A method for centroiding UHPLC-HRMS profile mode data

Abstract

The application discloses a UHPLC-HRMS profile mode data centroid conversion method, which comprises the following steps: collecting profile mode data based on UHPLC-HRMS, and extracting a local maximum value vector based on each mass spectrum data in the profile mode data; obtaining an outlier vector and a candidate peak vector through outlier detection based on the local maximum value vector; estimating instrument noise based on the outlier vector and the candidate peak vector; and performing centroid ion extraction based on the instrument noise and the candidate peak vector to complete UHPLC-HRMS profile mode data centroid conversion. The application can effectively separate the information of metabolites in each mass spectrum from the information of background noise, reduce the risk of compound information loss, and realize effective conversion of UHPLC-HRMS profile mode data.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of UHPLC-HRMS profile mode data centroid conversion. BACKGROUND

[0002] Ultra-High Performance Liquid Chromatography-High Resolution Mass Spectrometry (UHPLC-HRMS) is widely used in omics research, including metabolomics and lipidomics. In routine analysis, most of the instruments of the suppliers, such as Agilent, Waters, ThermoFisher, etc., support centroid and profile (or Continues) modes for UHPLC-HRMS data acquisition. Compared with the centroid mode, one advantage of the profile mode is that it can retain all the information in the mass spectrum to the maximum extent, thus providing a rather large data file for each analyzed sample. It is by no means an easy task for researchers to effectively and high-quality analyze the UHPLC-HRMS profile mode data.

[0003] There is no obvious difference between UHPLC-HRMS profile mode data analysis and centroid mode data analysis, except for the process of data centroid conversion. In brief, the workflow of profile mode data analysis includes centroid conversion, extracted ion chromatogram (EIC) construction, chromatographic peak extraction and annotation, chromatographic peak alignment and registration. Current UHPLC-HRMS data analysis tools can be briefly divided into two categories according to the implemented centroid conversion: i) the centroid conversion is completed with the help of third-party software such as MSConvert; ii) the data analysis tool itself supports centroid conversion.

[0004] The main purpose of centroiding is to clearly separate the information of metabolites within each mass spectrum from the information of background noise. Published data analysis tools have adopted various strategies to achieve this goal. For example, the most effective centroiding can be done by picking the maximum value of each mass spectral peak. In this case, centroiding can be done quickly during data acquisition, so it is adopted by vendor software such as Agilent MassHunter. XCMS further adopts a smoothing strategy to enhance the true peak signal in the original mass spectrum before mass spectral peak extraction. In MS-DIAL, a mass spectral peak extraction algorithm based on differential calculation and noise estimation is used to extract the peaks in each mass spectrum to perform centroiding. Another more complex strategy can be found in MSConvert, which uses wavelet analysis to detect mass spectral peaks in the mass spectrum for centroiding. Although current data analysis tools have developed various strategies, it seems that the influence of centroiding on downstream data analysis steps such as EIC construction, chromatographic peak extraction, etc. has not been optimized. In summary, it is still meaningful to develop high-quality data analysis tools to effectively process UHPLC-HRMS profile mode data sets in complex sample analysis. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a UHPLC-HRMS profile mode data centroiding conversion method, which can effectively realize the centroiding conversion of UHPLC-HRMS profile mode data, and improve the influence on downstream data analysis steps such as EIC construction, chromatographic peak extraction, etc.

[0006] To achieve the above-mentioned purpose, the present application provides a UHPLC-HRMS profile mode data centroiding conversion method, comprising the following steps:

[0007] (1) Based on the profile mode data obtained by UHPLC-HRMS acquisition, extracting a local maximum value vector based on each mass spectral data in the profile mode data: convoluting and smoothing the obtained mass spectrum with a Gaussian function to obtain a convolution mass spectral signal; extracting a local maximum value based on the convolution mass spectral signal to obtain a local maximum value vector;

[0008] (2) Based on the local maximum value vector, obtaining an outlier vector and a candidate peak vector by outlier detection: using a moving window strategy for outlier detection, and using a first derivative and a robust statistical method to calculate the signal-to-noise ratio of the elements in the local maximum value vector at the center of the window, and regarding the signal-to-noise ratio exceeding a first threshold value as an outlier element, and the rest as a candidate peak element, repeating the above steps until the window traverses the convolution mass spectral signal, obtaining an outlier vector and a candidate peak vector;

[0009] (3) Estimating instrument noise based on the outlier vector and candidate peak vector: searching in the original mass spectrum with the original position in the outlier vector as fixed points, estimating the missing values between two fixed points by linear interpolation strategy, and constructing the instrument noise at each point in the profile mode mass spectrum;

[0010] (4) Centroid ion extraction based on the instrument noise and candidate peak vector, completing the centroiding conversion of UHPLC-HRMS profile mode data: dividing the ion abundance of the elements in the candidate peak vector by the corresponding estimated instrument noise to calculate the signal-to-noise ratio of each element; retaining the elements in the candidate peak vector with a signal-to-noise ratio higher than a second threshold value, and correcting the m / z value and ion abundance of the two adjacent elements with an m / z difference within the allowable range, finally completing the extraction of centroid ions and realizing the centroiding conversion.

[0011] Further, in the step (1), the Gaussian function used in the convolution smoothing is a 5-point Gaussian function.

[0012] Further, in the step (1), the method of local maximum extraction is: for any convolution mass spectrum signal s, the local maximum is extracted by s i-1 >s i &s i <s i+1 ; wherein, s i is the i-th data point in the mass spectrum, s i-1 and s i+1 are the i-1 and i+1 data points, respectively.

[0013] Further, in the step (2), in the moving window strategy, the window size is 30.

[0014] Further, in the step (2), in the outlier detection using the moving window strategy, the signal-to-noise ratio first threshold value is 2.5.

[0015] Further, in the step (4), in the method of extracting centroid ions from the instrument noise and candidate peak vector, the signal-to-noise ratio second threshold value is 1.5, and the m / z tolerance of the two adjacent elements is 0.015.

[0016] Compared with the prior art, the present application has the following advantages and technical effects:

[0017] The application provides a UHPLC-HRMS profile mode data centroiding conversion method, which is realized based on a maximum value-based centroiding transformation (MVCT) strategy, and the basic principle is that the ion corresponding to a compound in a profile mode mass spectrum should be a local maximum value. The method first convolves and smooths the obtained mass spectrum with a Gaussian function, then extracts the maximum value to obtain a local maximum value vector; the moving window strategy is used for outlier detection, and the first derivative and robust statistical method are used for signal-to-noise ratio calculation to obtain the outlier vector and candidate peak vector; the mass spectrometer instrument noise is estimated according to the outlier vector by using the linear interpolation strategy; the ions in the candidate peak vector are screened according to the signal-to-noise ratio, and the m / z value and ion abundance of the adjacent two elements with the m / z difference within the tolerance range are corrected, so that the centroid ion extraction is finally completed, and the centroiding conversion is realized. The centroiding data obtained by the method can effectively separate the information of each metabolite in the mass spectrum from the information of the background noise, and reduce the risk of compound information loss. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the application, and are incorporated herein for purposes of illustrating the illustrative embodiments of the present application and the explanations provided herein and are not intended as a limitation. In the drawings:

[0019] Figure 1 A UHPLC-HRMS profile mode data centroiding conversion method flowchart of an embodiment of the application;

[0020] Figure 2 A whole workflow diagram of outlier detection in an embodiment of the application;

[0021] Figure 3 A UHPLC-HRMS profile mode data centroiding conversion method flowchart of an embodiment of the application, wherein, Figure 3 a in the formula (1) is a raw profile signal of a mass spectrum, Figure 3 b in the formula (2) is an analyzed mass spectrum, wherein the maximum value in x is represented as a point, Figure 3 c in the formula (3) is x in the selected spectrum, peak , Figure 3 d in the formula (4) is x in the selected spectrum, noise , Figure 3 e in the formula (5) is an estimated instrument noise, Figure 3 f in the formula (6) is the signal-to-noise ratio corresponding to the element in x, peak Figure 3 g in the formula (7) is a final centroiding result, Figure 3 ​h in the centroid of the signal in the mass spectrometry space;

[0022] Figure 4 is a brief comparison between the present application and MSConvert in the embodiment of the present application, wherein, Figure 4 a in the EIC number of four food matrices, MS 1 chromatographic peak, MS with MS / MS spectrum 1 chromatographic peak and MS with MF>0.7 1 chromatographic peak, Figure 4 b in the mass spectrometry peak extraction example;

[0023] Figure 5 is a mass spectrometry peak extraction schematic diagram under different parameter settings of MSConvert in the embodiment of the present application, wherein, Figure 5 a in the mass spectrometry peak extraction result schematic diagram of MSConvert using the CWT method (Min SNR=0.1, Min peak spacing=0.01), Figure 5 b in the mass spectrometry peak extraction result schematic diagram of MSConvert using the CWT method (Min SNR=0.01, Min peak spacing=0.1), Figure 5 c in the mass spectrometry peak extraction result schematic diagram of MSConvert using the CWT method (Min SNR=0.01, Min peak spacing=0.01), Figure 5 d in the mass spectrometry peak extraction result schematic diagram of MSConvert using the Vendor method;

[0024] Figure 6 is a centroid mass spectrum diagram obtained by the present application and MSConvert in the embodiment of the present application, wherein the retention time is 27.023 min, Figure 6 a in the original profile mass spectrum diagram with a retention time of 27.023 min, Figure 6 b in the centroid mass spectrum diagram obtained by the present application, Figure 6 c in the centroid mass spectrum diagram obtained by MSConvert using the Vendor method, Figure 6 d in the centroid mass spectrum diagram obtained by MSConvert using the CWT method (Min SNR=0.1, Min peak spacing=0.1), Figure 6Figure 1 is a schematic diagram of a centroid mass spectrum obtained by MSConvert using the CWT method (Min SNR = 0.1, Min peak spacing = 0.01) in the present application, Figure 6 Figure 2 is a schematic diagram of a centroid mass spectrum obtained by MSConvert using the CWT method (Min SNR = 0.01, Min peak spacing = 0.1) in the present application, Figure 6 Figure 3 is a schematic diagram of a centroid mass spectrum obtained by MSConvert using the CWT method (Min SNR = 0.01, Min peak spacing = 0.01) in the present application;

[0025] Figure 7 Figure 4 is a schematic diagram of a mass peak extraction process in the CWT in the present application, wherein, Figure 7 Figure 5 is a schematic diagram of an original profile spectrum in the present application, Figure 7 Figure 6 is a schematic diagram of wavelet coefficients at different wavelet scales in the present application, Figure 7 Figure 7 is a schematic diagram of contour plots of signals in the wavelet scale space in the present application. DETAILED DESCRIPTION

[0026] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0027] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0028] EMBODIMENTS

[0029] The following four plant matrices of licorice, chrysanthemum, honeysuckle and tobacco were used as samples, respectively, and UHPLC-HRMS analysis was performed by Waters ACQUITY UPLC-AB SCIEX TripleTOF TM 5600plus MS.

[0030] First, the profile mode data required in the present application was collected based on UHPLC-HRMS. In the present embodiment, the following steps were specifically included:

[0031] (1) Preparation of quality control (QC) samples: The preparation of QC samples was exemplified by Glycyrrhiza. Equal weight of each Glycyrrhiza sample powder was weighed and mixed to prepare the Glycyrrhiza QC sample. The QC samples of Chrysanthemum, Honeysuckle and Tobacco were prepared in the same way. All prepared QC samples were stored at -40 °C refrigerator for analysis and used as complex plant matrix for analysis.

[0032] (2) Sample pre-treatment process: For four plant matrices, including Glycyrrhiza, Chrysanthemum, Honeysuckle and Tobacco, about 20 mg of powder was weighed into an Eppendorf tube (2 mL size). About 1.5 mL of methanol water (CH3OH / H2O = 7 / 3) extract was added to each sample Eppendorf tube for metabolite extraction. After vortexing for 2 min, the centrifuge tube was ultrasonically treated at room temperature for 30 min. Then, the centrifuge tube was centrifuged at 13000 rpm for 10 min, and the supernatant was transferred to a chromatographic bottle for UHPLC-HRMS analysis.

[0033] (3) Analysis of Glycyrrhiza samples by Waters ACQUITY UPLC-AB SCIEX TripleTOF TM 5600plus MS:

[0034] The chromatographic conditions for analyzing Glycyrrhiza samples were as follows: separation was performed on a Waters ACQUITY UPLC BEH C18 column (100 mm x 2.1 mm, 1.7 μm particle size). The column temperature was set at 35 °C, and the flow rate was 0.2 mL / min. The mobile phases A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The elution gradient is shown in Table 1. The elution program was terminated within 36 min, followed by 2 min of column equilibration. The sample injection volume was 4 μL.

[0035] Table 1

[0036]

[0037] The mass spectrometric conditions for analyzing Glycyrrhiza samples were as follows: metabolite ionization and MS detection were achieved by an electrospray ionization source (ESI). The MS / MS spectra of IDA data of all Glycyrrhiza samples were collected using positive ion mode. The mass spectrometric conditions included MS (TOF-MS) and MS / MS (Product Ion) detection parameters. The parameter details are described as follows:

[0038] TOF-MS parameters: Mass scan range 100-1000 Da; Accumulation time 0.18 s; Ion source gas (GS1) flow rate 50 mL / min; Ion source gas (GS2) flow rate 50 mL / min; Curtain gas (CUR) 30 mL / min; Temperature (TEM) 500 V; Ion Sparay Voltage Floating (ISVF) 5500 V; De-clustering voltage (DP) 80 V; Collision energy (CE) 10 V.

[0039] Product Ion parameters: Mass scan range 50-1000 Da; Accumulation time 0.03 s; Cycle time 0.35 s; Ion source gas (GS1) flow rate 50 mL / min; Ion source gas (GS2) flow rate 50 mL / min; Curtain gas (CUR) 30 mL / min; Temperature (TEM) 500 V; Ion Sparay Voltage Floating (ISVF) 5500 V; De-clustering voltage (DP) 80 V; Collision energy (CE) 35 V; Collision energy spread (CES) 15 V; Ion release delay (IRD) 67; Ion release width (IRW) 25; Mass tolerance 100 mDa; Maximum number of candidate ions monitored per cycle 5; Exclusion of previous target ions for 5 s.

[0040] (4) Waters ACQUITY UPLC-AB SCIEX TripleTOF 5600plus MS analysis of chrysanthemum samples: TM 5600plus MS analysis of chrysanthemum samples:

[0041] The chromatographic conditions for analysis of chrysanthemum samples were as follows: separation was performed on a Waters ACQUITY UPLC BEH C18 column (100 mm x 2.1 mm, 1.7 μm particle size) at a column temperature of 35 °C and a flow rate of 0.2 mL / min. Mobile phases A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The elution gradient is shown in Table 2. The elution program was terminated within 40 min, followed by a 3 min column equilibration. The sample injection volume was 4 μL.

[0042] Table 2

[0043]

[0044] The mass spectrometric conditions for analysis of chrysanthemum samples were as follows: the selected mass spectrometric instrument parameters for chrysanthemum sample analysis were consistent with those selected for glycyrrhiza sample analysis. For details, please refer to the UHPLC-HRMS instrument analysis conditions for glycyrrhiza samples.

[0045] (5) Waters ACQUITY UPLC-AB SCIEX TripleTOF 5600plus MS analysis of chrysanthemum samples:TM 5600plus MS analysis of honeysuckle samples:

[0046] The chromatographic conditions for analyzing the honeysuckle samples were as follows: separation was performed using a Waters T3 column (100 mm x 2.1 mm, 1.7 μm particle size). The column temperature was set at 35 °C, and the flow rate was 0.2 mL / min. The mobile phases A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The elution gradient is shown in Table 3. The elution program was terminated within 64 min, followed by a 5 min column equilibration. The sample injection volume was 4 μL.

[0047] Table 3

[0048]

[0049] The mass spectrometric conditions for analyzing the honeysuckle samples were the same as those selected for analyzing the licorice samples, and the details are referred to the UHPLC-HRMS instrument analysis conditions of the licorice samples.

[0050] (6) Waters ACQUITY UPLC-AB SCIEX TripleTOF TM 5600plus MS analysis of tobacco samples:

[0051] The chromatographic conditions for analyzing the tobacco samples were as follows: separation was performed using a Waters T3 column (100 mm x 2.1 mm, 1.7 μm particle size). The column temperature was set at 35 °C, and the flow rate was 0.2 mL / min. The mobile phases A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. The elution gradient is shown in Table 4. The elution program was terminated within 30 min, followed by a 3 min column equilibration. The sample injection volume was 4 μL.

[0052] Table 4

[0053]

[0054] The mass spectrometric conditions for analyzing the tobacco samples were the same as those selected for analyzing the honeysuckle samples, and the details are referred to the UHPLC-HRMS instrument analysis conditions of the honeysuckle samples.

[0055] Secondly, the UHPLC-HRMS profile mode data centroid conversion method, as shown in Figure 1 includes the following steps:

[0056] S1. Profile mode data is acquired based on UHPLC-HRMS, and a local maximum vector is extracted based on each mass spectrum data in the profile mode data;

[0057] In this embodiment, the local maximum of each mass spectrum data is extracted:

[0058] The obtained mass spectrum s is convoluted with a Gaussian function. Then, according to the obtained convoluted mass spectrum signal, a new vector x is obtained by s i-1 >s i &s i <s i+1 The local maximum in the mass spectrum is extracted. Wherein, s i is the i-th data point in the mass spectrum, s i-1 and s i+1 are the i-1 and i+1 data points, respectively. Thus, a new vector x containing only the extracted local maximum can be obtained, and the elements are arranged in ascending order of the original position.

[0059] Wherein, the Gaussian function used for convolution smoothing is a 5-point Gaussian function.

[0060] S2. Based on the local maximum vector, an outlier vector and a candidate peak vector are obtained by outlier detection;

[0061] In this embodiment, the outlier vector and the candidate peak vector are extracted: the local maximum vector x containing elements from the instrument noise is subjected to an outlier detection step to distinguish elements corresponding to the instrument background noise and the compounds. Based on the basic fact that elements from the compounds should be much larger than the noise, if an element in x is much larger than its neighbors, it will be marked as an outlier. In this embodiment, a moving window strategy is used to find outliers. The element at the center position of the window will be verified by using robust statistics. At the same time, the first derivative of x is also used to check the outlier elements. Figure 2 The detailed process of local maximum extraction is shown. In the moving window strategy (left part), the signal-to-noise ratio of the center point of the window is calculated by S / N = max(|(x i -median(x w )) / σ|,|(x i -x i-1 ) / σ|), wherein x i is the center point of the moving window, and x w is the element in the window vector. σ = 1.483 * median|x j -median(x w )|(j = 1, …, J). σ is the estimated noise in the window; the constant 1.483 is a correction factor, so that σ belongs to the normal distribution. When the signal-to-noise ratio is greater than 2.5, x iwill be considered as outliers, corresponding to 99% confidence level under normal distribution. Regarding the first derivative based strategy (right part), the noise level of vector x is estimated by Noise = 1.483 * median |dx j -median(dx) | (j = 1,..., N), where dx is the first derivative of x; dx j is the jth data point in dx; N is the number of data points in dx. x j with signal to noise ratio exceeding a threshold value will be considered as outliers, the rest are candidate peak elements. Repeat the above steps until the window traverses the convolution mass spectrum signal, and finally obtain the outlier vector and candidate peak vector.

[0062] wherein the moving window strategy is used with window size of 30 and signal to noise ratio threshold value of 2.5.

[0063] S3. Estimate instrument noise based on the outlier vector and candidate peak vector;

[0064] In this embodiment, instrument noise estimation: temporarily mark the outliers in x as candidate peak vector (x peak ), the rest of x is considered as instrument noise vector (x noise ), and according to x noise , instrument noise is automatically estimated. The original position of the elements in outlier vector x noise is searched in the original mass spectrum and marked as fixed points. Then, the missing values between two consecutive fixed points are estimated using linear interpolation strategy to construct the instrument noise s noise at each point in profile mode mass spectrum.

[0065] S4. Centroid ion extraction based on the instrument noise and candidate peak vector, complete the centroiding conversion of UHPLC-HRMS profile mode data.

[0066] In this embodiment, centroid ion extraction: calculate the signal to noise ratio of each element in x peak by dividing the ion abundance of each element in candidate peak vector x peak by the corresponding estimated instrument noise. If the element in x peak with signal to noise ratio higher than the threshold value will be retained, and the m / z value and ion abundance of the adjacent two elements in x cen with m / z difference within the tolerance range are corrected by m / z = (Int1 * m / z1 + Int2 * m / z2) / (Int1 + Int2), wherein Int1 and Int2 are the ion intensities of the first and second ions respectively. m / z1 and m / z2 are high precision m / z values within the preset m / z tolerance. m / z cenThis is the high-precision m / z value of the merged ion. The larger of Int1 and Int2 will be used as the ionic strength of the merged ion, i.e., Int... cen =max(Int1,Int2).

[0067] The signal-to-noise ratio threshold is set to 1.5, and the tolerance between two adjacent elements m / z is 0.015.

[0068] The following are some of the analytical data results from this invention:

[0069] Further explanation of the working principle of this invention. (See attached document) Figure 3 As shown, Figure 3 The a-value provides three profile mode mass spectra, which were obtained in scans 4500, 4506, and 12827, respectively. Figure 3 Section b describes all local maxima extracted from a selected region in the 4500th spectrum s. Using a developed outlier detection step, these local maxima are divided into two groups, x peak (See Figure 3 c) and x noise (See Figure 3 (d) in the middle. Then according to x noise An adaptive estimate of the instrument noise was performed, such as... Figure 3 As shown in e), x was calculated. peak The signal-to-noise ratio (SNR) of all elements is calculated, and elements with an SNR below 1.5 are removed. Figure 3 As shown in f in the figure. After merging ions with m / z values ​​less than the preset m / z tolerance (0.015), a mass spectrum after centroid conversion can be obtained, as shown in the figure. Figure 3 As shown in g in the figure. The centroid transformation process is performed independently for each obtained profile mode mass spectrum to obtain the centroid-transformed centroid mode mass spectrum (see g in the figure). Figure 3 (h in the middle).

[0070] A comprehensive study of centroidal transformation was conducted using four food matrices from a non-targeted dataset, and compared with MSConvert. The results are attached. Figure 4 As shown. Since MSConvert focuses on file format conversion and does not provide other procedures for UHPLC-HRMS data analysis, the centroid-converted file is imported into AntDAS-Profiler in this invention to provide a fair comparison. First, the number of constructed EICs and the detected MS... 1 Performance was evaluated using the number of chromatographic peaks, and the results were as follows: Figure 4a. It can be found from the figure that MSConvert obtained the most EICs in all four food matrices. However, the extracted chromatographic peaks clearly show that the present application (abbreviated as AntDAS-Profiler) provided MS 1 chromatographic peaks than MSConvert. Since the extracted chromatographic peaks alone cannot be used to evaluate a method, the present application further compared the MS 1 chromatographic peaks since they can be more valuable for omics studies. From Figure 4 a, it can be found that AntDAS-Profiler obtained more EICs than MSConvert in all food matrices. In addition, the present study further introduced the MS / MS spectra of the two methods into a public compound library for compound identification, which can be downloaded from http: / / prime.psc.riken.jp / compms / msdial / main.html#MSP. The Match Factor (MF) was used to measure the quality of the MS / MS spectra, which was calculated according to the dot product between the constructed MS / MS spectra and the reference in the library, and the threshold of MF was set to 0.7. In this case, AntDAS-Profiler obtained more chromatographic peaks with MF > 0.7 than MSConvert in all four matrices. In order to further clarify the differences between AntDAS-Profiler and MSConvert in centroid conversion, the present embodiment illustrates this by an example, as shown in Figure 4 b. The original mass spectrum signal is shown in the left part of Figure 4 b, and it can be seen from the figure that there are two obvious Regions of Interest (ROIs). The centroid conversion result of MSConvert shows that only one ROI corresponding to m / z 425.2303 can be retained. The ROI of m / z 425.1960 is filtered due to low ion intensity. In contrast, with the help of AntDAS-Profiler, two ROIs (m / z 425.2081, 425.2290) can be satisfactorily retained after centroid conversion. In addition, several ions located in the low intensity region can be detected. Then, the present application optimized the parameters of CWT strategy centroid conversion in MSConvert, and the results are shown in Figure 5 a, where Figure 5 a is the MS spectrum peak extraction result schematic diagram of MSConvert using CWT method (Min SNR = 0.1, Min peak spacing = 0.01), Figure 5b in Figure 1 is a schematic diagram of the result of MSConvert using CWT method (Min SNR = 0.01, Min peak spacing = 0.1) for mass spectral peak extraction, Figure 5 c in Figure 1 is a schematic diagram of the result of MSConvert using CWT method (Min SNR = 0.01, Min peak spacing = 0.01) for mass spectral peak extraction, Figure 5 d in Figure 1 is a schematic diagram of the result of MSConvert using Vendor method for mass spectral peak extraction. Unfortunately, none of them can successfully extract the EIC of m / z 425.2081. Figure 5 b in Figure 1 shows that MSConvert can face the problem of identifying ions with no baseline separation. A very likely reason is the centroiding strategy based on CWT employed in MSConvert.

[0071] A brief comparison of the centroiding algorithm in AntDAS-Profiler and MSConvert is shown in Figure 2; in which, Figure 6 a in Figure 2 is a schematic diagram of the original profile mass spectrum at the retention time of 27.023 min, Figure 6 b in Figure 2 is a schematic diagram of the centroid mass spectrum obtained by the present application, Figure 6 c in Figure 2 is a schematic diagram of the centroid mass spectrum obtained by MSConvert using Vendor method, Figure 6 d in Figure 2 is a schematic diagram of the centroid mass spectrum obtained by MSConvert using CWT method (Min SNR = 0.1, Min peak spacing = 0.1), Figure 6 e in Figure 2 is a schematic diagram of the centroid mass spectrum obtained by MSConvert using CWT method (Min SNR = 0.1, Min peak spacing = 0.01), Figure 6 f in Figure 2 is a schematic diagram of the centroid mass spectrum obtained by MSConvert using CWT method (Min SNR = 0.01, Min peak spacing = 0.1), Figure 6 g in Figure 2 is a schematic diagram of the centroid mass spectrum obtained by MSConvert using CWT method (Min SNR = 0.01, Min peak spacing = 0.01). Figure 6 a in Figure 3 shows an original mass spectrum at the retention time of 27.023 min. AntDAS-Profiler obtains 104 ions (see Figure 6 b in Figure 3). MSConvert only obtains 17 ions (see Figure 6 c in Figure 3) in Vendor method.Figure 6 (c) Using the CWT method, MSConvert can obtain different ion numbers under various parameters. When focusing on ions near m / z 425.22, Figure 6 In the inset of 'a', two mass spectrometry peaks without baseline separation can be observed. Using AntDAS-Profiler, both ions can be successfully extracted. In contrast, MSConvert only yields one ion.

[0072] To clarify the cause, this invention further investigated the centroid transformation method based on CWT, and the results show in the appendix... Figure 7 middle. Figure 7 The 'a' in the image provides the original profile mass spectrum, where it can be observed that the two peaks did not achieve baseline separation. Using the "Mexican-Hat" mother wavelet, signals at different wavelet scales are shown in... Figure 7 In addition to b, Figure 7 Figure 'c' shows the contour plot of the signal in the wavelet scale space. Clearly, the contours indicate that only one mass spectral peak corresponding to the ion m / z 425.2304 can be extracted. Similar results are obtained in studies using various mother wavelet functions. These results demonstrate that the developed centroid conversion strategy is superior to the strategy in MSConvert.

[0073] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A UHPLC-HRMS profile mode data centroiding conversion method characterized in that, The method comprises the following steps: (1) obtaining profile mode data based on UHPLC-HRMS, and extracting a local maximum vector based on each mass spectrum data in the profile mode data: performing convolution smoothing on the obtained mass spectrum by using a Gaussian function to obtain a convolution mass spectrum signal; extracting a local maximum based on the convolution mass spectrum signal to obtain a local maximum vector; (2) obtaining an outlier vector and a candidate peak vector by outlier detection based on the local maximum vector: performing outlier detection by using a moving window strategy, and calculating a signal-to-noise ratio of an element in the local maximum vector at the center of the window by using a first derivative and a robust statistical method, and regarding an element with a signal-to-noise ratio higher than a first threshold value as an outlier element, and regarding the remaining elements as candidate peak elements, and repeating the above steps until the window traverses the convolution mass spectrum signal, to obtain an outlier vector and a candidate peak vector; (3) estimating instrument noise based on the outlier vector and the candidate peak vector: searching for an original position in the original mass spectrum by using the outlier vector, and marking the original position as a fixed point, estimating missing values between two fixed points by using a linear interpolation strategy, and constructing instrument noise at each point in the profile mode mass spectrum; (4) performing centroid ion extraction based on the instrument noise and the candidate peak vector to complete centroid conversion of UHPLC-HRMS profile mode data: calculating a signal-to-noise ratio of each element in the candidate peak vector by dividing the ion abundance of the element by the corresponding estimated instrument noise; retaining elements in the candidate peak vector with a signal-to-noise ratio higher than a second threshold value, and correcting m / z values and ion abundances of adjacent two elements with an m / z difference within a tolerance range, to finally complete extraction of centroid ions and realize centroid conversion.

2. The UHPLC-HRMS profile mode data centroiding conversion method of claim 1, wherein, In the step (1), the Gaussian function used for convolution smoothing is a 5-point Gaussian function.

3. The UHPLC-HRMS profile mode data centroiding conversion method of claim 1, wherein, In the step (1), the method of local maximum extraction is: for any volume integral mass spectrum signal s, the local maximum extraction is performed on s i-1 s i s i s i+1 s i is the i-th data point in the mass spectrum, s i-1 and s i+1 are the i-1 and i+1 data points respectively.

4. The UHPLC-HRMS profile mode data centroiding conversion method of claim 1, wherein, In the step (2), the window size in the moving window strategy is 30.

5. The UHPLC-HRMS profile mode data centroiding conversion method of claim 1, wherein, In the step (2), the signal-to-noise ratio first threshold value in the outlier detection by using the moving window strategy is 2.

5.

6. The UHPLC-HRMS profile mode data centroiding conversion method of claim 1, wherein, In the step (4), in the method of performing centroid ion extraction based on the instrument noise and the candidate peak vector, the signal-to-noise ratio second threshold value is 1.5, and the m / z tolerance of adjacent two elements is 0.015.

Images