A natural monomer fragrance raw material data analysis method and system based on HPLC-MRMS

Abstract

This invention belongs to the field of liquid chromatography-tandem magnetic resonance mass spectrometry data analysis technology, and relates to a method and system for analyzing natural single-component fragrance raw materials based on HPLC-MRMS. The core process is "extracted ion chromatogram construction - baseline correction - extraction of EIC peaks from extracted ion chromatograms - mass spectrometry reconstruction." Based on HPLC-MRMS, fragrance base data of natural single-component fragrance raw materials are collected and a total ion current chromatogram (TIC) is constructed. Effective peaks are extracted by constructing extracted ion chromatograms (EIC), correcting the baseline using a local minimum algorithm, and extracting effective peaks using a dynamic window smoothing strategy. Data preprocessing is completed, and the EIC peak signals of the extracted ion chromatograms are segmented. Information is integrated to generate segmented mass spectra, constructing a library of common and characteristic ions of natural single-component fragrance raw materials, and quantifying the overlap of characteristic ions to achieve fragrance raw material identification. This invention solves the problems of insufficient targeting, weak interference elimination ability, ion suppression effect, and low identification accuracy in the analysis of raw material fragrance materials.

Description

Technical Field

[0001] This invention belongs to the field of liquid chromatography-tandem magnetic resonance mass spectrometry data analysis technology, specifically involving a method and system for analyzing natural single-component aroma raw materials based on HPLC-MRMS. It is used for the extraction of characteristic ions, mass spectrometry reconstruction and accurate identification of natural single-component aroma raw materials, providing technical support for the component analysis and quality evaluation of natural single-component aroma raw materials in the tobacco, food and other industries. Background Technology

[0002] With the increasing demand for natural single-component flavoring raw materials from the tobacco, food, and other industries, the quality control and component analysis of these raw materials have become a key focus. As a crucial component of flavoring raw materials in the tobacco and food industries, the chemical composition of natural single-component flavoring raw materials is complex and diverse, easily influenced by factors such as raw material source and processing technology. Accurate analysis of the composition of flavoring raw materials is a core requirement for quality control and application optimization. Due to their hydrophilic nature, flavoring raw materials often contain high levels of sugars and macromolecular compounds. Commonly used gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods have certain limitations in analyzing these raw materials. While HPLC-MRMS technology, with its high separation efficiency and strong detection sensitivity, has been widely used for the component analysis of natural single-component flavoring raw materials, matrix components such as sugars and macromolecular impurities in natural single-component flavoring raw materials can easily produce ion suppression effects, leading to the masking of characteristic component signals and reducing the accuracy of analysis.

[0003] Existing LC-MS data analysis software is mostly a general-purpose tool, not specifically optimized for the matrix characteristics and analytical needs of natural single fragrance raw materials, and has the following shortcomings: First, it lacks an interference elimination mechanism for the matrix of natural single fragrance raw materials, making it difficult to effectively separate interfering components such as sugars from target characteristic components; second, the data processing workflow is fragmented, requiring manual completion of multiple steps such as ion flow map construction, baseline correction, and peak extraction, which is cumbersome and inefficient; third, it lacks dedicated mass spectrometry reconstruction and recognition algorithms, making it impossible to achieve accurate matching and rapid identification of characteristic components of natural single fragrance raw materials.

[0004] Therefore, solving the problems of low efficiency and insufficient accuracy in the analysis of flavoring raw materials is of great significance for promoting the characteristic analysis of flavoring raw materials in the tobacco industry and its digital flavoring technology. Summary of the Invention

[0005] This invention aims to solve the problems of low efficiency and insufficient accuracy in the analysis of natural single fragrance raw materials by systematically eliminating interference, reconstructing mass spectrometry, and matching features.

[0006] The specific technical solution of this invention is as follows: A method for analyzing data of natural monomeric fragrance raw materials based on HPLC-MRMS, comprising the following steps: The core workflow revolves around "extracted ion chromatogram construction - baseline correction - extraction of EIC peaks from extracted ion chromatograms - mass spectrometry reconstruction." Based on HPLC-MRMS, it collects data on natural single-component fragrance raw materials and fragrance bases, constructs a total ion current (TIC) chromatogram, and uses an adaptive ion linkage algorithm to construct the extracted ion chromatogram (EIC), a local minimum algorithm to correct the baseline, and a dynamic window smoothing strategy to extract effective peaks. This completes HPLC-MRMS data preprocessing, segments the extracted ion chromatogram EIC peak signals according to retention time, integrates the information to generate segmented mass spectra, and constructs a library of common and characteristic ions for natural single-component fragrance raw materials. The Jaccard coefficient is used to quantify the overlap of characteristic ions to achieve fragrance raw material identification. A dedicated analytical algorithm for natural single-component fragrance raw materials is integrated to effectively separate interfering components from characteristic components, thereby solving the problems of insufficient targeting, weak interference elimination ability, and low identification accuracy in existing technologies for natural single-component fragrance raw material data analysis.

[0007] Preferably, the natural monomeric fragrance raw materials and fragrance base data are in ASCII format.

[0008] Preferably, the step of constructing the extracted ion chromatogram (EIC) using the adaptive ion linkage algorithm specifically includes: constructing the ion chromatogram EIC based on ion mass-to-charge ratio (m / z) similarity clustering; iteratively merging adjacent ion clusters with m / z tolerances meeting the threshold by calculating the left / right boundaries of the ion clusters; screening high-quality ion chromatogram EICs by combining the number of consecutive points and the ratio of maximum to minimum intensity; and eliminating false positive ion chromatogram EICs caused by instrument noise.

[0009] More preferably, the left / right boundaries of the ion clustering are: in, , These represent the left and right boundaries, respectively, and m represents the mass of the ion. , , They represent the first , , The number of charges carried by each ion; the number of consecutive binding points is SPN≥10; and the ratio of the maximum to minimum intensity is L / S≥10.

[0010] Preferably, the local minimum algorithm for baseline correction specifically includes: based on the characteristic of slow change in the baseline signal, using the local minimum (LMV) algorithm, traversing the original chromatographic signal by moving the window, extracting the local minimum points within the window, and after outlier elimination, fitting a continuous baseline curve, and subtracting the baseline from the original signal to eliminate background drift.

[0011] More preferably, the size of the moving window is 60 points; the outlier elimination is specifically: based on the first derivative, if the derivative exceeds 2.5, it is marked as an outlier and corrected by linear interpolation.

[0012] Preferably, the step of extracting effective peaks using the dynamic window smoothing strategy specifically includes: estimating instrument noise using the dynamic window smoothing strategy, setting the noise window width, taking 80% quantiles within the window as the threshold, estimating the instrument noise below the pseudo-chromatographic peak corresponding to the local maximum value through linear interpolation, calculating the signal-to-noise ratio (SNR), retaining only effective chromatographic peaks with SNR > 10, and eliminating low-quality pseudo-peaks and noise peaks.

[0013] Preferably, in the step of generating segmented mass spectra by integrating information, after baseline correction and noise subtraction of the EIC peak signal of the ion chromatogram, the ion mass-to-charge ratio m / z and response intensity information of the effective ions are integrated to generate segmented average mass spectra, focusing on the enrichment segment of characteristic components.

[0014] Preferably, the step of identifying fragrance raw materials by quantifying the overlap of characteristic ions using the Jaccard coefficient specifically includes: ion preprocessing, screening ions with a signal-to-noise ratio > 50, fusing ions with similar mass-to-charge ratios according to a tolerance of 0.001 Da, and selecting the top 100 ions based on their response intensity; the Jaccard coefficient is calculated using the formula J(A, B) = |A∩B| / |A∪B|, where |A∩B| is the number of ions in ion A and ion B whose mass-to-charge ratio m / z deviation is ≤ 0.001 Da and whose response intensity meets the threshold, and |A∪B| is the total number of ions in ion A and ion B. The coefficient takes a value of 0-1, with a higher degree of matching indicating a closer match to 1.

[0015] This invention also discloses a data analysis system for natural monomeric fragrance raw materials based on HPLC-MRMS. This system is used to implement the above-mentioned data analysis method for natural monomeric fragrance raw materials. The system includes: The data acquisition module is used to acquire data on natural monomeric fragrance raw materials and fragrance bases based on HPLC-MRMS, and to construct total ion current chromatogram (TIC).

[0016] The data processing module is used for HPLC-MRMS data preprocessing of natural monomeric fragrance raw materials.

[0017] The mass spectrometry reconstruction module is used to segment and extract the EIC peak signal of the ion chromatogram according to the retention time, and integrate the information to generate segmented mass spectra.

[0018] Natural monomer fragrance raw material identification module: used to identify natural monomer fragrance raw materials by quantifying the overlap of characteristic ions using the Jaccard coefficient.

[0019] The beneficial effects of this invention are: 1. This invention integrates a dedicated analytical algorithm for natural single fragrance raw materials, achieving effective separation of interfering components and characteristic components, thereby solving the problems of insufficient targeting, weak interference elimination ability, and low identification accuracy of natural single fragrance raw material data analysis in the prior art.

[0020] 2. This invention provides an MRMS data analysis method and system for identifying natural monomeric flavoring raw materials in functional flavor base modules, which is of great significance for promoting the characteristic analysis of flavoring raw materials used in tobacco industry and its digital flavoring technology. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the process steps of a method and system for analyzing natural monomeric fragrance raw materials based on HPLC-MRMS according to the present invention. Figure 2 This is a schematic diagram of the TIC after data baseline correction according to the present invention; Figure 3 This is a schematic diagram of the EIC construction developed in this invention; Figure 4 This is a schematic diagram of the EIC peak extraction algorithm based on dynamic windows according to the present invention; Figure 5 This is a schematic diagram of mass spectrometry reconstruction of the fragment segment from the present invention over a period of 2-20 minutes; Figure 6 This is a schematic diagram showing the matching results of the fragrance base and fragrance raw materials of the present invention. Detailed Implementation

[0022] The relevant technologies of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0023] like Figures 1-6As shown in this embodiment, a method for analyzing natural single-component fragrance raw materials based on HPLC-MRMS is presented. This method uses "extracted ion chromatogram construction - baseline correction - EIC peak extraction - mass spectrometry reconstruction" as its core process. It collects data on natural single-component fragrance raw materials and fragrance bases using HPLC-MRMS and constructs a TIC (Total Ion Spectrometry Index). An adaptive ion linkage algorithm is used to construct the EIC, a local minimum algorithm is used to correct the baseline, and a dynamic window smoothing strategy is used to extract effective peaks. This completes HPLC-MRMS data preprocessing. The EIC peak signals are segmented according to retention time, and the information is integrated to generate segmented mass spectra. A library of common and characteristic ions of natural single-component fragrance raw materials is constructed, and the Jaccard coefficient is used to quantify the overlap of characteristic ions to achieve fragrance raw material identification. A dedicated analytical algorithm for natural single-component fragrance raw materials is integrated to achieve effective separation of interfering components and characteristic components, thereby solving the problems of insufficient targeting, weak interference elimination ability, and low identification accuracy in existing technologies for analyzing natural single-component fragrance raw material data.

[0024] To achieve the above objectives, the present invention adopts the following technical solution: (1) Data acquisition module: Data on natural single fragrance raw materials and fragrance bases (ASCII format files) were collected using HPLC-MRMS, and a total ion current chromatogram (TIC) was constructed based on the HPLC-MRMS data collected.

[0025] (2) Data processing module: The core functionality involves the preprocessing of HPLC-MRMS data for natural single-origin fragrance raw materials, comprising three key steps: S1: Construction of Extracted Ion Chromatography (EIC): An Adaptive Ion Connectivity (AiCN) algorithm was used to construct an EIC based on ion mass-to-charge ratio (m / z) similarity clustering. The left / right boundaries of the ion clusters were calculated using a formula. ; The process iteratively merges adjacent ion clusters with m / z tolerances that meet the threshold, and selects high-quality EICs by combining the number of consecutive points (SPN≥10) and the ratio of maximum to minimum intensity (L / S≥10), thus eliminating false positive EICs caused by instrument noise.

[0026] S2: Baseline correction: Based on the characteristic of slow changes in the baseline signal, the Local Minimum (LMV) algorithm is adopted. The original chromatographic signal is traversed through a moving window of 60 points to extract the local minimum points within the window (approximately representing the true baseline level). After outlier elimination (based on the first derivative judgment, if the derivative exceeds 2.5, it is marked as an outlier and corrected by linear interpolation), a continuous baseline curve is fitted, and the baseline is subtracted from the original signal to eliminate background drift.

[0027] S3: EIC peak extraction: A dynamic window smoothing strategy was used to estimate instrument noise. The noise window width was set to 25, and the 80% quantile within the window was taken as the threshold. For pseudo-chromatographic peaks corresponding to local maximum values, the instrument noise under them was estimated by linear interpolation. The signal-to-noise ratio (SNR = signal intensity / noise intensity) was calculated. Only effective chromatographic peaks with SNR > 10 were retained, and low-quality pseudo-peaks and noise peaks were removed.

[0028] (3) Mass spectrometry reconstruction module: The EIC peak signal is segmented according to the retention time, and after baseline correction and noise subtraction, the m / z and response intensity information of effective ions are integrated to generate a segmented average mass spectrum, which focuses on the enrichment segment of characteristic components in the selected time period.

[0029] (4) Identification of natural monomeric fragrance raw materials in the fragrance base module: For each natural single fragrance ingredient, ions that can be identified in all other fragrance ingredients are designated as common ions, and a corresponding common ion library is constructed. Based on the common ions, ions with an intensity greater than all other fragrance ingredients and unique to that specific natural single fragrance ingredient are identified as characteristic ions, and a corresponding characteristic ion library is constructed. Ions with a signal-to-noise ratio (S / N) greater than 50 and similar mass-to-charge ratios are fused. Ions with similar mass-to-charge ratios are fused with a tolerance of 0.001 Da, and the top 100 ions based on response intensity are selected for preprocessing. Finally, the Jaccard Index (JCD) is used as the core matching index, and the overlap of characteristic ions is quantified using the formula J(A, B) = |A∩B| / |A∪B|, where |A∩B| is the number of ions in A and B with an m / z deviation ≤ 0.001 Da and a response intensity meeting the threshold, and |A∪B| is the total number of ions in A and B. The coefficient ranges from 0 to 1, with values ​​closer to 1 indicating a higher degree of matching.

[0030] Example Fragrance base 1 contains two natural single fragrance ingredients: plum extract and 5-grape extract. According to the data analysis algorithm of this paper, among 60 natural single fragrance ingredients, plum extract ranked second and 5-grape extract ranked third in the top 10 natural single fragrance ingredients identified. The identification rate of the top 10 natural single fragrance ingredients is 100%. The top 10 matching results of fragrance base 1 with fragrance ingredients are shown in Table 1.

[0031] Fragrance Base 2 contains five fragrance ingredients: apple extract, malt extract, dandelion extract, chicory extract, and tamarind extract. Using our data analysis algorithm, 60 natural single fragrance ingredients were analyzed. Among the top 10 identified natural single fragrance ingredients, apple extract ranked 4th, malt extract ranked 3rd, chicory extract ranked 8th, and tamarind extract ranked 6th. Four of the top 10 natural single fragrance ingredients were identified, achieving an 80% recognition rate. The Top 10 matching results between Fragrance Base 2 and fragrance ingredients are shown in Table 2.

[0032] Flavor base 3 contains 10 flavoring ingredients, including compound tobacco extract, Virginia flue-cured tobacco extract, Zimbabwean tobacco extract, Yunnan tobacco extract, Brazilian tobacco extract, Maillard reactant, malt extract, tamarind extract, carob extract, and grape extract. The data analysis algorithm analyzed 60 natural single flavoring ingredients. Of the top 15 identified natural single flavoring ingredients, excluding Zimbabwean tobacco extract and tamarind extract, 8 were identified, resulting in an 80% identification rate. The Top 15 matching results between flavor base 3 and flavoring ingredients are shown in Table 3.

[0033] In summary, this invention achieves efficient analysis of HPLC-MRMS data of natural single-component flavor ingredients by constructing a complete technical process of "data acquisition-processing-mass spectrometry reconstruction-flavor ingredient identification." Specifically, the adaptive ion linkage algorithm effectively constructs high-quality EICs, the local minimum algorithm accurately corrects the baseline, the dynamic window smoothing strategy efficiently extracts effective chromatographic peaks, segmented mass spectrometry reconstruction focuses on characteristic components, and Jaccard coefficient matching enables accurate identification of flavor ingredients. Experimental verification shows that this method can achieve an identification rate of over 80% for natural single-component flavor ingredients in different flavor bases, significantly improving the efficiency and accuracy of natural single-component flavor ingredient analysis, and providing strong technical support for quality control and digital flavoring of natural single-component flavor ingredients in the tobacco, food, and other industries.

[0034] It should be emphasized that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A natural monomer fragrance raw material data analysis method based on HPLC-MRMS, characterized by, Includes the following steps: The core workflow is "extracted ion chromatogram construction - baseline correction - extraction of EIC peaks from extracted ion chromatograms - mass spectrometry reconstruction". Based on HPLC-MRMS, natural monomeric fragrance raw materials and fragrance base data are collected and a total ion current chromatogram (TIC) is constructed. An adaptive ion linkage algorithm is used to construct the extracted ion chromatogram (EIC), a local minimum algorithm is used to correct the baseline, and a dynamic window smoothing strategy is used to extract effective peaks. HPLC-MRMS data preprocessing is completed, and the extracted ion chromatogram (EIC) peak signals are segmented according to retention time. The information is integrated to generate segmented mass spectra, and a common and characteristic ion library of natural monomeric fragrance raw materials is constructed. The Jaccard coefficient is used to quantify the overlap of characteristic ions to achieve fragrance raw material identification.

2. The natural monomeric flavor raw material data analysis method based on HPLC-MRMS according to claim 1, characterized by, The natural monomeric fragrance raw materials and fragrance base data are in ASCII format.

3. The natural monomeric flavor raw material data analysis method based on HPLC-MRMS according to claim 1, characterized in that, The step of constructing the extracted ion chromatogram (EIC) using the adaptive ion linkage algorithm specifically includes: Ion chromatograms (EICs) are constructed based on ion mass-to-charge ratio (m / z) similarity clustering. By calculating the left and right boundaries of ion clusters, adjacent ion clusters with m / z tolerances meeting the threshold are iteratively merged. High-quality ion chromatograms (EICs) are selected by combining the number of consecutive points and the ratio of maximum to minimum intensity, and false positive ion chromatograms (EICs) caused by instrument noise are eliminated.

4. The natural monomer fragrance raw material data analysis method based on HPLC-MRMS according to claim 3, characterized in that, The left / right boundaries of the ion clustering are: in, , These represent the left and right boundaries, respectively, and m represents the mass of the ion. , , They represent the first , , The number of charges carried by each ion; the number of consecutive binding points is SPN≥10; and the ratio of the maximum to minimum intensity is L / S≥10.

5. The HPLC-MRMS-based natural monomeric flavor raw material data analysis method according to claim 1, characterized by, The local minimum algorithm for baseline correction specifically includes: Based on the characteristic of slow changes in the baseline signal, the Local Minimum (LMV) algorithm is adopted. By moving the window to traverse the original chromatographic signal, the local minimum points within the window are extracted. After outlier elimination, a continuous baseline curve is fitted, and the baseline is subtracted from the original signal to eliminate background drift.

6. The natural monomeric flavor raw material data analysis method based on HPLC-MRMS according to claim 5, characterized by, The size of the moving window is 60 points; the outlier elimination is specifically: based on the first derivative, if the derivative exceeds 2.5, it is marked as an outlier and corrected by linear interpolation.

7. The HPLC-MRMS-based natural monomeric flavor raw material data analysis method according to claim 1, characterized by, The steps for extracting effective peaks using the dynamic window smoothing strategy specifically include: A dynamic window smoothing strategy is used to estimate instrument noise. The noise window width is set, and the 80% quantile within the window is taken as the threshold. For pseudo-chromatographic peaks corresponding to local maximum values, the instrument noise under them is estimated by linear interpolation. The signal-to-noise ratio (SNR) is calculated, and only effective chromatographic peaks with SNR > 10 are retained, while low-quality pseudo-peaks and noise peaks are removed.

8. The natural monomeric flavor raw material data analysis method based on HPLC-MRMS according to claim 1, characterized by, In the step of generating segmented mass spectra by integrating information, after baseline correction and noise subtraction of the EIC peak signal of the ion chromatogram, the ion mass-to-charge ratio m / z and response intensity information of the effective ions are integrated to generate segmented average mass spectra, focusing on the enrichment segment of characteristic components.

9. The natural monomeric flavor raw material data analysis method based on HPLC-MRMS according to claim 1, characterized by, The step of identifying fragrance raw materials by quantifying the overlap of characteristic ions using the Jaccard coefficient specifically includes: Ion pretreatment involves screening ions with a signal-to-noise ratio > 50, fusing ions with similar mass-to-charge ratios according to a tolerance of 0.001 Da, and selecting the top 100 ions based on their response intensity. The Jaccard coefficient is calculated using the formula J(A, B) = |A∩B| / |A∪B|, where |A∩B| is the number of ions in ion A and ion B whose mass-to-charge ratio m / z deviation is ≤ 0.001 Da and whose response intensity meets the threshold, and |A∪B| is the total number of ions in ion A and ion B. The coefficient ranges from 0 to 1, with a higher degree of matching indicating a closer match to 1.

10. A natural monomer fragrance raw material data analysis system based on HPLC-MRMS, characterized by, The system is used to implement the natural monomeric fragrance raw material data analysis method according to any one of claims 1 to 9, the system comprising: The data acquisition module is used to acquire data on natural monomeric fragrance raw materials and fragrance bases based on HPLC-MRMS, and to construct total ion current chromatogram (TIC). The data processing module is used for HPLC-MRMS data preprocessing of natural monomeric fragrance raw materials: The mass spectrometry reconstruction module is used to segment and extract the EIC peak signal of the ion chromatogram according to the retention time, and integrate the information to generate segmented mass spectra. Natural monomer fragrance raw material identification module: used to identify fragrance raw materials by quantifying the overlap of characteristic ions using the Jaccard coefficient.

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