Method for detecting cyclopropene fatty acid in cottonseed cake based on gas chromatography

Abstract

The invention discloses a method for detecting cyclopropene fatty acid in cottonseed cake based on gas chromatography, and belongs to the field of detection of cyclopropene fatty acid in cottonseed cake, and the method comprises the following steps: generating and updating a detection method detail table, and establishing an optimization information base by a platform party according to the detection method detail table; identifying a reference detection method of the user; acquiring historical detection data of the reference detection method in real time; docking an optimization information base; analyzing the historical detection data according to the optimization information base to obtain a potential optimization mode corresponding to the reference detection method; generating an optimization information table according to the potential optimization mode, and performing recommendation marking in the optimization information table; displaying the optimization information table to a user, and optimizing and adjusting the reference detection method by the user according to the optimization information table; and detecting the cyclopropene fatty acid in the cottonseed cake meal according to a reference detection method to obtain a detection result.

Description

Technical Field

[0001] The invention belongs to the field of detection of cyclopropene fatty acids in cottonseed cake, and particularly relates to a method for detecting cyclopropene fatty acids in cottonseed cake based on gas chromatography technology. Background Art

[0002] Cyclopropene fatty acids (CFAs) are important quality indicators and potentially toxic components in cottonseed and cottonseed meal. However, their detection faces numerous technical challenges. Currently, gas chromatography (GC) is a leading method for determining fatty acid content, offering advantages such as high sensitivity, high resolution, and accuracy. However, there are some limitations in the detection of cyclopropene fatty acids in cottonseed and its cake. In traditional methods, the sample pretreatment steps are cumbersome, including multiple extraction, washing and concentration processes, which are not only time-consuming and labor-intensive, but also easily lead to the loss or degradation of the target compound, affecting the accuracy of the test results. Moreover, there are many types of fatty acids in cottonseed and its cake, and the matrix is complex. Traditional chromatographic conditions make it difficult to achieve complete separation of cyclopropene fatty acids from other fatty acids, resulting in overlapping chromatographic peaks and interference with the quantitative analysis of the target compound. Therefore, there are many influencing factors that interfere with the test results during the detection process, and the detection method needs to be optimized according to the actual situation, such as adding purification and enrichment to the detection steps, removing matrix interferences (such as pigments, waxes, etc.), and improving the purity and detection sensitivity of the target compound. For example, solid phase extraction (SPE): using silica gel or amino columns to enrich cyclopropene fatty acid methyl esters; thin layer chromatography (TLC): separating cyclopropene fatty acid methyl esters from other fatty acid methyl esters.

[0003] Therefore, how to intelligently optimize and adjust the detection method according to the detection situation has become a problem that needs to be solved. Summary of the Invention

[0004] In order to solve the problems existing in the above scheme, the present invention provides a method for detecting cyclopropene fatty acids in cottonseed cake based on gas chromatography technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography technology includes:

[0007] Generate and update a detailed list of detection methods, the detailed list of detection methods being used to count various reserve detection methods for detecting cyclopropene fatty acids in cottonseed meal; establish an optimization information library based on the detailed list of detection methods by the platform, the optimized information library being used to store various step optimization methods that have an optimization effect on the reserve detection methods and the optimized identification features corresponding to the corresponding step optimization methods;

[0008] Identify the user's benchmark detection method; obtain historical detection data of the benchmark detection method in real time; connect to the optimization information library; analyze the historical detection data based on the optimization information library to obtain potential optimization methods corresponding to the benchmark detection method;

[0009] Generate an optimization information table based on potential optimization methods and mark the recommended methods in the optimization information table; display the optimization information table to the user, and let the user optimize and adjust the benchmark detection method based on the optimization information table;

[0010] Cyclopropene fatty acids in cottonseed meal were detected according to the benchmark detection method to obtain the test results.

[0011] Furthermore, the platform shall calibrate and verify the detailed list of detection methods and the optimized information library.

[0012] Furthermore, the optimization adaptation characteristics include the optimization effect corresponding to the step optimization method, the scope of application, the detection problem solved, the manifestation of the detection problem, and the cost estimation method.

[0013] Furthermore, the platform sets corresponding record identification items for each step optimization method in the optimization information library, and labels the record identification items with labels corresponding to the corresponding step optimization methods.

[0014] Furthermore, historical test data is analyzed based on the optimized information base, including:

[0015] Identify the optimization identification features corresponding to the optimization methods of each step in the optimization information library, perform feature identification on the historical detection data according to the recorded identification items, and obtain the optimization material data of the optimization method of the step;

[0016] Determine whether the step optimization method meets the optimization criteria based on the optimization material data and the optimization identification features; and mark the step optimization method that meets the optimization criteria as a potential optimization method.

[0017] Furthermore, judging whether the step optimization method meets the optimization criteria based on the optimized material data and the optimized identification features includes:

[0018] An optimization judgment model is established. The expression of the optimization judgment model is:

[0019]

[0020] Where: (yh, YS, BZ) is the input data, yh is the optimization effect, YS is the optimized material data, and BZ is the optimization standard; the output data is the optimization judgment value YG(yh, YS, BZ), and the optimization judgment value is 1 or 0;

[0021] Determine the corresponding optimization effect according to the optimization identification feature, analyze the optimization effect and optimization material data according to the optimization judgment model, and obtain the optimization judgment value of the step optimization method;

[0022] When the optimization judgment value is 1, the optimization method of the judgment step meets the optimization standard;

[0023] When the optimization judgment value is 0, it is determined that the optimization method of the step does not meet the optimization criteria.

[0024] Furthermore, the recommended mark is made in the optimization information table, including:

[0025] Identify each potential optimization method corresponding to the optimization information table, estimate the optimization value corresponding to the corresponding potential optimization method; estimate the corresponding optimization cost based on the optimization identification characteristics of the potential optimization method;

[0026] Input the optimization cost and optimization value into the preset evaluation formula to calculate the optimization evaluation value of the corresponding potential optimization method. The evaluation formula is:

[0027]

[0028] Where: PU is the optimization evaluation value; τ is the adjustment coefficient; e is the natural coefficient; CB is the optimization cost; γ is the optimization value;

[0029] Potential optimization methods are recommended and marked according to the optimization evaluation value.

[0030] Furthermore, it also includes: obtaining the optimization adjustment records and optimization information table of the benchmark detection method in real time, generating a method information table based on the optimization adjustment records and optimization information table; generating a method evaluation result based on the method optimization table, the method evaluation result is whether the method is qualified or unqualified and the corresponding reserve detection method; and displaying the method evaluation result to the user.

[0031] A detection system for cyclopropene fatty acids in cottonseed meal based on gas chromatography technology, including a platform end and a user end;

[0032] The platform includes an optimization information library and an auxiliary optimization module;

[0033] The optimization information library is used to store various step optimization methods that have an optimization effect on the reserve detection method and the optimization identification features corresponding to the corresponding step optimization methods;

[0034] The auxiliary optimization module is used to assist the user in optimizing and adjusting the benchmark detection method;

[0035] The user terminal includes an optimization analysis module and a detection module;

[0036] The optimization analysis module optimizes and analyzes the user's benchmark detection method, obtains historical detection data of the benchmark detection method in real time, connects to the optimization information library, analyzes the historical detection data based on the optimization information library, and obtains potential optimization methods corresponding to the benchmark detection method;

[0037] Generate an optimization information table based on potential optimization methods and mark the recommended methods in the optimization information table; display the optimization information table to the user, and let the user optimize and adjust the benchmark detection method based on the optimization information table;

[0038] The detection module is used to detect cyclopropene fatty acids in cottonseed meal according to a benchmark detection method to obtain a detection result.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This method automatically adjusts the testing process based on the actual test results, avoiding the tedious and time-consuming sample pretreatment steps of multiple extractions, washings, and concentrations required in traditional methods. Through intelligent optimization mechanisms, it effectively reduces the time and labor costs required for testing, significantly improving the overall efficiency of cyclopropene fatty acid detection in cottonseed and cottonseed meal, enabling faster testing and meeting the needs of large-scale or emergency testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] like Figure 1 As shown in FIG, the method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography technology includes:

[0045] Platform side:

[0046] Real-time acquisition of various current detection methods for cyclopropene fatty acids in cottonseed meal, marking them as reserve detection methods; generating a detailed list of detection methods based on the reserve detection methods; and dynamically updating the detailed list of detection methods;

[0047] According to the detection method list, various step optimization methods that can be applied to the corresponding reserve detection methods are summarized in real time, such as purification and enrichment, removal of matrix interferences, and improvement of the purity and detection sensitivity of target compounds; sample pre-treatment optimization, batch processing optimization, etc.; dynamic search and analysis are carried out according to the development of existing technologies to determine the step optimization methods corresponding to each reserve detection method, and the optimization adaptation features such as optimization effect, solution to detection problems, manifestation of corresponding detection problems, cost estimation method, and scope of application corresponding to the step optimization methods are set. The optimization effect is the estimated optimization effect when the step optimization method is applied to the reserve detection method, and the scope of application is which reserve detection methods the step optimization method is applicable to; in order to ensure the accuracy of the step optimization method and optimization adaptation features, the platform shall verify and adjust the newly added step optimization method and optimization adaptation features; an optimization information library shall be established based on the step optimization method and optimization identification features; the optimization information library is generally set up in the cloud to facilitate sharing with various users.

[0048] User side:

[0049] Obtain the detection method used by the user to detect cyclopropene fatty acids in cottonseed meal, and mark it as a benchmark detection method; obtain historical detection data for detection using the benchmark detection method in real time; connect to the optimization information library established by the platform; analyze the historical detection data based on the optimization information library to obtain the step optimization method applicable to the benchmark detection method, and mark the corresponding step optimization method as a potential optimization method; generate an optimization information table based on the potential optimization method, which is used to count the information of each potential optimization method. The optimization information table may include a variety of information, such as an introduction, advantages and disadvantages, etc., and make a recommendation mark in the optimization information table, that is, mark the potential optimization method that is recommended for the user to apply; display the optimization information table to the user, and the user decides whether to optimize, and the platform provides assistance during the optimization; that is, the user optimizes and adjusts the benchmark detection method based on the optimization information table;

[0050] Cyclopropene fatty acids in cottonseed meal were detected according to the benchmark detection method to obtain the test results.

[0051] In one embodiment, analyzing historical detection data based on the optimization information library includes:

[0052] Identify the optimization identification features corresponding to the optimization methods of each step in the optimization information library, and set record identification items for the corresponding step optimization methods based on the optimization identification features, that is, set according to the optimization effects, detection problems, and performance phenomena in the optimization identification features, so as to facilitate the subsequent rapid identification and screening of feature data related to the step optimization method from historical detection data, and mark them as optimization material data, which is generally set by the platform; mark the step optimization methods that are not applicable to the benchmark detection method based on the optimization identification features, indicating that they are not applicable, and no subsequent analysis is required, and judgment is made based on the applicable scope of the optimization identification features;

[0053] Perform feature recognition on historical test data based on record identification items to obtain optimized material data for the corresponding step optimization method;

[0054] Determine whether the corresponding step optimization method meets the optimization criteria based on the optimized material data and optimized identification features. The lowest optimization criteria is that the optimization effect of the step optimization method is greater than the current detection effect corresponding to the optimized material data. For example, if the detection time of the step is 20 minutes, the detection time after optimization is 15 minutes. Set the optimization criteria according to user needs, such as optimizing by 10%. Otherwise, evaluate based on the lowest criteria. Mark the step optimization method that meets the optimization criteria as a potential optimization method.

[0055] In one embodiment, whether the optimization method of the corresponding step meets the optimization standard is judged based on the optimization material data and the optimization identification features, and the effect judgment can be performed based on existing technologies.

[0056] In one embodiment, judging whether the optimization method of the corresponding step meets the optimization criteria based on the optimization material data and the optimization identification features includes:

[0057] An optimization judgment model is established. The expression of the optimization judgment model is:

[0058]

[0059] Where: (yh, YS, BZ) is the input data, yh is the optimization effect, YS is the optimized material data, and BZ is the optimization standard; qualified means that the optimization effect meets the optimization standard, and the output data is the optimization judgment value YG(yh, YS, BZ), which is 1 or 0; use the corresponding historical data to set the corresponding training set for training.

[0060] Determine the corresponding optimization effect based on the optimization recognition features, analyze the optimization effect and optimized material data based on the optimization judgment model, and the optimization standard is pre-set; obtain the corresponding optimization judgment value;

[0061] When the optimization judgment value is 1, the step optimization method is to meet the optimization criteria;

[0062] When the optimization judgment value is 0, the step optimization method does not meet the optimization criteria.

[0063] In one embodiment, analyzing historical detection data based on the optimization information library includes:

[0064] Identify the optimization identification features corresponding to the optimization methods of each step in the optimization information library. Mark the step optimization methods that are not applicable to the benchmark detection method based on the optimization identification features, indicating that they are not applicable. No subsequent analysis is required, and judgment is made based on the applicable scope of the optimization identification features.

[0065] According to the optimization recognition features, the corresponding optimization effect of the corresponding unlabeled step optimization method when applied to the benchmark detection method is estimated, and the optimization effect is compared with the historical detection data to determine whether the corresponding step optimization method meets the optimization criteria.

[0066] In one embodiment, the recommendation mark is made in the optimization information table, including:

[0067] Identify the corresponding potential optimization methods in the optimization information table and estimate the optimization degree of the corresponding potential optimization method. That is, compare the optimization effect with the existing detection effect and the degree of superiority. For example, if the existing value is 1 and the optimization effect is 1.2, the optimization degree is (1.2-1) / 1, which is marked as the optimization value. The optimization value can also be estimated by other methods. The corresponding optimization cost is estimated based on the optimization identification characteristics of the potential optimization method.

[0068] Input the optimization cost and optimization value into the preset evaluation formula to calculate the corresponding optimization evaluation value. The evaluation formula is:

[0069]

[0070] Where: PU is the optimization evaluation value; τ is the adjustment coefficient, which is determined based on the optimization steps of the potential optimization method. Because the optimization weights of different steps are different, the more important the step, the larger the adjustment coefficient. The platform sets the corresponding adjustment coefficient for each step of the corresponding reserve detection method, and then matches it. It can also be adjusted according to user requirements; e is the natural coefficient; CB is the optimization cost; γ is the optimization value.

[0071] Potential optimization methods with optimization evaluation values greater than the threshold X1 are marked as recommended. The threshold X1 is initially set by the platform and subsequently set by the user as needed.

[0072] In one embodiment, as the number of potential optimization methods increases and the number of optimizations of the benchmark detection method increases, users may find that a better detection method is available. That is, the benchmark detection method may be technologically outdated. Therefore, in order to facilitate timely understanding by users, the following solution is adopted in this embodiment:

[0073] Obtain the optimization adjustment records and current optimization information table of the benchmark detection method in real time, generate a method information table based on the optimization adjustment records and the optimization information table, the method optimization table is used to count the potential optimization methods of the optimization application corresponding to the benchmark detection method and the optimization information table counted in the optimization information table; generate method evaluation results based on the method optimization table, the method evaluation results are qualified or unqualified methods and reserve detection methods; display the method evaluation results to the user.

[0074] In one embodiment, a method evaluation result is generated according to a method optimization table, an optimization simulation is performed on the benchmark detection method according to the method optimization table, the fully optimized benchmark detection method is determined, a comprehensive evaluation of the detection effect is performed on it and other reserve detection methods in the method details table, and a reserve detection method whose comprehensive detection effect is greater than that of the benchmark detection method is determined and marked as a candidate detection method; an evaluation is performed to determine whether the conversion from the benchmark detection method to the candidate detection method meets the actual application requirements, mainly from the perspective of cost and optimization effect (optimization value); the specific evaluation method can be implemented based on existing technology.

[0075] In one embodiment, a method evaluation result is generated according to a method optimization table, and an evaluation may be performed based on an existing method.

[0076] A detection system for cyclopropene fatty acids in cottonseed meal based on gas chromatography technology, including a platform end and a user end;

[0077] The platform includes an optimization information library and an auxiliary optimization module;

[0078] The optimization information library is used to store various step optimization methods that have an optimization effect on the reserve detection method and the optimization identification features corresponding to the corresponding step optimization methods;

[0079] The auxiliary optimization module is used to assist the user in optimizing and adjusting the benchmark detection method;

[0080] The user terminal includes an optimization analysis module and a detection module;

[0081] The optimization analysis module optimizes and analyzes the user's benchmark detection method, obtains historical detection data of the benchmark detection method in real time, connects to the optimization information library, analyzes the historical detection data based on the optimization information library, and obtains potential optimization methods corresponding to the benchmark detection method;

[0082] Generate an optimization information table based on potential optimization methods and mark the recommended methods in the optimization information table; display the optimization information table to the user, and let the user optimize and adjust the benchmark detection method based on the optimization information table;

[0083] The detection module is used to detect cyclopropene fatty acids in cottonseed meal according to a benchmark detection method to obtain a detection result.

[0084] The above formulas are all calculated by removing dimensions and taking their numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data.

[0085] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography, characterized in that: include: Generate and update a detailed list of detection methods, wherein the detailed list of detection methods is used to count various reserve detection methods for detecting cyclopropene fatty acids in cottonseed meal; The platform establishes an optimization information library based on the detailed list of detection methods. The optimization information library is used to store various step optimization methods that have an optimization effect on the reserve detection methods and the optimization identification features corresponding to the corresponding step optimization methods; Identify the user's benchmark detection method; obtain historical detection data of the benchmark detection method in real time; connect to the optimization information library; Analyze historical test data based on the optimization information library to obtain potential optimization methods corresponding to the benchmark test method; Generate an optimization information table based on potential optimization methods and make recommendation marks in the optimization information table; The optimization information table is displayed to the user, and the user optimizes and adjusts the benchmark detection method based on the optimization information table; Cyclopropene fatty acids in cottonseed meal were tested according to the benchmark test method to obtain test results.

2. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography according to claim 1, wherein The platform shall calibrate and verify the detailed list of detection methods and the optimized information library.

3. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography according to claim 1, wherein The optimization adaptation characteristics include the optimization effect corresponding to the step optimization method, the scope of application, the detection problem solved, the manifestation of the detection problem, and the cost estimation method.

4. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography according to claim 1, wherein The platform sets corresponding record identification items for the optimization methods of each step in the optimization information library, and labels the record identification items with labels corresponding to the optimization methods of the corresponding steps.

5. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography according to claim 1, wherein Analyze historical test data based on the optimized information base, including: Identify the optimization identification features corresponding to the optimization methods of each step in the optimization information library, perform feature identification on the historical detection data according to the recorded identification items, and obtain the optimization material data of the optimization method of the step; Determine whether the step optimization method meets the optimization criteria based on the optimization material data and the optimization identification features; and mark the step optimization method that meets the optimization criteria as a potential optimization method.

6. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography according to claim 5, wherein: Determine whether the step optimization method meets the optimization criteria based on the optimized material data and optimization identification features, including: An optimization judgment model is established. The expression of the optimization judgment model is: Where: (yh, YS, BZ) is the input data, yh is the optimization effect, YS is the optimized material data, and BZ is the optimization standard; the output data is the optimization judgment value YG(yh, YS, BZ), and the optimization judgment value is 1 or 0; Determine the corresponding optimization effect according to the optimization identification feature, analyze the optimization effect and optimization material data according to the optimization judgment model, and obtain the optimization judgment value of the step optimization method; When the optimization judgment value is 1, the optimization method of the judgment step meets the optimization standard; When the optimization judgment value is 0, it is determined that the optimization method of the step does not meet the optimization criteria.

7. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography technology according to claim 1, wherein Recommended markings are made in the optimization information table, including: Identify each potential optimization method corresponding to the optimization information table, estimate the optimization value corresponding to the corresponding potential optimization method; estimate the corresponding optimization cost based on the optimization identification characteristics of the potential optimization method; Input the optimization cost and optimization value into the preset evaluation formula to calculate the optimization evaluation value of the corresponding potential optimization method. The evaluation formula is: Where: PU is the optimization evaluation value; τ is the adjustment coefficient; e is the natural coefficient; CB is the optimization cost; γ is the optimization value; Potential optimization methods are recommended and marked according to the optimization evaluation value.

8. The method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography technology according to claim 1, wherein Also includes: Obtaining the optimization adjustment record and optimization information table of the benchmark detection method in real time, and generating a method information table based on the optimization adjustment record and optimization information table; Generate method evaluation results based on the method optimization table. The method evaluation results are qualified or unqualified methods and the corresponding reserve detection methods; the method evaluation results are displayed to the user.

9. A detection system for cyclopropene fatty acids in cottonseed meal based on gas chromatography technology, characterized in that: Execute the method for detecting cyclopropene fatty acids in cottonseed meal based on gas chromatography technology as described in any one of claims 1 to 8, comprising a platform end and a user end; The platform includes an optimization information library and an auxiliary optimization module; The optimization information library is used to store various step optimization methods that have an optimization effect on the reserve detection method and the optimization identification features corresponding to the corresponding step optimization methods; The auxiliary optimization module is used to assist the user in optimizing and adjusting the benchmark detection method; The user terminal includes an optimization analysis module and a detection module; The optimization analysis module optimizes and analyzes the user's benchmark detection method, obtains historical detection data of the benchmark detection method in real time, and connects to the optimization information database; Analyze historical test data based on the optimization information library to obtain potential optimization methods corresponding to the benchmark test method; Generate an optimization information table based on potential optimization methods and make recommendation marks in the optimization information table; The optimization information table is displayed to the user, and the user optimizes and adjusts the benchmark detection method based on the optimization information table; The detection module is used to detect cyclopropene fatty acids in cottonseed meal according to a benchmark detection method to obtain a detection result.

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