CN113762759A - Multi-index system evaluation method suitable for food detection - Google Patents
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Abstract
The invention discloses a multi-index system evaluation method suitable for food detection, which establishes a multi-factor evaluation system based on a TOPSIS method of comprehensive Radar analysis-entropy weight-improvement. The method firstly proposes that parameters such as absolute recovery rate, absolute matrix effect, total scanning of extraction solution, scanning of phospholipid characteristic precursor ions and the like are used as evaluation indexes, and on the basis of comparing various single-factor independent analysis and evaluation, radar analysis, relative contribution rate analysis, improved entropy weight-TOPSIS analysis and other multi-index comprehensive evaluation and analysis are carried out on normalized processing data. The established evaluation technology overcomes the defects of the evaluation unicity, subjectivity and incapability of quantification of the conventional food detection method, comprehensively considers various factors influencing the accuracy, the qualification and the quantification of the target object, serves for the rapid and accurate development, optimization and comparison of the food detection method, and has good application value in the field of food detection.
Description
Technical Field
The invention relates to a multi-index system evaluation method suitable for food detection, and belongs to the technical field of food detection.
Background
The food safety problem becomes a public safety problem concerned all over the world, and the severe current situation of food safety puts more strict requirements on food safety detection technology. The complex and various food matrixes and the great difference of the properties of target species bring great difficulty to the establishment of food detection methods, and how to establish a scientific, accurate, rapid and efficient detection method is the key point of food detection work. The method optimization process aiming at improving the extraction efficiency, the purification effect and the like to the maximum extent is the main work of the method establishment. In order to ensure the accuracy and reliability of the detection result, a scientific evaluation index and method are required to evaluate the optimized method, and finally, an optimal method is obtained. Evaluation of evaluation indices commonly used in the process of optimization of the currently reported methods a number of evaluation methods, in particular methods of their purification effect, are reported in food analysis, wherein the Recovery (RE) and Matrix Effect (ME) are the most considered. The appearance of the extract, the removal efficiency of the pigments, the gravimetric analysis of the total matrix content, was mainly directed to extracts with a high matrix content. The existing food detection method has the defects of single index and incomplete evaluation information, incapability of quantification, no correlation and the like caused by independent factor analysis. Furthermore, the reported evaluation methods do not give relative contribution rates and do not provide quantitative evaluation criteria for the performance of the methods. In consideration of the precision and accuracy requirements of quantitative analysis, it is necessary to establish a comprehensive system evaluation system with controllable detection method process and accurate method data.
The common single index evaluation method cannot meet the evaluation requirement of the detection method, and the comprehensive evaluation method directly applied to multiple fields is not applicable to the evaluation of the food detection method. Common comprehensive evaluation methods include an expert consultation method, an analytic hierarchy process, a fuzzy comprehensive evaluation method, an entropy weight-TOPSIS method and the like. The former methods have the disadvantage of depending on subjective experience or data phenols, respectively. The entropy weight-TOPSIS method introduces an entropy weight method to judge the weights of various performance indexes of different methods, and is suitable for evaluation of a food detection method based on double correlation analysis of the weights and results, so that an idea is provided for people. Due to the complexity of the food matrix and the difference of the properties of the target object, the optimization design of the method has multiple and complicated influence factors, and the single comprehensive evaluation method cannot well meet the optimization requirement of the method.
The evaluation model based on the multi-index comprehensive analysis can be generalized into three key parts, including determination of evaluation indexes, determination of weight coefficients and determination of evaluation methods. In food analysis, various evaluation indexes, particularly indexes for evaluating the purification effect, are reported, of which the recovery Rate (RE) and the Matrix Effect (ME) are most considered. Simple evaluation indexes such as the transparency of the extracting solution, the removal efficiency of the pigment, the gravimetric analysis of the total matrix content and the like mainly aim at the extract with high matrix content and can not meet the trace detection requirement. The development of sophisticated equipment has also led to evaluation methods based on detection techniques, such as full scan in mass spectrometry (F-scan) and residue analysis by thin layer chromatography, which are suitable for lower content extracts. Impurity analysis such as lipidomics or phospholipid analysis also brings similar evaluation ideas to us, and precursor ion scanning (P-scan) technology has been developed for screening and assaying phospholipids, caffeoylquinic acid derivatives and disinfection byproducts, particularly as a common strategy in shotgun lipidomics. The introduction of characteristic fragment ions of phospholipids (m/z 184 or m/z 241) to evaluate the removal efficiency of phospholipids in certain matrices is a very meaningful way of evaluating the purification effect. Common evaluation index weight assignment methods include a variation coefficient method, an Analytic Hierarchy Process (AHP) and an entropy weight method, the former two methods have the defects of depending on data variability and subjective experience, and the entropy weight method is used for more objectively judging the weights of various performance indexes of different methods.
In order to achieve the objective, intuitive and quantitative evaluation purpose of the detection method, a multi-factor comprehensive evaluation model is established, analysis and evaluation methods of different levels are integrated, a comprehensive system evaluation system with controllable detection method process and accurate method data is established, and a solid technical foundation is laid for food safety guarantee.
Disclosure of Invention
Aiming at the current situations of incomplete evaluation information, incapability of quantification, no correlation and the like caused by the unicity, independence and the like of evaluation indexes in food detection method evaluation, a set of comprehensive food detection method evaluation system based on a multi-factor analysis evaluation model is established, theoretical basis and technical means are provided for development, optimization and comparison of a food detection method, and the comprehensive food detection method evaluation system is used for efficient quality control and supervision of food safety.
A multi-index system evaluation method suitable for food detection comprises the following steps: the method comprises the following steps:
(1) establishing a food detection evaluation index set: the food detection and evaluation model at least comprises a pretreatment process evaluation unit and a detection process evaluation unit; the pretreatment process evaluation unit and the detection process evaluation unit correspond to a plurality of evaluation indexes, and the evaluation indexes at least comprise recovery rate, absolute matrix effect, extraction solution full scanning and phospholipid characteristic precursor ion scanning;
(2) constructing a judgment matrix of the evaluation index, and carrying out normalized data processing;
(3) performing two-dimensional analysis on the normalized data matrix, and visually presenting the correlation of the optimization factors on multiple evaluation indexes;
(4) entropy analysis is carried out on the normalized data matrix of the index factors by applying an entropy theory, and the information entropy and the weight of each index are determined;
(5) analyzing the weight of the index factors to obtain the contribution condition of each index factor in different methods;
(6) and performing TOPSIS analysis on the basis of the weight assignment of the index factors to obtain comprehensive quantitative evaluation indexes, thereby realizing the comprehensive evaluation of different food detection systems.
The invention is realized by the following technical scheme:
(1) and (4) selecting an evaluation index. Taking optimization of the method for detecting acrylamide in vegetable oil by improving QuEChERS as an example. Selecting key index factors based on factors such as detection matrix and target object, and selecting parameters such as absolute recovery rate (A-RE), absolute matrix effect (A-ME), extraction solution full scan (F-scan) and phospholipid characteristic precursor ion scan (P-scan, m/z 184 and m/z 241) as evaluation indexes of the detection method. Wherein the Recovery (RE) comprises absolute recovery (A-RE) and relative recovery (R-RE) depending on whether the quantitation result is calibrated using an internal standard or a matrix matching calibration curve; the Matrix Effect (ME) includes absolute matrix effect (A-ME) and relative matrix effect (R-ME).
The recovery Rate (RE) was evaluated by the ratio of the amount of change in the extract after actual labeling to the theoretical amount of addition, and RE% ((A))b-As) 100/M, wherein AbIs the content of target substance in the extract after adding the standard, AsThe content of the target substance in the matrix sample before the standard addition is shown, and M is the theoretical addition amount. The response matrix of the target substance in the added standard extracting solution matches the curve slope of the standard solution/the curve slope of the standard solution without the matrix-1). times.100 percent. Relative recovery (R-RE) is the recovery of the measurement after calibration with an internal standard or matrix-matched calibration curve, and absolute recovery (A-RE) is the recovery quantified directly with a pure solvent standard curve, without any form of correction or conversion.
The Matrix Effect (ME) is evaluated by the relative ratio of the blank extract to the pure solvent after the same level of the target component is added, and ME% ((A))m/Ae-1)×100％。AmResults measured for matrix-matched standard solutions, AeThe results are measured for matrix-free standard solutions. The relative matrix effect (R-ME) is the recovery of the measurement after calibration with an internal standard or matrix-matched calibration curve, and the absolute matrix effect (A-ME) is the recovery quantified directly from a pure solvent standard curve without any form of calibration or foldingAnd (4) calculating. A negative matrix effect indicates the presence of a matrix-inhibiting effect; a positive matrix effect indicates the presence of a matrix-enhancing effect, 0 is no matrix effect, and a larger absolute value indicates a stronger matrix effect.
The extraction solution full scan (F-scan) is that the monitoring mode at the detection end of the mass spectrum detector is a full ion scan mode, and in order to objectively and accurately reflect the full scan ion response under the detection condition, the conditions of other liquid phases and the ion source are the same as the detection condition except that the scanning mode is different in the experiment.
Phospholipid characteristic precursor ion scanning (P-scan, m/z 184 and m/z 241) is that a monitoring mode at the detection end of a mass spectrum detector is a precursor ion scanning mode, characteristic fragment ions are respectively set as m/z 184 and m/z 241, and in order to objectively and accurately reflect phospholipid response under detection conditions, except for different scanning modes, other liquid phase conditions and ion source conditions are the same as the detection conditions in an experiment.
(2) As a pre-analysis for developing subsequent comprehensive evaluations, a variety of single factor independent analyses (MFIA) were primarily optimized for the method for which the evaluation is to be developed.
(3) And respectively carrying out radar two-dimensional analysis, weight relative contribution rate analysis and entropy weight-TOPSIS analysis on the basis of the index data processed by the normalized data. First, based on the optimized index, the data is normalized for processing. The method comprises the following basic steps:
1) the evaluation index data form an initial index system and are expressed as the following matrix:
assume that there are m samples to be evaluated based on n indices. Wherein x isijThe observed values of the samples are i 1,2 … m, and j 1,2 … n.
2) And standardizing the evaluation index data to form a normalized matrix system of the analysis model. Considering the influence of different dimensions in the original matrix, dimensionless transformation is adopted for normalization, and meanwhile, the method is simple and convenient to operate. And processing the data in the same direction according to the characteristics of the indexes, so that the data have the same trend, wherein the larger the data, the better the data. Wherein the recovery rate takes 100% as a standard value, and the processing equation is as follows:
absolute matrix effect, extraction solution full scan (F-scan) and phospholipid characteristic precursor ion scan, with 0 as standard value for data processing:
then, each evaluation index is normalized:
and (3) expressing the proportions of the evaluation indexes after normalization treatment:
finally obtaining a normalized matrix Pij。
(4) For normalized data matrix PijAnd performing Radar two-dimensional analysis, and visually presenting the relevance of the optimization factors on multiple evaluation indexes.
(5) Application of entropy theory to the normalized data matrix P of the indicator factor of claim 4ijEntropy analysis is carried out to determine the information entropy E of each indexj
And determining each index weight wj
(6) And analyzing the weight of the index factors to obtain the contribution condition of each index factor in different methods.
(7) Performing TOPSIS analysis on the basis of the weight assignment of the index factors to obtain a final evaluation conclusion of the multi-factor evaluation model, and basically comprising the following steps of:
1) weighted normalization matrix ZijAnd (4) establishing.
wjpij
Wherein Z isijFor weighting the normalized values, PijTo a normalized value, wjAre entropy weights.
2) And determining positive and negative ideal reference points.
3) The distances to the positive and negative ideal reference points are calculated using the following formula.
Wherein Z isj +And Zj －Respectively, the values of the positive and negative ideal reference points, Di +And Dj －Respectively, the distance to the positive and negative ideal reference points.
4) Approximation coefficient (comprehensive evaluation coefficient) CiThe following formula is calculated.
According to the proximity coefficient CiTo determine the best protocol, CiThe larger the value, the better the scheme.
(8) The multi-factor evaluation model is applied to optimization of a pretreatment method in a method for detecting acrylamide in vegetable oil by QuEChERS, and comprises selection of sample amount, selection of an extraction solvent system and selection of type and dosage of a QuEChERS adsorbent.
Aiming at the conditions of various matrixes and large difference of target objects in food inspection, the invention comprehensively considers various influence factors to establish various evaluation indexes, establishes a multi-factor comprehensive analysis model respectively through various single-factor independent analysis, radar two-dimensional analysis, weight relative contribution rate analysis and improved analysis and evaluation methods such as entropy weight-TOPSIS, and the like, and pertinently applies different analysis methods to the evaluation of the food inspection method to form a comprehensive food detection method evaluation system with better applicability, thereby better serving food supervision and industrial development.
Advantageous effects
On the basis of optimizing various evaluation indexes, the invention develops various single-factor independent analyses and multi-factor correlation analyses (radar two-dimensional analysis, weight relative contribution rate analysis, improved entropy weight-TOPSIS and other evaluation analyses), and finally the model outputs various visual charts to realize comprehensive and objective evaluation of the detection method, thereby being applied to development, optimization, evaluation and comparison of food inspection methods. The method for analyzing impurity components of various disciplines such as innovation, lipidomics and the like is used for purification evaluation by optimizing various evaluation indexes in food detection and integrating the impurity components of various disciplines such as lipidomics. A multi-factor comprehensive evaluation system applied to food detection is established for the first time, and the defects of the existing food detection and evaluation are overcome. The improved entropy weight-TOPSIS analysis method is innovatively applied to quantitative evaluation of a food detection method. So far, no comprehensive evaluation system is applied to optimization, comparison and evaluation of food detection methods, particularly scientific, comprehensive and visual evaluation of complex animal-derived and high-fat matrix related detection methods. The invention expands and improves an evaluation model based on an entropy weight-ideal solution so as to solve the selection problem of an optimal detection method in food analysis.
Drawings
FIG. 1 is a flow chart of a multi-factor comprehensive evaluation model;
FIG. 2 is an application of radar map two-dimensional analysis of a multi-factor evaluation model in optimization of a method for detecting acrylamide in vegetable oil by QuEChERS;
FIG. 3 is an application of weight analysis of evaluation indexes in a multi-factor evaluation model in optimization of a method for detecting acrylamide in vegetable oil by QuEChERS;
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
In the embodiment, the work flow is as shown in the attached figure 1. The following examples are presented to illustrate the present invention in further detail in conjunction with the detailed description. A multi-index system evaluation method suitable for food detection is disclosed, which takes the improvement of the optimization of a method for detecting acrylamide in vegetable oil by QuEChERS as an example, and evaluates the optimization of the method and the comparison with other methods. The method comprises the following steps:
(1) and (4) selecting an evaluation index. Selecting key index factors based on factors such as detection matrix and target object, and selecting parameters such as absolute recovery rate (A-RE), absolute matrix effect (A-ME), extraction solution full scan (F-scan) and phospholipid characteristic precursor ion scan (P-scan, m/z 184 and m/z 241) as evaluation indexes of the detection method. Wherein the Recovery (RE) comprises absolute recovery (A-RE) and relative recovery (R-RE) depending on whether the quantitation result is calibrated using an internal standard or a matrix matching calibration curve; the Matrix Effect (ME) includes absolute matrix effect (A-ME) and relative matrix effect (R-ME).
The recovery Rate (RE) was evaluated by the ratio of the amount of change in the extract after actual labeling to the theoretical amount of addition, and RE% ((A))b-As) 100/M, wherein AbIs the content of target substance in the extract after adding the standard, AsThe content of the target substance in the matrix sample before the standard addition is shown, and M is the theoretical addition amount. The response matrix of the target substance in the added standard extracting solution matches the curve slope of the standard solution/the curve slope of the standard solution without the matrix-1). times.100 percent. Relative recovery (R-RE) is the recovery, absolute recovery, of the measurement corrected for an internal standard or matrix-matched calibration curveThe rate (A-RE) is the recovery quantified directly from a pure solvent standard curve without any form of correction or conversion.
The Matrix Effect (ME) is evaluated by the relative ratio of the blank extract to the pure solvent after the same level of the target component is added, and ME% ((A))m/Ae-1)×100％。AmResults measured for matrix-matched standard solutions, AeThe results are measured for matrix-free standard solutions. Relative matrix effect (R-ME) is the recovery of the measurement after calibration with an internal standard or matrix-matched calibration curve, and absolute matrix effect (A-ME) is the recovery quantified directly from a pure solvent standard curve without any form of calibration or conversion. A negative matrix effect indicates the presence of a matrix-inhibiting effect; a positive matrix effect indicates the presence of a matrix-enhancing effect, 0 is no matrix effect, and a larger absolute value indicates a stronger matrix effect.
The extraction solution full scan (F-scan) is that the monitoring mode at the detection end of the mass spectrum detector is a full ion scan mode, and in order to objectively and accurately reflect the full scan ion response under the detection condition, the conditions of other liquid phases and the ion source are the same as the detection condition except that the scanning mode is different in the experiment.
Phospholipid characteristic precursor ion scanning (P-scan, m/z 184 and m/z 241) is that a monitoring mode at the detection end of a mass spectrum detector is a precursor ion scanning mode, characteristic fragment ions are respectively set as m/z 184 and m/z 241, and in order to objectively and accurately reflect phospholipid response under detection conditions, except for different scanning modes, other liquid phase conditions and ion source conditions are the same as the detection conditions in an experiment.
(2) As a pre-analysis for developing subsequent comprehensive evaluations, a variety of single factor independent analyses (MFIA) were primarily optimized for the method for which the evaluation is to be developed.
(3) And respectively carrying out radar two-dimensional analysis, weight relative contribution rate analysis and entropy weight-TOPSIS analysis on the basis of the index data processed by the normalized data. First, based on the optimized index, the data is normalized for processing. The method comprises the following basic steps:
1) the evaluation index data form an initial index system and are expressed as the following matrix:
assume that there are m samples to be evaluated based on n indices. Wherein x isijThe observed values of the samples are i 1,2 … m, and j 1,2 … n.
2) And standardizing the evaluation index data to form a normalized matrix system of the analysis model. Considering the influence of different dimensions in the original matrix, dimensionless transformation is adopted for normalization, and meanwhile, the method is simple and convenient to operate. And processing the data in the same direction according to the characteristics of the indexes, so that the data have the same trend, wherein the larger the data, the better the data. Wherein the recovery rate takes 100% as a standard value, and the processing equation is as follows:
absolute matrix effect, extraction solution full scan (F-scan) and phospholipid characteristic precursor ion scan, with 0 as standard value for data processing:
then, each evaluation index is normalized:
and (3) expressing the proportions of the evaluation indexes after normalization treatment:
finally obtaining a normalized matrix Pij。
(4) For normalized data matrix PijRadar two-dimensional analysis is carried out, and relevance of optimization factors on multiple evaluation indexes is visually presented, as shown in the attached figure 2.
(5) Application of entropy theory to the normalized data matrix P of the indicator factor of claim 4ijEntropy analysis is carried out to determine the information entropy E of each indexj
And determining each index weight wj
(6) Analyzing the weight of the index factors to obtain the contribution of each index factor in different methods, as shown in fig. 3.
(7) Performing TOPSIS analysis on the basis of the weight assignment of the index factors to obtain a final evaluation conclusion of the multi-factor evaluation model, and basically comprising the following steps of:
1) weighted normalization matrix ZijAnd (4) establishing.
wjpij；
Wherein Z isijFor weighting the normalized values, PijTo a normalized value, wjAre entropy weights.
2) And determining positive and negative ideal reference points.
3) The distances to the positive and negative ideal reference points are calculated using the following formula.
Wherein Z isj +And Zj －Respectively, the values of the positive and negative ideal reference points, Di +And Dj －Respectively, the distance to the positive and negative ideal reference points.
4) Approximation coefficient (comprehensive evaluation coefficient) CiThe following formula is calculated.
According to the proximity coefficient CiTo determine the best protocol, CiThe larger the value, the better the protocol, as shown in attached Table 4.
(8) The multi-factor evaluation model is applied to optimization of a pretreatment method in a method for detecting acrylamide in vegetable oil by QuEChERS, and comprises selection of sample amount, selection of an extraction solvent system and selection of type and dosage of a QuEChERS adsorbent.
Table 1 application of entropy weight-TOPSIS analysis in multi-factor evaluation model in optimization of detection method for determining acrylamide in vegetable oil by QuEChERS
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such 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 such modifications and variations.
Claims (6)
1. A multi-index system evaluation method suitable for food detection is characterized by comprising the following steps:
(1) establishing a food detection evaluation index set: the food detection and evaluation model at least comprises a pretreatment process evaluation unit and a detection process evaluation unit; the pretreatment process evaluation unit and the detection process evaluation unit correspond to a plurality of evaluation indexes, and the evaluation indexes at least comprise recovery rate, absolute matrix effect, extraction solution full scanning and phospholipid characteristic precursor ion scanning;
(2) constructing a judgment matrix of the evaluation index, and carrying out normalized data processing;
(3) performing two-dimensional analysis on the normalized data matrix, and visually presenting the correlation of the optimization factors on multiple evaluation indexes;
(4) entropy analysis is carried out on the normalized data matrix of the index factors by applying an entropy theory to determine the information entropy of each index and the weight of each index;
(5) analyzing the weight of the index factors to obtain the contribution condition of each index factor in different methods;
(6) and performing TOPSIS analysis on the basis of the weight assignment of the index factors to obtain comprehensive quantitative evaluation indexes, thereby realizing the comprehensive evaluation of different food detection systems.
2. The method for evaluating a multi-index system suitable for food detection according to claim 1, wherein the normalized data processing steps are as follows:
1) the evaluation index data form an initial index system and are expressed as the following matrix:
assume that there are m samples to be evaluated according to n indices, wherein,x ij is an observed value of the sample and is,i=1, 2…m，j=1, 2…n；
2) considering the influence of different dimensions in the original matrix, the normalization adopts dimensionless transformation, and the data is processed in the same direction according to the characteristics of the indexes to have the same trend, wherein the larger the data is, the better the data is, the recovery rate takes 100% as a standard value, and the processing equation is as follows:
absolute matrix effect, data processing is carried out on three indexes of extraction solution full scan and phospholipid characteristic precursor ion scan by taking 0 as a standard value:
；
then, each evaluation index is normalized:
and (3) expressing the proportions of the evaluation indexes after normalization treatment:
finally obtaining a normalized matrixP ij 。
3. The method according to claim 1, wherein the entropy and the weight of each index information are as follows:
entropy of each index informationE j
And determining the weights of the indexesw j
4. The method of claim 1, wherein the TOPSIS analysis is as follows:
1) weighted normalization matrixZ ij Establishing:
wherein the content of the first and second substances,Z ij in order to weight the normalized value of the value,P ij in order to be a normalized value, the value,w j is an entropy weight;
2) determination of positive and negative ideal reference points:
3) the distance to the positive and negative ideal reference points is calculated using the following formula:
wherein the content of the first and second substances,Z j + and Z j － respectively the values of the positive and negative ideal reference points,D i + andD j － the distances to the positive and negative ideal reference points respectively;
4) approximation coefficient, i.e. comprehensive evaluation coefficientC i The following formula calculates:
according to the proximity coefficientC i To determine the best protocol for the experiment,C i the larger the value, the better the scheme.
5. The method as claimed in claim 1, wherein when the method is applied to QuEChERS for measuring acrylamide in vegetable oil, the pretreatment evaluation unit at least comprises a sample amount selection evaluation unit, an extraction solvent system selection evaluation unit, an adsorbent type evaluation unit and an adsorbent dosage selection evaluation unit; the detection process evaluation unit at least comprises a selection evaluation unit of a chromatographic column and an evaluation unit of chromatographic conditions.
6. The method of claim 1, wherein the recovery rate comprises an absolute recovery rate and a relative recovery rate; the matrix effect includes an absolute matrix effect and a relative matrix effect.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102324066A (en) * | 2011-08-26 | 2012-01-18 | 积成电子股份有限公司 | Radar chart representation method for early warning and assessment index of power system |
| KR20160011776A (en) * | 2014-07-22 | 2016-02-02 | 성균관대학교산학협력단 | Method for obtaining solutions based on weighting analytic hierarchy process, grey number and entropy for multiple-criteria group decision making problems |
| CN111398458A (en) * | 2020-04-01 | 2020-07-10 | 山东省食品药品检验研究院 | Improved method for rapidly determining acrylamide in vegetable oil by QuEChERS-L C-MS/MS |
| CN112098756A (en) * | 2020-09-16 | 2020-12-18 | 东风柳州汽车有限公司 | Method, device, equipment and storage medium for positioning electromagnetic compatibility problem |
| CN112465366A (en) * | 2020-12-02 | 2021-03-09 | 中国林业科学研究院亚热带林业研究所 | Comprehensive evaluation method for persimmon quality based on entropy weight TOPSIS model |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102324066A (en) * | 2011-08-26 | 2012-01-18 | 积成电子股份有限公司 | Radar chart representation method for early warning and assessment index of power system |
| KR20160011776A (en) * | 2014-07-22 | 2016-02-02 | 성균관대학교산학협력단 | Method for obtaining solutions based on weighting analytic hierarchy process, grey number and entropy for multiple-criteria group decision making problems |
| CN111398458A (en) * | 2020-04-01 | 2020-07-10 | 山东省食品药品检验研究院 | Improved method for rapidly determining acrylamide in vegetable oil by QuEChERS-L C-MS/MS |
| CN112098756A (en) * | 2020-09-16 | 2020-12-18 | 东风柳州汽车有限公司 | Method, device, equipment and storage medium for positioning electromagnetic compatibility problem |
| CN112465366A (en) * | 2020-12-02 | 2021-03-09 | 中国林业科学研究院亚热带林业研究所 | Comprehensive evaluation method for persimmon quality based on entropy weight TOPSIS model |
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