Automatic analysis method for gas chromatography-mass spectrometry data of biomass pyrolysis oil

Abstract

The application relates to the technical field of data analysis, and aims to provide a biomass pyrolysis oil-gas chromatography-mass spectrometry data automatic analysis method to solve the problem that existing gas chromatography-mass spectrometry data analysis and processing are dependent on manual operation. The technical scheme can be summarized as follows: acquiring gas chromatography-mass spectrometry data, identifying key columns in the gas chromatography-mass spectrometry data; filtering according to a peak area threshold set by a user; normalizing the filtered data; according to a classification priority set by the user, adopting a positive-negative keyword matching method to perform feature word matching classification on compound names in the data; extracting all elements and corresponding atom numbers of each compound from each molecular formula; according to carbon number statistical parameters and element statistical parameters set by the user, performing corresponding statistical analysis on all elements and corresponding atom numbers of each compound; and outputting analysis results. The application can realize full-automatic processing of gas chromatography-mass spectrometry data without manual intervention.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to an automated analysis method for biomass pyrolysis oil and gas combined with mass spectrometry data. Background Technology

[0002] With the increasing global demand for renewable energy and green chemicals, the high-value utilization of biomass has become a research hotspot. Biomass pyrolysis oil, in particular, has attracted significant attention due to its rich content of high-value-added chemicals and its potential to replace fossil fuels. Gas chromatography-mass spectrometry (GC-MS), relying on its chromatographic separation efficiency and mass spectrometric qualitative capabilities, has become a core characterization method for elucidating the chemical composition of biomass pyrolysis oil and its catalytic conversion derivatives. However, biomass pyrolysis oil is extremely complex, containing hundreds or even thousands of multiphase complex compounds, including acids, alcohols, aldehydes, ketones, phenols, furans, and aromatics. This massive amount of information presents a significant challenge to subsequent data processing and in-depth analysis. In traditional analysis, researchers often rely on the instrument manufacturer's software for basic inspection, followed by manual comparison and judgment of the data row by row and column by column. This highly manual interactive mode is not only time-consuming, labor-intensive, and inefficient, but also prone to human bias due to operator fatigue and experience differences, making it difficult to meet the urgent need for rapid and accurate analysis of the complex components of biomass pyrolysis oil.

[0003] To address the aforementioned issues, researchers have proposed various technical solutions for gas chromatography-mass spectrometry (GC-MS) data processing, aiming to reduce manual intervention and improve processing efficiency. For example, patent CN108351331A discloses a GC-MS data processing method and device that uses algorithm optimization to accurately extract the correlation between mass-to-charge ratio and signal intensity, automating spectral analysis and baseline calibration, effectively reducing manual intervention. Patent CN109983333A provides a data analysis device for GC-MS analysis that improves the accuracy and stability of primary mass spectrometry peak identification by aligning signals from a multidimensional detector. While these technical solutions optimize the basic "denoising-peak finding-qualification" process after data acquisition to some extent, their output still contains information on hundreds or even thousands of initially identified compounds. Faced with primary analytical data of biomass pyrolysis oil containing a large number of isomers and multifunctional derivatives, there is still a lack of mature and systematic solutions in the industry for how to achieve targeted and in-depth mining of phenols, carbon number statistics, and deoxygenation / heteroatom transformation patterns based on its unique chemical characteristics. This has become a key bottleneck restricting the in-depth mining and widespread application of biomass pyrolysis oil gas chromatography-mass spectrometry (GC-MS) data.

[0004] In summary, existing gas chromatography-mass spectrometry (GC-MS) data processing methods suffer from significant "post-processing disconnect" when dealing with complex biomass pyrolysis oil components. When in-depth analysis of pyrolysis oil components requires consideration of core indicators such as typical biomass derivative categories (e.g., phenols, furans, sugars, and aromatics), carbon chain length distribution, and the mass fraction or sample contribution value of specific elements (N, S, and Cl, etc.), researchers still need to export the initially identified compound data to third-party computational software and manually construct mathematical models to normalize peak areas. This is especially problematic when a large number of multifunctional compounds (e.g., guaiacol compounds containing both phenolic hydroxyl and methoxy groups) are present, easily leading to classification conflicts and qualitative ambiguities. This necessitates reliance on personal experience for classification, increasing data processing complexity and error risk, and severely limiting the overall efficiency and standardization of biomass pyrolysis oil chemical composition analysis. It is evident that existing gas chromatography-mass spectrometry (GC-MS) data processing applications in biomass pyrolysis oil suffer from technical bottlenecks such as excessive manual intervention during analysis, cumbersome operation, high repeatability, and large multifunctional group classification errors, requiring manual analysis and processing. Summary of the Invention

[0005] The purpose of this application is to provide an automated analysis method for biomass pyrolysis oil and gas-mass spectrometry data to solve the problem that existing gas chromatography-mass spectrometry data rely on manual analysis and processing.

[0006] The technical solution adopted in this application to solve the above-mentioned technical problems provides an automated analysis method for biomass pyrolysis oil, gas and mass spectrometry data, including the following steps:

[0007] Acquire gas chromatography-mass spectrometry (GC-MS) data and identify key columns in the GC-MS data, including compound name, peak area, and molecular formula;

[0008] Based on the peak area threshold set by the user, the corresponding compounds and data with peak areas lower than the peak area threshold in the gas chromatography-mass spectrometry data are filtered out.

[0009] The peak areas of each compound in the filtered gas chromatography-mass spectrometry data were normalized to bring the total peak area percentage back to 100%, thus obtaining the normalized peak area percentage of each compound.

[0010] Based on the user-defined classification priority, the positive and negative keyword matching method is used to classify the compound names in the filtered gas chromatography-mass spectrometry data by matching feature words, and obtain the compound type corresponding to each compound.

[0011] Extract all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data;

[0012] Based on the carbon number and elemental statistical parameters set by the user, corresponding statistical analysis is performed on all elements and their corresponding atomic numbers in each extracted compound.

[0013] Output the compound type, normalized peak area percentage, and corresponding statistical analysis for each compound.

[0014] In some embodiments, since the peak area in the key column of gas chromatography-mass spectrometry data may refer to either a peak area percentage or an absolute peak area, the peak area in the key column may include either a peak area percentage or an absolute peak area.

[0015] The peak area threshold includes a peak area percentage threshold;

[0016] When the peak area in the key column is the absolute value of the peak area, the absolute value of the peak area is converted into a peak area percentage. Then, based on the peak area threshold set by the user, the corresponding compounds and their data with peak areas lower than the peak area threshold in the gas chromatography-mass spectrometry data are filtered out.

[0017] In some embodiments, to provide a feasible method for normalizing the peak areas of each compound in filtered gas chromatography-mass spectrometry data, the calculation formula for normalizing the peak areas of each compound in filtered gas chromatography-mass spectrometry data includes:

[0018] ;

[0019] Among them, Normalized Area Pct i It refers to the normalized peak area percentage of the i-th compound, Area Pct i It refers to the peak area of ​​the i-th compound in gas chromatography-mass spectrometry data, Area Pct j This refers to the peak area of ​​the j-th compound in the gas chromatography-mass spectrometry (GC-MS) data, and n refers to the number of compounds in the filtered GC-MS data.

[0020] In some embodiments, to provide a feasible classification category, the compound types to be classified in the classification priority include:

[0021] Monocyclic aromatic hydrocarbons, polycyclic aromatic hydrocarbons, alkanes, alkenes, alkynes, acids, aldehydes, ketones, phenols, alcohols, esters, ethers, furans, and other heteroatom compounds.

[0022] In some embodiments, since there may be multifunctional compounds, in order to avoid the situation where the same compound contains two identical functional groups at the same time and cannot be classified during parallel classification, the step of classifying the compound names in the filtered gas chromatography-mass spectrometry data by using positive and negative keyword matching according to the classification priority set by the user is a serial classification based on the classification priority.

[0023] In some embodiments, to provide a feasible method for classifying compound names in filtered gas chromatography-mass spectrometry (GC-MS) data using positive and negative keyword matching, the method includes:

[0024] Read the pre-defined positive matching keywords, suffix matching keywords, and negative exclusion keywords for each compound type;

[0025] For any compound name, the positive matching keywords, suffix matching keywords, and negative exclusion keywords are used to match the compound name to obtain positive matching results, suffix matching results, and negative exclusion results respectively.

[0026] Add the positive matching results to the suffix matching results, and remove the compound type classifications that overlap with the negative exclusion results to obtain the classification matching results;

[0027] Based on the classification matching results, the compound type with the highest classification priority is selected as the compound type for the compound name.

[0028] In some embodiments, since the keywords of aromatic compounds (monocyclic aromatics and polycyclic aromatics) may be basically the same, in order to further refine the classification, the monocyclic aromatics and polycyclic aromatics in the pre-set positive matching keywords, suffix matching keywords and negative exclusion keywords for each type of compound are uniformly classified as aromatics.

[0029] Based on the classification matching results, the classification with the highest priority is selected as the compound type for the compound name. This also includes:

[0030] If the classification matching results include aromatic hydrocarbons, and a classification with higher priority than monocyclic aromatic hydrocarbons or polycyclic aromatic hydrocarbons cannot be selected based on classification priority, a regular expression is used to analyze the compound name to identify whether the compound name is a monocyclic aromatic hydrocarbon or a polycyclic aromatic hydrocarbon, the classification matching results are updated, and then the compound type with the highest classification priority is selected as the compound type of the compound name.

[0031] In some embodiments, since there may be cases where a certain compound has no classification matching results, the compound type of the compound name is selected based on the classification matching results, with the highest classification priority. This further includes:

[0032] When the classification matching result is empty, the molecular formula corresponding to the compound name is parsed to determine whether it contains heteroatoms. If it does, the classification matching result is set to other heteroatom compounds; otherwise, the classification matching result is set to unknown.

[0033] In some embodiments, since the molecular formula column may be empty in the original gas chromatography-mass spectrometry data, the extraction of all elements and corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data includes:

[0034] Extract all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data;

[0035] When the molecular formula of a certain compound is empty in the filtered gas chromatography-mass spectrometry data, a regular expression is used to extract all elements and the corresponding number of atoms from the corresponding compound name.

[0036] In some embodiments, since there are many carbon statistical parameters and elemental statistical parameters, in order to provide a feasible method for setting carbon statistical parameters and elemental statistical parameters, the user-defined carbon number statistical parameters and elemental statistical parameters include:

[0037] The carbon number statistics parameters include: the percentage of carbon atoms in the peak area of ​​a compound with a set carbon number and / or carbon number range classification parameters and / or carbon number statistics compound type parameters;

[0038] The elemental statistical parameters include: the element name in the mass fraction of the element in each compound and / or the element name in the contribution value of the element in the sample and / or the elemental statistical compound type parameter.

[0039] In some embodiments, to provide a feasible method for obtaining the weights of each edge based on its basic weight, business weight, and time-series correlation weight, the step of performing corresponding statistical analysis on all elements and corresponding atom numbers in each extracted compound according to user-defined carbon number statistical parameters and elemental statistical parameters includes:

[0040] When the carbon number statistics parameter includes the number of carbon atoms in the peak area percentage of compounds with a set number of carbon atoms, the normalized peak area percentage of all compounds with the same set number of carbon atoms is extracted from the carbon number screening set and added together to obtain the peak area percentage of the compound with that number of carbon atoms.

[0041] When the carbon number statistics parameter includes the carbon number range classification parameter, the normalized peak area percentage of the compound corresponding to the number of carbon atoms for each carbon number range classification parameter is extracted from the carbon number screening set. The normalized peak area percentages of each compound within the same carbon number range classification are added together to obtain the peak area percentage of the compound within each carbon number range classification.

[0042] When the carbon number statistics parameter includes the carbon number statistics compound type parameter, the carbon number statistics compound type parameter is read to obtain the selected compound type, and each compound that matches the selected compound type and its corresponding carbon number is selected as the carbon number screening set.

[0043] When no carbon number screening set is set, the filtered gas chromatography-mass spectrometry data is used as the carbon number screening set.

[0044] When the elemental statistics parameter includes the element name in the mass fraction of the set element in each compound, the element corresponding to the element name is taken as the first target element. All elements and corresponding atomic numbers of each compound containing the first target element are extracted from the element screening set, and the mass percentage of the first target element in each extracted compound is calculated respectively.

[0045] When the elemental statistics parameters include the element name in the contribution value of the set element in the sample, the element corresponding to the element name is taken as the second target element. The normalized peak area percentage, all elements and corresponding number of atoms of each compound containing the second target element are extracted from the element screening set. The mass percentage of the second target element in each extracted compound is calculated respectively. Then, the contribution value of the second target element in the sample is calculated based on the normalized peak area percentage of each compound containing the second target element and the mass percentage of the second target element in each compound.

[0046] When the elemental statistics parameters include the elemental statistics compound type parameters, the elemental statistics compound type parameters are read to obtain the selected compound type. The compounds that match the selected compound type, their corresponding elements, and their corresponding number of atoms are then selected as the elemental screening set.

[0047] When no element selection set is set, the filtered gas chromatography-mass spectrometry data is used as the element selection set.

[0048] The beneficial effect of this application is that, in the scheme described above, the automated analysis method of biomass pyrolysis oil-gas-mass combined data eliminates the need for tedious manual analysis. Based on the peak area threshold, carbon number statistical parameters, and elemental statistical parameters set by the user, the system can automatically process and obtain the corresponding statistical analysis, which is convenient for users. Detailed Implementation

[0049] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0050] It should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0051] An automated analysis method for biomass pyrolysis oil-gas-mass spectrometry data in this embodiment includes the following steps:

[0052] Acquire gas chromatography-mass spectrometry (GC-MS) data and identify key columns in the GC-MS data, including compound name, peak area, and molecular formula;

[0053] Based on the peak area threshold set by the user, the corresponding compounds and data with peak areas lower than the peak area threshold in the gas chromatography-mass spectrometry data are filtered out.

[0054] The peak areas of each compound in the filtered gas chromatography-mass spectrometry data were normalized to bring the total peak area percentage back to 100%, thus obtaining the normalized peak area percentage of each compound.

[0055] Based on the user-defined classification priority, the positive and negative keyword matching method is used to classify the compound names in the filtered gas chromatography-mass spectrometry data by matching feature words, and obtain the compound type corresponding to each compound.

[0056] Extract all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data;

[0057] Based on the carbon number and elemental statistical parameters set by the user, corresponding statistical analysis is performed on all elements and their corresponding atomic numbers in each extracted compound.

[0058] Output the compound type, normalized peak area percentage, and corresponding statistical analysis for each compound.

[0059] It is understandable that in the above embodiments, the acquired gas chromatography-mass spectrometry data were filtered and normalized in order to eliminate noise and unify the benchmark. Furthermore, the inclusion of classification priorities avoids the problem of difficult classification of multifunctional compounds.

[0060] Data can be obtained by users uploading data or directly from the gas chromatography-mass spectrometry instrument.

[0061] In some embodiments, since the peak area in the key column of gas chromatography-mass spectrometry data may refer to either a peak area percentage or an absolute peak area, the peak area mentioned in the key column may include either a peak area percentage or an absolute peak area.

[0062] The peak area threshold may include a peak area percentage threshold.

[0063] When the peak area in the key column is the absolute value of the peak area, the absolute value of the peak area is converted into a percentage of the peak area. Then, based on the peak area threshold set by the user, the corresponding compounds and their data with peak areas lower than the peak area threshold in the gas chromatography-mass spectrometry data are filtered out.

[0064] Understandably, the calculation method for converting the absolute peak area value to a peak area percentage is as follows: divide the absolute peak area value by the sum of the absolute peak area values ​​of all compounds in the gas chromatography-mass spectrometry (GC-MS) data (i.e., the total absolute peak area value in the GC-MS data), and the corresponding peak area percentage will be obtained. The peak area percentage threshold can be 0.5%, etc., and can be set according to the actual situation; no limitation is made here.

[0065] In some embodiments, to provide a feasible method for normalizing the peak areas of each compound in filtered gas chromatography-mass spectrometry data, the calculation formula for normalizing the peak areas of each compound in filtered gas chromatography-mass spectrometry data may include:

[0066] ;

[0067] Among them, Normalized Area Pct i It refers to the normalized peak area percentage of the i-th compound, Area Pct i It refers to the peak area of ​​the i-th compound in gas chromatography-mass spectrometry data, Area Pct j This refers to the peak area of ​​the j-th compound in the gas chromatography-mass spectrometry (GC-MS) data, and n refers to the number of compounds in the filtered GC-MS data.

[0068] It is understood that the peak area referred to in the above embodiments can be either a peak area percentage or an absolute peak area value, and the calculation result is a normalized peak area percentage.

[0069] In some embodiments, to provide a feasible classification category, the compound types to be classified in the classification priority may include:

[0070] Monocyclic aromatic hydrocarbons, polycyclic aromatic hydrocarbons, alkanes, alkenes, alkynes, acids, aldehydes, ketones, phenols, alcohols, esters, ethers, furans, and other heteroatom compounds, etc.

[0071] It is understandable that the above-mentioned compound types are commonly used, and modifications can be made according to the actual situation when using them, which will not be detailed here.

[0072] In some embodiments, since multifunctional compounds may exist, in order to avoid the situation where the same compound contains two identical functional groups and cannot be classified when parallel classification is performed, serial classification can be performed according to the classification priority when the compound names in the filtered gas chromatography-mass spectrometry data are classified by feature word matching using positive and negative keyword matching method according to the classification priority set by the user.

[0073] Understandably, by using sequential classification, the program categorizes compounds according to their priority. Once a compound matches a given classification type, the classification process stops, and the compound is assigned to that type. This further resolves the issue of classifying multifunctional compounds. For example, phenol has both a benzene ring and a hydroxyl group; if phenols have a higher priority than aromatics, they will be classified as phenols. Furthermore, sequential classification saves computational resources because it only finds the highest priority classification.

[0074] In some embodiments, to provide a feasible method for classifying compound names in filtered gas chromatography-mass spectrometry (GC-MS) data using positive and negative keyword matching, the method may include:

[0075] Read the pre-defined positive matching keywords, suffix matching keywords, and negative exclusion keywords for each compound type;

[0076] For any compound name, the positive matching keywords, suffix matching keywords, and negative exclusion keywords are used to match the compound name to obtain positive matching results, suffix matching results, and negative exclusion results respectively.

[0077] Add the positive matching results to the suffix matching results, and remove the compound type classifications that overlap with the negative exclusion results to obtain the classification matching results;

[0078] Based on the classification matching results, the compound type with the highest classification priority is selected as the compound type for the compound name.

[0079] Understandably, when matching positive keywords, the search term checks if the compound name contains any root word of that keyword. For example, "one" could be a positive keyword for ketones. Similarly, when matching suffix keywords, the search term checks if the compound name ends with any root word of that keyword. For example, "ol" could be a suffix keyword for alcohols. When matching negative exclusion keywords, the search term checks if the compound name contains any root word of that keyword. For instance, if a positive keyword match finds "one," the positive match results will include "one" and the corresponding ketones. However, if a negative exclusion keyword match finds "limonene," then the positive match results for "one" and ketones should be excluded; that is, the compound type category that overlaps with the negative exclusion results should be removed.

[0080] When performing serial classification, the selected positive matching keywords, suffix matching keywords, and negative exclusion keywords should be the positive matching keywords, suffix matching keywords, and negative exclusion keywords of the currently selected priority compound type. The classification matching result should be either a match or a non-match. If there is a non-match, the positive matching keywords, suffix matching keywords, and negative exclusion keywords of the next priority compound type should be changed and classification matching should be performed again until the classification matching result is a match. If all positive matching keywords, suffix matching keywords, and negative exclusion keywords of all compound types have been matched but no match has been found, the classification matching result will be empty.

[0081] In some embodiments, since the keywords of aromatic compounds (monocyclic aromatics and polycyclic aromatics) may be basically the same, in order to further refine them, the pre-defined positive matching keywords, suffix matching keywords and negative exclusion keywords for each type of compound can unify monocyclic aromatics and polycyclic aromatics in the category into aromatics.

[0082] Based on the classification matching results, the classification with the highest priority is selected as the compound type for the compound name. This may also include:

[0083] If the classification matching results include aromatic hydrocarbons, and a classification with higher priority than monocyclic aromatic hydrocarbons or polycyclic aromatic hydrocarbons cannot be selected based on classification priority, a regular expression is used to analyze the compound name to identify whether the compound name is a monocyclic aromatic hydrocarbon or a polycyclic aromatic hydrocarbon, the classification matching results are updated, and then the compound type with the highest classification priority is selected as the compound type of the compound name.

[0084] Understandably, the regular expression here can be:

[0085] Monocyclic aromatic hydrocarbons: Regular matching complete word boundaries such as \bbenzene\b (benzene), \btoluene\b (toluene), \bxylene\b (xylene), \bstyrene\b (styrene);

[0086] Polycyclic aromatic hydrocarbons: Regular matching complete word boundaries such as naphthalene, banthracene, fluorene, etc.

[0087] As a fallback clause, when the regular expression does not match a monocyclic aromatic hydrocarbon, it can be classified as a polycyclic aromatic hydrocarbon.

[0088] In some embodiments, since there may be cases where a certain compound has no classification matching results, the compound type for the compound name is selected based on the classification matching results, with the highest classification priority. This may further include:

[0089] When the classification matching result is empty, the molecular formula corresponding to the compound name is parsed to determine whether it contains heteroatoms. If it does, the classification matching result is set to other heteroatom compounds; otherwise, the classification matching result is set to unknown.

[0090] It is understandable that the heteroatoms here can be silicon (Si), bromine (Br), sulfur (S), chlorine (Cl), fluorine (F), nitrogen (N), and phosphorus (P), etc., or can be set by the user according to the situation. For example, if you want to classify compounds containing "nitrogen (N)", you can only set the heteroatoms to "nitrogen (N)".

[0091] In some embodiments, since the molecular formula column may be empty in the original gas chromatography-mass spectrometry data, extracting all elements and their corresponding atomic numbers from each molecular formula in the filtered gas chromatography-mass spectrometry data may include:

[0092] Extract all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data;

[0093] When the molecular formula of a certain compound is empty in the filtered gas chromatography-mass spectrometry data, a regular expression is used to extract all elements and the corresponding number of atoms from the corresponding compound name.

[0094] Understandably, the regular expression here can be used in code as follows:

[0095] “ ".here, This refers to the regular expression in the code. This regular expression is used to extract the molecular formula from the compound name, where [AZ][az]? matches element symbols (such as C, H, O, N, etc.), and d represents a number (i.e., \d). This means matching zero or more of the preceding characters (i.e.) ),but The question mark (?) indicates the number of atoms in the element being matched. It is a quantifier that indicates the preceding character appears zero or one time. Used to match bracket structures that may exist in the molecular formula (such as (CH3)2). For example, the compound name "2,6-dimethoxyphenol (C8H)2" is used. 10 Taking "O3)" as an example, this regular expression can extract "C8H" from it. 10 O3".

[0096] In some embodiments, since there are many carbon statistical parameters and elemental statistical parameters, in order to provide a feasible method for setting carbon statistical parameters and elemental statistical parameters, the user-defined carbon number statistical parameters and elemental statistical parameters include...

[0097] The carbon number statistics parameters may include: the percentage of carbon atoms in the peak area of ​​a compound with a set carbon number and / or carbon number range classification parameters and / or carbon number statistics compound type parameters;

[0098] The elemental statistical parameters may include: the element name in the mass fraction of the element in each compound and / or the element name in the contribution value of the element in the sample and / or the elemental statistical compound type parameter.

[0099] Understandably, the purpose of setting the carbon number statistics parameters in this way is to enable the program to subsequently calculate the peak area percentage of compounds with a certain number of carbon atoms and / or to perform statistical analysis on compounds within each set carbon number range and / or to perform carbon number statistics on the selected compound type, etc.

[0100] The purpose of setting the elemental statistics parameters in this way is to enable the program to subsequently calculate the mass fraction of a certain element in each compound and / or calculate the contribution value of a certain element in the sample and / or perform elemental statistics on the selected compound type.

[0101] In some embodiments, to provide a feasible method for obtaining the weights of each edge based on its basic weight, business weight, and time-series correlation weight, the statistical analysis of all elements and their corresponding atomic numbers in each extracted compound based on user-defined carbon number and elemental statistical parameters may include:

[0102] When the carbon number statistics parameter includes the number of carbon atoms in the peak area percentage of compounds with a set number of carbon atoms, the normalized peak area percentage of all compounds with the same set number of carbon atoms is extracted from the carbon number screening set and added together to obtain the peak area percentage of the compound with that number of carbon atoms.

[0103] When the carbon number statistics parameter includes the carbon number range classification parameter, the normalized peak area percentage of the compound corresponding to the number of carbon atoms for each carbon number range classification parameter is extracted from the carbon number screening set. The normalized peak area percentages of each compound within the same carbon number range classification are added together to obtain the peak area percentage of the compound within each carbon number range classification.

[0104] When the carbon number statistics parameter includes the carbon number statistics compound type parameter, the carbon number statistics compound type parameter is read to obtain the selected compound type, and each compound that matches the selected compound type and its corresponding carbon number is selected as the carbon number screening set.

[0105] When no carbon number screening set is set, the filtered gas chromatography-mass spectrometry data is used as the carbon number screening set.

[0106] When the elemental statistics parameter includes the element name in the mass fraction of the set element in each compound, the element corresponding to the element name is taken as the first target element. All elements and corresponding atomic numbers of each compound containing the first target element are extracted from the element screening set, and the mass percentage of the first target element in each extracted compound is calculated respectively.

[0107] When the elemental statistics parameters include the element name in the contribution value of the set element in the sample, the element corresponding to the element name is taken as the second target element. The normalized peak area percentage, all elements and corresponding number of atoms of each compound containing the second target element are extracted from the element screening set. The mass percentage of the second target element in each extracted compound is calculated respectively. Then, the contribution value of the second target element in the sample is calculated based on the normalized peak area percentage of each compound containing the second target element and the mass percentage of the second target element in each compound.

[0108] When the elemental statistics parameters include the elemental statistics compound type parameters, the elemental statistics compound type parameters are read to obtain the selected compound type. The compounds that match the selected compound type, their corresponding elements, and their corresponding number of atoms are then selected as the elemental screening set.

[0109] When no element selection set is set, the filtered gas chromatography-mass spectrometry data is used as the element selection set.

[0110] It is understood that in the above embodiments, the carbon number range classification parameters can be as follows: the carbon number range is divided into 3 (which can be defined as low carbon content compound, medium carbon content compound and high carbon content compound respectively). Then the carbon number range classification parameters can be the low carbon number threshold Cl and the high carbon number threshold Ch. When the carbon number of a compound is less than or equal to Cl, it is classified as a low carbon content compound. When the carbon number of a compound is higher than Cl but less than or equal to Ch, it is classified as a medium carbon content compound. When the carbon number of a compound is higher than Ch, it is classified as a high carbon content compound.

[0111] In addition, in the element statistics parameters, the first target element and the second target element can be the same, which can avoid double calculation.

[0112] The specific calculation method for the mass percentage of the first target element in each extracted compound can be as follows:

[0113] ;

[0114] Among them, W E,i CountE,i refers to the mass fraction of the first target element E in the i-th compound, Atomic MassE refers to the relative atomic mass of the first target element E, CountE,i refers to the number of atoms of the first target element in the molecular formula of the i-th compound, and Molecular Weight refers to the mass fraction of the first target element E in the i-th compound. i It refers to the relative molecular mass of the i-th compound.

[0115] The contribution of the second target element in the sample, calculated based on the normalized peak area percentage of each compound containing the second target element and the mass percentage of the second target element in each compound, can be expressed by the following formula:

[0116] ;

[0117] Where Total PercentE refers to the contribution value of the second target element E in the sample, and m refers to the number of each compound in the element screening set.

[0118] Finally, the output can be in the form of an Excel spreadsheet, and the display format can also be set by the user. For example, the rows of the table can be filled with the compound type or carbon number range classification category, while the columns can be filled with the file name of the original data, and the values ​​can be filled with the corresponding normalized peak area, etc.

[0119] As can be seen from the above embodiments, this automated analysis method for biomass pyrolysis oil-gas-mass spectrometry data can significantly improve the efficiency and accuracy of analysis without relying on manual intervention.

[0120] It should be understood that the order of the steps in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0121] Another embodiment of this application provides an automated data analysis system for biomass pyrolysis oil, gas and mass transfer.

[0122] The automated analysis system for biomass pyrolysis oil, gas and mass spectrometry data in this embodiment includes: a processing unit, a storage unit, and a computer program stored in the storage unit and executable on the processing unit. When the processing unit executes the computer program, it implements the steps in the above embodiments of the automated analysis method for biomass pyrolysis oil, gas and mass spectrometry data.

[0123] For example, the computer program can be divided into one or more modules / units, which are stored in the storage unit and executed by the processing unit to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the network fault diagnosis system based on dynamic weighted graphs. For example, the computer program can be divided into a data interaction module, a peak area normalization module, a data identification and filtering module, a compound classification module, and a statistical analysis module, with the specific functions of each module as follows:

[0124] The data interaction module is used to acquire gas chromatography-mass spectrometry data, input user-defined peak area thresholds, input user-defined classification priorities, input user-defined carbon number statistical parameters and elemental statistical parameters, and output the compound type, normalized peak area percentage and corresponding statistical analysis for each compound.

[0125] The data identification and filtering module is used to identify key columns in the gas chromatography-mass spectrometry data, including compound name, peak area and molecular formula; and to filter out corresponding compounds and data whose peak areas are lower than the peak area threshold in the gas chromatography-mass spectrometry data according to the peak area threshold set by the user.

[0126] The peak area normalization module is used to normalize the peak area of ​​each compound in the filtered gas chromatography-mass spectrometry data, so that the total peak area percentage returns to 100%, and the normalized peak area percentage of each compound is obtained.

[0127] The compound classification module is used to classify compound names in filtered gas chromatography-mass spectrometry data by matching positive and negative keywords according to the classification priority set by the user, so as to obtain the compound type corresponding to each compound.

[0128] The statistical analysis module is used to extract all elements and their corresponding atomic numbers from each molecular formula in the filtered gas chromatography-mass spectrometry data; and to perform corresponding statistical analysis on all elements and their corresponding atomic numbers in each extracted compound according to the user-defined carbon number statistical parameters and elemental statistical parameters.

[0129] The automated gas chromatography-mass spectrometry (GC-MS) data analysis system can be a desktop computer, laptop, handheld computer, or cloud server, among other computing devices. The automated GC-MS data analysis system may include, but is not limited to, a processing unit and a storage unit. Those skilled in the art will understand that the above description of the automated GC-MS data analysis system is merely an example and does not constitute a limitation on the system. It may include more or fewer components than described above, or combine certain components, or use different components. For example, the automated GC-MS data analysis system may also include input / output devices, network access devices, buses, etc.

[0130] The processing unit can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0131] The storage unit can be an internal storage unit of the automated gas chromatography-mass spectrometry (GC-MS) data analysis system, such as the hard drive or memory of the GC-MS data analysis system. The storage unit can also be an external storage device of the GC-MS data analysis system, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the GC-MS data analysis system. Furthermore, the storage unit can include both internal and external storage devices of the GC-MS data analysis system. The storage unit is used to store the computer program and other programs and data required by the GC-MS data analysis system. The storage unit can also be used to temporarily store data that has been output or will be output.

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0134] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0135] In the embodiments provided in this application, it should be understood that the disclosed systems / devices / terminal devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, and may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0138] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0139] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An automated analysis method for biomass pyrolysis oil-gas-mass spectrometry data, characterized in that, Includes the following steps: Acquire gas chromatography-mass spectrometry (GC-MS) data and identify key columns in the GC-MS data, including compound name, peak area, and molecular formula; Based on the peak area threshold set by the user, the corresponding compounds and data with peak areas lower than the peak area threshold in the gas chromatography-mass spectrometry data are filtered out. The peak areas of each compound in the filtered gas chromatography-mass spectrometry data were normalized to bring the total peak area percentage back to 100%, thus obtaining the normalized peak area percentage of each compound. Based on the user-defined classification priority, the positive and negative keyword matching method is used to classify the compound names in the filtered gas chromatography-mass spectrometry data by matching feature words, and obtain the compound type corresponding to each compound. The method of using positive and negative keyword matching to classify compound names in filtered gas chromatography-mass spectrometry data by feature word matching includes: Read the pre-defined positive matching keywords, suffix matching keywords, and negative exclusion keywords for each compound type; For any compound name, the positive matching keywords, suffix matching keywords, and negative exclusion keywords are used to match the compound name to obtain positive matching results, suffix matching results, and negative exclusion results respectively. Add the positive matching results to the suffix matching results, and remove the compound type classifications that overlap with the negative exclusion results to obtain the classification matching results; Based on the classification matching results, select the compound type with the highest classification priority as the compound type for the compound name; Extract all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data; Based on the carbon number and elemental statistical parameters set by the user, corresponding statistical analysis is performed on all elements and their corresponding atomic numbers in each extracted compound. Output the compound type, normalized peak area percentage, and corresponding statistical analysis for each compound.

2. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in claim 1, characterized in that, In the key column, the peak area includes a peak area percentage or a peak area absolute value; The peak area threshold includes a peak area percentage threshold; When the peak area in the key column is the absolute value of the peak area, the absolute value of the peak area is converted into a peak area percentage. Then, based on the peak area threshold set by the user, the corresponding compounds and their data with peak areas lower than the peak area threshold in the gas chromatography-mass spectrometry data are filtered out.

3. The automated analysis method for biomass pyrolysis oil-gas-mass spectrometry data as described in claim 1, characterized in that, The normalization of peak areas of each compound in the filtered gas chromatography-mass spectrometry data includes the following calculation formulas: ； Among them, Normalized Area Pct i It refers to the normalized peak area percentage of the i-th compound, Area Pct i It refers to the peak area of ​​the i-th compound in gas chromatography-mass spectrometry data, Area Pct j This refers to the peak area of ​​the j-th compound in the gas chromatography-mass spectrometry (GC-MS) data, and n refers to the number of compounds in the filtered GC-MS data.

4. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in claim 1, characterized in that, The classification priority includes the following compound types: Monocyclic aromatic hydrocarbons, polycyclic aromatic hydrocarbons, alkanes, alkenes, alkynes, acids, aldehydes, ketones, phenols, alcohols, esters, ethers, furans, and other heteroatom compounds.

5. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in claim 4, characterized in that, Among the pre-defined positive matching keywords, suffix matching keywords, and negative exclusion keywords for each compound type, monocyclic aromatic hydrocarbons and polycyclic aromatic hydrocarbons in the category are uniformly classified as aromatic hydrocarbons. Based on the classification matching results, the classification with the highest priority is selected as the compound type for the compound name. This also includes: If the classification matching results include aromatic hydrocarbons, and a classification with higher priority than monocyclic aromatic hydrocarbons or polycyclic aromatic hydrocarbons cannot be selected based on classification priority, a regular expression is used to analyze the compound name to identify whether the compound name is a monocyclic aromatic hydrocarbon or a polycyclic aromatic hydrocarbon, the classification matching results are updated, and then the compound type with the highest classification priority is selected as the compound type of the compound name.

6. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in claim 4, characterized in that, The step of selecting the classification with the highest priority from the classification matching results as the compound type for the compound name also includes: When the classification matching result is empty, the molecular formula corresponding to the compound name is parsed to determine whether it contains heteroatoms. If it does, the classification matching result is set to other heteroatom compounds; otherwise, the classification matching result is set to unknown.

7. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in claim 1, characterized in that, The extraction of all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data includes: Extract all elements and their corresponding atomic numbers from each compound in each molecular formula from the filtered gas chromatography-mass spectrometry data; When the molecular formula of a certain compound is empty in the filtered gas chromatography-mass spectrometry data, a regular expression is used to extract all elements and the corresponding number of atoms from the corresponding compound name.

8. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in any one of claims 1-7, characterized in that, Among the user-defined carbon number statistics parameters and elemental statistics parameters, The carbon number statistics parameters include: the percentage of carbon atoms in the peak area of ​​a compound with a set carbon number and / or carbon number range classification parameters and / or carbon number statistics compound type parameters; The elemental statistical parameters include: the element name in the mass fraction of the element in each compound and / or the element name in the contribution value of the element in the sample and / or the elemental statistical compound type parameter.

9. The automated analysis method for biomass pyrolysis oil-gas-mass co-processing data as described in claim 8, characterized in that, The step of performing statistical analysis on all elements and their corresponding atom numbers in each extracted compound based on user-defined carbon number and elemental statistical parameters includes: When the carbon number statistics parameter includes the number of carbon atoms in the peak area percentage of compounds with a set number of carbon atoms, the normalized peak area percentage of all compounds with the same set number of carbon atoms is extracted from the carbon number screening set and added together to obtain the peak area percentage of the compound with that number of carbon atoms. When the carbon number statistics parameter includes the carbon number range classification parameter, the normalized peak area percentage of the compound corresponding to the number of carbon atoms for each carbon number range classification parameter is extracted from the carbon number screening set. The normalized peak area percentages of each compound within the same carbon number range classification are added together to obtain the peak area percentage of the compound within each carbon number range classification. When the carbon number statistics parameter includes the carbon number statistics compound type parameter, the carbon number statistics compound type parameter is read to obtain the selected compound type, and each compound that matches the selected compound type and its corresponding carbon number is selected as the carbon number screening set. When no carbon number screening set is set, the filtered gas chromatography-mass spectrometry data is used as the carbon number screening set. When the elemental statistics parameter includes the element name in the mass fraction of the set element in each compound, the element corresponding to the element name is taken as the first target element. All elements and corresponding atomic numbers of each compound containing the first target element are extracted from the element screening set, and the mass percentage of the first target element in each extracted compound is calculated respectively. When the elemental statistics parameters include the element name in the contribution value of the set element in the sample, the element corresponding to the element name is taken as the second target element. The normalized peak area percentage, all elements and corresponding number of atoms of each compound containing the second target element are extracted from the element screening set. The mass percentage of the second target element in each extracted compound is calculated respectively. Then, the contribution value of the second target element in the sample is calculated based on the normalized peak area percentage of each compound containing the second target element and the mass percentage of the second target element in each compound. When the elemental statistics parameters include the elemental statistics compound type parameters, the elemental statistics compound type parameters are read to obtain the selected compound type. The compounds that match the selected compound type, their corresponding elements, and their corresponding number of atoms are then selected as the elemental screening set. When no element selection set is set, the filtered gas chromatography-mass spectrometry data is used as the element selection set.

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