A detection method and system based on new pollutant pretreatment data

Abstract

The present invention discloses a detection method and system based on new pollutant pre-treatment data, which relates to the technical field of pollutant detection data processing, including determining the processing degree index of the pre-treatment stage by analyzing the pre-treatment data; inputting the new pollutant sample after pre-treatment into the mass spectrometer to obtain multiple mass spectra, identifying and distinguishing the peaks and noise in the mass spectra by distinguishing the threshold; and aligning multiple peaks in the multiple mass spectra according to the reference mass spectra and the distance metric; normalizing all peaks on the mass spectra, and performing diversified analysis according to the normalized mass spectra. The distinguishing threshold of the mass spectra is set according to the fluctuation category of the mass spectra and the processing degree index of the pre-treatment stage, accurately distinguishing the difference between the peaks and the noise, aligning and normalizing the peaks, and performing diversified analysis, combining the pre-treatment stage with the detection process, and improving the accuracy and adaptability of new pollutant detection.

Description

Technical Field

[0001] The present invention relates to the technical field of pollutant detection data processing, and in particular to a detection method and system based on new pollutant pre-processing data. Background Art

[0002] Emerging pollutants have characteristics such as biological toxicity and environmental persistence, posing a threat to ecology and human health. Due to their low concentrations and diversity, their detection faces challenges. Sample pretreatment technology is crucial in the detection of emerging pollutants, aiming to simplify samples, remove interference, and improve detection accuracy and reliability. Although there are many detection technologies currently, their sensitivity and wide applicability still need to be improved. In order to meet the challenges, sample pretreatment technology is developing in the direction of high efficiency, miniaturization, automation, combined technology and intelligence, in order to improve processing efficiency, detection flexibility and accuracy, reduce human errors, and achieve rapid, accurate and efficient detection of new pollutants, providing support for effective monitoring and prevention and control.

[0003] In the existing technology, new pollutants are often detected only based on mass spectrometry technology, without considering the impact of the data in the pre-processing stage of new pollutants on the detection, resulting in poor accuracy and low adaptability of new pollutant detection.

[0004] Therefore, how to improve the accuracy and adaptability of new pollutant detection is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problems of poor accuracy and low adaptability of new pollutant detection in the prior art, and to propose a detection method based on new pollutant pre-treatment data, the method comprising:

[0006] Pre-treat new pollutant samples and obtain pre-treatment data of the pre-treatment stage, and determine the treatment degree index of the pre-treatment stage by analyzing the pre-treatment data;

[0007] Inputting the new pollutant sample after pre-treatment into the mass spectrometer to obtain multiple mass spectra, determining the fluctuation category of the mass spectra, and setting the discrimination threshold of the mass spectra according to the fluctuation category of the mass spectra and the treatment degree index of the pre-treatment stage, and identifying and distinguishing the peaks and noise in the mass spectra through the discrimination threshold;

[0008] Selecting a mass spectrum from the multiple mass spectra as a reference mass spectrum, defining a distance between two peaks in the mass spectrum as a distance metric, and aligning multiple peaks in the multiple mass spectra based on the reference mass spectrum and the distance metric;

[0009] All peaks on the mass spectrum are normalized, and various analyses are performed based on the normalized mass spectrum to detect new pollutants.

[0010] In some embodiments of the present application, the processing degree index of the pre-processing stage is determined by analyzing the pre-processing data, including:

[0011] The pre-processed data is divided into key data and non-key data, and box plots of the key data and non-key data are established respectively;

[0012] Determine the box length, boundary distance and outliers through the box plot, and divide the outliers into top outliers and bottom outliers according to their positions in the box plot relative to the upper and lower boundaries. Evaluate the accuracy index of each key data and non-key data based on the box length, boundary distance and outliers.

[0013]

[0014] Among them, P is the precision index, X1 is the box length, X2 is the upper limit of the box plot, X3 is the lower limit of the box plot, and n1 is the number of top outliers. is the i1th top outlier, n2 is the number of bottom outliers, is the i2th bottom outlier, k1 and k2 are the first constant and the second constant respectively;

[0015] The processing degree index of the pre-processing stage is evaluated according to the accuracy index of each key data and non-key data.

[0016] In some embodiments of the present application, the processing degree index of the pre-processing stage is evaluated according to the accuracy index of each key data and non-key data, including:

[0017]

[0018] Among them, L is the processing degree index, α1 and α2 are the weights of key data and non-key data respectively, m1 is the number of key data, is the accuracy index of the j1th key data, m2 is the number of non-key data, It is the accuracy index of the j2th non-critical data.

[0019] In some embodiments of the present application, determining the fluctuation category of the mass spectrum includes:

[0020] The fluctuation categories of mass spectra include stable mass spectra and unstable mass spectra;

[0021] Calculating the ratio between the maximum signal intensity and the minimum signal intensity in the mass spectrum, dividing the mass spectrum into a plurality of parts according to the maximum signal intensity, calculating the average signal intensity of each part, and determining the difference between the average signal intensity of the plurality of parts;

[0022] If the ratio between the maximum signal intensity and the minimum signal intensity exceeds a first threshold, and the difference between the average values ​​of the signal intensities of the plurality of parts exceeds a second threshold, the mass spectrum is determined to be an unstable mass spectrum;

[0023] Otherwise, the mass spectrum is determined to be a stable mass spectrum.

[0024] In some embodiments of the present application, the mass spectrum differentiation threshold is set according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, including:

[0025] For a stable mass spectrum, determining a baseline and a standard deviation of the entire mass spectrum, determining a first multiplication factor according to the standard deviation, and determining an initial discrimination threshold of the entire mass spectrum according to the baseline, the standard deviation and the first multiplication factor;

[0026] For a non-stable mass spectrum, the mass spectrum is divided into a plurality of sub-regions according to different intervals of signal intensity, a baseline and a standard deviation of each sub-region are determined, a second multiplication factor is determined according to the standard deviation of the sub-region, and an initial discrimination threshold of each sub-region of the mass spectrum is determined according to the baseline, the standard deviation and the second multiplication factor;

[0027] The initial discrimination threshold is adjusted by the processing degree index in the pre-processing stage to obtain the discrimination threshold, and the discrimination threshold is applied as the discrimination threshold of the mass spectrum or the sub-region of the mass spectrum.

[0028] In some embodiments of the present application, selecting a mass spectrum from multiple mass spectra as a reference mass spectrum includes:

[0029] The quality indicators of each mass spectrum are analyzed, including resolution, signal-to-noise ratio and peak shape. These quality indicators are weighted and summed to obtain a comprehensive quality indicator. The mass spectrum with the highest comprehensive quality indicator is used as the reference mass spectrum.

[0030] In some embodiments of the present application, the distance between two peaks in a mass spectrum is defined as a distance metric, including:

[0031] Determine the peak characteristics of each peak in the mass spectrum, and define a distance metric based on the peak characteristics and the processing degree index in the pre-processing stage;

[0032] ;

[0033] Among them, S a，b is the distance measure between the a-th peak and the b-th peak, v is the number of peak features, a c is the cth peak feature of the ath peak, b c is the cth peak characteristic of the bth peak, exp is the exponential function, τ is the correction factor, and L→τ represents the correction factor obtained by mapping the processing degree index.

[0034] In some embodiments of the present application, all peaks on the mass spectrum are normalized, including:

[0035] Select the type of internal standard, add the same concentration of internal standard to each new pollutant sample, perform mass spectrometry analysis on the new pollutant sample after adding the internal standard, and obtain the peak area of ​​each peak and the internal standard;

[0036] The peak area of ​​each peak was divided by the peak area of ​​the internal standard to obtain the normalized relative content.

[0037] In some embodiments of the present application, the diverse analysis includes fragment analysis, quantitative analysis and qualitative analysis.

[0038] Correspondingly, the present application also provides a detection system based on new pollutant pre-treatment data, including:

[0039] The first module is used to pre-treat new pollutant samples and obtain pre-treatment data of the pre-treatment stage, and determine the treatment degree index of the pre-treatment stage by analyzing the pre-treatment data;

[0040] The second module is used to input the new pollutant sample after pre-treatment into the mass spectrometer to obtain multiple mass spectra, determine the fluctuation category of the mass spectra, and set the discrimination threshold of the mass spectra according to the fluctuation category of the mass spectra and the processing degree index of the pre-treatment stage, and identify and distinguish the peaks and noise in the mass spectra through the discrimination threshold;

[0041] The third module is used to select a mass spectrum from the multiple mass spectra as a reference mass spectrum, define a distance between two peaks in the mass spectrum as a distance metric, and align multiple peaks in the multiple mass spectra according to the reference mass spectrum and the distance metric;

[0042] The fourth module is used to normalize all peaks on the mass spectrum and perform various analyses based on the normalized mass spectrum to detect new pollutants.

[0043] By applying the above technical solution, the new pollutant sample is pre-processed, and the pre-processing data of the pre-processing stage is obtained, and the processing degree index of the pre-processing stage is determined by analyzing the pre-processing data; the new pollutant sample after pre-processing is input into the mass spectrometer to obtain multiple mass spectra, determine the fluctuation category of the mass spectrum, and set the discrimination threshold of the mass spectrum according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, and identify and distinguish the peaks and noise in the mass spectrum by the discrimination threshold; select a mass spectrum from multiple mass spectra as a reference mass spectrum, define the distance between two peaks in the mass spectrum, record it as a distance metric, and align multiple peaks in multiple mass spectra according to the reference mass spectrum and the distance metric; normalize all peaks on the mass spectrum, and perform diversified analysis based on the normalized mass spectrum to achieve the detection of new pollutants. This application determines the processing degree index of the pre-processing stage by analyzing the pre-processing data, so as to accurately describe the processing degree of the pre-processing stage, facilitate the subsequent adaptive adjustment of the detection process, and ensure the adaptability of new pollutant detection. The discrimination threshold of the mass spectrum is set according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, the difference between peaks and noise is accurately distinguished, the peaks are aligned and normalized, and diversified analysis is performed. The pre-processing stage is combined with the detection process, which improves the accuracy and adaptability of new pollutant detection and ensures the reliability and safety of the detection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a process of a detection method based on new pollutant pre-treatment data proposed by the present invention;

[0045] Figure 2 This is a schematic diagram of the structure of a detection system based on new pollutant pre-treatment data proposed by the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] Reference Figure 1 , a detection method based on new pollutant pre-treatment data, comprising the following steps:

[0048] Step S101, pre-processing the new pollutant sample, and obtaining pre-processing data of the pre-processing stage, and determining the processing degree index of the pre-processing stage by analyzing the pre-processing data.

[0049] In this embodiment, new pollutants refer to pollutants that have been recently discovered or concerned, pose risks to the ecological environment or human health, have not yet been included in management or existing management measures are not sufficient to effectively prevent and control their risks. The pre-treatment of new pollutants refers to a series of treatments on the collected samples before the new pollutant detection is carried out, so as to effectively separate the target compound from the complex matrix and improve the sensitivity and accuracy of the detection. This process usually includes the steps of sample collection, storage, transportation, extraction, purification and concentration. Because multiple samples or samples will be processed, a large amount of pre-treatment data will be generated. The processing degree index is used to describe the processing accuracy or processing completion of each step in the pre-treatment stage. The higher the processing degree index, the better the sample pre-treatment effect.

[0050] The main significance of pre-treatment of new pollutants:

[0051] Removal of interfering substances: Environmental samples often contain a large number of interfering substances with properties similar to those of the target compounds, which can seriously affect the accuracy of the analysis results. The pretreatment process can remove these interfering substances through physical or chemical methods to improve the reliability of detection.

[0052] Enrichment of target compounds: Since the concentration of new pollutants in environmental samples is often very low, direct analysis and detection is difficult to meet the sensitivity requirements. The pretreatment process can enrich the target compounds through extraction, adsorption and other methods to improve the sensitivity of detection.

[0053] In some embodiments of the present application, the processing degree index of the pre-processing stage is determined by analyzing the pre-processing data, including:

[0054] The pre-processed data is divided into key data and non-key data, and box plots of the key data and non-key data are established respectively;

[0055] Determine the box length, boundary distance and outliers through the box plot, and divide the outliers into top outliers and bottom outliers according to their positions in the box plot relative to the upper and lower boundaries. Evaluate the accuracy index of each key data and non-key data based on the box length, boundary distance and outliers.

[0056]

[0057] Among them, P is the precision index, X1 is the box length, X2 is the upper limit of the box plot, X3 is the lower limit of the box plot, and n1 is the number of top outliers. is the i1th top outlier, n2 is the number of bottom outliers, is the i2th bottom outlier, k1 and k2 are the first constant and the second constant respectively;

[0058] The processing degree index of the pre-processing stage is evaluated according to the accuracy index of each key data and non-key data.

[0059] In this embodiment, the pre-processing data is divided into key data and non-key data. Here, whether the data is key data can be divided according to the impact on the results. Key data can be target compound related data, interfering substance removal effect data, etc., and non-key data can be temperature, time, rotation speed, etc.

[0060] In the present embodiment, tools such as Excel, Python (matplotlib or pandas library), R language or professional statistical software can be used to draw box plot.Select the option of drawing box plot in the tool, and import the prepared data set.Box plot will automatically calculate and display the minimum value, lower quartile (Q1), median (Q2), upper quartile (Q3) and maximum value of each data set.The box body part of box plot is defined by Q1 and Q3, and the median is represented inside the box body with a line, and the box body length (i.e. the distance between Q3 and Q1) reflects the discrete degree of data, and outliers (outliers) are usually defined as less than Q1-1.5IQR (lower bound) or greater than Q3+1.5IQR (upper bound), and the difference between upper bound and lower bound is the boundary distance.According to the position of the upper bound and lower bound in the box plot where the outlier is located, the outlier is divided into top outlier and bottom outlier, and the outlier located above the upper bound is the top outlier, and the outlier located below the lower bound is the bottom outlier.

[0061] In this embodiment, It indicates the correction of the relative dispersion degree by the distance between the top outlier value and the bottom outlier value and the upper and lower bounds respectively.

[0062] In some embodiments of the present application, the processing degree index of the pre-processing stage is evaluated according to the accuracy index of each key data and non-key data, including:

[0063]

[0064] Among them, L is the processing degree index, α1 and α2 are the weights of key data and non-key data respectively, m1 is the number of key data, is the accuracy index of the j1th key data, m2 is the number of non-key data, It is the accuracy index of the j2th non-critical data.

[0065] In this embodiment, the processing degree index is described by the class average value, and the class average value is greater than the average value.

[0066] In step S102, the new pollutant sample after pre-treatment is input into the mass spectrometer to obtain multiple mass spectra, determine the fluctuation category of the mass spectra, and set the discrimination threshold of the mass spectra according to the fluctuation category of the mass spectra and the processing degree index of the pre-treatment stage, and identify and distinguish the peaks and noise in the mass spectra through the discrimination threshold.

[0067] In the present embodiment, the signal intensity variation fluctuation of some mass spectra is large, and such definition is unstable mass spectra, and the definition of signal intensity variation fluctuation is small stable mass spectra. If the pre-treatment precision is not high, it may cause the positional offset of peaks between different samples to be large, increasing the difficulty and error of peak alignment. Therefore, it is necessary to help set the discrimination threshold of mass spectra according to the processing degree index of the pre-treatment stage, distinguish the peak and noise in mass spectra, and the discrimination threshold here can be signal-to-noise ratio threshold, peak detection threshold etc. The threshold may be used to filter out the noise peaks of low signal intensity, and only retains the peaks with signal intensity higher than threshold for further analysis.

[0068] In some embodiments of the present application, determining the fluctuation category of the mass spectrum includes:

[0069] The fluctuation categories of mass spectra include stable mass spectra and unstable mass spectra;

[0070] Calculating the ratio between the maximum signal intensity and the minimum signal intensity in the mass spectrum, dividing the mass spectrum into a plurality of parts according to the maximum signal intensity, calculating the average signal intensity of each part, and determining the difference between the average signal intensity of the plurality of parts;

[0071] If the ratio between the maximum signal intensity and the minimum signal intensity exceeds a first threshold, and the difference between the average values ​​of the signal intensities of the plurality of parts exceeds a second threshold, the mass spectrum is determined to be an unstable mass spectrum;

[0072] Otherwise, the mass spectrum is determined to be a stable mass spectrum.

[0073] In this embodiment, signal intensity refers to the relative abundance or signal intensity of each ion represented by the ordinate in the mass spectrogram. In a mass spectrogram, the ordinate usually represents the relative abundance of ions, that is, the signal intensity generated by each ion during the detection process. This signal intensity can be counts, percentages, or relative intensity, which reflects the content or possibility of the ion in the sample.

[0074] In this embodiment, the ratio between the maximum signal intensity and the minimum signal intensity indicates the fluctuation of the signal intensity. The mass spectrum is split into several parts according to the maximum signal intensity. The greater the maximum signal intensity, the more the number of splits. The difference in the average signal intensity between the multiple parts also indicates the fluctuation. The greater the ratio of the maximum signal intensity to the minimum signal intensity, the greater the fluctuation. When both meet the requirements, it is determined that the mass spectrum fluctuates greatly.

[0075] In some embodiments of the present application, the mass spectrum differentiation threshold is set according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, including:

[0076] For a stable mass spectrum, determining a baseline and a standard deviation of the entire mass spectrum, determining a first multiplication factor according to the standard deviation, and determining an initial discrimination threshold of the entire mass spectrum according to the baseline, the standard deviation and the first multiplication factor;

[0077] For a non-stable mass spectrum, the mass spectrum is divided into a plurality of sub-regions according to different intervals of signal intensity, a baseline and a standard deviation of each sub-region are determined, a second multiplication factor is determined according to the standard deviation of the sub-region, and an initial discrimination threshold of each sub-region of the mass spectrum is determined according to the baseline, the standard deviation and the second multiplication factor;

[0078] The initial discrimination threshold is adjusted by the processing degree index in the pre-processing stage to obtain the discrimination threshold, and the discrimination threshold is applied as the discrimination threshold of the mass spectrum or the sub-region of the mass spectrum.

[0079] In this embodiment, the threshold is set according to the mean value and standard deviation of the signal. For example, the threshold can be set using the formula of "baseline + n × standard deviation", where n is a multiplication factor. Different standard deviations correspond to different multiplication factors, and the first multiplication factor and the second multiplication factor have different corresponding relationships with the standard deviation. If the data distribution is more concentrated, that is, the standard deviation is relatively small, then it may be necessary to select a smaller n value to ensure that the threshold is not too high, thereby missing some important abnormal signals. If the data distribution is more dispersed, that is, the standard deviation is large, then it may be necessary to select a larger n value to avoid misjudging too many normal signals as abnormal signals.

[0080] In this embodiment, for the unstable mass spectrum, the peak and the noise are distinguished by the distinction threshold of each sub-region.

[0081] Step S103, selecting a mass spectrum from the multiple mass spectra as a reference mass spectrum, defining a distance between two peaks in the mass spectrum as a distance metric, and aligning multiple peaks in the multiple mass spectra based on the reference mass spectrum and the distance metric.

[0082] In this embodiment, the DTW algorithm is applied for alignment.

[0083] Define distance metric: In the DTW algorithm, a distance metric needs to be defined to calculate the distance between two time points or two mass spectrum peaks to describe the difference between the two peaks. Commonly used distance metrics include Euclidean distance, Manhattan distance, etc., which are defined here by adjusted Euclidean distance. For mass spectra, the height, area, or mass-to-charge ratio (m / z) of the mass spectrum peak is usually used as a feature to calculate the distance. Here, all peak features are combined to calculate the distance.

[0084] Construct a distance matrix: Arrange the time points or mass peaks of the reference mass spectrum and the mass spectrum to be aligned in a certain order to construct a distance matrix. Each element in the matrix represents the distance between a time point in the reference mass spectrum and a time point in the mass spectrum to be aligned.

[0085] Dynamic programming solution: Use dynamic programming to solve the DTW path. The dynamic programming algorithm fills the distance matrix through iterative calculations and finds a path through the matrix grid points so that the cumulative distance on the path is minimized. This path is the optimal alignment path between the two mass spectra.

[0086] Path backtracking: Based on the cumulative distance matrix calculated by dynamic programming, backtrack to find the best alignment path. Each point on the path corresponds to a time point or mass spectrum peak in the reference mass spectrum and the mass spectrum to be aligned.

[0087] In some embodiments of the present application, selecting a mass spectrum from multiple mass spectra as a reference mass spectrum includes:

[0088] The quality indicators of each mass spectrum are analyzed, including resolution, signal-to-noise ratio and peak shape. These quality indicators are weighted and summed to obtain a comprehensive quality indicator. The mass spectrum with the highest comprehensive quality indicator is used as the reference mass spectrum.

[0089] In some embodiments of the present application, the distance between two peaks in a mass spectrum is defined as a distance metric, including:

[0090] Determine the peak characteristics of each peak in the mass spectrum, and define a distance metric based on the peak characteristics and the processing degree index in the pre-processing stage;

[0091]

[0092] Among them, S a，b is the distance measure between the a-th peak and the b-th peak, v is the number of peak features, a c is the cth peak feature of the ath peak, b c is the cth peak characteristic of the bth peak, exp is the exponential function, τ is the correction factor, and L→τ represents the correction factor obtained by mapping the processing degree index.

[0093] In this embodiment, if the pre-processing accuracy is not high, it may cause the positional offset of the peaks between different samples to be large, increasing the difficulty and error of peak alignment, so it is necessary to adjust the distance metric according to the pre-processing degree index. Peak characteristics include height, area, mass-to-charge ratio, resolution, broadband, etc.

[0094] Step S104, normalizing all peaks on the mass spectrum, and performing diversified analysis based on the normalized mass spectrum to detect new pollutants.

[0095] In some embodiments of the present application, all peaks on the mass spectrum are normalized, including:

[0096] Select the type of internal standard, add the same concentration of internal standard to each new pollutant sample, perform mass spectrometry analysis on the new pollutant sample after adding the internal standard, and obtain the peak area of ​​each peak and the internal standard;

[0097] The peak area of ​​each peak was divided by the peak area of ​​the internal standard to obtain the normalized relative content.

[0098] It should be noted that in addition to internal standard normalization, there are other normalization methods, such as TIC normalization, neutral loss normalization (NLN), etc.

[0099] Selection of normalization method: The appropriate normalization method should be selected according to the specific situation and objectives of the experiment. For example, when the concentration difference between samples is large, TIC normalization may be more applicable; when the instrument response difference needs to be eliminated, internal standard normalization may be more appropriate.

[0100] Selection of internal standard: For internal standard normalization method, the selection of internal standard is crucial. It should be selected with good stability, consistent response and similar physical and chemical properties to the measured component.

[0101] In some embodiments of the present application, the diverse analysis includes fragment analysis, quantitative analysis and qualitative analysis.

[0102] In this embodiment, mass spectrum analysis is also included:

[0103] Mass spectrometry software was used to analyze the mass spectrum and identify proton peaks, molecular ion peaks and fragment ion peaks.

[0104] The molecular weight and structural information of the compound are inferred based on the peak position (mass-to-charge ratio m / z) and relative abundance.

[0105] Fragmentation Analysis:

[0106] For tandem mass spectrometry (MS / MS) data, fragmentation analysis was performed to obtain more detailed structural information of the compounds.

[0107] Analyze the formation process of fragment ions and infer the fragmentation pathway and functional group information of the compounds.

[0108] Qualitative analysis:

[0109] The mass spectrometry data obtained from the experiment are compared with a database of known compounds to identify the compounds present in the sample.

[0110] Use specialized mass spectrometry search software (such as Mascot, SEQUEST, etc.) to perform database searching and matching.

[0111] Quantitative analysis:

[0112] The target compounds were quantitatively analyzed using the standard curve method, internal standard method or external standard method.

[0113] Calculate the concentration or relative amount of compounds in the sample.

[0114] By applying the above technical solution, the new pollutant sample is pre-processed, and the pre-processing data of the pre-processing stage is obtained, and the processing degree index of the pre-processing stage is determined by analyzing the pre-processing data; the new pollutant sample after pre-processing is input into the mass spectrometer to obtain multiple mass spectra, determine the fluctuation category of the mass spectrum, and set the discrimination threshold of the mass spectrum according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, and identify and distinguish the peaks and noise in the mass spectrum by the discrimination threshold; select a mass spectrum from multiple mass spectra as a reference mass spectrum, define the distance between two peaks in the mass spectrum, record it as a distance metric, and align multiple peaks in multiple mass spectra according to the reference mass spectrum and the distance metric; normalize all peaks on the mass spectrum, and perform diversified analysis based on the normalized mass spectrum to achieve the detection of new pollutants. This application determines the processing degree index of the pre-processing stage by analyzing the pre-processing data, so as to accurately describe the processing degree of the pre-processing stage, facilitate the subsequent adaptive adjustment of the detection process, and ensure the adaptability of new pollutant detection. The discrimination threshold of the mass spectrum is set according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, the difference between peaks and noise is accurately distinguished, the peaks are aligned and normalized, and diversified analysis is performed. The pre-processing stage is combined with the detection process, which improves the accuracy and adaptability of new pollutant detection and ensures the reliability and safety of the detection process.

[0115] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0116] Correspondingly, the present application also provides a detection system based on new pollutant pre-treatment data, such as Figure 2 As shown, including:

[0117] The first module is used to pre-treat new pollutant samples and obtain pre-treatment data of the pre-treatment stage, and determine the treatment degree index of the pre-treatment stage by analyzing the pre-treatment data;

[0118] The second module is used to input the new pollutant sample after pre-treatment into the mass spectrometer to obtain multiple mass spectra, determine the fluctuation category of the mass spectra, and set the discrimination threshold of the mass spectra according to the fluctuation category of the mass spectra and the processing degree index of the pre-treatment stage, and identify and distinguish the peaks and noise in the mass spectra through the discrimination threshold;

[0119] The third module is used to select a mass spectrum from the multiple mass spectra as a reference mass spectrum, define a distance between two peaks in the mass spectrum as a distance metric, and align multiple peaks in the multiple mass spectra according to the reference mass spectrum and the distance metric;

[0120] The fourth module is used to normalize all peaks on the mass spectrum and perform various analyses based on the normalized mass spectrum to detect new pollutants.

[0121] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0122] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A detection method based on new pollutant pre-treatment data, characterized in that: include: Pre-treat new pollutant samples and obtain pre-treatment data of the pre-treatment stage, and determine the treatment degree index of the pre-treatment stage by analyzing the pre-treatment data; Inputting the new pollutant sample after pre-treatment into the mass spectrometer to obtain multiple mass spectra, determining the fluctuation category of the mass spectra, and setting the discrimination threshold of the mass spectra according to the fluctuation category of the mass spectra and the treatment degree index of the pre-treatment stage, and identifying and distinguishing the peaks and noise in the mass spectra through the discrimination threshold; Selecting a mass spectrum from the multiple mass spectra as a reference mass spectrum, defining a distance between two peaks in the mass spectrum as a distance metric, and aligning multiple peaks in the multiple mass spectra based on the reference mass spectrum and the distance metric; Normalize all peaks on the mass spectrum and perform multiple analyses based on the normalized mass spectrum to detect new pollutants; in, By analyzing the pre-processing data, the processing degree indicators of the pre-processing stage are determined, including: The pre-processed data is divided into key data and non-key data, and box plots of the key data and non-key data are established respectively; Determine the box length, boundary distance and outliers through the box plot, and divide the outliers into top outliers and bottom outliers according to their positions in the box plot relative to the upper and lower boundaries. Evaluate the accuracy index of each key data and non-key data based on the box length, boundary distance and outliers. Among them, P is the precision index, X1 is the box length, X2 is the upper limit of the box plot, X3 is the lower limit of the box plot, and n1 is the number of top outliers. is the i1th top outlier, n2 is the number of bottom outliers, is the i2th bottom outlier, k1 and k2 are the first constant and the second constant respectively; Evaluate the processing degree index of the pre-processing stage according to the accuracy index of each key data and non-key data; The processing degree indicators of the pre-processing stage are evaluated according to the accuracy indicators of each key data and non-key data, including: Among them, L is the processing degree index, α1 and α2 are the weights of key data and non-key data respectively, m1 is the number of key data, is the accuracy index of the j1th key data, m2 is the number of non-key data, is the accuracy index of the j2th non-critical data; Define the distance between two peaks in the mass spectrum, denoted as the distance metric, including: Determine the peak characteristics of each peak in the mass spectrum, and define a distance metric based on the peak characteristics and the processing degree index in the pre-processing stage; Among them, S a，b is the distance measure between the a-th peak and the b-th peak, v is the number of peak features, a c is the cth peak feature of the ath peak, b c is the cth peak characteristic of the bth peak, exp is the exponential function, τ is the correction factor, and L→τ represents the correction factor obtained by mapping the processing degree index.

2. The detection method based on new pollutant pre-treatment data according to claim 1 is characterized in that: Determine the fluctuation category of the mass spectrum, including: The fluctuation categories of mass spectra include stable mass spectra and unstable mass spectra; Calculating the ratio between the maximum signal intensity and the minimum signal intensity in the mass spectrum, dividing the mass spectrum into a plurality of parts according to the maximum signal intensity, calculating the average signal intensity of each part, and determining the difference between the average signal intensity of the plurality of parts; If the ratio between the maximum signal intensity and the minimum signal intensity exceeds a first threshold, and the difference between the average values ​​of the signal intensities of the plurality of parts exceeds a second threshold, the mass spectrum is determined to be an unstable mass spectrum; Otherwise, the mass spectrum is determined to be a stable mass spectrum.

3. The detection method based on new pollutant pre-processing data according to claim 2 is characterized in that: And the mass spectrum distinction threshold is set according to the fluctuation category of the mass spectrum and the processing degree index of the pre-processing stage, including: For a stable mass spectrum, determining a baseline and a standard deviation of the entire mass spectrum, determining a first multiplication factor according to the standard deviation, and determining an initial discrimination threshold of the entire mass spectrum according to the baseline, the standard deviation and the first multiplication factor; For a non-stable mass spectrum, the mass spectrum is divided into a plurality of sub-regions according to different intervals of signal intensity, a baseline and a standard deviation of each sub-region are determined, a second multiplication factor is determined according to the standard deviation of the sub-region, and an initial discrimination threshold of each sub-region of the mass spectrum is determined according to the baseline, the standard deviation and the second multiplication factor; The initial distinction threshold is adjusted by the processing degree index in the pre-processing stage to obtain the distinction threshold, and the distinction threshold is applied as the distinction threshold of the mass spectrum or the sub-region of the mass spectrum.

4. The detection method based on new pollutant pre-treatment data according to claim 1 is characterized in that: Select a mass spectrum from multiple mass spectra as a reference mass spectrum, including: The quality indicators of each mass spectrum are analyzed, including resolution, signal-to-noise ratio and peak shape. These quality indicators are weighted and summed to obtain a comprehensive quality indicator. The mass spectrum with the highest comprehensive quality indicator is used as the reference mass spectrum.

5. The detection method based on new pollutant pre-processing data according to claim 1 is characterized in that: Normalize all peaks in the mass spectrum, including: Select the type of internal standard, add the same concentration of internal standard to each new pollutant sample, perform mass spectrometry analysis on the new pollutant sample after adding the internal standard, and obtain the peak area of ​​each peak and the internal standard; The peak area of ​​each peak was divided by the peak area of ​​the internal standard to obtain the normalized relative content.

6. The detection method based on new pollutant pre-processing data according to claim 1 is characterized in that: The diverse analysis includes fragment analysis, quantitative analysis and qualitative analysis.

7. A detection system based on new pollutant pre-treatment data, characterized in that: For implementing the detection method based on new pollutant pre-treatment data as described in any one of claims 1 to 6, the system comprises: The first module is used to pre-treat new pollutant samples and obtain pre-treatment data of the pre-treatment stage, and determine the treatment degree index of the pre-treatment stage by analyzing the pre-treatment data; The second module is used to input the new pollutant sample after pre-treatment into the mass spectrometer to obtain multiple mass spectra, determine the fluctuation category of the mass spectra, and set the discrimination threshold of the mass spectra according to the fluctuation category of the mass spectra and the processing degree index of the pre-treatment stage, and identify and distinguish the peaks and noise in the mass spectra through the discrimination threshold; The third module is used to select a mass spectrum from the multiple mass spectra as a reference mass spectrum, define a distance between two peaks in the mass spectrum as a distance metric, and align multiple peaks in the multiple mass spectra according to the reference mass spectrum and the distance metric; The fourth module is used to normalize all peaks on the mass spectrum and perform various analyses based on the normalized mass spectrum to detect new pollutants.

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