A method for optimizing chromatographic data of a gas chromatograph

Abstract

The present application relates to a kind of chromatographic data optimization processing method of gas chromatograph, belong to data analysis technical field, comprising: based on the detection data of septum, determine the aging state grade of septum;Based on the aging state grade of septum and the material of septum, determine the release material of septum;Obtain the chromatographic data of sample analysis by gas chromatograph, remove the interference peak corresponding to the release material of septum from chromatographic data, generate first chromatographic optimization data;Based on first chromatographic optimization data and the aging state grade of septum, determine sample material loss compensation numerical value, and optimize chromatographic peak area value in first chromatographic optimization data, generate second chromatographic optimization data.The method is based on septum aging prediction, interference peak removal and quantitative correction linkage, both solve the problem of peak shape pollution, and also solve the problem of quantitative inaccuracy caused by aging, and then the reliability of gas chromatographic data and the accuracy of analysis result can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and in particular to a chromatographic data optimization processing method of a gas chromatograph. BACKGROUND

[0002] As an important technology in the field of modern chemical analysis, gas chromatography analysis is widely used in environmental monitoring, food safety, drug analysis and other key scenarios. Its high sensitivity and high separation efficiency make it the preferred method for detecting complex sample components.

[0003] Through the gas chromatograph, different chemical components in the sample can be separated in the chromatographic column and converted into quantifiable signal peaks. However, it is found in research that in high-throughput and long-time running scenarios, small changes in instrument components can cause significant analysis deviation. For example, the septum, as a key component of the gas chromatograph, has not been fully considered in terms of aging problems. Existing methods usually assume that the septum performance remains stable within a certain use cycle, and only process samples through a fixed pretreatment process, without dynamically adapting to changes caused by septum aging, making it difficult to deal with the interference caused by septum aging in complex sample analysis, resulting in gradual accumulation of analysis results deviation. SUMMARY

[0004] To solve the above technical problems of the prior art, the present application provides a chromatographic data optimization processing method of a gas chromatograph.

[0005] In a first aspect, the present application provides a chromatographic data optimization processing method of a gas chromatograph, comprising: determining the aging state level of a septum of the gas chromatograph based on detection data of the septum; wherein the detection data at least includes the puncture times of the septum; determining the release material of the septum based on the aging state level of the septum and the material of the septum; obtaining chromatographic data of a sample analyzed by the gas chromatograph, removing the interference peak corresponding to the release material of the septum from the chromatographic data, and generating first chromatographic optimization data; determining the sample material loss compensation value based on the first chromatographic optimization data and the aging state level of the septum, and optimizing the chromatographic peak area value in the first chromatographic optimization data to generate second chromatographic optimization data.

[0006] Optionally, the detection data further comprises temperature distribution data, pressure detection data and hardness detection data of the septum; wherein the temperature distribution data is used to determine a thermal conductivity degradation area in the septum; the pressure detection data is pressure retention data of the thermal conductivity degradation area under a standard sampling pressure; the detection data of the septum based on the gas chromatograph is used to determine an aging state level of the septum, comprising: determining a sealing performance index of the septum based on the pressure detection data and a standard retention time; determining a hardness drop percentage of the septum based on the hardness detection data and an initial hardness of the septum; determining the aging state level of the septum based on the puncture times of the septum, the sealing performance index of the septum and the hardness drop percentage of the septum.

[0007] Optionally, the step of determining the interference peaks corresponding to the release substances of the septum from the chromatographic data comprises: obtaining a retention time range of the release substances of the septum from a constructed material release substance database based on the aging state level of the septum and the material of the septum; determining a first candidate peak set from the chromatographic data based on the retention time range; performing feature frequency band screening on the first candidate peak set to determine a second candidate peak set; performing peak shape parameter screening on the second candidate peak set to determine a third candidate peak set; wherein the third candidate peak set comprises the interference peaks corresponding to the release substances of the septum.

[0008] Optionally, the step of determining the first candidate peak set from the chromatographic data based on the retention time range comprises: converting the chromatographic data into a frequency domain signal, calculating a baseline drift value of the frequency domain signal after separating periodic fluctuations and noise components; correcting the chromatographic data based on the baseline drift value, and determining the first candidate peak set by performing first-order derivation on the corrected chromatographic signal data.

[0009] Optionally, the step of performing peak shape parameter screening on the second candidate peak set to determine the third candidate peak set comprises: extracting a peak height value, a half-peak width value and a peak shape symmetry parameter of each peak from the second candidate peak set; wherein the peak shape parameters comprise the peak height value, the half-peak width value and the peak shape symmetry parameter; determining the third candidate peak set based on the peak height value, the half-peak width value and the peak shape symmetry parameter of each peak.

[0010] Optionally, before the step of removing the interference peaks corresponding to the release substances of the septum from the chromatographic data, the method further comprises: determining that an extra peak intensity of the interference peak exceeds a preset threshold; wherein the extra peak intensity of the interference peak is calculated by the following steps, comprising: obtaining a peak area value of the interference peak; determining the extra peak intensity of the interference peak based on a ratio of the peak area value of the interference peak to an adjacent normal chromatographic peak area value.

[0011] Optionally, the removing the interference peak corresponding to the release material of the septum from the chromatographic data to generate the first chromatographic optimization data comprises: applying high-pass filtering processing to the chromatographic data to obtain preliminary purification signal data; performing moving window baseline correction on the preliminary purification signal data to eliminate residual baseline drift to obtain baseline correction signal data; replacing the signal in the interference interval by using an interpolation method for the interference peak in the baseline correction signal data to obtain an interference removal signal; and performing high-frequency noise removal processing on the interference removal signal to generate the first chromatographic optimization data.

[0012] Optionally, based on the first chromatographic optimization data and the aging state grade of the septum, a sample material loss compensation value is determined, and the chromatographic peak area value in the first chromatographic optimization data is optimized to generate second chromatographic optimization data, comprising: based on the calculated chromatographic peak area value and the retention time of the chromatographic peak in the first chromatographic optimization data; based on the retention time of the chromatographic peak and the aging state grade of the septum, a preset loss compensation reference table is queried to determine the sample material loss compensation value corresponding to the chromatographic peak; and based on the sample material loss compensation value corresponding to the chromatographic peak, the chromatographic peak area value in the first chromatographic optimization data is optimized to generate second chromatographic optimization data.

[0013] Optionally, the method further comprises: modifying the evaporation temperature setting value according to the optimized chromatographic peak area value, predicting the residence time window of the sample target compound according to the modified evaporation temperature setting value, and optimizing the injection time interval in combination with the residence time window to obtain an optimized injection time interval.

[0014] Optionally, the method further comprises: if the predicted residence time of the sample target compound deviates from the standard range, adjusting the carrier gas flow rate of the chromatographic column to obtain a stable separation efficiency evaluation result.

[0015] The beneficial effects of the present application include: traditional chromatographic data do not consider the interference caused by the release material generated by the aging of the septum, and the sample component loss caused by the aging of the septum, and the present application estimates the aging state grade of the septum through the puncture times of the septum, and determines the release material of the septum and the sample material loss compensation value in combination with the material of the septum and the chromatographic data of the sample, and then realizes double optimization, and improves the purity of the chromatographic data and the accuracy of the quantitative analysis.

[0016] That is, the embodiment of the present application is based on septum aging prediction, and interference peak removal and quantitative correction are linked, which solves the peak shape pollution problem and the quantitative inaccuracy problem caused by aging, and further improves the reliability of the gas chromatographic data and the accuracy of the analysis result. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A step flow chart of a chromatographic data optimization processing method of a gas chromatograph according to an embodiment of the present application is provided in the following;

[0018] Figure 2 A step flow chart of another chromatographic data optimization processing method of a gas chromatograph according to an embodiment of the present application is provided in the following;

[0019] Figure 3 A step flow chart of still another chromatographic data optimization processing method of a gas chromatograph according to an embodiment of the present application is provided in the following. DETAILED DESCRIPTION

[0020] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, and circuits are omitted so as not to obscure the description of the present application with unnecessary detail.

[0021] In addition, in the description of the present application and the appended claims, the terms "first", "second", "third", etc. are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0022] Through a gas chromatograph, different chemical components in a sample can be separated in a chromatographic column and converted into quantifiable signal peaks. However, it is found in research that in the context of high-throughput and long-time operation, slight changes in instrument components can cause significant analysis deviation. For example, the aging problem of the septum, as a key component of the gas chromatograph, has not been fully considered. Existing methods usually assume that the septum performance remains stable within a certain use cycle, and only process samples through a fixed pretreatment process, without dynamically adapting to the changes caused by septum aging, making it difficult to deal with the interference caused by septum aging in complex sample analysis, resulting in gradual accumulation of analysis result deviation.

[0023] And in further research, it is found that with the increase of the number of uses, the septum material gradually decomposes and releases trace amounts of chemicals that form additional interference peaks in the chromatographic analysis, masking or distorting the signal of the target component. More complex is that the decline in the sealing performance of the septum also leads to the loss of light components in the sample in the injection port, which is not a regular change, but after repeated puncture of the septum, it shows a slow and then sharp growth trend. And the decline in sealing performance causes subtle changes in pressure and temperature distribution in the injection port, leading to uneven sample evaporation rates, with some light components lost before entering the chromatographic column, while other components may be thermally decomposed or adsorbed due to prolonged residence time. This phenomenon is manifested in the actual operation as abnormal fluctuations in the chromatographic peak area. For example, when analyzing volatile organic compounds, the peak area of some low-boiling components may be significantly reduced due to the loss of light components, and the appearance of interference peaks further masks the target signal, greatly reducing the accuracy of quantitative analysis. How to optimize the sample pretreatment process to reduce interference peaks and compensate for the loss of light components under the dynamic conditions of septum aging leading to a decline in sealing performance and changes in sample residence time has become a key to improving the quantitative accuracy of gas chromatographic analysis.

[0024] In view of the above problems, the following embodiments are proposed to solve the above technical problems.

[0025] Please refer to Figure 1 The embodiment of the present application provides a chromatographic data optimization processing method of a gas chromatograph, specifically comprising: steps 101-104.

[0026] Step 101: determining the aging state level of the septum based on the detection data of the septum of the gas chromatograph.

[0027] Among them, the detection data at least includes the puncture times of the septum. That is, the puncture times of the septum can be obtained according to the historical use records of the gas chromatograph, and the aging state level of the septum is determined.

[0028] In an embodiment, the aging state level of the septum can be represented by a numerical value, or can be subdivided into normal, slight aging, moderate aging, and severe aging.

[0029] Step 102: determining the release material of the septum based on the aging state level of the septum and the material of the septum.

[0030] Among them, the release material of the septum can be obtained from the constructed material release material database based on the aging state level of the septum and the material of the septum.

[0031] That is, the material release material database can be constructed in advance, which contains different release materials corresponding to different aging state levels of the septum and materials of the septum.

[0032] The materials of the septum may include, but are not limited to, silicone rubber and fluororubber; the substances released by the septum may include, but are not limited to, siloxanes, plasticizers, and antioxidants.

[0033] Step 103: Obtain chromatographic data of the sample by gas chromatography, remove the interfering peaks corresponding to the released substances of the septum from the chromatographic data, and generate the first chromatographic optimization data.

[0034] Then, considering that the septum material gradually decomposes and releases trace amounts of chemicals, which can form additional interfering peaks in chromatographic analysis and mask or distort the signal of the target component, the first step of optimization is to remove the interfering peaks corresponding to the released substances from the chromatographic data.

[0035] Step 104: Based on the first chromatographic optimization data and the aging status level of the septum, determine the sample material loss compensation value, and optimize the chromatographic peak area value in the first chromatographic optimization data to generate the second chromatographic optimization data.

[0036] Next, considering that the decreased sealing performance of the septum would also lead to the loss of light components in the sample inlet, the second optimization method was to determine the sample material loss compensation value based on the first chromatographic optimization data and the aging status level of the septum, and to optimize the chromatographic peak area value in the first chromatographic optimization data.

[0037] In summary, the gas chromatographic data optimization processing method provided in this application has the following beneficial effects, including:

[0038] Traditional chromatographic data do not take into account the interference caused by substances released during septum aging, nor the loss of sample components due to septum aging. This application addresses this issue by estimating the aging status level of the septum through the number of punctures, and by combining the septum material and the chromatographic data of the sample to determine the compensation values ​​for substances released by the septum and the loss of sample material, thereby achieving dual optimization and improving the purity of chromatographic data and the accuracy of quantitative analysis.

[0039] That is, the embodiments of this application are based on septum aging prediction, and link interference peak removal with quantitative correction, which solves both the peak shape contamination problem and the quantitative inaccuracy problem caused by aging, thereby improving the reliability of gas chromatography data and the accuracy of analysis results.

[0040] Optionally, in one embodiment, the detection data further includes septum temperature distribution data, pressure detection data, and hardness detection data. The temperature distribution data is used to determine the thermal conductivity degradation region within the septum; the pressure detection data is the pressure retention data of the thermal conductivity degradation region under standard injection pressure.

[0041] It should be noted that the septum aging has local non-uniformity, such as the frequently punctured area and the edge close to the sample inlet heating area, which will first appear thermal conductivity performance degradation (material thermal conductivity is poor, local temperature is higher than the surrounding) due to long-term high temperature and physical wear. Here, the temperature distribution data can be collected by a thermal imager, and the area where the temperature difference exceeds the threshold (such as 5°C) can be identified, that is, the thermal conductivity degradation area, which is a high-risk area of septum aging, and the sealing performance decreases first. By determining the thermal conductivity degradation area, subsequent pressure testing can avoid covering the entire septum without distinction. That is, if the pressure of the entire septum is directly measured, the normal sealing performance of the non-degradation area will lower the accuracy of the overall sealing evaluation, and only the pressure of the thermal conductivity degradation area can accurately capture the weak link of the real aging of the septum.

[0042] In an embodiment, the thermal conductivity degradation area identification is realized by real-time monitoring of the septum surface temperature distribution by a thermal imager. The thermal imager can collect infrared images of the septum surface at a frequency of 30 frames per second, divide the image into a 5x5 grid area, and calculate the temperature value of the center point of each grid. When the temperature difference between adjacent grids exceeds 5 degrees Celsius, it is determined that the internal structure of the material in the area has changed, the heat conduction path is blocked, and it is marked as a thermal conductivity degradation area.

[0043] Please refer to Figure 2 , accordingly, the above step of determining the aging state grade of the septum based on the septum detection data of the gas chromatograph can specifically include steps 201-203.

[0044] Step 201: Determine the sealing performance index of the septum based on the pressure detection data and the standard holding time.

[0045] Specifically, the sealing performance index can be equal to the pressure detection data divided by the standard holding time, such as a standard holding time of 30s and a pressure detection data of 18s, and the sealing performance index is equal to 0.6. It should be noted that the standard holding time is the pressure holding time of the new septum under the same standard sample pressure, which can be determined in advance by experiment as the benchmark of unaged sealing performance.

[0046] Step 202: Determine the hardness drop percentage of the septum based on the hardness detection data and the initial hardness of the septum.

[0047] In one possible implementation, hardness detection can use a Shore hardness tester to uniformly select 9 test points on the surface of the septum to form a 3x3 test matrix. Each test point is applied with the same pressure for 3 seconds, and then the hardness value is read and compared with the initial hardness value of the septum to calculate the drop percentage.

[0048] It should be noted that the hardness reduction percentage reflects the severity of the metamorphic spacer material, the higher the percentage, the more elastic loss and the greater the brittleness of the material, such as a percentage > 40% means that the material has been seriously aged and is prone to cracking during puncture.

[0049] Step 203: Determine the aging state level of the spacer based on the number of punctures of the spacer, the sealing performance index of the spacer, and the hardness reduction percentage of the spacer.

[0050] Finally, the aging state level of the spacer is determined based on the number of punctures of the spacer, the sealing performance index of the spacer, and the hardness reduction percentage of the spacer.

[0051] In an embodiment, the aging state level of the spacer can be divided by the following examples, including:

[0052] Normal: puncture times ≤ 30 times; sealing performance index range: 0.9-1.0; hardness reduction percentage: ≤ 10%.

[0053] Mild aging: puncture times ≤ 50 times; sealing performance index range: 0.7-0.89; hardness reduction percentage: 10%-20%;

[0054] Moderate aging: puncture times ≤ 51-200 times; sealing performance index range: 0.4-0.69; hardness reduction percentage: 21%-40%.

[0055] Severe aging: puncture times > 200 times; sealing performance index range: < 0.4; hardness reduction percentage: > 40%.

[0056] It should be noted that the above is only an example for reference and does not limit the numerical range. In addition, in actual application, when there is an intersection between data, priority judgment can also be made according to the importance of the parameters.

[0057] In summary, the embodiments of the present application propose the detection of temperature, pressure, and hardness data, which evaluates from three dimensions of physical wear (puncture times), functional failure (sealing performance), and material metamorphism (hardness), covering all key performances of spacer aging, avoiding misjudgment of aging state level due to single parameter, and making the aging state level more consistent with the actual use state of the spacer. At the same time, in the present application, the thermal conductivity degradation area is first determined by the temperature distribution data, which can avoid subsequent pressure testing covering the entire spacer without distinction. That is, if the pressure of the entire spacer is directly measured, the normal sealing performance of the non-degradation area will lower the accuracy of the overall sealing evaluation, and only the pressure of the thermal conductivity degradation area is measured, which can accurately capture the weak link of the real aging of the spacer, further improving the accuracy of the level determination.

[0058] Please refer to Figure 3Optionally, the step of determining the interference peak corresponding to the release substance of the gasket from the chromatographic data can further include steps 301-304.

[0059] Step 301: Based on the aging state level of the gasket and the material of the gasket, the retention time range of the release substance of the gasket is obtained from the constructed material release substance database.

[0060] The pre-constructed material release substance database records the release substance type and the retention time range corresponding to the gaskets of different aging levels and different materials, and the material release substance database can be queried.

[0061] Step 302: Based on the retention time range, the first candidate peak set is determined from the chromatographic data.

[0062] The first candidate peak set can exclude normal peaks that do not match the retention time at all, and initially narrow the interference peak screening range.

[0063] Step 303: The first candidate peak set is subjected to characteristic frequency band screening to determine the second candidate peak set.

[0064] The common frequency band can be verified by experiment in advance, such as 0.3-0.8 Hz as the screening reference, and the peaks concentrated in the 0.3-0.8 Hz characteristic frequency band are screened to generate the second candidate peak set.

[0065] Step 304: The second candidate peak set is subjected to peak shape parameter screening to determine the third candidate peak set; wherein the third candidate peak set includes the interference peak corresponding to the release substance of the gasket.

[0066] Then, the third candidate peak set can be determined based on the peak shape parameter screening, and the interference peak corresponding to the release substance of the gasket is finally locked, and the sample substance peak with the same time and frequency band but normal peak shape is excluded.

[0067] As can be seen, the present application narrows the range layer by layer through three levels of screening of retention time, characteristic frequency band, and peak shape parameter; the retention time first defines the preliminary range, then verifies whether the frequency attribute is interference through the characteristic frequency band, and the peak shape parameter locks the unique tailing characteristics of the interference peak. The three can effectively distinguish normal peaks and interference peaks.

[0068] Optionally, the above step of determining the first candidate peak set from the chromatographic data based on the retention time range includes: converting the chromatographic data into a frequency domain signal, calculating the baseline drift value of the frequency domain signal after separating the periodic fluctuation and noise components; correcting the chromatographic data based on the baseline drift value, and performing first-order derivation according to the corrected chromatographic signal data to determine the first candidate peak value set.

[0069] It should be noted that the above steps core solve the problem that the original chromatographic data contains noise and baseline drift, which leads to inaccurate peak interval positioning. Specifically, through four steps of frequency domain denoising, baseline correction, derivative positioning, and time filtering, the first candidate peak interval within the retention time range is accurately extracted.

[0070] First, separate the effective periodic fluctuations (target peaks and interference peaks) from the invalid random noise in the original chromatographic data (time domain signal) to avoid noise interference with subsequent baseline drift calculation and peak identification. Then, the moving average method can be used to calculate the baseline drift value: set a sliding window with a width of 30 data points, calculate the signal mean value in each window, and take the mean value as the baseline drift value of the center time point of the window; continuously calculate the baseline drift values of all time points to form a baseline drift curve, reflecting the baseline offset law of the original chromatographic data over time. Then, baseline correction can be performed by subtracting the baseline drift curve from the time domain signal of the original chromatographic data to obtain the baseline-corrected chromatographic signal data. After baseline correction, the five-point difference formula is used to calculate the first derivative value of each time point, the key nodes of the peaks are determined by the sign change of the first derivative value, and the peak interval set of the full spectrum is formed. Then, the candidate peaks with retention time within the characteristic range of the release substance of the septum can be selected from the full spectrum peak interval, i.e., the determination of the first candidate peak set.

[0071] In summary, this method can solve the problem of inaccurate baseline drift calculation caused by noise in the original data, and improve the accuracy of baseline correction. At the same time, by positioning the peak interval through the first derivative, the problem of peak boundary ambiguity caused by baseline drift is solved.

[0072] Optionally, the above steps perform peak shape parameter screening on the second candidate peak set to determine a third candidate peak set, including: extracting the peak height value, half-peak width value, and peak shape symmetry parameter of each peak from the second candidate peak set; wherein the peak shape parameters include peak height value, half-peak width value, and peak shape symmetry parameter; based on the peak height value, half-peak width value, and peak shape symmetry parameter of each peak, the third candidate peak set is determined.

[0073] It should be noted that the peak height value reflects the signal intensity of the peak; the half-peak width value reflects the separation purity and tailing trend of the peak; the septum release substance (such as siloxane, plasticizer) has strong interaction with the chromatographic column stationary phase, which causes slow desorption of the peak tail, resulting in tailing, and the peak shape symmetry parameter is much greater than 1.0. Therefore, the three types of peak shape parameters are used together to identify interference peaks in the embodiments of the present application.

[0074] In the embodiments of the present application, the peak height (intensity), half-peak width (separation degree), and symmetry (morphological nature) are combined, and the symmetry parameter > 1.5 is used as the core threshold, supplemented by the deviation verification of the peak height and half-peak width, which can effectively distinguish normal peaks and interference peaks that resemble interference peaks.

[0075] Optionally, before removing the interference peak corresponding to the release substance of the septum from the chromatographic data, the method further comprises: determining that the additional peak intensity of the interference peak exceeds a preset threshold.

[0076] Wherein, the additional peak intensity of the interference peak is calculated by the following steps, including: obtaining the peak area value of the interference peak; determining the additional peak intensity of the interference peak based on the ratio of the peak area value of the interference peak to the adjacent normal chromatographic peak area value.

[0077] It should be noted that the additional peak intensity of the interference peak in this application reflects the relative influence weight of the interference peak on the adjacent normal peak through the relative area ratio. Then, based on the preset threshold, it is determined whether the interference peak needs to be removed.

[0078] For example, if the additional peak intensity > preset threshold (such as 5%), it means that the interference peak has significantly affected the qualitative (peak shape overlap) or quantitative (signal superposition leading to high results) of the adjacent normal peak, and the subsequent removal operation needs to be performed.

[0079] If the additional peak intensity ≤ preset threshold: it means that the influence of the interference peak on the normal peak can be ignored (lower than the allowed error of the method), and there is no need to remove it to avoid introducing baseline fluctuation due to excessive processing.

[0080] In the above process, the setting of the additional peak intensity threshold can be based on the statistical rules of historical analysis data. By analyzing a large amount of chromatographic data of normal samples, the average signal intensity of the target compound peak is determined. When the interference peak intensity generated by the release substance of the septum exceeds 15% of the average intensity of the target peak, it is determined that signal purification treatment is needed to avoid the significant influence of the interference peak on the quantitative analysis results.

[0081] That is, the above-mentioned method can quantify the interference effect by relative area ratio, avoiding the disadvantages of absolute removal and excessive removal. It makes up for the defects of traditional identification and removal mode, and improves the accuracy, reliability and flexibility of data optimization.

[0082] The determination process of the interference peak will be described in detail below.

[0083] According to the aging state grade of the sample inlet septum, the preset septum material release substance database is queried, and the database records the characteristic retention time range of siloxane substances, plasticizer substances and antioxidant substances corresponding to different aging grades.

[0084] The original chromatographic signal data of the current sample analysis is collected, the time domain signal is converted into the frequency domain signal by Fourier transform, the periodic fluctuation component and the random noise component in the signal are separated, and the background noise spectrum distribution data is obtained. For the low frequency component in the background noise spectrum distribution data, the moving average method is used to calculate the drift value of the chromatographic baseline in different time windows, the original chromatographic signal is corrected according to the baseline drift value, the first derivative value of each time point is calculated on the corrected signal, the peak vertex position is identified through the turning point of the first derivative sign from positive to negative, and the first derivative from zero to positive and from negative to zero are marked as the peak starting point and the ending point respectively, to obtain the first candidate peak set.

[0085] Then the first candidate peak set is screened by the characteristic frequency band, and the second candidate peak set is determined.

[0086] Then the peak height value, the half-peak width value and the peak shape symmetry parameter of the interval are extracted from the second candidate peak set, the peak area is estimated by the product of the peak height value and the half-peak width value, and whether it is an interference peak produced by the release of the spacer material is judged according to the peak shape symmetry parameter. For the signal interval determined as an interference peak, the peak value accurate height is determined by fitting the peak shape curve using the least square method, the interference peak area value is calculated by integration, the interference strength coefficient is equal to the interference peak area value divided by the adjacent normal chromatographic peak area value, the retention time position and the interference strength coefficient of the interference peak are recorded, and the specific position and strength value of the additional peak of the release of the spacer material are obtained.

[0087] Specifically, the spacer material release substance database is established by long-term experimental data accumulation. The characteristic substances released by the sample inlet spacers of different brands and different materials at each aging stage are collected, the chemical components are determined by gas chromatography-mass spectrometry analysis, the retention time interval of siloxane substances is recorded in the interval of 5.2 to 8.7 minutes, the retention time interval of plasticizer substances is recorded in the interval of 12.3 to 15.8 minutes, and the retention time interval of antioxidant substances is recorded in the interval of 18.5 to 22.1 minutes. The core of Fourier transform processing of chromatographic signal is frequency domain feature separation.

[0088] Exemplarily, the original chromatogram data contains three types of components, i.e. target compound peak, interference peak caused by septum release substance and random noise, which have significant differences in frequency characteristics. Fourier transform converts time domain signal into frequency domain signal. In the frequency domain, the target compound peak appears as a low frequency component, with a frequency range of 0.01 to 0.5 Hz; the interference caused by the septum release substance has specific frequency characteristics, which are concentrated in the range of 0.3 to 0.8 Hz; and the random noise is mainly distributed in the high frequency region, more than 1 Hz. By setting a frequency window function, the frequency components in the range of 0.3 to 0.8 Hz are separated, and this part of the signal mainly reflects the interference characteristics of the septum release substance. The power spectral density of the separated frequency components is analyzed, and the energy distribution of each frequency component is calculated. When the power spectral density of a certain frequency component exceeds 1.5 times the average value, the time interval corresponding to the frequency is marked as a potential interference region. Baseline drift is particularly evident in long-term chromatographic analysis, mainly caused by temperature fluctuations and changes in carrier gas flow rate. The moving average method uses a sliding window with a width of 30 data points, and the signal mean value in each window is calculated as the baseline value at that time point. The continuous baseline values form a baseline drift curve.

[0089] In one possible implementation, the derivative analysis method realizes peak recognition through numerical differentiation. For each data point of the corrected chromatogram signal, the first derivative value is calculated using the five-point difference formula, i.e. the signal values of the current point and the two points before and after it are used to calculate a weighted average. The first derivative reflects the rate of change of the signal, which is positive at the rising edge of the peak, negative at the falling edge, and zero at the peak top. By monitoring the sign change of the first derivative, the starting point, the vertex and the end point of the peak interval are accurately located, forming a complete peak profile description.

[0090] Specifically, the retention time matching adopts a tolerance window mechanism. Considering the influence of instrument state and environmental factors, the actual retention time may deviate from the standard value, and a tolerance window of ±0.2 minutes is set. When the retention time of the candidate peak falls within the characteristic time range recorded in the database and its tolerance window, it is preliminarily determined as a potential interference peak.

[0091] For example, the peak shape parameter extraction process involves multi-dimensional feature analysis. The peak height value is obtained by subtracting the baseline value from the peak top signal intensity; the half-peak width value is measured at half the peak height, reflecting the chromatographic separation effect; and the peak symmetry parameter is determined by calculating the slope ratio of the front and back edges of the peak. The symmetry parameter of a normal chromatographic peak is close to 1.0, while the interference peak caused by the septum release substance often shows obvious tailing, with a symmetry parameter greater than 1.5. The interference peak determination result is verified by peak area estimation, and the area under the peak is calculated using the trapezoidal integration method. When the deviation between the estimated area and the standard substance peak area exceeds 20%, it is confirmed as an interference peak.

[0092] In an embodiment, the least square fitting determines the optimal peak shape parameters by iterative optimization. A Gaussian function is selected as the fitting model, and the residual sum of squares between the fitting curve and the measured data is minimized by adjusting three parameters of peak height, peak position and peak width. The fitting process uses the Levenberg-Marquardt algorithm for nonlinear optimization, and converges after 5 to 10 iterations to obtain accurate peak height and peak shape description.

[0093] In an embodiment, the interference intensity coefficient (the additional peak intensity of the interference peak) directly reflects the degree of influence of the release substance of the spacer on the analysis result. When the interference intensity coefficient exceeds 0.1, it indicates that the area of the interference peak reaches more than 10% of the normal peak, and signal correction is required; the coefficient is between 0.05 and 0.1, the influence is smaller but still needs to be recorded; the coefficient is lower than 0.05, the influence can be ignored.

[0094] Optionally, the above step removes the interference peak corresponding to the release substance of the spacer from the chromatographic data to generate the first chromatographic optimization data, including: applying high-pass filtering to the chromatographic data to obtain preliminary purification signal data; performing moving window baseline correction on the preliminary purification signal data to eliminate residual baseline drift to obtain baseline corrected signal data; replacing the interference interval signal with an interpolation method for the interference peak in the baseline corrected signal data to obtain interference removed signal; and performing high frequency noise removal processing on the interference removed signal to generate the first chromatographic optimization data.

[0095] That is, based on the frequency characteristics of the interference peak of the release substance of the spacer (confirmed by the material release substance database), the cutoff frequency of the high-pass filter (such as 0.5 Hz) can be set to ensure that the highest frequency of the interference peak is covered and the target peak is not damaged, so that the preliminary purification signal data is obtained. Then, the moving window method eliminates the interference of drift on peak shape judgment by dynamically fitting the baseline, ensuring the accuracy of subsequent interpolation replacement. Then, the interpolation method is used to replace the interference signal with simulated normal signal to avoid signal discontinuity caused by traditional one-size-fits-all deletion. Finally, high frequency noise removal processing is performed on the interference removed signal to generate the first chromatographic optimization data.

[0096] It can be seen that the embodiments of the present application realize the balance between precise interference removal and signal fidelity through a four-step coordinated process, thereby improving data quality, ensuring analysis reliability, and adapting to actual application needs.

[0097] Next, the removal process of the interference peak is described in detail.

[0098] First, the sampling frequency and signal length parameters of the chromatographic signal are obtained. A high-pass filter is designed based on the frequency characteristics of the interference peak. The cutoff frequency of the filter is set to the product of the main frequency of the interference signal and a preset coefficient. The chromatographic signal is processed by forward filtering followed by reverse filtering to eliminate the phase shift during the filtering process and obtain preliminary purified signal data.

[0099] For the initial purified signal data, a moving window method is used to identify the long-term drift trend of the baseline. Within each time window, a polynomial curve is fitted using the least squares method as the local baseline value. The baseline function can be expressed as:

[0100] ;

[0101] In this method, Indicates the first The polynomial baseline function within a time window Represents a time variable. Indicates the first The polynomial in the nth window coefficient of the secondary term, The polynomial represents the highest degree, and this formula describes the mathematical expression of fitting a local baseline using a polynomial function within each moving window. Smoothing is performed based on the difference in baseline values ​​between adjacent windows to obtain a continuous global baseline curve. Subtracting the global baseline curve from the initial purified signal data yields the baseline-corrected signal data. Based on the temporal position of residual interference peaks in the baseline-corrected signal data, a predetermined number of data points are extracted before and after the start and end points of the interference peaks. An interpolation method is used to estimate the signal trajectory within the interference peak interval based on the extracted data points. An interpolation curve is determined by maintaining the continuity of the signal value and first derivative at the interpolation point. The interpolation curve replaces the signal value within the interference peak interval, resulting in the interference-removed signal. For the high-frequency noise components in the interference-removed signal, multi-scale decomposition separates the signal into components at different frequency levels. Thresholding is applied to the high-frequency components to reduce noise amplitude, while retaining the target compound peak characteristics in the low-frequency components. The processed components are reconstructed to obtain the pure signal peak of the target compound in the sample, generating the first chromatographic optimization data.

[0102] In the above process, the high-pass filter adopts the Butterworth type frequency response characteristic, and the cutoff frequency is determined according to the spectrum analysis result of the interference signal. The main frequency of the interference signal is obtained by fast Fourier transform, and the cutoff frequency is set to the main frequency value multiplied by a coefficient of 0.8, which ensures the removal of low-frequency interference components while retaining the high-frequency characteristics of the target compound. Forward filtering processing will introduce phase delay, causing the peak position of the chromatogram to shift. By time reversing the signal after forward filtering, passing it through the same filter again, and then time reversing it again, zero-phase filtering is achieved. This bidirectional filtering method makes each frequency component experience the same positive and negative phase shift, and the final phase shift cancels each other out. The filtered signal keeps the original peak position unchanged, and only eliminates the baseline drift and interference components of low frequency. In the preliminary purification signal data, the amplitude of the interference peak is reduced to less than 30% of the original intensity, while the shape and position of the target compound peak remain unchanged.

[0103] In the above process, the baseline drift is mainly caused by the slow change of the injection port temperature and the slight fluctuation of the carrier gas pressure, which shows a slow upward or downward trend of the overall signal in long-time running chromatographic analysis, affecting the accurate calculation of peak area.

[0104] In the above process, the moving window method realizes global baseline correction through local baseline estimation. The specific process includes setting the window width to 50 data points, starting from the beginning of the signal, and moving 10 data points each time to form overlapping windows. In each window, a cubic polynomial is fitted using the least squares method, and the polynomial coefficients are obtained by solving the normal equations. The baseline values of adjacent windows differ in the overlapping area, and are smoothed by weighted averaging method, and the weight coefficient is determined according to the distance from the window center. The global baseline curve is formed by connecting each local baseline value, showing a smooth and continuous change trend. After subtracting the global baseline curve from the preliminary purification signal, the baseline-corrected signal in the non-peak area tends to zero, eliminating the systematic deviation.

[0105] In the above process, the selection of the interpolation method needs to consider the smoothing characteristics and continuity requirements of the chromatographic peak. The cubic spline interpolation, due to its second derivative continuity, can accurately simulate the natural shape of the chromatographic peak, and becomes the ideal choice for interference peak replacement. Further, the implementation of the interpolation constraint condition involves the reasonable setting of the boundary condition. 20 data points are extracted before and after the starting point and the ending point of the interference peak, respectively, which represent the normal signal not affected by the interference. The interpolation function not only ensures the equality of the function value at the boundary point, but also ensures the continuity of the first derivative, ensuring the smooth connection of the interpolation curve with the original signal. By solving the linear equation set with constraints, the coefficients of the interpolation polynomial are determined, and the interpolation curve obtained presents a natural peak shape change in the interference peak interval. Multi-scale decomposition uses wavelet transform to realize the frequency separation of the signal. By selecting a wavelet basis function with tight support characteristics, the signal is decomposed into different scale detail coefficients and approximation coefficients. The detail coefficients correspond to the high-frequency noise components, and the approximation coefficients retain the low-frequency peak shape characteristics.

[0106] It can be understood that the threshold processing realizes noise reduction through a soft threshold function. When the absolute value of the detail coefficient is less than the set threshold, it is set to zero; when it is greater than the threshold, its amplitude is scaled down in proportion. The threshold is determined according to the estimated value of the noise level, and is usually taken as 1.4826 times the median of the finest scale detail coefficient. The processed coefficient is reconstructed by inverse wavelet transform to obtain a pure signal peak that retains the characteristic information of the target compound, and the noise level is reduced to less than 20% of the original signal, realizing the elimination of the interference of the release substance of the spacer.

[0107] Optionally, based on the first chromatographic optimization data and the aging state level of the spacer, a sample material loss compensation value is determined, and the chromatographic peak area value in the first chromatographic optimization data is optimized to generate second chromatographic optimization data, which can specifically include: based on the calculated chromatographic peak area value and the retention time of the chromatographic peak in the first chromatographic optimization data; based on the retention time of the chromatographic peak and the aging state level of the spacer, querying a preset loss compensation reference table to determine the sample material loss compensation value corresponding to the chromatographic peak; based on the sample material loss compensation value corresponding to the chromatographic peak, optimizing the chromatographic peak area value in the first chromatographic optimization data to generate second chromatographic optimization data.

[0108] It should be noted that the time coordinates of the peak start point and the peak end point are identified from the pure signal peak of the sample target compound, the peak retention time is determined by the time coordinate of the peak vertex, the initial peak area value and the corresponding retention time value are obtained by using the numerical integration method to accumulate the product of the signal intensity value and the time interval of each sampling point in the interval from the peak start point to the peak end point, the loss compensation control table is queried according to the retention time value and the aging state grade of the septum, the loss coefficient of each retention time period of the compound under different aging grades is recorded in the control table, the loss compensation coefficient corresponding to the current aging state can be obtained by linear interpolation between the loss coefficients of adjacent aging grades, and the loss compensation coefficient of the sample material reflects the loss degree of the light component in the sample inlet. The initial peak area value is corrected by using the sample material loss compensation coefficient, the corrected peak area is equal to the initial peak area divided by the difference between one and the compensation coefficient, if the deviation of the corrected peak area and the average peak area of the same compound in the historical analysis record exceeds the preset threshold, the compensation coefficient is adjusted according to the deviation ratio, and the adjusted chromatographic peak area value is obtained.

[0109] In the above process, the peak start point is marked when the intensity starts to rise from the baseline level, and the peak end point is marked when the intensity falls back to the baseline level, and the peak vertex corresponds to the time coordinate of the maximum signal intensity.

[0110] In the above process, the core of the numerical integration method for calculating the peak area is the accumulation processing of the discrete data points.

[0111] In the above process, the chromatographic signal is recorded at a fixed sampling rate, and each sampling point contains a time stamp and a signal intensity value. From the peak start point, the average signal intensity of the adjacent two sampling points is taken as the representative intensity of this time period, and the time interval is multiplied to obtain the area contribution value of this section. All area contribution values from the start point to the end point are accumulated to obtain the initial peak area value. This trapezoidal integration method has higher precision than the rectangular integration method, especially in the area where the peak shape changes rapidly.

[0112] In the above process, the preset loss compensation control table is established by a large amount of experimental data, and the loss rules of various compounds under different aging degrees are recorded. The loss coefficient of light alkanes is 0.05 at the beginning of aging, and can reach 0.25 at the later stage of aging; the loss coefficient of aromatic compounds is relatively small.

[0113] In the above process, the linear interpolation calculates the compensation coefficient in proportion according to the position of the current aging state between the adjacent two standard aging grades. If the current aging state is between light and moderate, and is closer to moderate, the compensation coefficient is closer to the loss coefficient value corresponding to moderate aging.

[0114] In the above process, the correction formula is based on the principle of mass conservation. The amount of sample actually entering the chromatographic column is equal to the initial sample amount minus the loss amount. Therefore, the corrected peak area needs to be divided by the retention ratio. Further, comparing with historical analysis records can verify the rationality of the compensation. When the same batch of samples is analyzed under different aging conditions, the peak area after compensation correction should remain relatively stable. If the deviation exceeds 15%, the compensation coefficient needs to be adjusted for recalculation.

[0115] Optionally, after generating the second chromatographic optimization data, the method further comprises: modifying the evaporation temperature setting value according to the optimized chromatographic peak area value, predicting the residence time window of the target compounds of the sample according to the modified evaporation temperature setting value, and optimizing the injection time interval in combination with the residence time window to obtain an optimized injection time interval.

[0116] Specifically, the relative content percentage of each volatile component is calculated according to the adjusted chromatographic peak area value. By comparing the content data of the same component in continuous multiple analyses, the content difference value and the time interval of adjacent two analyses are calculated to obtain the content change rate. The change trend of the content decrease of light components and the content increase of heavy components is identified. The heating temperature time series data recorded by the temperature sensor of the sample pretreatment device is extracted. According to the loss rate of light components in the content change trend, in combination with the average temperature value in the heating temperature time series data, the relationship coefficient between the loss rate and the temperature is determined by linear fitting. If the loss rate of light components exceeds the preset threshold, the evaporation temperature setting value is reduced according to the relationship coefficient. According to the modified temperature value and the boiling point difference of each target compound, the relative volatility of each compound at the temperature is calculated. The relative volatility and the carrier gas flow rate parameter are used to calculate the dead time according to the length of the chromatographic column divided by the carrier gas linear velocity. The retention time is predicted by the product of the partition coefficient of each compound and the dead time. The partition coefficient is determined according to the polarity of the compound and the type of the stationary phase of the chromatographic column. The start and end time of the residence time window of each target compound is obtained. According to the width of the residence time window and the time interval of adjacent compound windows, the separation index value is calculated. The separation degree is equal to the difference between the retention times of two adjacent peaks divided by the average peak width. If the separation degree is lower than the preset standard value, the injection time interval is increased to delay the analysis start time of the subsequent batch of samples, and the optimized injection time interval parameter is obtained.

[0117] In the above process, the dynamic monitoring of the content of volatile components is the basis for realizing adaptive temperature control.

[0118] In the above process, by continuously collecting chromatographic analysis data of multiple batches of samples, a time series record of component content is established. Each data point contains analysis time, component name and peak area correction value. These data reflect the cumulative effect of sample loss during the septum aging process.

[0119] In the above process, the calculation of the content change rate involves the difference processing of time series data.

[0120] In the above process, for a certain volatile component such as n-hexane, its percentage content at the nth analysis is denoted as Cn, and the percentage content at the (n-1)th analysis is denoted as Cn-1, and the time interval between the two analyses is Δt hours.

[0121] In the above process, the content change rate is equal to (Cn-Cn-1) / Δt, with units of percentage per hour. When the change rates of three consecutive data points are all negative, it is determined that the component presents a loss trend. The relationship between the loss rate and the temperature is obtained by collecting loss rate data at different pretreatment temperatures, and the linear relationship coefficient is fitted by the least squares method, which represents the increase in the loss rate when the temperature increases by 1 degree Celsius. The linear relationship coefficient can be determined by the following formula:

[0122] ;

[0123] In the formula, represents the linear relationship coefficient obtained by least squares fitting, represents the total number of data points, represents the th temperature data point, represents the th loss rate data point, represents the average value of the temperature data, represents the average value of the loss rate data. When the loss rate of light components such as toluene exceeds the threshold value of 0.5% per hour, the temperature value that needs to be reduced is calculated according to the relationship coefficient, so that the loss rate falls back to an acceptable range. This real-time data-based temperature adjustment avoids excessive loss caused by fixed temperature settings.

[0124] In the above process, after the temperature adjustment, the volatile characteristics of each component need to be re-evaluated. The modified evaporation temperature affects the vapor-liquid equilibrium state of different components in the sample, and thus changes their rate of entering the chromatographic column and concentration distribution.

[0125] Optionally, the method further comprises adjusting the carrier gas flow rate of the chromatographic column if the predicted residence time of the target compound of the sample deviates from the standard range, to obtain stable separation efficiency evaluation results.

[0126] If the predicted sample target compound retention time deviates from the standard range, calculate the deviation percentage of the actual retention time from the standard value, according to the inverse relationship between retention time and carrier gas flow rate, when the deviation is positive, reduce the flow rate, when the deviation is negative, increase the flow rate, adjust the opening degree of the carrier gas flow rate control valve by half of the deviation percentage, obtain the adjusted carrier gas flow rate value. Run the chromatographic separation using the adjusted carrier gas flow rate value, monitor the peak time of adjacent compound peaks in real time, obtain the peak distance data by subtracting the later peak time from the earlier peak time, measure the peak width at 10% of the peak height, calculate the ratio of the distance from the peak top to the peak front to the distance from the peak top to the peak back, which is the peak shape symmetry factor. According to the peak distance data, judge whether the adjacent peaks reach baseline separation, according to the peak shape symmetry factor, judge the quality of the peak shape, if the symmetry factor is within the preset range and the peak distance meets the separation requirement, record the peak distance and symmetry factor at the current flow rate as the separation efficiency evaluation result, if it does not meet the requirement, continue to adjust the flow rate in the deviation direction until a stable state is reached.

[0127] In the above process, when the actual retention time of the target compound deviates from the expected value, the flow rate of the carrier gas can be adjusted to achieve rapid correction, avoiding peak overlap and separation degree decline.

[0128] In the above process, the physical basis of the inverse relationship between carrier gas flow rate and retention time.

[0129] In the above process, when the carrier gas flow rate increases from 30 ml / min to 35 ml / min, the linear velocity of the compound in the chromatographic column increases, and the retention time correspondingly shortens. The calculation of the deviation percentage uses the relative deviation formula, i.e. actual value minus standard value, divided by standard value, multiplied by 100%. According to the positive and negative nature of the deviation, determine the adjustment direction, positive deviation indicates that the retention time is too long and the flow rate needs to be increased, negative deviation is the opposite. Use half of the deviation percentage as the adjustment amplitude to avoid excessive adjustment causing system oscillation.

[0130] In the above process, the measurement of the peak shape symmetry factor is carried out at 10% of the peak height. By measuring the horizontal distance a from the peak front to the peak top and the horizontal distance b from the peak top to the peak back at this height, calculate the ratio b / a to obtain the symmetry factor. The ideal Gaussian peak symmetry factor is 1.0, and the actual chromatographic peak is usually between 0.8 and 1.2 due to the influence of column efficiency and dead volume.

[0131] For example, the judgment standard of baseline separation is that the peaks return to the baseline level between adjacent peaks, at which time the peak distance is greater than the sum of the widths of the two peaks. When the symmetry factor deviates from the ideal range or the peak distance is insufficient, the flow rate is automatically adjusted and re-evaluated. Further, through repeated flow rate fine-tuning and effect evaluation, the ideal separation conditions are gradually approached, realizing the dynamic optimization of separation efficiency and improving the reliability of analysis.

[0132] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of optimizing processing of chromatographic data of a gas chromatograph, characterized by, The method comprises the following steps: determining the aging state level of the septum based on the detection data of the septum by a gas chromatograph; wherein the detection data at least includes the puncture times of the septum; determining the release substance of the septum based on the aging state level of the septum and the material of the septum; obtaining the chromatographic data of the sample analyzed by the gas chromatograph, removing the interference peak corresponding to the release substance of the septum from the chromatographic data, and generating first chromatographic optimization data; determining the sample material loss compensation value based on the first chromatographic optimization data and the aging state level of the septum, and optimizing the chromatographic peak area value in the first chromatographic optimization data to generate second chromatographic optimization data; wherein the detection data further includes temperature distribution data, pressure detection data and hardness detection data of the septum; wherein the temperature distribution data is used to determine the thermal conductivity degradation area in the septum; the pressure detection data is the pressure retention data of the thermal conductivity degradation area under the standard sampling pressure; determining the aging state level of the septum based on the detection data of the septum by a gas chromatograph, comprising: determining the sealing performance index of the septum based on the pressure detection data and the standard retention time; determining the hardness drop percentage of the septum based on the hardness detection data and the initial hardness of the septum; determining the aging state level of the septum based on the puncture times of the septum, the sealing performance index of the septum and the hardness drop percentage of the septum.

2. The method of claim 1, wherein the method further comprises: The step of determining the interference peak corresponding to the release substance of the septum from the chromatographic data comprises: based on the aging state level of the septum and the material of the septum, obtaining the retention time range of the release substance of the septum from the constructed material release substance database; determining a first candidate peak set from the chromatographic data based on the retention time range; performing feature band screening on the first candidate peak set to determine a second candidate peak set; performing peak shape parameter screening on the second candidate peak set to determine a third candidate peak set; wherein the third candidate peak set includes the interference peak corresponding to the release substance of the septum.

3. The method of claim 2, wherein the method further comprises: The step of determining a first candidate peak set from the chromatographic data based on the retention time range comprises: transforming the chromatographic data into a frequency domain signal, calculating the baseline drift value of the frequency domain signal after separating the periodic fluctuation and noise components; correcting the chromatographic data based on the baseline drift value, performing first-order derivation based on the corrected chromatographic signal data to determine the first candidate peak value set.

4. The method of claim 2, wherein the method further comprises: The step of performing peak shape parameter screening on the second candidate peak set to determine a third candidate peak set comprises: extracting the peak height value, half-peak width value and peak shape symmetry parameter of each peak from the second candidate peak set; wherein the peak shape parameter includes the peak height value, half-peak width value and peak shape symmetry parameter; determining the third candidate peak set based on the peak height value, half-peak width value and peak shape symmetry parameter of each peak.

5. The method of claim 2, wherein the method further comprises: Before removing the interference peak corresponding to the release substance of the septum from the chromatographic data, the method further comprises: determining that the extra peak intensity of the interference peak exceeds a preset threshold value; wherein the extra peak intensity of the interference peak is calculated by the following steps, comprising: obtaining a peak area value of the interference peak; determining the extra peak intensity of the interference peak based on a ratio of the peak area value of the interference peak to an adjacent normal chromatographic peak area value.

6. The method of claim 1, wherein the method further comprises: The removing the interference peak corresponding to the release substance of the septum from the chromatographic data to generate the first chromatographic optimization data comprises: applying high-pass filtering processing to the chromatographic data to obtain preliminary purification signal data; performing moving window baseline correction on the preliminary purification signal data to eliminate residual baseline drift to obtain baseline correction signal data; using an interpolation method to replace the interference interval signal for the interference peak in the baseline correction signal data to obtain interference removal signal; performing high-frequency noise removal processing on the interference removal signal to generate the first chromatographic optimization data.

7. The method of claim 1, wherein the method further comprises: Based on the first chromatographic optimization data and the aging state grade of the septum, a sample material loss compensation value is determined, and the chromatographic peak area value in the first chromatographic optimization data is optimized to generate second chromatographic optimization data, comprising: calculating the chromatographic peak area value and the retention time of the chromatographic peak based on the first chromatographic optimization data; querying a preset loss compensation reference table based on the retention time of the chromatographic peak and the aging state grade of the septum to determine the sample material loss compensation value corresponding to the chromatographic peak; optimizing the chromatographic peak area value in the first chromatographic optimization data based on the sample material loss compensation value corresponding to the chromatographic peak to generate the second chromatographic optimization data.

8. The method of claim 1, wherein the method further comprises: The method further comprises: modifying the evaporation temperature setting value according to the optimized chromatographic peak area value, predicting the residence time window of the sample target compound according to the modified evaporation temperature setting value, and optimizing the injection time interval in combination with the residence time window to obtain an optimized injection time interval.

9. The method for optimizing chromatographic data of a gas chromatograph according to claim 8, characterized in that, The method further comprises: if the predicted residence time of the sample target compound deviates from the standard range, adjusting the carrier gas flow rate of the chromatographic column to obtain a stable separation efficiency evaluation result.

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