Method for predicting peanut oil hot cooking oil fume particulate matter content based on hot-pressed peanut oil components

Abstract

The invention discloses a method for predicting peanut oil hot cooking oil fume particulate matter content based on hot-pressed peanut oil components, and the method comprises the following steps: S1, measuring a peanut oil sample characteristic detection value; comprise oil acid value, oxidation induction time, alpha-tocopherol content, beta-tocopherol content, gamma-tocopherol content, delta-tocopherol content, total tocopherol content, fatty acid C18: 1 content and heating temperature; and S2, substituting the feature detection value into the prediction model, and outputting a prediction result, namely the oil smoke particle content. The method starts from a grease application end, takes peanut oil hot cooking oil fume particles as a main inspection point, and constructs the content of the oil fume particles generated in the peanut oil heating process by analyzing the grease content of the hot-pressed peanut oil. The method is of great significance in guaranteeing grease nutrition and safety quality, providing a basis for product upgrading and specific application of peanut oil and promoting healthy and sustainable development of the edible oil industry.

Description

Technical Field

[0001] The invention relates to the field of edible oil application and quality control, and in particular to a method for predicting the content of peanut oil hot cooking fume particles and their hazards based on hot pressed peanut oil components. Background Art

[0002] As a healthy oil, peanut oil ranks third in consumption among domestic edible oils. Peanut oil contains many beneficial ingredients, such as sterols and vitamin E. Regular consumption helps protect blood vessel walls and prevent thrombosis. Currently, the peanut oil sold on the market is mainly divided into hot-pressed peanut oil and cold-pressed peanut oil. Hot-pressed peanut oil is deeply loved by consumers due to its mellow flavor. It is also the mainstream product of peanut oil on the market due to its high oil yield.

[0003] At present, the hot-pressed peanut oil sold in the domestic market is mainly divided according to the difference in oleic acid content, including peanut oil with an oleic acid content of less than 45%, peanut oil with an oleic acid content of 45%-75%, and high oleic peanut oil containing more than 75% oleic acid. Oleic acid is the most typical fatty acid component in peanut oil. Studies have found that it can reduce the blood lipid level of patients with hyperlipidemia and prevent cardiovascular diseases. High oleic peanut oil has a higher price than ordinary peanut oil due to its high oleic acid content, more stable properties, and higher nutritional value, making it more popular with consumers.

[0004] At present, most of the optimization of peanut oil focuses on the influence of raw materials and processing technology, and there is relatively little research on the application end. As one of the major oils, peanut oil is often used for cooking and frying in daily life. Under high-temperature heating, the generated fumes will cause irreversible damage to the human body, including lung diseases, eye diseases, etc. Hot cooking fumes are mainly caused by the thermal oxidation, cracking, polymerization and other chemical reactions of oils and other cooking ingredients during the heating of edible oil or hot cooking of food. The complex mixture of solid, liquid and gas, including particulate matter, tiny liquid oil droplets, volatile and semi-volatile organic matter, etc. Among them, particulate matter, as a symbolic pollutant, is often used to quantify the degree of air pollution, especially inhalable fine particulate matter with small particle size and large specific surface area, which is easier to adsorb harmful substances and deposit in the human bronchi or even deep in the lungs, inducing lung damage and a variety of respiratory diseases. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a method for predicting the content of hot cooking oil fume particles in peanut oil and its hazards based on the components of hot pressed peanut oil. Starting from the oil application end, the present invention takes the hot cooking oil fume particles of peanut oil as the main investigation point, and constructs the content of oil fume particles generated during the heating process of peanut oil by analyzing the oil content of hot pressed peanut oil. The present invention is of great significance for ensuring the nutritional and safety quality of oil, providing a basis for the product upgrading and specific application of peanut oil, and promoting the healthy and sustainable development of the edible oil industry.

[0006] The technical solution of the present invention is as follows:

[0007] The first object of the present invention is to provide a model for predicting the content of peanut oil hot cooking fume particles based on hot pressed peanut oil components. The prediction model is as follows:

[0008] PM 1.0 Content prediction equation: Y PM1 =0.968*heating temperature+0.007*γ-VE+0.006*β-VE+0.005*C18:1+0.004*AV+0.001*OSI+0.001*α-VE+0.004*δ-VE+0.003*total VE;

[0009] PM 2.5 Content prediction equation: Y PM2.5 =0.969*heating temperature+0.007*γ-VE+0.006*δ-VE+0.005*total VE+0.005*β-VE+0.003*AV+0.003*OSI+0.001*C18:1+0.001*α-VE;

[0010] PM 4.0 Content prediction equation: Y PM4 =0.9729*heating temperature+0.009*total VE+0.005*γ-VE+0.004*δ-VE+0.004*β-VE+0.002*C18:1+0.002*AV+0.001*OSI+0.001*α-VE;

[0011] PM 10.0 Content prediction equation: Y PM10 =0.972*heating temperature+0.009*total VE+0.006*γ-VE+0.003*β-VE+0.002*C18:1+0.002*AV+0.001*α-VE+0.004*δ-VE;

[0012] Among them, α-V E , α-tocopherol content, mg / kg;

[0013] β-VE, β-tocopherol content, mg / kg;

[0014] γ-VE, γ-tocopherol content, mg / kg;

[0015] δ-VE, δ-tocopherol content, mg / kg;

[0016] Total VE, total tocopherol content, mg / kg;

[0017] AV, oil acid value, mg / g;

[0018] OSI, oxidation induction time, h;

[0019] C18:1, fatty acid C18:1 content, %.

[0020] Oxidation induction time is the time when oil begins to undergo auto-catalytic oxidation reaction under certain pro-oxidation conditions.

[0021] In one embodiment of the present invention, the heating temperature is 180-280°C.

[0022] The second object of the present invention is to provide a method for constructing a model for predicting the content of peanut oil hot cooking fume particles based on hot pressed peanut oil components, comprising the following steps:

[0023] Step 1: Select three hot-pressed peanut oils and determine their characteristic detection values ​​according to the national standard method;

[0024] Step 2: Place a monitor within the cooking breathing zone to heat the oil sample, collect the particulate matter in the generated oil fume after heating, and obtain relevant data;

[0025] Step 3: Construct a correlation model with the mass concentration of the particle components in the monitored particle size range and the characteristic detection value of hot-pressed peanut oil, perform numerical calculations based on the established model, and output the prediction results of hot-pressed peanut oil fume particles.

[0026] In one embodiment of the present invention, the oil samples are 45% oleic acid content peanut oil, 55% oleic acid content peanut oil and 75% oleic acid content peanut oil.

[0027] In one embodiment of the present invention, the heating temperature is 180-280°C.

[0028] Preferably, the heating temperature is 190°C, 220°C, or 280°C.

[0029] In one embodiment of the present invention, the collecting is performed 25 to 35 minutes after the start of heating.

[0030] In one embodiment of the present invention, the particulate matter is PM 1 、PM2.5 、PM 4 、PM 10 .

[0031] In one embodiment of the present invention, the relevant data are the mass concentration and particle size components of the particulate matter.

[0032] In one embodiment of the present invention, the monitor is an aerosol monitor.

[0033] In one embodiment of the present invention, the position of the monitor is 1300-1500 mm vertically from the ground and 400-600 mm from the center of the iron pot.

[0034] The third object of the present invention is to provide a method for predicting the content of peanut oil hot cooking fume particles based on hot pressed peanut oil components, comprising the following steps:

[0035] S1: Determine the characteristic detection value of peanut oil sample;

[0036] S2: Substitute the feature detection value into the above prediction model and output the prediction result, i.e., the oil fume particle content.

[0037] In one embodiment of the present invention, the characteristic detection values ​​of the peanut oil sample include oil acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, total tocopherol content, fatty acid C18:1 content and heating temperature.

[0038] In one embodiment of the present invention, the tocopherol content is detected using GB / T 26635-2011 "High Performance Liquid Chromatography Method for Determination of Tocopherol and Tocotrienol Content in Animal and Plant Oils and Fats".

[0039] In one embodiment of the present invention, the acid value detection adopts GB5009.229-2016 "National Food Safety Standard Determination of Acid Value in Food".

[0040] In one embodiment of the present invention, the oxidation induction time is detected using GB / T 21121-2007 "Determination of oxidative stability of animal and vegetable oils and fats (accelerated oxidation test)".

[0041] In one embodiment of the present invention, the fatty acid content is detected using GB 5009.168-2016 "National Food Safety Standard Determination of Fatty Acids in Foods".

[0042] In one embodiment of the present invention, the heating temperature is 180-280°C.

[0043] The predicted time for oil fume particles to be generated is 25 to 35 minutes after heating, at a vertical distance of 1300 to 1500 mm from the ground and 400 to 600 mm from the center of the cooking iron pan.

[0044] The prediction model of oil fume particle content and oil sample composition is calculated using Python. The model is based on the random forest algorithm, which constructs multiple decision trees and votes or averages their results to get the final prediction while preventing overfitting.

[0045] The prediction model of oil fume particle content and oil sample composition was obtained by performing correlation analysis, variable screening and processing, parameter fitting, model calibration and correction on the oil component contents of hot-pressed peanut oil with different oleic acid contents and the oil fume particle content in its hot cooking fume.

[0046] The hazard of hot-pressed peanut oil fume particle deposition can be further calculated based on the predicted fume particle content.

[0047] The beneficial technical effects of the present invention are:

[0048] The present invention deeply studies the composition characteristics of oil fume particles and oil sample components, and provides a new prediction model that fully considers the safety of hot-pressed peanut oil.

[0049] The present invention provides a method for in-depth analysis of oil fume composition and establishing a comprehensive relationship with oil quality, which helps to improve the understanding of the safety risks of oil use and its correlation with oil quality, and has practical significance for guiding oil processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 An overall flow chart of the model and application for the present invention.

[0051] Figure 2 This is a schematic diagram of collecting oil smoke particles in the breathing zone according to a specific embodiment of the present invention.

[0052] Figure 3 This is the prediction result for sample 1.

[0053] Figure 4 This is the prediction result for sample 2.

[0054] Figure 5 This is the prediction result for sample 3.

[0055] Figure 6 This is the prediction result for sample 4. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0057] Attached Figure 3-6 Mainly through the random forest algorithm, 10 groups of data were randomly selected from 30% of the test set for prediction, and a line chart comparing the true value and the predicted value was obtained. Each inflection point in the figure represents a group of predicted samples, the horizontal axis represents the 10 groups of randomly selected data, and the vertical axis represents the corresponding particulate matter content.

[0058] Examples 1 to 3 are the operation flow of hot pressed peanut oil fume particle prediction, the flow chart is as follows: Figure 1 As shown, the specific steps include:

[0059] Step 1: Detect the oil components of the hot-pressed peanut oil sample, including oil fatty acid composition, acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, and total tocopherol content;

[0060] Step 2: Place a TSI8534 aerosol monitor within the cooking breathing zone, which is 1400 mm vertically from the ground and 500 mm from the center of the iron pan. Use a 2.8KW natural gas cassette stove as the heating device to collect data on particulate matter (PM1, PM2.5, PM4, PM10) in the oil smoke generated by 80g hot-pressed peanut oil from the beginning of heating to the next 30 minutes.

[0061] Step 3: Analyze the mass concentration and particle size components in the collected particulate matter data;

[0062] Step 4: Import the obtained particulate matter monitoring data results into the established prediction model of oil fume particle content and hot-pressed peanut oil high-quality component composition, that is, input the mass concentration and distribution ratio of the particle components in the 4 monitored particle size ranges for program operation; the correlation model establishment process uses Python to expand the operation, and the model is established based on the random forest algorithm, which constructs multiple decision trees and votes or averages their results to obtain the final prediction, while preventing overfitting. During the model establishment process, all samples were randomly divided into two data sets, of which the training set was used to train the machine learning model, and the test set was used to test the screening effect of the model. The training and test variable screening were based on the correlation research results between the oil components of peanut oil and the generation of oil fume particles. The generated model is shown in Table 1 below:

[0063] Table 1 Prediction model of hot pressed peanut oil fume particles*

[0064]

[0065] *Note: PM, particulate matter; α-V E, α-tocopherol; β-VE, β-tocopherol; γ-VE, γ-tocopherol; δ-VE, δ-tocopherol; total VE, total tocopherol content; AV, acid value; OSI, oxidation induction time; C18:1, fatty acid C18:1 content; *, indicates the multiplication of two numbers.

[0066] The model was constructed with particulate matter content as the dependent variable, and peanut oil heating temperature, fatty acid C18:1 content, acid value (AV), oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content and total tocopherol content as independent variables to obtain models for the content of different types of particulate matter.

[0067] Source of raw materials

[0068] The peanut oil sample 1 used in Example 1 is a hot-pressed peanut oil with 45% oleic acid content, and the heating temperatures are 190°C, 220°C and 280°C respectively.

[0069] The peanut oil sample 2 used in Example 2 is a hot-pressed peanut oil with 75% oleic acid content, and the heating temperatures are 190°C, 220°C and 280°C respectively.

[0070] The peanut oil sample 3 used in Example 3 is a hot-pressed peanut oil with 55% oleic acid content, and the heating temperatures are 190°C, 220°C and 280°C respectively.

[0071] The peanut oil sample 4 used in Comparative Example 1 is a cold-pressed peanut oil with 45% oleic acid content, and the heating temperatures are 190°C, 220°C and 280°C respectively.

[0072] Example 1

[0073] Step 1: Detect the oil components of hot-pressed peanut oil sample 1, including oil fatty acid composition, acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, and total tocopherol content. The specific contents are shown in Table 2 below;

[0074] Table 2 Content of various oil components in peanut oil sample 1

[0075] Oil content 45% oleic acid hot pressed peanut oil C18：1(％) 44.68 Acid value (mg / g) 0.39 Oxidation induction time (h) 3.41 α-Tocopherol (mg / kg) 160.06 β-Tocopherol (mg / kg) 114.83 γ-Tocopherol (mg / kg) 39.55 δ-Tocopherol (mg / kg) 19.11 Total tocopherol (mg / kg) 335.50

[0076] Step 2: According to the method described above, a TSI8534 aerosol monitor was placed within the cooking breathing zone, i.e., 1400 mm vertically from the ground and 500 mm from the center of the iron pot. A 2.8 kW natural gas cassette stove was used as a heating device. 80 g of peanut oil sample was heated to 190°C, 220°C, and 280°C, and then the heat source was removed. The particulate matter (PM) in the oil fume was collected from the beginning of heating. 1 、PM 2.5 、PM 4 、PM 10) data, the collection process lasted 30min;

[0077] Step 3: The obtained raw data of particle mass concentration and particle size components are shown in Table 3 below;

[0078] Table 3 Mean concentration of particles obtained in peanut oil sample 1

[0079]

[0080] Step 4: Substitute the detected peanut oil components and heating temperature into the prediction model of oil fume particles to obtain the prediction image and prediction results as shown in the figure below: Figure 3 and as shown in Table 4;

[0081] Table 4 Predicted mean concentration of particulate matter in peanut oil sample 1

[0082]

[0083]

[0084] Depend on Figure 3 It can be seen that according to the prediction results, the true value and the predicted value have a high degree of overlap, and the determination coefficient R 2 The value is 0.987, and the mean square error is 0.137, which indicates that the smoke generation conditions in this embodiment match the data acquisition conditions for model establishment, and the evaluation results of the method of the present invention have a high degree of coincidence with the actual results, which is feasible.

[0085] Example 2

[0086] Step 1: Detect the oil components of hot-pressed peanut oil sample 2, including oil fatty acid composition, acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, and total tocopherol content; the specific contents are shown in Table 9 below;

[0087] Table 5 Content of each oil component in peanut oil sample 2

[0088] Oil content 75% oleic acid content peanut oil C18：1(％) 76.02 Acid value (mg / g) 0.34 Oxidation induction time (h) 9.96 α-Tocopherol (mg / kg) 203.39 β-Tocopherol (mg / kg) 22.84 γ-Tocopherol (mg / kg) 165.17 δ-Tocopherol (mg / kg) 17.92 Total tocopherol (mg / kg) 409.32

[0089] Step 2: According to the method described above, a TSI8534 aerosol monitor was placed within the cooking breathing zone, i.e., 1400 mm vertically from the ground and 500 mm from the center of the iron pot. A 2.8 kW natural gas cassette stove was used as a heating device. 80 g of peanut oil sample was heated to 190°C, 220°C, and 280°C, and then the heat source was removed. The particulate matter (PM) in the generated oil fume was collected from the beginning of heating. 1 、PM 2.5 、PM 4 、PM 10 ) data, the collection process lasted 30min;

[0090] Step 3: The obtained raw data of particle mass concentration and particle size components are shown in Table 6 below;

[0091] Table 6 Mean concentration of particles obtained in peanut oil sample 2

[0092]

[0093] Step 4: Substitute the detected peanut oil components and heating temperature into the prediction model of oil fume particulate matter content, and obtain the following prediction images and prediction results respectively as follows: Figure 4 and as shown in Table 7;

[0094] Table 7 Predicted mean concentration of particles in peanut oil sample 2

[0095]

[0096] Depend on Figure 4 It can be seen that according to the prediction results, the true value and the predicted value have a high degree of overlap, and the determination coefficient R 2 The value is 0.992, and the mean square error is 0.065, which indicates that the smoke generation conditions in this embodiment match the data acquisition conditions for model establishment, and the evaluation results of the method of the present invention have a high degree of coincidence with the actual results, which is feasible.

[0097] Example 3

[0098] Step 1: Detect the oil components of hot-pressed peanut oil sample 3, including oil fatty acid composition, acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, and total tocopherol content. The specific contents are shown in Table 8 below;

[0099] Table 8 Content of each oil component in peanut oil sample 3

[0100] Oil content 55% oleic acid hot pressed peanut oil C18：1(％) 55.89 Acid value (mg / g) 0.57 Oxidation induction time (h) 6.23 α-Tocopherol (mg / kg) 167.69 β-Tocopherol (mg / kg) 30.41 γ-Tocopherol (mg / kg) 191.63 δ-Tocopherol (mg / kg) 21.50 Total tocopherol (mg / kg) 411.23

[0101] Step 2: According to the method described above, a TSI8534 aerosol monitor was placed within the cooking breathing zone, i.e., 1400 mm vertically from the ground and 500 mm from the center of the iron pot. A 2.8KW natural gas cassette stove was used as a heating device. 80 g of peanut oil sample was heated to 190°C, 220°C, and 280°C, and then the heat source was removed. The particulate matter (PM) in the oil fume was collected from the beginning of heating. 1 、PM 2.5 、PM 4 、PM 10 ) data, the collection process lasted 30min;

[0102] Step 3: The obtained raw data of particle mass concentration and particle size components are shown in Table 9 below;

[0103] Table 9 Mean concentration of particles obtained in peanut oil sample 3

[0104]

[0105] Step 4: Substitute the detected peanut oil components and heating temperature into the prediction model of oil fume particles to obtain the following prediction images and prediction results: Figure 5 As shown in Table 10;

[0106] Table 10 Predicted mean concentration of particulate matter in peanut oil sample 3

[0107]

[0108] Depend on Figure 5 It can be seen that according to the prediction results, the true value and the predicted value have a high degree of overlap, and the determination coefficient R 2 The value is 0.992, and the mean square error is 0.065, which indicates that the smoke generation conditions in this embodiment match the data acquisition conditions for model establishment, and the evaluation results of the method of the present invention have a high degree of coincidence with the actual results, which is feasible.

[0109] Comparative Example 1

[0110] Step 1: Detect the oil components of cold-pressed peanut oil sample 4, including oil fatty acid composition, acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, and total tocopherol content. The specific contents are shown in Table 11 below;

[0111] Table 11 Content of each oil component in peanut oil sample 4

[0112]

[0113]

[0114] Step 2: According to the method described above, a TSI8534 aerosol monitor was placed within the cooking breathing zone, i.e., 1400 mm vertically from the ground and 500 mm from the center of the iron pot. A 2.8 kW natural gas cassette stove was used as a heating device. 80 g of peanut oil sample was heated to 190°C, 220°C, and 280°C, and then the heat source was removed. The particulate matter (PM) in the oil fume was collected from the beginning of heating. 1 、PM 2.5 、PM 4 、PM 10 ) data, the collection process lasted 30min;

[0115] Step 3: The obtained raw data of particle mass concentration and particle size components are shown in Table 12 below:

[0116] Table 12 Mean concentration of particles obtained in peanut oil sample 4

[0117]

[0118] Step 4: Substitute the detected peanut oil components and heating temperature into the prediction model of oil fume particles to obtain the following prediction images and prediction results: Figure 6 As shown in Table 13;

[0119] Table 13 Predicted mean concentration of particulate matter in peanut oil sample 4

[0120]

[0121] Depend on Figure 6 It can be seen that according to the prediction results, the overlap between the true value and the predicted value is low, and the determination coefficient R 2 The value is 0.522, and the mean square error is 0.378, which indicates that the smoke generation conditions in this comparative example do not match the data acquisition conditions for model establishment, and the evaluation results of the method of the present invention have a high degree of coincidence with the actual results, which is feasible.

[0122] Although the smoke-resistant conditions used in Comparative Example 1 are within the numerical range of the model, the experiment found that the cold-pressed peanut oil sample is not suitable for this model.

[0123] The embodiments provided above are not intended to limit the scope of the present invention, and the steps described are not intended to limit the execution order thereof. Those skilled in the art may make obvious improvements to the present invention in combination with existing common knowledge, which also fall within the scope of protection defined by the claims of the present invention.

Claims

1. A model for predicting the content of hot cooking fume particles in peanut oil based on hot pressed peanut oil components, characterized in that: The prediction model is as follows: PM 1.0 Content prediction equation: Y PM1 =0.968*heating temperature+0.007*γ-VE+0.006*β-VE+0.005*C18:1+0.004*AV+0.001*OSI+0.001*α-VE+0.004*δ-VE+0.003*total VE; PM 2.5 Content prediction equation: Y PM2.5 =0.969*heating temperature+0.007*γ-VE+0.006*δ-VE+0.005*total VE+0.005*β-VE+0.003*AV+0.003*OSI+0.001*C1 8:1+0.001*α-VE; PM 4.0 Content prediction equation: Y PM4 =0.9729*heating temperature+0.009*total VE+0.005*γ-VE+0.004*δ-VE+0.004*β-VE+0.002*C18:1+0.002*AV+0.001*OSI+0.001*α-VE; PM 10.0 Content prediction equation: Y PM10 =0.972*heating temperature+0.009*total VE+0.006*γ-VE+0.003*β-VE+0.002*C18:1+0.002*AV+0.001*α-VE+0.004*δ-VE; Among them, α-V E , α-tocopherol content, mg / kg; β-VE, β-tocopherol content, mg / kg; γ-VE, γ-tocopherol content, mg / kg; δ-VE, δ-tocopherol content, mg / kg; Total VE, total tocopherol content, mg / kg; AV, oil acid value, mg / g; OSI, oxidation induction time, h; C18:1, fatty acid C18:1 content, %.

2. The model according to claim 1, characterized in that The heating temperature is 180-280°C.

3. A method for predicting the content of peanut oil hot cooking fume particles based on hot pressed peanut oil components, characterized in that: The steps include: S1: Determine the characteristic detection value of peanut oil sample; S2: Substitute the feature detection value into the model described in claim 1 and output the prediction result, i.e., the oil fume particle content.

4. The method according to claim 3, characterized in that: The characteristic test values ​​of peanut oil samples include oil acid value, oxidation induction time, α-tocopherol content, β-tocopherol content, γ-tocopherol content, δ-tocopherol content, total tocopherol content, fatty acid C18:1 content and heating temperature.

5. The method according to claim 4, characterized in that The tocopherol content was determined using GB / T 26635-2011 “High Performance Liquid Chromatography for Determination of Tocopherol and Tocotrienol Content in Animal and Plant Oils and Fats”.

6. The method according to claim 4, characterized in that The fatty acid content was tested according to GB 5009.168-2016 “National Food Safety Standard - Determination of Fatty Acids in Foods”.

7. The method according to claim 4, characterized in that The acid value test adopts GB5009.229-2016 "National Food Safety Standard Determination of Acid Value in Food".

8. The method according to claim 4, characterized in that The oxidation induction time test adopts GB / T 21121-2007 “Determination of oxidative stability of animal and vegetable oils and fats (accelerated oxidation test)”.

9. The method according to claim 4, characterized in that The heating temperature is 180-280°C.

10. The method according to claim 4, characterized in that The predicted time for oil fume particles to be generated is 25 to 35 minutes after heating, at a vertical distance of 1300 to 1500 mm from the ground and 400 to 600 mm from the center of the cooking iron pan.

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