Comparison and evaluation of physicochemical properties and nutritional quality on ten germplasms of Actinidia arguta seed oils by ultrasonic-assisted extraction

Comparison and evaluation of physicochemical properties and nutritional quality on ten germplasms of Actinidia arguta seed oils by ultrasonic-assisted extraction | 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 Comparison and evaluation of physicochemical properties and nutritional quality on ten germplasms of Actinidia arguta seed oils by ultrasonic-assisted extraction Miao Yan, Song Pan, Heran Xu, Guanlin Qian, Huanyu Wang, Lin Hui, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6226482/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract As a by-product of processed products, the best treatment of fruit seeds is oil extraction. Actinidia arguta seeds account for 7–10% of fruit weight, and the current oil yield was 20.8%. To make more efficient use of Actinidia arguta seeds, the ultrasonic-assisted seed extraction method was adopted in this experiment, and the optimal oil extraction technology was obtained through a single-factor experiment and response surface experiment. The physical and chemical indexes of seed oil, including acid value, peroxide value, iodine value, color difference (L*, a*, b*), main fatty acids (linolenic acid and linoleic acid), and antioxidant activity (DPPH and FRAP), were compared and analyzed. Ten germplasms were comprehensively evaluated by principal component analysis and correlation analysis methods to explore the relationship between physical and chemical indexes and antioxidant indexes. The results showed the optimal oil extraction process: the liquid-solid ratio was 10:1mL/g, the extraction time was 98 min, the extraction power was 161 W, the extraction temperature was 40 min, and the oil extraction rate was 30.06 ± 0.21%. Through comprehensive evaluation, No. 14 had the highest score and the most potential to develop into oil. Iodine value was correlated with linoleic acid, DPPH and FRAP were positively correlated, and linoleic acid was negatively correlated with linoleic acid. This study improved seed oil yield, reduced by-product loss, screened out the most potential seed oil resources, and provided a theoretical basis for the future development of seed oil in the food and cosmetics industry. Actinidia arguta Germplasms Seed oils Ultrasonic-assisted extraction Process optimization Quality evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Actinidia arguta ( A.arguta ) are botanically classified as berries from the Actinidiaceae family, also known as hardy kiwi and kiwi berry, which is popular with consumers for its rich vitamin C[ 1 ]. It is now commercially cultivated in the United States, Chile, New Zealand, Australia, China, and most of Europe, and it has extensive wild resources in northeast China[ 2 ]. The fruit is smooth and hairless and can be eaten directly. Still, due to its poor storability, processing is the best treatment alternative to fresh food, and the proper utilization of processing by-products is a challenge for the agri-food industry and civil society[ 3 ]. The fruit and vegetable losses and waste up to 60% of the total production, which mainly consists of peels, pomades, flowers, stems, leaves, seeds, and pulp[ 4 , 5 ]. As one of the main by-products, the seeds of A.arguta accounted for 7–10% of the quality of the fresh fruit and had the potential to develop oil. In addition to natural antioxidants, plant seed oil with antioxidant properties has recently become a research focus[ 6 ]. For example, grape seed oil has shown antioxidant activity in the body by alleviating oxidative stress conditions, scavenging free radicals, inhibiting lipid oxidation, or reducing hydroperoxide formation[ 7 ]. Gasparini et al.(2023) evaluated the potential use of apple seed oil by analyzing its physicochemical properties and antioxidant activity[ 8 ]. In another study by Ozden et al. (2023), the antioxidant activity and fatty acids composition of Kiwifruit seed oil were characterized. Fruit oils of berries (Strawberry, blueberry, blackberry, cranberry, and red raspberry) have been studied as potential for food and cosmetic applications due to their antioxidant activity[ 9 ]. However, more reports were needed on the physicochemical composition antioxidants of A.arguta seed oil. Therefore, effective extraction and accurate quantification of fruit oil were necessary for the by-product processing and utilization. At present, there have been few reports on fruit oil extraction. Several techniques had played a role in fruit seed oil extraction, including ultrasonic-assisted extraction (UAE)[ 10 ], solvent extraction[ 11 ], cold pressing[ 12 ], supercritical fluid extraction[ 13 ], microwave-assisted extraction (MAE)[ 14 ], UAE was regarded as an efficient method to improve the extraction yield and reduce extraction time and solvent consumption, and possess good scalability[ 15 , 16 ]. In the ultrasonic-assisted solvent extraction process, microbubbles are formed due to the contraction and expansion of the liquid, and the energy given by ultrasound gradually increases the bubbles and accumulates energy in the interior until the bubbles burst, called acoustic cavitation[ 17 ]. The bubble bursting will release a large amount of thermal energy conducive to destroying the plant cell wall, thus speeding up the extraction speed and increasing the extraction rate[ 18 ]. Eventually, over time, when the plant cell wall breaks, the intercellular oil will be released[ 19 ]. The researchers studied the effects of ultrasonic extraction variables (i.e., ultrasonic power, time, temperature, and solvent-solid ratio) on oil recovery from different plant sources[ 20 – 22 ]. Under the optimum extraction conditions, the yield of pomegranate oil was higher than the conventional method [ 23 ]. However, no research report on extracting A.arguta seed oil under different ultrasonic extraction conditions existed. This study aimed to establish an efficient ultrasonic-assisted extraction method for A.arguta seed oil. A single-factor experiment screened the critical parameters of seed oil extraction, and the optimal extraction conditions of seed oil were determined by Box-Behnken design (BBD). Secondly, ten germplasms of A.arguta seed oils were extracted under the optimal extraction conditions, and their physicochemical properties (acid value, peroxide value, iodine value, color, fatty acids) and antioxidant activities were studied to explore the application potential of the obtained A.arguta seed oil in food and cosmetics industry. This study will provide theoretical guidance for the processing and utilization of A.arguta seed oil and improve the efficient utilization of industrial by-products of A.arguta . 2. Material and methods 2.1 Materials The fruits of A. arguta were picked at the same maturity stage in the Northeast Wild A. arguta resource nursery (Shenyang) of the Ministry of Agriculture. The ten germplasms included Changjiang No. 1(CJ No.1), Huan You(HY), No. 5, No. 2, No. 14, No. 36, No. 18, No. 6, No. 3 and No. 4, and CJ No.1 was used for oil extraction process optimization. The harvested fruit were immediately transported to the laboratory.Then the seeds were manually picked out and dried at room temperature (25 ± 2℃). After drying to the constant weight, the seeds were powered with particle size by mills and placed in refrigerated condition for further use. 2.2 Ultrasonic-assisted solvent extraction(USSE) Ultrasonic-assisted solvent extraction was achieved using an ultrasonic cleaner (KQ-300DE, Kunshan, China). The device can adjust ultrasonic time, ultrasonic temperature, and ultrasonic power. The ultrasonic-assisted solvent extraction process was as follows: CJ No.1 A.arguta seed powder (5 g) and different volumes of n-hexane (depending on the liquid-solid ratio ) were added to the flask and extracted, after which the solvent was evaporated with a rotary evaporator at 40 ° C, and measured by gravimetry, and the formula was: The oil extraction yield (%) = extracted oil mass/ dry material mass ×100% 2.3 Single-factor experiments The effects of different liquid-solid ratios (6:1, 8:1, 10:1, 12:1,14:1 mL/g) extraction time (60, 80, 100, 120, 140 min), extraction power (100, 130, 160, 190, 240 W) and extraction temperature (30, 35, 40, 45, 50℃ ) on the yield of A.arguta seed oil were studied 2.4 Box-Behnken experimental design(BBD) Based on the results of the single-factor experiments, along with the response surface methodology (RSM) was employed to optimize the extraction process conditions, liquid-solid ratio (X 1 , 6:1–10:1 mL/g), extraction time (X 2, 80–120 min), extraction power (X 3 , 130–190 W), and extraction temperature (X 4 , 30–50 ℃) were chosen for further optimization with BBD. The experimental scheme of the BBD was shown in Table 1 . A 29-randomized experiment containing three replicates in the center point was designed to establish the quadratic equation model. The relationship between the predicted oil yield (Y) and the independent variables (Xi ) was expressed. 2.5 Physicochemical property 2.5.1 The acid value, peroxidation value, and iodine value The contents of acid value(AV), peroxidation value(PV), and iodine value(IV) of A.arguta seed oils were determined according to the China standards GB/5009.229–2016, GB/5009.227–2023, GB/T5532-2022, respectively. 2.5.2. The color CIE LAB color system was used to characterize color differences, and L*, a*, and b* of A.arguta seed oils were measured by color difference meter (CR400, Japanese Konica Minolta). 2.5.3 Fatty acids Analysis of A.arguta seed oils by gas chromatography-mass spectrometry (7890A-5975A, Agilent, USA). The operating conditions were modified according to Lolli et al.(2023): the oven temperature was scheduled to be maintained from 40 ℃ for 5 minutes, at 6 ℃/min to 220 ℃ for 20 minutes, the shunt ratio was 20:1, and the solvent delay was 3 minutes. 2.6 Antioxidant activity 2.6.1. DPPH free radical scavenging rate Seed oils' DPPH radical scavenging activity was determined using a slightly modified DPPH assay of Yao et al.(2020). The seed oils (2 mL) were added to a DPPH solution diluted with 0.05 mg/mL ethanol. It was shaken gently and stored in darkness at room temperature for 30 minutes, and then measured at 517 nm by a UV spectrophotometer (UV2700, SHIMADZU, Japan). Inhibition rate was used to express the DPPH scavenging activity of seed oils. DPPH(%) = (1 − A Sample / A control ) ×100% 2.6.2 FRAP The FRAP of seed oils was modified according to Deng et al.( 2018). First, the working liquid was prepared: acetic acid buffer (200 mM, pH 3.6), TPTZ (10 mM), and FeCl 3 (20 mM) were mixed at a ratio of 10:1:1 and incubated at 37℃ for reserve. Then, 3 mL seed oils and 3 ml FRAP working liquid were reacted in the darkness for 2 hours, and the absorbance was measured at 593 nm by a UV spectrophotometer (UV2700, SHIMADZU, Japan). Trolox (6.25 ~ 200 µM) was used to establish the standard curve. The result is expressed in mg Trolox/kg oil. 2.7. Statistical analysis All experiments were conducted three times, and the data were expressed as mean values ± standard deviation. BBD was performed using Design Expert 13.0 software (Stat Ease Inc., Minneapolis, MN, USA)). Principal component analysis(PCA) and correlation analysis and their significant differences were processed by Origin 2023b (OriginLab Corporation., Northampton, MA, USA) and SPSS 26.0 (IBM, New York, USA). 3. Results and discussion 3.1 Single-factor experiments Four factors, including liquid-solid ratio, extraction time, extraction power, and extraction temperature, were selected to optimize the extraction effect. The influences of different factors on the oil yield of A.arguta seed oil were shown in Fig. 1 . The range of liquid-solid ratio, extraction time, extraction power, and extraction temperature were 23.37–28.51%, 23.52–29.04%, 23.41–29.32%, and 23.62–29.58%, respectively. The influence trends of the four factors were the same, increasing and then decreasing. To obtain the optimal extraction process, liquid-solid ratio (8:1, 10:1, 12:1 mL/g), extraction time (80, 100, 120 min), extraction power (140, 160, 180 W), and extraction temperature (35, 40, 45 ℃) were selected for RSM. 3.2 Optimization of crucial influencing factors by Box–Behnken design(BBD) The results of optimization using BBD were shown in Table 1 . The oil yield of the seeds was 23.01–30.02%. Table 2 showed the adjustment results and analysis of variance (ANOVA) of A.arguta oil yield. The significance difference of each coefficient in the regression equation fitted by the optimized analysis model was tested by Fischer's "F statistic" value (F-value) and probability value (p-value), and the relationship between each variable and the response value[ 27 , 28 ]. Generally, when model P < 0.05, it means that the model is statistically significant. At the same time, each variable factor's influenced degree and statistical significance could be obtained according to the F-value and p-value. When P > 0.05, the difference was not significant; When P < 0.01, the difference was very significant. When 0.01 < P < 0.05, the difference was significant. Table 1 Box-Behnken design with the observed responses and predicted values for yield of A.arguta seed oil Run Liquid-solid ratio(X 1 ) Extraction time(X 2 ) Extraction power(X 3 ) Extraction temperature(X 4 ) Eexp (%) Epredicted (%) 1 10 100 160 40 30.02 29.92 2 10 100 130 35 25.42 25.29 3 12 100 160 45 28.46 28.5 4 10 100 160 40 29.84 29.92 5 10 100 130 45 25.65 25.49 6 10 80 190 40 25.16 24.89 7 12 100 130 40 23.43 23.6 8 8 100 160 45 27.95 27.95 9 10 100 190 35 25.79 25.94 10 12 100 190 40 24.22 24.34 11 10 100 190 45 26.35 26.47 12 10 120 130 40 24.29 24.5 13 10 120 190 40 25.3 25.28 14 10 120 160 35 28.96 28.95 15 10 100 160 40 29.85 29.92 16 12 120 160 40 27.54 27.35 17 8 100 130 40 23.01 22.95 18 10 80 160 35 28.63 28.76 19 12 80 160 40 27.16 27.07 20 8 80 160 40 26.16 26.34 21 12 100 160 35 28.21 28.16 22 8 100 190 40 23.95 23.84 23 8 100 160 35 27.66 27.57 24 10 120 160 45 29.62 29.55 25 10 100 160 40 29.98 29.92 26 8 120 160 40 26.85 26.93 27 10 100 160 40 29.93 29.92 28 10 80 160 45 28.82 28.89 29 10 80 130 40 24.06 24.03 It can be seen from Table 2 that the parameters of linear non-combination items X 1 , X 2 , X 3 , X 4 and quadratic combination items X 1 2 , X 2 2 , X 3 2 had statistically significant effects on seed oil yield. Besides, the F value corresponding to the response value was 342.02, and the p-value of the model formation probability ability was extremely significant (P < 0.0001), which indicated that the corresponding model of the relationship between the oil yield and various factor variables had statistical significance. The experimental results showed that the oil yield of the optimized experiment could be well-fitted and predicted. According to the F value obtained from the analysis of variance, the influence degree of the existence of non-combined influencing factors on the oil extraction rate was determined: X 3 > X 1 > X 2 > X 4 . It was consistent with F's research results. Table 2 Adjustment and analysis of variance (ANOVA) of A.arguta oil yield . Term Sum of Squares DF Mean Square F-Value p-Value Model 139.17 14 9.94 342.02 < 0.0001 X 1 0.9861 1 0.9861 33.93 < 0.0001 X 2 0.5504 1 0.5504 18.94 0.0007 X 3 2.01 1 2.01 69.12 < 0.0001 X 4 0.396 1 0.396 13.63 0.0024 X 1 X 2 0.024 1 0.024 0.8266 0.3786 X 1 X 3 0.0056 1 0.0056 0.1935 0.6667 X 1 X 4 0.0004 1 0.0004 0.0138 0.9083 X 2 X 3 0.002 1 0.002 0.0697 0.7957 X 2 X 4 0.0552 1 0.0552 1.9 0.1897 X 3 X 4 0.0272 1 0.0272 0.9367 0.3495 X 1 2 25.88 1 25.88 890.42 < 0.0001 X 2 2 6.53 1 6.53 224.82 < 0.0001 X 3 2 116.81 1 116.81 4019.18 < 0.0001 X 4 2 0.0897 1 0.0897 3.09 0.1008 Residual 0.4069 14 0.0291 Lack of Fit 0.382 10 0.0382 6.13 0.0477 Pure Error 0.0249 4 0.0062 Core Total 139.57 28 R 2 = 0.9971 Adjusted R²=0.9942 Predicted R²=0.9840 The accuracy coefficient R 2 of the regression model was high, which indicates that the model was meaningful. The oil yield R 2 was about 0.9971. After model fitting, the adjusted R 2 value of the oil yield was 0.9942, which was highly consistent with the R 2 value, indicating that the experimental prediction level of BBD optimization was very high. In Table 2 , the coefficient of variation of oil yield is 0.6320, which was not high. The comprehensive evaluation shows that the model has high fitting ability and precision. When the "sufficient accuracy" is greater than 4, the signal strength meets the requirements of BBD, and the "sufficient accuracy" values of the oil yield are 56.8832[ 29 ]. The expression of oil yield was as follows: Y = 29.92 + 0.2867×X 1 + 0.2142×X 2 + 0.4092×X 3 + 0.1817×X 4 -0.0775×X 1 X 2 -0.0375×X 1 X 3 -0.01×X 1 X 4 -0.0225×X 2 X 3 + 0.1175×X 2 X 4 + 0.0825×X 3 X 4 -2×X 1 2 -X 2 2 -4.24×X 3 2 + 0.1176×X 4 2 To effectively investigate the influence of the interaction of various variable factors on oil yield, the X and Y axes were taken as variables, the response value was taken as the Z axis, and the other single factors were kept at zero (0 level) to form a three-dimensional umbrella graph. The response surface of the interaction of various factors on the oil yield was shown in Fig. 2 . The steeper the response surface 3D map was, the more significant the effect was. It could be seen that the impact of extraction power (X 3 ) and extraction temperature (X 4 ) on yield was the largest (Fig. 2 F), while the effects of liquid-solid ratio (X 1 ) and extraction temperature (X 4 ) on BSO yield was the smallest (Fig. 2 C). The results implied that the interaction between factors had the following effects on the yield of A.arguta seed: X 3 X 4 > X 1 X 3 >X 2 X 3 >X 1 X 2 >X 2 X 4 >X 1 X 4 . Combined with ANOVA, X 1 X 2 , X 1 X 3 , X 1 X 4 , X 2 X 3 , X 2 X 4 , and X 3 X 4 interaction terms had no significant effect on oil yield (P > 0.05), while X 1 X 2 , X 3 , and X 4 had significant effects on oil yield (P < 0.01). The yield increased significantly with the liquid-solid ratio and declined after 10:1 mL/g. To a certain extent, when the liquid-solid ratio was low, the appropriate increase of the liquid-solid ratio could promote the circulation of substances in the extracted liquid system, the flow of energy and the penetration of solvents into plant tissues, which was conducive to extraction[ 30 , 31 ]. On the contrary, the high liquid-solid ratio and excessive energy absorption in the extraction solution hindered the development of the extraction system[ 20 ]. The increase in oil yield can be explained by a more extended period, as the formation and subsequent collapse of the cavitation bubble then promotes the rupture of the cell wall, allowing the solvent to penetrate the cell and release the intercellular oil into the solution. However, when the extraction time exceeds the threshold (100 min), the overall oil yield decreases slightly. This phenomenon might be due to the low efficiency of the extraction process due to solvent evaporation. In addition, due to the porous nature of the raw material particles, the broken cell wall may reabsorb the extracted oil during a longer extraction time[ 32 , 33 ] When the extraction temperature exceeded 40 ℃, the oil yield decreased. This phenomenon occurred because although the thermal effect improves the diffusion of the solvent in the extraction solution in the matrix, the intensity of the cavitation effect caused by the ultrasonic effect should be reduced due to the high vapor pressure of the solvent. The high vapor pressure will also inhibit the generation of cavitation bubbles during the compression cycle[ 34 ]. Within a specific range, the oil yield increased significantly with the increase of extraction power. Still, the change amplitude was not noticeable. It tended to be stable after 160 W. This was mainly because the cavitation effect increases with increasing amplitude, resulting in better diffusion of the oil molecules into the solvent[ 35 ]. However, due to the nature of the ultrasonic cavitation effect, the extraction rate decreases beyond this range. When the ultrasonic amplitude was too large, cavitation bubbles could not form generally due to insufficient time. Too high an ultrasonic frequency can also affect the rupture of the formed bubbles, resulting in an overall reduction in the cavitation effect required to extract the oil from the feedstock effectively[ 36 ]. Therefore, the use of appropriate extraction power was necessary for the extraction of target substances. The optimal process conditions were obtained through BBD optimization and analysis of critical factors: liquid-solid ratio of 10:1 mL/g, extraction time of 98min, extraction power of 161W, and extraction temperature of 40℃. The optimal experimental conditions predicted were as follows: solid-liquid ratio of 10.24 mL/g, ultrasonic time of 97.72 min, ultrasonic power of 160.55 W, and ultrasonic temperature of 39.60 ℃. The verification experiments were carried out under certain conditions. The actual yield was (30.06 ± 0.21)%, close to the predicted value of 30.15% by the regression model. Therefore, the model fits to represent true variable relationships between the selected parameters. 3.3 Physicochemical property and antioxidant activity 3.3.1 Color Because the visual representations produced by the colors of oils are important for their potential applications in food and cosmetics. The main color compounds in vegetable oils are carotenes and chlorophylls[ 37 ]. The ten germplasms of A.arguta seed oils extracted according to the optimal extraction process were shown in Fig. 3 A. It can be intuitively observed that the colors of A.arguta seed oils were light yellow, and the seed oils of No. 2 and No. 3 were significantly lighter than others. The L*(brightness), a*(redness), and b*(yellowness) values were shown in Fig. 3 B, and the L* value measures brightness, ranging from 100 for perfect white to 0 for black. a* value: Red is measured when positive, grey when zero, and green when negative. b* value: yellow is measured when it is positive, grey is measured when it is zero, and blue is negative. L*, b*, and a* values of A.arguta seed oils were significant (p < 0.05), consistent with the results presented in Fig. 3 A. L* values in No. 2 and No. 3 were significantly higher than others, and b* was significantly lower than others. In comparing of a* values, all negative values were more inclined to green, and No. 3 seed oil had the smallest green value. The b* value indicated that all oils were yellow, with No. 14 seed oil having the highest yellow value. The main visual effect was to evaluate the appearance of the oil by removing seeds and particles. 3.3.2 Acid value and peroxide value Acid value and peroxide value are essential indexes to characterize oil quality. It had been reported that the acid value and peroxide value of fresh oil should be less than 4mg KOH/g and 10 mmol/kg, respectively [ 38 ]. All the ten germplasms of A.arguta seed oils in this study meet the standard. The acid value is directly related to the free fatty acid (FFA) content in oil. The higher the FFA level, the higher the index. The size of the acid value can see the content of free fatty acids in the oil to judge the quality of the oil [ 39 ]. The size of acid value represents the degree of oxidation of oil. The smaller the value, the smaller the degree of oxidation, the better the quality of oil, and the worse the quality of oil (M. et al., 2024). The acid value of A.arguta seed oils was 1.50–2.03 mg KOH/g oil, with a significant difference (P < 0.05). The highest acid value was No. 3, and the lowest was No. 36. Bialek et al. (2016) evaluated the quality of 17 plant oils as cosmetic ingredients with acid values ranging from 0.17 to 3.96 mg KOH/g and found the characterization of these oils to be acceptable for cosmetic applications[ 40 ]. Therefore the acid value of A.arguta seed oils meet its application in the cosmetic industry. The peroxide value is another critical parameter used to characterize the edibility of oils and fats. The oxidation of grease can be seen according to the peroxide value. The higher the peroxide value, the higher the content of primary oxidation products in the oil, which indicates the stronger the rancidity of the oil [ 41 ]. The peroxidation value of A.arguta seed oils was 6.13–8.94 mmol/kg with significant difference (P < 0.05), among which the highest peroxidation value was No. 3, and the lowest was HY. Deng et al. (2018) analyzed the physical and chemical indexes of kiwifruit oil, and the peroxide value was 7.13 mmol/kg, consistent with this study[ 26 ]. Although fresh oil meets the standard, it is easy to rancidity during storage, so suitable storage conditions should be selected. 3.3.3 Iodine value The number of grams of iodine required by 100 g of oil in addition to reaction with iodine under specific conditions is the iodine value of the oil. The iodine value can judge the degree of unsaturated oil, and the iodine value was positively correlated with the degree of unsaturated oil. The size of the iodine value could also evaluate the degree of dryness of the fat. Drying oil grease was a kind of oil with an iodine value higher than 130 I 2 g/100g oil, such as linseed oil and tung oil. The iodine value of semi-dry oil was between 100 and 130 I 2 g/100g oil. Non-drying oil referred to iodine value less than 100 I 2 g /100g oil, such as coconut oil, palm oil, peanut oil, etc. The iodine value of edible oil was mainly about 100 I 2 g /100g oil, which was semi-drying and non-drying oil grease. The iodine value of A.arguta seed oil were as high as 140.16-165.85 gI 2 /100g oil, indicating that A.arguta seed oil contains a large number of unsaturated double bonds and belonged to the dry oil, which was consistent with the research results of unsaturated fatty acid content of more than 80%, among which the highest iodine value is No. 6 (Fig. 4 C). 3.3.4 Fatty acids The two primary unsaturated fatty acids(PUFA) in A.arguta seed oils were linolenic acid and linoleic acid, and their contents were shown in Fig. 4 C. The highest linolenic acid content was No. 5, the lowest was No. 14, the highest was No. 3, and the lowest was No. 5. Oil can be used in the cosmetic industry as an emulsion because it contains fatty acids, compounds that can help maintain skin integrity[ 42 ]. Linoleic acid has a direct role in maintaining the integrity of the skin's water barrier, promoting hydration, and assisting in the healing process of skin diseases and sunburns[ 43 ]. 3.3.5 DPPH and FRAP The antioxidant activity of A.arguta seed oils were shown in Fig. 4 D. The differences in DPPH and FRAP contents of oil samples among all germplasms were statistically significant (P ≤ 0.05). In the FRAP assay, the highest value of No.36 was 74.71 ± 2.98 mg Trolox/kg, and the lowest value of No.5 was 59.20 ± 1.56 mg Trolox/kg. The highest value of No.36 was 29.50 ± 0.85% in the DPPH assay, while the lowest value of No. 5 was 27.06 ± 0.35%.The divergence in antioxidant activity among germplasms may due to the genotypes, growing region, and to different antioxidant activity evaluations employed by other studies such as ABTS and FRAP assays[ 27 ]. The expression results of two kinds of antioxidant capacity were consistent. Studies had shown that the size of FRAP is related to the content of oleic acid and linoleic acid, the highest linoleic acid was in No.36, and the lowest linoleic acid was in No.5. Natural antioxidants used in the cosmetic industry were able to reduce skin oxidative stress or prevent oxidative degradation of products[ 44 ]. In addition, the intake of antioxidants prevents intracellular oxidation, which was associated with promoting health and preventing most degenerative diseases[ 45 ]. 3.4 Principal component analysis and correlation analysis 3.4.1Principal component analysis Many factors determine the quality of oil, and it is difficult to evaluate the comprehensive quality of oil by a single index. Principal component analysis is an effective mathematical method that can reduce the dimensionality of multivariate data while retaining most of the variance[ 27 ]. With the PCA factor loading graph, we can determine the degree of contribution of each indicator. In this study, principal component analysis was performed on the physicochemical indexes (acid value, peroxide value, iodine value, color difference L*, a*, b*, linolenic acid, linoleic acid) and antioxidant indexes (DPPH and FRAP) of ten germplasms of A.arguta seed oils. The results were shown in Fig. 5 and Table 3 . Table 3 Eigenvalue, variance contribution rate, and the cumulative variance contribution rate of the three principal components. Indexes PC1 PC2 PC3 Acid value 0.908 -0.371 -0.118 Peroxide value 0.838 -0.473 -0.122 Iodine value -0.548 -0.428 0.589 L* 0.954 -0.03 -0.174 a* -0.963 -0.156 -0.09 b* -0.932 0.067 0.285 Linolenic -0.413 0.052 -0.828 Linoleic 0.589 -0.083 0.789 DPPH 0.308 0.91 0.195 FRAP 0.241 0.95 0.045 Eigenvalue 5.204 2.314 1.843 variance contribution rate(%) 52.037 23.14 18.426 cumulative variance contribution rate(%) 52.037 75.177 93.603 The first leading factor (PC1) explained 52.1% of the variation between samples, the second leading factor (PC2) explained 23.1% of the variation, the third leading factor (PC3) explained 18.4% of the variation, and the cumulative variance contribution of PC1, PC2, and PC3 was 93.6% (> 85%). In PC1, the main contributions are L, acid value, and peroxide value, reflecting the physicochemical quality of seed oil; in PC2, the main contributions are DPPH and PRAP, reflecting the antioxidant capacity of seed oil; in PC3, the main contributions are linoleic acid and iodine value. By multiplying the score of each factor by the arithmetic square root of the eigenvalue by its corresponding contribution rate, a comprehensive evaluation model was obtained: Y = 0.520×PC1 + 0.231×PC2 + 0.184×PC3 According to the above model, the seed oil quality scores of ten germplasms A.arguta were obtained, which were No. 4, No. 3, HY, No. 14, CJ No. 1, No. 5, No. 2, No. 18, No. 6, and No. 36 in order from the largest to the smallest. The No. 4 A.arguta seed oil should be promoted vigorously. The results were shown in Table 4 . Table 4 Principal component score and comprehensive evaluation index score of ten germplasms A.arguta seed oils. Germplasms PCA Evaluation index score PC1 PC2 PC3 Y CJ No.1 0.174 -0.160 -0.064 0.042 No.5 0.161 -0.204 -0.066 0.024 No.2 -0.105 -0.185 0.320 -0.038 No.14 0.183 -0.013 -0.095 0.075 No.36 -0.185 -0.068 -0.049 -0.121 No.18 -0.179 0.029 0.154 -0.058 No.6 -0.079 0.023 -0.449 -0.118 No.3 0.113 -0.036 0.428 0.129 No.4 0.059 0.393 0.106 0.141 HY 0.046 0.410 0.025 0.123 3.4.2 Correlation analysis Values of correlation coefficient (r) among studied parameters, summarized in Fig. 6 , reflect the ratio of explained variation to that of total variation. The results showed that the acid value was positively correlated with the peroxide value (r = 0.972), L* value (r = 0.880), and linoleic acid (r = 0.482), and the peroxide value was positively correlated with L* (r = 0.779) and linoleic acid (r = 0.441). Iodine value was positively correlated with b* value (r = 0.630), a* value (r = 0.510), and linoleic acid (r = 0.180), L* value was positively correlated with linoleic acid (r = 0.420), DPPH (r = 0.220), FRAP (r = 0.176), and b* (r = 0.830). Linolenic acid was positively correlated with a* (r = 0.180), b* (r = 0.412), DPPH was positively correlated with linoleic acid (r = 0.265) and FRAP (r = 0.975), and FRAP was positively correlated with linoleic acid (r = 0.232). The change in acid value can affect the change of L* value by more than 80%, and the correlation between DPPH and FRAP is very significant, consistent with the changes in A.arguta seed oils. In addition, acid value was negatively correlated with a* (r=-0.822) and b* (r=-0.907), peroxide value was negatively correlated with a* (r=-0.760) and b* (r=-0.820), and L* value was negatively correlated with a (r=-0.871) and b (r=-0.972). Linoleic acid was negatively correlated with linolenic acid (r=-0.870) and a* (r=-0.633). Studies have shown that the antioxidant activity was positively correlated with PUFA[ 27 ], linoleic acid and linolenic acid were both unsaturated fatty acids but were negatively correlated, so linoleic acid was positively correlated with DPPH and FRAP. It was reasonable that linolenic acid was negatively correlated with DPPH and FRAP. 4. Conclusion In this study, the optimal oil extraction process was determined using single-factor and response surface experiments, and then ten germplasms of A.arguta seeds were extracted under optimal technological conditions. The physical and chemical properties (acid value, peroxide value, iodine value, color, linolenic acid, and linoleic acid) and antioxidant activities (DPPH and FRAP) of ten germplasms of seed oils were compared and analyzed. The results showed that the color of No. 2 and No. 3 seed oils significantly differed from other seed oils. The acid value and peroxide value of seed oil No. 3 were the highest, and the iodine value and linoleic acid content of seed oil No. 14 were the highest. No. 5 had the highest linolenic acid content and the strongest antioxidant activity in No. 36. The seed oil quality was comprehensively evaluated by principal component analysis score, and No. 4 was selected as the most developed seed oil resource. The correlation analysis of the above indexes proved that iodine value was positively correlated with linoleic acid, DPPH was strongly positively correlated with FRAP, and linoleic acid was strongly negatively correlated with linoleic acid. This study improved seed oil yield, reduced seed powder loss, screened out the most potential seed oil resources, and provided a theoretical basis for the future development of seed oil in the food and cosmetics industry. Declarations The authors declare that there are no conflicts of interests or personal relationships that could have appeared to influence the work reported in this paper. Authors' contributions Conceptualization: Song Pan, Methodology: Guanlin Qian, Formal analysis and investigation: Heran Xu; Writing - original draft preparation: Miao Yan; Writing - review and editing: Miao Yan; Funding acquisition: Huanyu Wang; Resources: Lin Hui,Yuli Zhang; Supervision: Guang Xin. Acknowledgment and funding This work was supported by the National Key Research and Development Program of China [Project No. 2024YFD15015021], Provincial Department of Agriculture, Shenyang seed industry developement [No.01080122003/01080123001] , and Liaoning Province, Shenyang Agricultural University, high-end talent introduction fund [Project No. SYAU20160003]. References Song M, Xu H, Xin G, Liu C, Sun X, Zhi Y, et al. 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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-6226482","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431265993,"identity":"a5d324b7-4b4c-4251-8180-721da04b2298","order_by":0,"name":"Miao Yan","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Yan","suffix":""},{"id":431265998,"identity":"48fc65d1-8823-45d7-b252-0c27f5c96d15","order_by":1,"name":"Song Pan","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Pan","suffix":""},{"id":431266000,"identity":"d23ad7a2-58ab-4750-a82c-434c4406fc86","order_by":2,"name":"Heran Xu","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Heran","middleName":"","lastName":"Xu","suffix":""},{"id":431266002,"identity":"bf6c009b-2e18-42a2-8b5e-fecb06ff546e","order_by":3,"name":"Guanlin Qian","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guanlin","middleName":"","lastName":"Qian","suffix":""},{"id":431266007,"identity":"ad2f9d75-0605-4217-b3e5-305c822c8849","order_by":4,"name":"Huanyu Wang","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Huanyu","middleName":"","lastName":"Wang","suffix":""},{"id":431266010,"identity":"efb4c4e2-4423-437f-9139-441206ebdbbf","order_by":5,"name":"Lin Hui","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Hui","suffix":""},{"id":431266014,"identity":"3f7201c2-e23d-4b58-a77a-8f333e13a077","order_by":6,"name":"Yuli Zhang","email":"","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuli","middleName":"","lastName":"Zhang","suffix":""},{"id":431266015,"identity":"aaca6c4f-bcd1-49e6-8a22-0d061591531d","order_by":7,"name":"Guang Xin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYPACGx5+9sbGhx9I0JImI9lzuNlYggQth20MbqS3CfAQo9bg/BrDzwW/mHkYbj5sY5BgsJPTbSCgRXLGG2PpmX1sPIyzE9seFDAkG5sdIKCFX+KMgTRvDw8Ps3Riu4EEw4HEbYS0sEmcMf7N2yPBwyZ5sE2Chxgt/Pw9ZtI8Pwx4eCQYidQiOYOtzJq3IYFHgicRGMgGRPjF4Pzhzbd5/vy3tz9+/OHDDxV2cgS1MEgkMDAwtsFNIKQcBPhBhv4hRuUoGAWjYBSMWAAA9so+2iOByBMAAAAASUVORK5CYII=","orcid":"","institution":"Shenyang Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Guang","middleName":"","lastName":"Xin","suffix":""}],"badges":[],"createdAt":"2025-03-14 12:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6226482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6226482/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78915742,"identity":"085b673b-66a6-48b0-b232-8f2bc303f8d6","added_by":"auto","created_at":"2025-03-20 18:29:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26360,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of liquid-solid ratio, extraction time, extractionpower, and extraction temperature on the yield of \u003cem\u003eA.arguta\u003c/em\u003e seed oil.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/573b017613057737d8ceb73b.png"},{"id":78915744,"identity":"c55e3bd6-c936-4d29-864e-2d3a9df164d3","added_by":"auto","created_at":"2025-03-20 18:29:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":555611,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional response surface graphs illustrating the interaction between A) liquid-solid ratio with extraction time; B) liquid-solid ratio with extraction power; C) liquid-solid ratio with extraction temperature; D) extraction time with extraction power; E) extraction time with extraction temperature and F) extraction power with extraction temperature on the yield of \u003cem\u003eA.arguta\u003c/em\u003e seed oil.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/037325674852cf1e5215b0f4.png"},{"id":78915743,"identity":"42a06ded-6913-45ee-b149-affc4e15a141","added_by":"auto","created_at":"2025-03-20 18:29:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":475908,"visible":true,"origin":"","legend":"\u003cp\u003eThe A) source and actual color and B) the results of color difference meterof ten germplasms \u003cem\u003eA.arguta\u003c/em\u003e seed oils.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/84710fcf50ce60fca07768a6.png"},{"id":78916052,"identity":"bba7b716-1d87-4d7b-9118-1a78e5373228","added_by":"auto","created_at":"2025-03-20 18:37:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":417338,"visible":true,"origin":"","legend":"\u003cp\u003eThe A) acid value and peroxide value; B) iodine value; C) main fatty acid contents (linolneic acid and linolnic acid); D) antioxidant activity (DPPH and FRAP) of ten germplasms \u003cem\u003eA.arguta\u003c/em\u003e seed oils.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/9cb0d5e3a391a0501b4f40ff.png"},{"id":78916537,"identity":"29a3d16d-0162-42c8-98e3-667143ddb5e5","added_by":"auto","created_at":"2025-03-20 18:53:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8939,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) load on physicochemical properties and antioxidant activity of ten germplasms \u003cem\u003eA.arguta\u003c/em\u003e seed oils.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/8829c0cebbb4fe05d41c87d4.png"},{"id":78916059,"identity":"1022640b-6fe6-448a-bcd0-77c42f21a018","added_by":"auto","created_at":"2025-03-20 18:37:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15150,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heat map on physicochemical properties and antioxidant activity of ten germplasms \u003cem\u003eA.arguta\u003c/em\u003eseed oils.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/5f2558ab016c10a14c16f510.png"},{"id":78916793,"identity":"42852634-1c1d-4341-a43e-b5d9727966c3","added_by":"auto","created_at":"2025-03-20 19:01:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2812579,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/fbad8f09-2aab-4fb8-bf35-37c747227f33.pdf"},{"id":78915747,"identity":"448b771b-2fb7-422e-8c5a-e402059477f0","added_by":"auto","created_at":"2025-03-20 18:29:51","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":281600,"visible":true,"origin":"","legend":"","description":"","filename":"HighlightsandGraphicalabstract.doc","url":"https://assets-eu.researchsquare.com/files/rs-6226482/v1/280cc6d0f2e13b06a918fe64.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison and evaluation of physicochemical properties and nutritional quality on ten germplasms of Actinidia arguta seed oils by ultrasonic-assisted extraction","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eActinidia arguta\u003c/em\u003e (\u003cem\u003eA.arguta\u003c/em\u003e) are botanically classified as berries from the Actinidiaceae family, also known as hardy kiwi and kiwi berry, which is popular with consumers for its rich vitamin C[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is now commercially cultivated in the United States, Chile, New Zealand, Australia, China, and most of Europe, and it has extensive wild resources in northeast China[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The fruit is smooth and hairless and can be eaten directly. Still, due to its poor storability, processing is the best treatment alternative to fresh food, and the proper utilization of processing by-products is a challenge for the agri-food industry and civil society[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The fruit and vegetable losses and waste up to 60% of the total production, which mainly consists of peels, pomades, flowers, stems, leaves, seeds, and pulp[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs one of the main by-products, the seeds of \u003cem\u003eA.arguta\u003c/em\u003e accounted for 7\u0026ndash;10% of the quality of the fresh fruit and had the potential to develop oil. In addition to natural antioxidants, plant seed oil with antioxidant properties has recently become a research focus[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, grape seed oil has shown antioxidant activity in the body by alleviating oxidative stress conditions, scavenging free radicals, inhibiting lipid oxidation, or reducing hydroperoxide formation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Gasparini et al.(2023) evaluated the potential use of apple seed oil by analyzing its physicochemical properties and antioxidant activity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In another study by Ozden et al. (2023), the antioxidant activity and fatty acids composition of Kiwifruit seed oil were characterized. Fruit oils of berries (Strawberry, blueberry, blackberry, cranberry, and red raspberry) have been studied as potential for food and cosmetic applications due to their antioxidant activity[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, more reports were needed on the physicochemical composition antioxidants of \u003cem\u003eA.arguta\u003c/em\u003e seed oil. Therefore, effective extraction and accurate quantification of fruit oil were necessary for the by-product processing and utilization.\u003c/p\u003e \u003cp\u003eAt present, there have been few reports on fruit oil extraction. Several techniques had played a role in fruit seed oil extraction, including ultrasonic-assisted extraction (UAE)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], solvent extraction[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], cold pressing[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], supercritical fluid extraction[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], microwave-assisted extraction (MAE)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], UAE was regarded as an efficient method to improve the extraction yield and reduce extraction time and solvent consumption, and possess good scalability[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the ultrasonic-assisted solvent extraction process, microbubbles are formed due to the contraction and expansion of the liquid, and the energy given by ultrasound gradually increases the bubbles and accumulates energy in the interior until the bubbles burst, called acoustic cavitation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The bubble bursting will release a large amount of thermal energy conducive to destroying the plant cell wall, thus speeding up the extraction speed and increasing the extraction rate[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Eventually, over time, when the plant cell wall breaks, the intercellular oil will be released[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The researchers studied the effects of ultrasonic extraction variables (i.e., ultrasonic power, time, temperature, and solvent-solid ratio) on oil recovery from different plant sources[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Under the optimum extraction conditions, the yield of pomegranate oil was higher than the conventional method [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, no research report on extracting \u003cem\u003eA.arguta\u003c/em\u003e seed oil under different ultrasonic extraction conditions existed.\u003c/p\u003e \u003cp\u003eThis study aimed to establish an efficient ultrasonic-assisted extraction method for \u003cem\u003eA.arguta\u003c/em\u003e seed oil. A single-factor experiment screened the critical parameters of seed oil extraction, and the optimal extraction conditions of seed oil were determined by Box-Behnken design (BBD). Secondly, ten germplasms of \u003cem\u003eA.arguta\u003c/em\u003e seed oils were extracted under the optimal extraction conditions, and their physicochemical properties (acid value, peroxide value, iodine value, color, fatty acids) and antioxidant activities were studied to explore the application potential of the obtained \u003cem\u003eA.arguta\u003c/em\u003e seed oil in food and cosmetics industry. This study will provide theoretical guidance for the processing and utilization of \u003cem\u003eA.arguta\u003c/em\u003e seed oil and improve the efficient utilization of industrial by-products of \u003cem\u003eA.arguta\u003c/em\u003e.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cp\u003eThe fruits of \u003cem\u003eA. arguta\u003c/em\u003e were picked at the same maturity stage in the Northeast Wild \u003cem\u003eA. arguta\u003c/em\u003e resource nursery (Shenyang) of the Ministry of Agriculture. The ten germplasms included Changjiang No. 1(CJ No.1), Huan You(HY), No. 5, No. 2, No. 14, No. 36, No. 18, No. 6, No. 3 and No. 4, and CJ No.1 was used for oil extraction process optimization. The harvested fruit were immediately transported to the laboratory.Then the seeds were manually picked out and dried at room temperature (25\u0026thinsp;\u0026plusmn;\u0026thinsp;2℃). After drying to the constant weight, the seeds were powered with particle size by mills and placed in refrigerated condition for further use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ultrasonic-assisted solvent extraction(USSE)\u003c/h2\u003e \u003cp\u003eUltrasonic-assisted solvent extraction was achieved using an ultrasonic cleaner (KQ-300DE, Kunshan, China). The device can adjust ultrasonic time, ultrasonic temperature, and ultrasonic power. The ultrasonic-assisted solvent extraction process was as follows: CJ No.1 \u003cem\u003eA.arguta\u003c/em\u003e seed powder (5 g) and different volumes of n-hexane (depending on the liquid-solid ratio ) were added to the flask and extracted, after which the solvent was evaporated with a rotary evaporator at 40 \u0026deg; C, and measured by gravimetry, and the formula was:\u003c/p\u003e \u003cp\u003eThe oil extraction yield (%)\u0026thinsp;=\u0026thinsp;extracted oil mass/ dry material mass \u0026times;100%\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Single-factor experiments\u003c/h2\u003e \u003cp\u003eThe effects of different liquid-solid ratios (6:1, 8:1, 10:1, 12:1,14:1 mL/g) extraction time (60, 80, 100, 120, 140 min), extraction power (100, 130, 160, 190, 240 W) and extraction temperature (30, 35, 40, 45, 50℃ ) on the yield of \u003cem\u003eA.arguta\u003c/em\u003e seed oil were studied\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Box-Behnken experimental design(BBD)\u003c/h2\u003e \u003cp\u003eBased on the results of the single-factor experiments, along with the response surface methodology (RSM) was employed to optimize the extraction process conditions, liquid-solid ratio (X\u003csub\u003e1\u003c/sub\u003e, 6:1\u0026ndash;10:1 mL/g), extraction time (X\u003csub\u003e2,\u003c/sub\u003e 80\u0026ndash;120 min), extraction power (X\u003csub\u003e3\u003c/sub\u003e, 130\u0026ndash;190 W), and extraction temperature (X\u003csub\u003e4\u003c/sub\u003e, 30\u0026ndash;50 ℃) were chosen for further optimization with BBD. The experimental scheme of the BBD was shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A 29-randomized experiment containing three replicates in the center point was designed to establish the quadratic equation model. The relationship between the predicted oil yield (Y) and the independent variables (Xi ) was expressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Physicochemical property\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 The acid value, peroxidation value, and iodine value\u003c/h2\u003e \u003cp\u003eThe contents of acid value(AV), peroxidation value(PV), and iodine value(IV) of \u003cem\u003eA.arguta\u003c/em\u003e seed oils were determined according to the China standards GB/5009.229\u0026ndash;2016, GB/5009.227\u0026ndash;2023, GB/T5532-2022, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. The color\u003c/h2\u003e \u003cp\u003eCIE LAB color system was used to characterize color differences, and L*, a*, and b* of \u003cem\u003eA.arguta\u003c/em\u003e seed oils were measured by color difference meter (CR400, Japanese Konica Minolta).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Fatty acids\u003c/h2\u003e \u003cp\u003eAnalysis of \u003cem\u003eA.arguta\u003c/em\u003e seed oils by gas chromatography-mass spectrometry\u003c/p\u003e \u003cp\u003e(7890A-5975A, Agilent, USA). The operating conditions were modified according to Lolli et al.(2023): the oven temperature was scheduled to be maintained from 40 ℃ for 5 minutes, at 6 ℃/min to 220 ℃ for 20 minutes, the shunt ratio was 20:1, and the solvent delay was 3 minutes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Antioxidant activity\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1. DPPH free radical scavenging rate\u003c/h2\u003e \u003cp\u003eSeed oils' DPPH radical scavenging activity was determined using a slightly modified DPPH assay of Yao et al.(2020). The seed oils (2 mL) were added to a DPPH solution diluted with 0.05 mg/mL ethanol. It was shaken gently and stored in darkness at room temperature for 30 minutes, and then measured at 517 nm by a UV spectrophotometer (UV2700, SHIMADZU, Japan). Inhibition rate was used to express the DPPH scavenging activity of seed oils.\u003c/p\u003e \u003cp\u003eDPPH(%) = (1\u0026thinsp;\u0026minus;\u0026thinsp;A \u003csub\u003eSample\u003c/sub\u003e / A \u003csub\u003econtrol\u003c/sub\u003e) \u0026times;100%\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 FRAP\u003c/h2\u003e \u003cp\u003e The FRAP of seed oils was modified according to Deng et al.( 2018). First, the working liquid was prepared: acetic acid buffer (200 mM, pH 3.6), TPTZ (10 mM), and FeCl\u003csub\u003e3\u003c/sub\u003e (20 mM) were mixed at a ratio of 10:1:1 and incubated at 37℃ for reserve. Then, 3 mL seed oils and 3 ml FRAP working liquid were reacted in the darkness for 2 hours, and the absorbance was measured at 593 nm by a UV spectrophotometer (UV2700, SHIMADZU, Japan). Trolox (6.25\u0026thinsp;~\u0026thinsp;200 \u0026micro;M) was used to establish the standard curve. The result is expressed in mg Trolox/kg oil.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll experiments were conducted three times, and the data were expressed as mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. BBD was performed using Design Expert 13.0 software (Stat Ease Inc., Minneapolis, MN, USA)). Principal component analysis(PCA) and correlation analysis and their significant differences were processed by Origin 2023b (OriginLab Corporation., Northampton, MA, USA) and SPSS 26.0 (IBM, New York, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Single-factor experiments\u003c/h2\u003e \u003cp\u003eFour factors, including liquid-solid ratio, extraction time, extraction power, and extraction temperature, were selected to optimize the extraction effect. The influences of different factors on the oil yield of \u003cem\u003eA.arguta\u003c/em\u003e seed oil were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The range of liquid-solid ratio, extraction time, extraction power, and extraction temperature were 23.37\u0026ndash;28.51%, 23.52\u0026ndash;29.04%, 23.41\u0026ndash;29.32%, and 23.62\u0026ndash;29.58%, respectively. The influence trends of the four factors were the same, increasing and then decreasing. To obtain the optimal extraction process, liquid-solid ratio (8:1, 10:1, 12:1 mL/g), extraction time (80, 100, 120 min), extraction power (140, 160, 180 W), and extraction temperature (35, 40, 45 ℃) were selected for RSM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Optimization of crucial influencing factors by Box\u0026ndash;Behnken design(BBD)\u003c/h2\u003e \u003cp\u003eThe results of optimization using BBD were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The oil yield of the seeds was 23.01\u0026ndash;30.02%. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed the adjustment results and analysis of variance (ANOVA) of \u003cem\u003eA.arguta\u003c/em\u003e oil yield. The significance difference of each coefficient in the regression equation fitted by the optimized analysis model was tested by Fischer's \"F statistic\" value (F-value) and probability value (p-value), and the relationship between each variable and the response value[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Generally, when model P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, it means that the model is statistically significant. At the same time, each variable factor's influenced degree and statistical significance could be obtained according to the F-value and p-value. When P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, the difference was not significant; When P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, the difference was very significant. When 0.01\u0026thinsp;\u0026lt;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the difference was significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBox-Behnken design with the observed responses and predicted values for yield of \u003cem\u003eA.arguta\u003c/em\u003e seed oil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiquid-solid ratio(X\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtraction time(X\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtraction power(X\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtraction temperature(X\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEexp (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEpredicted (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.03\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\u003eIt can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that the parameters of linear non-combination items X\u003csub\u003e1\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003e, X\u003csub\u003e3\u003c/sub\u003e, X\u003csub\u003e4\u003c/sub\u003e and quadratic combination items X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e had statistically significant effects on seed oil yield. Besides, the F value corresponding to the response value was 342.02, and the p-value of the model formation probability ability was extremely significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), which indicated that the corresponding model of the relationship between the oil yield and various factor variables had statistical significance. The experimental results showed that the oil yield of the optimized experiment could be well-fitted and predicted. According to the F value obtained from the analysis of variance, the influence degree of the existence of non-combined influencing factors on the oil extraction rate was determined: X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;X\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;X\u003csub\u003e2\u003c/sub\u003e \u0026gt; X\u003csub\u003e4\u003c/sub\u003e. It was consistent with F's research results.\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\u003eAdjustment and analysis of variance (ANOVA) of \u003cem\u003eA.arguta\u003c/em\u003e oil yield .\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e342.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e890.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4019.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCore Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdjusted R\u0026sup2;=0.9942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePredicted R\u0026sup2;=0.9840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe accuracy coefficient R\u003csup\u003e2\u003c/sup\u003e of the regression model was high, which indicates that the model was meaningful. The oil yield R\u003csup\u003e2\u003c/sup\u003e was about 0.9971. After model fitting, the adjusted R\u003csup\u003e2\u003c/sup\u003e value of the oil yield was 0.9942, which was highly consistent with the R\u003csup\u003e2\u003c/sup\u003e value, indicating that the experimental prediction level of BBD optimization was very high. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the coefficient of variation of oil yield is 0.6320, which was not high. The comprehensive evaluation shows that the model has high fitting ability and precision. When the \"sufficient accuracy\" is greater than 4, the signal strength meets the requirements of BBD, and the \"sufficient accuracy\" values of the oil yield are 56.8832[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The expression of oil yield was as follows:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;29.92\u0026thinsp;+\u0026thinsp;0.2867\u0026times;X\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.2142\u0026times;X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.4092\u0026times;X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1817\u0026times;X\u003csub\u003e4\u003c/sub\u003e-0.0775\u0026times;X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e-0.0375\u0026times;X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e-0.01\u0026times;X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e-0.0225\u0026times;X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1175\u0026times;X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0825\u0026times;X\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e-2\u0026times;X\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-X\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-4.24\u0026times;X\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.1176\u0026times;X\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTo effectively investigate the influence of the interaction of various variable factors on oil yield, the X and Y axes were taken as variables, the response value was taken as the Z axis, and the other single factors were kept at zero (0 level) to form a three-dimensional umbrella graph. The response surface of the interaction of various factors on the oil yield was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The steeper the response surface 3D map was, the more significant the effect was. It could be seen that the impact of extraction power (X\u003csub\u003e3\u003c/sub\u003e) and extraction temperature (X\u003csub\u003e4\u003c/sub\u003e) on yield was the largest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), while the effects of liquid-solid ratio (X\u003csub\u003e1\u003c/sub\u003e) and extraction temperature (X\u003csub\u003e4\u003c/sub\u003e) on BSO yield was the smallest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The results implied that the interaction between factors had the following effects on the yield of \u003cem\u003eA.arguta\u003c/em\u003e seed: X\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u0026gt;X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e\u0026gt;X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e\u0026gt;X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e\u0026gt;X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e. Combined with ANOVA, X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e2\u003c/sub\u003e, X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e, X\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e3\u003c/sub\u003e, X\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e, and X\u003csub\u003e3\u003c/sub\u003eX\u003csub\u003e4\u003c/sub\u003e interaction terms had no significant effect on oil yield (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while X\u003csub\u003e1\u003c/sub\u003e X\u003csub\u003e2\u003c/sub\u003e, X\u003csub\u003e3\u003c/sub\u003e, and X\u003csub\u003e4\u003c/sub\u003e had significant effects on oil yield (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe yield increased significantly with the liquid-solid ratio and declined after 10:1 mL/g. To a certain extent, when the liquid-solid ratio was low, the appropriate increase of the liquid-solid ratio could promote the circulation of substances in the extracted liquid system, the flow of energy and the penetration of solvents into plant tissues, which was conducive to extraction[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On the contrary, the high liquid-solid ratio and excessive energy absorption in the extraction solution hindered the development of the extraction system[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe increase in oil yield can be explained by a more extended period, as the formation and subsequent collapse of the cavitation bubble then promotes the rupture of the cell wall, allowing the solvent to penetrate the cell and release the intercellular oil into the solution. However, when the extraction time exceeds the threshold (100 min), the overall oil yield decreases slightly. This phenomenon might be due to the low efficiency of the extraction process due to solvent evaporation. In addition, due to the porous nature of the raw material particles, the broken cell wall may reabsorb the extracted oil during a longer extraction time[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eWhen the extraction temperature exceeded 40 ℃, the oil yield decreased. This phenomenon occurred because although the thermal effect improves the diffusion of the solvent in the extraction solution in the matrix, the intensity of the cavitation effect caused by the ultrasonic effect should be reduced due to the high vapor pressure of the solvent. The high vapor pressure will also inhibit the generation of cavitation bubbles during the compression cycle[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin a specific range, the oil yield increased significantly with the increase of extraction power. Still, the change amplitude was not noticeable. It tended to be stable after 160 W. This was mainly because the cavitation effect increases with increasing amplitude, resulting in better diffusion of the oil molecules into the solvent[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, due to the nature of the ultrasonic cavitation effect, the extraction rate decreases beyond this range. When the ultrasonic amplitude was too large, cavitation bubbles could not form generally due to insufficient time. Too high an ultrasonic frequency can also affect the rupture of the formed bubbles, resulting in an overall reduction in the cavitation effect required to extract the oil from the feedstock effectively[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, the use of appropriate extraction power was necessary for the extraction of target substances.\u003c/p\u003e \u003cp\u003eThe optimal process conditions were obtained through BBD optimization and analysis of critical factors: liquid-solid ratio of 10:1 mL/g, extraction time of 98min, extraction power of 161W, and extraction temperature of 40℃. The optimal experimental conditions predicted were as follows: solid-liquid ratio of 10.24 mL/g, ultrasonic time of 97.72 min, ultrasonic power of 160.55 W, and ultrasonic temperature of 39.60 ℃. The verification experiments were carried out under certain conditions. The actual yield was (30.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21)%, close to the predicted value of 30.15% by the regression model. Therefore, the model fits to represent true variable relationships between the selected parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Physicochemical property and antioxidant activity\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Color\u003c/h2\u003e \u003cp\u003eBecause the visual representations produced by the colors of oils are important for their potential applications in food and cosmetics. The main color compounds in vegetable oils are carotenes and chlorophylls[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The ten germplasms of \u003cem\u003eA.arguta\u003c/em\u003e seed oils extracted according to the optimal extraction process were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. It can be intuitively observed that the colors of \u003cem\u003eA.arguta\u003c/em\u003e seed oils were light yellow, and the seed oils of No. 2 and No. 3 were significantly lighter than others. The L*(brightness), a*(redness), and b*(yellowness) values were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, and the L* value measures brightness, ranging from 100 for perfect white to 0 for black. a* value: Red is measured when positive, grey when zero, and green when negative. b* value: yellow is measured when it is positive, grey is measured when it is zero, and blue is negative. L*, b*, and a* values of \u003cem\u003eA.arguta\u003c/em\u003e seed oils were significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. L* values in No. 2 and No. 3 were significantly higher than others, and b* was significantly lower than others. In comparing of a* values, all negative values were more inclined to green, and No. 3 seed oil had the smallest green value. The b* value indicated that all oils were yellow, with No. 14 seed oil having the highest yellow value. The main visual effect was to evaluate the appearance of the oil by removing seeds and particles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Acid value and peroxide value\u003c/h2\u003e \u003cp\u003eAcid value and peroxide value are essential indexes to characterize oil quality. It had been reported that the acid value and peroxide value of fresh oil should be less than 4mg KOH/g and 10 mmol/kg, respectively [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. All the ten germplasms of \u003cem\u003eA.arguta\u003c/em\u003e seed oils in this study meet the standard. The acid value is directly related to the free fatty acid (FFA) content in oil. The higher the FFA level, the higher the index. The size of the acid value can see the content of free fatty acids in the oil to judge the quality of the oil [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The size of acid value represents the degree of oxidation of oil. The smaller the value, the smaller the degree of oxidation, the better the quality of oil, and the worse the quality of oil (M. et al., 2024). The acid value of \u003cem\u003eA.arguta\u003c/em\u003e seed oils was 1.50\u0026ndash;2.03 mg KOH/g oil, with a significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The highest acid value was No. 3, and the lowest was No. 36. Bialek et al. (2016) evaluated the quality of 17 plant oils as cosmetic ingredients with acid values ranging from 0.17 to 3.96 mg KOH/g and found the characterization of these oils to be acceptable for cosmetic applications[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore the acid value of \u003cem\u003eA.arguta\u003c/em\u003e seed oils meet its application in the cosmetic industry.\u003c/p\u003e \u003cp\u003eThe peroxide value is another critical parameter used to characterize the edibility of oils and fats. The oxidation of grease can be seen according to the peroxide value. The higher the peroxide value, the higher the content of primary oxidation products in the oil, which indicates the stronger the rancidity of the oil [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The peroxidation value of \u003cem\u003eA.arguta\u003c/em\u003e seed oils was 6.13\u0026ndash;8.94 mmol/kg with significant difference (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), among which the highest peroxidation value was No. 3, and the lowest was HY. Deng et al. (2018) analyzed the physical and chemical indexes of kiwifruit oil, and the peroxide value was 7.13 mmol/kg, consistent with this study[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Although fresh oil meets the standard, it is easy to rancidity during storage, so suitable storage conditions should be selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Iodine value\u003c/h2\u003e \u003cp\u003eThe number of grams of iodine required by 100 g of oil in addition to reaction with iodine under specific conditions is the iodine value of the oil. The iodine value can judge the degree of unsaturated oil, and the iodine value was positively correlated with the degree of unsaturated oil. The size of the iodine value could also evaluate the degree of dryness of the fat. Drying oil grease was a kind of oil with an iodine value higher than 130 I\u003csub\u003e2\u003c/sub\u003e g/100g oil, such as linseed oil and tung oil. The iodine value of semi-dry oil was between 100 and 130 I\u003csub\u003e2\u003c/sub\u003e g/100g oil. Non-drying oil referred to iodine value less than 100 I\u003csub\u003e2\u003c/sub\u003e g /100g oil, such as coconut oil, palm oil, peanut oil, etc. The iodine value of edible oil was mainly about 100 I\u003csub\u003e2\u003c/sub\u003e g /100g oil, which was semi-drying and non-drying oil grease. The iodine value of \u003cem\u003eA.arguta\u003c/em\u003e seed oil were as high as 140.16-165.85 gI\u003csub\u003e2\u003c/sub\u003e/100g oil, indicating that \u003cem\u003eA.arguta\u003c/em\u003e seed oil contains a large number of unsaturated double bonds and belonged to the dry oil, which was consistent with the research results of unsaturated fatty acid content of more than 80%, among which the highest iodine value is No. 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Fatty acids\u003c/h2\u003e \u003cp\u003eThe two primary unsaturated fatty acids(PUFA) in \u003cem\u003eA.arguta\u003c/em\u003e seed oils were linolenic acid and linoleic acid, and their contents were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC. The highest linolenic acid content was No. 5, the lowest was No. 14, the highest was No. 3, and the lowest was No. 5. Oil can be used in the cosmetic industry as an emulsion because it contains fatty acids, compounds that can help maintain skin integrity[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Linoleic acid has a direct role in maintaining the integrity of the skin's water barrier, promoting hydration, and assisting in the healing process of skin diseases and sunburns[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.5 DPPH and FRAP\u003c/h2\u003e \u003cp\u003eThe antioxidant activity of \u003cem\u003eA.arguta\u003c/em\u003e seed oils were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD. The differences in DPPH and FRAP contents of oil samples among all germplasms were statistically significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.05). In the FRAP assay, the highest value of No.36 was 74.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98 mg Trolox/kg, and the lowest value of No.5 was 59.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56 mg Trolox/kg. The highest value of No.36 was 29.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85% in the DPPH assay, while the lowest value of No. 5 was 27.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35%.The divergence in antioxidant activity among germplasms may due to the genotypes, growing region, and to different antioxidant activity evaluations employed by other studies such as ABTS and FRAP assays[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The expression results of two kinds of antioxidant capacity were consistent. Studies had shown that the size of FRAP is related to the content of oleic acid and linoleic acid, the highest linoleic acid was in No.36, and the lowest linoleic acid was in No.5. Natural antioxidants used in the cosmetic industry were able to reduce skin oxidative stress or prevent oxidative degradation of products[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, the intake of antioxidants prevents intracellular oxidation, which was associated with promoting health and preventing most degenerative diseases[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Principal component analysis and correlation analysis\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1Principal component analysis\u003c/h2\u003e \u003cp\u003eMany factors determine the quality of oil, and it is difficult to evaluate the comprehensive quality of oil by a single index. Principal component analysis is an effective mathematical method that can reduce the dimensionality of multivariate data while retaining most of the variance[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith the PCA factor loading graph, we can determine the degree of contribution of each indicator. In this study, principal component analysis was performed on the physicochemical indexes (acid value, peroxide value, iodine value, color difference L*, a*, b*, linolenic acid, linoleic acid) and antioxidant indexes (DPPH and FRAP) of ten germplasms of \u003cem\u003eA.arguta\u003c/em\u003e seed oils. The results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eEigenvalue, variance contribution rate, and the cumulative variance contribution rate of the three principal components.\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\u003eIndexes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcid value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeroxide value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIodine value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinolenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinoleic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFRAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariance contribution rate(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecumulative variance contribution rate(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.603\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\u003eThe first leading factor (PC1) explained 52.1% of the variation between samples, the second leading factor (PC2) explained 23.1% of the variation, the third leading factor (PC3) explained 18.4% of the variation, and the cumulative variance contribution of PC1, PC2, and PC3 was 93.6% (\u0026gt;\u0026thinsp;85%). In PC1, the main contributions are L, acid value, and peroxide value, reflecting the physicochemical quality of seed oil; in PC2, the main contributions are DPPH and PRAP, reflecting the antioxidant capacity of seed oil; in PC3, the main contributions are linoleic acid and iodine value. By multiplying the score of each factor by the arithmetic square root of the eigenvalue by its corresponding contribution rate, a comprehensive evaluation model was obtained:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;0.520\u0026times;PC1\u0026thinsp;+\u0026thinsp;0.231\u0026times;PC2\u0026thinsp;+\u0026thinsp;0.184\u0026times;PC3\u003c/p\u003e \u003cp\u003eAccording to the above model, the seed oil quality scores of ten germplasms \u003cem\u003eA.arguta\u003c/em\u003e were obtained, which were No. 4, No. 3, HY, No. 14, CJ No. 1, No. 5, No. 2, No. 18, No. 6, and No. 36 in order from the largest to the smallest. The No. 4 \u003cem\u003eA.arguta\u003c/em\u003e seed oil should be promoted vigorously. The results were shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003ePrincipal component score and comprehensive evaluation index score of ten germplasms \u003cem\u003eA.arguta\u003c/em\u003e seed 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGermplasms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEvaluation index score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCJ No.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Correlation analysis\u003c/h2\u003e \u003cp\u003eValues of correlation coefficient (r) among studied parameters, summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, reflect the ratio of explained variation to that of total variation. The results showed that the acid value was positively correlated with the peroxide value (r\u0026thinsp;=\u0026thinsp;0.972), L* value (r\u0026thinsp;=\u0026thinsp;0.880), and linoleic acid (r\u0026thinsp;=\u0026thinsp;0.482), and the peroxide value was positively correlated with L* (r\u0026thinsp;=\u0026thinsp;0.779) and linoleic acid (r\u0026thinsp;=\u0026thinsp;0.441). Iodine value was positively correlated with b* value (r\u0026thinsp;=\u0026thinsp;0.630), a* value (r\u0026thinsp;=\u0026thinsp;0.510), and linoleic acid (r\u0026thinsp;=\u0026thinsp;0.180), L* value was positively correlated with linoleic acid (r\u0026thinsp;=\u0026thinsp;0.420), DPPH (r\u0026thinsp;=\u0026thinsp;0.220), FRAP (r\u0026thinsp;=\u0026thinsp;0.176), and b* (r\u0026thinsp;=\u0026thinsp;0.830). Linolenic acid was positively correlated with a* (r\u0026thinsp;=\u0026thinsp;0.180), b* (r\u0026thinsp;=\u0026thinsp;0.412), DPPH was positively correlated with linoleic acid (r\u0026thinsp;=\u0026thinsp;0.265) and FRAP (r\u0026thinsp;=\u0026thinsp;0.975), and FRAP was positively correlated with linoleic acid (r\u0026thinsp;=\u0026thinsp;0.232). The change in acid value can affect the change of L* value by more than 80%, and the correlation between DPPH and FRAP is very significant, consistent with the changes in \u003cem\u003eA.arguta\u003c/em\u003e seed oils. In addition, acid value was negatively correlated with a* (r=-0.822) and b* (r=-0.907), peroxide value was negatively correlated with a* (r=-0.760) and b* (r=-0.820), and L* value was negatively correlated with a (r=-0.871) and b (r=-0.972). Linoleic acid was negatively correlated with linolenic acid (r=-0.870) and a* (r=-0.633). Studies have shown that the antioxidant activity was positively correlated with PUFA[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], linoleic acid and linolenic acid were both unsaturated fatty acids but were negatively correlated, so linoleic acid was positively correlated with DPPH and FRAP. It was reasonable that linolenic acid was negatively correlated with DPPH and FRAP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, the optimal oil extraction process was determined using single-factor and response surface experiments, and then ten germplasms of \u003cem\u003eA.arguta\u003c/em\u003e seeds were extracted under optimal technological conditions. The physical and chemical properties (acid value, peroxide value, iodine value, color, linolenic acid, and linoleic acid) and antioxidant activities (DPPH and FRAP) of ten germplasms of seed oils were compared and analyzed. The results showed that the color of No. 2 and No. 3 seed oils significantly differed from other seed oils. The acid value and peroxide value of seed oil No. 3 were the highest, and the iodine value and linoleic acid content of seed oil No. 14 were the highest. No. 5 had the highest linolenic acid content and the strongest antioxidant activity in No. 36. The seed oil quality was comprehensively evaluated by principal component analysis score, and No. 4 was selected as the most developed seed oil resource. The correlation analysis of the above indexes proved that iodine value was positively correlated with linoleic acid, DPPH was strongly positively correlated with FRAP, and linoleic acid was strongly negatively correlated with linoleic acid. This study improved seed oil yield, reduced seed powder loss, screened out the most potential seed oil resources, and provided a theoretical basis for the future development of seed oil in the food and cosmetics industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;The authors declare that there are no conflicts of interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Song Pan, Methodology: Guanlin Qian, Formal analysis and investigation: Heran Xu; Writing - original draft preparation: Miao Yan; Writing - review and editing: Miao Yan; Funding acquisition: Huanyu Wang; Resources: Lin Hui,Yuli Zhang; Supervision: Guang Xin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment and funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China [Project No. 2024YFD15015021], Provincial Department of Agriculture, Shenyang seed industry developement [No.01080122003/01080123001] , and Liaoning Province, Shenyang Agricultural University, high-end talent introduction fund [Project No. SYAU20160003].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSong M, Xu H, Xin G, Liu C, Sun X, Zhi Y, et al. 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Antioxidants. 2021;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox10030433\u003c/span\u003e\u003cspan address=\"10.3390/antiox10030433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"agricultural-products-processing-and-storage","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Agricultural Products Processing and Storage](https://link.springer.com/journal/44462)","snPcode":"44462","submissionUrl":"https://submission.springernature.com/new-submission/44462/3","title":"Agricultural Products Processing and Storage","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Actinidia arguta, Germplasms, Seed oils, Ultrasonic-assisted extraction, Process optimization, Quality evaluation","lastPublishedDoi":"10.21203/rs.3.rs-6226482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6226482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a by-product of processed products, the best treatment of fruit seeds is oil extraction. \u003cem\u003eActinidia arguta\u003c/em\u003e seeds account for 7\u0026ndash;10% of fruit weight, and the current oil yield was 20.8%. To make more efficient use of \u003cem\u003eActinidia arguta\u003c/em\u003e seeds, the ultrasonic-assisted seed extraction method was adopted in this experiment, and the optimal oil extraction technology was obtained through a single-factor experiment and response surface experiment. The physical and chemical indexes of seed oil, including acid value, peroxide value, iodine value, color difference (L*, a*, b*), main fatty acids (linolenic acid and linoleic acid), and antioxidant activity (DPPH and FRAP), were compared and analyzed. Ten germplasms were comprehensively evaluated by principal component analysis and correlation analysis methods to explore the relationship between physical and chemical indexes and antioxidant indexes. The results showed the optimal oil extraction process: the liquid-solid ratio was 10:1mL/g, the extraction time was 98 min, the extraction power was 161 W, the extraction temperature was 40 min, and the oil extraction rate was 30.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21%. Through comprehensive evaluation, No. 14 had the highest score and the most potential to develop into oil. Iodine value was correlated with linoleic acid, DPPH and FRAP were positively correlated, and linoleic acid was negatively correlated with linoleic acid. This study improved seed oil yield, reduced by-product loss, screened out the most potential seed oil resources, and provided a theoretical basis for the future development of seed oil in the food and cosmetics industry.\u003c/p\u003e","manuscriptTitle":"Comparison and evaluation of physicochemical properties and nutritional quality on ten germplasms of Actinidia arguta seed oils by ultrasonic-assisted extraction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 18:29:47","doi":"10.21203/rs.3.rs-6226482/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-30T10:00:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-28T16:26:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-26T14:20:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106119732586224563734635643285707168253","date":"2025-03-21T13:29:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210603428556492774028827031263447366794","date":"2025-03-19T22:53:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-19T02:58:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T13:04:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T13:02:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agricultural Products Processing and Storage","date":"2025-03-14T12:47:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agricultural-products-processing-and-storage","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Agricultural Products Processing and Storage](https://link.springer.com/journal/44462)","snPcode":"44462","submissionUrl":"https://submission.springernature.com/new-submission/44462/3","title":"Agricultural Products Processing and Storage","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83331347-f52b-4f64-9961-844e2d07763d","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-08T03:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 18:29:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6226482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6226482","identity":"rs-6226482","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}