A method for identifying the quality of rapeseed oil

Abstract

The application discloses a rapeseed oil quality identification method, comprising the following steps: obtaining the concentration data of the processing residual electroactive substances in the rapeseed oil by using an electrochemical detection method; collecting the oxygen partial pressure, relative humidity, thermodynamic temperature and light intensity data of the storage environment; constructing a synergistic action quantification model to obtain a synergistic action coefficient; constructing a nonlinear decay acceleration model to obtain a quality decay acceleration factor; detecting the acid value and peroxide value data; and combining a synergistic action deviation correction mechanism to calculate a comprehensive quality index. The method solves the problem that the existing technology cannot capture the implicit quality decay by cross-domain technology fusion, realizes accurate identification, and provides a reliable scheme for rapeseed oil quality evaluation.

Description

Technical Field

[0001] This invention relates to the intersection of food testing and electrochemical technology, specifically to a method for identifying the quality of rapeseed oil. Background Technology

[0002] Rapeseed oil, as one of my country's main edible oils, is directly related to food safety and consumer health. Current methods for identifying rapeseed oil quality mostly focus on detecting static component indicators such as acid value and peroxide value, neglecting the implicit impact of the synergistic effects of processing residues of electroactive substances and the storage environment on quality. These synergistic effects accelerate the oxidative deterioration of rapeseed oil, leading to a sharp decline in quality in the later stages of storage. However, due to the complexity and difficulty in quantifying these mechanisms, current technologies cannot capture this implicit quality degradation process; they can only make judgments after deterioration has become apparent, failing to achieve early warning and accurate assessment.

[0003] Based on the above problems, there is an urgent need for an identification method that can quantify the synergistic effect of electroactive substances and the environment and accurately characterize the dynamic degradation of quality, so as to solve the core problem that existing technologies are not sensitive to latent quality changes. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a method for identifying the quality of rapeseed oil, comprising the following steps:

[0005] S1. The concentration data of residual electroactive substances in rapeseed oil samples were obtained by electrochemical detection method;

[0006] S2. Collect data on oxygen partial pressure, relative humidity, thermodynamic temperature, and light intensity of the rapeseed oil sample storage environment;

[0007] S3. Based on the concentration data of the electroactive substance and the data of the storage environment, construct a synergistic effect quantification model and calculate the synergistic effect coefficient;

[0008] S4. Based on the aforementioned synergistic effect coefficient, construct a nonlinear decay acceleration model and calculate the quality decay acceleration factor.

[0009] S5. Detect acid value and peroxide value data of rapeseed oil samples;

[0010] S6. Based on the quality degradation acceleration factor, the acid value data, and the peroxide value data, combined with the synergistic effect deviation correction mechanism, the comprehensive quality index of rapeseed oil is calculated to complete the quality identification of rapeseed oil.

[0011] Preferably, a sample pretreatment step is included before step S1. The sample pretreatment step is as follows: take 50 mL of rapeseed oil sample, filter it using a 0.45 μm organic phase filter membrane to remove mechanical impurities from the sample; take 10 mL of the filtered rapeseed oil sample and inject it into the electrochemical detection cell, and let it stand for 30 min.

[0012] Preferably, the electroactive substance in step S1 is phospholipid; the electrochemical detection method in step S1 is implemented through a three-electrode system, which includes a working electrode, a reference electrode, and a counter electrode; the concentration data in step S1 is obtained by detecting the oxidation peak current of phospholipid using cyclic voltammetry, and then converting it using a calibration curve of oxidation peak current versus phospholipid concentration, the expression of which is C = k1·I ox +k0, where C is the molar concentration of phospholipid, k1 is the slope of the calibration curve, and k0 is the intercept of the calibration curve. ox This represents the oxidation peak current of phospholipids.

[0013] Preferably, the storage environment data in step S2 is collected through a sensor group, which includes an oxygen partial pressure sensor, a humidity sensor, a temperature sensor, and a light sensor; during the collection process, three sets of storage environment data are continuously acquired, and the average value of the three sets of data is taken as the input data for step S3.

[0014] Preferably, the synergistic effect quantification model in step S3 is a trace electroactive substance synergistic effect coefficient model. The trace electroactive substance synergistic effect coefficient model is constructed based on the law of mass action and the van der Hoff equation. The synergistic effect strength is quantified by using the phospholipid electroactivity characteristic constant, humidity synergistic correction coefficient, temperature influence constant, and the concentration data and storage environment data of the electroactive substance.

[0015] Preferably, the nonlinear decay acceleration model in step S4 is a quality decay acceleration factor model, which is constructed based on the Arrhenius equation and achieves nonlinear quantification of the quality decay rate through the baseline decay rate, temperature acceleration coefficient, light sensitivity coefficient, the synergistic effect coefficient, and storage environment data.

[0016] Preferably, the synergistic deviation correction mechanism in step S6 is a rapeseed oil comprehensive quality index calculation mechanism. The rapeseed oil comprehensive quality index calculation mechanism uses acid value weight coefficient, decay factor weight coefficient, peroxide value weight coefficient, and synergistic deviation correction coefficient, combined with the quality decay acceleration factor, acid value data, peroxide value data, and synergistic effect coefficient, to realize the correction of detection deviation and the quantification of comprehensive quality.

[0017] Preferably, the working electrode of the three-electrode system is a glassy carbon electrode, the reference electrode is an Ag / AgCl electrode, and the counter electrode is a platinum wire electrode; the scanning range of the cyclic voltammetry is -0.2V to 1.0V, and the scanning rate is 50mV / s; the calibration curve is established as follows: five phospholipid-rapeseed oil standard samples with different molar concentrations are prepared, and the phospholipid oxidation peak current of each standard sample is detected by cyclic voltammetry. The calibration curve is obtained by linear regression analysis with the phospholipid molar concentration as the abscissa and the oxidation peak current as the ordinate.

[0018] Preferably, the characteristic constant of phospholipid electroactivity is determined by the following method: Phospholipid-rapeseed oil standard samples with different known molar concentrations are prepared, the oxidation peak current of each standard sample is obtained, and the standard values ​​of the stored environment data are substituted into the trace electroactive substance synergistic effect coefficient model, and the specific value is obtained by least squares fitting. The humidity synergistic correction coefficient is determined by the following method: under different relative humidity conditions, the synergistic effect intensity of the same phospholipid-rapeseed oil standard sample is detected, and the specific value is obtained by nonlinear regression fitting based on the correspondence between the detection results and relative humidity. The temperature influence constant is derived based on the van der Hoff equation, and the derivation process is combined with the activation energy of the reaction between phospholipid and oxygen and the gas constant calculation.

[0019] Preferably, the acid value in step S6 is detected by titration, and the peroxide value is detected by iodometric titration. The acid value weighting coefficient, attenuation factor weighting coefficient, and peroxide value weighting coefficient are determined by the analytic hierarchy process (AHP), which includes three steps: constructing a quality evaluation index system, constructing a judgment matrix, calculating weights, and performing consistency checks. The synergistic deviation correction coefficient is determined as follows: under different synergistic effect coefficients, the acid value and peroxide value of the same rapeseed oil sample are tested, and the deviations between the test results and the standard values ​​are compared. Based on the correspondence between the deviation and the synergistic effect coefficient, a specific value is obtained through nonlinear regression fitting. The upper limit of the national standard for rapeseed oil acid value is 3 mg KOH / g, and the reference value for peroxide value of fresh rapeseed oil is 0.4 mmol / kg.

[0020] Technical Effects: This invention, through the cross-disciplinary integration of electrochemical detection and environmental parameter modeling, constructs a synergistic effect quantification model, a nonlinear decay acceleration model, and a deviation correction mechanism, creatively solving the core problem that existing technologies cannot quantify latent quality degradation. Its technical points directly address the unresolved pain point of the synergistic effect between electroactive substances and the environment, enabling dynamic and accurate identification of rapeseed oil quality and providing a reliable basis for quality assessment. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for identifying the quality of rapeseed oil according to this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] The existing technology has the following technical problems: it cannot quantify the synergistic effect between residual electroactive substances in processing and the storage environment, making it difficult to capture the hidden quality degradation process of rapeseed oil, and can only make a judgment after the deterioration phenomenon appears.

[0024] Based on this, please refer to Figure 1 This embodiment provides a method for identifying the quality of rapeseed oil, including the following steps:

[0025] S1. The concentration data of residual electroactive substances in rapeseed oil samples were obtained by electrochemical detection method;

[0026] S2. Collect data on oxygen partial pressure, relative humidity, thermodynamic temperature, and light intensity of the rapeseed oil sample storage environment;

[0027] S3. Based on the concentration data of the electroactive substance and the storage environment data, construct a synergistic effect quantification model and calculate the synergistic effect coefficient;

[0028] S4. Based on the aforementioned synergistic effect coefficient, construct a nonlinear decay acceleration model and calculate the quality decay acceleration factor.

[0029] S5. Detect the acid value and peroxide value data of rapeseed oil samples;

[0030] S6. Based on the quality degradation acceleration factor, acid value data, and peroxide value data, combined with the synergistic effect deviation correction mechanism, the comprehensive quality index of rapeseed oil is calculated to complete the quality identification of rapeseed oil.

[0031] The implementation of this technical solution must strictly adhere to the step-by-step logic and model construction principles to ensure the reliability and accuracy of data at each stage. Firstly, the detection of electroactive substance concentration requires a high-precision electrochemical workstation. A three-electrode system is used to build the detection platform. The working electrode is a glassy carbon electrode that has been polished to ensure surface smoothness and improve signal response sensitivity. The reference electrode is an Ag / AgCl electrode, and the counter electrode is a platinum wire electrode. These three components form a stable electrochemical detection circuit. During detection, a pretreated rapeseed oil sample is injected into the detection cell, immersing the three electrodes in the sample. Cyclic voltammetry is used for scanning, with the scan range set from -0.2V to 1.0V and the scan rate at 50mV / s. This parameter combination effectively stimulates the redox reaction of phospholipids, generating characteristic oxidation peaks. The oxidation peak current is recorded by the electrochemical workstation, and the molar concentration of phospholipids is calculated based on a pre-constructed calibration curve. The calibration curve must be obtained by fitting at least five standard samples of different concentrations to ensure the linear correlation and accuracy of the concentration conversion.

[0032] Data acquisition for the storage environment requires a dedicated sensor array. An electrochemical sensor is used, covering a measurement range of 0 to 25 kPa, meeting the detection needs of typical storage environments. A capacitive humidity sensor ensures stable response within a relative humidity range of 10% to 90%. A platinum resistance temperature sensor provides a measurement accuracy of ±0.1 K, and a silicon photodiode sensor is used, adaptable to light intensity ranges from 0 to 10000 lux. During data acquisition, the sensor array should be placed in the center of the rapeseed oil storage environment to ensure data representativeness. Three sets of data should be collected consecutively, with a 10-second interval between each set. The average value is then used as the input data to compensate for the effects of instantaneous fluctuations in environmental parameters.

[0033] The construction of a synergistic effect quantification model is one of the core technical points, and its mathematical expression is: 'in , is the synergistic effect coefficient of trace electroactive substances, and is dimensionless; The characteristic constant of phospholipid electroactivity, with dimensions of ; The molar concentration of phospholipids in rapeseed oil, with dimensions of ; The partial pressure of oxygen in the storage environment, with dimensions of ; This is the humidity co-correction coefficient, which is dimensionless. The relative humidity of the storage environment is dimensionless. Let be the temperature effect constant, with dimensions . ; The thermodynamic temperature of the storage environment, with dimensions of The logical derivation of this formula is based on the law of mass action and the van der Hoff equation. The law of mass action states that the rate of a chemical reaction is directly proportional to the product of the concentrations of the reactants. The oxidation rate of phospholipids and oxygen is affected by the concentrations of both, therefore the molar concentration of phospholipids is introduced. partial pressure with oxygen The product term directly reflects the fundamental contribution of reactant concentration to the concerted effect. The van der Hoff equation reveals the effect of temperature on the equilibrium constant; increased temperature accelerates the oxidation reaction, thereby enhancing the concerted effect. Therefore, an exponential term is introduced. ,in The temperature-dependent constant is the activation energy of the reaction between phospholipids and oxygen. and gas constant It is derived that, Dimensions are To ensure that the exponential terms are dimensionless, the effect of humidity on the synergistic effect is not linear. Experiments have shown that when the relative humidity exceeds 60%, the dispersibility of phospholipids in the oil phase significantly improves, and the intensity of the synergistic effect increases sharply. Therefore, the following approach is adopted. The term characterizes this nonlinear relationship and is corrected by a humidity-coordinated correction coefficient. Make adjustments. The values ​​were obtained by fitting experimental data under different humidity conditions to ensure accurate quantification of the effect of humidity. Phospholipid electroactivity characteristic constants. Dimensional design for Its function is to offset of Dimensions and of Dimensionality, so that the formula outputs a dimensionless coefficient of synergy. , The value ranges from 0 to 2, with a larger value indicating a stronger synergistic effect.

[0034] The mathematical expression for the nonlinear decay acceleration model is: ,in, The quality degradation acceleration factor has a dimension of 1 / day. The baseline decay rate is expressed in units of 1 / day, specifically at 25°C, without light, and... The inherent decay rate of rapeseed oil; The temperature acceleration coefficient has dimensions of . ; The thermodynamic temperature of the storage environment, with dimensions of ; The light sensitivity coefficient has the following dimensions: ; The light intensity of the storage environment, with dimensions of ; The coefficient representing the synergistic effect of trace electroactive substances is dimensionless. The derivation of this model is based on the Arrhenius equation, which shows that the reaction rate constant has an exponential relationship with temperature; therefore, the coefficient is introduced... The item, where 298.15K is the reference temperature of 25℃, and the temperature acceleration factor is... The dimensions are The values ​​were obtained by fitting experimental data to ensure an accurate characterization of the accelerating effect of temperature on the decay rate. Light intensity. The effect of light intensity on lipid oxidation is related to photon energy. Experiments have shown that the square root of light intensity has a linear relationship with the oxidation rate. Therefore, [the following method was adopted]. Item, light sensitivity coefficient The dimensions are This is used to adjust the weighting of the effect of illumination. Synergistic effect coefficient. It has a catalytic effect on the decay rate, when When the catalytic effect is 0, the formula is as follows: ,at this time That is, the reference decay rate, when As the size increases, the catalytic effect is enhanced. The coefficient design makes The catalytic effect reaches saturation at a certain time to avoid over-amplification and ensure the rationality of the model. The dimension is 1 / day, which directly represents the daily rate of quality decay and is consistent with the actual physical meaning.

[0035] The mathematical expression for the synergistic bias correction mechanism is: ,in, The comprehensive quality index of rapeseed oil is dimensionless. This is the acid value weighting coefficient, which is dimensionless. For the measured acid value, the dimensions are: ; This is the upper limit of the national standard for acid value of rapeseed oil, with dimensions of [dimensions missing]. ; , is the attenuation factor weighting coefficient, which is dimensionless; The quality degradation acceleration factor has a dimension of 1 / day. This refers to the number of days rapeseed oil can be stored, measured in days. This is the peroxide value weighting coefficient, and its dimensionless value. The peroxide value of fresh rapeseed oil is a reference value, with dimensions of [dimensions missing]. ; For actual measurement of peroxide value, the dimension is: ; This is the coefficient for correction of coordination deviation, and its dimension is dimensionless. The coefficient representing the synergistic effect of trace electroactive substances is dimensionless. This mechanism is constructed based on weighted summation and bias correction theory. First, the acid value weighting coefficient is determined using the analytic hierarchy process (AHP). Attenuation factor weighting coefficient Peroxide value weighting coefficient The sum of the three is 1, ensuring the reasonableness of the weighted summation. Acid value term. middle, The value is 3 mg KOH / g, which is the upper limit of the national standard for rapeseed oil acid value. This item reflects the impact of the current free fatty acid content on quality by comparing the measured acid value with the standard value. The smaller the ratio, the larger the value of this item, and the better the quality. (Decrease trend item) Based on first-order reaction kinetics, quality varies with storage time. It decays exponentially. The product is dimensionless, ensuring the exponent term output is reasonable. Peroxide value term. middle, The peroxide value is 0.4 mmol / kg, which is the reference value for fresh rapeseed oil. This value reflects the current degree of oxidation by comparing the reference value with the measured value. The higher the ratio, the lower the degree of oxidation and the better the quality. (Cooperational bias correction term) To address the detection bias caused by synergistic effects, when When the deviation is minimal, the correction factor is 1. When the value deviates from this value, the correction factor is adjusted accordingly to offset the effect of the deviation. The values ​​are obtained through experimental fitting to ensure accurate correction results. The output is a dimensionless value from 0 to 100, which intuitively reflects the overall quality.

[0036] This scheme achieves the quantification and dynamic assessment of latent quality degradation through the cross-disciplinary integration of electrochemical detection and environmental parameter modeling. Each model parameter has a clear theoretical basis and experimental support, and the dimensional design strictly follows physical laws, ensuring the scientific nature and feasibility of the technical solution.

[0037] The existing technology has the following technical problems: mechanical impurities in the sample can interfere with the accuracy of electroactive substance concentration detection and affect the accuracy of subsequent model calculations.

[0038] Based on this, a sample pretreatment step is included before step S1. The sample pretreatment step is as follows: take 50 mL of rapeseed oil sample, filter it using a 0.45 μm organic phase filter membrane to remove mechanical impurities from the sample, and inject 10 mL of the filtered rapeseed oil sample into the electrochemical detection cell and let it stand for 30 min.

[0039] The implementation of this technical solution requires strict control of all operating parameters to ensure stable pretreatment results. When taking a 50mL rapeseed oil sample, a calibrated pipette must be used to ensure the accuracy of the sample volume. This volume ensures representativeness of the detection, avoids detection deviations due to insufficient sample volume, and prevents sample waste. The selection of the organic phase filter membrane must meet two core requirements: a pore size of 0.45μm, which can accurately trap mechanical impurities in rapeseed oil without blocking phospholipid molecules; and an organic phase compatible material to avoid chemical reactions between the filter membrane and rapeseed oil or phospholipids, ensuring that the sample composition is not altered. The filtration process uses vacuum filtration, controlling the filtration rate at 5mL / min to avoid insufficient impurity trapping due to excessive speed, or affecting experimental efficiency due to excessively slow speed. After filtration, take 10mL of the filtered sample. This volume is precisely matched with the volume of the electrochemical detection cell, ensuring that the three electrodes are completely immersed in the sample, and that the sample volume is sufficient to cover the electrode reaction area. After injection into the detection cell, the sample is allowed to stand for 30 minutes. This time is designed to eliminate air bubbles in the sample, as air bubbles can lead to insufficient contact between the electrode and the sample, affecting the stability and accuracy of the electrochemical signal. The 30-minute standing time ensures that all air bubbles rise to the surface, avoiding interference with the detection process. The standardized operation of the entire pretreatment process provides a pure and stable sample basis for subsequent electroactive substance concentration detection, effectively improving the reliability of the detection data.

[0040] The existing technology has the following technical problems: there is a lack of accurate detection methods for residual electroactive substances in rapeseed oil, and reliable concentration data cannot be obtained.

[0041] Based on this, the electroactive substance in step S1 is phospholipid, and the electrochemical detection method in step S1 is implemented through a three-electrode system, which includes a working electrode, a reference electrode, and a counter electrode. The concentration data in step S1 is obtained by detecting the oxidation peak current of phospholipid using cyclic voltammetry, and then converting it using a calibration curve of oxidation peak current versus phospholipid concentration. The expression for the calibration curve is as follows: ,in, This is the molar concentration of phospholipids, with dimensions of _____. ; To calibrate the slope of the curve, the dimension is... ; To calibrate the intercept of the curve, the dimension is... ; This represents the oxidation peak current of phospholipids, with dimensions in μA.

[0042] The implementation of this technical solution requires a focus on the stability of the detection system and the accuracy of the calibration curve. The electroactive substance is clearly identified as phospholipids, as they are the most significant residual electroactive substance in the rapeseed oil degumming process. Their content is directly related to the processing technology and has a significant synergistic catalytic effect on rapeseed oil quality degradation. Targeted detection of phospholipid concentration can accurately reflect the potential impact of processing residues on quality. The three-electrode system was rigorously selected. A glassy carbon electrode was chosen as the working electrode, possessing good electrochemical stability, conductivity, and surface regenerability. After polishing with 0.05μm alumina powder, its surface roughness is less than 0.1μm, effectively improving the response sensitivity and repeatability of the oxidation peak current. An Ag / AgCl electrode with saturated KCl solution was selected as the reference electrode. This electrode has a stable electrode potential with a potential drift of less than 0.1mV / h, ensuring the stability of the potential reference during detection. A platinum wire electrode was selected as the counter electrode. The platinum wire has a diameter of 0.5mm and a length of 5mm, exhibiting excellent catalytic performance, enabling rapid electron transfer, promoting the counter electrode reaction, and avoiding polarization phenomena that could affect the detection results. The cyclic voltammetry parameters were optimized, with a scan range of -0.2V to 1.0V. This range fully covers the redox potential range of phospholipids, avoiding both missing oxidation peaks due to an overly narrow scan range and introducing irrelevant reaction signals due to an overly wide range. The scan rate was 50mV / s, balancing signal response intensity and peak clarity. Too high a rate would increase peak current but broaden the peak shape, while too low a rate would increase signal noise. The 50mV / s rate ensured symmetrical oxidation peaks and stable peak currents. The construction of calibration curves followed strict standard procedures. Five phospholipid-rapeseed oil standard samples with different molar concentrations, ranging from 0.01mmol / L to 0.1mmol / L, were prepared, covering the common phospholipid content range in actual rapeseed oil. Using the same three-electrode system and cyclic voltammetry parameters as the sample detection, the oxidation peak current of each standard sample was measured. Three sets of peak current data were recorded for each concentration, and the average value was taken as the characteristic current value for that concentration. The phospholipid molar concentration was used as the basis for the calibration curves. x-axis represents oxidation peak current Using the ordinate as the vertical axis, a linear regression analysis was performed using the least squares method to obtain the calibration curve expression. Requires linear correlation coefficient To ensure a significant linear relationship between concentration and peak current and reduce conversion errors, the oxidation peak current of phospholipids was recorded during sample testing. By substituting the values ​​into the calibration curve, the molar concentration of phospholipids in the sample can be accurately calculated. This provides reliable core input data for subsequent synergistic effect quantification models.

[0043] The existing technology has the following technical problems: the accuracy and stability of the storage environment parameter acquisition are insufficient, which affects the reliability of subsequent model calculations.

[0044] Based on this, the storage environment data mentioned in step S2 is collected by a sensor group, which includes an oxygen partial pressure sensor, a humidity sensor, a temperature sensor, and a light sensor. During the collection process, three sets of storage environment data are continuously acquired, and the average value of the three sets of data is taken as the input data for step S3.

[0045] The implementation of this technical solution requires a focus on the standardization of sensor selection and data acquisition processes. Each sensor in the sensor array is selected based on the specific environmental parameter detection requirements. The oxygen partial pressure sensor is an electrochemical sensor, based on the reduction reaction of oxygen on the electrode surface. The reaction current is linearly related to the oxygen partial pressure, with a detection range of 0 to 25 kPa, a resolution of 0.01 kPa, and a response time of less than 10 seconds, enabling rapid capture of changes in oxygen partial pressure. The humidity sensor is a capacitive sensor, based on the principle that changes in humidity lead to changes in capacitance. It has a detection range of 10% to 90% relative humidity, an accuracy of ±2% relative humidity, and a response time of less than 10 seconds. The response time is less than 5 seconds to avoid data distortion caused by humidity response lag. A PT100 platinum resistance thermometer is used for temperature measurement. Based on the characteristic that the resistance of platinum resistance changes with temperature, the detection range is 273.15K to 333.15K, with an accuracy of ±0.1K and a resolution of 0.01K, enabling precise measurement of the thermodynamic temperature of the storage environment. A silicon photodiode sensor is used for light intensity measurement. Based on the photoelectric effect, the detection range is 0 to 10000 lux, with a resolution of 1 lux and a response time of less than 1 second, suitable for light intensity detection in different indoor and outdoor storage scenarios. The installation position of the sensor group must be strictly controlled. It should be installed at the center height of the rapeseed oil storage container, at least 5cm away from the container wall, to avoid the influence of temperature and humidity differences of the container wall on the detection results. Simultaneously, ensure that the sensor probe is unobstructed and in full contact with the environment. The data acquisition process is standardized. After starting the sensor array, a 30-minute warm-up is performed to ensure the sensors reach a stable operating state. Then, three sets of data are continuously acquired, with a 10-second interval between each set. This interval is designed to avoid the influence of instantaneous fluctuations. The average of the three sets of data is calculated using the arithmetic mean method. If the deviation of a set of data from the other two sets exceeds 5%, a new set of data is acquired to replace the abnormal data before recalculating the average, ensuring the stability and reliability of the input data. Regular calibration of the sensor array is crucial. The oxygen partial pressure sensor is calibrated monthly using a standard gas, the humidity sensor is calibrated quarterly using a standard humidity generator, the temperature sensor is calibrated semi-annually using a standard constant temperature bath, and the light sensor is calibrated annually using a standard light source. The calibration data and correction coefficients are recorded during the calibration process to ensure the long-term stability of the sensor's detection accuracy.

[0046] The existing technology has the following technical problems: it cannot quantify the synergistic effect between electroactive materials and the storage environment, making it difficult to characterize the triggering conditions for latent quality degradation.

[0047] Based on this, the synergistic effect quantification model mentioned in step S3 is a synergistic effect coefficient model of trace electroactive substances. The synergistic effect coefficient model of trace electroactive substances is constructed based on the law of mass action and the van der Hoff equation. The synergistic effect strength is quantified by using the phospholipid electroactivity characteristic constant, humidity synergistic correction coefficient, temperature influence constant, and the concentration data and storage environment data of the electroactive substances.

[0048] In practical implementation, the mathematical expression of this model is: ,in, , is the synergistic effect coefficient of trace electroactive substances, and is dimensionless; The characteristic constant of phospholipid electroactivity, with dimensions of ; The molar concentration of phospholipids in rapeseed oil, with dimensions of _____. ; The partial pressure of oxygen in the storage environment, with dimensions of ; This is the humidity co-correction coefficient, which is dimensionless. The relative humidity of the storage environment is dimensionless. Let be the temperature effect constant, with dimensions . ; The thermodynamic temperature of the storage environment, with dimensions of .

[0049] The formula is constructed based on multidisciplinary theories, ensuring the scientific validity and rationality of the quantitative logic. The definitions, dimensions, and derivation processes of each parameter are clear and explicit, supporting implementation by those skilled in the art. From a theoretical perspective, the law of mass action is the core foundation. This law states that the rate of a chemical reaction is directly proportional to the product of the reactant concentrations. The oxidation reaction of phospholipids with oxygen is a key reaction in the degradation of rapeseed oil quality, and its reaction rate is directly affected by the phospholipid concentration and oxygen partial pressure. Therefore, the formula incorporates... and The product term directly reflects the fundamental contribution of both concentrations to the synergistic effect. This design conforms to the basic laws of chemical reactions, ensuring the theoretical rationality of the model. The van der Hoff equation provides a basis for quantifying the effect of temperature. The van der Hoff equation shows that for every 10K increase in temperature, the reaction rate constant increases by approximately 2 to 4 times. Temperature affects the reaction rate by influencing the activation energy, thus affecting the strength of the synergistic effect; therefore, an exponential term is introduced. ,in The temperature-dependent constant is the activation energy of the reaction between phospholipids and oxygen. and gas constant It is derived that, gas constant The value is 8.314 J / (mol·K), which is the activation energy of the reaction between phospholipids and oxygen. Measured by differential scanning calorimetry, the value is typically 30,000 J / mol to 50,000 J / mol. The numerical range is from 3600K to 6000K, and the dimensions are... Ensure that the index items are within Since it is a dimensionless quantity, the output of the exponential function is also dimensionless, which is consistent with mathematical logic.

[0050] The mechanism by which humidity affects the synergistic effect is complex. Experimental verification shows that changes in relative humidity affect the solubility and dispersibility of phospholipids in rapeseed oil. Under low humidity conditions, phospholipid molecules tend to aggregate, resulting in a weaker synergistic effect. Under high humidity conditions, phospholipid molecules are more uniformly dispersed, increasing the contact area with oxygen and significantly enhancing the synergistic effect. Moreover, this effect is not linear; when the relative humidity exceeds 60%, the rate of increase in the intensity of the synergistic effect accelerates significantly. Therefore, using... The term characterizes this nonlinear relationship, with a humidity-coordinated correction coefficient. The weights used to adjust for the influence of humidity were determined through experimental fitting. The specific process involved preparing standard samples of phospholipid-rapeseed oil at fixed concentrations, and maintaining constant temperature and oxygen partial pressure in environments with relative humidity of 20%, 40%, 60%, and 80%. The strength of the synergistic effect was measured, and based on the correlation between the measured results and the square of the relative humidity, a nonlinear regression fitting was used to obtain the final value. The value is typically in the range of 0.01 to 0.05, and the dimensionless value ensures... The term is dimensionless and does not affect the overall dimensions of the formula.

[0051] Phospholipid electroactivity characteristic constants The design is key to dimensional unification, and its dimensions are... The determination of this dimension is based on the requirement of overall dimensional balance in the formula. The dimensions are , The dimensions are The dimensions of their product are , Multiplying by this product cancels out the dimensions, resulting in a dimensionless product. Multiplying by other dimensionless terms ultimately makes... The dimensionless nature of this value aligns with the physical meaning of the synergistic effect coefficient. The numerical range is 0 to 2, when When, it indicates no synergistic effect. When the value reaches saturation, it indicates that the synergistic effect has reached saturation. This value range has been verified by experimental data and can accurately cover the intensity range of synergistic effects in actual storage scenarios.

[0052] The logical derivation of this formula is complete, and from the theoretical basis to the parameter design and dimensional balance, it has been rigorously verified. The methods for determining each parameter are clear and repeatable. Those skilled in the art can accurately construct this model and calculate the synergistic effect coefficient by following the above instructions. .

[0053] The existing technology has the following technical problems: it cannot characterize the nonlinear decay acceleration effect of temperature and light on rapeseed oil quality, making it difficult to dynamically assess quality changes.

[0054] Based on this, the nonlinear decay acceleration model in step S4 is a quality decay acceleration factor model. The quality decay acceleration factor model is constructed based on the Arrhenius equation and achieves nonlinear quantification of the quality decay rate through the baseline decay rate, temperature acceleration coefficient, light sensitivity coefficient, the synergistic effect coefficient, and storage environment data.

[0055] In practical implementation, the mathematical expression of this model is: ,in, The quality degradation acceleration factor has a dimension of 1 / day. The baseline decay rate is expressed in units of 1 / day, specifically at 25°C, without light, and... The inherent decay rate of rapeseed oil; The temperature acceleration coefficient has dimensions of . ; The thermodynamic temperature of the storage environment, with dimensions of ; The light sensitivity coefficient has the following dimensions: ; The light intensity of the storage environment, with dimensions of ; The coefficient for the synergistic effect of trace electroactive substances is dimensionless.

[0056] The formula is constructed by integrating chemical kinetics and experimental verification data, with sufficient theoretical basis and full disclosure. The physical meaning, dimensions, and derivation process of each parameter are clearly defined, ensuring that those skilled in the art can implement it. The core theoretical basis is the Arrhenius equation, a classic formula describing the relationship between the reaction rate constant and temperature. This equation shows that the reaction rate constant increases exponentially with temperature. The degradation of rapeseed oil quality is essentially a first-order reaction of oil oxidation, and its degradation rate conforms to the law of the Arrhenius equation. Therefore, it is introduced... The item, where 298.15K is the reference temperature of 25℃, is a common food storage reference temperature, facilitating data comparison, and includes the temperature acceleration factor. The dimensions are The value was determined through experimental fitting. The specific process was as follows: Fresh rapeseed oil samples were selected and kept in constant temperature environments of 283.15K, 293.15K, 303.15K, 313.15K, and 323.15K, respectively, while keeping other conditions constant. Quality indicators were tested periodically, and the decay rate constant at different temperatures was calculated. Based on the exponential relationship between the rate constant and temperature, the value was obtained through fitting. The value, typically ranging from 0.03 to 0.051 / K, ensures accurate characterization of the accelerating effect of temperature on the decay rate.

[0057] The effect of light on the quality degradation of rapeseed oil stems from the action of photon energy. Photon energy can excite electronic transitions in oil molecules, accelerating the oxidation reaction. Experimental verification shows that there is a linear relationship between light intensity and the square root of the degradation rate. This is because the transfer of photon energy is proportional to the square root of the light intensity; therefore, the formula uses... Item, light sensitivity coefficient The dimensions are The value was determined experimentally. The specific process was as follows: Fresh rapeseed oil samples were selected and placed in light environments of 1000 lux, 3000 lux, 5000 lux, 7000 lux, and 9000 lux, respectively, while maintaining constant temperature and oxygen partial pressure. Quality indicators were periodically tested, and the decay rate constant under different light intensities was calculated. Based on the linear relationship between the rate constant and the square root of the light intensity, the value was obtained through fitting. The value typically ranges from 0.001 to 0.0031 / lux. 0 · 5 This ensures the accuracy of quantifying the effects of light.

[0058] Synergistic effect coefficient The catalytic effect on the decay rate is achieved through... This design is based on catalytic reaction kinetics, when... At that time, the catalytic effect is 0. ,at this time That is, the reference decay rate. At 25°C, without light and The inherent degradation rate of rapeseed oil, determined experimentally, typically ranges from 0.002 to 0.005 L / day, with dimensions of 1 / day. As the size increases, the catalytic effect gradually strengthens. The coefficient design makes hour, The catalytic effect reaches a reasonable range, avoiding If the value is too large, the catalytic effect will be excessively amplified. To ensure the model's rationality, this coefficient has been determined through multiple experiments and can accurately reflect... The catalytic relationship between the decay rate and the decay rate.

[0059] The dimensional balance of the formula has been rigorously verified. The dimension is 1 / day, and both the exponential and logarithmic terms are dimensionless. The dimension is 1 / day, consistent with the physical meaning of the quality degradation acceleration factor, i.e., the daily rate of quality degradation. This dimension design conforms to physical laws, ensuring the rationality of the calculation results. The logical derivation process of the entire formula starts from a theoretical model, combines experimental data to optimize parameters, and forms a complete quantitative system. The method for determining each parameter is repeatable. Those skilled in the art can accurately construct the model and calculate the results according to the above description. It fully meets the requirements of Article 26, Paragraph 4 of the Patent Law.

[0060] The existing technology has the following technical problems: the synergistic effect can lead to deviations in the detection of static quality indicators, affecting the accuracy of the overall quality assessment.

[0061] Based on this, the synergistic deviation correction mechanism mentioned in step S6 is a rapeseed oil comprehensive quality index calculation mechanism. The rapeseed oil comprehensive quality index calculation mechanism uses acid value weight coefficient, decay factor weight coefficient, peroxide value weight coefficient, and synergistic deviation correction coefficient, combined with the quality decay acceleration factor, acid value data, peroxide value data and synergistic effect coefficient, to realize the correction of detection deviation and the quantification of comprehensive quality.

[0062] In practical implementation, the mathematical expression of this mechanism is: ,in, The comprehensive quality index of rapeseed oil is dimensionless. This is the acid value weighting coefficient, which is dimensionless. For the measured acid value, the dimensions are: ; This is the upper limit of the national standard for acid value of rapeseed oil, with dimensions of [dimensions missing]. ; , is the attenuation factor weighting coefficient, which is dimensionless; The quality degradation acceleration factor has a dimension of 1 / day. This refers to the number of days rapeseed oil can be stored, measured in days. This is the peroxide value weighting coefficient, and its dimensionless value. The peroxide value of fresh rapeseed oil is a reference value, with dimensions of [dimensions missing]. ; For actual measurement of peroxide value, the dimension is: ; This is the coefficient for correction of coordination deviation, and its dimension is dimensionless. The coefficient for the synergistic effect of trace electroactive substances is dimensionless.

[0063] The formula is constructed based on multi-index evaluation theory and deviation correction theory, with sufficient theoretical basis. The definitions, dimensions, and derivation processes of each parameter are detailed and clear, and fully disclosed. It can support implementation by those skilled in the art and provides sufficient specification support for the claims. The core design idea is to integrate static quality indicators and dynamic decay trends, and to correct for detection deviations caused by synergistic effects, thereby achieving accurate quantification of comprehensive quality.

[0064] Static quality indicators include acid value and peroxide value. Acid value reflects the content of free fatty acids in rapeseed oil and is an important indicator for measuring oil rancidity. Peroxide value reflects the content of primary products of oil oxidation and directly reflects the degree of oxidation. Both are national standard indicators for rapeseed oil quality evaluation, with clearly defined testing methods and standard values. Acid value item middle, For the measured acid value, the dimensions are: , This is the upper limit of the national standard for acid value of rapeseed oil, with a value of 3 mg KOH / g, and dimensions of... Consistent, therefore Dimensionless The value ranges from 0 to 1; a higher value indicates a lower acid value and better quality. (Peroxide value item) middle, For actual measurement of peroxide value, the dimension is: , The reference value for peroxide value of fresh rapeseed oil is 0.4 mmol / kg, with dimensions of [missing value]. Consistent, therefore It is dimensionless, with a numerical range of 0 to 1. The larger the value, the lower the peroxide value, the lighter the degree of oxidation, and the better the quality.

[0065] Dynamic decay trend term Based on first-order reaction kinetics, the quality degradation of rapeseed oil follows the first-order reaction law, meaning that the quality decreases exponentially with storage time. This is the quality degradation acceleration factor, with dimensions of 1 / day. To store the number of days, the unit of measurement is days, therefore As a dimensionless quantity, the output of the exponential term is made reasonable, with a value range of 0 to 1. A larger value indicates a smoother decay trend and better quality maintenance. This design incorporates the dynamic decay process into the comprehensive quality evaluation, making up for the shortcomings of existing technologies that only focus on static indicators, and can more comprehensively reflect the quality status of rapeseed oil.

[0066] Weighting coefficient , , The determination of the weights was achieved using the Analytic Hierarchy Process (AHP), a commonly used scientific method in multi-index evaluation that can objectively allocate the weights of each index. The specific process is as follows: A quality evaluation index system is constructed, with the target layer being the overall quality of rapeseed oil and the criteria layer including acid value, decay trend, and peroxide value; a judgment matrix is ​​constructed, and five experts in food testing are invited to conduct pairwise comparisons of the importance of each criterion layer index, assigning values ​​using a 1-9 scale; the weight vector is calculated and a consistency test is performed, with consistency test indicators... When this is achieved, it indicates that the judgment matrix has consistency and the weight allocation is reasonable, ultimately yielding... , , The value, usually The range is 0.3 to 0.4. The range is 0.4 to 0.5. The range is from 0.1 to 0.2, and all three are dimensionless. This ensures the reasonableness of the weighted summation.

[0067] Cooperative deviation correction term The core of solving the detection bias problem is the synergistic effect coefficient. Experiments have shown that this coefficient is crucial for addressing detection bias. The test results for acid value and peroxide value may be affected by bias. At that time, the deviation is minimal, and as... As the deviation from this value increases, the deviation gradually increases; therefore, the following method is adopted. Characterizing the degree of deviation, the coefficient of coordination deviation correction The values ​​were determined through experimental fitting. The specific process was as follows: standard rapeseed oil samples were selected, whose standard values ​​for acid value and peroxide value were known. Under the given conditions, the acid value and peroxide value of the tested samples are analyzed, and the deviation between the test results and the standard values ​​is calculated. Based on the deviation and... The correspondence was obtained by nonlinear regression fitting. The value is usually in the range of 0.1 to 0.2, and the dimension is dimensionless. This ensures that the correction term is dimensionless and does not affect the overall dimension of the formula.

[0068] The formula's dimensional balance design is reasonable, with 100 as the normalization coefficient, making... The output range is 0 to 100, dimensionless, for easy intuitive understanding. All terms in the weighted summation are dimensionless. The term remains dimensionless, and the correction term is also dimensionless. The overall dimensionless nature of the formula aligns with the physical meaning of the comprehensive quality index. The logical derivation of the entire formula, from indicator selection, weight allocation, dynamic trend quantification to deviation correction, forms a complete system. The methods for determining each parameter are scientific and repeatable, and those skilled in the art can accurately calculate it according to the above explanation. This enables accurate assessment of overall quality.

[0069] The existing technology has the following technical problems: the electrode type of the three-electrode system and the parameter settings of the cyclic voltammetry are not clear, the calibration curve is not constructed in a standardized manner, which affects the accuracy of phospholipid concentration detection.

[0070] Based on this, the working electrode of the three-electrode system is a glassy carbon electrode, the reference electrode is an Ag / AgCl electrode, and the counter electrode is a platinum wire electrode. The scanning range of the cyclic voltammetry is -0.2V to 1.0V, and the scanning rate is 50mV / s. The calibration curve is established as follows: five phospholipid-rapeseed oil standard samples with different molar concentrations are prepared, and the phospholipid oxidation peak current of each standard sample is detected by cyclic voltammetry. The calibration curve is obtained by linear regression analysis with the phospholipid molar concentration as the abscissa and the oxidation peak current as the ordinate.

[0071] The implementation of this technical solution requires strict standardization of all operational details to ensure the stability of the detection system and the accuracy of the calibration curve. The electrode selection for the three-electrode system has been verified through multiple experiments. A glassy carbon electrode with a diameter of 3 mm is selected as the working electrode. The surface treatment process of the glassy carbon electrode is standardized. First, it is coarsely polished on a polishing cloth with 1.0 μm alumina powder, and then finely polished with 0.05 μm alumina powder until the electrode surface has a mirror-like gloss. Then, it is ultrasonically cleaned with anhydrous ethanol and deionized water for 5 min in sequence to remove residual polishing powder and impurities. Finally, cyclic voltammetry is performed in 0.5 mol / L H2SO4 solution with a scan range of -0.2 V to 1.0 V and a scan rate of 50 mV / s until the cyclic voltammetry curve is stable to ensure consistent electrochemical activity of the electrode surface. The reference electrode is an Ag / AgCl electrode with saturated KCl solution. Before use, the salt bridge must be checked for unobstructed flow to ensure stable electrode potential. The counter electrode is a platinum wire electrode with a diameter of 0.5 mm and a length of 5 mm. The surface of the platinum wire is electrochemically polished to improve catalytic activity. The installation positions of the three electrodes must be fixed. The distance between the working electrode and the counter electrode is 5 mm, and the distance between the reference electrode and the working electrode is 2 mm to avoid potential drop caused by excessive electrode spacing or mutual interference caused by insufficient electrode spacing.

[0072] The parameters of the cyclic voltammetry were optimized by the system, with a scan range of -0.2V to 1.0V. This range was determined based on the redox characteristics of phospholipids. The oxidation potential of phospholipids is approximately 0.6V, and the reduction potential is approximately -0.1V. This scan range can completely capture the oxidation and reduction peaks of phospholipids without including redox signals from other irrelevant components in rapeseed oil. The scan rate is 50mV / s. This rate balances peak current intensity and peak shape resolution. A scan rate that is too fast will increase the peak current but broaden the peak shape, making it difficult to distinguish the oxidation peak from other impurity peaks. A scan rate that is too slow will decrease the peak current and increase signal noise. A scan rate of 50mV / s makes the oxidation peak of phospholipids sharp and symmetrical, and the peak current stable, which is convenient for accurate reading.

[0073] The construction of the calibration curve is crucial for concentration conversion and must adhere to strict standard procedures. Standard samples were prepared using a gravimetric method, with precise weighing of phospholipid standards and rapeseed oil as the solvent. Five standard samples with different molar concentrations were prepared: 0.01 mmol / L, 0.03 mmol / L, 0.05 mmol / L, 0.07 mmol / L, and 0.1 mmol / L. This concentration range covers the common phospholipid content range in actual rapeseed oil, ensuring the applicability of the calibration curve. The detection of standard samples must use the exact same three-electrode system, cyclic voltammetry parameters, and environmental conditions as the sample detection. Each standard sample was measured three times, and the oxidation peak current was recorded for each measurement. The average value was taken as the characteristic current value corresponding to that concentration. The oxidation peak current was read using the peak height method, with the baseline as the reference, to ensure consistency in the reading method. Data processing employed linear regression analysis, with phospholipid molar concentration as the x-axis and oxidation peak current as the y-axis. The calibration curve was obtained by fitting the data using the least squares method, requiring a linear correlation coefficient. ,intercept The absolute value is less than 0.001 mmol / L, and the slope is... The relative standard deviation is less than 5%, ensuring a significant linear relationship in the calibration curve and that the conversion error is within acceptable limits. The calibration curve is valid for 3 months. If the electrode is changed or the detection environment changes during this period, the calibration curve must be reconstructed to ensure the accuracy of the concentration conversion.

[0074] The existing technology has the following technical problems: the key parameters in the synergistic effect coefficient model of trace electroactive substances are not clearly determined, which affects the accuracy of the model calculation.

[0075] Based on this, the characteristic constants of phospholipid electroactivity were determined as follows: Phospholipid-rapeseed oil standard samples with different known molar concentrations were prepared, and the oxidation peak current of each standard sample was obtained using the aforementioned detection method. The standard values ​​of the stored environmental data were then substituted into the trace electroactive substance synergistic effect coefficient model, and the specific values ​​were obtained through least squares fitting. The humidity synergistic correction coefficient was determined as follows: Synergistic effect intensity was detected on the same phospholipid-rapeseed oil standard sample under different relative humidity conditions. Based on the correspondence between the detection results and relative humidity, the specific values ​​were obtained through nonlinear regression fitting. The temperature influence constant was derived based on the van der Hoff equation, and the derivation process incorporated the activation energy of the reaction between phospholipids and oxygen and the gas constant calculations.

[0076] The implementation of this technical solution needs to focus on the scientific validity and repeatability of parameter determination, ensuring the accuracy and reliability of the values ​​for each key parameter. Phospholipid electroactivity characteristic constants. The determination of the phospholipid concentration required experimental fitting using multiple concentration standard samples. The standard samples were configured identically to those used in the calibration curve, with a concentration range of 0.01 mmol / L to 0.1 mmol / L, comprising five concentration points. The standard values ​​for the storage environment were set as follows: oxygen partial pressure 21 kPa, relative humidity 50%, and thermodynamic temperature 298.15 K. These standard values ​​simulated typical conditions of a conventional storage environment, ensuring the universality of the parameter fitting. Using the described electrochemical detection method, the oxidation peak current of each standard sample was obtained, and the phospholipid molar concentration was calculated by combining it with the calibration curve. Then The standard values ​​of the storage environment data were substituted into the synergistic effect coefficient model of trace electroactive substances. Simultaneously, the synergistic effect strength of each standard sample was determined through independent experiments. The synergistic effect strength was measured using differential scanning calorimetry, characterized by detecting the exothermic rate of the oxidation reaction. Based on the correspondence between the measured synergistic effect strength and the model calculation value, the least squares method was used for fitting. The specific values ​​are fixed during the fitting process. and The initial values ​​were 0.03 and 4800K, respectively. After fitting, the relative error between the model's calculated values ​​and the experimentally measured values ​​was required to be less than 5%, ensuring... The accuracy.

[0077] Humidity Co-correction Factor The determination of the synergistic effect required experiments under different humidity conditions. A phospholipid-rapeseed oil standard sample with a concentration of 0.05 mmol / L was selected, as this concentration is intermediate and representative. Other parameters of the storage environment were set as follows: oxygen partial pressure 21 kPa, thermodynamic temperature 298.15 K, and relative humidity set at four gradients of 20%, 40%, 60%, and 80%, covering common humidity ranges. Under each humidity condition, differential scanning calorimetry was used to determine the synergistic effect strength. Data from the storage environment is substituted into the model and fixed. and The numerical values ​​were obtained by nonlinear regression fitting based on the correlation between the strength of the synergistic effect and the square of the relative humidity. For specific numerical values, an exponential function is used as the fitting model, and the coefficient of determination of the fitted curve is required. ,make sure It can accurately characterize the nonlinear effects of humidity.

[0078] Temperature effect constant The determination is based on the van der Hoff equation, which is expressed as follows: ,in For the enthalpy change of the reaction, the oxidation reaction of phospholipids with oxygen, It can be determined by differential scanning calorimetry, typically in the range of -30 kJ / mol to -50 kJ / mol. The negative sign indicates an exothermic reaction, and the gas constant is... Based on the derivation of the van der Hoff equation, ,because It is a negative value. The value is positive, typically ranging from 3600K to 6000K. During the derivation process, the reaction equilibrium constant at different temperatures needs to be experimentally determined and verified. The numerical values ​​were obtained by selecting three different temperatures: 288.15 K, 298.15 K, and 308.15 K, and determining the reaction equilibrium constants at each temperature. These constants were then substituted into the van der Hoff equation for calculation. The average value is used to ensure the reliability of the derivation results. Detailed experimental data is recorded for each parameter determination process, including standard sample configuration data, detection data, and fitting curves, ensuring the traceability and repeatability of parameter values ​​and providing support for the accuracy of model calculations.

[0079] The existing technology has the following technical problems: the detection methods for static basic quality indicators are not clear, and the determination methods for weighting coefficients and synergistic deviation correction coefficients are not standardized, which affects the accuracy of the comprehensive quality index calculation.

[0080] Based on this, the acid value mentioned in step S6 is detected by titration, and the peroxide value is detected by iodometric titration. The acid value weighting coefficient, attenuation factor weighting coefficient, and peroxide value weighting coefficient are determined by the analytic hierarchy process (AHP), which includes three steps: constructing a quality evaluation index system, constructing a judgment matrix, calculating weights, and performing consistency checks. The synergistic deviation correction coefficient is determined as follows: under different synergistic effect coefficients, the acid value and peroxide value of the same rapeseed oil sample are tested, and the deviations of the test results from the standard values ​​are compared. Based on the correspondence between the deviation and the synergistic effect coefficient, a specific value is obtained through nonlinear regression fitting. The upper limit of the national standard for rapeseed oil acid value is 3 mg KOH / g, and the reference value for peroxide value of fresh rapeseed oil is 0.4 mmol / kg.

[0081] The implementation of this technical solution requires standardized testing methods and parameter determination procedures to ensure the accuracy and standardization of each step. The acid value is determined using a titration method, strictly following the national standard GB / T5009.229-2016. The testing procedure is as follows: Weigh 5.0g of rapeseed oil sample and place it in a 250mL Erlenmeyer flask. Add 50mL of neutral ether-ethanol mixed solvent, shake to completely dissolve the sample, add 3 drops of phenolphthalein indicator, and titrate with 0.1mol / L potassium hydroxide standard solution until the solution turns pink and does not fade within 30 seconds. Record the volume of potassium hydroxide standard solution consumed. The formula for calculating the acid value is: ,in The volume of potassium hydroxide standard solution consumed is expressed in mL. 56.1 represents the concentration of the potassium hydroxide standard solution, in mol / L; 56.1 represents the molar mass of potassium hydroxide, in g / mol. For sample quality, the unit is During the testing process, each sample was tested three times, and the average value was taken as the final test result, with a relative deviation of less than 2%.

[0082] The peroxide value was determined using the iodometric method, following the national standard GB / T5009.227-2016. The procedure was as follows: Weigh 2.0 g of rapeseed oil sample and place it in a 250 mL iodine flask. Add 30 mL of a chloroform-glacial acetic acid mixture and shake to completely dissolve the sample. Add 1 mL of saturated potassium iodide solution, shake well, and let stand in the dark for 3 minutes. Add 100 mL of water and titrate with 0.01 mol / L sodium thiosulfate standard solution until the solution turns pale yellow. Add 1 mL of starch indicator and continue titrating until the blue color disappears. Record the volume of sodium thiosulfate standard solution consumed. The peroxide value is calculated using the following formula: ,in The volume of sodium thiosulfate standard solution consumed is expressed in mL. 0.1269 is the concentration of sodium thiosulfate standard solution, in mol / L; 0.1269 is the ratio of the molar mass of iodine to 2, in g / mmol. For sample quality, the unit is Each sample was tested three times, and the average value was taken as the final test result, with a relative deviation of less than 3%.

[0083] The weighting coefficients were determined using the Analytic Hierarchy Process (AHP), with the following steps: First, a quality evaluation index system was constructed. The target layer was the overall quality of rapeseed oil, and the criteria layer included acid value, decay trend, and peroxide value. Second, a judgment matrix was constructed. Five food testing experts were invited to compare the importance of each indicator in the criteria layer pairwise, assigning values ​​using a 1-9 scale: 1 indicates equal importance, 3 indicates slightly more important, 5 indicates more important, 7 indicates more important, and 9 indicates extremely important. The reverse was used for the less important indicators. Third, weights were calculated by normalizing the judgment matrix and calculating the eigenvectors, which represent the weighting coefficients of each indicator. Fourth, a consistency check was performed, calculating the consistency index. ,in To determine the largest eigenvalue of a matrix, This plan specifies the number of indicators. Find the average random consistency index ,when If the judgment matrix is ​​consistent and the weight allocation is reasonable, then the judgment matrix needs to be adjusted and recalculated to obtain the final weight coefficients. , , Must meet This ensures the reasonableness of the weighted summation.

[0084] Cooperative deviation correction coefficient The determination was achieved through experimental fitting. Standard rapeseed oil samples were selected, and their acid value and peroxide value were determined by an authoritative testing institution, ensuring accuracy and reliability. Different concentrations of phospholipid-rapeseed oil mixed samples were prepared to obtain different synergistic coefficients. The sample size ranged from 0.2 to 1.8, with intervals of 0.4, totaling 5 samples. For each sample, the acid value and peroxide value were determined using the titration and iodometric methods described above. The absolute deviation between the test results and the standard values ​​was calculated. Based on the deviation and... The correspondence was obtained by nonlinear regression fitting. The numerical values ​​are given, and a quadratic function is selected as the fitting model. The coefficient of determination of the fitted curve is required. ,make sure Able to accurately compensate for different The detection deviation is limited. The upper limit of the national standard for acid value of rapeseed oil and the reference value for peroxide value of fresh rapeseed oil are determined based on national standards and industry consensus to ensure the uniformity and authority of quality evaluation.

[0085] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for the identification of the quality of a rapeseed oil, characterized in that, Includes the following steps: S1. The concentration data of residual electroactive substances in rapeseed oil samples were obtained by electrochemical detection method; S2. Collect data on oxygen partial pressure, relative humidity, thermodynamic temperature, and light intensity of the rapeseed oil sample storage environment; S3. Based on the concentration data of the electroactive substance and the data of the storage environment, construct a synergistic effect quantification model and calculate the synergistic effect coefficient; S4. Based on the aforementioned synergistic effect coefficient, construct a nonlinear decay acceleration model and calculate the quality decay acceleration factor. S5. Detect acid value and peroxide value data of rapeseed oil samples; S6. Based on the quality decay acceleration factor, the acid value data and the peroxide value data, combined with the synergistic effect deviation correction mechanism, the comprehensive quality index of rapeseed oil is calculated to complete the quality identification of rapeseed oil. The electroactive substance in step S1 is phospholipid; the electrochemical detection method in step S1 is implemented through a three-electrode system, which includes a working electrode, a reference electrode, and a counter electrode; the concentration data in step S1 is obtained by detecting the oxidation peak current of phospholipid using cyclic voltammetry, and then converting it using a calibration curve of oxidation peak current versus phospholipid concentration. The expression for the calibration curve is C = k1·I ox +k0, where C is the molar concentration of phospholipids, k1 is the slope of the calibration curve, and k0 is the intercept of the calibration curve. ox The oxidation peak current of phospholipids is used. Step S3's synergistic effect quantification model is a trace electroactive substance synergistic effect coefficient model, constructed based on the law of mass action and the van der Hoff equation. It quantifies the synergistic effect strength using phospholipid electroactivity characteristic constants, humidity synergistic correction coefficients, temperature influence constants, and the concentration data and storage environment data of the electroactive substances. Step S4's nonlinear decay acceleration model is a quality decay acceleration factor model, constructed based on the Arrhenius equation. It nonlinearly quantifies the quality decay rate using the baseline decay rate, temperature acceleration coefficient, light sensitivity coefficient, synergistic effect coefficient, and storage environment data. Step S6's synergistic effect deviation correction mechanism is a rapeseed oil comprehensive quality index calculation mechanism. This mechanism uses acid value weighting coefficients, decay factor weighting coefficients, peroxide value weighting coefficients, and synergistic deviation correction coefficients, combined with the quality decay acceleration factor, acid value data, peroxide value data, and synergistic effect coefficients, to correct detection deviations and quantify comprehensive quality.

2. The method for identifying the quality of rapeseed oil according to claim 1, characterized in that, Before step S1, there is also a sample pretreatment step, which is as follows: take 50 mL of rapeseed oil sample, filter it with a 0.45 μm organic phase filter membrane to remove mechanical impurities in the sample; take 10 mL of the filtered rapeseed oil sample and inject it into the electrochemical detection cell, and let it stand for 30 min.

3. The method for identifying the quality of rapeseed oil according to claim 1, characterized in that, The storage environment data mentioned in step S2 is collected through a sensor group, which includes an oxygen partial pressure sensor, a humidity sensor, a temperature sensor, and a light sensor. During the collection process, three sets of storage environment data are continuously acquired, and the average value of the three sets of data is taken as the input data for step S3.

4. The method for identifying the quality of rapeseed oil according to claim 1, characterized in that, The working electrode of the three-electrode system is a glassy carbon electrode, the reference electrode is an Ag / AgCl electrode, and the counter electrode is a platinum wire electrode. The scanning range of the cyclic voltammetry is -0.2V to 1.0V, and the scanning rate is 50mV / s. The calibration curve was established as follows: five phospholipid-rapeseed oil standard samples with different molar concentrations were prepared, and the phospholipid oxidation peak current of each standard sample was detected by cyclic voltammetry. The calibration curve was obtained by linear regression analysis with the phospholipid molar concentration as the abscissa and the oxidation peak current as the ordinate.

5. The method for identifying the quality of rapeseed oil according to claim 1, characterized in that, The characteristic constants of phospholipid electroactivity were determined as follows: Phospholipid-rapeseed oil standard samples with different known molar concentrations were prepared, and the oxidation peak current of each standard sample was obtained. These values, combined with the standard values ​​from the stored environmental data, were substituted into the trace electroactive substance synergistic effect coefficient model, and the specific values ​​were obtained through least squares fitting. The humidity synergistic correction coefficient was determined as follows: The synergistic effect intensity of the same phospholipid-rapeseed oil standard sample was detected under different relative humidity conditions. Based on the correspondence between the detection results and relative humidity, the specific values ​​were obtained through nonlinear regression fitting. The temperature influence constant was derived based on the van der Hoff equation, and the derivation process incorporated calculations of the activation energy of the reaction between phospholipids and oxygen, as well as the gas constant.

6. The method for identifying the quality of rapeseed oil according to claim 1, characterized in that, The acid value mentioned in step S6 is detected by titration, and the peroxide value is detected by iodometric titration. The weighting coefficients of acid value, decay factor, and peroxide value are determined by the analytic hierarchy process (AHP), which includes three steps: constructing a quality evaluation index system, constructing a judgment matrix, calculating weights, and performing consistency checks. The synergistic deviation correction coefficient is determined as follows: under different synergistic effect coefficients, the acid value and peroxide value of the same rapeseed oil sample are tested, and the deviations of the test results from the standard values ​​are compared. Based on the correspondence between the deviation and the synergistic effect coefficient, a specific value is obtained through nonlinear regression fitting. The upper limit of the national standard for rapeseed oil acid value is 3 mg KOH / g, and the reference value for peroxide value of fresh rapeseed oil is 0.4 mmol / kg.

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