1. An intelligent calibration method for online monitoring of GCMS equipment is characterized by comprising the following steps: the method comprises the following steps:

obtaining calibration data;

dividing the calibration data into two batches, processing the first batch of calibration data, and processing the second batch of calibration data based on the result of the first batch of calibration data to obtain the peak position with the highest correlation of the selected comprehensive mass spectrogram as the characteristic ion and the retention time of the substance;

recording and storing the processed data;

associating the calibration data to generate a standard curve;

and (6) auditing the standard curve.

2. The intelligent calibration method for the online monitoring GCMS device according to claim 1, wherein: the first batch calibration data:

acquiring a standard mass spectrogram of the ion source substance by substance, and confirming all ions of the ion source substance;

taking out the chromatogram of each ion of the substance, and finding out all wave crests;

obtaining detection mass spectrograms of the detection mass spectrograms at all the wave peak positions, and calculating the correlation between the detection mass spectrograms and the substance standard mass spectrograms;

and selecting five peak time points with the highest mass spectrogram correlation as candidate retention time of the substance, and taking the ions with the peaks as candidate characteristic ions.

3. The intelligent calibration method for the online monitoring GCMS device according to claim 2, wherein: the second batch of calibration data:

finding peak positions within a certain range nearby the candidate retention time and subsequent characteristic ions of the first batch of data; if the wave crest cannot be found, candidate characteristic ions can be removed from the ions;

obtaining a detection mass spectrogram of the residual wave crest positions, and calculating the correlation with a substance standard mass spectrogram; if the correlation is smaller than a set threshold value, removing candidate characteristic ions from the corresponding ions;

the remaining candidate feature ions and retention time are involved in the calculation of the next batch.

4. The intelligent calibration method for the online monitoring GCMS device according to claim 3, wherein: the second batch of calibration data:

and if a plurality of characteristic ions can be selected finally, selecting a plurality of batches of actual sample data after calibration, performing qualitative and quantitative analysis by adopting different characteristic ions, and selecting the ions capable of separating substances better as final characteristic ions.

5. The intelligent calibration method for the online monitoring GCMS device according to claim 1, wherein: the recording and storing of the processed data:

by applying the data storage technology, data query and update can be carried out simultaneously, and operation records can be traced.

6. The intelligent calibration method for the online monitoring GCMS device according to claim 1, wherein: the method further comprises the following steps:

manually correcting the curves which are not approved.

7. The intelligent calibration method for the online monitoring GCMS device according to claim 6, wherein: the manual correction comprises the following steps:

the method supports the function of superposition of multiple spectrograms, and more conveniently checks the association relation among various characteristic peaks among multiple data.

8. The intelligent calibration method for the online monitoring GCMS device according to claim 7, wherein: the manual correction comprises the following steps:

and a material knowledge base and a multi-ion spectrogram overlapping function of the specified materials are supported, and a user does not need to record a common characteristic ion set of each material.

Background

The gas chromatography-mass spectrometer (GC/MS) is widely applied to separation and identification of complex components, has high resolution of GC and high sensitivity of mass spectrum, and is widely used in the fields of environmental protection industry, electronic industry, textile industry, petrochemical industry, essence and spice industry, pharmaceutical industry, agriculture, food safety and the like; before each detection, calibration is needed to be carried out to make a standard curve (standard yeast), while the current standard yeast making process is responsible for difficult operation and time consumption; the gas chromatography-mass spectrometry combined instrument is used for monitoring volatile organic compounds in real time on line in the field of environmental protection, equipment needs to be calibrated and a standard curve needs to be made regularly, and analysis and detection needs to be carried out on more than one hundred compounds, so that higher requirements are provided for the convenience of the operation process;

the existing procedures and technologies for calibrating and making standard yeast are as follows:

1. injecting a plurality of batches of standard gas into a gas chromatography-mass spectrometer to obtain an ion chromatogram of each batch;

2. the characteristic ions and retention times were confirmed manually from substance to substance:

selecting several pairs of ion chromatograms for each substance according to experience, judging the peak time of the substance in the chromatogram according to experience, and finding out the peak value of the chromatogram in the peak time range; and calculating a mass spectrogram at the peak value of the chromatogram, comparing the mass spectrogram with a standard mass spectrogram of the substance, and determining the most appropriate retention time and characteristic ions.

3. And determining the starting and stopping time of the peak on the characteristic ion chromatogram one by one, and calculating the peak area.

4. The peak area of each batch is fitted with the standard concentration to form a curve and the curve is the standard curve of each substance.

When the existing standard curve is manufactured, higher labor cost is needed to be invested to correct calibration data, and new uncertainty is introduced due to manual participation. In the actual implementation process of the business, in order to ensure the correctness of the standard curve/calibration method, manual correction needs to be performed based on each result record of each substance by manual participation, and finally, qualitative and quantitative results of calibration data are obtained. This process is cumbersome, labor intensive, and relatively complex. Analysts need not only high professional literacy, but also sufficient patience and care to ensure the correctness of the final result.

First, in the conventional art, the association between data is maintained by the operator himself, not by software. Therefore, the work of querying data, correlating data, analyzing data correlations, etc. introduces a large amount of work, further increasing the possibility of errors. Second, manual correction introduces a large subjective factor in the process. Not only may qualitative errors occur, but also qualitative results may be inconsistent. Finally, in the face of doubt data, an analyst needs to rely on a mass spectrometry technology to perform analysis and judgment, so that the professional requirements on the analyst are increased. If the abilities of the analysts do not meet the standards, manpower and material resources may be wasted and accurate results cannot be obtained.

Disclosure of Invention

Technical problem to be solved

Aiming at the defects of the prior art, the invention provides the intelligent calibration method for the online monitoring GCMS equipment, which has the advantage of improving the working efficiency and solves the problems of poor accuracy and low efficiency.

(II) technical scheme

In order to achieve the purpose, the invention provides the following technical scheme: an intelligent calibration method for online monitoring of GCMS equipment comprises the following steps:

obtaining calibration data;

dividing the calibration data into two batches, processing the first batch of calibration data, and processing the second batch of calibration data based on the result of the first batch of calibration data to obtain the peak position with the highest correlation of the selected comprehensive mass spectrogram as the characteristic ion and the retention time of the substance;

recording and storing the processed data;

associating the calibration data to generate a standard curve;

and (6) auditing the standard curve.

Preferably, the first lot calibration data is:

acquiring a standard mass spectrogram of the ion source substance by substance, and confirming all ions of the ion source substance;

taking out the chromatogram of each ion of the substance, and finding out all wave crests;

obtaining detection mass spectrograms of the detection mass spectrograms at all the wave peak positions, and calculating the correlation between the detection mass spectrograms and the substance standard mass spectrograms;

and selecting five peak time points with the highest mass spectrogram correlation as candidate retention time of the substance, and taking the ions with the peaks as candidate characteristic ions.

Preferably, the second batch of calibration data is:

finding peak positions within a certain range nearby the candidate retention time and subsequent characteristic ions of the first batch of data; if the wave crest cannot be found, candidate characteristic ions can be removed from the ions;

obtaining a detection mass spectrogram of the residual wave crest positions, and calculating the correlation with a substance standard mass spectrogram; if the correlation is smaller than a set threshold value, removing candidate characteristic ions from the corresponding ions;

the remaining candidate feature ions and retention time are involved in the calculation of the next batch.

Preferably, the second batch of calibration data is:

and if a plurality of characteristic ions can be selected finally, selecting a plurality of batches of actual sample data after calibration, performing qualitative and quantitative analysis by adopting different characteristic ions, and selecting the ions capable of separating substances better as final characteristic ions.

Preferably, the recording and storing the processed data:

by applying the data storage technology, data query and update can be carried out simultaneously, and operation records can be traced.

Preferably, the method further comprises:

manually correcting the curves which are not approved.

Preferably, the manual correction:

the method supports the function of superposition of multiple spectrograms, and more conveniently checks the association relation among various characteristic peaks among multiple data.

Preferably, the manual correction:

and a material knowledge base and a multi-ion spectrogram overlapping function of the specified materials are supported, and a user does not need to record a common characteristic ion set of each material.

(III) advantageous effects

Compared with the prior art, the invention provides an intelligent calibration method for online monitoring of GCMS equipment, which has the following beneficial effects:

the intelligent calibration method for the online monitoring GCMS equipment can output results in real time when calibration is carried out, and if the calibration data has problems, the problems can be found and processed in time; the whole process needs fewer parts needing manual participation, the operation complexity can be greatly reduced, the labor cost is saved, and the efficiency is improved.

Drawings

FIG. 1 is a schematic view of the flow structure of the present invention;

FIG. 2 is a schematic diagram of a calibration data processing flow according to the present invention;

FIG. 3 is a single-substance multi-data superposition spectrum in the present invention;

FIG. 4 is a spectrum of a plurality of ion stacks according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example one

An intelligent calibration method for online monitoring of GCMS equipment comprises the following steps:

obtaining calibration data;

dividing the calibration data into two batches, processing the first batch of calibration data, and processing the second batch of calibration data based on the result of the first batch of calibration data to obtain the peak position with the highest correlation of the selected comprehensive mass spectrogram as the characteristic ion and the retention time of the substance;

recording and storing the processed data;

associating the calibration data to generate a standard curve;

and (6) auditing the standard curve.

Specifically, the first batch of calibration data is:

based on the range of substances to be detected, the following is taken for each substance: obtaining a standard mass spectrogram of the ion source, and confirming all ions of the ion source;

taking out the chromatogram of each ion of the substance, and finding out all wave crests;

obtaining detection mass spectrograms of the detection mass spectrograms at all the wave peak positions, and calculating the correlation between the detection mass spectrograms and the substance standard mass spectrograms;

the correlation coefficient calculation formula is as follows:

wherein X is a set of species concentrations and Y is a set of species responses. If the substance is quantified by an internal standard method, X is a substance concentration ratio set, and Y is a substance corresponding ratio set.

And selecting five peak time points with the highest mass spectrogram correlation as candidate retention time of the substance, and taking the ions with the peaks as candidate characteristic ions.

Specifically, the second batch of calibration data is:

finding peak positions within a certain range (supporting customization and defaulting to 0.2 minutes) nearby the candidate retention time and subsequent characteristic ions of the first batch of data; if the wave crest cannot be found, candidate characteristic ions can be removed from the ions;

obtaining a detection mass spectrogram of the residual wave crest positions, and calculating the correlation with a substance standard mass spectrogram; if the correlation is smaller than a set threshold (self-definition is supported, and the default is 0.98), removing candidate characteristic ions from the corresponding ions;

the remaining candidate feature ions and retention time are involved in the calculation of the next batch.

Specifically, the second batch of calibration data is:

and if a plurality of characteristic ions can be selected finally, selecting a plurality of batches of actual sample data after calibration, performing qualitative and quantitative analysis by adopting different characteristic ions, and selecting the ions capable of separating substances better as final characteristic ions.

In this embodiment, the intelligent calibration model: automatically outputting an algorithm model of material quantitative configuration and a standard curve based on the material characteristics and the calibration data; material peak time estimation model: and estimating the peak emergence time of each substance by adopting a supervised learning method based on sample data.

With regard to the automatic calculation function of quantitative configuration in the intelligent calibration model, the system performs processing in two steps.

In a first step, characteristic ions are determined. The system adopts the characteristics of two dimensions to carry out comprehensive judgment: in the first dimension, the interference situation [ left-right (2-side) interference, left/right (1-side) interference, no interference ]; the second dimension, peak height. Briefly, the characteristic ions to be selected should have a peak height response that is as good as possible without interference.

And secondly, calculating a standard curve. And calculating the standard curve by adopting a calculation mode of the recent environment department guide file. Currently, the system performs calibration calculations on substances by calculating (relative) response factors.

Regarding the estimation model of the substance peak time, the system adopts a linear fitting method to carry out supervised learning, comprehensively considers the chromatographic peak characteristics of the internal standard substance and the chromatographic peak characteristics of the currently selected substance, and estimates the peak time of each substance.

Example two

The method for storing the processing data is added on the basis of the first embodiment.

Specifically, the recording and storing of the processed data:

by applying the data storage technology, data query and update can be carried out simultaneously, and operation records can be traced.

In this embodiment, the data association application scheme: and a system design scheme for providing quick data navigation and data application for a client.

EXAMPLE III

And a method for correcting data is added on the basis of the second embodiment.

Specifically, the method further comprises:

manually correcting the curves which are not approved.

Specifically, the manual correction:

the method supports the function of superposition of multiple spectrograms, and more conveniently checks the association relation among various characteristic peaks among multiple data.

The multiple data superposition function includes two kinds:

first, single-material, multiple-data stacking. As shown in fig. 3 below, spectrograms of the substance in the plurality of data are extracted and displayed in an overlapping manner, so that a visual effect is obtained.

Second, single data multi-ion superposition. As shown in fig. 4 below, a plurality of groups of ion spectrograms in one data are extracted and displayed in an overlapping manner, so that a visual effect is obtained.

Specifically, the manual correction:

and a material knowledge base and a multi-ion spectrogram overlapping function of the specified materials are supported, and a user does not need to record a common characteristic ion set of each material.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.