u0026ndash; SBO: soybean oil \u0026ndash; SFO: sunflower oil \u0026ndash; SSO: safflower oil\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe main fatty acids in all oils with their ranges were palmitic (C\u003csub\u003e16:0\u003c/sub\u003e) 6.35\u0026ndash;10.17%; stearic (C\u003csub\u003e18:0\u003c/sub\u003e) 2.31\u0026ndash;4.80%; oleic (C\u003csub\u003e18:1\u003c/sub\u003e) 11.43\u0026ndash;22.24%; linoleic or ω-6 (C\u003csub\u003e18:2\u003c/sub\u003e) 54.17\u0026ndash;78.46%; linolenic or ω-3 (C\u003csub\u003e18:3\u003c/sub\u003e) 0.039\u0026ndash;6.37%. Saturated fatty acids (SFA) ranged 9.602\u0026ndash;15.928%; unsaturated fatty acids (USFA) ranged 83.09-90.281%; monounsaturated fatty acids (MUFA) ranged 11.738\u0026ndash;22.55%; polyunsaturated fatty acids (PUFA) ranged 60.54-78.543%; and the ratio USFA/SFA ranged 5.127\u0026ndash;9.402.\u003c/p\u003e \u003cp\u003eIt is well-observed the high similarity in fatty acid composition between safflower and sunflower oils, with higher oleic acid (C\u003csub\u003e18:1\u003c/sub\u003e) in sunflower oil and higher linoleic acid (C\u003csub\u003e18:2\u003c/sub\u003e) in safflower oils. The fatty acid compositions were in the range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210\u0026ndash;1999, 2019). Also, the recorded values were in good agreement when compared with measurements performed by different authors for soybean and sunflower oils (Almoselhy \u003cem\u003eet al.\u003c/em\u003e, 2020; Almoselhy \u003cem\u003eet al.\u003c/em\u003e, 2021, Ayouaz \u003cem\u003eet al.\u003c/em\u003e, 2022), and safflower oil (Ghiasy-Oskoee \u0026amp; AghaAlikhani, 2023; Song \u003cem\u003eet al.\u003c/em\u003e, 2023; Stojanović \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Lipid nutritional indices of SSO, SFO, and SBO\u003c/h2\u003e \u003cp\u003eLipid nutritional indices are calculated from the fatty acid composition of the oils under investigation for the possible assessment of health-related benefits of oils as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLipid nutritional indices of SSO, SFO, and SBO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e№\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid nutritional indices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSO\u003c/p\u003e \u003cp\u003eGiza1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSO\u003c/p\u003e \u003cp\u003eKharga1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSFO\u003c/p\u003e \u003cp\u003eGiza120\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSBO\u003c/p\u003e \u003cp\u003e(RBD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePUFA/SFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eω-6/ω-3 (C\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e/ C\u003c/b\u003e\u003csub\u003e\u003cb\u003e18:3\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e945.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1998.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e274.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIndex of atherogenicity (IA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIndex of thrombogenicity (IT)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHypocholesterol./hypercholesterol. (HH)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHealth-promoting index (HPI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnsaturation index (UI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIodine value (IV calculated)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePeroxidability index (PI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAllylic Position equivalent (APE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e176.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBis-Allylic position equivalent (BAPE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOxidation Stability Index (OSI)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOxidizability value (COX)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.2136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.5824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSBO: soybean oil \u0026ndash; SFO: sunflower oil \u0026ndash; SSO: safflower oil\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eConsidering the important ratio between polyunsaturated fatty acids and saturated fatty acids, or PUFA/SFA, it was ranged 3.8\u0026ndash;8.18, with the highest values assigned for SSO, followed by SFO, then SBO, and it is considered an important index generally used to evaluate the effect of diet on the cardiovascular health (CVH), the higher PUFA/SFA, the healthier the effect on CVH besides the important ratio of ω-6/ω-3, or C\u003csub\u003e18:2\u003c/sub\u003e/C\u003csub\u003e18:3,\u003c/sub\u003e which was ranged 8.5-1998.2 for the edible oils under investigation, with the highest values for SSO, followed by SFO, then SBO with the lowest value.\u003c/p\u003e \u003cp\u003eThe index of atherogenicity, or IA, of the oils under investigation ranged from 0.075\u0026ndash;0.126, which is considered an excellent value for a safe fatty acid profile, as IA demonstrates the relationship between SFA (which are considered proatherogenic, increasing cholesterol in blood with deposition on walls of arteries) and USFA (as an antiatherogenic agent). The smaller the IA value, the healthier the edible oil, with the best minimum value assigned for SFO, followed by SSO, and SBO, with slight differences between SFO and SSO owing to the high similarity in fatty acid composition of the two oils.\u003c/p\u003e \u003cp\u003eThe index of thrombogenicity, or IT, of the studied oils ranged from 0.198\u0026ndash;0.261, which is considered another excellent parameter confirming the healthy profile of fatty acids in the edible oils under examination, as IT exhibits the thrombogenic effect of fatty acids, with affinity to form accumulations or clots in blood vessels, with the best minimum value assigned for SSO, followed by SFO, and SBO.\u003c/p\u003e \u003cp\u003eIt is noteworthy to mention that both IA and IT can be used to evaluate the possible effects of fatty acid composition on CVH, where the fatty acid composition with lower values of IA and IT presents better nutritional quality, and its consumption can reduce the risk of coronary heart disease (CHD).\u003c/p\u003e \u003cp\u003eThe hypocholesterolemic/hypercholesterolemic or HH index ranged from 8.084\u0026ndash;13.759, which is an indicator of a healthy fatty acid profile, as the higher ratio demonstrates the relationship between the hypocholesterolemic fatty acid (\u003cem\u003ecis\u003c/em\u003e-C\u003csub\u003e18:1\u003c/sub\u003e and PUFA) and the hypercholesterolemic fatty acids to evaluate the effect of the fatty acid composition on cholesterol. The evaluated HH index for all edible oils under study was higher than 1.0, suggesting the positive effect on CVDs (Stoyanova \u0026amp; Romova, 2024), with the highest values assigned for SFO, followed by SSO with small differences, and then SBO had the last order.\u003c/p\u003e \u003cp\u003eConsidering the health-promoting index, or HPI, it was ranged from 7.951 to 13.421, and it is simply the inverse of the IA with the same indication to ensure the safety of these consumed edible oils for health. Overall, the abovementioned indices (IA, IT, HH) are well-known calculated indices from the fatty acid composition to be used in the evaluation of the potential effects of fatty acids on cardiovascular diseases (Chen \u0026amp; Liu, 2020).\u003c/p\u003e \u003cp\u003eThe unsaturation index (UI) for the edible oils under investigation ranged from 150 to 168.907, with the highest value for SSO, followed by SFO, and SBO in the last order of decreasing unsaturation. This is the same order of USFA, which was highest in SSO, followed by SFO, and SBO when calculating USFA directly from the fatty acid composition analysis without the sophisticated mathematical equations of UI, which resulted in a similar trend as in the calculation of USFA.\u003c/p\u003e \u003cp\u003eThe iodine value (IV) ranged from 115.08 to 130.96, with the highest value for SFO followed by SSO, then SBO, which is greatly similar to the trend of the unsaturation index (UI), and the USFA percent with slight differences emerged from the variation in the source and variety of SFO, which made it preceding SSO in IV despite the superiority of SSO with higher USFA.\u003c/p\u003e \u003cp\u003ePeroxidability index (PI) evaluation based on fatty acid composition was found to range between 72.67 and 78.92 which is considered a good indicator for the good stability of oils under study according to the review of literature mentioning the measured values from 7.10 (olive oils) to 111.87 (perilla oils), where malondialdehyde (MDA), as the secondary product in the lipid oxidation process, was produced more in oils with higher PI without induced oxidative stress (Yun \u0026amp; Surh, 2012).\u003c/p\u003e \u003cp\u003eThe rate of oxidation of fatty constituents depends on the double bond number and their relative positions per mole, as demonstrated by Stoyanova \u0026amp; Romova (2024), considering the allylic position equivalent, or APE (-H\u003csub\u003e2\u003c/sub\u003eC\u0026thinsp;=\u0026thinsp;CH-CH\u003csub\u003e2\u003c/sub\u003e-), and the bis-allylic position equivalent, or BAPE (R-CH\u0026thinsp;=\u0026thinsp;CH-CH\u003csub\u003e2\u003c/sub\u003e-CH\u0026thinsp;=\u0026thinsp;CH-R). The APE value for the oils under study ranged from 165.56 to 179.946 and the BAPE value ranged from 66.91 to 78.626, which are expected due to their high-unsaturated composition. The higher the results for these two indices are, the higher is the susceptibility of the oil to oxidation. The unsaturation index (UI), with its range ranging from 150 to 168.907, matches the PI, APE, and BAPE in the same tendencies.\u003c/p\u003e \u003cp\u003eOxidation Stability Index (OSI) can be used to predict the oil shelf life (Pinto \u003cem\u003eet al.\u003c/em\u003e, 2021). Oxidizability value (COX) calculated according to the formula mentioned by Fatemi \u0026amp; Hammond (1980) of the studied oils ranged 7.1778\u0026ndash;8.2136. The OSI of the studied oil ranged from 0.37183 to 0.89905, which was found to be inversely correlated to the APE, BAPE, and COX values. The recorded values were in good agreement when compared with measurements performed by different authors for safflower oils (Longoria-Sanchez \u003cem\u003eet al.\u003c/em\u003e, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Monitoring changes in FFA, PV, and TPC of oil blends during frying procedure\u003c/h2\u003e \u003cp\u003eThe effect of the deep frying procedure on the quality of oil blends was studied by performing two frying schemes with different ratios of two oils to be carried out for safflower with soybean oils and soybean with sunflower oils, in order to reach the best blends for deep frying through the exact monitoring of free fatty acid, peroxide value, and total polar compounds.\u003c/p\u003e \u003cp\u003eDuring the deep frying procedure, the oil comes into contact with air, moisture, and foodstuffs at a high temperature (180 \u003csup\u003eo\u003c/sup\u003eC), where many changes occur, including oxidation, hydrolysis, polymerization, and thermal degradation for the oil through deteriorative reactions with the formation of many hazardous volatile and non-volatile components, which significantly reduce the nutritional value of the oil (Aşkın \u0026amp; Kaya, 2020; Kittipongpittaya \u003cem\u003eet al.\u003c/em\u003e, 2020). Peroxide levels (PV) were found to peak during frying and then fall at the end of the frying procedure. Oxidation is more likely to occur in linoleic acids. TPC measures directly the level of the degraded components in an oil. The maximum value of TPC for commercial frying oils is accepted as 24% in several European countries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonitoring changes in FFA, PV, and TPC of SSO blends with SBO during frying\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrying oil blend\u003c/p\u003e \u003cp\u003eSSO\u0026thinsp;+\u0026thinsp;SBO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eFrying hours\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFFA%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO (Pure 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (20:80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (40:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (60:40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (80:20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (50:50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003ePV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO (Pure 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (20:80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (40:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (60:40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (80:20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (50:50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eTPC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO (Pure 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (20:80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (40:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (60:40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (80:20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSSO:SBO (50:50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFFA: free fatty acids \u0026ndash; PV: peroxide value \u0026ndash; SBO: soybean oil \u0026ndash; SSO: safflower oil \u0026ndash;\u003c/p\u003e \u003cp\u003eTPC: total polar compounds\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonitoring changes in FFA, PV, and TPC of SFO blends with SBO during frying\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFrying oil blend\u003c/p\u003e \u003cp\u003eSFO\u0026thinsp;+\u0026thinsp;SBO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eFrying hours\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eFFA%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO (Pure 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (20:80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (40:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (60:40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (80:20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (50:50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003ePV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO (Pure 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (20:80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (40:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (60:40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (80:20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (50:50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eTPC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO (Pure 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (20:80)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (40:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (60:40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (80:20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSFO:SBO (50:50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFFA: free fatty acids \u0026ndash; PV: peroxide value \u0026ndash; SBO: soybean oil \u0026ndash; SFO: sunflower oil\u003c/p\u003e \u003cp\u003eTPC: total polar compounds\u003c/p\u003e \u003cp\u003e \u003cb\u003eMonitoring changes in FFA, PV, and TPC of SSO blends with SBO during frying\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor SSO blends with SBO, during the repeated frying, the deterioration of the frying oil blend was detected after 2 hours, as indicated by the increase in peroxide values, which exceeded the permitted range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210\u0026ndash;1999, 2019). Whereas, for SFO blends with SBO, during the repeated frying, the deterioration of the frying oil blend was detected after 3 hours, as indicated by the increase in peroxide values, which exceeded the permitted range stipulated by the Codex standard for named vegetable oils (CODEX STAN 210\u0026ndash;1999, 2019). Therefore, it is recommended to avoid the repeated usage of these frying oil blends in deep frying processes after the end of the determined period of their validity for human consumption to avoid the health risks resulting from consumption of the deteriorated used oil. Also, it is noteworthy to mention that the cold-pressed safflower oil is characterized by superior quality and safety, as it does not involve additional refining, bleaching, and deodorizing (RBD) processes as many vegetable and seed oils (Almoselhy \u003cem\u003eet al.\u003c/em\u003e, 2020), as the refining processes at high temperatures are possibly accompanied by hazardous processing contaminants such as 3-MCPD (Almoselhy \u003cem\u003eet al.\u003c/em\u003e, 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Novelty impact statement","content":"\u003cp\u003eThe novelty of the current research can be summarized as a new approach to studying in-depth the physicochemical characteristics of safflower oil to validate its potential for expansion in production in Egypt. The significance of this study appears mainly in the sustainable economic utilization of safflower as a non-traditional oilseed crop capable of growing under high temperatures, drought, salinity, and marginal environments in order to bridge the edible oil gap accompanied by negative impacts of climate change on the agroecological settings for common oilseed crop productivity. A new innovative achievement is the detailed presentation of the lipid nutritional indices for the first time in this original research paper to reveal the great health benefits of safflower seed oil on a scientific basis, applying the evidence-based approach using all available information and mathematical equations to calculate the lipid nutritional indices from the fatty acids composition. Therefore, this work should be of special value to researchers requiring up-to-date information on safflower seed oil, including physicochemical characteristics with lipid nutritional indices, in an informative and concise way.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eSafflower seed oil, a valuable natural source of sustainable and economical non-traditional edible oil with capability for growing under high temperatures, drought, salinity, and marginal environments, needs to be expanded in production in Egypt to help in bridging the edible oil gap, which is considered a serious threat to food security in the edible oils sector. The physicochemical characteristics and fatty acid composition of safflower seed oil are highly similar to the common sunflower oil, with slight differences. The most innovative are the lipid nutritional indices calculated from the fatty acid composition of safflower seed oil, signifying its medicinal benefits. Being a novel non-traditional edible oil of plant origin rich in ω-6 fatty acids with optimum indices of atherogenicity (IA), thrombogenicity (IT), and hypocholesterolemic/hypercholesterolemic (HH) with the health-promoting index (HPI), along with the powerful antioxidant effect of a high content of α-tocopherol with superior health benefits compared with animal fat, safflower seed oil successfully demonstrated its potential as a promising non-traditional edible oil qualified for expansion in production in Egypt.\u003c/p\u003e"},{"header":"6. Recommendations for expansion in production of safflower oil","content":"\u003cp\u003eIt is highly recommended to support the expansion in production of safflower seed oil in different ways, such as by giving technical support and incentives to farmers for potential cultivation of safflower and highlighting the economic importance and health benefits of safflower. The association between farmers, stakeholders, experts, industry leaders, policymakers, representatives from regulatory bodies, production companies, and scientific research institutions, should play a crucial role in facilitating the cultivation of safflower and production of safflower oil in Egypt.\u003c/p\u003e"},{"header":"7. Future directions and challenges in harnessing medicinal potential of safflower","content":"\u003cp\u003e\u003cstrong\u003e7.1. Future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.1.1. Clinical trials and standardization:\u003c/strong\u003e To realize the full potential of safflower, comprehensive clinical trials are necessary. It is necessary to develop standardized processes for dosage and administration in order to evaluate the medication\u0026apos;s efficacy and safety across a range of patient populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.1.2. Mechanistic understanding:\u003c/strong\u003e Determining the precise molecular mechanisms underlying safflower\u0026apos;s medicinal effects would shed insight on the plant\u0026apos;s mode of action and make the development of targeted remedies for certain ailments easier.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.1.3. Formulation development:\u003c/strong\u003e Examining innovative formulations and techniques of administration, like liposomes, nanoparticles, and nanoemulsions, can improve the stability and bioavailability of bioactive compounds, therefore augmenting their therapeutic effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.1.4. Drug interactions and safety profile:\u003c/strong\u003e It is essential to investigate potential drug interactions and evaluate the plant\u0026apos;s long-term safety profile before incorporating safflower into traditional medicine and ensuring patient safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2. Challenges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.1. Diversity in regulatory status:\u003c/strong\u003e One problem is that different countries and regions have varied safflower regulatory statuses. Some nations accept it as traditional medicine or pharmaceutical preparation, while others ban its use because of safety and efficacy concerns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.2. Quality control and standardization:\u003c/strong\u003e One major obstacle to maintaining consistent quality and potency of safflower-based products is the absence of standard operating procedures for safflower production, harvesting, and extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.3. Complex authentication and analytical techniques:\u003c/strong\u003e Authentication procedures can be costly and intricate. It\u0026apos;s always difficult to come up with easier, less expensive ways to confirm the legitimacy of safflower products. GC, HPLC, and other spectrometric techniques are just a few of the expensive and complex analytical techniques utilized in quality evaluation. Widespread adoption requires the development of more affordable, simple, and approachable procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.4. Geographical variation:\u003c/strong\u003e The origins of safflower cultivars have a major influence on the end products. Developing consistent and trustworthy authentication techniques is hampered by the need to comprehend and account for this geographic heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.5. Bioavailability issues:\u003c/strong\u003e Safflower\u0026apos;s therapeutic efficacy is limited by the low bioavailability of several of its bioactive components, which calls for creative methods to improve absorption and systemic administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.6. Regulatory hurdles:\u003c/strong\u003e In order to guarantee the quality, safety, and effectiveness of safflower-based products for consumer use, regulatory frameworks and norms must be established before they can be commercialized.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.7. Global market dynamics:\u003c/strong\u003e Complexity increases when one must satisfy international standards while responding to the demand of the worldwide market for verified food quality. The issue in realizing safflower\u0026apos;s full potential lies in finding a balance between customs and modern market demands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.8. Global awareness and accessibility:\u003c/strong\u003e Raising public knowledge of safflower\u0026apos;s possible health advantages and making it available, particularly in areas with limited resources, continue to be major obstacles to its broad adoption and use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.9. Educational awareness:\u003c/strong\u003e It is crucial to educate consumers, producers, and healthcare professionals about the changing role of safflower from a traditional treatment to a pharmaceutical preparation. Closing the information gap guarantees acceptance and well-informed choices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.10. Integration into pharmacopeia:\u003c/strong\u003e Establishing precise administration guidelines, consistent dosages, and guaranteeing respect to pharmacological norms are obstacles in the process of including safflower in pharmacopeias. Its approval and usage in pharmacies depend on overcoming these obstacles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.2.11. Maintaining therapeutic integrity:\u003c/strong\u003e One of the major challenges is making sure that safflower products\u0026apos; medicinal value is preserved in their pharmaceutical formulations. It is a hard task to balance standardization without sacrificing the variety of medicinal chemicals found in safflower.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida, O.P., de Freitas Marques, M.B., de Oliveira, J.P., da Costa, J.M.G., Rodrigues, A.P. \u003cem\u003eet al.\u003c/em\u003e (2022). Encapsulation of safflower oil in nanostructured lipid carriers for food application. \u003cem\u003eJ Food Sci Technol.\u003c/em\u003e,59(2):805-814. https://doi.org/10.1007/s13197-021-05078-5\u003c/li\u003e\n\u003cli\u003eAlmoselhy, R.I.M., Eid, M.M., Abd El-Baset, W.S. \u0026amp; Aboelhassan, A.F.A. (2021). Determination of 3-MCPD in Some Edible Oils using GC-MS/MS. \u003cem\u003eEgypt. J. Chem.\u003c/em\u003e, 64(3):1639-1652. https://doi.org/10.21608/ejchem.2021.64084.3373\u003c/li\u003e\n\u003cli\u003eAlmoselhy, R.I.M., Eid, M.M., Abd El-Mageed, S.M.M. \u0026amp; Youness, R.A. (2020). Using Nanotechnology in Bleaching Vegetable Oils. \u003cem\u003eEgypt. J. Chem.\u003c/em\u003e, 63(7):2699-2706. https://doi.org/10.21608/ejchem.2020.23625.2407\u003c/li\u003e\n\u003cli\u003eAşkın, B. \u0026amp; Kaya, Y. (2020). Effect of deep frying process on the quality of the refined oleic/linoleic sunflower seed oil and olive oil. \u003cem\u003eJ Food Sci Technol.\u003c/em\u003e, 57(12):4716-4725. https://doi.org/10.1007/s13197-020-04655-4\u003c/li\u003e\n\u003cli\u003eAyouaz, S., Bensadia, D., Hamitri-Guerfi, F., Muhammad, D.R.A., Mouhoubi, K. \u003cem\u003eet al.\u003c/em\u003e (2022). Impact of incorporating sesame oil (\u003cem\u003eSesamum indicum\u003c/em\u003e L.) in an Algerian frying oil and margarine formulation. \u003cem\u003eThe North African Journal of Food and Nutrition Research\u003c/em\u003e, 6(14):165\u0026ndash;177. https://doi.org/10.51745/najfnr.6.14.165-177\u003c/li\u003e\n\u003cli\u003eBadem, Ş. \u0026amp; Başt\u0026uuml;rk, A. (2023). Oxidative stability and characterization of oleogels obtained from safflower oil-based beeswax and rice bran wax and their effect on the quality of cake samples. \u003cem\u003eJ Am Oil Chem Soc\u003c/em\u003e., 100(8):635\u0026ndash;649. https://doi.org/10.1002/aocs.12694\u003c/li\u003e\n\u003cli\u003eBenmeziane, F., Araba, K. \u0026amp; Belahcene, A. (2024). The effect of deep-frying process on the physicochemical characteristics of two edible vegetable oils marketed in Algeria. \u003cem\u003eThe North African Journal of Food and Nutrition Research\u003c/em\u003e, 8(17):21 \u0026ndash; 31. https://doi.org/10.51745/najfnr.8.17.21-31\u003c/li\u003e\n\u003cli\u003eBotella-Mart\u0026iacute;nez, C., P\u0026eacute;rez-\u0026Aacute;lvarez, J.\u0026Aacute;., Sayas-Barber\u0026aacute;, E., Navarro Rodr\u0026iacute;guez de Vera, C., Fern\u0026aacute;ndez-L\u0026oacute;pez, J. \u003cem\u003eet al.\u003c/em\u003e (2023). Healthier Oils: A New Scope in the Development of Functional Meat and Dairy Products: A Review. \u003cem\u003eBiomolecules\u003c/em\u003e, 13(5):778. https://doi.org/10.3390/biom13050778\u003c/li\u003e\n\u003cli\u003eChen, J. \u0026amp; Liu, H. (2020). Nutritional Indices for Assessing Fatty Acids: A Mini-Review. \u003cem\u003eInt J Mol Sci.\u003c/em\u003e, 21(16):5695. https://doi.org/10.3390/ijms21165695\u003c/li\u003e\n\u003cli\u003eCheng, H., Yang, C., Ge, P., Liu, Y., Zafar, M.M. \u003cem\u003eet al.\u003c/em\u003e (2024). Genetic diversity, clinical uses, and phytochemical and pharmacological properties of safflower (\u003cem\u003eCarthamus tinctorius\u003c/em\u003e L.): an important medicinal plant \u003cem\u003eFront. Pharmacol.\u003c/em\u003e, \u003cem\u003eSec. Ethnopharmacology\u003c/em\u003e, 15 \u0026ndash; 2024. https://doi.org/10.3389/fphar.2024.1374680\u003c/li\u003e\n\u003cli\u003eDeliorman Orhan, D., Pekacar, S., Ulutaş, O.K., \u0026Ouml;z\u0026uuml;pek, B., S\u0026uuml;mmeoğlu, D. \u003cem\u003eet al.\u003c/em\u003e (2022). Assessment of Commercially Safflower Oils (Carthami Oleum Raffinatum) in Terms of European Pharmacopoeia Criteria and Their Weight Control Potentials. \u003cem\u003eTurk J Pharm Sci.\u003c/em\u003e, 19(3):273-279. https://doi.org/10.4274/tjps.galenos.2021.84484\u003c/li\u003e\n\u003cli\u003eFatemi, S.H. \u0026amp; Hammond, E.G. (1980), Analysis of oleate, linoleate and linolenate hydroperoxides in oxidized ester mixtures. \u003cem\u003eLipids\u003c/em\u003e, 15:379-385. https://doi.org/10.1007/BF02533555\u003c/li\u003e\n\u003cli\u003eGhiasy-Oskoee, M. \u0026amp; AghaAlikhani, M. (2023). Towards utilizing Asteraceae alternative oilseed species on marginal lands: Agronomic performance, fatty acid composition, oil biocompounds, and oil physicochemical properties of Asteraceae species. \u003cem\u003eJournal of Agriculture and Food Research\u003c/em\u003e, 14, 100799, https://doi.org/10.1016/j.jafr.2023.100799\u003c/li\u003e\n\u003cli\u003eHan, L., Chen, M., Li, Y., Wu, S., Zhang, L. \u003cem\u003eet al.\u003c/em\u003e (2022). Discrimination of different oil types and adulterated safflower seed oil based on electronic nose combined with gas chromatography-ion mobility spectrometry. \u003cem\u003eJournal of Food Composition and Analysis\u003c/em\u003e,114, 104804. https://doi.org/10.1016/j.jfca.2022.104804\u003c/li\u003e\n\u003cli\u003eHou, N.C., Gao, H.H., Qiu, Z.J., Deng, Y.H., Zhang, Y.T. \u003cem\u003eet al.\u003c/em\u003e (2024). Quality and active constituents of safflower seed oil: A comparison of cold pressing, hot pressing, Soxhlet extraction and subcritical fluid extraction, \u003cem\u003eLWT\u003c/em\u003e, 200, 116184. https://doi.org/10.1016/j.lwt.2024.116184\u003c/li\u003e\n\u003cli\u003eIskakov, B., Kakimov, M., Kudelski, R., Mursalykova, M., Kassenov \u003cem\u003eet al.\u003c/em\u003e (2023). Improving the Technology of Primary Purification of the Safflower Oil Using Secondary Products of Processing on a Biological Basis. \u003cem\u003eFoods\u003c/em\u003e, 12(17):3275. https://doi.org/10.3390/foods12173275\u003c/li\u003e\n\u003cli\u003eJeong, E.H., Yang, H., Kim, J.E. \u0026amp; Lee, K.W. (2020). Safflower Seed Oil and Its Active Compound Acacetin Inhibit UVB-Induced Skin Photoaging. \u003cem\u003eJ Microbiol Biotechnol.\u003c/em\u003e, 30(10):1567-1573. https://doi.org/10.4014/jmb.2003.03064\u003c/li\u003e\n\u003cli\u003eKang, Z.L., Xie, J.J., Hu, Z.L., Li, Y.P. \u0026amp; Ma, H.J. (2023). Effect of safflower oil and magnetic field-modified soy 11S globulin on rheological and emulsifying properties of oil-in-water emulsions. \u003cem\u003eFood Hydrocolloids\u003c/em\u003e, 142, 108775, https://doi.org/10.1016/j.foodhyd.2023.108775\u003c/li\u003e\n\u003cli\u003eKhan, A., Nadeem, M., Ullah, R., Gulzar, N., Al-Asmari, F. \u003cem\u003eet al.\u003c/em\u003e (2024). Fatty Acid Composition, Phenolic Compounds, Phytosterols, and Lipid Oxidation of Single- and Double-Fractionated Olein of Safflower Oil Produced by Low-Temperature Crystallization. \u003cem\u003eACS Omega\u003c/em\u003e\u003cem\u003e9\u003c/em\u003e (6):6787-6796. https://doi.org/10.1021/acsomega.3c08099\u003c/li\u003e\n\u003cli\u003eKh\u0026eacute;miri, I., Essghaier, B., Sadfi-Zouaoui, N. \u0026amp; Bitri, L. (2020). Antioxidant and Antimicrobial Potentials of Seed Oil from \u003cem\u003eCarthamus tinctorius\u003c/em\u003e L. in the Management of Skin Injuries. \u003cem\u003eOxid Med Cell Longev\u003c/em\u003e, 4103418. https://doi.org/10.1155/2020/4103418\u003c/li\u003e\n\u003cli\u003eKittipongpittaya, K., Panya, A., Prasomsri, T. \u0026amp; Sueaphet, P. (2020). Tropical Oil Blending and Their Effects on Nutritional Content and Physicochemical Properties during Deep Fat Frying. \u003cem\u003eJ Nutr Sci Vitaminol\u003c/em\u003e (Tokyo), 66(Supplement):S206-S214. https://doi.org/10.3177/jnsv.66.s206\u003c/li\u003e\n\u003cli\u003eKyriakidis, N.B. \u0026amp; Katsiloulis, T. (2000). Calculation of iodine value from measurements of fatty acid methyl esters of some oils: Comparison with the relevant American Oil Chemists Society method. \u003cem\u003eJ Am Oil Chem Soc\u003c/em\u003e, 77(12):1235-1238. https://doi.org/10.1007/s11746-000-0193-3\u003c/li\u003e\n\u003cli\u003eLongoria-Sanchez, A., Morales, M.V., Oomah, B.D., Ochoa-Espinoza, X.M., G\u0026oacute;ngora-G\u0026oacute;mez, A.M. \u003cem\u003eet al.\u003c/em\u003e (2019). Characteristics and Antioxidant Properties of Cold Pressed High Oleic and Linoleic Oils from Mexican Safflower Varieties. \u003cem\u003eEmirates Journal of Food and Agriculture\u003c/em\u003e, 31(9):679-87. https://doi:10.9755/ejfa.2019.v31.i9.1999\u003c/li\u003e\n\u003cli\u003eMursalykova, M., Kakimov, M., Kassenov, A., Iskakov, B., Sergibayeva, Z. \u003cem\u003eet al.\u003c/em\u003e (2023). Mathematical Modeling of Screw Press Configuration for Processing Safflower Oil. \u003cem\u003eApplied Sciences\u003c/em\u003e, 13(5):3057. https://doi.org/10.3390/app13053057\u003c/li\u003e\n\u003cli\u003ePinto, T. I., Coelho, J. A., Pires, B. I., Neng, N. R., Nogueira, J. M. \u003cem\u003eet al.\u003c/em\u003e (2021). Supercritical Carbon Dioxide Extraction, Antioxidant Activity, and Fatty Acid Composition of Bran Oil from Rice Varieties Cultivated in Portugal. \u003cem\u003eSeparations\u003c/em\u003e, 8(8):115. https://doi.org/10.3390/separations8080115\u003c/li\u003e\n\u003cli\u003ePotdar, S., Bagale, U., Potoroko, I., Hakke, V.S., Maralla, Y. \u003cem\u003eet al.\u003c/em\u003e (2022). Sonochemical approach for the synthesis of safflower oil based low fat emulsion: Effect of ultrasonic parameters. \u003cem\u003eMaterials Today: Proceedings\u003c/em\u003e, 57(4):1619-1625, https://doi.org/10.1016/j.matpr.2021.12.232\u003c/li\u003e\n\u003cli\u003eRuyvaran, M., Zamani, A., Mohamadian, A., Zarshenas, M.M., Eftekhari, M.H. \u003cem\u003eet al.\u003c/em\u003e (2022). Safflower (\u003cem\u003eCarthamus tinctorius\u003c/em\u003e L.) oil could improve abdominal obesity, blood pressure, and insulin resistance in patients with metabolic syndrome: A randomized, double-blind, placebo-controlled clinical trial. \u003cem\u003eJournal of Ethnopharmacology\u003c/em\u003e, 282, 114590. https://doi.org/10.1016/j.jep.2021.114590\u003c/li\u003e\n\u003cli\u003eShahid, M., Jaradat, A. \u0026amp; Rao, N.K. (2020). Safflower: A Multipurpose Crop for the Marginal Lands. In: Hirich, A., Choukr-Allah, R., Ragab, R. (eds) Emerging Research in Alternative Crops. \u003cem\u003eEnvironment \u0026amp; Policy\u003c/em\u003e, 58. Springer, Cham., Switzerland, 58, 279-294. https://doi.org/10.1007/978-3-319-90472-6_12\u003c/li\u003e\n\u003cli\u003eSong, L., Geng, S. \u0026amp; Liu, B (2023). Characterization of Wei Safflower Seed Oil Using Cold-Pressing and Solvent Extraction. \u003cem\u003eFoods\u003c/em\u003e, 12(17):3228. https://doi.org/10.3390%2Ffoods12173228\u003c/li\u003e\n\u003cli\u003eStojanović, Z.S., Uletilović, D.D., Kravić, S.Ž., Kevre\u0026scaron;an, Ž.S., Grahovac, N.L. \u003cem\u003eet al. \u003c/em\u003e(2023). Comparative Study of the Nutritional and Chemical Composition of New Oil Rape, Safflower and Mustard Seed Varieties Developed and Grown in Serbia. \u003cem\u003ePlants\u003c/em\u003e, 12(11):2160. https://doi.org/10.3390/plants12112160\u003c/li\u003e\n\u003cli\u003eStoyanova, L.S., \u0026amp; Romova, M.Y.A. (2024). Bioactive Compounds and Nutritive Composition of Waste Seeds from \u003cem\u003eNicotiana tobacum\u003c/em\u003e L. (Solanaceae). \u003cem\u003eCurrent Research in Nutrition and Food Science,\u003c/em\u003e 12(1):374-383. http://dx.doi.org/10.12944/CRNFSJ.12.1.30\u003c/li\u003e\n\u003cli\u003eWang, R., Wang, D., Yu, J., Wang, T., Yu, D. \u003cem\u003eet al.\u003c/em\u003e (2024). Conjugated safflower seed oil and its oleogels stabilization properties: Thermodynamic properties, rheology, oxidative stability. \u003cem\u003eFood Bioscience\u003c/em\u003e, 59, 104203, https://doi.org/10.1016/j.fbio.2024.104203\u003c/li\u003e\n\u003cli\u003eWong, M.L., Timms, R.E. \u0026amp; Goh, E.M. (1988). Colorimetric determination of total tocopherols in plam oil, olein and stearin. \u003cem\u003eJ Am Oil Chem Soc\u003c/em\u003e, 65:258\u0026ndash;261. https://doi.org/10.1007/BF02636412\u003c/li\u003e\n\u003cli\u003eYun, J.M. \u0026amp; Surh, J. (2012). Fatty Acid Composition as a Predictor for the Oxidation Stability of Korean Vegetable Oils with or without Induced Oxidative Stress. \u003cem\u003ePrev Nutr Food Sci.\u003c/em\u003e, 17(2):158-165. https://doi.org/10.3746/pnf.2012.17.2.158\u003c/li\u003e\n\u003cli\u003eZou, Z., Wang, Q., Li, M., Zhen, J., Yuan, D. \u003cem\u003eet al.\u003c/em\u003e (2024). Combination of gas chromatography-mass spectrometry and hyperspectral imaging for identification of adulterated Safflower seed oil. \u003cem\u003eJournal of Food Composition and Analysis\u003c/em\u003e, 135, 106593, https://doi.org/10.1016/j.jfca.2024.106593\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Carthamus tinctorius L., safflower oil, edible oil gap, lipid nutritional indices, frying stability, non-traditional edible oils","lastPublishedDoi":"10.21203/rs.3.rs-5159596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5159596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eIncreasing demand for sustainable and economical non-traditional edible oils as alternatives to common oils is pivotal to bridge the edible oils gap, accompanied by negative impacts of climate change on the agroecological settings for common oilseed crop productivity. Safflower is one of the fast-growing medicinal oilseed crops rich in polyunsaturated fatty acids, known as the “king of linoleic acid”, with capability for growing under high temperatures, drought, salinity, and marginal environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims: \u003c/strong\u003eThe current research aimed to study in-depth the physicochemical characteristics along with the lipid nutritional indices of safflower oil to validate its potential for expansion in production in Egypt.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods: \u003c/strong\u003eSafflower oils extracted from seeds of two spineless varieties of Egypt were subjected to proximate composition, physicochemical, fatty acid composition, and α-tocopherol analyses. A frying stability test was carried out for safflower oil and its blends with soybean oil in different ratios, monitored by analyses of free fatty acid, peroxide value, and total polar compounds. Lipid nutritional indices were calculated to explore their health-related applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSafflower oil revealed similar proximate composition as sunflower oil with similar physicochemical characteristics. The fatty acid composition of safflower oil was greatly similar to sunflower oil, with smaller oleic acid and greater linoleic acid contents, along with recognized stability in the frying process. Lipid nutritional indices calculated from the fatty acid composition supported the medicinal uses of safflower oil as a valuable source of ω-6 fatty acids and revealed optimum indices of atherogenicity (IA), thrombogenicity (IT), and hypocholesterolemic/hypercholesterolemic (HH) with the health-promoting index (HPI) along with the powerful antioxidant effect of the high content of α-tocopherol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eSafflower oil successfully demonstrated its potential as a promising non-traditional edible oil qualified for expansion in production in Egypt.\u003c/p\u003e","manuscriptTitle":"Physicochemical characteristics of safflower oil to expand its production in Egypt","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 11:33:04","doi":"10.21203/rs.3.rs-5159596/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":"d8743393-f9e7-4387-9fcb-a577493eb6ab","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38246591,"name":"Food Science \u0026 Technology"}],"tags":[],"updatedAt":"2024-11-11T17:15:38+00:00","versionOfRecord":{"articleIdentity":"rs-5159596","link":"https://doi.org/10.51745/najfnr.8.18.140-153","journal":{"identity":"the-north-african-journal-of-food-and-nutrition-research","isVorOnly":true,"title":"The North African Journal of Food and Nutrition Research"},"publishedOn":"2024-10-16 00:00:00","publishedOnDateReadable":"October 16th, 2024"},"versionCreatedAt":"2024-09-27 11:33:04","video":"","vorDoi":"10.51745/najfnr.8.18.140-153","vorDoiUrl":"https://doi.org/10.51745/najfnr.8.18.140-153","workflowStages":[]},"version":"v1","identity":"rs-5159596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5159596","identity":"rs-5159596","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}