Grease detection device and method for edible oil production and processing

Abstract

The invention relates to the technical field of tourism services, in particular to a grease detection device and method for edible oil production and processing, and a system comprises a spectral data acquisition module, a component ratio calculation module, a spectral feature analysis module, a chemical component deviation evaluation module and a smell component fluctuation detection module. According to the method, accurate identification of edible oil component characteristics is improved by using the laser Raman spectrometer, fine analysis of complex components is realized by combining gas and liquid chromatograms and mass spectrometers, release of volatile substances is monitored by the electronic nose sensor, and environmental adaptability and dynamic response capability of data are enhanced. Through automatic abnormal data elimination and normalization processing, the data consistency and standardization level are improved, the accuracy of sample classification and edible oil safety assessment is further improved through multi-parameter ratio calculation and peak position offset analysis, the data processing flow is optimized, automation and intelligence of the detection system are enhanced, and the detection efficiency is improved. And the efficiency and the precision of edible oil detection are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tourism services, and in particular to a device and method for detecting grease used in the production and processing of edible oil. Background Art

[0002] The field of oil and fat detection technology includes component analysis, quality assessment, adulteration identification and safety monitoring of edible oil and related oil products. The core content of this technical field includes the determination of key parameters such as acid value, peroxide value, fatty acid composition (such as oleic acid, erucic acid, etc.), solvent residue, benzopyrene content, etc. of edible oil. The measurement of these parameters usually relies on methods such as gravimetric analysis, electrochemical detection, chromatographic analysis and spectral detection. However, the current standard detection methods generally have problems such as long time consumption, high cost, use of organic solvents, generation of hazardous waste and complex operation, which not only increases the difficulty of detection, but also limits the detection efficiency and popularity. In recent years, Raman spectroscopy technology has gradually been used in the field of oil and fat detection due to its advantages such as non-destructive detection, no need for complex sample pretreatment and high detection convenience. It can be used to construct a quality assessment model based on physical and chemical analysis values ​​to achieve rapid determination of multiple quality parameters of edible oil.

[0003] Among them, the oil and fat detection device for edible oil production and processing refers to equipment used to detect multiple quality parameters of edible oil during production and processing. The device mainly detects indicators such as acid value, peroxide value, fatty acid composition, solvent residue and benzopyrene content. The detection methods include Raman spectroscopy for spectral intensity analysis and component identification, electrochemical sensors for acid value and peroxide value determination, gas chromatography for solvent residue detection, and high-performance liquid chromatography for benzopyrene content determination. In addition, the device is combined with an automatic sampling system to achieve real-time online monitoring, and uses data modeling and analysis technology to match physical and chemical analysis values ​​through spectral scanning data to quickly determine the target parameters. This can reduce the need for sample pretreatment while reducing the detection steps, thereby improving detection efficiency and applicability.

[0004] Existing technologies rely on a single method in the detection process, making it difficult to build a systematic multi-parameter analysis system, which limits the accuracy of component comparison and risk assessment. Some detection methods are sensitive to changes in environmental conditions, and data stability is difficult to ensure. Especially in the analysis of volatile components, the current methods have low adaptability to the influence of different environmental factors. In terms of data standardization, traditional detection methods fail to effectively eliminate abnormal data, resulting in large comparison errors between samples from different batches, reducing data consistency. In terms of component ratio calculation, existing methods have not formed a refined ratio matrix, making it difficult to accurately evaluate the correspondence between fatty acid composition and quality characteristics. Spectral analysis mainly relies on static measurement and lacks dynamic change analysis, resulting in insufficient recognition of peak shift characteristics. Adulteration detection methods mostly use a single indicator for evaluation, making it difficult to comprehensively analyze the trend of chemical component shifts for risk prediction, resulting in low accuracy in adulteration identification. Odor component fluctuation analysis relies on chromatographic methods, which have limited recognition capabilities for abnormal release patterns and make it difficult to achieve efficient screening. Overall, the existing technology has deficiencies in multi-dimensional component analysis, data stability, abnormal data processing, ratio calculation accuracy, spectral dynamic analysis and comprehensive evaluation of doping detection, which affects the accuracy of the test results and the intelligence level of the detection process. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a grease detection device and method for edible oil production and processing.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A fat detection device for edible oil production and processing comprises: The spectral data acquisition module obtains edible oil samples, uses a laser Raman spectrometer to record Raman scattering signals, extracts characteristic spectral data, and combines gas and liquid chromatography to analyze compound components, and generates a standardized data set after normalization processing; The component ratio calculation module calls the standardized data set, calculates the ratio of saturated and unsaturated fatty acids, compares the database benchmark value, analyzes the deviation range, matches the minimum deviation sample, and generates a component ratio matching result; The spectral feature analysis module analyzes the spectral peak shift based on the component ratio matching result, calculates the frequency change, and compares the Raman spectrum database to match the characteristic peak shift amplitude to generate spectral feature matching data; The chemical composition shift assessment module calls the spectral feature matching data, calculates the oxidation product ratio, compares the polar compound database, analyzes the shift trend, sets the doping threshold, determines the doping or oxidation risk, and generates chemical composition shift determination data; The odor component fluctuation detection module calls the chemical component deviation judgment data, analyzes the release trend of volatile components, calculates the concentration change curve, compares the database to match the abnormal release curve, and generates abnormal detection data.

[0007] As a further scheme of the present invention, the standardized data set includes characteristic spectral data, fatty acid composition, polar compound content, and volatile organic compound characteristics; the component ratio matching results include target sample ratio, database benchmark ratio, deviation range, and matching sample category; the spectral feature matching data includes spectral peak shift, frequency change value, peak shift interval, and matching reference sample; the chemical component shift judgment data includes polar compound ratio, oxidation product ratio, ratio shift trend, and doping risk level; the abnormal detection data includes volatile component release trend, key component release rate, concentration change curve, and abnormal release pattern.

[0008] As a further solution of the present invention, the spectral data acquisition module includes: The spectral detection submodule obtains edible oil production and processing samples, uses a laser Raman spectrometer to irradiate the samples, records Raman scattering signals, analyzes the frequency changes of Stokes and anti-Stokes scattered photons, extracts characteristic spectral peak positions, bandwidths, and intensity information from spectral data, and performs baseline correction to eliminate signal drift and obtain spectral characteristic data; The component analysis submodule uses a gas chromatograph to separate fatty acids, polar compounds and volatile organic compounds based on the spectral characteristic data, records the chromatographic peak position, peak area and retention time of each component, and combines with a liquid chromatography-mass spectrometer to calculate the content index of each component, construct a ratio matrix of fatty acids, polar compounds and volatile organic compounds, and obtain component ratio data; The data processing submodule calls the component ratio data, uses an electronic nose sensor to detect the release rate of volatile substances under differentiated environmental conditions, calculates the release rate and concentration changes of key volatile components, removes abnormal data points, and performs normalization processing to finally generate a standardized data set.

[0009] As a further solution of the present invention, the formula for calculating the characteristic value of the component content ratio is specifically: ; in, represents the characteristic value of the component content ratio, Representative sample The peak area of ​​the target compound, Representative The retention time of the target compound, represents the total mass of the sample, Represents the ratio matrix The ratio of polar compounds, represents the mean of the ratios of all polar compounds, represents the total number of target compounds, Represents the total number of polar compounds in the ratio matrix.

[0010] As a further solution of the present invention, the component ratio calculation module includes: The fatty acid extraction submodule calls the standardized data set, screens fatty acid-related data, extracts the content information of saturated fatty acids and unsaturated fatty acids, removes non-target component data, and calculates the mass fraction ratio of similar fatty acids to establish fatty acid composition data; The ratio calculation submodule calculates the ratio of saturated fatty acids to unsaturated fatty acids based on the fatty acid composition data, constructs a ratio matrix, analyzes the proportion relationship of each component in the ratio matrix, and obtains a fatty acid ratio matrix; The deviation matching submodule calls the fatty acid ratio matrix, compares it with the stored edible oil component ratio database, calculates the deviation range between the target sample ratio and the database benchmark ratio, matches the deviation sample category, and generates a component ratio matching result.

[0011] As a further solution of the present invention, the spectral feature analysis module includes: The spectral data extraction submodule calls the component ratio matching result, screens the Raman spectral signal data, extracts the spectral peak information, separates the Stokes and anti-Stokes scattering signals, analyzes the signal intensity and spectral width, calculates the signal distribution index of the frequency point, and establishes the spectral characteristic parameters; The peak shift calculation submodule analyzes the spectral peak shift based on the spectral characteristic parameters, calculates the photon frequency change value, selects the signal points where the peak shift amplitude exceeds the set threshold, counts the peak shift interval, and obtains the peak shift data; The spectrum matching submodule calls the peak shift data, compares the Raman spectrum database, analyzes the peak shift amplitudes of the target sample and the reference sample, matches the characteristic peak shift trends, and generates spectrum feature matching data.

[0012] As a further solution of the present invention, the photon frequency change value calculation formula is specifically: ; in, represents the change value of photon frequency, represents the speed of light, represents the incident laser wavelength, represents the spectral peak position measured for the sample, Represents the standard reference spectrum peak position.

[0013] As a further solution of the present invention, the chemical composition deviation assessment module includes: The polar compound calculation submodule calls the spectral feature matching data, extracts the polar compound content data of the target sample, screens the target polar compound category, calculates the ratio of each component, and establishes the ratio relationship between the compounds to obtain the polar compound ratio data; The oxidation product comparison submodule calculates the oxidation product concentration ratio based on the polar compound ratio data, screens the ratio range of the target oxidation marker, compares the polar compound reference database, analyzes the change trend of the oxidation product concentration, and obtains the oxidation product concentration ratio; The doping risk assessment submodule calls the oxidation product concentration ratio, sets the doping risk threshold, calculates the deviation index of the target sample, compares the stored risk assessment reference value, determines whether it exceeds the safety limit, and generates chemical composition deviation determination data.

[0014] As a further solution of the present invention, the odor component fluctuation detection module includes: The volatile component extraction submodule calls the chemical component deviation determination data, extracts the volatile organic compound data of the target sample, screens the key components, analyzes the concentration ratio of each component, removes the data outside the detection error range, and establishes the component distribution trend to obtain the volatile organic compound component data; The release rate calculation submodule determines the release trend under set environmental conditions based on the volatile organic compound component data, calculates the release rate of key components, extracts the concentration change curve in the time dimension, and compares the reference release baseline value to obtain the release rate of key components; The abnormal pattern matching submodule calls the key component release rate, compares it with the volatile organic compound database, calculates the release pattern deviation value of the target sample, matches the abnormal release curve stored in the database, screens the abnormal sample category, and generates abnormal detection data.

[0015] A method for detecting fats and oils used in edible oil production and processing, comprising the following steps: S1: Obtain edible oil samples, use laser Raman spectrometer to record Raman scattering signals, and analyze compound components by gas chromatography and liquid chromatography, and generate standardized data sets after normalization processing; S2: calling the standardized data set, calculating the ratio of saturated to unsaturated fatty acids, comparing the database benchmark value, analyzing the deviation range, and generating a component ratio matching result; S3: Based on the component ratio matching result, analyze the spectral peak shift, calculate the frequency change, and compare with the Raman spectrum database to generate spectral feature matching data; S4: calling the spectral feature matching data, calculating the oxidation product ratio, comparing the polar compound database, setting the doping threshold, judging the doping or oxidation risk, and generating chemical composition deviation determination data; S5: calling the chemical component deviation determination data, analyzing the release trend of volatile components, comparing the database to match the abnormal release curve, and generating abnormal detection data.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the use of a laser Raman spectrometer improves the accurate identification of edible oil component characteristics, and the combination of gas chromatography and liquid chromatography-mass spectrometry enables the fine analysis of complex components. The electronic nose sensor monitors the release of volatile substances, enhances the environmental adaptability and dynamic response capability of data, and improves the consistency and standardization level of data through automated abnormal data elimination and normalization processing. The ratio calculation of multiple parameters and peak shift analysis further enhance the accuracy of sample classification and edible oil safety assessment, optimizes the data processing flow, enhances the automation and intelligence of the detection system, and significantly improves the efficiency and accuracy of edible oil detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 It is a submodule flow chart of the present invention; Figure 3 It is a flow chart of the spectrum data acquisition module of the present invention; Figure 4 It is a flow chart of the component ratio calculation module of the present invention; Figure 5 It is a flow chart of the spectrum characteristic analysis module of the present invention; Figure 6 The flow chart of the chemical composition deviation assessment module of the present invention is as follows; Figure 7 This is a flow chart of the odor component fluctuation detection module of the present invention; Figure 8 is a flow chart of the steps of the method of the present invention; Fig. 9 This is an information diagram of the device of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0019] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0020] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0021] See also Figure 1-Figure 9 , a grease detection device for edible oil production and processing comprises: The spectral data acquisition module obtains edible oil production and processing samples, uses a laser Raman spectrometer to irradiate the samples, records Raman scattering signals, analyzes the frequency changes of Stokes and anti-Stokes scattered photons, extracts characteristic spectral peak positions, bandwidths and intensity data, uses a gas chromatograph to separate fatty acids, polar compounds and volatile organic components, combines a liquid chromatography-mass spectrometer to calculate the compound content and ratio matrix, uses an electronic nose sensor to detect the release rate of volatile substances under differentiated environmental conditions, removes abnormal data and performs normalization processing to generate a standardized data set; The component ratio calculation module calls the standardized data set, extracts the fatty acid composition data, calculates the ratio of saturated fatty acids to unsaturated fatty acids, compares the target sample ratio matrix with the database benchmark ratio based on the stored edible oil component ratio database, analyzes the deviation range between the target sample ratio and the database benchmark value, matches the sample category with the minimum deviation, and generates a component ratio matching result; The spectral feature analysis module calls the component ratio matching result, extracts the Raman spectral signal data, analyzes the spectral peak offset, calculates the frequency change, compares the peak offset interval of the target sample and the reference data with the Raman spectral database, matches the reference sample with the characteristic peak offset amplitude, and generates spectral feature matching data; The chemical composition shift assessment module calls the spectral feature matching data, extracts the polar compound ratio data, calculates the oxidation product concentration ratio, compares the stored polar compound reference database to analyze the ratio shift trend, sets the doping risk threshold, determines whether the target sample has doping or oxidation risks, and generates chemical composition shift determination data; The odor component fluctuation detection module calls the chemical component deviation judgment data, extracts the volatile organic compound data, determines the release trend under the set environmental conditions, calculates the release rate and concentration change curve of the key volatile components, compares the volatile organic compound database, analyzes the release pattern deviation between the target sample and the stored data, matches the abnormal release curve, and generates abnormal detection data.

[0022] The standardized data set includes characteristic spectral data, fatty acid composition, polar compound content, and volatile organic compound characteristics. The component ratio matching results include target sample ratio, database benchmark ratio, deviation range, and matching sample category. The spectral feature matching data includes spectral peak shift, frequency change value, peak shift interval, and matching reference sample. The chemical composition shift judgment data includes polar compound ratio, oxidation product ratio, ratio shift trend, and doping risk level. The abnormal detection data includes volatile component release trend, key component release rate, concentration change curve, and abnormal release pattern.

[0023] See also Figure 3 and Figure 2 , the spectral data acquisition module includes: The spectral detection submodule obtains edible oil production and processing samples, uses a laser Raman spectrometer to irradiate the samples, records Raman scattering signals, analyzes the frequency changes of Stokes and anti-Stokes scattered photons, extracts characteristic spectral peak positions, bandwidths, and intensity information from spectral data, and performs baseline correction to eliminate signal drift and obtain spectral characteristic data; Obtain edible oil production and processing samples, collect edible oil samples from different sources and batches through sampling equipment, and package them according to standard specifications to reduce environmental impact, use laser Raman spectrometer to irradiate the samples, select a laser source with a laser wavelength in the range of 532nm to 785nm to optimize the signal acquisition effect, and control the laser incident power between 50mW and 200mW to avoid the influence of photothermal effect on the sample. The laser beam is vertically incident on the sample surface, and the Raman scattering signal is collected by a lens and recorded by a spectrometer. By detecting the frequency offset of Stokes and anti-Stokes scattered photons, the characteristic spectral peak position, bandwidth and intensity information are extracted according to the Raman scattering spectrum characteristics, wherein the peak position data is wavelength calibrated through the detector array of the spectrometer to ensure the accuracy of the data, and the bandwidth information is calculated by half-peak width, and its value is defined as the peak width at 50% of the maximum height of the spectral peak, in cm -1The intensity information is obtained by integrating the area under the spectral peak to obtain the relative intensity of the characteristic peak. After that, baseline correction is performed. The Savitzky-Golay filtering method is used to remove background noise and smooth the spectral curve. At the same time, the polynomial fitting method is used to eliminate the baseline drift and normalize the signal to reduce the influence of the instrument system error, and finally the standardized spectral characteristic data is obtained.

[0024] The component analysis submodule uses gas chromatography to separate fatty acids, polar compounds and volatile organic compounds based on spectral feature data, records the chromatographic peak position, peak area and retention time of each component, and combines liquid chromatography-mass spectrometry to calculate the content index of each component, construct a ratio matrix of fatty acids, polar compounds and volatile organic compounds, and obtain component ratio data; The specific calculation formula of the component content ratio characteristic value is: ; in, represents the characteristic value of the component content ratio, Representative sample The peak area of ​​the target compound, Representative The retention time of the target compound, represents the total mass of the sample, Represents the ratio matrix The ratio of polar compounds, represents the mean of the ratios of all polar compounds, represents the total number of target compounds, Represents the total number of polar compounds in the ratio matrix: Parameter acquisition: : The sample The peak area of ​​each target compound. The sample is tested by gas chromatography-mass spectrometry (GC-MS) to obtain the peak area of ​​each target compound. Assume that three target compounds are detected, and their peak areas are 1500, 2000 and 2500 respectively.

[0025] : No. The retention time of each target compound is obtained by GC-MS detection. Assume that the corresponding retention times are 5.2 minutes, 7.8 minutes and 10.1 minutes respectively.

[0026] : Total mass of the sample. Weigh the sample mass using a precision balance. Assume the total mass of the sample is 50 grams.

[0027] : The first The ratio of polar compounds is calculated by liquid chromatography-mass spectrometry (LC-MS) to detect the sample and calculate the ratio of each polar compound. Assume that the ratios of the four polar compounds detected are 0.12, 0.15, 0.14 and 0.13 respectively.

[0028] : The average of all polar compound ratios. The calculation method is: ; Substitute the above The numerical value is calculated: ; Formula calculation derivation: Molecular part: Calculate the product of the peak area and retention time of each target compound and sum them: ; Specific calculation: ; ; ; Add the above results together: ; Denominator: Calculate the square of the difference between the ratio of each polar compound and the mean and sum them: ; Specific calculation: ; ; ; ; Add the above results together: ; Calculate the denominator part of the standard deviation: ; Specific calculation: ; ; Final calculation: Substituting the numerator and denominator into the formula: ; The results show that the characteristic value of the component content ratio is 43520.75. This value reflects the comprehensive content characteristics of the target compound in the sample and is helpful for further analysis of the component ratio data of the sample.

[0029] The data processing submodule calls the component ratio data, uses the electronic nose sensor to detect the release rate of volatile substances under differentiated environmental conditions, calculates the release rate and concentration changes of key volatile components, removes abnormal data points, and performs normalization processing to finally generate a standardized data set; The component ratio data was called up, and the electronic nose sensor was used to detect the release rate of volatile substances in edible oil under different environmental conditions. First, the electronic nose sensor array was set, including various types of sensors such as metal oxide sensors (MOS) and photoionization detectors (PID), and pre-calibrated to make the reference signal at zero. By controlling environmental variables such as temperature (25℃, 40℃, 60℃) and humidity (40%, 60%, 80%), the release rate of the target volatile components under different conditions was recorded respectively. The micro-airflow control technology was used to pass the sample container at a flow rate of 0.5L / min, and the sensor response signal was recorded within 10 minutes. The concentration changes of key volatile components were calculated by the linear regression model, and the noise data points were eliminated, that is, the data points exceeding the mean ±3 times the standard deviation were judged as outliers and deleted. Then, the data were normalized, and the calculation formula was: ; in, is the normalized data, is the original data, and are the minimum and maximum values ​​in the data set respectively. After normalization, the values ​​of all data are mapped to the range of 0 to 1 to ensure the consistency of data processing and finally generate a standardized data set.

[0030] See also Figure 4 and Figure 2 , the component ratio calculation module includes: The fatty acid extraction submodule calls the standardized data set, screens fatty acid-related data, extracts the content information of saturated fatty acids and unsaturated fatty acids, removes non-target component data, and calculates the mass fraction ratio of similar fatty acids to establish fatty acid composition data; The standardized data set was called and the associated information related to fatty acids in the data set was screened. First, the fatty acid target data was extracted. By retrieving the component items marked as fatty acid categories in the data set, the name, structural information and mass score data of each component were called to determine its carbon chain length and degree of saturation. The distinction between saturated fatty acids and unsaturated fatty acids was defined. Fatty acids without double bonds in the carbon chain were classified as saturated fatty acids, and fatty acids with one or more double bonds were classified as unsaturated fatty acids. The extracted fatty acid data were grouped by type, and the mass score of each category of fatty acids was calculated. The mass score was calculated as the ratio of the mass of the fatty acid component to the total mass of the sample. The calculation formula is as follows: ; in, is the mass fraction of the target fatty acid, Represents the mass of the fatty acid. Represents the total mass of the sample. Assuming that the total mass of a edible oil sample is 100g, of which the mass of palmitic acid (C16:0) is 12g and the mass of oleic acid (C18:1) is 28g, the mass fraction of palmitic acid is calculated to be 0.12 and the mass fraction of oleic acid is 0.28. Then, the non-target component data is eliminated, and the impurity data other than fatty acids is removed by judging the structural characteristics of the compounds and the matching rules in the fatty acid database. Finally, the mass fraction ratio of the quasi-fatty acids is calculated, that is, the mass of the quasi-fatty acids in the sample is compared with the total mass of the fatty acids. The calculation method is: ; in, Representative fatty acid mass percentage, Represents the total mass of fatty acids, Represents the sum of the masses of all fatty acids. If the mass of the fatty acid is 5g and the total mass of fatty acids is 50g, the calculated ratio is 0.10, and the fatty acid composition data is finally established.

[0031] The ratio calculation submodule calculates the ratio of saturated fatty acids to unsaturated fatty acids based on the fatty acid composition data, constructs a ratio matrix, analyzes the proportion of each component in the ratio matrix, and obtains the fatty acid ratio matrix; Based on the fatty acid composition data, the mass fractions of saturated fatty acids and unsaturated fatty acids were first extracted, and the ratio between the two was calculated. The ratio calculation rule was defined, and the ratio of the mass fraction of saturated fatty acids to the mass fraction of unsaturated fatty acids was used as an indicator. The calculation formula is as follows: ; in, Represents the ratio of saturated fatty acids to unsaturated fatty acids. represents the mass fraction of saturated fatty acids, Represents the mass fraction of unsaturated fatty acids. Assuming that the mass fraction of saturated fatty acids in a sample is 0.35 and the mass fraction of unsaturated fatty acids is 0.65, the calculated ratio is 0.538. The ratio data is stored in the ratio matrix, and the proportion of each component in the ratio matrix is ​​analyzed to determine the proportional relationship between different fatty acid types in order to parse out the content comparison of the main fatty acids. The data in the ratio matrix is ​​structured and organized, and the data in the comparison value matrix is ​​stratified according to the saturation and structural characteristics of fatty acids. The ratios of long-chain saturated fatty acids and short-chain saturated fatty acids are calculated respectively, and the ratios are stored in the ratio matrix respectively to obtain the fatty acid ratio matrix.

[0032] The deviation matching submodule calls the fatty acid ratio matrix, compares it with the stored edible oil component ratio database, calculates the deviation range between the target sample ratio and the database benchmark ratio, matches the deviation sample category, and generates the component ratio matching result; Call the fatty acid ratio matrix and compare it with the stored edible oil composition ratio database. First, read the benchmark ratio data in the database, extract the ratio data of the current target sample, and calculate the deviation range between the target sample ratio and each standard ratio in the database in turn. Use the deviation calculation formula: ; in, Represents the percentage deviation between the target sample ratio and the benchmark ratio. represents the standard ratio in the database, Represents the ratio of the target sample. Assuming that the standard ratio of a certain type of oil in the database is 0.50 and the ratio of the target sample is 0.55, the calculated deviation is 10%. The matching rules are set according to the deviation range. If the deviation is less than 5%, it is judged as a high match. If the deviation is between 5% and 15%, it is judged as a moderate match. If the deviation is greater than 15%, it is judged as a low match. Finally, based on the ratio matching results, the target samples are classified and matched with the corresponding sample categories to generate the component ratio matching results.

[0033] See also Figure 5 and Figure 2 , the spectral feature analysis module includes: The spectral data extraction submodule calls the component ratio matching results, screens the Raman spectral signal data, extracts the spectral peak information, separates the Stokes and anti-Stokes scattering signals, analyzes the signal intensity and spectral width, calculates the signal distribution index of the frequency point, and establishes the spectral characteristic parameters; The component ratio matching result is called, and the Raman spectrum signal data involved is screened. First, the spectrum data of the sample is extracted. By analyzing the recorded Raman spectrum data table, the spectrum peak position information corresponding to the Raman spectrum signal is read, and the interference data with signal intensity lower than the background noise is eliminated. The data in the effective signal area is separated and divided into Stokes scattering signal and anti-Stokes scattering signal. The Stokes scattering signal corresponds to the data area where the frequency of the Raman scattered photon is lower than the frequency of the incident light, and the anti-Stokes scattering signal corresponds to the data area where the frequency is higher than the incident light. The spectrum intensity in each signal area is analyzed, and the energy distribution of the overall spectrum is obtained by numerically integrating the spectrum peak intensity, and the spectrum width information is extracted. The spectrum width is defined by the half-maximum width (FWHM), that is, the width value at 50% of the maximum intensity of the spectrum peak, and the unit is cm -1 , calculate the signal distribution index of the frequency point, where the calculation of the signal distribution is based on the normalized intensity of the spectral signal, and the calculation formula is as follows: ; in, is the normalized signal distribution value, is the spectrum signal intensity at the current frequency point, and are the minimum and maximum values ​​of the signal respectively. Assuming that the signal strength at a certain frequency point is 120, the minimum signal strength is 80, and the maximum signal strength is 200, the normalized signal distribution value is calculated to be 0.25, and finally the spectral characteristic parameters are established.

[0034] The peak shift calculation submodule analyzes the spectral peak shift based on the spectral characteristic parameters, calculates the photon frequency change value, selects the signal points where the peak shift amplitude exceeds the set threshold, counts the peak shift interval, and obtains the peak shift data; The specific calculation formula for the photon frequency change value is: ; in, represents the change value of photon frequency, represents the speed of light, represents the incident laser wavelength, represents the spectral peak position measured for the sample, Represents the standard reference spectrum peak position.

[0035] Detailed explanation of the parameters used in the formula to calculate the change in photon frequency: It is the photon frequency change value, which is used to indicate the frequency difference between the sample peak position and the standard peak position.

[0036] is the speed of light, a fixed value Meters per second, used to convert spatial distance into frequency changes in time units.

[0037] is the wavelength of the incident laser, which is obtained through instrument settings. In the experiment, if a 532nm laser is used, nm.

[0038] It is the spectral peak position measured by the sample, the data obtained by the spectral analysis equipment.

[0039] It is the standard reference spectrum peak position, obtained from standard samples or known data.

[0040] Specific calculation process example: Assume that the measured sample peak position cm -1 , standard peak position , incident laser wavelength , first convert the wavelength units to units more suitable for frequency calculations, .

[0041] Calculation steps: Calculate the absolute value of the peak position deviation: ; Convert the peak position deviation to a frequency change using the speed of light and wavelength: ; Note that the frequency unit needs to be unified, here cm -1 Convert to the appropriate unit m -1 , each cm -1 Equal to 100m -1 ,so: ; Further calculation: ; This calculation result This indicates that the change in the spectral peak position causes the frequency to change This change helps researchers evaluate the difference between the sample and the standard, thereby analyzing the material composition or structural changes of the sample.

[0042] The spectrum matching submodule calls the peak shift data, compares the Raman spectrum database, analyzes the peak shift amplitude of the target sample and the reference sample, matches the characteristic peak shift trend, and generates spectrum feature matching data; Call the peak shift data, and compare the Raman spectrum data of the target sample with the reference spectrum data in the database, read the standard peak data in the database, calculate the shift amplitude of each peak position of the target sample, and record the peak positions with a high degree of matching, calculate the shift trend of the target sample and the reference sample at the key characteristic peak position, and measure the matching situation with the peak shift change rate. The calculation formula is as follows: ; in, is the peak shift change rate, is the peak position shift, is the standard peak position, assuming that the offset of a characteristic peak position of the target sample is 4cm -1 , the corresponding standard peak position is 1600cm -1 , the calculated shift change rate is 0.25%. The matching judgment rule is set. If the shift change rate is less than 1%, it is judged as a high match. If the change rate is between 1% and 3%, it is judged as a moderate match. If the change rate is greater than 3%, it is judged as a low match. Finally, the shift trend of the spectral feature peak is matched according to the calculation results to generate spectral feature matching data.

[0043] See also Figure 6 and Figure 2 , the chemical composition deviation assessment module includes: The polar compound calculation submodule calls the spectral feature matching data, extracts the polar compound content data of the target sample, screens the target polar compound category, calculates the ratio of each component, and establishes the ratio relationship between the compounds to obtain the polar compound ratio data; Call the spectral feature matching data and extract the polar compound content data of the target sample from it. First, analyze the spectral data of the sample, screen out the spectral peaks related to the polar compounds, and read the corresponding signal intensity. After normalization, remove the signal interference and obtain the relative content data. Screen the target polar compound category. According to the sample component database, set the screening range of the target polar compound, calculate the ratio of each component, define the calculation rules of the polar compound components, and use the ratio of the mass fraction of the target compound to the total mass of the polar compound as an indicator. The calculation formula is as follows: ; in, is the target compound ratio, represents the mass of the target polar compound, Represents the total mass of polar compounds. Assuming that the mass of a polar compound in the sample is 8g and the total mass of polar compounds is 40g, the calculated ratio is 0.20. The ratio relationship between the components is established. For different types of polar compounds, their respective ratio data are calculated respectively, and a ratio matrix is ​​constructed to finally obtain the polar compound ratio data.

[0044] The oxidation product comparison submodule calculates the oxidation product concentration ratio based on the polar compound ratio data, screens the ratio range of the target oxidation marker, compares it with the polar compound reference database, analyzes the change trend of the oxidation product concentration, and obtains the oxidation product concentration ratio; Based on the polar compound ratio data and the calculation of the oxidation product concentration ratio, the mass information of each oxidation product in the sample is first extracted, and the mass ratio of each oxidation product is calculated. The calculation formula is as follows: ; in, represents the concentration ratio of oxidation products, represents the mass of the target oxidation product, Represents the total mass of the sample. Assuming that the mass of the oxidation product in a sample is 5g and the total mass of the sample is 100g, the calculated oxidation product concentration ratio is 0.05. The ratio range of the target oxidation marker is screened. According to the polar compound reference database, the concentration threshold range of different oxidation products is set. If the concentration ratio of an oxidation marker exceeds the database setting range, the abnormal value is recorded, and the changing trend of the oxidation product concentration is analyzed. By comparing the change amplitude of the sample oxidation product concentration ratio, the intensity and change rate of the oxidation reaction are judged, and finally the oxidation product concentration ratio is obtained.

[0045] The doping risk assessment submodule calls the oxidation product concentration ratio, sets the doping risk threshold, calculates the deviation index of the target sample, and compares it with the stored risk assessment benchmark value to determine whether it exceeds the safety limit and generate chemical composition deviation judgment data; Call the oxidation product concentration ratio and set the doping risk threshold. First, read the stored risk assessment database and extract the safety limit data of different types of edible oils to calculate the deviation index of the target sample. The calculation formula is as follows: ; in, represents the shift percentage of the oxidation product of the target sample, It represents the reference oxidation product ratio in the database. Assuming that the standard oxidation product ratio of a certain type of oil in the database is 0.03 and the oxidation product ratio of the target sample is 0.05, the calculated deviation rate is 66.67%. Based on the adulteration risk threshold, it is determined whether the safety limit is exceeded. If the deviation rate is less than 10%, it is judged as low risk. If the deviation rate is between 10% and 30%, it is judged as medium risk. If the deviation rate exceeds 30%, it is judged as high risk and the deviation value is recorded. Finally, it is compared with the stored risk assessment benchmark value and the chemical composition deviation judgment data is generated based on the calculation result.

[0046] See also Figure 7 and Figure 2 , the odor component fluctuation detection module includes: The volatile component extraction submodule calls the chemical component deviation judgment data, extracts the volatile organic compound data of the target sample, screens the key components, analyzes the concentration ratio of each component, removes the data outside the detection error range, and establishes the component distribution trend to obtain the volatile organic compound component data; The chemical composition deviation judgment data is called, and the volatile organic compound data of the target sample is extracted from it. First, the gas chromatography-mass spectrometry (GC-MS) data of the sample is analyzed, and the chromatographic peak position, peak area and retention time information of the volatile organic compounds are extracted. According to the stored volatile compound database, the key components that meet the target category are screened, and the concentration ratio of each component is calculated. The peak area data of the target component is extracted, and its ratio relative to the sum of the peak areas of all volatile organic compounds is calculated. If the peak area of ​​a component is 5000 and the sum of the peak areas of all volatile organic compounds is 50000, its concentration ratio is calculated to be 0.10. The data outside the detection error range is eliminated, and the detection error threshold is set to ±2%. Abnormal data beyond the error range is excluded, and the concentration changes of each volatile component are statistically analyzed. By calculating the average concentration value and standard deviation of each component, the component distribution trend is established, and finally the volatile organic compound component data is obtained.

[0047] The release rate calculation submodule determines the release trend under set environmental conditions based on the volatile organic compound component data, calculates the release rate of key components, extracts the concentration change curve in the time dimension, and compares it with the reference release benchmark value to obtain the release rate of key components; Based on the volatile organic compound component data and measuring the release trend under set environmental conditions, first set the environmental parameters such as temperature (25℃, 40℃, 60℃), humidity (40%, 60%, 80%) and wind speed (0.1m / s, 0.5m / s, 1.0m / s), and record the concentration changes of volatile components under different conditions. The measurement time interval is set to 10min, calculate the release rate of key components, and extract the concentration change data in the time dimension. If the concentration of a component increases from 0.10 to 0.30 within 30min under the conditions of temperature 40℃, humidity 60% and wind speed 0.5m / s, the calculated release rate is 0.0067 / min. Compared with the reference release benchmark value, the benchmark value range is set to 0.002 / min to 0.008 / min. If the release rate of the target sample exceeds this range, record the abnormal release situation, and finally obtain the release rate of the key component.

[0048] The abnormal pattern matching submodule calls the release rate of key components, compares it with the volatile organic compound database, calculates the release pattern deviation value of the target sample, matches the abnormal release curve stored in the database, screens the abnormal sample category, and generates abnormal detection data; The release rate of key components is called and compared with the volatile organic compound database. First, the normal and abnormal release pattern data stored in the database are extracted, and the release pattern deviation value of the target sample is calculated. If the standard release rate of a target component in the database is 0.005 / min, and the release rate of the target sample is 0.007 / min, the calculated deviation is 40%. The matching threshold range is set. If the deviation is less than 10%, it is judged as normal release. If the deviation is between 10%-30%, it is judged as a slight abnormality. If the deviation exceeds 30%, it is judged as a serious abnormality. The abnormal release curves stored in the database are matched, the abnormal sample categories are screened, and abnormal detection data is generated based on the calculation results.

[0049] See also Figure 8 , a method for detecting fats and oils used in edible oil production and processing, comprising the following steps: S1: Obtain edible oil samples, use laser Raman spectrometer to record Raman scattering signals, and analyze compound components by gas chromatography and liquid chromatography, and generate standardized data sets after normalization processing; S2: Call the standardized data set, calculate the ratio of saturated to unsaturated fatty acids, compare the database benchmark value, analyze the deviation range, and generate the component ratio matching result; S3: Based on the component ratio matching results, analyze the spectral peak shift, calculate the frequency change, and compare it with the Raman spectrum database to generate spectral feature matching data; S4: Call spectral feature matching data, calculate oxidation product ratio, compare polar compound database, set doping threshold, judge doping or oxidation risk, and generate chemical composition deviation judgment data; S5: Call the chemical composition deviation judgment data, analyze the release trend of volatile components, compare the database to match the abnormal release curve, and generate abnormal detection data.

[0050] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0051] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 computer, 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.

[0053] 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 A function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0055] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A fat detection device for edible oil production and processing, characterized in that: The system comprises: The spectral data acquisition module obtains edible oil samples, uses a laser Raman spectrometer to record Raman scattering signals, extracts characteristic spectral data, and combines gas and liquid chromatography to analyze compound components, and generates a standardized data set after normalization processing; The component ratio calculation module calls the standardized data set, calculates the ratio of saturated and unsaturated fatty acids, compares the database benchmark value, analyzes the deviation range, matches the minimum deviation sample, and generates a component ratio matching result; The spectral feature analysis module analyzes the spectral peak shift based on the component ratio matching result, calculates the frequency change, and compares the Raman spectrum database to match the characteristic peak shift amplitude to generate spectral feature matching data; The chemical composition shift assessment module calls the spectral feature matching data, calculates the oxidation product ratio, compares the polar compound database, analyzes the shift trend, sets the doping threshold, determines the doping or oxidation risk, and generates chemical composition shift determination data; The odor component fluctuation detection module calls the chemical component deviation judgment data, analyzes the release trend of volatile components, calculates the concentration change curve, compares the database to match the abnormal release curve, and generates abnormal detection data.

2. The grease detection device for edible oil production and processing according to claim 1, characterized in that: The standardized data set includes characteristic spectral data, fatty acid composition, polar compound content, and volatile organic compound characteristics. The component ratio matching results include target sample ratio, database benchmark ratio, deviation range, and matching sample category. The spectral feature matching data includes spectral peak shift, frequency change value, peak shift interval, and matching reference sample. The chemical component shift judgment data includes polar compound ratio, oxidation product ratio, ratio shift trend, and doping risk level. The abnormal detection data includes volatile component release trend, key component release rate, concentration change curve, and abnormal release pattern.

3. The grease detection device for edible oil production and processing according to claim 1, characterized in that: The spectral data acquisition module comprises: The spectral detection submodule obtains edible oil production and processing samples, uses a laser Raman spectrometer to irradiate the samples, records Raman scattering signals, analyzes the frequency changes of Stokes and anti-Stokes scattered photons, extracts characteristic spectral peak positions, bandwidths, and intensity information from spectral data, and performs baseline correction to eliminate signal drift and obtain spectral characteristic data; The component analysis submodule uses a gas chromatograph to separate fatty acids, polar compounds and volatile organic compounds based on the spectral characteristic data, records the chromatographic peak position, peak area and retention time of each component, and combines with a liquid chromatography-mass spectrometer to calculate the content index of each component, construct a ratio matrix of fatty acids, polar compounds and volatile organic compounds, and obtain component ratio data; The data processing submodule calls the component ratio data, uses an electronic nose sensor to detect the release rate of volatile substances under differentiated environmental conditions, calculates the release rate and concentration changes of key volatile components, removes abnormal data points, and performs normalization processing to finally generate a standardized data set.

4. The grease detection device for edible oil production and processing according to claim 3, characterized in that: The specific calculation formula of the component content ratio characteristic value is: ； in, represents the characteristic value of the component content ratio, Representative sample The peak area of ​​the target compound, Representative The retention time of the target compound, represents the total mass of the sample, Represents the ratio matrix The ratio of polar compounds, represents the mean of the ratios of all polar compounds, represents the total number of target compounds, Represents the total number of polar compounds in the ratio matrix.

5. The grease detection device for edible oil production and processing according to claim 1, characterized in that: The component ratio calculation module comprises: The fatty acid extraction submodule calls the standardized data set, screens fatty acid-related data, extracts the content information of saturated fatty acids and unsaturated fatty acids, removes non-target component data, and calculates the mass fraction ratio of similar fatty acids to establish fatty acid composition data; The ratio calculation submodule calculates the ratio of saturated fatty acids to unsaturated fatty acids based on the fatty acid composition data, constructs a ratio matrix, analyzes the proportion relationship of each component in the ratio matrix, and obtains a fatty acid ratio matrix; The deviation matching submodule calls the fatty acid ratio matrix, compares it with the stored edible oil component ratio database, calculates the deviation range between the target sample ratio and the database benchmark ratio, matches the deviation sample category, and generates a component ratio matching result.

6. The grease detection device for edible oil production and processing according to claim 1, characterized in that: The spectral feature analysis module comprises: The spectral data extraction submodule calls the component ratio matching result, screens the Raman spectral signal data, extracts the spectral peak information, separates the Stokes and anti-Stokes scattering signals, analyzes the signal intensity and spectral width, calculates the signal distribution index of the frequency point, and establishes the spectral characteristic parameters; The peak shift calculation submodule analyzes the spectral peak shift based on the spectral characteristic parameters, calculates the photon frequency change value, selects the signal points where the peak shift amplitude exceeds the set threshold, counts the peak shift interval, and obtains the peak shift data; The spectrum matching submodule calls the peak shift data, compares the Raman spectrum database, analyzes the peak shift amplitudes of the target sample and the reference sample, matches the characteristic peak shift trends, and generates spectrum feature matching data.

7. The grease detection device for edible oil production and processing according to claim 6, characterized in that: The photon frequency change value calculation formula is specifically: ； in, represents the change value of photon frequency, represents the speed of light, represents the incident laser wavelength, represents the spectral peak position measured for the sample, Represents the standard reference spectrum peak position.

8. The grease detection device for edible oil production and processing according to claim 1, characterized in that: The chemical composition deviation assessment module includes: The polar compound calculation submodule calls the spectral feature matching data, extracts the polar compound content data of the target sample, screens the target polar compound category, calculates the ratio of each component, and establishes the ratio relationship between the compounds to obtain the polar compound ratio data; The oxidation product comparison submodule calculates the oxidation product concentration ratio based on the polar compound ratio data, screens the ratio range of the target oxidation marker, compares the polar compound reference database, analyzes the change trend of the oxidation product concentration, and obtains the oxidation product concentration ratio; The doping risk assessment submodule calls the oxidation product concentration ratio, sets the doping risk threshold, calculates the deviation index of the target sample, compares the stored risk assessment reference value, determines whether it exceeds the safety limit, and generates chemical composition deviation determination data.

9. The grease detection device for edible oil production and processing according to claim 1, characterized in that: The odor component fluctuation detection module includes: The volatile component extraction submodule calls the chemical component deviation determination data, extracts the volatile organic compound data of the target sample, screens the key components, analyzes the concentration ratio of each component, removes the data outside the detection error range, and establishes the component distribution trend to obtain the volatile organic compound component data; The release rate calculation submodule determines the release trend under set environmental conditions based on the volatile organic compound component data, calculates the release rate of key components, extracts the concentration change curve in the time dimension, and compares the reference release baseline value to obtain the release rate of key components; The abnormal pattern matching submodule calls the key component release rate, compares it with the volatile organic compound database, calculates the release pattern deviation value of the target sample, matches the abnormal release curve stored in the database, screens the abnormal sample category, and generates abnormal detection data.

10. A method for detecting fats and oils used in edible oil production and processing, characterized in that: According to the edible oil production and processing oil detection device according to any one of claims 1 to 9, the method comprises the following steps: S1: Obtain edible oil samples, use laser Raman spectrometer to record Raman scattering signals, and analyze compound components by gas chromatography and liquid chromatography, and generate standardized data sets after normalization processing; S2: calling the standardized data set, calculating the ratio of saturated to unsaturated fatty acids, comparing the database benchmark value, analyzing the deviation range, and generating a component ratio matching result; S3: Based on the component ratio matching result, analyze the spectral peak shift, calculate the frequency change, and compare with the Raman spectrum database to generate spectral feature matching data; S4: calling the spectral feature matching data, calculating the oxidation product ratio, comparing the polar compound database, setting the doping threshold, judging the doping or oxidation risk, and generating chemical composition deviation determination data; S5: calling the chemical component deviation determination data, analyzing the release trend of volatile components, comparing the database to match the abnormal release curve, and generating abnormal detection data.

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