Chromatographic peak identification method and equipment for supercritical fluid chromatography system and medium

Abstract

The invention discloses a chromatographic peak identification method and equipment for a supercritical fluid chromatographic system and a medium, and relates to the technical field of chromatographic analysis. The chromatographic peak identification method comprises the following steps: acquiring original chromatographic data of a sample to be detected; performing noise reduction on the original chromatographic data through wavelet transform, and removing signal low-frequency components based on wavelet energy distribution to obtain a chromatogram after baseline correction; and adjusting a peak height threshold value and a slope threshold value on the chromatogram after baseline correction by using a sliding window, analyzing a signal value and a slope change trend of a detection point section by section, and determining the position of a characteristic point of a chromatographic peak. By the adoption of the method, the peak height threshold value and the slope threshold value can be dynamically adjusted, the method better fits the actual situation, the capacity of detecting weak signals and complex peak shapes is enhanced, the positions of the chromatographic peak feature points can be more finely and accurately recognized, and the precision of subsequent analysis results is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of chromatographic analysis, and particularly to a chromatographic peak identification method, device, and medium for a supercritical fluid chromatography system. Background Art

[0002] A supercritical fluid chromatography (SFC) system uses carbon dioxide as the main mobile phase. Through the interaction between compounds and the stationary phase, it can efficiently separate and analyze natural products, macromolecular compounds, thermally unstable substances, chiral compounds, etc. In a supercritical fluid chromatography system, the separated chromatographic signals may exhibit chromatographic peak overlap, tailing, or incomplete separation due to the similar retention behavior of target compounds and sample complexity, affecting the accuracy of subsequent quantitative analysis.

[0003] Currently, related technologies detect the rise and fall points of chromatographic peaks through fixed thresholds or simple signal intensity changes. However, in the face of overlapping peaks, asymmetric peaks, and complex baseline drifts, these methods often struggle to accurately capture key feature points, prone to omissions and misjudgments. At the same time, actual measurements are inevitably affected by factors such as noise interference, further reducing the stability and accuracy of detection. Summary of the Invention

[0004] In view of the above defects or deficiencies in the related technologies, it is desirable to provide a chromatographic peak identification method, device, and medium for a supercritical fluid chromatography system that can accurately identify the chromatographic peaks of each component and improve the accuracy of subsequent analysis results.

[0005] In a first aspect, the present application provides a chromatographic peak identification method for a supercritical fluid chromatography system, the chromatographic peak identification method comprising:

[0006] Obtaining the original chromatographic data of the sample to be measured;

[0007] Denosing the original chromatographic data through wavelet transform and removing the low-frequency components of the signal based on wavelet energy distribution to obtain a chromatogram after baseline correction;

[0008] On the chromatogram after baseline correction, adjusting the peak height threshold and slope threshold using a sliding window, and analyzing the signal values and slope change trends of the detection points segment by segment to determine the positions of the characteristic points of the chromatographic peaks.

[0009] Optionally, in some embodiments of the present application, the adjusting the peak height threshold and slope threshold using a sliding window on the chromatogram after baseline correction includes:

[0010] Initialize the peak height threshold and the slope threshold, and select a target interval on the chromatogram after baseline correction to set the sliding window, where the target interval includes the rising curve;

[0011] Traverse the signal values and slopes of each detection point in the sliding window in sequence. If the signal value and slope of the detection point are both greater than the currently recorded maximum signal value and maximum slope, update the current consecutive rising count and the total rising count. When the total rising count reaches the preset rising threshold, use the signal value corresponding to the total rising count as the preset peak height threshold and the slope corresponding to the total rising count as the preset slope threshold.

[0012] Optionally, in some embodiments of the present application, analyzing the changing trends of the signal values and slopes of the detection points segment by segment to determine the characteristic point positions of the chromatographic peaks includes:

[0013] When the slope of the detection point in the sliding window changes from zero to a positive number and shows an increasing trend, and the rising amplitude of the slope of the detection point reaches the preset slope threshold, determine the detection point as the starting point of the chromatographic peak;

[0014] When the slope of the detection point in the sliding window changes from a positive number to a negative number and shows a decreasing trend, determine the detection point as the apex of the chromatographic peak;

[0015] When the slope of the detection point in the sliding window changes from a negative number to zero and shows a horizontal trend, and the signal value of the detection point remains unchanged, determine the detection point as the end point of the chromatographic peak.

[0016] Optionally, in some embodiments of the present application, the method further includes:

[0017] When the slope of the detection point in the sliding window changes from a negative number to a positive number and shows an increasing trend, the rising amplitude of the slope of the detection point reaches the preset slope threshold, and the signal value of the detection point shows an increasing trend and the rising amplitude reaches the preset peak height threshold, determine the detection point as the overlapping peak valley point.

[0018] Optionally, in some embodiments of the present application, denoising the original chromatographic data by wavelet transform includes:

[0019] According to different wavelet bases and decomposition levels, extract the signal values of the original chromatographic data and calculate the signal-to-noise ratio and mean square error of each layer. Take the combination corresponding to the maximum signal-to-noise ratio and the minimum mean square error as the target wavelet base and the first target decomposition level;

[0020] Use the target wavelet base and the first target decomposition level to decompose and reconstruct the original chromatographic data to obtain the denoised chromatographic data.

[0021] Optionally, in some embodiments of the present application, removing the low-frequency components of the signal based on the wavelet energy distribution to obtain a chromatogram after baseline correction includes:

[0022] Using the target wavelet basis, performing multi-level decomposition on the denoised chromatographic data and calculating the wavelet detail frequency and wavelet approximation frequency of each level;

[0023] Using the wavelet detail frequency and the wavelet approximation frequency of each level, calculating the frequency coefficient of each level, taking the decomposition level corresponding to the maximum frequency coefficient as the second target decomposition level, and setting the low-frequency approximation coefficients below the second target decomposition level to zero and reconstructing the signal to obtain the chromatogram after baseline correction.

[0024] Optionally, the wavelet detail frequency Fd in some embodiments of the present application n is obtained by the following formula:

[0025]

[0026] The wavelet approximation frequency Fa n is obtained by the following formula:

[0027]

[0028] where cD n represents the wavelet detail value energy, cA n represents the wavelet approximation value energy, n = 1, 2,..., N, N represents the signal length; ld n represents the number of discrete values of cD n and la n represents the number of discrete values of cA n .

[0029] Optionally, the frequency coefficient k in some embodiments of the present application is obtained by the following formula:

[0030]

[0031] where Fd n represents the wavelet detail frequency and Fa n represents the wavelet approximation frequency.

[0032] In a second aspect, the present application provides a terminal device, which includes a processor and a memory. At least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of the chromatographic peak recognition method described in any item of the first aspect.

[0033] In a third aspect, the present application provides a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the steps of the chromatographic peak identification method according to any one of the first aspect.

[0034] As can be seen from the above technical solutions, the embodiments of the present application have the following advantages:

[0035] The embodiments of the present application provide a chromatographic peak identification method, device and medium for a supercritical fluid chromatography system. The wavelet transform is used to denoise the original chromatographic data of the sample to be measured, avoiding noise interference. The wavelet energy distribution is used to remove the low-frequency components of the signal, retaining the key detailed features of the signal. At the same time, the accuracy and reliability of the data are significantly improved. Therefore, the peak height threshold and slope threshold can be dynamically adjusted by using a sliding window on the obtained chromatogram after baseline correction, which is more in line with the actual situation, enhancing the ability to detect weak signals and complex peak shapes. Furthermore, by analyzing the signal value and slope change trend of the detection points segment by segment, the position of the chromatographic peak feature points can be more accurately identified, effectively improving the accuracy of the subsequent analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 It is a schematic flowchart of a chromatographic peak identification method for a supercritical fluid chromatography system provided by an embodiment of the present application;

[0038] Figure 2 It is a chromatogram corresponding to the original chromatographic data of a sample to be measured provided by an embodiment of the present application;

[0039] Figure 3 It is a chromatogram corresponding to the denoised chromatographic data provided by an embodiment of the present application;

[0040] Figure 4 It is a chromatogram after baseline correction provided by an embodiment of the present application;

[0041] Figure 5 It is a schematic diagram of the chromatographic peak identification result provided by an embodiment of the present application;

[0042] Figure 6 It is a provided by an embodiment of the present application Figure 5 Partial enlarged view of the shown chromatographic peak identification result;

[0043] Figure 7 It is a structural block diagram of a chromatographic peak identification device provided by an embodiment of the present application;

[0044] Figure 8 It is a structural block diagram of a terminal device provided by an embodiment of the present application. Specific embodiments

[0045] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0046] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific embodiments.

[0047] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The following Figures 1 to 8 elaborates in detail the chromatographic peak identification method, device, and medium for a supercritical fluid chromatography system provided by the embodiments of the present application.

[0048] Please refer to Figure 1 , which is a flow schematic diagram of a chromatographic peak identification method for a supercritical fluid chromatography system provided by an embodiment of the present application. The chromatographic peak identification method specifically includes the following steps:

[0049] S101, obtain the original chromatographic data of the sample to be measured.

[0050] In some embodiments of the present application, the sample to be measured may be a conjugated linoleic acid sample, and the chromatogram corresponding to its original chromatographic data is as Figure 2 shown. The abscissa represents time, with the unit of second (s), and the ordinate represents signal intensity, with the unit of millivolt (mv).

[0051] S102, perform noise reduction on the original chromatographic data through wavelet transform, and remove the low-frequency components of the signal based on the wavelet energy distribution to obtain a chromatogram after baseline correction.

[0052] In some embodiments of the present application, the original chromatographic data can be used in a Matlab simulation experiment to extract the signal values of the original chromatographic data and calculate the signal-to-noise ratio SNR and mean square error MSE of each layer according to different wavelet bases and decomposition levels. The combination corresponding to the maximum signal-to-noise ratio and the minimum mean square error is used as the target wavelet base and the first target decomposition level. For example, the signal-to-noise ratio SNR can be obtained through Equation (1), that is:

[0053]

[0054] The mean square error MSE can be obtained by Equation (2), that is:

[0055]

[0056] In Equations (1) and (2), d(t) represents the noisy chromatographic signal, represents the denoised chromatographic signal, N represents the signal length, and t represents time. When the signal-to-noise ratio SNR is larger, the denoising effect is better, and when the mean square error MSE is smaller, the signal restoration effect is better. Finally, the original chromatographic data is decomposed and reconstructed using the db1 wavelet basis and 4 decomposition levels to obtain the denoised chromatographic data, and the processing results are as Figure 3 shown. It can be seen from the figure that a chromatographic signal with a high signal-to-noise ratio and a low root mean square error can be obtained after wavelet decomposition denoising, the high-frequency noise is eliminated, and the distortion degree of the chromatographic signal is effectively reduced.

[0057] Furthermore, the target wavelet basis db1 can be first used to perform multi-level decomposition on the denoised chromatographic data and calculate the wavelet detail frequency Fd n and the wavelet approximation frequency Fa n . For example, the wavelet detail frequency Fd n can be obtained by Equation (3), that is:

[0058]

[0059] The wavelet approximation frequency Fa n can be obtained by Equation (4), that is:

[0060]

[0061] In Equations (3) and (4), cD n represents the energy of the wavelet detail value, cA n represents the energy of the wavelet approximation value, n = 1, 2,..., N, N represents the signal length; ld n represents the number of discrete values of cD n , la n represents the number of discrete values of cA n .

[0062] Then, using the wavelet detail frequency Fd n and the wavelet approximation frequency Fa n of each layer, calculate the frequency coefficient k of each layer, and take the decomposition layer corresponding to the largest frequency coefficient as the second target decomposition layer. For example, the frequency coefficient k can be obtained by Equation (5), that is:

[0063]

[0064] In formula (5), Fd n represents the wavelet detail frequency, and Fa n represents the wavelet approximation frequency. By comparing the frequency coefficients k of different decomposition levels of the db1 wavelet basis, it is obtained that when the decomposition level is 5, the frequency coefficient k = 1.14 reaches the maximum. That is to say, the second target decomposition level is 5. Therefore, the low-frequency approximation coefficients below this second target decomposition level are set to zero and the signal is reconstructed, thereby obtaining the chromatogram after baseline correction. The processing result is as Figure 4 shown. It can be seen from the figure that all baseline intervals are accurately identified, and when the baseline of the denoised signal is subtracted, the noise signals in the sub-intervals are effectively removed.

[0065] S103. On the chromatogram after baseline correction, use a sliding window to adjust the peak height threshold and slope threshold, and analyze the signal values and slope change trends of the detection points segment by segment to determine the positions of the characteristic points of the chromatographic peaks.

[0066] In some embodiments of the present application, the peak height threshold and slope threshold can be initialized first, and a target interval including the rising curve is selected on the chromatogram after baseline correction to set a sliding window. For example, both the initialized peak height threshold and slope threshold are 2 times the signal noise standard deviation, and the sliding window includes 6 detection points. Then, sequentially traverse the signal values and slopes (i.e., the first derivative) of each detection point in the sliding window. If the signal value and slope of the detection point are both greater than the currently recorded maximum signal value and maximum slope, then update the current rising continuous count and the total rising count. That is to say, the total rising count = the previous total rising count + the current rising continuous count. Otherwise, the current rising continuous count is zero, and the total rising count = the previous total rising count. When the total rising count reaches the preset rising threshold, it indicates that there is a significant rising trend. The signal value corresponding to the total rising count is used as the preset peak height threshold, and the slope corresponding to the total rising count is used as the preset slope threshold. Similarly, when judging a significant falling trend, the target interval includes the falling curve. If the signal value and slope of the detection point are both less than the currently recorded minimum signal value and minimum slope, then update the current falling continuous count and the total falling count. That is to say, the total falling count = the previous total falling count + the current falling continuous count. Otherwise, the current falling continuous count is zero, and the total falling count = the previous total falling count. When the total falling count reaches the preset falling threshold, the signal value corresponding to the total falling count is used as the preset peak height threshold, and the slope corresponding to the total falling count is used as the preset slope threshold.

[0067] Further, a sliding window is used to segment the chromatogram after baseline correction, and trend analysis is performed within each sliding window. For example, when the slope of the detection points within the sliding window changes from zero to a positive number and shows an increasing trend, and the rising amplitude of the slope of the detection points reaches a preset slope threshold, the detection point is determined as the starting point of the chromatographic peak; when the slope of the detection points within the sliding window changes from a positive number to a negative number and shows a decreasing trend, the detection point is determined as the apex of the chromatographic peak; when the slope of the detection points within the sliding window changes from a negative number to zero and shows a horizontal trend, and the signal value of the detection points remains unchanged, the detection point is determined as the end point of the chromatographic peak; and when the slope of the detection points within the sliding window changes from a negative number to a positive number and shows an increasing trend, the rising amplitude of the slope of the detection points reaches a preset slope threshold, and the signal value of the detection points shows an increasing trend and the rising amplitude reaches a preset peak height threshold, the detection point is determined as the overlapping peak valley point. The processing results are as Figure 5 and Figure 6 shown. It can be seen from the figure that by combining the sliding window and slope trend analysis, the characteristic points of the chromatographic peak are effectively identified, significantly improving the accuracy and robustness, providing a reliable guarantee for the subsequent analysis of the components of the sample to be tested.

[0068] The chromatographic peak recognition method provided by the embodiments of the present application uses wavelet transform to denoise the original chromatographic data of the sample to be tested, avoiding noise interference, and uses wavelet energy distribution to remove the low-frequency components of the signal, retaining the key detailed features of the signal. At the same time, the accuracy and reliability of the data are significantly improved. Thus, the peak height threshold and slope threshold can be dynamically adjusted using a sliding window on the obtained chromatogram after baseline correction, which is more in line with the actual situation, enhancing the ability to detect weak signals and complex peak shapes. Furthermore, by analyzing the signal value and slope change trend of the detection points segment by segment, the position of the characteristic points of the chromatographic peak can be more accurately and delicately identified, effectively improving the accuracy of the subsequent analysis results.

[0069] Based on the foregoing embodiments, the embodiments of the present application provide a chromatographic peak recognition device for a supercritical fluid chromatography system. The chromatographic peak recognition device 100 can be applied to Figures 1 to 6 the chromatographic peak recognition method corresponding to the embodiment. Please refer to Figure 7 , the chromatographic peak recognition device 100 includes:

[0070] An acquisition module 101 configured to acquire the original chromatographic data of the sample to be tested;

[0071] A preprocessing module 102 configured to denoise the original chromatographic data by wavelet transform and remove the low-frequency components of the signal based on wavelet energy distribution to obtain a chromatogram after baseline correction;

[0072] The determination module 103 is configured to adjust the peak height threshold and the slope threshold by using a sliding window on the chromatogram after baseline correction, and analyze the signal values and the slope change trends of the detection points segment by segment to determine the positions of the characteristic points of the chromatographic peaks.

[0073] Optionally, in some embodiments of the present application, the determination module 103 is specifically configured to initialize the peak height threshold and the slope threshold, and select a target interval on the chromatogram after baseline correction to set a sliding window, where the target interval includes an ascending curve;

[0074] Traverse the signal values and slopes of each detection point in the sliding window in sequence. If the signal value and slope of the detection point are both greater than the currently recorded maximum signal value and maximum slope, update the current ascending continuous count and the total ascending count. When the total ascending count reaches a preset ascending threshold, use the signal value corresponding to the total ascending count as the preset peak height threshold and the slope corresponding to the total ascending count as the preset slope threshold.

[0075] Optionally, in some embodiments of the present application, the determination module 103 is specifically configured to, when the slope of the detection point in the sliding window changes from zero to a positive number and shows an increasing trend, and the rising amplitude of the slope of the detection point reaches the preset slope threshold, determine the detection point as the starting point of the chromatographic peak;

[0076] When the slope of the detection point in the sliding window changes from a positive number to a negative number and shows a decreasing trend, determine the detection point as the apex of the chromatographic peak;

[0077] When the slope of the detection point in the sliding window changes from a negative number to zero and shows a horizontal trend, and the signal value of the detection point remains unchanged, determine the detection point as the end point of the chromatographic peak.

[0078] Optionally, in some embodiments of the present application, the determination module 103 is further specifically configured to, when the slope of the detection point in the sliding window changes from a negative number to a positive number and shows an increasing trend, the rising amplitude of the slope of the detection point reaches the preset slope threshold, and the signal value of the detection point shows an increasing trend and the rising amplitude reaches the preset peak height threshold, determine the detection point as an overlapping peak valley point.

[0079] Optionally, in some embodiments of the present application, the preprocessing module 102 is specifically configured to extract the signal values of the original chromatographic data and calculate the signal-to-noise ratio and the mean square error of each layer according to different wavelet bases and decomposition levels, and use the combination corresponding to the maximum signal-to-noise ratio and the minimum mean square error as the target wavelet base and the first target decomposition level;

[0080] Decompose and reconstruct the original chromatographic data by using the target wavelet base and the first target decomposition level to obtain denoised chromatographic data.

[0081] Optionally, in some embodiments of the present application, the preprocessing module 102 is specifically configured to perform multi-layer decomposition on the denoised chromatographic data by using the target wavelet base and calculate the wavelet detail frequency and the wavelet approximation frequency of each layer;

[0082] Using the wavelet detail frequency and wavelet approximation frequency of each layer, calculate the frequency coefficient of each layer. Take the decomposition layer corresponding to the largest frequency coefficient as the second target decomposition layer, set the low-frequency approximation coefficients below the second target decomposition layer to zero and reconstruct the signal to obtain the chromatogram after baseline correction.

[0083] Optionally, in some embodiments of the present application, the wavelet detail frequency Fd n is obtained by the following formula:

[0084]

[0085] The wavelet approximation frequency Fa n is obtained by the following formula:

[0086]

[0087] where cD n represents the wavelet detail value energy, cA n represents the wavelet approximation value energy, n = 1, 2,..., N, and N represents the signal length; ld n represents the number of discrete values of cD n and la n represents the number of discrete values of cA n .

[0088] Optionally, in some embodiments of the present application, the frequency coefficient k is obtained by the following formula:

[0089]

[0090] where Fd n represents the wavelet detail frequency, and Fa n represents the wavelet approximation frequency.

[0091] It should be noted that the descriptions of the same steps and the same content in this embodiment and other embodiments can be referred to the descriptions in other embodiments, and will not be repeated here.

[0092] The chromatographic peak recognition device provided by the embodiments of the present application uses wavelet transform to denoise the original chromatographic data of the sample to be measured, avoiding noise interference, and uses wavelet energy distribution to remove the low-frequency components of the signal, retaining the key detail features of the signal. At the same time, it significantly improves the accuracy and reliability of the data. Therefore, the peak height threshold and slope threshold can be dynamically adjusted using a sliding window on the obtained chromatogram after baseline correction, which is more in line with the actual situation, enhances the ability to detect weak signals and complex peak shapes, and then accurately identifies the positions of the chromatographic peak feature points more delicately by analyzing the signal value and slope change trend of each detection point segment by segment, effectively improving the accuracy of the subsequent analysis results.

[0093] Based on the foregoing embodiments, an embodiment of the present application provides a terminal device. Please refer to Figure 8 , the terminal device 200 may include a processor 201 and a memory 202. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory 202, and the instruction, program, code set, or instruction set is loaded and executed by the processor 201 to implement Figures 1 to 6 the steps of the chromatographic peak recognition method corresponding to the embodiment.

[0094] As another aspect, an embodiment of the present application provides a computer-readable storage medium for storing program code, and the program code is used to execute any one of the implementation manners in the chromatographic peak recognition method corresponding to the foregoing Figures 1 to 6 embodiment.

[0095] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0096] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other can be indirect couplings or communication connections through some interfaces, devices, or modules, and can be in electrical, mechanical, or other forms. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] In addition, in each embodiment of the present application, the functional modules can be integrated into a processing unit, or each module exists physically alone, or two or more units can be integrated into one module. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0098] Based on such understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the chromatographic peak recognition method in various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0099] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0100] Specific examples are used in this article to elaborate on the principle and implementation manner of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A chromatographic peak identification method for a supercritical fluid chromatography system, characterized in that: The chromatographic peak identification method comprises: Obtaining original chromatographic data of the sample to be tested; De-noising the raw chromatographic data by wavelet transform, and removing low-frequency components of the signal based on wavelet energy distribution to obtain a chromatogram after baseline correction; The peak height threshold and the slope threshold are adjusted using a sliding window on the chromatogram after baseline correction, and the signal value and slope change trend of the detection point are analyzed section by section to determine the characteristic point position of the chromatographic peak.

2. The chromatographic peak identification method according to claim 1, characterized in that: The step of adjusting the peak height threshold and the slope threshold using a sliding window on the chromatogram after baseline correction comprises: Initializing the peak height threshold and the slope threshold, and selecting a target interval on the baseline-corrected chromatogram to set the sliding window, wherein the target interval includes an ascending curve; The signal value and slope of each detection point in the sliding window are traversed in turn. If the signal value and slope of the detection point are greater than the maximum signal value and maximum slope of the current record, the number of consecutive rises and the total number of rises are updated. When the total number of rises reaches the preset rising threshold, the signal value corresponding to the total number of rises is used as the preset peak height threshold and the slope corresponding to the total number of rises is used as the preset slope threshold.

3. The chromatographic peak identification method according to claim 2, characterized in that: The step of analyzing the signal value and slope change trend of the detection point section by section to determine the characteristic point position of the chromatographic peak includes: When the slope of the detection point in the sliding window changes from zero to a positive number and shows an increasing trend, and the rising amplitude of the slope of the detection point reaches the preset slope threshold, the detection point is determined as the starting point of the chromatographic peak; When the slope of the detection point in the sliding window changes from a positive number to a negative number and shows a decreasing trend, the detection point is determined as the apex of the chromatographic peak; When the slope of the detection point in the sliding window changes from a negative number to zero and presents a horizontal trend, and the signal value of the detection point remains unchanged, the detection point is determined as the end point of the chromatographic peak.

4. The chromatographic peak identification method according to claim 3, characterized in that: The method further comprises: When the slope of the detection point in the sliding window changes from negative to positive and shows an increasing trend, the rising amplitude of the slope of the detection point reaches the preset slope threshold, and the signal value of the detection point shows an increasing trend and the rising amplitude reaches the preset peak height threshold, the detection point is determined as an overlapping peak valley point.

5. The chromatographic peak identification method according to any one of claims 1 to 4, characterized in that: The denoising of the original chromatographic data by wavelet transform comprises: According to different wavelet bases and decomposition levels, the signal value of the original chromatographic data is extracted and the signal-to-noise ratio and mean square error of each layer are calculated, and the combination corresponding to the maximum signal-to-noise ratio and the minimum mean square error is used as the target wavelet base and the first target decomposition level; The original chromatographic data are decomposed and reconstructed using the target wavelet basis and the first target decomposition level to obtain denoised chromatographic data.

6. The chromatographic peak identification method according to claim 5, characterized in that: The method of removing low-frequency components of the signal based on wavelet energy distribution to obtain a chromatogram after baseline correction includes: Using the target wavelet basis, performing multi-layer decomposition on the denoised chromatographic data and calculating the wavelet detail frequency and wavelet approximate frequency of each layer; The frequency coefficient of each layer is calculated using the wavelet detail frequency and the wavelet approximation frequency of each layer, the decomposition layer number corresponding to the maximum frequency coefficient is used as the second target decomposition layer number, and the low-frequency approximation coefficients below the second target decomposition layer number are set to zero and the signal is reconstructed to obtain the chromatogram after baseline correction.

7. The chromatographic peak identification method according to claim 6, characterized in that: The wavelet detail frequency Fd n Obtained by the following formula: The wavelet approximation frequency Fa n Obtained by the following formula: Among them, cD n represents the wavelet detail value energy, cA n represents the wavelet approximation energy, n=1,2,…,N, N represents the signal length; ld n Indicates cD n The number of discrete values ​​of n Indicates cA n The number of discrete values ​​of .

8. The chromatographic peak identification method according to claim 7, characterized in that: The frequency coefficient k is obtained by the following formula: Among them, Fd n represents the wavelet detail frequency, Fa n Represents the wavelet approximation frequency.

9. A terminal device, characterized in that: The terminal device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the steps of the chromatographic peak identification method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the chromatographic peak identification method according to any one of claims 1 to 8.

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