CN117233274B - Method and system for detecting and correcting content of semi-volatile organic compounds in soil - Google Patents
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Abstract
The invention provides a method and a system for detecting and correcting the content of semi-volatile organic matters in soil, which relate to the technical field of organic matter detection, and solve the technical problems that when the content of the soil is detected, fuzzy calibration can only be carried out on abnormal detection data, the accuracy of overall detection information is insufficient due to the existence of data deviation, deviation abnormal recognition and relative abnormal recognition are carried out on the chromatographic detection data, parallel detection and calibration based on data are ensured, abnormal points are accurately positioned, prediction and supplement are carried out, and the actual consistency of the content detection of the soil organic matters is ensured.
Description
Technical Field
The invention relates to the technical field of organic matter detection, in particular to a method and a system for detecting and correcting the content of semi-volatile organic matters in soil.
Background
Soil organic matters are important indexes for measuring soil quality, and are further directly related to aspects of agricultural production and environmental protection, and the detection accuracy of the organic matters needs to be strictly controlled. At present, the measurement of the content of the organic matters is mainly carried out by combining related equipment to carry out direct detection or carrying out organic chemical reaction, and the traditional detection technology has certain limitations. When the prior art detects the content of the soil organic matters, fuzzy calibration can only be carried out aiming at abnormal detection data, and the accuracy of the whole detection information is insufficient due to the existence of data deviation.
Disclosure of Invention
The application provides a method and a system for detecting and correcting the content of semi-volatile organic compounds in soil, which are used for solving the technical problem that when the content of the soil organic compounds is detected in the prior art, fuzzy calibration can only be carried out on abnormal detection data, and the accuracy of overall detection information is insufficient due to the existence of data deviation.
In view of the above problems, the present application provides a method and a system for detecting and correcting the content of semi-volatile organic compounds in soil.
In a first aspect, the present application provides a method for detecting and correcting the content of a semi-volatile organic compound in soil, the method comprising:
obtaining a pretreatment sample solution of the picked soil, and preparing pretreatment sample solutions with a plurality of concentrations through an automatic sampler;
injecting the pretreated sample solution with the plurality of concentrations into a gas chromatograph-mass spectrometer for detection to obtain a chromatograph detection data set;
performing deviation anomaly identification on the chromatographic detection data set, positioning the anomaly detection data set, and performing blank resetting on the anomaly detection data set from the chromatographic detection data set to obtain a chromatographic detection data set after one resetting;
performing relative anomaly identification on the primary-reset chromatographic detection data set, positioning a secondary anomaly detection data set, and performing blank resetting on the secondary anomaly detection data set to obtain a secondary-reset chromatographic detection data set;
carrying out Markov chain prediction on the secondary-reset chromatographic detection data set, outputting correction data corresponding to all blank-reset data, and generating a corrected chromatographic detection data set;
and drawing a color spectrum detection curve by using the corrected color spectrum detection data set with the peak area as an ordinate and the concentration as an abscissa.
In a second aspect, the present application provides a system for detecting and correcting the content of semi-volatile organic compounds in soil, the system comprising:
the sample solution acquisition module is used for obtaining pretreated sample solutions of the collected soil, and the pretreated sample solutions with a plurality of concentrations are prepared through the automatic sampler;
the chromatographic detection module is used for injecting a gas chromatograph-mass spectrometer to detect the pretreated sample solutions with the plurality of concentrations to obtain a chromatographic detection data set;
the deviation anomaly detection module is used for carrying out deviation anomaly identification on the chromatographic detection data set, positioning the anomaly detection data set, and carrying out blank resetting on the anomaly detection data set from the chromatographic detection data set to obtain a chromatographic detection data set after one resetting;
the relative anomaly identification module is used for carrying out relative anomaly identification on the chromatographic detection data set subjected to primary resetting, positioning a secondary anomaly detection data set, and carrying out blank resetting on the secondary anomaly detection data set to obtain the chromatographic detection data set subjected to secondary resetting;
the data prediction module is used for outputting correction data corresponding to all blank reset data through Markov chain prediction on the chromatographic detection data set subjected to secondary reset, and generating a corrected chromatographic detection data set;
and the curve drawing module is used for drawing a color spectrum detection curve by taking the peak area as an ordinate and the concentration as an abscissa.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method for detecting and correcting the content of the semi-volatile organic compounds in the soil, the pretreated sample solution of the collected soil is obtained, the pretreated sample solution with a plurality of concentrations is prepared through an automatic sampler, the pretreated sample solution with a plurality of concentrations is injected into a gas chromatograph-mass spectrometer to detect, a chromatograph detection data set is obtained and is subjected to deviation anomaly identification, the anomaly detection data set is positioned to carry out blank arrangement, the chromatograph detection data set after primary arrangement is obtained, further the relative anomaly identification is carried out, the secondary anomaly detection data set is positioned and is subjected to blank arrangement, the chromatograph detection data set after secondary arrangement is obtained, markov chain prediction is carried out on the chromatograph detection data set after secondary arrangement, correction data corresponding to all blank arrangement data are output, and the corrected chromatograph detection data set is generated; the method has the advantages that the peak area is taken as an ordinate, the concentration is taken as an abscissa, the drawing of a chromatographic detection curve is carried out, the technical problems that fuzzy calibration can only be carried out aiming at abnormal detection data when the content of soil organic matters is detected in the prior art, the accuracy of overall detection information is insufficient due to the existence of data deviation are solved, deviation abnormal recognition and relative abnormal recognition are carried out on the chromatographic detection data, abnormal points are accurately positioned and predicted and supplemented based on parallel detection and calibration of the data, and the actual consistency of the content detection of the soil organic matters is ensured.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting and correcting the content of semi-volatile organic compounds in soil;
FIG. 2 is a schematic diagram of a structure connection flow in a method for detecting and correcting the content of semi-volatile organic compounds in soil;
fig. 3 is a schematic structural diagram of a system for detecting and correcting the content of semi-volatile organic compounds in soil.
Reference numerals illustrate: the device comprises a sample solution acquisition module 11, a chromatographic detection module 12, a deviation anomaly detection module 13, a relative anomaly identification module 14, a data prediction module 15 and a curve drawing module 16.
Detailed Description
According to the method and the system for detecting and correcting the content of the semi-volatile organic compounds in the soil, a pretreated sample solution of the collected soil is obtained, the pretreated sample solution is injected into a gas chromatograph-mass spectrometer, a chromatograph detection data set is obtained and subjected to deviation anomaly identification and blank arrangement, the chromatograph detection data set after primary arrangement is obtained, the chromatograph detection data set after secondary arrangement is obtained and subjected to Markov chain prediction correction, the corrected chromatograph detection data set is generated and subjected to drawing of a chromatograph detection curve, and the method and the system are used for solving the technical problems that when the content of the soil organic compounds is detected in the prior art, fuzzy calibration can only be carried out aiming at anomaly detection data, and the accuracy of overall detection information is insufficient due to the deviation of the data.
Example 1
As shown in fig. 1 and 2, the present application provides a method for detecting and correcting the content of semi-volatile organic compounds in soil, which includes:
s1: obtaining a pretreatment sample solution of the picked soil, and preparing pretreatment sample solutions with a plurality of concentrations through an automatic sampler;
wherein, this application S1 still includes:
s11: after each preparation of a pretreated sample solution with one concentration, the automatic sampler carries out residual liquid concentration detection of the last concentration on the liquid storage inner wall of the automatic sampler;
s12: and generating first feed-back data according to the concentration of the residual liquid, wherein the first feed-back data is used for feeding back the adjusted feed-back amount when the automatic sampler adjusts the pretreatment sample solution with the next concentration.
Soil organic matters are important indexes for measuring soil quality, and are further directly related to aspects of agricultural production and environmental protection, and the detection accuracy of the organic matters needs to be strictly controlled. According to the method for detecting and correcting the content of the semi-volatile organic compounds in the soil, deviation anomaly identification and relative anomaly identification are carried out on chromatographic detection data, anomaly points are accurately positioned and predicted and supplemented based on parallel detection and calibration of the data, and the actual consistency of the content detection of the soil organic compounds is guaranteed.
The method is characterized in that target soil to be subjected to organic content detection is randomly sampled, the sampled soil is pretreated to sample an organic solution, and the organic extraction can be performed based on a pressurized fluid extraction mode. Specifically, the collected soil is dehydrated, and a proper amount of diatomite is added and ground. Extracting with n-hexane-acetone mixed solvent as extraction solution, and preparing extraction conditions, wherein the extraction temperature is 100 ℃ and the extraction pressure is 1500psi, the static extraction time is 5min, the leaching is 60% of the pool volume, the nitrogen purging time is 60s, and the extraction cycle times are 2 times. After completion of extraction and collection of the solution. Purifying the extracting solution by adopting a magnesium silicate small column, concentrating the sample based on a parallel concentrator to reach the standard concentration, and fixing the volume to 1.0ml based on an acetone-n-hexane mixed solvent to finish pretreatment of the extracted soil to obtain the pretreated sample solution.
The pretreatment sample solution is prepared based on the automatic sampler, wherein the pretreatment sample solution comprises semi-volatile organic compounds such as o-toluidine, p-toluidine, o-nitrotoluene, p-nitrotoluene, m-dinitrobenzene, p-dinitrobenzene, o-dinitrobenzene and the like, pretreatment sample solutions with different concentrations are prepared based on the automatic sampler, and specific preparation modes are the same, so that the pretreatment sample solutions with the concentrations are obtained.
When the automatic sampler is used for preparing, after the pretreatment sample solution with one concentration is prepared, the feedback adjustment of the lower preparation liquid inlet amount is performed based on the residual of the inner wall so as to improve the preparation precision of the pretreatment sample solution. Specifically, after the automatic sampler completes the preparation of the pretreatment sample solution with one concentration, the concentration detection of the residual liquid with the previous concentration is carried out on the liquid storage inner wall of the automatic sampler, and the residual quantity is determined for marking. And performing feedback adjustment on the liquid inlet amount when the automatic sampler adjusts the pretreatment sample solution with the next concentration based on the residual liquid concentration, for example, performing concentration conversion based on the residual liquid concentration and the residual amount, converting the residual liquid concentration and the residual amount into liquid amount based on the next concentration, and performing difference calculation on the liquid inlet amount required by the next concentration and the liquid amount to serve as the first liquid inlet feedback data. The residual detection and the feed liquid feedback adjustment are carried out to accurately control the feed liquid amount, so that the preparation precision of the pretreatment sample solutions with the concentrations is improved.
S2: injecting the pretreated sample solution with the plurality of concentrations into a gas chromatograph-mass spectrometer for detection to obtain a chromatograph detection data set;
s3: performing deviation anomaly identification on the chromatographic detection data set, positioning the anomaly detection data set, and performing blank resetting on the anomaly detection data set from the chromatographic detection data set to obtain a chromatographic detection data set after one resetting;
and respectively removing the pretreatment sample solutions with the plurality of concentrations based on the automatic sampler, and injecting the pretreatment sample solutions into the gas chromatograph-mass spectrometer for chromatographic detection. Preferably, based on detection requirements, a reasonable mass spectrometer can be selected for combination use aiming at the meteorological chromatograph so as to improve chromatographic detection effect. Traversing the pretreatment sample solutions with the plurality of concentrations, and respectively sampling and injection detection based on the automatic sampler. Specifically, the mixed organic matters in the pretreated sample solution are gasified and separated through gas chromatography, all the components sequentially enter a mass spectrometer according to the sequence of retention time, ionization and electric signal conversion are carried out, chromatographic data, such as a chromatogram and the like, are obtained after computer processing, and the chromatographic detection data set is determined, wherein the chromatographic detection data set corresponds to the pretreated sample solutions with the concentrations one by one. The chromatographic detection data set is used as a reference for judging the organic matter content.
Furthermore, a certain detection error exists in the chromatographic detection data set, and in order to ensure the detection accuracy of the organic matter content, detection, identification and calibration of abnormal data are performed. Specifically, the anomaly identification is performed on the chromatographic detection data set, and the chromatographic detection data set is exemplarily based on the concentration differentiation, the data fluctuation state and the concentration fluctuation of the chromatographic data of the same organic matters under different concentrations are positively correlated, and based on the data fluctuation state and the concentration fluctuation, the chromatographic detection data set is in a certain trend range, the deviation analysis is performed on the data in the chromatographic detection data set, if the chromatographic detection data deviates from the overall amplitude trend, the detection data of the corresponding position is judged to be anomaly, and the detection data of the corresponding position is added into the anomaly detection data set. And further determining the positions of the abnormal detection data in the abnormal detection data set in the chromatographic detection data set, performing blank resetting of the corresponding positions, namely performing blank resetting, completing resetting once, and acquiring the chromatographic detection data set after resetting once.
S4: performing relative anomaly identification on the primary-reset chromatographic detection data set, positioning a secondary anomaly detection data set, and performing blank resetting on the secondary anomaly detection data set to obtain a secondary-reset chromatographic detection data set;
wherein, carry out relative unusual discernment to chromatographic detection dataset after once put down, this application S4 still includes:
s41: outputting a plurality of parallel detection data sets by carrying out a plurality of parallel detection on the same organic matter;
s42: acquiring a chromatographic detection data set after primary resetting of a first parallel detection data set, and generating a first chromatographic detection data set by correcting Markov chain prediction of the chromatographic detection data set after primary resetting;
s43: and carrying out relative anomaly identification by taking the first chromatographic detection data set as a relative group of the second parallel detection data set, and positioning the second parallel detection data set.
Wherein, after locating the second anomaly detection dataset of the second parallel detection, the application S43 further includes:
s431: blank resetting is carried out according to a secondary anomaly detection data set of the secondary parallel detection, so that a chromatographic detection data set after the secondary resetting of the secondary parallel detection is obtained;
s432: performing Markov chain prediction correction on the chromatographic detection data set subjected to secondary resetting of the secondary parallel detection to generate a second chromatographic detection data set;
s433: and carrying out sectional optimization according to the first chromatographic detection data set and the second chromatographic detection data set, and outputting the optimized first chromatographic detection data set and the optimized second chromatographic detection data set.
S5: carrying out Markov chain prediction on the secondary-reset chromatographic detection data set, outputting correction data corresponding to all blank-reset data, and generating a corrected chromatographic detection data set;
wherein, through carrying out Markov chain prediction to the chromatographic detection data set after secondary localization, this application S5 still includes:
s51: generating prediction nodes according to blank bits in the chromatographic detection data set after secondary placement, taking the prediction nodes as output targets, predicting the detection data under each prediction node by using a Markov chain,
the prediction of the Markov chain takes the first feedback information as the adjustment information of the feedback activation layer, performs feedback prediction on the detection data under each prediction node, and outputs the prediction data corresponding to the prediction node;
s52: and carrying out complementary optimization on the chromatographic detection data set after secondary resetting according to the prediction data corresponding to the prediction node.
And carrying out multiple parallel detection on the same organic matter, namely carrying out multiple repeated detection based on the same processing mode according to all analysis steps of the sample, mapping and correlating a parallel detection result with the organic matter, and obtaining multiple parallel detection data sets, wherein the specific detection times can be set by user definition based on a person skilled in the art. Based on the multiple parallel detection data sets, extracting the first parallel detection data set detected for the first time, and executing primary resetting processing on the first parallel detection data set, wherein the blank resetting processing mode of abnormal data appearing in the embodiment of the application is the same, and the blank resetting processing mode comprises primary resetting and secondary resetting, wherein the primary resetting is data blank resetting aiming at deviation abnormality; the secondary homing is data blank homing for relative anomalies for parallel detection.
And carrying out Markov chain prediction on the once-reset chromatographic detection data set, carrying out prediction supplementation on blank reset data, and exemplarily, calling a history detection record and carrying out link association integration on the chromatographic detection data of the organic matters, carrying out link node matching and extraction on the blank reset data as a prediction chain, and supplementing the once-reset chromatographic detection data set to generate the first chromatographic detection data set.
Wherein, because the first parallel detection data set does not have a comparison group, the first parallel detection data set is subjected to one-time resetting and then is directly subjected to blank resetting data correction, and the first chromatographic detection data are obtained; and performing primary resetting on the parallel detection data sets of the rest times, further taking the first chromatographic detection data set as a comparison group, performing relative anomaly detection, namely judging the detection data with the overrun difference value as anomaly detection data and performing secondary resetting, and further performing prediction calibration on the parallel detection data with the primary resetting and the secondary resetting by combining a Markov chain to serve as chromatographic detection data corresponding to the parallel times.
Similarly, the second set of parallel detection data is extracted based on the plurality of sets of parallel detection data. And taking the first chromatographic detection data set as a relative reference group, performing mapping and checking with the second parallel detection data set, and extracting chromatographic data with differences in the second parallel detection data set compared with the first chromatographic detection data set as a second parallel detection data set. Preferably, a deviation allowable section, that is, a data deviation within an allowable range due to a technique or the like, is set, and data having a data deviation larger than the deviation allowable section is mapped to the second parallel detection data set and the first color spectrum detection data set as the second anomaly detection data set for the second parallel detection.
Further, in the second parallel detection data set, blank arrangement is performed on the second anomaly detection data set of the second parallel detection, and a chromatographic detection data set after the second parallel detection is subjected to the second arrangement is obtained. And similarly, carrying out Markov chain prediction on the chromatographic detection data set subjected to secondary resetting of the second parallel detection, carrying out blank resetting identification on the chromatographic detection data set subjected to secondary resetting, taking the identified blank point as a prediction node, and taking the prediction node as an output target, namely data to be predicted. The Markov chain is connected with a feedback activation layer, namely a network layer for executing feedback processing, the first feedback information is used as adjustment information of the feedback activation layer and is consistent with the prediction node, detection data prediction of the prediction node is carried out in the Markov chain, prediction data is obtained, and mapping and output are carried out with the prediction node. Supplementing the obtained prediction data in the second parallel detection data set to obtain the second chromatographic detection data set. The Markov chain prediction correction steps in the embodiment of the application are the same.
The first chromatographic detection data set and the second chromatographic detection data set are chromatographic detection data sets which are determined based on fixed step correction, and the first chromatographic detection data set and the second chromatographic detection data set are subjected to sectional optimization in order to ensure the actual fit degree of the chromatographic detection data. That is, the first chromatographic detection data set is fixed, the second chromatographic detection data set is subjected to optimizing adjustment, for example, a data adjustable section is determined, random disturbance of corresponding data is performed in the data adjustable section, a correction is performed on a random disturbance data set, an optimal data set is determined, the second chromatographic detection data set is determined to be adjusted, further, the second chromatographic detection data set is fixed, the first chromatographic detection data set is subjected to optimizing adjustment, the first chromatographic detection data set is determined to be adjusted, the first chromatographic detection data set and the second chromatographic detection data set are subjected to mapping correction, and the data difference degree is determined.
Similarly, based on the steps, iterative adjustment and data difference measurement after adjustment are sequentially performed, and when the maximum number of iterations is reached, the data difference is screened to be the smallest, namely the first chromatographic detection data set and the second chromatographic detection data set approach infinitely and are used as the optimized first chromatographic detection data set and the optimized second chromatographic detection data set. Wherein, since the first parallel detection data set and the second parallel detection data set are detection results of homologous samples based on the same detection step, the corresponding parallel detection results should be approximately consistent. And determining a corrected chromatographic detection data set based on the first chromatographic detection data set and the second chromatographic detection data set, for example, performing mean value calculation of mapping data on the first chromatographic detection data set and the second chromatographic detection data set as the corrected chromatographic detection data set so as to maximally ensure the accuracy of the corrected chromatographic detection data set.
Preferably, if the multiple parallel detection data sets further include nth parallel detection data, the nth chromatographic detection data set is obtained by processing based on the steps of resetting and calibrating the second parallel detection data set, and when the chromatographic detection data set is optimized, the first chromatographic detection data set, the second chromatographic detection data set and the nth chromatographic detection data set are subjected to sectional optimization, and the optimization result approaches infinitely.
S6: and drawing a color spectrum detection curve by using the corrected color spectrum detection data set with the peak area as an ordinate and the concentration as an abscissa.
Wherein, this application S6 further includes:
s61: determining the types of the organic matters to be detected, and respectively carrying out multiple parallel detection on each organic matter type to obtain multiple parallel detection data corresponding to each organic matter type;
s62: and generating a chromatographic detection curve of each organic matter type according to the multiple parallel detection data corresponding to each organic matter type.
Wherein, this application S62 further includes:
s621: fitting the chromatographic detection curves of all the organic matter types to obtain fitting quality error indexes and correlation coefficient errors;
s622: and when any error of the fitting quality error and the correlation coefficient error is larger than a preset error, generating first feedback information, and respectively correcting and optimizing the chromatographic detection curves corresponding to the organic matter types according to the first feedback information.
And identifying the type of the organic matter to be detected based on the corrected chromatographic detection data set, and executing multiple parallel detection of each organic matter type, wherein the parallel detection steps are consistent, and multiple parallel detection data corresponding to each organic matter type are acquired. And further performing chromatographic detection curve conversion on the multiple parallel detection data corresponding to each organic matter type, and completing data conversion processing based on a computer. Specifically, a chromatographic curve coordinate system is built by taking the peak area as an ordinate and the concentration as an abscissa, and curve conversion is performed on multiple parallel detection data corresponding to each organic matter type. And then fitting the chromatographic detection curves corresponding to the organic matter types, determining a comprehensive trend curve, and measuring curve fitting errors, wherein the curve fitting errors comprise fitting quality error indexes and correlation coefficient errors, the correlation coefficient errors are correlation errors among correlation data, and the fitting quality error indexes are standards for measuring curve fitting quality, for example, trend difference degrees of the chromatographic detection curves are positively correlated with the fitting quality, for example, the measured organic matter contents in the parallel measurement detection data are summed, and the fitting quality error indexes are obtained based on the data difference degrees.
And setting the preset error, namely, based on a critical error value which is custom set by a person skilled in the art based on the precision requirement, respectively checking the fitting quality error and the correlation coefficient error with the preset error, and if any error is larger than the preset error, indicating that the current fitting error is out of limit, and generating the first feedback information. The first feedback information is feedback adjustment data determined based on the error overrun scale and the direction of the measurement, and the chromatographic detection curves corresponding to all organic matter types are corrected and optimized based on the first feedback information, so that the accuracy of the chromatographic detection curves is guaranteed, and the consistency of the chromatographic detection curves and the content of the organic matters in the actual soil is maximally guaranteed.
The method for detecting and correcting the content of the semi-volatile organic compounds in the soil has the following technical effects:
1. the method comprises the steps of obtaining a pretreated sample solution, injecting the pretreated sample solution into a gas chromatograph-mass spectrometer, obtaining a chromatographic detection data set for deviation anomaly identification, obtaining a chromatographic detection data set after primary resetting, carrying out relative anomaly identification, obtaining a chromatographic detection data set after secondary resetting, carrying out Markov chain prediction correction, generating a corrected chromatographic detection data set, carrying out drawing of a chromatographic detection curve, solving the technical problem that when the content of soil organic matters is detected in the prior art, only fuzzy calibration can be carried out on the anomaly detection data, the accuracy of the whole detection information is insufficient due to the existence of deviation of the data, and accurately positioning anomaly points and carrying out prediction supplementation based on parallel detection and calibration of the data by carrying out deviation anomaly identification and relative anomaly identification on the chromatographic detection data, so that the actual consistency of the content detection of the soil organic matters is ensured.
2. By performing multiple parallel detection, performing primary homing for offset anomalies of the chromatographic detection data sets, performing secondary homing for relative anomalies between the parallel detection data sets, performing blank homing for anomaly data, performing predictive replenishment in combination with a Markov chain, performing positioning correction and segmentation optimization of the anomaly data accurately, and maximizing detection accuracy of soil organic matter content.
3. When the chromatographic curve is drawn, the parallel detection and curve fitting of each organic matter type are carried out for multiple times in the same way so as to carry out reference analysis, curve calibration is carried out based on fitting errors, the existence errors in the processing process are avoided, and the processing precision is improved.
Example two
Based on the same inventive concept as the method for detecting and correcting the content of the semi-volatile organic compounds in the soil in the foregoing embodiment, as shown in fig. 3, the present application provides a system for detecting and correcting the content of the semi-volatile organic compounds in the soil, the system comprising:
a sample solution obtaining module 11, wherein the sample solution obtaining module 11 is used for obtaining a pretreated sample solution of the collected soil, and the pretreated sample solution with a plurality of concentrations is prepared by an automatic sampler;
the chromatographic detection module 12 is used for injecting a gas chromatograph-mass spectrometer to detect the pretreated sample solutions with the plurality of concentrations, so as to obtain a chromatographic detection data set;
the deviation anomaly detection module 13 is used for carrying out deviation anomaly identification on the chromatographic detection data set, positioning the anomaly detection data set, and carrying out blank resetting on the anomaly detection data set from the chromatographic detection data set to obtain a chromatographic detection data set after one resetting;
the relative anomaly identification module 14 is configured to perform relative anomaly identification on the first-time-reset chromatographic detection data set, locate a second-time anomaly detection data set, and perform blank reset on the second-time anomaly detection data set to obtain a second-time-reset chromatographic detection data set;
the data prediction module 15 is configured to perform markov chain prediction on the secondary-configured chromatographic detection data set, output correction data corresponding to all blank-configured data, and generate a corrected chromatographic detection data set;
and the curve drawing module 16 is used for drawing a color spectrum detection curve by using the corrected color spectrum detection data set with the peak area as an ordinate and the concentration as an abscissa.
Wherein the relative anomaly identification module 14 further comprises:
the parallel detection module is used for outputting a plurality of parallel detection data sets by carrying out multiple parallel detection on the same organic matter;
the first chromatographic detection data set generation module is used for acquiring a chromatographic detection data set after primary resetting of the first parallel detection data set and generating a first chromatographic detection data set by carrying out Markov chain prediction correction on the chromatographic detection data set after primary resetting;
the secondary anomaly detection data set positioning module is used for carrying out relative anomaly identification by taking the first chromatographic detection data set as a relative group of the second parallel detection data set and positioning the second anomaly detection data set of the second parallel detection.
Wherein, the secondary anomaly detection dataset locating module further comprises:
the data blank arrangement module is used for carrying out blank arrangement according to a secondary abnormal detection data set of the secondary parallel detection to obtain a chromatographic detection data set of the secondary parallel detection after the secondary arrangement;
the second chromatographic detection data set generation module is used for carrying out Markov chain prediction correction on the chromatographic detection data set subjected to secondary resetting of the second parallel detection to generate a second chromatographic detection data set;
the data segmentation optimization module is used for carrying out segmentation optimization according to the first chromatographic detection data set and the second chromatographic detection data set, and outputting the optimized first chromatographic detection data set and the optimized second chromatographic detection data set.
Wherein the curve plotting module 16 further comprises:
the parallel detection module is used for determining the types of the organic matters to be detected, and carrying out multiple parallel detection on each organic matter type respectively to obtain multiple parallel detection data corresponding to each organic matter type respectively;
the chromatographic detection curve generation module is used for generating a chromatographic detection curve of each organic matter type according to the multiple parallel detection data corresponding to each organic matter type.
Wherein, the chromatographic detection curve generation module further comprises:
the curve fitting module is used for fitting the chromatographic detection curves of all the organic matter types to obtain fitting quality error indexes and correlation coefficient errors;
and the curve feedback correction module is used for generating first feedback information when any error of the fitting quality error and the correlation coefficient error is larger than a preset error, and respectively correcting and optimizing the chromatographic detection curves corresponding to the organic matter types according to the first feedback information.
Wherein the data prediction module 15 further comprises:
a node data prediction module, which is used for generating prediction nodes according to blank bits in the chromatographic detection data set after the secondary placement, taking the prediction nodes as output targets, predicting the detection data under each prediction node by using a Markov chain,
the prediction of the Markov chain takes the first feedback information as the adjustment information of the feedback activation layer, performs feedback prediction on the detection data under each prediction node, and outputs the prediction data corresponding to the prediction node;
and the data supplementing and optimizing module is used for supplementing and optimizing the chromatographic detection data set after the secondary resetting according to the prediction data corresponding to the prediction node.
Wherein the sample solution obtaining module 11 further comprises:
the device comprises a liquid storage inner wall of the automatic sampler, a residual liquid concentration detection module, a liquid concentration detection module and a liquid concentration detection module, wherein the liquid storage inner wall of the automatic sampler is used for carrying out residual liquid concentration detection of the previous concentration after the automatic sampler prepares a pretreatment sample solution of one concentration;
the liquid inlet feedback data generation module is used for generating first liquid inlet feedback data according to the concentration of residual liquid, wherein the first liquid inlet feedback data are used for feeding back the adjusted liquid inlet amount when the automatic sampler adjusts the pretreatment sample solution with the next concentration.
The foregoing detailed description of the method for detecting and correcting the content of the semi-volatile organic compound in the soil will be clear to those skilled in the art, and the method and the system for detecting and correcting the content of the semi-volatile organic compound in the soil in this embodiment are relatively simple for the device disclosed in the embodiments, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. A method for detecting and correcting the content of semi-volatile organic compounds in soil, which is characterized by comprising the following steps:
obtaining a pretreatment sample solution of the picked soil, and preparing pretreatment sample solutions with a plurality of concentrations through an automatic sampler;
injecting the pretreated sample solution with the plurality of concentrations into a gas chromatograph-mass spectrometer for detection to obtain a chromatograph detection data set;
performing deviation anomaly identification on the chromatographic detection data set, positioning the anomaly detection data set, and performing blank resetting on the anomaly detection data set from the chromatographic detection data set to obtain a chromatographic detection data set after one resetting;
performing relative anomaly identification on the primary-reset chromatographic detection data set, positioning a secondary anomaly detection data set, and performing blank resetting on the secondary anomaly detection data set to obtain a secondary-reset chromatographic detection data set;
carrying out Markov chain prediction on the secondary-reset chromatographic detection data set, outputting correction data corresponding to all blank-reset data, and generating a corrected chromatographic detection data set;
outputting a plurality of parallel detection data sets by carrying out a plurality of parallel detection on the same organic matter;
acquiring a chromatographic detection data set after primary resetting of a first parallel detection data set, and generating a first chromatographic detection data set by correcting Markov chain prediction of the chromatographic detection data set after primary resetting;
performing relative anomaly identification by taking the first chromatographic detection data set as a relative group of a second parallel detection data set, and positioning the second parallel detection data set;
blank resetting is carried out according to a secondary anomaly detection data set of the secondary parallel detection, so that a chromatographic detection data set after the secondary resetting of the secondary parallel detection is obtained;
performing Markov chain prediction correction on the chromatographic detection data set subjected to secondary resetting of the secondary parallel detection to generate a second chromatographic detection data set;
performing segment optimization according to the first chromatographic detection data set and the second chromatographic detection data set, and outputting an optimized first chromatographic detection data set and an optimized second chromatographic detection data set;
generating prediction nodes according to blank bits in the secondary-reset chromatographic detection data set, and predicting detection data under each prediction node by using a Markov chain by taking the prediction nodes as output targets;
the method comprises the steps that a Markov chain is predicted, first feedback information is used as adjusting information of a feedback activation layer, feedback prediction is conducted on detection data under each prediction node, and prediction data corresponding to the prediction nodes are output;
performing complementary optimization on the chromatographic detection data set after secondary resetting according to the prediction data corresponding to the prediction nodes;
and drawing a color spectrum detection curve by using the corrected color spectrum detection data set with the peak area as an ordinate and the concentration as an abscissa.
2. The method of claim 1, wherein the method further comprises:
determining the types of the organic matters to be detected, and respectively carrying out multiple parallel detection on each organic matter type to obtain multiple parallel detection data corresponding to each organic matter type;
and generating a chromatographic detection curve of each organic matter type according to the multiple parallel detection data corresponding to each organic matter type.
3. The method of claim 2, wherein the method further comprises:
fitting the chromatographic detection curves of all the organic matter types to obtain fitting quality error indexes and correlation coefficient errors;
and when any error of the fitting quality error and the correlation coefficient error is larger than a preset error, generating first feedback information, and respectively correcting and optimizing the chromatographic detection curves corresponding to the organic matter types according to the first feedback information.
4. The method of claim 1, wherein the method further comprises:
after each preparation of a pretreated sample solution with one concentration, the automatic sampler carries out residual liquid concentration detection of the last concentration on the liquid storage inner wall of the automatic sampler;
and generating first feed-back data according to the concentration of the residual liquid, wherein the first feed-back data is used for feeding back the adjusted feed-back amount when the automatic sampler adjusts the pretreatment sample solution with the next concentration.
5. A system for detecting and correcting the content of semi-volatile organic compounds in soil, wherein the system is used for executing the method as claimed in any one of claims 1 to 4, and the system comprises:
the sample solution acquisition module is used for obtaining pretreated sample solutions of the collected soil, and the pretreated sample solutions with a plurality of concentrations are prepared through the automatic sampler;
the chromatographic detection module is used for injecting a gas chromatograph-mass spectrometer to detect the pretreated sample solutions with the plurality of concentrations to obtain a chromatographic detection data set;
the deviation anomaly detection module is used for carrying out deviation anomaly identification on the chromatographic detection data set, positioning the anomaly detection data set, and carrying out blank resetting on the anomaly detection data set from the chromatographic detection data set to obtain a chromatographic detection data set after one resetting;
the relative anomaly identification module is used for carrying out relative anomaly identification on the chromatographic detection data set subjected to primary resetting, positioning a secondary anomaly detection data set, and carrying out blank resetting on the secondary anomaly detection data set to obtain the chromatographic detection data set subjected to secondary resetting;
the data prediction module is used for outputting correction data corresponding to all blank reset data through Markov chain prediction on the chromatographic detection data set subjected to secondary reset, and generating a corrected chromatographic detection data set;
the parallel detection module is used for outputting a plurality of parallel detection data sets by carrying out multiple parallel detection on the same organic matter;
the first chromatographic detection data set generation module is used for acquiring a chromatographic detection data set after primary resetting of the first parallel detection data set and generating a first chromatographic detection data set by carrying out Markov chain prediction correction on the chromatographic detection data set after primary resetting;
the secondary anomaly detection data set positioning module is used for carrying out relative anomaly identification by taking the first chromatographic detection data set as a relative group of the second parallel detection data set and positioning the second anomaly detection data set of the second parallel detection;
the data blank arrangement module is used for carrying out blank arrangement according to a secondary abnormal detection data set of the secondary parallel detection to obtain a chromatographic detection data set of the secondary parallel detection after the secondary arrangement;
the second chromatographic detection data set generation module is used for carrying out Markov chain prediction correction on the chromatographic detection data set subjected to secondary resetting of the second parallel detection to generate a second chromatographic detection data set;
the data segmentation optimization module is used for carrying out segmentation optimization according to the first chromatographic detection data set and the second chromatographic detection data set and outputting an optimized first chromatographic detection data set and an optimized second chromatographic detection data set;
the node data prediction module is used for generating prediction nodes according to blank bits in the chromatographic detection data set after the secondary resetting, taking the prediction nodes as output targets, and predicting detection data under each prediction node by using a Markov chain;
the prediction of the Markov chain takes the first feedback information as the adjustment information of the feedback activation layer, performs feedback prediction on the detection data under each prediction node, and outputs the prediction data corresponding to the prediction node;
the data supplementing and optimizing module is used for supplementing and optimizing the chromatographic detection data set after the secondary resetting according to the prediction data corresponding to the prediction node;
and the curve drawing module is used for drawing a color spectrum detection curve by taking the peak area as an ordinate and the concentration as an abscissa.
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