A method and system for baseline correction of a p-tert-butylphenol chromatogram

Abstract

The application relates to the technical field of chromatogram baseline correction, in particular to a method and system for baseline correction of m-tert-butyl phenol chromatogram, which comprises the following steps: obtaining a sample chromatogram and a standard chromatogram of m-tert-butyl phenol, and voltage, temperature and pressure data at each sampling time; obtaining reference sampling points in the sample chromatogram; obtaining weight coefficients of the reference sampling points according to the shortest time interval between each reference sampling point and a characteristic peak, the possibility that the characteristic peak corresponding to the shortest time interval belongs to a pseudo peak, and the fluctuation degree of voltage, temperature and pressure data and the jitter degree of signal intensity at the time when each reference sampling point is located; performing weighted fitting on the ordinate of all the reference sampling points to obtain a baseline fitting curve of the sample chromatogram, and then performing baseline correction on the sample chromatogram. The baseline correction accuracy of the chromatogram is improved by performing weighted fitting on all the reference sampling points.

Description

Technical Field

[0001] This application relates to the field of chromatographic baseline correction technology, specifically to a baseline correction method and system for m-tert-butylphenol chromatograms. Background Technology

[0002] When using high-performance liquid chromatography (HPLC) to determine the purity of m-tert-butylphenol, instability in operating conditions such as voltage, temperature, and mobile phase, as well as the continuous elution of contaminants or stationary phase within the chromatographic column, can lead to baseline drift in the chromatograms acquired by the HPLC due to noise. Baseline drift affects the calculation of peak areas in the chromatograms, thereby reducing the accuracy of the m-tert-butylphenol purity determination results. Therefore, baseline correction processing is required for the acquired chromatograms.

[0003] Among baseline correction methods for chromatograms, polynomial fitting has become a widely used method due to its mathematical simplicity, high computational efficiency, and strong interpretability. This method typically uses the least squares method to fit the baseline in the chromatogram and then subtracts the fitted baseline from the chromatogram to achieve baseline correction. However, this method usually applies the same weight to all sampling points outside the characteristic peaks in the chromatogram, ignoring data changes caused by low-frequency and high-frequency noise, as well as spurious peaks caused by noise with frequencies comparable to the spectral peaks. This makes it easy to capture too many data changes caused by noise interference and data changes near spurious peaks during baseline fitting, while ignoring the data changes near the characteristic peaks of the actual components in the chromatogram. This leads to deviations in the final baseline fitting curve, resulting in inaccurate baseline correction results and consequently reducing the accuracy of subsequent m-tert-butylphenol purity detection. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a baseline correction method and system for the chromatogram of m-tert-butylphenol, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of this application provide a baseline correction method for a m-tert-butylphenol chromatogram, the method comprising the following steps:

[0006] Obtain the sample chromatogram of m-tert-butylphenol sample solution throughout the entire detection process, as well as the voltage, temperature, and pressure data at each sampling time; the horizontal and vertical axes of the chromatogram are retention time and signal intensity, respectively;

[0007] Extract the time intervals of each characteristic peak in the sample chromatogram, and record all sampling points in the sample chromatogram that are outside all the time intervals as reference sampling points; obtain the standard chromatogram of m-tert-butylphenol; based on the shortest time interval between each reference sampling point and all characteristic peaks in the sample chromatogram, and the difference between the retention time of the characteristic peak corresponding to the shortest time interval and the retention time of each characteristic peak in the standard chromatogram, obtain the relative distance of the pseudo peaks of each reference sampling point;

[0008] Based on the fluctuation of voltage, temperature, and pressure data at each benchmark sampling point at the given time, the low-frequency interference coefficient of each benchmark sampling point is obtained. Based on the jitter of signal intensity at each benchmark sampling point in the sample chromatogram at the given time, the high-frequency interference coefficient of each benchmark sampling point is obtained. Combined with the relative distance of pseudo-peaks and the low-frequency interference coefficient of each benchmark sampling point, the weight coefficient of each benchmark sampling point is obtained. The ordinate of all benchmark sampling points is then weighted and fitted to obtain the baseline fitting curve of the sample chromatogram, and the baseline of the sample chromatogram is then corrected.

[0009] Preferably, the time interval of each characteristic peak in the sample chromatogram refers to the time interval between the start and end points of each characteristic peak in the sample chromatogram.

[0010] Preferably, the method for obtaining the relative distance between the pseudo-peaks of each reference sampling point is as follows:

[0011] Based on the retention time differences between each characteristic peak in the sample chromatogram and all characteristic peaks in the standard chromatogram, the pseudo-peak characteristic values ​​of each characteristic peak in the sample chromatogram are obtained;

[0012] The minimum absolute difference between the retention time of each baseline sampling point and the retention time of all characteristic peaks in the chromatogram of the statistical sample;

[0013] The relative distance between the pseudo-peaks at each benchmark sampling point is positively correlated with the minimum value, and negatively correlated with the pseudo-peak characteristic value of the characteristic peak corresponding to the minimum value.

[0014] Preferably, the pseudo-peak characteristic value of each characteristic peak in the sample chromatogram refers to the minimum absolute difference between the retention time of each characteristic peak in the sample chromatogram and the retention time of all characteristic peaks in the standard chromatogram.

[0015] Preferably, the low-frequency interference coefficient of each reference sampling point refers to the mean of the standard deviation of the data points in the sliding window of the voltage sequence, temperature sequence, and pressure sequence at the time of each reference sampling point; wherein, the voltage sequence, temperature sequence, and pressure sequence refer to the sequences composed of all voltage, temperature, and pressure data arranged in chronological order throughout the entire detection process.

[0016] Preferably, the method for obtaining the high-frequency interference coefficient of each reference sampling point is as follows:

[0017] All baseline sampling points are divided into multiple sampling point subsets according to the retention times of all characteristic peaks in the sample chromatogram;

[0018] Smooth the signal intensity of all reference sampling points in each sampling point subset;

[0019] The absolute difference between the signal strength of each reference sampling point before and after smoothing is recorded as the high-frequency interference coefficient of each reference sampling point.

[0020] Preferably, the weighting coefficient of each reference sampling point is positively correlated with the relative distance of the pseudo-peak, and negatively correlated with the low-frequency interference coefficient and the high-frequency interference coefficient, respectively.

[0021] Preferably, the process for obtaining the baseline fitting curve of the sample chromatogram is as follows:

[0022] The result of the Softmax function processing of the weight coefficients of each benchmark sampling point is recorded as the fitting weight of each benchmark sampling point;

[0023] The weighted least squares polynomial fitting method is used to fit the ordinate of all reference sampling points in the sample chromatogram. During the fitting process, the fitting weight of each reference sampling point is used as the weight of the corresponding reference sampling point. The resulting fitting curve is the baseline fitting curve of the sample chromatogram.

[0024] Preferably, the specific process of baseline correction of the sample chromatogram is as follows: the ordinate of each sampling point in the sample chromatogram is reassigned to its original ordinate value minus its corresponding baseline fitting value, and the chromatogram obtained after reassignment is the chromatogram of the sample chromatogram after baseline correction.

[0025] Secondly, embodiments of this application also provide a baseline correction system for a m-tert-butylphenol chromatogram, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the baseline correction method for a m-tert-butylphenol chromatogram described in any one of the above-mentioned methods.

[0026] This application has at least the following beneficial effects:

[0027] This application constructs a relative distance for spurious peaks by analyzing the relative distances between each reference sampling point and characteristic peak in the sample chromatogram, as well as the probability that the characteristic peak closest to each reference sampling point is a spurious peak. This effectively assesses the relative distance between each reference sampling point and spurious peaks in the sample chromatogram caused by noise. By analyzing the fluctuations in the power supply voltage, detector temperature, and column pressure data of the high-performance liquid chromatograph, a low-frequency interference coefficient is constructed. This accurately assesses the low-frequency noise level at each reference sampling point in the sample chromatogram when voltage, temperature, and mobile phase are unstable. By analyzing the signal intensity jitter in the sample chromatogram, a high-frequency interference coefficient is constructed. This assesses the high-frequency interference level at each reference sampling point. Combining the low-frequency interference coefficient and the relative distance for spurious peaks, the fitting weights of each reference sampling point are obtained. This assigns different weights to each reference sampling point in the sample chromatogram during baseline fitting. Compared to the traditional polynomial fitting baseline correction method using the same weights, this effectively reduces the impact of noise-affected reference sampling points in the sample chromatogram on baseline fitting, thereby enabling more accurate baseline estimation in the sample chromatogram and improving the accuracy of baseline correction. Attached Figure Description

[0028] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating the steps of a baseline correction method for a m-tert-butylphenol chromatogram provided in one embodiment of this application;

[0030] Figure 2 This is a flowchart illustrating the process of obtaining the weight coefficients of each benchmark sampling point according to one embodiment of this application. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a baseline correction method and system for m-tert-butylphenol chromatograms proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0033] The following description, in conjunction with the accompanying drawings, details the specific scheme of the baseline correction method and system for the chromatogram of m-tert-butylphenol provided in this application.

[0034] Please see Figure 1 The diagram illustrates a flowchart of a baseline correction method for a m-tert-butylphenol chromatogram according to an embodiment of this application. The method includes the following steps:

[0035] Step 1: Obtain the chromatogram of the m-tert-butylphenol sample solution throughout the entire detection process, as well as the voltage, temperature, and pressure data at each sampling time.

[0036] First, a sample solution of m-tert-butylphenol is prepared. Specifically, a certain amount of the prepared m-tert-butylphenol is accurately weighed and dissolved and diluted with methanol to obtain a sample solution. In this embodiment, the sample solution is a 1 ml solution containing 2 mg of m-tert-butylphenol. Then, a Thermo Fisher Ultimate 3000 high-performance liquid chromatograph (HPLC) is used to acquire the chromatogram of the m-tert-butylphenol sample solution. The HPLC mainly consists of a high-pressure pump, an injector, a column, a detector, and an integrator. The specific process of acquiring the chromatogram is as follows: the HPLC is set to the preset chromatographic conditions, and the prepared sample solution is injected into the injector of the HPLC. The injected sample solution is carried into the column by the mobile phase. The components in the sample solution are separated in the column and enter the detector for detection. The integrator then acquires the chromatogram of the sample solution during the entire detection process, which is recorded as the sample chromatogram. It should be noted that the horizontal and vertical axes in the chromatogram represent retention time and signal intensity, respectively.

[0037] Meanwhile, throughout the entire detection process of the detector, the voltage data of the power supply used by the high-performance liquid chromatograph is collected in real time using a voltage sensor, the temperature data of the detector is collected in real time using a temperature sensor, and the pressure data inside the chromatographic column is collected in real time using a pressure sensor. The sampling rate of voltage, temperature, and pressure data is consistent with the sampling rate when the detector collects chromatograms. In this embodiment, the sampling rate of the detector is 10Hz.

[0038] All voltage, temperature, and pressure data collected during the entire detection process are arranged in chronological order to obtain the voltage sequence, temperature sequence, and pressure sequence for the entire detection process. This sequence is used to characterize the changes in voltage, temperature, and pressure data corresponding to each sampling point in the chromatogram of the sample over time.

[0039] To avoid the impact of dimensional inconsistencies on subsequent data analysis, the Min-Max normalization method was used to normalize the collected voltage, temperature, and pressure sequences respectively, eliminating the influence of data dimensions and obtaining normalized voltage, temperature, and pressure sequences. The Min-Max normalization method is a well-known technique, and its specific process will not be elaborated further.

[0040] Step 2: Extract the time intervals of each characteristic peak in the sample chromatogram, and record all sampling points in the sample chromatogram that are outside all the time intervals as reference sampling points; obtain the standard chromatogram of m-tert-butylphenol; based on the shortest time interval between each reference sampling point and all characteristic peaks in the sample chromatogram, and the difference between the retention time of the characteristic peak corresponding to the shortest time interval and the retention time of each characteristic peak in the standard chromatogram, obtain the relative distance of the pseudo-peaks of each reference sampling point.

[0041] Since the retention time in a chromatogram is determined by thermodynamic factors during the chromatographic process, any substance will have a definite retention time under certain chromatographic operating conditions. Therefore, under the same chromatographic conditions, the characteristic peaks of each component in the sample chromatogram should have retention times that are relatively close to those of each component in the standard chromatogram of m-tert-butylphenol. However, spurious peaks generated by noise interference in the sample chromatogram of the sample solution do not have this characteristic due to their randomness.

[0042] Based on the above analysis, using the same chromatographic conditions as when collecting the sample chromatogram, a standard chromatogram of m-tert-butylphenol was obtained, along with the retention times corresponding to each characteristic peak in the standard chromatogram. The standard chromatogram refers to a noise-free chromatogram collected for m-tert-butylphenol. In this embodiment, the components corresponding to all characteristic peaks in the standard chromatogram include phenol, m-tert-butylphenol, p-tert-butylphenol, o-tert-butylphenol, and 3,5-di-tert-butylphenol. The retention times of the characteristic peaks corresponding to these components in the standard chromatogram are 12.9, 24.1, 24.4, 26.4, and 31.7, respectively.

[0043] Furthermore, a derivative-based peak detection method is used to extract the start point, peak point, and end point of all characteristic peaks in the sample chromatogram. The time interval between the start and end points of each characteristic peak is recorded as the time interval of each characteristic peak. All sampling points in the sample chromatogram located outside the time intervals of all characteristic peaks are recorded as the baseline sampling points of the sample chromatogram, which will be used as sampling points for subsequent baseline fitting of the sample chromatogram. The derivative-based peak detection method is a well-known technique, and its specific process will not be described in detail.

[0044] Taking any characteristic peak 'a' in the sample chromatogram as an example, calculate the absolute difference between the retention time of characteristic peak 'a' and the retention times of all characteristic peaks in the standard chromatogram. Record the minimum of all obtained absolute differences as the pseudo-peak characteristic value of characteristic peak 'a'. The larger the pseudo-peak characteristic value, the more likely characteristic peak 'a' is a pseudo-peak caused by noise interference in the sample chromatogram, rather than the true characteristic peaks corresponding to the components in the sample solution in the sample chromatogram. The retention time of each characteristic peak refers to the retention time corresponding to the peak point of each characteristic peak.

[0045] In a preferred embodiment, the relative distance of spurious peaks at each benchmark sampling point is obtained based on the shortest time interval between each benchmark sampling point and all characteristic peaks in the sample chromatogram, and the difference between the retention time of the characteristic peak corresponding to the shortest time interval and the retention time of each characteristic peak in the standard chromatogram. This distance is used to assess the probability that each benchmark sampling point is near a spurious peak in the sample chromatogram. The method for obtaining the relative distance of spurious peaks at each benchmark sampling point is as follows: statistically analyze the spurious peak characteristic values ​​of each characteristic peak in the sample chromatogram; statistically analyze the minimum absolute difference between the retention time of each benchmark sampling point and the retention time of all characteristic peaks in the sample chromatogram; the relative distance of spurious peaks at each benchmark sampling point is positively correlated with the minimum value and negatively correlated with the spurious peak characteristic value of the characteristic peak corresponding to the minimum value. Here, the positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), and the negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases).

[0046] Preferably, in this embodiment, the relative distance of the pseudo-peak at the reference sampling point b is denoted as... Its specific expression is: In the formula, The relative distance between the pseudo-peaks at the reference sampling point b; It is the minimum absolute difference between the retention time of the baseline sampling point b and the retention times of all characteristic peaks in the sample chromatogram; The pseudo-peak characteristic value is the minimum absolute difference between the retention time of the baseline sampling point b and the retention times of all characteristic peaks in the sample chromatogram. This is a preset constant used to prevent the denominator from being 0, and its value range is [0.0001, 0.01]. In this embodiment, the preset constant is... Set to 0.0001.

[0047] income The smaller the value, the greater the likelihood that the baseline sampling point b is near a spurious peak. Therefore, when performing baseline fitting on the sample chromatogram, the weight of the baseline sampling point b in the baseline fitting process should be smaller to reduce the impact of sampling points near spurious peaks caused by noise interference in the sample chromatogram on the accuracy of subsequent baseline fitting.

[0048] Step 3: Based on the fluctuation of voltage, temperature, and pressure data at each benchmark sampling point at the given time, obtain the low-frequency interference coefficient of each benchmark sampling point; based on the jitter of signal intensity at each benchmark sampling point in the sample chromatogram at the given time, obtain the high-frequency interference coefficient of each benchmark sampling point; and combine the relative distance of the pseudo-peaks and the low-frequency interference coefficient of each benchmark sampling point to obtain the weighting coefficient of each benchmark sampling point. Use this to perform a weighted fitting of the ordinate of all benchmark sampling points to obtain the baseline fitting curve of the sample chromatogram, and then perform baseline correction on the sample chromatogram.

[0049] Furthermore, due to the instability of operating conditions such as power supply voltage, detector temperature, and mobile phase in the high-performance liquid chromatograph (HPLC), low-frequency noise can be introduced into the sample chromatogram. Therefore, the greater the fluctuation in the power supply voltage, detector temperature, or column pressure of the HPLC at a given moment, the greater the degree of low-frequency noise interference on the signal intensity of the corresponding moment in the sample chromatogram. Therefore, when performing baseline correction on the sample chromatogram of the sample solution, in order to reduce the impact of data at benchmark sampling points with high levels of low-frequency noise interference on the baseline fitting results, it is necessary to analyze the low-frequency noise interference experienced by each benchmark sampling point.

[0050] Specifically, the standard deviation of each data point in the normalized voltage, temperature, and pressure sequences is obtained using the moving standard deviation method. Each standard deviation represents the degree of local fluctuation of the corresponding data point in the voltage, temperature, and pressure sequences. The larger the standard deviation of a data point, the greater its local data fluctuation in the voltage, temperature, or pressure sequence. In this embodiment, the sliding window width in the moving standard deviation method is set to 11. It should be noted that in the sliding windows for the starting and ending data of the voltage, temperature, and pressure sequences, the mean of all data within the window is used to fill in any missing data. The moving standard deviation method is a well-known technique, and its specific process will not be elaborated further.

[0051] Taking the baseline sampling point b of the sample chromatogram as an example, the mean of the standard deviations of the corresponding data points in the voltage, temperature, and pressure sequences at the time of baseline sampling point b is recorded as the low-frequency interference coefficient of baseline sampling point b. This coefficient is used to evaluate the degree of low-frequency noise introduced by the baseline sampling point b in the sample chromatogram due to voltage, temperature, and mobile phase instability during data acquisition. The larger the value, the greater the degree of low-frequency noise at the baseline sampling point b. Therefore, when performing baseline fitting on the sample chromatogram, the weight of the baseline sampling point b in the baseline fitting process should be set smaller to reduce the impact of data changes caused by the introduction of low-frequency noise in the sample chromatogram on the final fitted baseline.

[0052] Furthermore, in an ideal situation, the data changes at sampling points outside the characteristic peaks in a chromatogram are smooth and continuous. However, high-frequency noise in the sample chromatogram usually causes data jitter at the sampling points. Therefore, in order to reduce the impact of data at sampling points affected by high-frequency noise in the collected sample chromatogram on the baseline fitting results, it is also necessary to analyze the high-frequency noise interference at each benchmark sampling point.

[0053] The retention times of all characteristic peaks in the sample chromatogram are used as segmentation time points. All benchmark sampling points are then divided into multiple sampling point subsets according to these time points. The Savitzky Golay Filter (SG Filter) is used to smooth the signal intensity of all benchmark sampling points in each subset. The absolute difference between the signal intensity of each benchmark sampling point before and after smoothing is recorded as the high-frequency interference coefficient of each benchmark sampling point. This coefficient is used to evaluate the data jitter of the signal intensity at each benchmark sampling point. A larger value indicates greater data jitter and thus greater high-frequency noise at that point. Therefore, when performing baseline fitting on the sample chromatogram, the weight of the benchmark sampling point should be set smaller to reduce the impact of data changes caused by high-frequency noise in the sample chromatogram on the final fitted baseline. The SG Filter algorithm is a well-known technique, and its specific process will not be described in detail here.

[0054] Furthermore, the Min-Max normalization method is used to normalize the relative distance of the pseudo-peaks, the low-frequency interference coefficient, and the high-frequency interference coefficient of all reference sampling points. The Min-Max normalization method is a well-known technique, and the specific process will not be described in detail.

[0055] In a preferred embodiment, weight coefficients for each benchmark sampling point are obtained based on the relative distance of the spurious peaks, the low-frequency interference coefficient, and the high-frequency interference coefficient. These weight coefficients are used to evaluate the weight of each benchmark sampling point in the baseline fitting process when performing baseline fitting on the sample chromatogram. The weight coefficients of each benchmark sampling point are positively correlated with the relative distance of the spurious peaks and negatively correlated with the low-frequency interference coefficient and the high-frequency interference coefficient, respectively. The flowchart for obtaining the weight coefficients of each benchmark sampling point is shown below. Figure 2 As shown.

[0056] In this embodiment, the weighting coefficient of the reference sampling point b is denoted as Its specific expression is: In the formula, The weighting coefficient for the reference sampling point b; This represents the normalized result of the relative distance between the pseudo-peaks at the reference sampling point b. This represents the mean between the normalized results of the low-frequency interference coefficient and the high-frequency interference coefficient at the reference sampling point b. This is a preset constant used to prevent the denominator from being 0. Its value range is [0.0001, 0.01]. In this embodiment, it is set to 0.0001.

[0057] Furthermore, the Softmax function is used to process the weight coefficients of all benchmark sampling points, ensuring that the values ​​of all processed weight coefficients fall within the range of (0,1) and that the sum of all processed weight coefficients is 1. The Softmax function processing result of the weight coefficients of each benchmark sampling point is recorded as the fitting weight of each benchmark sampling point, which is used as the weight magnitude of each benchmark sampling point in the subsequent baseline fitting process of the sample chromatogram. The Softmax function is a well-known technique, and its specific process will not be elaborated further.

[0058] Furthermore, a weighted least squares polynomial fitting method is used to fit the ordinates of all benchmark sampling points in the sample chromatogram. During the fitting process, the calculated fitting weights of each benchmark sampling point are used as the weights of their respective benchmark sampling points. The resulting fitted curve is the baseline fitted curve of the sample chromatogram. The weighted least squares polynomial fitting method is a well-known technique, and its specific process will not be elaborated further.

[0059] The ordinate of each sampling point in the sample chromatogram is reassigned to its original ordinate value minus its corresponding baseline fitting value. The resulting chromatogram after reassignment is the baseline-corrected chromatogram of the sample chromatogram, thus completing the baseline correction process for the chromatogram of the m-tert-butylphenol sample solution.

[0060] Based on the same inventive concept as the above method, this application embodiment also provides a baseline correction system for a m-tert-butylphenol chromatogram, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method described in the above-mentioned baseline correction method for a m-tert-butylphenol chromatogram.

[0061] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0062] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0063] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A baseline correction method for a chromatogram of m-tert-butylphenol, characterized in that, The method includes the following steps: Obtain the sample chromatogram of m-tert-butylphenol sample solution throughout the entire detection process, as well as the voltage, temperature, and pressure data at each sampling time; the horizontal and vertical axes of the chromatogram are retention time and signal intensity, respectively; Extract the time intervals of each characteristic peak in the sample chromatogram, and record all sampling points in the sample chromatogram that are outside all the time intervals as reference sampling points; obtain the standard chromatogram of m-tert-butylphenol; based on the shortest time interval between each reference sampling point and all characteristic peaks in the sample chromatogram, and the difference between the retention time of the characteristic peak corresponding to the shortest time interval and the retention time of each characteristic peak in the standard chromatogram, obtain the relative distance of the pseudo peaks of each reference sampling point; Based on the fluctuation of voltage, temperature, and pressure data at each benchmark sampling point at the given time, the low-frequency interference coefficient of each benchmark sampling point is obtained. Based on the jitter of signal intensity at each benchmark sampling point in the sample chromatogram at the given time, the high-frequency interference coefficient of each benchmark sampling point is obtained. Combined with the relative distance of the pseudo-peaks and the low-frequency interference coefficient of each benchmark sampling point, the weighting coefficient of each benchmark sampling point is obtained. The ordinate of all benchmark sampling points is then weighted and fitted to obtain the baseline fitting curve of the sample chromatogram, and the baseline of the sample chromatogram is then corrected. The method for obtaining the relative distance between the pseudo-peaks at each reference sampling point is as follows: Based on the retention time differences between each characteristic peak in the sample chromatogram and all characteristic peaks in the standard chromatogram, the pseudo-peak characteristic values ​​of each characteristic peak in the sample chromatogram are obtained; The minimum absolute difference between the retention time of each baseline sampling point and the retention time of all characteristic peaks in the chromatogram of the statistical sample; The relative distance between the pseudo-peaks at each benchmark sampling point is positively correlated with the minimum value, and negatively correlated with the pseudo-peak characteristic value of the characteristic peak corresponding to the minimum value. The low-frequency interference coefficient of each reference sampling point refers to the mean of the standard deviation of the data points in the sliding window of the voltage, temperature, and pressure sequences at the time of each reference sampling point; where the voltage sequence, temperature sequence, and pressure sequence refer to the sequences composed of all voltage, temperature, and pressure data arranged in chronological order throughout the entire detection process. The method for obtaining the high-frequency interference coefficients at each reference sampling point is as follows: All baseline sampling points are divided into multiple sampling point subsets according to the retention times of all characteristic peaks in the sample chromatogram; Smooth the signal intensity of all reference sampling points in each sampling point subset; The absolute difference between the signal strength of each reference sampling point before and after smoothing is recorded as the high-frequency interference coefficient of each reference sampling point.

2. The baseline correction method for a m-tert-butylphenol chromatogram as described in claim 1, characterized in that, The time interval of each characteristic peak in a sample chromatogram refers to the time interval between the start and end of each characteristic peak in the sample chromatogram.

3. The baseline correction method for a m-tert-butylphenol chromatogram as described in claim 1, characterized in that, The spurious peak characteristic value of each characteristic peak in the sample chromatogram refers to the minimum absolute difference between the retention time of each characteristic peak in the sample chromatogram and the retention time of all characteristic peaks in the standard chromatogram.

4. The baseline correction method for a m-tert-butylphenol chromatogram as described in claim 1, characterized in that, The weighting coefficients of each reference sampling point are positively correlated with the relative distance of the pseudo-peak, and negatively correlated with the low-frequency interference coefficient and the high-frequency interference coefficient, respectively.

5. The baseline correction method for a m-tert-butylphenol chromatogram as described in claim 1, characterized in that, The process of obtaining the baseline fitting curve of the sample chromatogram is as follows: The result of the Softmax function processing of the weight coefficients of each benchmark sampling point is recorded as the fitting weight of each benchmark sampling point; The weighted least squares polynomial fitting method is used to fit the ordinate of all reference sampling points in the sample chromatogram. During the fitting process, the fitting weight of each reference sampling point is used as the weight of the corresponding reference sampling point. The resulting fitting curve is the baseline fitting curve of the sample chromatogram.

6. The baseline correction method for a m-tert-butylphenol chromatogram as described in claim 1, characterized in that, The specific process of baseline correction for sample chromatograms is as follows: the ordinate of each sampling point in the sample chromatogram is reassigned to its original ordinate value minus its corresponding baseline fitting value. The chromatogram obtained after reassignment is the chromatogram of the sample chromatogram after baseline correction.

7. A baseline correction system for a m-tert-butylphenol chromatogram, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the baseline correction method for a m-tert-butylphenol chromatogram as described in any one of claims 1-6.

Images