Mass spectrum recognition method and system for liquid chromatograph-mass spectrometer

Abstract

The invention relates to the field of mass spectrum recognition, in particular to a mass spectrum recognition method and system for a liquid chromatograph-mass spectrometer, and the method comprises the steps: obtaining a mass spectrum and a mass spectrum baseline, carrying out the preliminary denoising of the mass spectrum through an LMS algorithm, obtaining an initial mass spectrum, adjusting the initial step length factor in the LMS algorithm, obtaining an optimal step length factor, and carrying out the recognition of the optimal step length factor. And de-noising the mass spectrum again so as to identify the mass spectrum. The peak side fluctuation coefficient and the burst noise characteristic coefficient are utilized to adjust the initial step length factor to obtain the optimal step length factor, and finally the LMS algorithm is utilized to de-noise the mass spectrum, so that the de-noising effect of the mass spectrum is improved, and the mass spectrum can be conveniently identified.

Description

Technical Field

[0001] The present invention relates to the field of mass spectrometry identification, and particularly to a method and system for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument. Background Art

[0002] A liquid chromatography-mass spectrometry instrument is an analytical instrument that combines liquid chromatography and mass spectrometry technologies and is applied in fields such as chemistry, pharmacy, environmental science, and food safety. The liquid chromatography-mass spectrometry instrument is used for qualitative and quantitative analysis of complex samples. As a means of analyzing the composition of substances based on the principle of measuring the mass-to-charge ratio of ions, mass spectrometry technology has become a key detection technology in the food safety monitoring system due to its high sensitivity and specificity.

[0003] The Chinese patent application document with the publication number CN116500118A discloses a method and device for identifying ion fragment peak regions of a Vocs mass spectrometry diagram based on deep learning. The method includes: obtaining a target Vocs mass spectrometry diagram, inputting the target Vocs mass spectrometry diagram into a trained target DeepGCMSPeak model, where the target DeepGCMSPeak model includes a first CNN network and a second CNN network; identifying a target ion fragment peak region from the target Vocs mass spectrometry diagram through the first CNN network; inputting the target ion fragment peak region into the second CNN network, and identifying the target region area of the target ion fragment peak region through the second CNN network; determining the target ion fragment peak region and the target region area as the target identification result of the target Vocs mass spectrometry diagram.

[0004] The accuracy of the detection result is affected by the mass spectrometry diagram, and the quality of the mass spectrometry diagram is affected by many factors during the detection process, resulting in problems such as low signal-to-noise ratio, peak shape overlap, and resolution decline in the mass spectrometry diagram. Conventional mass spectrometry analysis schemes often have difficulty meeting the requirements of accurate identification when dealing with samples with complex matrix interference. Therefore, it is necessary to perform denoising processing on the mass spectrometry diagram to improve the quality of the mass spectrometry diagram. Summary of the Invention

[0005] In order to perform denoising on the mass spectrometry diagram and improve the quality of the mass spectrometry diagram, the present invention provides a method and system for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument.

[0006] In a first aspect, the present invention provides a method for identifying a mass spectrometry diagram for a liquid chromatography-mass spectrometry instrument, adopting the following technical solution: Obtain a mass spectrometry diagram and a mass spectrometry baseline, perform preliminary denoising on the mass spectrometry diagram using the LMS algorithm to obtain an initial mass spectrometry diagram, adjust the initial step factor in the LMS algorithm to obtain an optimal step factor, and perform secondary denoising on the mass spectrometry diagram for identifying the mass spectrometry diagram; Among them, the calculation method of the optimal step factor is as follows: perform peak detection on the initial mass spectrum to obtain multiple peak points, where the peak points include the first peak point and the second peak point. Determine whether the first peak point is an abnormal peak point. If so, take the average value of the absolute values of the differences between the amplitudes of the first peak point and the second peak points on both sides as the abnormal factor; otherwise, take 0 as the abnormal factor. Take the sum of the abnormal factors of all first peak points as the peak-side fluctuation coefficient. Calculate the drift degree of the mass spectrum baseline and the burst noise characteristic coefficient of the mass spectrum. The burst noise characteristic coefficient is positively correlated with the baseline drift degree; use the ratio of the burst noise characteristic coefficient to the peak-side fluctuation coefficient to correct the preset initial step factor to obtain the optimal step factor.

[0007] According to the fact that the peak-side fluctuation coefficient can reflect the oscillation situation of the LMS algorithm during the denoising process, and the burst noise characteristic coefficient can reflect the characteristics of the sudden pulse signal in the mass spectrum, use the peak-side fluctuation coefficient and the burst noise characteristic coefficient to adjust the initial step factor to obtain the optimal step factor. Finally, use the LMS algorithm to denoise the mass spectrum, improving the denoising effect of the mass spectrum and facilitating the identification of the mass spectrum.

[0008] Preferably, the method for obtaining the first peak point is as follows: compare the amplitudes of the peak point and the adjacent peak points, and take the peak point with amplitudes greater than the amplitudes on both sides as the first peak point.

[0009] Preferably, the method for determining whether the first peak point is an abnormal peak point is as follows: calculate the absolute value of the mass difference between the first peak point and the adjacent first peak points. If the absolute value of the mass difference is within the preset interval, then the first peak point is an abnormal peak point; otherwise, the first peak point is a normal peak point.

[0010] Identifying the first peak point can initially determine whether the corresponding first peak point is abnormal, providing a theoretical basis for subsequent adjustment of the step factor.

[0011] Preferably, the method further includes calculating the sharpness factor of the peak point: obtain the abscissas of the wave troughs on both sides of the peak point, take the absolute value of the difference between the abscissas of the two wave troughs as the characteristic value, and take the ratio of the amplitude of the peak point to the characteristic value as the sharpness factor.

[0012] By calculating the sharpness factor of the peak point, the sharpness factor reflects the characteristics of the sudden pulse signal in the mass spectrum, facilitating the calculation of the burst noise characteristic coefficient.

[0013] Preferably, the calculation method of the burst noise characteristic coefficient is as follows: calculate the range of the sharpness factors of multiple peak points, and take the product of the range and the baseline drift degree as the burst noise characteristic coefficient.

[0014] Preferably, the expression of the optimal step factor is:

[0015] In the formula, is the optimal step size factor of the LMS algorithm, is the initial step size factor of the LMS algorithm, is the burst noise characteristic coefficient of the mass spectrum, is the peak side fluctuation characteristic coefficient of the mass spectrum, is the logical mapping function.

[0016] By adjusting the initial step size factor using the burst noise characteristic coefficient and the peak side fluctuation characteristic coefficient to obtain the optimal step size factor, the mass spectrum can be quickly denoised, and the phenomenon of oscillation of the LMS algorithm during the denoising process is reduced.

[0017] Preferably, the method further includes the step of normalizing the abscissa and amplitude.

[0018] Preferably, the AMPD algorithm is used to detect the peaks of the mass spectrum to obtain multiple peak points.

[0019] Preferably, the position change amount of the mass spectrum baseline is used as the drift degree of the mass spectrum baseline.

[0020] Through the baseline drift, it can be further judged whether there is burst noise in the mass spectrum, improving the accuracy of the calculation result of the burst noise characteristic coefficient.

[0021] In a second aspect, the present invention provides a mass spectrum recognition system for a liquid chromatography-mass spectrometry instrument, adopting the following technical solution: A mass spectrum recognition system for a liquid chromatography-mass spectrometry instrument, including: a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, it realizes a mass spectrum recognition method for a liquid chromatography-mass spectrometry instrument according to the above.

[0022] Generate a computer program for the above-mentioned mass spectrum recognition method for a liquid chromatography-mass spectrometry instrument and store it in the memory to be loaded and executed by the processor. Thus, a system is made according to the memory and the processor, which is convenient to use.

[0023] The present invention has the following technical effects: According to the peak side fluctuation coefficient, it can reflect the oscillation situation of the LMS algorithm during the denoising process. According to the burst noise characteristic coefficient, it can reflect the characteristics of the burst pulse signal in the mass spectrum. The initial step size factor is adjusted using the peak side fluctuation coefficient and the burst noise characteristic coefficient to obtain the optimal step size factor. Finally, the LMS algorithm is used to denoise the mass spectrum, improving the denoising effect of the mass spectrum and facilitating the recognition of the mass spectrum. Description of the Drawings

[0024] Figure 1 This is a flow chart of a mass spectrometry diagram recognition method for a liquid chromatography-mass spectrometry instrument according to the present invention. Specific embodiments

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0026] An embodiment of the present invention discloses a mass spectrometry diagram recognition method for a liquid chromatography-mass spectrometry instrument. Refer to Figure 1 , including the following steps: S1: Obtain a mass spectrometry diagram and a mass spectrometry baseline, and perform preliminary denoising on the mass spectrometry diagram using the LMS algorithm to obtain an initial mass spectrometry diagram.

[0027] Pre-treat the food to be detected, select a solvent according to the polarity of the target substance. For example, when detecting the residue of non-compliant components in food, use acidified acetonitrile combined with SPE column purification to remove protein and fat interference. Then, use a liquid chromatography-mass spectrometry instrument to obtain the mass spectrometry diagram of the food to be detected, and obtain the mass spectrometry baseline. In the mass spectrometry diagram, the abscissa of the mass spectrometry diagram represents the mass-to-charge ratio, with the unit of m / z, and the ordinate represents the relative abundance of ions. Use the LMS algorithm to perform preliminary denoising on the mass spectrometry diagram to obtain an initial mass spectrometry diagram. At this time, the step factor in the LMS algorithm uses a preset initial step factor.

[0028] S2: Adjust the initial step factor in the LMS algorithm to obtain an optimal step factor.

[0029] The LMS algorithm minimizes the mean square error between the signal and the noise by dynamically adjusting the filter weights, thereby effectively suppressing noise interference. In the mass spectrometry diagram, the noise in the mass spectrometry diagram is usually random noise introduced by the instrument. The LMS algorithm has a good suppression effect on noise, thereby improving the recognition effect of the mass spectrometry diagram. In the LMS algorithm, when the step factor is large, the convergence speed is large, approaching the optimal solution quickly, which may cause algorithm oscillation, such as fluctuations on both sides of the ion peak in the mass spectrometry diagram, resulting in an increase in the residual noise energy; when the step factor is small, the convergence is slow, and more iterations are required to achieve a stable effect, otherwise it will not be able to effectively suppress the sudden noise caused by instrument pulse interference. Therefore, it is necessary to calculate the optimal step factor to adapt to the LMS algorithm for filtering and denoising the mass spectrometry diagram.

[0030] S21: Perform peak detection on the initial mass spectrometry diagram to obtain multiple peak points.

[0031] The initial mass spectrum is subjected to peak detection using the AMPD algorithm to obtain multiple peak points. The amplitudes of the peak points are compared with those of adjacent peak points. The peak points with amplitudes greater than those on both the left and right sides are taken as the first peak points, and the remaining peak points are taken as the second peak points. The mass value and amplitude of each peak point are obtained. It can be understood that the mass value is the abscissa value of the peak point, and the amplitude is the ordinate value of the peak point.

[0032] When an abnormal situation of algorithm oscillation occurs during the denoising process of the mass spectrum, fluctuations will appear on both sides of the ion peak, and the amplitude of the fluctuation is smaller than that of the ion peak. When the average value of the absolute value of the difference in amplitude between the first peak point and the adjacent second peak point is larger, it indicates that the possibility of an abnormal situation of oscillation occurring during the denoising process of the LSM algorithm is greater.

[0033] S22: Determine whether the first peak point is an abnormal peak point. If so, take the average value of the absolute value of the difference in amplitude between the first peak point and the second peak points on both the left and right sides as the abnormal factor. Otherwise, take 0 as the abnormal factor.

[0034] Calculate the absolute value of the mass difference between the first peak point and the adjacent first peak point. If the absolute value of the mass difference is within the preset interval, then the first peak point is an abnormal peak point. Otherwise, the first peak point is a normal peak point.

[0035] The fluctuating peaks in the mass spectrum have a certain correlation with the mass number regularity. Under normal circumstances, the mass difference between the molecular ion peak and its adjacent molecular ion peak in the mass spectrum conforms to a certain rule. For example: the mass difference is 15 (alkyl cleavage), 18 (alcohol and carboxylic acid dehydration), and 28 (carbonyl cleavage); when the mass difference between the molecular ion peak and its adjacent peak is between 4 and 14, and between 20 and 25, it cannot correspond to typical chemical bond cleavage or isotope effect. At this time, when the residual noise energy in the mass spectrum increases, the mass difference between the molecular ion peak and the molecular ion peaks on both sides is in an abnormal interval.

[0036] Therefore, when the absolute value of the mass difference between the first peak point and the adjacent first peak points on both the left and right is in the intervals (4, 14) and (20, 25), then the corresponding first peak is an abnormal peak point; when the absolute value of the mass difference between the first peak point and the adjacent first peak points on both the left and right is not in the intervals (4, 14) and (20, 25), then the corresponding first peak is a normal peak point.

[0037] Exemplarily, for the 2nd first peak point, the mass difference from the 1st first peak point is 5, and the mass difference from the 3rd first peak point is 15. Then the 2nd first peak point is an abnormal peak point, that is, if one of the two obtained mass differences falls into the corresponding interval, then the corresponding first peak point is determined to be an abnormal peak point.

[0038] If the first peak point is an abnormal peak point, use the Z-Score algorithm to normalize the amplitudes of the first peak point and the second peak point, calculate the absolute value of the difference between the amplitude of the first peak point and the amplitude of the second peak point adjacent to the left, and the absolute value of the difference between the amplitude of the first peak point and the amplitude of the second peak point adjacent to the right. Take the mean of the two absolute values of the differences as the abnormal factor of the corresponding first peak point. Conversely, if the first peak point is a normal peak point, the abnormal factor of the corresponding first peak point is 0.

[0039] The larger the abnormal factor, the greater the possibility of abnormal oscillation of the LMS algorithm during the initial denoising process of the mass spectrometry diagram using the LMS algorithm. At this time, the step size factor of the LMS algorithm should be reduced to reduce the steady-state error.

[0040] S23: Take the sum of the abnormal factors of all the first peak points as the peak-side fluctuation coefficient.

[0041] For the initial mass spectrometry diagram, calculate the sum of the abnormal factors of all the first peak points, and take the sum of the abnormal factors as the peak-side fluctuation coefficient of the mass spectrometry diagram.

[0042] S24: Calculate the sharpness factor of the peak point.

[0043] Obtain the abscissas of the wave troughs on the left and right sides of the peak point and the ordinate of the peak point, use the Z-Score algorithm to normalize the abscissa and ordinate, take the absolute value of the difference between the abscissas of the two wave troughs as the characteristic value, and take the ratio of the amplitude of the peak point to the characteristic value as the sharpness factor. When the peak point presents the characteristics of a sharp signal, the larger the amplitude of the peak point and the smaller the absolute value of the difference in the abscissa between the wave troughs on the left and right sides closest to the peak point, the larger the sharpness factor of the peak point, the more in line with the characteristics of a sudden pulse signal, indicating that the corresponding peak point is more likely to be a sudden noise.

[0044] S25: Calculate the sudden noise characteristic coefficient.

[0045] In one embodiment, calculate the drift degree of the mass spectrometry baseline, calculate the range of the sharpness factors of multiple peak points, and take the product of the range and the drift degree of the baseline as the sudden noise characteristic coefficient.

[0046] There is sudden noise in the mass spectrometry diagram caused by instrument pulse interference, and at the same time, it causes the drift of the mass spectrometry baseline. The drift degree of the mass spectrometry baseline is usually within ±10%. The calculation method of the drift degree of the mass spectrometry baseline is a prior art. Exemplarily, in the mass spectrometry diagram, the mass spectrometry baseline drifts from 1.0×10 6 counts to 1.2×10 6 counts in 1 hour, then the drift degree of the mass spectrometry baseline is +20%.

[0047] Sudden noise appears in the form of pulses, presenting as sharp pulse signals on the mass spectrometry diagram that are significantly different in shape from normal molecular ion peaks. Therefore, the sudden noise characteristic coefficient can comprehensively reflect whether the noise in the mass spectrometry diagram conforms to the characteristics of sudden noise. The larger the sudden noise characteristic coefficient, the greater the possibility of sudden noise existing in the mass spectrometry diagram. At this time, it is necessary to increase the step size factor of the LMS algorithm to accelerate the algorithm convergence.

[0048] S26: Modify the preset initial step size factor using the ratio of the sudden noise characteristic coefficient to the peak-side fluctuation coefficient to obtain the optimal step size factor.

[0049] For a mass spectrometry diagram with more obvious sudden noise characteristics at the peak, the corresponding sudden noise characteristic coefficient is larger. In order to quickly denoise the mass spectrometry diagram, it is necessary to increase the step size factor of the LMS algorithm; while the larger the peak-side fluctuation characteristic coefficient, it indicates that there are fluctuations on both sides of the molecular ion peak in the mass spectrometry diagram, resulting in an increase in the residual noise energy and causing algorithm oscillation. At this time, the step size factor of the LMS algorithm should be reduced.

[0050] Therefore, the expression for the optimal step size factor is:

[0051] In the formula, is the optimal step size factor of the LMS algorithm, is the initial step size factor of the LMS algorithm, is the sudden noise characteristic coefficient of the mass spectrometry diagram, is the peak-side fluctuation characteristic coefficient of the mass spectrometry diagram, is the logical mapping function.

[0052] During the process of using the LMS algorithm to denoise the mass spectrometry diagram, the initial step size factor is within the interval [0.01, 1]. In this embodiment, the median 0.505 of the interval is selected as the initial step size factor of the LMS algorithm. Implementers can choose other values within the interval [0.01, 1] as the initial step size factor and modify the initial step size within the interval based on the peak-side fluctuation characteristic coefficient and the sudden noise characteristic coefficient. The logical mapping function is to map the value to between 0 and 1, so that the initial step size factor remains within the interval [0.01, 1] after being modified.

[0053] S3: Use the LMS algorithm to denoise the mass spectrometry diagram again for identifying the mass spectrometry diagram.

[0054] After obtaining the optimal step size factor, use the LMS algorithm to denoise the mass spectrometry diagram again to obtain the denoised mass spectrometry diagram. Then, perform component analysis based on the denoised mass spectrometry diagram.

[0055] An embodiment of the present invention also discloses a mass spectrometry identification system for a liquid chromatography-mass spectrometry instrument, which includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for identifying mass spectrometry diagrams of a liquid chromatography-mass spectrometry instrument according to the present invention is implemented.

[0056] The above system further includes other components well-known to those skilled in the art, such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be described in detail here.

[0057] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument, characterized in that, Including the steps: Obtain a mass spectrum and a mass spectrometry baseline, preliminarily denoise the mass spectrum using the LMS algorithm to obtain an initial mass spectrum, adjust the initial step factor in the LMS algorithm to obtain an optimal step factor, and denoise the mass spectrum again for mass spectrum identification; Among them, the calculation method of the optimal step factor is as follows: perform peak detection on the initial mass spectrum to obtain multiple peak points, the peak points include a first peak point and a second peak point, determine whether the first peak point is an abnormal peak point, if so, take the average value of the absolute value of the difference between the amplitudes of the first peak point and the second peak points on both sides as the abnormal factor, otherwise, take 0 as the abnormal factor, and take the sum of the abnormal factors of all the first peak points as the peak-side fluctuation coefficient; Calculate the drift degree of the mass spectrometry baseline and the burst noise characteristic coefficient of the mass spectrum, and the burst noise characteristic coefficient is positively correlated with the baseline drift degree; use the ratio of the burst noise characteristic coefficient to the peak-side fluctuation coefficient to correct the preset initial step factor to obtain the optimal step factor.

2. The mass spectrometry pattern recognition method for a liquid chromatography-mass spectrometry instrument according to claim 1, characterized in that, The method for obtaining the first peak point is: compare the amplitudes of the peak point and the adjacent peak points, and take the peak point with amplitudes greater than the amplitudes on both sides as the first peak point.

3. A method for identifying a mass spectrometry diagram for a liquid chromatography-mass spectrometry instrument according to claim 1, characterized in that, The method for determining whether the first peak point is an abnormal peak point is: calculate the absolute value of the mass difference between the first peak point and the adjacent first peak point, if the absolute value of the mass difference is within a preset interval, then the first peak point is an abnormal peak point, otherwise, the first peak point is a normal peak point.

4. A method for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument according to claim 1, characterized in that, The method also includes calculating the sharpness factor of the peak point: obtain the abscissas of the troughs on both sides of the peak point, take the absolute value of the difference between the abscissas of the two troughs as the characteristic value, and take the ratio of the amplitude of the peak point to the characteristic value as the sharpness factor.

5. A method for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument according to claim 4, characterized in that The calculation method of the burst noise characteristic coefficient is: calculate the range of the sharpness factors of multiple peak points, and take the product of the range and the baseline drift degree as the burst noise characteristic coefficient.

6. A method for identifying a mass spectrometry diagram for a liquid chromatography-mass spectrometry instrument according to claim 1, characterized in that, The expression of the optimal step factor is: wherein, is the optimal step size factor of the LMS algorithm, is the initial step size factor of the LMS algorithm, is the burst noise characteristic coefficient of the mass spectrum, is the peak side fluctuation characteristic coefficient of the mass spectrum, is the logical mapping function.

7. A method for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument according to claim 4, characterized in that, The method also includes the step of normalizing the abscissa and amplitude.

8. A method for identifying a mass spectrum of a liquid chromatography-mass spectrometry instrument according to claim 1, characterized in that Use the AMPD algorithm to perform peak detection on the mass spectrum to obtain multiple peak points.

9. A method for identifying mass spectrometry diagrams for a liquid chromatography-mass spectrometry instrument according to claim 1, characterized in that, Take the amount of change in the position of the mass spectrometry baseline as the drift degree of the mass spectrometry baseline.

10. A mass spectrometry pattern recognition system for a liquid chromatography-mass spectrometry instrument, characterized in that, Including: A processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for identifying a mass spectrum for a liquid chromatography-mass spectrometry instrument according to any one of claims 1-9 is implemented.

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