Chromatographic data optimization processing method of gas chromatograph

Abstract

The invention relates to a chromatographic data optimization processing method of a gas chromatograph, which belongs to the technical field of data analysis, and comprises the following steps: determining the aging state grade of a shock insulator based on the detection data of the shock insulator; based on the aging state grade of the isolation pad and the material of the isolation pad, determining a release substance of the isolation pad; the method comprises the following steps: acquiring chromatographic data obtained by analyzing a sample through a gas chromatograph, and removing an interference peak corresponding to a release substance of a shock insulator from the chromatographic data to generate first chromatographic optimization data; and determining a sample material loss compensation value based on the first chromatographic optimization data and the aging state grade of the isolation pad, and optimizing a chromatographic peak area value in the first chromatographic optimization data to generate second chromatographic optimization data. According to the method, on the basis of aging prediction of the shock insulator, interference peak removal and quantitative correction are linked, the problem of peak shape pollution is solved, the problem of inaccurate quantification caused by aging is solved, and then the reliability of gas chromatography data and the accuracy of analysis results can be improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method for optimizing chromatographic data processing for gas chromatographs. Background Technology

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

[0003] Gas chromatography (GC) allows for the separation of different chemical components in a sample within a chromatographic column, converting them into quantifiable signal peaks. However, research has revealed that in high-throughput, long-term operation scenarios, even minor changes in instrument components can lead to significant analytical biases. For instance, the aging of the injection port septum, a critical component of the GC, has not been adequately considered. Existing methods typically assume that septum performance remains stable within a certain service life, relying solely on standardized sample pretreatment procedures without dynamically adapting to changes caused by septum aging. This makes it difficult to address interference from septum aging in the analysis of complex samples, leading to a gradual accumulation of biases in the analytical results. Summary of the Invention

[0004] To address the aforementioned problems in the prior art, this invention provides a method for optimizing chromatographic data processing for a gas chromatograph.

[0005] In a first aspect, embodiments of this application provide a method for optimizing chromatographic data of a gas chromatograph, comprising: determining the aging state level of the septum based on detection data of the septum of the gas chromatograph; wherein the detection data includes at least the number of punctures of the septum; determining the released substances of the septum based on the aging state level of the septum and the material of the septum; acquiring chromatographic data of a sample analyzed by the gas chromatograph, removing interfering peaks corresponding to the released substances of the septum from the chromatographic data, and generating first chromatographic optimization data; determining a 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 values ​​in the first chromatographic optimization data to generate second chromatographic optimization data.

[0006] Optionally, 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 region in the septum; the pressure detection data is the pressure holding data of the thermal conductivity degradation region under standard injection pressure; the determination of the aging state level of the septum based on the gas chromatograph septum detection data includes: determining the sealing performance index of the septum based on the pressure detection data and standard holding time; determining the percentage decrease in hardness of the septum based on the hardness detection data and the initial hardness of the septum; and determining the aging state level of the septum based on the number of punctures, the sealing performance index, and the percentage decrease in hardness of the septum.

[0007] Optionally, the step of determining the interfering peaks corresponding to the released substances of the septum from the chromatographic data includes: obtaining the retention time range of the released 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 characteristic frequency band screening on the first candidate peak set to determine a second candidate peak set; and 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 interfering peaks corresponding to the released substances of the septum.

[0008] Optionally, 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, separating periodic fluctuations and noise components, and calculating the baseline drift value of the frequency domain signal; correcting the chromatographic data based on the baseline drift value, and performing first-order differentiation based on the corrected chromatographic signal data to determine the first candidate peak set.

[0009] Optionally, the step of filtering the peak shape parameters of the second candidate peak set to determine the third candidate peak set includes: extracting the peak height, half-peak width, and peak shape symmetry parameter of each peak from the second candidate peak set; wherein the peak shape parameters include the peak height, half-peak width, and peak shape symmetry parameter; and determining the third candidate peak set based on the peak height, half-peak width, and peak shape symmetry parameter of each peak.

[0010] Optionally, before removing the interfering peak corresponding to the released substance of the septum from the chromatographic data, the method further includes: determining that the additional peak intensity of the interfering peak exceeds a preset threshold; wherein the additional peak intensity of the interfering peak is calculated by the following steps: obtaining the peak area value of the interfering peak; and determining the additional peak intensity of the interfering peak based on the ratio of the peak area value of the interfering peak to the area value of an adjacent normal chromatographic peak.

[0011] Optionally, removing interference peaks corresponding to the released substances from the chromatographic data to generate first chromatographic optimized data includes: applying high-pass filtering to the chromatographic data to obtain preliminary purified signal data; performing moving window baseline correction on the preliminary purified signal data to eliminate residual baseline drift and obtain baseline corrected signal data; replacing the interference interval signals in the baseline corrected signal data with interpolation methods to obtain interference removal signals; and performing high-frequency noise removal processing on the interference removal signals to generate the first chromatographic optimized data.

[0012] Optionally, based on the first chromatographic optimization data and the aging state level 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. This includes: 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 lookup table based on the retention time of the chromatographic peak and the aging state level of the septum to determine the sample material loss compensation value corresponding to the chromatographic peak; and 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 second chromatographic optimization data.

[0013] Optionally, the method further includes: modifying the evaporation temperature setting value according to the optimized chromatographic peak area value, predicting the residence time window of the target compound in 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 the optimized injection time interval.

[0014] Optionally, the method further includes: if the residence time of the target compound in the predicted sample 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 this invention include: traditional chromatographic data do not take into account the interference caused by the release of substances generated by septum aging, or the loss of sample components caused by septum aging. In this application, the aging state level of the septum is estimated by the number of punctures, and the release of substances from the septum and the chromatographic data of the sample are combined to determine the compensation values ​​for the loss of sample materials and the release of substances from the septum, thereby achieving dual optimization and improving the purity of chromatographic data and the accuracy of quantitative analysis.

[0016] 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. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the steps of a gas chromatograph chromatographic data optimization processing method provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the steps of another gas chromatograph chromatographic data optimization processing method provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the steps of another gas chromatograph chromatographic data optimization processing method provided in an embodiment of the present invention. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0019] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] Gas chromatography (GC) allows for the separation of different chemical components in a sample within a chromatographic column, converting them into quantifiable signal peaks. However, research has revealed that in high-throughput, long-term operation scenarios, even minor changes in instrument components can lead to significant analytical biases. For instance, the aging of the injection port septum, a critical component of the GC, has not been adequately considered. Existing methods typically assume that septum performance remains stable within a certain service life, relying solely on standardized sample pretreatment procedures without dynamically adapting to changes caused by septum aging. This makes it difficult to address interference from septum aging in the analysis of complex samples, leading to a gradual accumulation of biases in the analytical results.

[0021] Furthermore, further research revealed that with increased use, the septum material gradually decomposes, releasing trace amounts of chemicals. These substances can form additional interfering peaks in chromatographic analysis, masking or distorting the signal of the target component. More complexly, the decreased sealing performance of the septum can also lead to the loss of light components from the sample within the injection port. This loss is not a regular pattern, but rather exhibits a slow initially followed by a rapid increase after repeated septum punctures. Moreover, the decreased sealing performance causes subtle changes in the pressure and temperature distribution within the injection port, resulting in uneven sample evaporation rates. Some light components are lost before entering the chromatographic column, while other components may undergo thermal decomposition or adsorption due to prolonged residence time. This phenomenon manifests in practical operation as abnormal fluctuations in chromatographic peak areas. For example, when analyzing volatile organic compounds, the peak area of ​​certain low-boiling-point components may be significantly reduced due to the loss of light components, and the appearance of interfering peaks further masks the target signal, significantly compromising the accuracy of quantitative analysis. Optimizing the sample pretreatment process to reduce interfering peaks and compensate for the loss of light components under dynamic conditions such as decreased sealing performance due to septum aging and changes in sample residence time has become the key to improving quantitative accuracy in gas chromatography analysis.

[0022] In view of the above problems, this application proposes the following embodiments to solve the above technical problems.

[0023] Please see Figure 1 This application provides a method for optimizing chromatographic data of a gas chromatograph, specifically including steps 101 to 104.

[0024] Step 101: Determine the aging status level of the septum based on the gas chromatograph's septum detection data.

[0025] The test data includes at least the number of septum punctures. That is, the number of septum punctures can be obtained from the historical usage records of the gas chromatograph, and the aging status level of the septum can be determined accordingly.

[0026] In one embodiment, the aging status level of the spacer can be represented by a numerical value, or it can be further subdivided into normal, slightly aged, moderately aged, and severely aged.

[0027] Step 102: Determine the substances released from the septum based on the aging status level of the septum and the material of the septum.

[0028] Specifically, the released substances of the septum can be obtained from the constructed material release substance database based on the aging status level of the septum and the material of the septum.

[0029] That is, a database of material release substances can be pre-built, which includes the aging status level of different septa and the different release substances corresponding to the materials of the septa.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] In summary, the gas chromatographic data optimization processing method provided in this application has the following beneficial effects, including: 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.

[0036] 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.

[0037] 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.

[0038] It's important to note that septum aging exhibits localized unevenness. Areas with frequent punctures, such as the edges near the injection port heating zone, will experience thermal conductivity degradation (poor material thermal conductivity, resulting in localized temperatures higher than the surrounding area) due to prolonged high temperatures and physical wear. Temperature distribution data can be collected using a thermal imager to identify areas where the temperature difference exceeds a threshold (e.g., 5°C), i.e., areas of thermal conductivity degradation. These areas are high-risk zones for septum aging, where sealing performance declines preferentially. Identifying these thermal conductivity degradation areas avoids indiscriminately covering the entire septum during subsequent pressure testing. In other words, directly measuring the pressure of the entire septum will lower the accuracy of the overall sealing assessment due to the normal sealing performance of non-degraded areas. However, measuring pressure only in areas of thermal conductivity degradation allows for precise identification of the weak points in the septum's true aging process.

[0039] In one embodiment, the identification of thermally degraded regions is achieved by real-time monitoring of the temperature distribution on the septum surface using a thermal imager. The thermal imager can acquire infrared images of the septum surface at a frequency of 30 frames per second, divide the images into 5×5 grid regions, and calculate the temperature value at 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 that region has changed, the heat conduction path is blocked, and it is marked as a thermally degraded region.

[0040] Please see Figure 2 Accordingly, the above steps, based on the gas chromatograph's septum detection data, determine the aging status level of the septum, and may specifically include: steps 201 to 203.

[0041] Step 201: Determine the sealing performance index of the septum based on the pressure test data and standard holding time.

[0042] Specifically, the sealing performance index can be equal to the pressure test data divided by the standard holding time. For example, if the standard holding time is 30 seconds and the pressure test data is 18 seconds, the sealing performance index is 0.6. It should be noted that the standard holding time is the pressure holding time of a new septum under the same standard injection pressure, which can be determined in advance through experiments and serves as a benchmark for the sealing performance before aging.

[0043] Step 202: Based on the hardness test data and the initial hardness of the septum, determine the percentage decrease in hardness of the septum.

[0044] In one possible implementation, hardness testing can be performed using a Shore hardness tester to uniformly select nine test points on the septum surface, forming a 3×3 test matrix. The same pressure is applied to each test point and held for 3 seconds before the hardness value is read. The result is then compared to the initial hardness value of the septum to calculate the percentage decrease.

[0045] It should be noted that the percentage decrease in hardness reflects the severity of the deterioration of the septum material. The higher the percentage, the more elasticity the material loses and the more brittle it becomes. For example, a percentage greater than 40% means that the material has been severely aged and is prone to cracking during puncture.

[0046] Step 203: Determine the aging status level of the septum based on the number of punctures, the sealing performance index of the septum, and the percentage decrease in hardness of the septum.

[0047] Finally, the aging status level of the septum was determined by combining three test data: the number of punctures of the septum, the sealing performance index of the septum, and the percentage decrease in the hardness of the septum.

[0048] In one embodiment, the aging status level of the spacers can be classified by the following examples: Normal: Number of punctures ≤ 30; Sealing performance index range: 0.9~1.0; Percentage decrease in hardness: ≤ 10%.

[0049] Slight aging: ≤50 punctures; Sealing performance index range: 0.7~0.89; Hardness reduction percentage: 10%~20%; Moderate aging: puncture count ≤51~200 times; sealing performance index range: 0.4~0.69; hardness reduction percentage: 21%~40%.

[0050] Severe aging: Number of punctures > 200; Sealing performance index range: < 0.4; Percentage decrease in hardness: > 40%.

[0051] It should be noted that the above are merely examples for reference and are not intended to limit the range of values. Furthermore, in practical applications, when there is overlap between data, priority can be determined based on the importance of the parameters.

[0052] In summary, this application proposes the detection of temperature, pressure, and hardness data, evaluating aging from three dimensions: physical wear (puncture count), functional failure (sealing performance), and material deterioration (hardness). This covers all key manifestations of septum aging, avoiding misjudgments of aging status levels due to a single parameter, and making the aging status level more closely reflect the actual usage condition of the septum. Furthermore, this application first identifies the thermal conductivity degradation area through temperature distribution data, preventing subsequent pressure testing from indiscriminately covering the entire septum. That is, if the pressure of the entire septum is directly measured, the normal sealing performance of non-degraded areas will reduce the accuracy of the overall sealing assessment. However, measuring pressure only in the thermal conductivity degradation area can accurately capture the weak points of the septum's true aging, further improving the accuracy of the aging level determination.

[0053] Please see Figure 3Optionally, the step of determining the interfering peak corresponding to the released substance from the chromatographic data may further include steps 301 to 304.

[0054] Step 301: Based on the aging status level of the septum and the material of the septum, obtain the retention time range of the released substances of the septum from the constructed material release substance database.

[0055] The pre-built material release substance database records the types of released substances and their retention time ranges corresponding to different aging levels and different material septa, and can be queried through the constructed material release substance database.

[0056] Step 302: Determine the first candidate peak set from the chromatographic data based on the retention time range.

[0057] The first candidate peak set can exclude normal peaks with completely mismatched retention times, thus initially narrowing down the range of interference peaks to be screened.

[0058] Step 303: Perform characteristic frequency band filtering on the first candidate peak set to determine the second candidate peak set.

[0059] Common frequency bands can be verified in advance through experiments, such as 0.3Hz~0.8Hz as the screening benchmark, and peaks concentrated in the characteristic frequency band of 0.3Hz~0.8Hz can be selected to generate a second set of candidate peaks.

[0060] Step 304: Screen the peak shape parameters of the second candidate peak set to determine the third candidate peak set; wherein, the third candidate peak set includes the interference peaks corresponding to the released substances of the septum.

[0061] Then, a third set of candidate peaks can be determined based on the peak shape parameters, and finally the interference peaks corresponding to the septum release substances can be locked, excluding sample substance peaks that match in time and frequency band but have normal peak shapes.

[0062] As can be seen, this application narrows down the scope by using three levels of screening: retention time, characteristic frequency band, and peak shape parameter. The retention time first defines the initial range, then the characteristic frequency band verifies whether the frequency attribute is interference, and the peak shape parameter locks in the unique tailing characteristics of the interference peak. The three factors work together to effectively distinguish between normal peaks and interference peaks.

[0063] Optionally, the above-mentioned determination of the first candidate peak set from chromatographic data based on retention time range includes: converting chromatographic data into a frequency domain signal, separating periodic fluctuations and noise components, and calculating the baseline drift value of the frequency domain signal; correcting the chromatographic data based on the baseline drift value, and performing first-order differentiation based on the corrected chromatographic signal data to determine the first candidate peak set.

[0064] It should be noted that the above steps are designed to address the problem of inaccurate peak location caused by noise and baseline drift in the original chromatographic data. Specifically, through four steps—frequency domain denoising, baseline correction, derivative positioning, and time screening—the first candidate peak interval that meets the retention time range is accurately extracted.

[0065] First, effective periodic fluctuations (target peaks and interfering peaks) and invalid random noise are separated from the raw 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: a sliding window with a width of 30 data points is set, and the signal mean is calculated within each window. This mean is used as the baseline drift value at the center time point of the window. The baseline drift values ​​are continuously calculated for all time points to form a baseline drift curve, reflecting the baseline shift pattern of the raw chromatographic data over time. Next, baseline correction is performed by subtracting the baseline drift curve from the time-domain signal of the raw chromatographic data to obtain the baseline-corrected chromatographic signal data. Then, the first derivative value at each time point is calculated using the five-point difference formula on the baseline-corrected chromatographic signal data. The sign change of the first derivative value determines the key nodes of the peaks, forming a set of peak intervals in the full spectrum. Candidate peaks whose retention times conform to the characteristic range of septum-released substances can then be screened from the full spectrum peak intervals; this is the determination of the first candidate peak set.

[0066] In summary, this method can solve the problem of inaccurate baseline drift calculation caused by noise in the original data, thus improving the accuracy of baseline correction. Simultaneously, by using the first derivative to locate peak intervals, it addresses the issue of blurred peak boundaries caused by baseline drift.

[0067] Optionally, the above steps involve filtering the peak shape parameters of the second candidate peak set to determine the third candidate peak set, including: extracting the peak height, half-peak width, and peak shape symmetry parameter of each peak from the second candidate peak set; wherein the peak shape parameters include the peak height, half-peak width, and peak shape symmetry parameter; and determining the third candidate peak set based on the peak height, half-peak width, and peak shape symmetry parameter of each peak.

[0068] It should be noted that peak height reflects the signal intensity of the peak; half-maximum width reflects the separation purity and tailing tendency of the peak; septum-released substances (such as siloxanes and plasticizers) have strong interactions with the chromatographic column stationary phase, resulting in slow desorption at the peak trailing edge, manifesting as tailing, and the peak shape symmetry parameter is much greater than 1.0. Therefore, the embodiments of this application identify interfering peaks based on the joint identification of three types of peak shape parameters.

[0069] In this embodiment, peak height (intensity), half-width (separation), and symmetry (morphological essence) are combined, and the symmetry parameter >1.5 can be used as the core threshold. With the deviation verification of peak height and half-width, it can effectively distinguish normal peaks and interference peaks that resemble interference peaks.

[0070] Optionally, before removing the interfering peaks corresponding to the released substances from the chromatographic data, the method further includes determining that the additional peak intensity of the interfering peak exceeds a preset threshold.

[0071] The additional peak intensity of the interfering peak is calculated through the following steps: obtaining the peak area value of the interfering peak; and determining the additional peak intensity of the interfering peak based on the ratio of the peak area value of the interfering peak to the area value of the adjacent normal chromatographic peak.

[0072] It should be noted that in this application, the additional peak intensity of the interference peak reflects the relative influence weight of the interference peak on adjacent normal peaks through the relative area ratio. Then, a preset threshold is used to determine whether the interference peak needs to be removed.

[0073] For example, if the extra peak intensity is greater than a preset threshold (e.g., 5%), it means that the interference peak has significantly affected the qualitative (peak overlap) or quantitative (signal superposition leads to an overestimation of the result) of the adjacent normal peak, and subsequent removal operations need to be performed.

[0074] If the additional peak intensity is less than or equal to the preset threshold, it means that the influence of the interference peak on the normal peak is negligible (below the allowable error of the method) and does not need to be removed, thus avoiding excessive processing that could introduce baseline fluctuations.

[0075] In the above process, the setting of the additional peak intensity threshold can be based on the statistical regularity of historical analysis data. By analyzing the chromatographic data of a large number of normal samples, the average signal intensity of the target compound peak is determined. When the intensity of the interfering peak generated by the septum-released substance exceeds 15% of the average intensity of the target peak, it is determined that signal purification processing is required to avoid the interfering peak from having a significant impact on the quantitative analysis results.

[0076] In other words, the above method can quantify the impact of interference by using relative area ratios, thus avoiding the drawbacks of absolute removal and over-removal. It overcomes the shortcomings of traditional identification-removal models and improves the accuracy, reliability, and flexibility of data optimization.

[0077] The following is a complete explanation of the process for determining the interference peaks.

[0078] The database of substances released from septum materials is queried based on the aging status level of the septum at the injection port. The database records the characteristic retention time ranges of siloxanes, plasticizers, and antioxidants corresponding to different aging levels.

[0079] The raw chromatographic signal data of the current sample analysis is collected, and the time-domain signal is converted into a frequency-domain signal using Fourier transform. Periodic fluctuation components and random noise components in the signal are separated to obtain the background noise spectral distribution data. For the low-frequency components in the background noise spectral distribution data, the moving average method is used to calculate the drift value of the chromatographic baseline within different time windows. The raw chromatographic signal is corrected based on the baseline drift value. The first derivative value at each time point is calculated on the corrected signal. The peak position is identified by the inflection point where the first derivative changes from positive to negative. The positions where the first derivative changes from zero to positive and from negative to zero are marked as the peak start point and end point, respectively, to obtain the first candidate peak set.

[0080] Then, the first candidate peak set is filtered by characteristic frequency bands to determine the second candidate peak set.

[0081] Then, the peak height, half-width at half-maximum (HWHM), and peak shape symmetry parameters of this interval are extracted from the second candidate peak set. The peak area is estimated by multiplying the peak height and HWHM, and the peak shape symmetry parameters are used to determine whether it is an interfering peak caused by substances released from the septum material. For the signal interval determined to be an interfering peak, the least squares method is used to fit the peak shape curve to determine the accurate peak height. The area value of the interfering peak is calculated by integration, and the interference intensity coefficient is equal to the area value of the interfering peak divided by the area value of the adjacent normal chromatographic peak. The retention time position and interference intensity coefficient of the interfering peak are recorded to obtain the specific position and intensity value of the additional peak of the substances released from the septum material.

[0082] Specifically, the database of substances released from septum materials was established through long-term accumulation of experimental data. Characteristic substances released by inlet septums of different brands and materials at various aging stages were collected. Their chemical composition was determined by gas chromatography-mass spectrometry (GC-MS), recording the retention times of siloxanes (5.2–8.7 minutes), plasticizers (12.3–15.8 minutes), and antioxidants (18.5–22.1 minutes). The core of Fourier transform processing of chromatographic signals lies in frequency domain feature separation.

[0083] For example, the raw chromatographic data contains three types of components: target compound peaks, interference peaks from septum-released substances, and random noise, with significantly different frequency characteristics. Fourier transform converts the time-domain signal into a frequency-domain signal. In the frequency domain, the target compound peaks appear as low-to-mid-frequency components, typically ranging from 0.01 to 0.5 Hz; the interference from septum-released substances has specific frequency characteristics, concentrated between 0.3 and 0.8 Hz; while random noise is mainly distributed in the high-frequency region, exceeding 1 Hz. By setting a frequency window function, the frequency components from 0.3 to 0.8 Hz are separated; this part of the signal mainly reflects the interference characteristics of the septum material's released substances. Power spectral density analysis is performed on the separated frequency components to calculate the energy distribution of each frequency component. When the power spectral density of a certain frequency component exceeds 1.5 times the average value, the time interval corresponding to that frequency is marked as a potential interference region. Baseline drift is particularly pronounced in long-term chromatographic analyses, 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. The mean value of the signal is calculated in each window as the baseline value at that time point, and the continuous baseline values ​​constitute the baseline drift curve.

[0084] In one possible implementation, derivative analysis identifies peak values ​​through numerical differentiation. For each data point of the corrected chromatographic signal, the first derivative is calculated using a five-point difference formula, which involves a weighted average of the signal values ​​at the current point and the two points before and after it. The first derivative reflects the rate of change of the signal; it is positive at the rising edge of the peak, negative at the falling edge, and zero at the peak apex. By monitoring the change in the sign of the first derivative, the start, peak, and end points of the peak interval are accurately located, forming a complete peak profile description.

[0085] Specifically, retention time matching employs a tolerance window mechanism. Considering the influence of instrument status and environmental factors, the actual retention time may deviate from the standard value. A tolerance window of ±0.2 minutes is set. When the retention time of a candidate peak falls within the characteristic time range recorded in the database and its tolerance window, it is initially determined to be a potential interference peak.

[0086] For example, peak shape parameter extraction involves multi-dimensional feature analysis. Peak height is obtained by subtracting the baseline value from the peak signal intensity; half-width at half-maximum (HWHM) is the width of the peak measured at half its height, reflecting the chromatographic separation effect; peak symmetry parameter is determined by calculating the ratio of the slope of the peak leading edge to the slope of the trailing edge. The symmetry parameter of a normal chromatographic peak is close to 1.0, while interfering peaks produced by septum-released substances often exhibit obvious tailing, with a symmetry parameter greater than 1.5. The interference peak determination results are verified by peak area estimation. The trapezoidal integral method is used to calculate the area under the peak. When the deviation between the estimated area and the standard substance peak area exceeds 20%, it is confirmed as an interfering peak.

[0087] In one embodiment, the least squares fitting method determines the optimal peak shape parameters through iterative optimization. A Gaussian function is selected as the fitting model, and the sum of squared residuals between the fitted curve and the measured data is minimized by adjusting three parameters: peak height, peak position, and peak width. The fitting process employs the Levenberg-Marquardt algorithm for nonlinear optimization, converging after 5 to 10 iterations to obtain an accurate description of the peak height and peak shape.

[0088] In one embodiment, the interference intensity coefficient (the additional peak intensity of the interference peak) directly reflects the degree of influence of the septum-released substance on the analytical results. 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; when the coefficient is between 0.05 and 0.1, the impact is small but still needs to be recorded; when the coefficient is below 0.05, its impact can be ignored.

[0089] Optionally, the above steps for removing interfering peaks corresponding to the released substances from the chromatographic data to generate the first chromatographic optimization data include: applying a high-pass filter to the chromatographic data to obtain preliminary purified signal data; performing moving window baseline correction on the preliminary purified signal data to eliminate residual baseline drift and obtain baseline corrected signal data; replacing the interfering interval signals in the baseline corrected signal data with interpolation methods to obtain interference removal signals; and performing high-frequency noise removal processing on the interference removal signals to generate the first chromatographic optimization data.

[0090] That is, based on the frequency characteristics of the interfering peaks of the released substances from the septum (confirmed through a material release substance database), the cutoff frequency of the high-pass filter (e.g., 0.5Hz) can be set to ensure coverage of the highest frequency of the interfering peaks without damaging the target peak, thus obtaining preliminary purified signal data. Then, the moving window method dynamically fits the baseline to eliminate the interference of drift on peak shape judgment, ensuring the accuracy of subsequent interpolation and replacement. Next, interpolation is used to replace the interfering signal with a simulated normal signal, avoiding signal fragmentation 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.

[0091] As can be seen, the embodiments of this application achieve a balance between precise removal of interference and signal fidelity through a four-step collaborative process, thereby improving data quality, ensuring the reliability of analysis, and adapting to practical application needs.

[0092] The process of removing interference peaks will be explained in detail below.

[0093] 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.

[0094] 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: ; 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.

[0095] In the above process, the high-pass filter employs Butterworth-type frequency response characteristics, and its cutoff frequency is determined based on the spectral analysis results of the interference signal. The dominant frequency of the interference signal is obtained through Fast Fourier Transform, and the cutoff frequency is set to a coefficient of 0.8 multiplied by the dominant frequency value to ensure that low-frequency interference components are filtered out while retaining the high-frequency characteristics of the target compound. Forward filtering introduces phase delay, causing a shift in the chromatographic peak position. By inverting the time of the forward-filtered signal, processing it again through the same filter, and then inverting the time again, zero-phase filtering is achieved. This bidirectional filtering method ensures that each frequency component experiences the same positive and negative phase shifts, ultimately canceling each other out. The filtered signal maintains the original peak position, eliminating only low-frequency baseline drift and interference components. In the preliminary purified signal data, the amplitude of the interference peak is reduced to less than 30% of its original intensity, while the shape and position of the target compound peak remain unchanged.

[0096] In the above process, baseline drift mainly originates from the slow change in injection port temperature and the small fluctuations in carrier gas pressure. In long-term chromatographic analysis, it manifests as a slow overall upward or downward trend in the signal, affecting the accurate calculation of peak area.

[0097] In the above process, the moving window method achieves global baseline correction through local baseline estimation. Specifically, the process involves setting the window width to 50 data points, moving 10 data points at a time from the signal's starting position to form overlapping windows. Within each window, a cubic polynomial is fitted using the least squares method, with the polynomial coefficients obtained by solving a system of normal equations. Since the baseline values ​​of adjacent windows differ in the overlapping region, a weighted averaging method is used for smoothing, with the weighting coefficients determined based on the distance from the window center. The global baseline curve is formed by connecting the local baseline values, exhibiting a smooth and continuous trend. After subtracting the global baseline curve from the initially purified signal, the baseline-corrected signal's signal value approaches zero in the peakless region, eliminating systematic bias.

[0098] In the above process, the selection of interpolation methods needs to consider the smoothness and continuity requirements of chromatographic peaks. Cubic spline interpolation, due to its continuous second derivative, can accurately simulate the natural shape of chromatographic peaks, making it an ideal choice for replacing interference peaks. Furthermore, the implementation of interpolation constraints involves the reasonable setting of boundary conditions. Twenty data points are extracted before the start point and after the end point of the interference peak; these data points represent normal signals unaffected by interference. The interpolation function must not only ensure equal function values ​​at the boundary points but also ensure the continuity of the first derivative, ensuring a smooth connection between the interpolation curve and the original signal. By solving the constrained linear equation system, the coefficients of the interpolation polynomial are determined, and the resulting interpolation curve exhibits a natural peak shape variation within the interference peak interval. Multi-scale decomposition uses wavelet transform to achieve frequency separation of the signal. Wavelet basis functions with tight support characteristics are selected to decompose the signal into detail coefficients and approximation coefficients at different scales. The detail coefficients correspond to high-frequency noise components, while the approximation coefficients preserve the low-frequency peak shape characteristics.

[0099] Understandably, thresholding achieves noise reduction through a soft thresholding function. When the absolute value of a detail coefficient is less than a set threshold, it is set to zero; when it is greater than the threshold, its amplitude is reduced proportionally. The threshold is determined based on an estimate of the noise level, typically 1.4826 times the median of the finest-scale detail coefficients. The processed coefficients are reconstructed using inverse wavelet transform, resulting in a clean signal peak that retains the characteristic information of the target compound, reducing the noise level to below 20% of the original signal, thus eliminating interference from the septum's release material.

[0100] Optionally, based on the first chromatographic optimization data and the aging state level 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. This can specifically include: calculating the chromatographic peak area value and 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 level of the septum to determine the sample material loss compensation value corresponding to the chromatographic peak; and 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 second chromatographic optimization data.

[0101] It should be noted that the time coordinates of the peak start and end points are identified from the pure signal peaks of the target compound in the sample. The retention time of the chromatographic peak is determined by the time coordinate of the peak apex. The product of the signal intensity value and the time interval of each sampling point within the interval from the peak start to the end point is accumulated using a numerical integration method to obtain the initial peak area value and the corresponding retention time value. Based on the retention time value and the septum aging state level, a preset loss compensation reference table is consulted. The reference table records the loss coefficient of the compound at each retention time under different aging levels. By performing linear interpolation between the loss coefficients of adjacent aging levels, the sample material loss compensation coefficient corresponding to the current aging state can be obtained. The sample material loss compensation coefficient reflects the degree of loss of light components at the injection port. The initial peak area value is corrected using the sample material loss compensation coefficient. The corrected peak area is equal to the initial peak area divided by one minus the compensation coefficient. If the deviation between the corrected peak area and the average peak area of ​​the same compound in the historical analysis records exceeds a preset threshold, the compensation coefficient is adjusted according to the deviation ratio and recalculated to obtain the adjusted chromatographic peak area value.

[0102] In the above process, by monitoring the rate of change of signal strength, the peak start point is marked when the strength starts to rise from the baseline level, and the peak end point is marked when the strength falls back to the baseline level. The peak point corresponds to the time coordinate of the maximum signal strength.

[0103] In the above process, the core of calculating the peak area using the numerical integration method lies in the cumulative processing of discrete data points.

[0104] In the above process, the chromatographic signal is recorded at a fixed sampling rate, and each sampling point includes a timestamp and signal intensity value. Starting from the peak initiation point, the average signal intensity of two adjacent sampling points is taken as the representative intensity for that time period, and multiplied by the time interval to obtain the area contribution value for that segment. All area contribution values ​​from the initiation point to the termination point are accumulated segment by segment to obtain the initial peak area value. This trapezoidal integration method has higher accuracy than rectangular integration, especially in regions where the peak shape changes rapidly.

[0105] In the above process, a pre-set loss compensation control table was established using a large amount of experimental data, recording the loss patterns of various compounds under different aging degrees. The loss coefficient of light alkanes was 0.05 in the early stage of aging and could reach 0.25 in the later stage; the loss coefficient of aromatic compounds was relatively small.

[0106] In the above process, linear interpolation calculates the compensation coefficient proportionally based on the position of the current aging state between two adjacent standard aging levels. If the current aging state is between mild and moderate, and closer to moderate, the compensation coefficient is closer to the loss coefficient value corresponding to moderate aging.

[0107] In the above process, the correction formula is based on the principle of mass conservation. The actual amount of sample entering the column equals the initial injection amount minus the loss. Therefore, the corrected peak area needs to be divided by the retention ratio. Furthermore, comparison 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 should remain relatively stable. If the deviation exceeds 15%, the compensation coefficient needs to be adjusted and recalculated.

[0108] Optionally, after generating the second chromatographic optimization data, the method further includes: modifying the evaporation temperature setting value according to the optimized chromatographic peak area value, predicting the residence time window of the target compound in 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 the optimized injection time interval.

[0109] Specifically, the relative percentage of each volatile component is calculated based on the adjusted peak area. By comparing the content data of the same component in multiple consecutive analyses, the ratio of the content difference between two adjacent analyses to the time interval is calculated to obtain the content change rate. This identifies the decreasing trend of light component content and the increasing trend of heavy component content. The heating temperature time series data recorded by the temperature sensor of the sample pretreatment device is extracted. Based on the loss rate of light components in the content change trend, combined with the average temperature value in the heating temperature time series data, a linear fitting is used to determine the relationship coefficient between the loss rate and temperature. If the loss rate of light components exceeds a preset threshold, the evaporation temperature setting is reduced according to the relationship coefficient. Based on the modified temperature value and the boiling point differences of each target compound, the relative volatility of each compound at that temperature is calculated. Using the relative volatility and carrier gas flow rate parameters, the dead time is calculated by dividing the column length by the carrier gas linear velocity. The retention time is predicted by multiplying the distribution coefficient of each compound by the dead time. The distribution coefficient is determined based on the compound polarity and the stationary phase type of the column, thus obtaining the start and end times of the residence time window for each target compound. Based on the residence time window width and the time interval between adjacent compound windows, the resolution index value is calculated. The resolution is equal to the difference in retention time between two adjacent peaks divided by the average peak width. If the resolution is lower than the preset standard value, the injection time interval is increased to delay the start time of analysis of subsequent batches of samples, thus obtaining the optimized injection time interval parameter.

[0110] In the above process, dynamic monitoring of the volatile component content is the foundation for achieving adaptive temperature control.

[0111] In the above process, by continuously collecting chromatographic analysis data of multiple batches of samples, a time series record of component content was established. Each data point includes the analysis time, component name, and peak area correction value. These data reflect the cumulative effect of sample loss during septum aging.

[0112] In the above process, the calculation of the rate of change of content involves the differential processing of time series data.

[0113] In the above process, for a specific volatile component such as n-hexane, its content percentage in the nth analysis is denoted as Cn, and its content percentage in the (n-1)th analysis is denoted as Cn-1. The time interval between the two analyses is Δt hours.

[0114] In the above process, the rate of change of content is equal to (Cn-Cn-1) / Δt, in percentage per hour. When the rate of change for three consecutive data points is negative, the component is considered to be showing a loss trend. The relationship between the loss rate and temperature is obtained by collecting loss rate data at different pretreatment temperatures and using the least squares method to fit a linear relationship coefficient, representing the increase in the loss rate for every 1 degree Celsius increase in temperature. The linear relationship coefficient can be determined by the following formula: ; In this formula, The coefficients represent the linear relationship obtained by fitting using the least squares method. Indicates the total number of data points. Indicates the first One temperature data point, Indicates the first Each loss rate data point This represents the average value of the temperature data. This represents the average of the loss rate data. When the loss rate of light components such as toluene exceeds the threshold of 0.5% per hour, the required temperature reduction is calculated based on the relationship coefficient to bring the loss rate back to an acceptable range. This real-time data-based temperature adjustment avoids excessive losses caused by fixed temperature settings.

[0115] In the above process, the volatility characteristics of each component need to be reassessed after temperature adjustment. The modified evaporation temperature affects the gas-liquid equilibrium state of different components in the sample, thereby changing their entry rate and concentration distribution into the chromatographic column.

[0116] Optionally, the method further includes: if the predicted residence time of the target compound in the sample deviates from the standard range, adjusting the carrier gas flow rate of the chromatographic column to obtain a stable separation efficiency evaluation result.

[0117] If the predicted residence time of the target compound in the sample deviates from the standard range, calculate the percentage deviation between the actual residence time and the standard value. Based on the inverse relationship between retention time and carrier gas flow rate, decrease the flow rate when the deviation is positive and increase it when the deviation is negative. Adjust the carrier gas flow rate control valve by half the percentage deviation to obtain the adjusted carrier gas flow rate. Use the adjusted carrier gas flow rate to run chromatographic separation, monitoring the elution times of adjacent compound peaks in real time. Subtract the earlier elution time from the later elution time to obtain the interpeak distance data. Measure the peak width at 10% of the peak height and calculate the ratio of the distance from the peak apex to the peak front to the distance from the peak apex to the peak rear edge; this ratio is used as the peak shape symmetry factor. Determine whether adjacent peaks have achieved baseline separation based on the interpeak distance data. Assess peak shape quality based on the peak shape symmetry factor. If the symmetry factor is within a preset range and the interpeak distance meets the separation requirements, record the interpeak distance and symmetry factor at the current flow rate as the separation efficiency evaluation result. If the requirements are not met, continue adjusting the flow rate in the direction of deviation until a stable state is reached.

[0118] In the above process, when the actual residence time of the target compound deviates from the expected value, rapid correction can be achieved by adjusting the carrier gas flow rate to avoid peak overlap and decreased separation.

[0119] The physical basis for the inverse relationship between carrier gas flow rate and retention time in the above process.

[0120] 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 residence time decreases accordingly. The percentage deviation is calculated using the relative deviation formula: actual value minus standard value, then divided by the standard value and multiplied by 100%. The direction of adjustment is determined by the sign of the deviation; a positive deviation indicates that the residence time is too long and the flow rate needs to be increased, while a negative deviation indicates the opposite. Half of the deviation percentage is used as the adjustment range to avoid over-adjustment that could cause system oscillation.

[0121] In the above process, the peak shape symmetry factor is measured at 10% of the peak height. The symmetry factor is obtained by calculating the ratio b / a by measuring the horizontal distance *a* from the peak tip to the peak apex and the horizontal distance *b* from the peak apex to the trailing edge at this height. The ideal Gaussian peak symmetry factor is 1.0, while in practice, due to column efficiency and dead volume, it is typically between 0.8 and 1.2.

[0122] For example, the criterion for baseline separation is that adjacent peaks return to the baseline level, at which point the distance between peaks is greater than the sum of the two peak widths. When the symmetry factor deviates from the ideal range or the distance between peaks is insufficient, the flow rate is automatically adjusted and reassessed. Furthermore, through repeated fine-tuning of the flow rate and evaluation of the effect, the ideal separation conditions are gradually approximated, achieving dynamic optimization of separation efficiency and improving the reliability of the analysis.

[0123] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing chromatographic data from a gas chromatograph, characterized in that, include: Based on the gas chromatograph's detection data of the septum, the aging status level of the septum is determined; wherein, the detection data includes at least the number of punctures of the septum; The substances released from the septum are determined based on the aging status level of the septum and the material of the septum; Acquire chromatographic data of the sample analyzed by the gas chromatograph, remove interference peaks corresponding to the released substances of the septum from the chromatographic data, and generate first chromatographic optimization data; Based on the first chromatographic optimization data and the aging state level of the septum, the sample material loss compensation value is determined, and the chromatographic peak area value in the first chromatographic optimization data is optimized to generate the second chromatographic optimization data.

2. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 1, characterized in that, The detection data also 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 region in the septum; the pressure detection data is the pressure holding data of the thermal conductivity degradation region under standard injection pressure; The determination of the aging status level of the septum based on the gas chromatograph detection data includes: Based on the pressure detection data and standard holding time, the sealing performance index of the septum is determined; Based on the hardness test data and the initial hardness of the septum, the percentage decrease in hardness of the septum is determined; The aging status level of the septum is determined based on the number of punctures, the sealing performance index of the septum, and the percentage decrease in hardness of the septum.

3. The method for optimizing chromatographic data of a gas chromatograph according to claim 1, characterized in that, The step of determining the interfering peaks corresponding to the released substances from the chromatographic data includes: Based on the aging status level of the septum and the material of the septum, the retention time range of the released substances of the septum is obtained from the constructed material release substance database; A first set of candidate peaks is determined from the chromatographic data based on the retention time range; The first candidate peak set is filtered by characteristic frequency bands to determine the second candidate peak set; The second candidate peak set is screened for peak shape parameters to determine the third candidate peak set; wherein, the third candidate peak set includes the interference peaks corresponding to the released substances of the septum.

4. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 3, characterized in that, The step of determining the first candidate peak set from the chromatographic data based on the retention time range includes: The chromatographic data is converted into a frequency domain signal, and the baseline drift value of the frequency domain signal is calculated after separating periodic fluctuations and noise components. The chromatographic data is corrected based on the baseline drift value, and the first candidate peak set is determined by performing first-order differentiation on the corrected chromatographic signal data.

5. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 3, characterized in that, The step of filtering the peak shape parameters of the second candidate peak set to determine the third candidate peak set includes: Extract the peak height, half-width at half-maximum (WHM), and peak shape symmetry parameter for each peak from the second candidate peak set; wherein, the peak shape parameter includes the peak height, WHM, and peak shape symmetry parameter. The third candidate peak set is determined based on the peak height, half-peak width, and peak shape symmetry parameters of each peak.

6. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 3, characterized in that, Before removing the interfering peaks corresponding to the released substances from the chromatographic data, the method further includes: The additional peak intensity of the interference peak is determined to exceed a preset threshold; The additional peak intensity of the interference peak is calculated through the following steps: Obtain the peak area value of the interference peak; The additional peak intensity of the interfering peak is determined based on the ratio of the peak area of ​​the interfering peak to the area of ​​the adjacent normal chromatographic peak.

7. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 1, characterized in that, The step of removing interfering peaks corresponding to the released substances from the chromatographic data to generate the first chromatographic optimization data includes: High-pass filtering is applied to the chromatographic data to obtain preliminary purification signal data; The preliminary purified signal data is subjected to moving window baseline correction to eliminate residual baseline drift and obtain baseline corrected signal data; For the interference peaks in the baseline correction signal data, an interpolation method is used to replace the interference interval signal to obtain the interference removal signal; The interference removal signal is subjected to high-frequency noise removal processing to generate the first chromatographic optimization data.

8. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 1, characterized in that, Based on the first chromatographic optimization data and the aging state level of the septum, the sample material loss compensation value is determined, and the chromatographic peak area values ​​in the first chromatographic optimization data are optimized to generate second chromatographic optimization data, including: The peak area and retention time of the chromatographic peaks are calculated based on the first chromatographic optimization data. Based on the retention time of the chromatographic peak and the aging status level of the septum, a preset loss compensation reference table is consulted 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, the chromatographic peak area value in the first chromatographic optimization data is optimized to generate the second chromatographic optimization data.

9. The method for optimizing chromatographic data processing of a gas chromatograph according to claim 1, characterized in that, The method further includes: The evaporation temperature setting is modified based on the optimized chromatographic peak area value. The residence time window of the target compound in the sample is predicted based on the modified evaporation temperature setting value. The injection time interval is then optimized based on the residence time window to obtain the optimized injection time interval.

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

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