A rapid detection method and system for evaluating sesame oil quality

Abstract

The invention belongs to the technical field of edible oil production quality detection, and more specifically, relates to a rapid detection method and system for evaluating the quality of sesame oil. When the infrared spectroscopy method is used to detect the peroxide content in the sesame oil, the invention proposes a sampling data preprocessing method combined with the dielectric constant data of the sesame oil sample in the spectrum data sampling stage to reduce the sampling data error and lay a data foundation for the subsequent accurate evaluation of the quality of the sesame oil. At the same time, when establishing a standard relationship, the invention first obtains 8 standard points for establishing the standard relationship through solution configuration, then abandons one standard point for each of the 8 standard points to establish an initial standard relationship, and considers the concentration difference calculated between the abandoned point and the standard relationship to regenerate different numbers of standard points, thereby weakening the influence of inaccurate standard points, and then establishes the standard relationship according to all the standard points, so that the standard relationship is established more accurately.

Description

Technical Field

[0001] The invention belongs to the technical field of edible oil production quality detection, and more specifically, relates to a rapid detection method and system for sesame oil quality evaluation. Background Art

[0002] With the rise of the consumption concept of the Chinese people, consumers are eager to buy safe and reliable sesame oil products. Therefore, a fast, non-destructive and simple sesame oil detection method is particularly important. There are many methods for testing edible oils, and traditional chemical indexes are usually used to evaluate the quality of edible oils. Since these chemical index tests require a long time and a large amount of chemical reagents, and it is difficult to evaluate the changes in the quality of edible oils on site, this method is difficult to be widely used.

[0003] In the prior art, infrared spectroscopy is generally used for rapid detection of edible oil. However, the scheme in the prior art realizes spectrum acquisition by averaging multiple samples when collecting infrared spectra, resulting in low data acquisition accuracy. In addition, the process of establishing the standard relationship in the prior art generally configures 7-10 standard triphenylphosphine oxide solutions for infrared spectrum detection and establishes the standard relationship, resulting in low measurement accuracy. Summary of the invention

[0004] The object of the present invention is to provide a rapid detection method and system for evaluating the quality of sesame oil, so as to improve the accuracy of detecting the peroxide content in sesame oil.

[0005] In order to solve the above technical problems, the present invention specifically provides the following technical solutions: a rapid detection method for evaluating the quality of sesame oil, which is used to detect peroxides in sesame oil, specifically comprising the steps of:

[0006] S1: Obtain sesame oil samples for sesame oil quality evaluation;

[0007] S2: collecting dielectric characteristic values ​​of the sesame oil sample;

[0008] S3: collecting infrared spectrum data of the sesame oil sample;

[0009] S4: performing a first preprocessing operation on the infrared spectrum data to obtain first preprocessed infrared spectrum data;

[0010] S5: performing a second preprocessing operation on the first preprocessed infrared spectrum data to obtain second preprocessed infrared spectrum data;

[0011] S6: Obtaining characteristic values ​​of the infrared spectrum curve of the sesame oil sample according to the second preprocessed infrared spectrum data;

[0012] S7: establishing a standard relationship for detecting peroxides in the sesame oil sample;

[0013] The S7 is specifically:

[0014] S7.1: Prepare triphenylphosphine oxide stock solution;

[0015] S7.2: mixing the triphenylphosphine oxide stock solution with sesame oil samples of different masses, and diluting the triphenylphosphine oxide stock solution to 8 different concentrations, thereby obtaining 8 triphenylphosphine oxide solutions of different concentrations as standard triphenylphosphine oxide solutions;

[0016] S7.3: Detecting the eight standard triphenylphosphine oxide solutions of different concentrations by infrared spectroscopy to obtain the characteristics of the infrared spectrum curve, thereby obtaining standard points of the eight standard triphenylphosphine oxide solutions of different concentrations;

[0017] S7.4: establishing a standard relationship for peroxide detection in the sesame oil sample according to the standard points of the 8 standard triphenylphosphine oxide solutions of different concentrations;

[0018] Wherein, the standard relationship for peroxide detection in the sesame oil sample established according to the characteristics of the infrared spectrum curve is specifically:

[0019] S7.4.1: Select 7 standard points from the 8 standard points to establish an initial standard relationship for peroxide detection in the sesame oil sample, and establish a total of 8 initial standard relationship;

[0020] S7.4.2: Enter the independent variable of the unselected standard point into each initial standard relationship to obtain the concentration deviation calculated using the initial standard relationship;

[0021] S7.4.3: Automatically generate a preset number of standard points according to each initial standard relationship, wherein the number of standard points generated by each initial standard relationship is determined according to the concentration deviation value;

[0022] S7.4.4: Linearly fit all standard point data to obtain a standard relationship for peroxide detection in the sesame oil sample;

[0023] S8: Input the characteristic value of the infrared spectrum curve of the sesame oil sample obtained in S6 into the standard relationship formula to calculate the peroxide content value of the sesame oil sample.

[0024] Preferably, S4 is specifically:

[0025] S4.1: Calculate the standard deviation of 16 infrared spectrum data of sesame oil sample at each scanning frequency;

[0026] Wherein, the calculation formula of the standard deviation of 16 infrared spectrum data of the sesame oil sample at each scanning frequency is:

[0027]

[0028] In the formula, σ is the standard deviation, x i is the i-th infrared spectrum data, μ is the average value of 16 infrared spectrum data;

[0029] S4.2: Determine a comparison threshold of the standard deviation according to the dielectric characteristic value of the sesame oil sample;

[0030] The comparison threshold σ of the standard deviation is determined according to the dielectric characteristic value of the sesame oil sample. N Specifically:

[0031]

[0032] Wherein, C is the dielectric characteristic value of the sesame oil sample;

[0033] S4.3: Compare the standard deviation obtained in S4.1 with the comparison threshold of the standard deviation. If the standard deviation obtained in S4.1 is less than the comparison threshold of the standard deviation, proceed to S4.4; otherwise, proceed to S4.5;

[0034] S4.4: taking an average value of 16 infrared spectrum data of the sesame oil sample as the infrared spectrum data at the scanning frequency;

[0035] S4.5: Calculate a first peroxide content value of the sesame oil sample according to the dielectric characteristic value of the sesame oil sample;

[0036] S4.6: Calculate a second peroxide content value of the sesame oil sample according to the first peroxide content value of the sesame oil sample and a method conversion coefficient; wherein the method conversion coefficient is a ratio of the peroxide value of the sesame oil sample calculated by the dielectric characteristic value method to the peroxide value of the sesame oil sample calculated by the infrared spectroscopy method;

[0037] S4.7: Determine the number of data to be deleted based on the standard deviation between the second peroxide content value and the standard deviation obtained in S4.1;

[0038] The formula for determining the number of data n to be deleted based on the standard deviation between the second peroxide content value and the standard deviation obtained in S4.1 is:

[0039]

[0040] Wherein, a, b are coefficients, m is the second peroxide content value, m0 is the peroxide content reference value, and σ0 is the standard deviation reference value;

[0041] S4.8: Sort the infrared spectrum data from large to small according to the difference between each infrared spectrum data and the mean value of the infrared spectrum data, delete the first n infrared spectrum data according to the number n of deleted data determined in S4.7, and average the remaining infrared spectrum data to obtain the infrared spectrum data at the scanning frequency.

[0042] Preferably, S4.5 is specifically: using the capacitance value of sesame oil samples with different peroxide contents and known peroxide contents collected at the same preset temperature as the dielectric characteristic value, setting the preset temperature to 25°C, and then performing xy fitting on the peroxide content and the capacitance value to obtain a fitting relationship between the peroxide content and the capacitance value, and the fitting relationship is:

[0043] y=34.21x-17.91,R 2 =0.973;

[0044] The horizontal axis x is the capacitance value of the sesame oil sample, and the vertical axis y is the peroxide content value of the sesame oil sample. 2 is the correlation coefficient of the fitting equation, and the first peroxide content value of the sesame oil sample is obtained through the fitting equation.

[0045] Preferably, in S2, the dielectric characteristic value is a capacitance value.

[0046] Preferably, the capacitance value of the sesame oil sample is collected by a network analyzer; wherein the network analyzer is a 85071C network analyzer, and the 85071C network analyzer also includes a sample cavity, a coaxial probe, data processing software and a microcomputer;

[0047] Before using a network analyzer to collect the capacitance value of the sesame oil sample, the network analyzer is first calibrated; after the calibration is qualified, the capacitance value of the sesame oil sample can be calculated and collected.

[0048] Preferably, the network analyzer further comprises a temperature controller, which is connected to the sample chamber and is used to control the temperature of the sample chamber to a set temperature; the set temperature is 25°C.

[0049] Preferably, S3 is specifically:

[0050] S3.1: mixing the sesame oil sample with an acetone solution of triphenylphosphine to obtain an infrared spectrometry solution of the sesame oil sample;

[0051] S3.2: Preheat the infrared spectrometer;

[0052] S3.3: Collect infrared spectrum data of the sesame oil sample.

[0053] Preferably, the S3.3 is specifically as follows: 10 μL of the infrared spectrum determination liquid of the sesame oil sample is transferred to cover the sampling crystal surface of the infrared spectrometer, and sampling parameters are set to realize the collection of infrared spectrum data of the sesame oil sample;

[0054] The sampling parameters are: infrared spectrum detection range: 4000-400cm -1 , sampling resolution is 4cm -1 ; Each sesame oil sample was scanned 16 times.

[0055] Preferably, in S5, the second preprocessing operation is: inputting the first preprocessed infrared spectrum data into Matlab, and then performing baseline correction, moving average denoising, standard normal variable transformation, multivariate scattering correction and derivative spectrum correction respectively.

[0056] According to another aspect of the present invention, a rapid detection system for evaluating the quality of sesame oil is provided, wherein the system adopts the rapid detection method for evaluating the quality of sesame oil, and the system further comprises:

[0057] A sample acquisition module, used for acquiring sesame oil samples for sesame oil quality evaluation;

[0058] A dielectric characteristic value sampling module, used for collecting the dielectric characteristic value of the sesame oil sample;

[0059] An infrared spectrum data sampling module, used for collecting infrared spectrum data of the sesame oil sample;

[0060] A first preprocessing module, used for performing a first preprocessing operation on the infrared spectrum data to obtain first preprocessed infrared spectrum data;

[0061] A second preprocessing module performs a second preprocessing operation on the first preprocessed infrared spectrum data to obtain second preprocessed infrared spectrum data;

[0062] A characteristic value calculation module, used for obtaining a characteristic value of the infrared spectrum curve of the sesame oil sample according to the second preprocessed infrared spectrum data;

[0063] A standard relationship formula establishment module, used to establish a standard relationship formula for peroxide detection in the sesame oil sample;

[0064] The quality evaluation module is used to input the characteristic value of the infrared spectrum curve of the sesame oil sample obtained by the characteristic value calculation module into the standard relationship, calculate the peroxide content value of the sesame oil sample, and realize the evaluation of the sesame oil quality.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] When the infrared spectroscopy method is used to detect the peroxide content in sesame oil, the present invention proposes a sampling data preprocessing method combined with the dielectric constant data of the sesame oil sample in the spectrum data sampling stage to reduce the sampling data error and lay a data foundation for the subsequent accurate evaluation of the quality of the sesame oil;

[0067] At the same time, when establishing a standard relationship, the present invention first obtains 8 standard points for establishing the standard relationship through solution configuration, then abandons one standard point for each of the 8 standard points to establish an initial standard relationship, and considers the concentration difference between the abandoned point and the standard relationship calculated to generate different numbers of standard points, thereby weakening the influence of inaccurate standard points, and then establishes a standard relationship based on all the standard points, so that the standard relationship is established more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0069] Figure 1 A flowchart of a rapid detection method for evaluating the quality of sesame oil provided by an embodiment of the present invention;

[0070] Figure 2 A flow chart of collecting infrared spectrum data of sesame oil samples provided by an embodiment of the present invention;

[0071] Figure 3 A fitting diagram of peroxide content and capacitance provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] The following first describes the concepts involved in the present application in conjunction with the accompanying drawings. It should be noted that the following description of each concept is only to make the content of the present application easier to understand, and does not limit the scope of protection of the present application; at the same time, the embodiments and features in the embodiments of the present application can be combined with each other in the absence of conflict. The present application will be described in detail with reference to the accompanying drawings and in conjunction with the embodiments.

[0074] Embodiment 1, as Figure 1 As shown, the present invention provides a rapid detection method for evaluating the quality of sesame oil, which is used to detect peroxides in sesame oil, wherein the detection method specifically includes:

[0075] S1: Obtain sesame oil samples for sesame oil quality evaluation;

[0076] Using a pipette gun to take sesame oil of a preset mass from a sesame oil finished product production line as a sesame oil sample for sesame oil quality evaluation, and placing the sesame oil sample in a reflux bottle;

[0077] In this step, the preset mass is 100 g.

[0078] S2: collecting dielectric characteristic values ​​of the sesame oil sample;

[0079] In fact, the content of peroxide in the sesame oil sample will cause a large change in the capacitance value of the sesame oil sample. Therefore, in this embodiment, the dielectric characteristic value is the capacitance value;

[0080] Specifically, the capacitance value of the sesame oil sample is collected by a network analyzer; wherein the network analyzer is a 85071C network analyzer (Agilent Technologies, the 85071C network analyzer also includes a sample chamber, a coaxial probe, data processing software and a microcomputer;

[0081] Before using a network analyzer to collect the capacitance value of the sesame oil sample, the network analyzer is first calibrated, that is, the coaxial probe is calibrated with 25° C. deionized water; after the calibration is qualified, the capacitance value of the sesame oil sample can be calculated and collected;

[0082] It is worth emphasizing that the network analyzer further comprises a temperature controller, which is connected to the sample chamber and is used to control the temperature of the sample chamber to a set temperature;

[0083] In this step, the set temperature is 25°C.

[0084] S3: collecting infrared spectrum data of the sesame oil sample;

[0085] Infrared spectroscopy is an analytical method that studies the structure and composition of substances based on their absorption characteristics of infrared light. This method is one of the fastest-growing and most eye-catching green analytical technologies since the 1990s, and is particularly suitable for rapid analysis and screening of oil quality.

[0086] The principle of infrared spectroscopy technology for detecting oil quality is: material molecules are composed of atoms connected by chemical bonds. The atoms in the molecules are constantly vibrating near the equilibrium position. When the molecules vibrate, their dipole moments will change, so that they can absorb infrared light of a specific wavelength. Different chemical bonds or functional groups have different vibration frequencies, so they absorb infrared light at different wavelengths, which forms a characteristic infrared absorption spectrum. When infrared light is irradiated on a molecule, if the frequency of the light matches the vibration frequency of a chemical bond or functional group in the molecule, the molecule will absorb the energy of the infrared light, thereby causing the chemical bond or functional group to transition in vibration from the ground state to the excited state. This absorption process is selective, and different chemical bonds or functional groups will absorb By collecting infrared light in a specific frequency range and measuring the absorption of infrared light of different wavelengths by molecules, an infrared absorption spectrum can be obtained. The infrared spectrum has information such as the position, intensity and shape of the absorption peak. The position, intensity and shape of the absorption peak on the infrared spectrum reflects the characteristics of different chemical bonds or functional groups in the molecule. Therefore, the peroxide content in sesame oil can be detected by infrared spectroscopy, thereby quickly detecting the quality of sesame oil. When using infrared spectroscopy to detect the quality of sesame oil, since the sample preparation process is relatively simple, for many samples, only simple processing is required for testing. It usually takes only a few minutes to more than ten minutes from sample measurement to obtaining the spectral results, and the analysis results can be provided quickly.

[0087] The high concentration of triphenylphosphine in acetone solution is reacted with the peroxide in the sesame oil sample to generate triphenylphosphine oxide. The reaction chemical formula is as follows:

[0088]

[0089] According to the above reaction chemical formula, the peroxide in the sesame oil sample has a chemical reaction relationship with triphenylphosphine of 1:1, and the chemical reaction product triphenylphosphine oxide has a relationship with both reactants of 1:1:1, that is, 1 mol of peroxide reacts with 1 mol of triphenylphosphine to generate 1 mol of triphenylphosphine oxide; therefore, the amount of peroxide in the sesame oil sample can be obtained by measuring the molar amount of triphenylphosphine oxide by infrared spectroscopy.

[0090] Specifically, as attached Figure 2 As shown, the S3 is specifically:

[0091] S3.1: mixing the sesame oil sample with an acetone solution of triphenylphosphine to obtain an infrared spectrometry solution of the sesame oil sample;

[0092] Wherein, about 40 μL of 5% triphenylphosphine (TPP) acetone solution is added to a mixing bottle containing the sesame oil sample, the mixing bottle stopper is tightly closed, and the mixture is shaken and mixed evenly, so that the peroxide in the oil sample reacts with triphenylphosphine (TPP) to generate triphenylphosphine oxide (TPPO), thereby preparing an infrared spectrum measurement solution for the sesame oil sample;

[0093] S3.2: Preheat the infrared spectrometer;

[0094] In this step, the infrared spectrometer is a Nicolet Fourier transform infrared spectrometer, which is turned on for 30 minutes to ensure that the Nicolet' Fourier transform infrared spectrometer is fully predicted.

[0095] S3.3: Collect infrared spectrum data of the sesame oil sample;

[0096] Pipette about 10 μL of the infrared spectrum determination liquid of the sesame oil sample, evenly cover the sampling crystal surface of the infrared spectrometer, and set the sampling parameters to realize the collection of infrared spectrum data of the sesame oil sample;

[0097] In this step, the sampling parameters are: infrared spectrum detection range: 4000-400cm -1 , sampling resolution is 4cm -1 ; Each sesame oil sample was scanned 16 times.

[0098] S4: performing a first preprocessing operation on the infrared spectrum data to obtain first preprocessed infrared spectrum data;

[0099] Considering that the infrared spectrum data collected by the infrared spectrometer not only contains the essential information of the sample, but also contains more external interference information, it is very important to adopt a reasonable pre-processing method to eliminate interference factors. Generally, through the above-mentioned infrared spectrum data collection step, in order to reduce the data collection error, each sample is generally scanned and collected repeatedly at the same sampling resolution. In S3, each sample is repeatedly sampled 16 times at the same sampling resolution. After multiple samplings, the sampling data at each scanning frequency are generally averaged as the sampling value of the sample at the scanning frequency in the prior art; however, the above-mentioned pre-processing method is actually a rough pre-processing method, which leads to a large error in the sampling data. According to the above-mentioned defects, this step proposes a sampling data pre-processing method combined with the dielectric constant data of the sesame oil sample to reduce the sampling data error and lay a data foundation for the subsequent accurate evaluation of the quality of the sesame oil;

[0100] Specifically, the S4 is as follows:

[0101] S4.1: Calculate the standard deviation of 16 infrared spectrum data of sesame oil sample at each scanning frequency;

[0102] Wherein, the calculation formula of the standard deviation of 16 infrared spectrum data of the sesame oil sample at each scanning frequency is:

[0103]

[0104] In the formula, σ is the standard deviation, x i is the i-th infrared spectrum data, μ is the average value of 16 infrared spectrum data;

[0105] S4.2: Determine a comparison threshold of the standard deviation according to the dielectric characteristic value of the sesame oil sample;

[0106] In this step, since the dielectric characteristic value of sesame oil is relatively easy to obtain, and the dielectric characteristic value of sesame oil has a good linear relationship with the peroxide content in the sesame oil, the peroxide content in the sesame oil can be reflected by the dielectric characteristic value, and for sesame oil samples with different peroxide contents, when the peroxide content is low, the linearity of the dielectric characteristic value, that is, the capacitance value and the peroxide content is relatively poor, and when the peroxide content is high, the linearity of the dielectric characteristic value, that is, the capacitance value and the peroxide content is relatively good. When the peroxide content in the sesame oil is low, the accuracy of the collected data is required to be higher. Therefore, in the embodiment, different comparison thresholds are set according to the dielectric characteristic value of sesame oil, that is, the capacitance value, which improves the accuracy of data preprocessing while also improving the robustness of the sampling data preprocessing method.

[0107] Wherein, the comparison threshold σ of the standard deviation is determined according to the dielectric characteristic value of the sesame oil sample. N Specifically:

[0108]

[0109] Wherein, C is the dielectric characteristic value of the sesame oil sample;

[0110] S4.3: Compare the standard deviation obtained in S4.1 with the comparison threshold of the standard deviation. If the standard deviation obtained in S4.1 is less than the comparison threshold of the standard deviation, proceed to S4.4; otherwise, proceed to S4.5;

[0111] S4.4: taking an average value of 16 infrared spectrum data of the sesame oil sample as the infrared spectrum data at the scanning frequency;

[0112] In fact, if the standard deviation obtained in S4.1 is greater than the comparison threshold σ of the standard deviation N, then the sampling quality of the sampling points under this scanning frequency is better, that is, the relative deviation between the 16 sampling data is small, so the averaging method can be used to obtain more accurate sampling data;

[0113] S4.5: Calculate a first peroxide content value of the sesame oil sample according to the dielectric characteristic value of the sesame oil sample;

[0114] The capacitance values ​​of sesame oil samples with known peroxide contents and different peroxide contents collected at the same preset temperature are used as dielectric characteristic values, the preset temperature is set to 25°C, and then the peroxide content and the capacitance value are xy fitted to obtain the fitting relationship between the peroxide content and the capacitance value, wherein the attached Figure 3 The figure shows the fitting diagram of peroxide content and capacitance value; Figure 3 The horizontal coordinate x of the fitting graph is the capacitance value of the sesame oil sample (unit: 100nF), and the vertical coordinate y is the peroxide content value (mmol / kg) of the sesame oil sample, wherein the fitting relationship is:

[0115]

[0116] Among them, R 2 is the correlation coefficient of the fitting relationship, and the first peroxide content value of the sesame oil sample can be obtained by the fitting relationship;

[0117] S4.6: Calculate a second peroxide content value of the sesame oil sample according to the first peroxide content value of the sesame oil sample and a method conversion coefficient; wherein the method conversion coefficient is a ratio of the peroxide value of the sesame oil sample calculated by the dielectric characteristic value method to the peroxide value of the sesame oil sample calculated by the infrared spectroscopy method;

[0118] In fact, each detection method of active substances in sesame oil samples has its inherent error. Therefore, in this step, a method conversion coefficient is set to calculate the peroxide value of the sesame oil sample according to the dielectric characteristic value method to predict the peroxide value of the sesame oil sample using infrared spectroscopy;

[0119] S4.7: Determine the number of data to be deleted based on the standard deviation between the second peroxide content value and the standard deviation obtained in S4.1;

[0120] In this step, the formula for determining the number of data n to be deleted based on the standard deviation between the second peroxide content value and the standard deviation obtained in S4.1 is:

[0121]

[0122] Wherein, a, b are coefficients, m is the second peroxide content value, m0 is the peroxide content reference value, and σ0 is the standard deviation reference value;

[0123] S4.8: Sort the infrared spectrum data from large to small according to the difference between each infrared spectrum data and the mean value of the infrared spectrum data, and delete the first n infrared spectrum data according to the number n of deleted data determined in S4.7, and average the remaining infrared spectrum data to obtain the infrared spectrum data at the scanning frequency;

[0124] S5: performing a second preprocessing operation on the first preprocessed infrared spectrum data to obtain second preprocessed infrared spectrum data;

[0125] The second preprocessing operation is: inputting the first preprocessed infrared spectrum data into Matlab, and then performing baseline correction, moving average denoising, standard normal variable transformation, multivariate scattering correction and derivative spectrum correction respectively;

[0126] The baseline correction is used to remove the baseline offset or drift caused by the instrument, sample container or other factors to ensure that the spectrum reflects the characteristics of the sample itself; the moving average denoising is used to reduce the random noise in the spectral data, the standard normal variable transformation (SNV) is used to correct the scattering effect and perform spectral correction, the multivariate scattering correction (MSC) is used to correct the sample scattering effect, and the derivative spectral correction highlights the details of the spectral curve by calculating the first-order, second-order or higher-order derivatives, which helps to separate overlapping peaks and eliminate the influence of baseline drift.

[0127] S6: Obtaining characteristic values ​​of the infrared spectrum curve of the sesame oil sample according to the second preprocessed infrared spectrum data;

[0128] Wherein, in this step, the characteristic value of the infrared spectrum curve is 542cm -1 The peak height of the characteristic absorption peak.

[0129] S7: establishing a standard relationship for detecting peroxides in the sesame oil sample;

[0130] It can be seen from the above steps that the high-concentration triphenylphosphine acetone solution is reacted with the peroxide in the sesame oil sample to generate triphenylphosphine oxide, and the content of the peroxide is in a 1:1 relationship with the content of the generated triphenylphosphine oxide. Therefore, in this embodiment, a mixed solution is obtained by adding a quantitative triphenylphosphine oxide solution to the sesame oil sample, and different amounts of sesame oil are added respectively to obtain a triphenylphosphine oxide solution as a standard triphenylphosphine oxide solution. The standard triphenylphosphine oxide solutions of different concentrations are detected by infrared spectroscopy, and then a relationship is established between the characteristics of the obtained infrared spectrum curve and the content of triphenylphosphine oxide to obtain a relationship between the characteristics of the infrared spectrum curve and the content of triphenylphosphine oxide, and a standard relationship between the peroxide content and the characteristics of the infrared spectrum curve can be obtained;

[0131] Specifically, the S7 is as follows:

[0132] S7.1: Prepare triphenylphosphine oxide stock solution;

[0133] Grind triphenylphosphine oxide into powder, accurately weigh 1 g of the triphenylphosphine oxide powder using a high-precision balance, and then dissolve it in a small amount of ethanol, then treat the sesame oil sample to remove peroxides in the sesame oil sample and mix it with the triphenylphosphine oxide ethanol solution to obtain a triphenylphosphine oxide stock solution;

[0134] In this step, since the amount of triphenylphosphine oxide is known and the mass of the stock solution can be obtained by a balance, the content of the triphenylphosphine oxide stock solution is known.

[0135] S7.2: mixing the triphenylphosphine oxide stock solution with sesame oil samples of different masses, and diluting the triphenylphosphine oxide stock solution to 8 different concentrations, thereby obtaining 8 triphenylphosphine oxide solutions of different concentrations as standard triphenylphosphine oxide solutions;

[0136] S7.3: Detecting the eight standard triphenylphosphine oxide solutions of different concentrations by infrared spectroscopy to obtain the characteristics of the infrared spectrum curve, thereby obtaining standard points of the eight standard triphenylphosphine oxide solutions of different concentrations;

[0137] In this step, the infrared spectrum curve is characterized by 542 cm -1 The peak height of the characteristic absorption peak obtained, that is, the data of each standard point includes the concentration of the triphenylphosphine oxide solution at the standard point and the peak height of the characteristic absorption peak;

[0138] S7.4: establishing a standard relationship for peroxide detection in the sesame oil sample according to the standard points of the 8 standard triphenylphosphine oxide solutions of different concentrations;

[0139] Among them, in the prior art, 7-10 standard triphenylphosphine oxide solutions are generally configured for infrared spectrum detection to establish a standard relationship. However, the standard curve established by the above scheme may be affected by the sampling accuracy of the standard points, which may lead to low accuracy in establishing the standard curve. Therefore, this embodiment proposes a standard curve establishment method for the above situation to improve the accuracy of establishing the standard curve.

[0140] Wherein, the standard relationship for peroxide detection in the sesame oil sample established according to the characteristics of the infrared spectrum curve is specifically:

[0141] S7.4.1: Select 7 standard points from the 8 standard points to establish an initial standard relationship for peroxide detection in the sesame oil sample, and establish a total of 8 initial standard relationship;

[0142] Wherein, in this step, linear fitting is performed on the data of the seven standard points to establish an initial standard relationship; the independent variable of the initial standard relationship is the peak height of the characteristic absorption peak, and the dependent variable is the concentration of the triphenylphosphine oxide solution;

[0143] S7.4.2: Enter the independent variable of the unselected standard point into each initial standard relationship to obtain the concentration deviation calculated using the initial standard relationship;

[0144] For example, the initial standard relationship is established using the 1st to 7th standard points, and the peak height value of the 8th point is input into the initial standard relationship established using the 1st to 7th standard points, and a calculated concentration value is obtained, which is subtracted from the concentration value corresponding to the 8th point to obtain a concentration deviation value;

[0145] S7.4.3: Automatically generate a preset number of standard points according to each initial standard relationship, wherein the number of standard points generated by each initial standard relationship is determined according to the concentration deviation value;

[0146] The method of determining the number of standard points generated by each initial standard relationship according to the concentration deviation value is as follows: the absolute values ​​of the concentration deviation values ​​are sorted from large to small, and the number of standard points generated by the initial standard relationship corresponding to the absolute value of each concentration deviation value is 50, 45, 40, 35, 30, 25, 20, 15 respectively;

[0147] S7.4.4: Linearly fit all standard point data to obtain a standard relationship for peroxide detection in the sesame oil sample;

[0148] In this embodiment, the standard relationship is:

[0149] y=0.0004lx+0.00074

[0150] In the embodiment, 8 standard points for establishing a standard relationship are first obtained by solution configuration, and then one standard point is discarded for each of the 8 standard points to establish an initial standard relationship, and different numbers of standard points are generated considering the concentration difference calculated between the discarded point and the standard relationship, thereby weakening the influence of inaccurate standard points, and then a standard relationship is established based on all the standard points, so that the standard relationship is established more accurately.

[0151] S8: Input the characteristic value of the infrared spectrum curve of the sesame oil sample obtained in S6 into the standard relationship formula to calculate the peroxide content value of the sesame oil sample.

[0152] Embodiment 2: This embodiment further includes a rapid detection system for evaluating the quality of sesame oil. The system adopts the rapid detection method for evaluating the quality of sesame oil in Embodiment 1. The system further includes:

[0153] A sample acquisition module, used for acquiring sesame oil samples for sesame oil quality evaluation;

[0154] A dielectric characteristic value sampling module, used for collecting the dielectric characteristic value of the sesame oil sample;

[0155] An infrared spectrum data sampling module, used for collecting infrared spectrum data of the sesame oil sample;

[0156] A first preprocessing module, used for performing a first preprocessing operation on the infrared spectrum data to obtain first preprocessed infrared spectrum data;

[0157] A second preprocessing module performs a second preprocessing operation on the first preprocessed infrared spectrum data to obtain second preprocessed infrared spectrum data;

[0158] A characteristic value calculation module, used for obtaining a characteristic value of the infrared spectrum curve of the sesame oil sample according to the second preprocessed infrared spectrum data;

[0159] A standard relationship formula establishment module, used to establish a standard relationship formula for peroxide detection in the sesame oil sample;

[0160] The quality evaluation module is used to input the characteristic value of the infrared spectrum curve of the sesame oil sample obtained by the characteristic value calculation module into the standard relationship, calculate the peroxide content value of the sesame oil sample, and realize the evaluation of the sesame oil quality.

[0161] Embodiment three, this embodiment includes an electronic device, including a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the various steps of the rapid detection method for evaluating the quality of sesame oil in embodiment one are implemented, and the same technical effect can be achieved.

[0162] Embodiment 4: This embodiment includes a computer-readable storage medium on which a data processing program is stored. The data processing program is executed by a processor to execute the various steps of the rapid detection method for evaluating the quality of sesame oil in embodiment 1.

[0163] It will be appreciated by those skilled in the art that the embodiments herein may be provided as methods, devices (equipment), or computer program products. Therefore, this article may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Including but not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, disk storage or other magnetic storage device, or any other medium that can be used to store desired information and can be accessed by a computer, etc. In addition, it is well known to those of ordinary skill in the art that communication media generally contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0164] This document is described with reference to flowcharts and / or block diagrams of methods, apparatuses (devices) and computer program products according to the embodiments of this document. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0165] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods of the technology of the present invention, and are not intended to impose any formal restrictions on the implementation methods of the technology of the present invention. Any person skilled in the art may make some changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are essentially the same as the present invention. Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here, and that various obvious changes, readjustments and substitutions can be made to those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention is described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may also include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A rapid detection method for evaluating the quality of sesame oil, used for detecting peroxides in sesame oil, characterized in that: The specific steps include: S1: Obtain sesame oil samples for sesame oil quality evaluation; S2: collecting dielectric characteristic values ​​of the sesame oil sample; S3: collecting infrared spectrum data of the sesame oil sample; S4: performing a first preprocessing operation on the infrared spectrum data to obtain first preprocessed infrared spectrum data; S5: performing a second preprocessing operation on the first preprocessed infrared spectrum data to obtain second preprocessed infrared spectrum data; S6: Obtaining characteristic values ​​of the infrared spectrum curve of the sesame oil sample according to the second preprocessed infrared spectrum data; S7: establishing a standard relationship for detecting peroxides in the sesame oil sample; The standard relationship is: y=0.00041x+0.00074; The S7 is specifically: S7.1: Prepare triphenylphosphine oxide stock solution; S7.2: mixing the triphenylphosphine oxide stock solution with sesame oil samples of different masses, and diluting the triphenylphosphine oxide stock solution to 8 different concentrations, thereby obtaining 8 triphenylphosphine oxide solutions of different concentrations as standard triphenylphosphine oxide solutions; S7.3: The eight standard triphenylphosphine oxide solutions of different concentrations are respectively detected by infrared spectroscopy to obtain the characteristics of the infrared spectrum curve, thereby obtaining the standard points of the eight standard triphenylphosphine oxide solutions of different concentrations; the characteristic value of the infrared spectrum curve is 542cm -1 The peak height of the characteristic absorption peak at ; S7.4: establishing a standard relationship for peroxide detection in the sesame oil sample according to the standard points of the 8 standard triphenylphosphine oxide solutions of different concentrations; Wherein, the standard relationship for peroxide detection in the sesame oil sample established according to the characteristics of the infrared spectrum curve is specifically: S7.4.1: Select 7 standard points from the 8 standard points to establish an initial standard relationship for peroxide detection in the sesame oil sample, and establish a total of 8 initial standard relationship; S7.4.2: Enter the independent variable of the unselected standard point into each initial standard relationship to obtain the concentration deviation calculated using the initial standard relationship; S7.4.3: Automatically generate a preset number of standard points according to each initial standard relationship, wherein the number of standard points generated by each initial standard relationship is determined according to the concentration deviation value; S7.4.4: Linearly fit all standard point data to obtain a standard relationship for peroxide detection in the sesame oil sample; S8: Input the characteristic value of the infrared spectrum curve of the sesame oil sample obtained in S6 into the standard relationship formula to calculate the peroxide content value of the sesame oil sample.

2. A rapid detection method for evaluating the quality of sesame oil according to claim 1, characterized in that: The S4 is specifically: S4.1: Calculate the standard deviation of 16 infrared spectrum data of sesame oil sample at each scanning frequency; Wherein, the calculation formula of the standard deviation of 16 infrared spectrum data of the sesame oil sample at each scanning frequency is: In the formula, σ is the standard deviation, x i is the i-th infrared spectrum data, μ is the average value of 16 infrared spectrum data; S4.2: Determine a comparison threshold of the standard deviation according to the dielectric characteristic value of the sesame oil sample; The comparison threshold σ of the standard deviation is determined according to the dielectric characteristic value of the sesame oil sample. N Specifically: Wherein, C is the dielectric characteristic value of the sesame oil sample; S4.3: Compare the standard deviation obtained in S4.1 with the comparison threshold of the standard deviation. If the standard deviation obtained in S4.1 is less than the comparison threshold of the standard deviation, proceed to S4.4; otherwise, proceed to S4.5; S4.4: taking an average value of 16 infrared spectrum data of the sesame oil sample as the infrared spectrum data at the scanning frequency; S4.5: Calculate a first peroxide content value of the sesame oil sample according to the dielectric characteristic value of the sesame oil sample; S4.6: Calculate a second peroxide content value of the sesame oil sample according to the first peroxide content value of the sesame oil sample and a method conversion coefficient; wherein the method conversion coefficient is a ratio of the peroxide value of the sesame oil sample calculated by the dielectric characteristic value method to the peroxide value of the sesame oil sample calculated by the infrared spectroscopy method; S4.7: Determine the number of data to be deleted based on the standard deviation between the second peroxide content value and the standard deviation obtained in S4.1; The formula for determining the number of data n to be deleted based on the standard deviation between the second peroxide content value and the standard deviation obtained in S4.1 is: Wherein, a, b are coefficients, m is the second peroxide content value, m0 is the peroxide content reference value, and σ0 is the standard deviation reference value; S4.8: Sort the infrared spectrum data from large to small according to the difference between each infrared spectrum data and the mean value of the infrared spectrum data, delete the first n infrared spectrum data according to the number n of deleted data determined in S4.7, and average the remaining infrared spectrum data to obtain the infrared spectrum data at the scanning frequency.

3. A rapid detection method for evaluating the quality of sesame oil according to claim 2, characterized in that: The S4.5 is specifically as follows: the capacitance value of the sesame oil samples with different peroxide contents and known peroxide contents collected at the same preset temperature is used as the dielectric characteristic value, the preset temperature is set to 25°C, and then the peroxide content and the capacitance value are xy fitted to obtain the fitting relationship between the peroxide content and the capacitance value, and the fitting relationship is: y=34.21x-17.91 R 2 =0.973; The horizontal coordinate x is the capacitance value of the sesame oil sample, and the vertical coordinate y is the peroxide content value of the sesame oil sample. The first peroxide content value of the sesame oil sample is obtained by the fitting relationship.

4. A rapid detection method for evaluating the quality of sesame oil according to claim 1, characterized in that: In S2, the dielectric characteristic value is a capacitance value.

5. A rapid detection method for evaluating the quality of sesame oil according to claim 4, characterized in that: The capacitance value of the sesame oil sample is collected by a network analyzer; wherein the network analyzer is a 85071C network analyzer, and the 85071C network analyzer also includes a sample cavity, a coaxial probe, data processing software and a microcomputer; Before using a network analyzer to collect the capacitance value of the sesame oil sample, the network analyzer is first calibrated; after the calibration is qualified, the capacitance value of the sesame oil sample can be calculated and collected.

6. A rapid detection method for evaluating the quality of sesame oil according to claim 5, characterized in that: The network analyzer further comprises a temperature controller, which is connected to the sample chamber and is used to control the temperature of the sample chamber to a set temperature; the set temperature is 25°C.

7. A rapid detection method for evaluating the quality of sesame oil according to claim 1, characterized in that: The S3 is specifically: S3.1: mixing the sesame oil sample with an acetone solution of triphenylphosphine to obtain an infrared spectrometry solution of the sesame oil sample; S3.2: Preheat the infrared spectrometer; S3.3: Collect infrared spectrum data of the sesame oil sample.

8. A rapid detection method for evaluating the quality of sesame oil according to claim 7, characterized in that: The S3.3 is specifically as follows: 10 μL of the infrared spectrum determination liquid of the sesame oil sample is transferred to cover the sampling crystal surface of the infrared spectrometer, and sampling parameters are set to realize the collection of infrared spectrum data of the sesame oil sample; The sampling parameters are: infrared spectrum detection range: 4000-400cm -1 , sampling resolution is 4cm -1 ; Each sesame oil sample was scanned 16 times.

9. A rapid detection method for evaluating the quality of sesame oil according to claim 7, characterized in that: In S5, the second preprocessing operation is: inputting the first preprocessed infrared spectrum data into Matlab, and then performing baseline correction, moving average denoising, standard normal variable transformation, multivariate scattering correction and derivative spectrum correction respectively.

10. A rapid detection system for evaluating the quality of sesame oil, characterized in that: The system adopts the rapid detection method for evaluating the quality of sesame oil according to any one of claims 1 to 9, and the system further comprises: A sample acquisition module, used for acquiring sesame oil samples for sesame oil quality evaluation; A dielectric characteristic value sampling module, used for collecting the dielectric characteristic value of the sesame oil sample; An infrared spectrum data sampling module, used for collecting infrared spectrum data of the sesame oil sample; A first preprocessing module, used for performing a first preprocessing operation on the infrared spectrum data to obtain first preprocessed infrared spectrum data; A second preprocessing module performs a second preprocessing operation on the first preprocessed infrared spectrum data to obtain second preprocessed infrared spectrum data; A characteristic value calculation module, used for obtaining a characteristic value of the infrared spectrum curve of the sesame oil sample according to the second preprocessed infrared spectrum data; A standard relationship formula establishment module, used to establish a standard relationship formula for peroxide detection in the sesame oil sample; The quality evaluation module is used to input the characteristic value of the infrared spectrum curve of the sesame oil sample obtained by the characteristic value calculation module into the standard relationship, calculate the peroxide content value of the sesame oil sample, and realize the evaluation of the sesame oil quality.

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