Method and device for screening compounds with biological accumulation potential in sample to be detected

Abstract

The invention provides a method and device for screening compounds with biological accumulation potential in a to-be-detected sample, and the method comprises the following steps: analyzing the to-be-detected sample by using a comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain a plurality of primarily screened compounds in the to-be-detected sample and retention time information of each primarily screened compound; respectively calculating a first orthogonal vector of each primary screening compound in the one-dimensional gas chromatographic column and a second orthogonal vector of each primary screening compound in the two-dimensional gas chromatographic column according to the retention time information; inputting the first orthogonal vector and the second orthogonal vector into a first target distribution coefficient prediction model to obtain a first target distribution coefficient of each primary screening compound; the first orthogonal vector and the second orthogonal vector are input into a second target distribution coefficient prediction model, a second target distribution coefficient of each preliminary screening compound is obtained, and the first target distribution coefficient prediction model and the second target distribution coefficient prediction model have different model parameters; and according to the first target partition coefficient, the second target partition coefficient and a biological accumulation potential discrimination condition, screening the multiple preliminary screening compounds to obtain a target compound.

Description

Technical Field

[0001] The present invention relates to the technical field of sample detection, and in particular to a method and device for screening compounds with bioaccumulation potential in a sample to be detected based on full two-dimensional gas chromatography retention time. Background Art

[0002] Long-term exposure to environmental pollutants can have adverse effects on human health. When compounds with bioaccumulation potential enter the body, they will continue to accumulate and cause long-term harm to health. Therefore, it is crucial to quickly detect target compounds with bioaccumulation potential through effective data screening methods.

[0003] In the process of implementing the present invention, it was found that the existing method of screening compounds with bioaccumulation potential is based on the quantitative structure-property relationship to predict the distribution characteristics of the compound for screening. This method requires the structural identification of a large number of compounds for non-target screening one by one. However, due to the extremely complex organic composition in the sample to be tested, the efficiency from separating and analyzing these complex mixtures to identifying the structure of each compound is low, time-consuming and labor-intensive, and it is difficult to quickly identify the target compound with bioaccumulation potential among the thousands of compounds detected. Summary of the invention

[0004] In view of the above problems, the present invention provides a method and device for screening compounds with bioaccumulation potential in a sample to be tested.

[0005] In one aspect of the present invention, a method for screening compounds with bioaccumulation potential in a sample to be tested is provided, comprising: analyzing the sample to be tested using a full two-dimensional gas chromatography-time-of-flight mass spectrometry method to obtain multiple preliminary screening compounds in the sample to be tested and retention time information of each preliminary screening compound; calculating the first orthogonal vector of each preliminary screening compound in a one-dimensional gas chromatography column and the second orthogonal vector in a two-dimensional gas chromatography column according to the retention time information; inputting the first orthogonal vector and the second orthogonal vector into a first target distribution coefficient prediction model to obtain the first target distribution coefficient of each preliminary screening compound; inputting the first orthogonal vector and the second orthogonal vector into a second target distribution coefficient prediction model to obtain the second target distribution coefficient of each preliminary screening compound, wherein the first target distribution coefficient prediction model and the second target distribution coefficient prediction model have different model parameters; and screening the target compound from the multiple preliminary screening compounds according to the first target distribution coefficient, the second target distribution coefficient and the bioaccumulation potential discrimination condition.

[0006] According to an embodiment of the present invention, the first orthogonal vector of each primary screening compound in a one-dimensional gas chromatography column and the second orthogonal vector in a two-dimensional gas chromatography column are calculated respectively according to the retention time information, including: calculating the first initial distribution coefficient of each primary screening compound in the one-dimensional gas chromatography column and the second initial distribution coefficient in the two-dimensional gas chromatography column respectively according to the retention time information; orthogonalizing the first initial distribution coefficient and the second initial distribution coefficient to obtain a first orthogonal vector and a second orthogonal vector.

[0007] According to an embodiment of the present invention, orthogonalizing the first initial allocation coefficient and the second initial allocation coefficient comprises adopting the following method:

[0008]

[0009]

[0010] in, is the first orthogonal vector, is the second orthogonal vector, is the first initial distribution coefficient, is the second initial distribution coefficient, is a constant.

[0011] According to an embodiment of the present invention, a first target distribution coefficient prediction model is constructed by the following method: multiple parameters in a multi-parameter linear relationship model are simplified to obtain a two-parameter linear relationship model; the model parameters of the two-parameter linear relationship model are fitted using a first orthogonal vector sample of multiple compound samples in a one-dimensional gas chromatography column, a second orthogonal vector sample in a two-dimensional gas chromatography column, and a logarithmic transformed octanol-water distribution coefficient sample of the compound sample to obtain the first target distribution coefficient prediction model.

[0012] According to an embodiment of the present invention, the method for screening compounds with bioaccumulation potential in a sample to be tested further comprises: analyzing the sample to be tested by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain an initial gas chromatogram of the sample to be tested.

[0013] According to an embodiment of the present invention, a target compound is screened from multiple primary screening compounds according to a first target distribution coefficient, a second target distribution coefficient and a bioaccumulation potential discrimination condition, including: generating a first contour line according to the first target distribution coefficient; generating a second contour line according to the second target distribution coefficient; mapping the first contour line and the second contour line to an initial gas chromatogram to obtain a target gas chromatogram; and determining the range of the target compound based on the target gas chromatogram according to the bioaccumulation potential discrimination condition.

[0014] According to an embodiment of the present invention, the bioaccumulation potential discrimination conditions include: the first target partition coefficient is greater than 2, the first target partition coefficient is greater than 5, and the second target partition coefficient is greater than 6 and less than 12.

[0015] According to an embodiment of the present invention, according to the bioaccumulation potential discrimination conditions, the range of the target compound is determined based on the target gas chromatography, including: in the target gas chromatogram, the preliminary screening compounds with a first target distribution coefficient greater than 5 are determined as target compounds, or, in the target gas chromatogram, the preliminary screening compounds with a first target distribution coefficient greater than 2 and a second target distribution coefficient greater than 6 and less than 12 are determined as target compounds.

[0016] Another aspect of the present invention provides a device for screening compounds with bioaccumulation potential in a sample to be tested, comprising: an analysis module, used to analyze the sample to be tested using a full two-dimensional gas chromatography-time-of-flight mass spectrometry method to obtain multiple primary screening compounds in the sample to be tested and retention time information of each primary screening compound; a calculation module, used to calculate the first orthogonal vector of each primary screening compound in a one-dimensional gas chromatography column and the second orthogonal vector in a two-dimensional gas chromatography column according to the retention time information; a first prediction module, used to input the first orthogonal vector and the second orthogonal vector into a first target distribution coefficient prediction model to obtain the first target distribution coefficient of each primary screening compound; a second prediction module, used to input the first orthogonal vector and the second orthogonal vector into a second target distribution coefficient prediction model to obtain the second target distribution coefficient of each primary screening compound, wherein the first target distribution coefficient prediction model and the second target distribution coefficient prediction model have different model parameters; a screening module, used to screen and obtain a target compound from multiple primary screening compounds according to the first target distribution coefficient, the second target distribution coefficient and the bioaccumulation potential discrimination condition.

[0017] According to an embodiment of the present invention, based on the pre-constructed first target partition coefficient prediction model or the second target partition coefficient prediction model, the input of the target partition coefficient prediction model for predicting the equilibrium partition of organic compounds in various media is converted into a first or second orthogonal vector related to the retention time of the compound, so as to calculate the orthogonal partition coefficient according to the retention time, and the first orthogonal vector and the second orthogonal vector are used as two parameters (two different dimensions) of the target partition coefficient prediction model, so that the first target partition coefficient ( ) and the second target distribution coefficient ( ). Furthermore, according to the bioaccumulation potential discrimination conditions, from all the preliminarily selected compounds, the compounds with bioaccumulation potential are determined as target compounds according to the output results of the target partition coefficient prediction model, so as to reduce the entire scope and calculation amount of measuring the compounds one by one, and improve the efficiency of screening and identification of compounds with bioaccumulation potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0019] Figure 1 A flow chart of a method for screening a compound with bioaccumulation potential in a sample to be tested according to an embodiment of the present invention is schematically shown;

[0020] Figure 2A A schematic diagram showing a comparison of the results of the first target allocation coefficient prediction model according to an embodiment of the present disclosure is shown;

[0021] Figure 2B A schematic diagram showing a comparison of the results of the second target allocation coefficient prediction model according to an embodiment of the present disclosure is shown;

[0022] Figure 3 A flow chart schematically shows a method for determining the scope of a target compound according to an embodiment of the present invention;

[0023] Figure 4A Schematically showing a first contour line corresponding to a first target allocation coefficient according to an embodiment of the present invention;

[0024] Figure 4B Schematically showing a second contour line corresponding to a second target allocation coefficient according to an embodiment of the present invention;

[0025] Figure 5 A target gas chromatograph according to an embodiment of the present invention is schematically shown;

[0026] Figure 6 The structural block diagram of the device for screening compounds with bioaccumulation potential in a sample to be tested according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0027] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0028] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0029] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0030] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0031] One aspect of the present invention provides a method for screening a sample for compounds with bioaccumulation potential.

[0032] Figure 1 The following is a schematic flow chart of a method for screening compounds with bioaccumulation potential in a sample according to an embodiment of the present invention. Figure 1 As shown, the method includes operations S110 to S150.

[0033] In operation S110, the sample to be tested is analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain a plurality of primary screening compounds in the sample to be tested and retention time information of each of the primary screening compounds.

[0034] According to an embodiment of the present invention, in comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC×GC), compounds with different distribution characteristics will have different separation effects in GC×GC. This two-dimensional separation method uses two chromatographic columns with different separation principles and independent of each other to separate the compounds in the sample to be tested, which greatly improves the separation effect of the compounds, so that different components in the sample to be tested can be more finely separated in two dimensions.

[0035] In operation S120, a first orthogonal vector in the one-dimensional gas chromatography column and a second orthogonal vector in the two-dimensional gas chromatography column are calculated for each primary screening compound according to the retention time information.

[0036] According to an embodiment of the present invention, the retention time information reflects the interaction between the compound and the chromatographic column stationary phase and the mobile phase under specific chromatographic conditions. By calculating the first orthogonal vector and the second orthogonal vector according to the retention time information, the retention time can be reflected in the first orthogonal vector and the second orthogonal vector. Further, the first orthogonal vector and the second orthogonal vector are used as the input of the first target distribution coefficient prediction model and the second target distribution coefficient prediction model. Since the orthogonal distribution coefficient is calculated based on the information reflecting the interaction between the compound and the chromatographic column such as the retention time, they more directly reflect the essential characteristics of the compound. Using it as an input can provide more targeted information for the prediction model, thereby improving the accuracy of the prediction of the target distribution coefficient.

[0037] According to an embodiment of the present invention, the first orthogonal vector and the second orthogonal vector may be obtained through orthogonalization processing, the purpose of which is to make different distribution coefficients more independent, so as to better analyze and understand the behavior of compounds in different chromatographic columns.

[0038] In operation S130, the first orthogonal vector and the second orthogonal vector are input into a first target partition coefficient prediction model to obtain a first target partition coefficient of each primary screening compound.

[0039] According to an embodiment of the present invention, the first target allocation coefficient prediction model is as shown in formula (1):

[0040] Formula (1)

[0041] in, is the first target partition coefficient of compound i, i.e., the logarithmic transformed octanol-water partition coefficient of compound i, is the first orthogonal vector of compound i, is the second orthogonal vector of compound i, The model parameters of the first objective allocation coefficient prediction model are assigned.

[0042] In operation S140, the first orthogonal vector and the second orthogonal vector are input into a second target partition coefficient prediction model to obtain a second target partition coefficient of each primary screening compound, wherein the first target partition coefficient prediction model and the second target partition coefficient prediction model have different model parameters.

[0043] According to an embodiment of the present invention, the second target allocation coefficient prediction model is as shown in formula (2):

[0044] Formula (2)

[0045] in, is the second target partition coefficient of compound i, i.e., the logarithmic transformed octanol-air partition coefficient of compound i, Model parameters of the coefficient prediction model are allocated to the second objective.

[0046] In operation S150, a target compound is screened from a plurality of pre-screened compounds according to the first target partition coefficient, the second target partition coefficient, and the bioaccumulation potential discrimination condition.

[0047] According to an embodiment of the present invention, the bioaccumulation potential discrimination conditions include: the first target partition coefficient is greater than 2, the first target partition coefficient is greater than 5, and the second target partition coefficient is greater than 6 and less than 12.

[0048] According to an embodiment of the present invention, if the first target partition coefficient of any compound i in the primary screening compounds is greater than 5 ( >5), compound i is determined as the target compound; or, if the first target partition coefficient of any compound i in the initial screening compounds is greater than 2 and the second target partition coefficient is greater than 6 and less than 12 ( >2 and ), compound i was identified as the target compound.

[0049] According to an embodiment of the present invention, based on the pre-constructed first target partition coefficient prediction model or the second target partition coefficient prediction model, the input of the target partition coefficient prediction model for predicting the equilibrium partition of organic compounds in various media is converted into a first or second orthogonal vector related to the retention time of the compound, so as to calculate the orthogonal partition coefficient according to the retention time, and the first orthogonal vector and the second orthogonal vector are used as two parameters (two different dimensions) of the target partition coefficient prediction model, so that the first target partition coefficient ( ) and the second target distribution coefficient ( ). Furthermore, according to the bioaccumulation potential discrimination conditions, from all the preliminarily selected compounds, the compounds with bioaccumulation potential are determined as target compounds according to the output results of the target partition coefficient prediction model, so as to reduce the entire scope and calculation amount of measuring the compounds one by one, and improve the efficiency of screening and identification of compounds with bioaccumulation potential.

[0050] According to an embodiment of the present invention, the first target allocation coefficient prediction model is constructed by the following method, including steps 11 and 12.

[0051] In step 11, multiple parameters in the multi-parameter linear relationship model are simplified to obtain a two-parameter linear relationship model.

[0052] According to the embodiment of the present invention, the simplification process may adopt any existing method, such as principal component analysis or singular value decomposition, etc., which will not be described in detail here.

[0053] In step 12, the model parameters of the two-parameter linear relationship model are fitted using the first orthogonal vector samples of the multiple compound samples in the one-dimensional gas chromatography column, the second orthogonal vector samples in the two-dimensional gas chromatography column, and the logarithmic transformation octanol-water partition coefficient samples of the compound samples to obtain a first target partition coefficient prediction model.

[0054] According to an embodiment of the present invention, before step 12, steps 121 to 126 are also included.

[0055] In step 121, a plurality of compound samples and a group of normal alkanes are analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain retention time sample information of the plurality of compound samples and the group of normal alkanes.

[0056] In step 122, based on the model parameters and molecular descriptors of the adsorption site model (ASM model), the ASM model is used to calculate the first initial partition coefficient samples ( ) and the second initial partition coefficient sample ( ).

[0057] In step 123, according to the retention time sample information and the first initial distribution coefficient sample ( ) to obtain the model parameters of the first initial model, refer to formula (3):

[0058] Formula (3)

[0059] in, is the first initial distribution coefficient sample, are the model parameters of the first initial model, is the number of carbon atoms in normal alkanes.

[0060] In step 124, according to the retention time sample information and the second initial partition coefficient sample ( ) to obtain the model parameters of the second initial model, refer to formula (4):

[0061] Formula (4)

[0062] in, is the second initial distribution coefficient sample, is the number of carbon atoms in normal alkanes, is the retention time sample information of compound sample j in the two-dimensional gas chromatography column, is the retention time sample information of normal alkanes in the two-dimensional gas chromatography column, are the model parameters of the second initial model.

[0063] In step 125, based on the first initial allocation coefficient sample ( ) and the carbon number of normal alkanes, the model parameters of the first initial model are obtained by fitting .

[0064] In step 126, based on the second initial allocation coefficient sample ( ), the carbon number and retention time sample information of normal alkanes, and the model parameters of the second initial model are obtained by fitting .

[0065] According to an embodiment of the present invention, after fitting the model parameters of the first initial model and the second initial model through the above steps 121 to 126, the retention time information of the sample to be tested can be input into formula (3) and formula (4) in the application process to obtain the first initial distribution coefficient and the second initial distribution coefficient of the sample to be tested.

[0066] According to an embodiment of the present invention, the first initial allocation coefficient sample and the second initial partition coefficient sample Orthogonalization processing to obtain the first orthogonal vector sample and the second orthogonal vector sample .

[0067] According to an embodiment of the present invention, based on the model parameters and molecular descriptors of the adsorption site model (ASM model), the ASM model is used to calculate the logarithmic transformed octanol-water partition coefficient samples of the plurality of compound samples. .

[0068] According to an embodiment of the present invention, a plurality of compound samples are used to transform the octanol-water partition coefficient samples. , the first orthogonal vector sample , the second orthogonal vector sample , model parameters for fitting a two-parameter linear relationship model , and obtain the first target allocation coefficient prediction model, as shown in formula (1).

[0069] According to an embodiment of the present invention, by constructing a two-dimensional gas chromatography retention behavior and distribution characteristic model (a first target distribution coefficient prediction model or a second target distribution coefficient prediction model), the distribution characteristics of unknown structure compounds are predicted based on retention time.

[0070] According to an embodiment of the present invention, the process of constructing the second target partition coefficient prediction model (Formula (2)) is the same as the process of constructing the first target partition coefficient prediction model (Formula (1)), and specific reference may be made to Steps 11 to 12, with the only difference being that: according to an embodiment of the present invention, based on the model parameters and molecular descriptors of the adsorption site model (ASM model), the ASM model is used to calculate the logarithmic transformed octanol-air partition coefficient samples of the plurality of compound samples. ; Use the logarithmic transformation of octanol-air partition coefficient samples of multiple compound samples , the first orthogonal vector sample , the second orthogonal vector sample , fit the model parameters of another two-parameter linear relationship model , and obtain the second target allocation coefficient prediction model, as shown in formula (2).

[0071] According to the embodiment of the present invention, since the , , , Both are calculated by the ASM model. After constructing the first target distribution coefficient prediction model and the second target distribution coefficient prediction model, the retention time information of the compound samples is used to further verify the authenticity of the two models.

[0072] According to an embodiment of the present invention, the first target allocation coefficient is calculated by using a known ASM model. , the second target allocation coefficient , the first orthogonal vector and the second orthogonal vector , thereby constructing a first target partition coefficient prediction model and a second target partition coefficient prediction model. Further, the retention time of the validation set compound samples is used to verify the accuracy of the first initial model, the second initial model, the first target partition coefficient prediction model and the second target partition coefficient prediction model, so that the first target partition coefficient prediction model and the second target partition coefficient prediction model are associated with the retention time of the compound. Thus, when detecting unknown samples, the first target partition coefficient and the second target partition coefficient can be directly predicted based on the retention time information obtained by the comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry, so as to further determine the target compound according to the bioaccumulation potential discrimination condition.

[0073] Exemplarily, there are 82 compound samples, of which 66 compound samples are training sets and 16 compound samples are validation sets.

[0074] 66 compound samples were analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain the retention time information of 66 compound samples. Specifically, three chromatographic temperature increase programs (A, B and C) were used: the initial temperature of the first column oven was set to 60°C and maintained for 1 min. The temperature was increased to 100°C at a rate of 20°C / min, 10°C / min and 5°C / min, respectively, and maintained for 1 min. Subsequently, the temperature was increased to 310°C at a rate of 5°C / min, 3°C / min and 2°C / min, respectively, and maintained at this temperature for 5 min. The modulator cycles were 6s, 8s and 10s, respectively.

[0075] The other parameters of the comprehensive two-dimensional gas chromatography-high-resolution time-of-flight mass spectrometry (GC×GC-HRTOF MS) were the same, and the specific conditions were as follows: chromatographic column combination was Rxi-5ms (30 m×0.25 mm×0.25 μm) + BPX-50 (1 m×0.1 mm×0.1 μm); carrier gas flow rate was 1.0 mL / min; splitless injection; inlet temperature was 280°C; the second column oven was always 10°C higher than the first column oven; the modulator was always 15°C higher than the second column oven; the transfer line temperature was 310°C; the solvent delay was 350s; EI mode; ionization energy was 70 eV; ion source temperature was 250°C; data acquisition range was m / z 35-600; acquisition speed was 200 spectra / s.

[0076] Using the 66 compound samples in the training set, the ASM model was used to calculate the , , , .

[0077] Further, and Orthogonalize to get the first orthogonal vector and the second orthogonal vector , the value of βorth is calculated to be 1.1472.

[0078] Exemplarily, the model parameters of the first target partition coefficient prediction model and the second target partition coefficient prediction model obtained by fitting using 66 compound samples are shown in Table 1.

[0079] Table 1. Regression coefficients obtained based on the fitting of 66 compound sample pairs

[0080]

[0081] As shown in Table 1, the fitting effect is good. The square of the correlation coefficient (r 2 ) is 0.997, The square of the correlation coefficient (r 2 ) is 0.950, and The root mean square error (RMSE) is 0.076 and 0.378 log units respectively.

[0082] Under three chromatographic temperature programs, 66 training set compounds and a group of normal alkanes were analyzed by GC×GC-HRTOFMS to obtain the two-dimensional retention time, and then the model parameters of formulas (3) and (4) were fitted, as shown in Table 2. The distribution coefficient of the compound sample in the chromatographic column fits well with the retention time under the three temperature programs, r 2 The value is 0.992 ~0.999, and the RMSE value is 0.019 ~ 0.107 log units.

[0083] Table 2. Regression coefficients of equations (3) and (4) using training set compound samples under three chromatographic temperature programs (n = 66)

[0084]

[0085] The model parameters of the first target distribution coefficient prediction model are obtained by fitting Or the model parameters of the second target distribution coefficient prediction model Finally, in order to verify the accuracy of the model's prediction of the two distribution characteristics, the distribution characteristics predicted by two-dimensional gas chromatography retention time of compound samples in the training set and validation set were compared with the experimental values ​​reported in the literature.

[0086] Figure 2A A comparison diagram of the results of the first target allocation coefficient prediction model according to an embodiment of the present disclosure is schematically shown. Figure 2B A comparison diagram of the results of the second target allocation coefficient prediction model according to an embodiment of the present disclosure is schematically shown.

[0087] like Figure 2A and 2B As shown, and R 2 The predicted values ​​of the target distribution coefficient prediction model of the embodiment of the present invention were compared with those of the ASM model. The results showed that the distribution characteristic values ​​predicted by the two models were very consistent. 2 The predicted values ​​of this model were compared with those of other software. and The values ​​are compared. 2 are 0.913 and 0.756 respectively, and the RMSE are 0.420 and 0.810 log units respectively.

[0088] Table 3 Comparison of predicted values, experimental values, and predicted values ​​of other models (ASM) according to the embodiments of the present invention

[0089]

[0090] According to an embodiment of the present invention, the first orthogonal vector of each primary screening compound in the one-dimensional gas chromatography column and the second orthogonal vector in the two-dimensional gas chromatography column are calculated respectively according to the retention time information, including steps 21 and 22.

[0091] In step 21, the first initial distribution coefficient of each primary screening compound in the one-dimensional gas chromatography column and the second initial distribution coefficient in the two-dimensional gas chromatography column are calculated respectively according to the retention time information.

[0092] In step 22, orthogonal processing is performed on the first initial allocation coefficient and the second initial allocation coefficient to obtain a first orthogonal vector and a second orthogonal vector.

[0093] According to an embodiment of the present invention, step 22 can be calculated by referring to formula (5) and formula (6).

[0094] Formula (5)

[0095] Formula (6)

[0096] in, is the first orthogonal vector, is the second orthogonal vector, is the first initial distribution coefficient, is the second initial distribution coefficient, is a constant.

[0097] Based on the above embodiment, the retention time information of each preliminary screening compound in the sample to be tested is analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to calculate the first target distribution coefficient and the second target distribution coefficient according to the first target distribution coefficient prediction model and the second target distribution coefficient prediction model.

[0098] At the same time, the sample to be tested is analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry, and an initial gas chromatogram of the sample to be tested is also obtained.

[0099] Figure 3 The flowchart of the method for determining the scope of a target compound according to an embodiment of the present invention is schematically shown.

[0100] As Figure 3 shown, the method includes operations S310 to S340.

[0101] In operation S310, a first contour line is generated according to a first target distribution coefficient.

[0102] In operation S320, a second contour line is generated according to a second target distribution coefficient.

[0103] In operation S330, the first contour line and the second contour line are mapped to an initial gas chromatogram to obtain a target gas chromatogram.

[0104] In operation S340, according to the bioaccumulation potential discrimination condition, the range of target compounds is determined based on the target gas chromatogram.

[0105] According to an embodiment of the present invention, determining the range of target compounds based on the target gas chromatogram according to the bioaccumulation potential discrimination condition includes steps 31 to 32.

[0106] In step 31, in the target gas chromatogram, a pre-screened compound with a first target distribution coefficient greater than 5 is determined as a target compound, or, in step 32, in the target gas chromatogram, a pre-screened compound with a first target distribution coefficient greater than 2 and a second target distribution coefficient greater than 6 and less than 12 is determined as a target compound.

[0107] According to an embodiment of the present invention, the position of a compound on a GC×GC chromatogram reflects the unique distribution characteristics of the compound in different environmental media. The distribution characteristics of any compound can be quickly evaluated according to the peak position in the chromatogram. The bioaccumulation potential of a compound is closely related to the distribution characteristics of the compound. According to experience, >5 is used as a screening criterion for bioaccumulation, or, >2 and 6< <12 compounds have potential bioaccumulation. Therefore, pay attention to >5 or >2 and 6< <12 compounds. By separating the compounds in the sample to be measured by GC×GC-HRTOFMS and mapping the distribution characteristics onto the GC×GC chromatogram, compounds with bioaccumulation potential can be screened intuitively and quickly.

[0108] According to an embodiment of the present invention, the distribution characteristics are mapped on a two-dimensional gas chromatogram, and the distribution characteristics of the compounds in the chromatogram are divided into regions. The distribution characteristics of the compound can be quickly understood based on the peak position in the chromatogram. Based on the relationship between bioaccumulation and distribution characteristics, compounds with bioaccumulation potential can be quickly located on the chromatogram, reducing the number of compounds that require qualitative analysis in the massive data of non-target screening. GC×GC-TOF MS, which has higher separation power, peak capacity and sensitivity than traditional one-dimensional chromatography, is used to achieve simultaneous separation of complex and trace organic pollutants in complex samples, providing a guarantee for the accurate identification of unknown substances.

[0109] Hereinafter, specific embodiments are provided to exemplarily illustrate the above embodiments.

[0110] Embodiment 1:

[0111] Breast milk is a commonly used biological matrix for monitoring human exposure to organic pollutants. Breast milk contains a large amount of lipids, and lipophilic exogenous compounds that enter the human body through diet, breathing and skin absorption tend to accumulate in breast milk. Existing technologies mainly use targeted analysis of pollutants in breast milk, but compared with the thousands of pollutants that humans are exposed to, this only reveals the tip of the iceberg of harmful chemicals in the human body. Non-target screening is an effective method to identify unknown risk compounds in complex matrices. However, non-target screening of pollutants in biological matrices remains challenging.

[0112] The breast milk matrix is ​​complex and contains low concentrations of exogenous compounds. GC×GC-HRTOF MS has high separation ability, peak capacity and sensitivity, and can effectively separate thousands of organic pollutants in complex samples at the same time. Screening for compounds with bioaccumulation potential in breast milk will help to discover compounds that pose potential risks to human health. In the specific implementation of the present invention, a full two-dimensional gas chromatography retention behavior and distribution characteristic model is constructed, and GC×GC-HRTOF MS is used to analyze breast milk samples. The model is used to achieve rapid screening of compounds with bioaccumulation potential and perform structural identification. Specifically, taking breast milk samples as an example of the sample to be tested, the following steps are included:

[0113] 1. Sample pretreatment

[0114] The breast milk samples were freeze-dried, concentrated by accelerated solvent extraction (ASE extraction) with n-hexane: dichloromethane (1:1, V / V), and subjected to gel permeation chromatography (GPC) and Florisil column to remove lipids from the samples. After concentration, the volume was fixed to 50 μl with n-hexane for analysis. 5 μl was taken from each sample and mixed to prepare QC samples.

[0115] 2. Analysis of samples using GC×GC-HRTOF MS

[0116] Use GC×GC-HRTOF MS to analyze the samples to be tested. Select one of the three heating programs used when building the model (for example, select the heating program (A) with a shorter analysis time to analyze breast milk samples) to ensure that the compounds in the sample are completely separated. Other instrument parameters remain unchanged. When analyzing the samples to be tested, it is necessary to add a data quality evaluation step, that is, through repeated analysis of QC samples, observe whether the detected compounds have obvious signal drift, and characterize the stability of the detection method.

[0117] The specific instrument parameters used during the operation include: splitless injection; injection volume 1 μl; injection port temperature 280°C; carrier gas flow rate 1.0 mL / min; chromatographic column combination Rxi-5ms (30 m×0.25 mm×0.25 μm) + BPX-50 (1 m×0.1 mm×0.1 μm); the first column oven starts at 60°C, holds for 1 min, increases to 100°C at a rate of 20°C / min, holds for 1 min, then increases to 310°C at a rate of 5°C / min, holds for 5 min; the second column oven is always 10°C higher than the first column oven; the modulator is 15°C higher than the second column oven; the modulation period is 6 s; the transfer line temperature is 310°C; the ion source is in EI mode; the ionization energy is 70 eV; the ion source temperature is 250°C; the data acquisition range is m / z 35-600; the acquisition rate is 200 spectra / s. Thousands of volatile and semi-volatile organic pollutants can be effectively separated simultaneously in one injection.

[0118] (III) Calculate the first target allocation coefficient and the second target allocation coefficient

[0119] The specific calculation process is referred to the above embodiment and will not be described in detail here.

[0120] 4. Draw contour lines and map them to the initial gas chromatogram

[0121] Generate the first contour line according to the first target allocation coefficient, such as Figure 4A shown.

[0122] The second contour line is generated according to the second target allocation coefficient, such as Figure 4B shown.

[0123] The first contour line and the second contour line are mapped to the initial gas chromatogram to obtain the target gas chromatogram, such as Figure 5 shown.

[0124] (V) Structural identification of compounds marked as having bioaccumulation potential in the chromatogram

[0125] For one breast milk sample, deconvolution showed 1260 compounds, of which 361 had confidence level 3 or higher. Although the breast milk sample contained a complex composition, Figure 5 As shown in Figure 1, the contour lines on the GC×GC chromatogram clearly divide the distribution characteristics of the compounds. The chromatographic peaks of 156 compounds appear in the predetermined bioaccumulation potential region. Filtering the data before structural identification significantly reduces the number of compounds that require structural identification.

[0126] Screening of bioaccumulative compounds in 20 breast milk samples. Screening of 1286 compounds with bioaccumulation potential from more than 3000 compounds. Structural identification of these compounds. Software was used to perform mass correction, deconvolution and integration of the detected data. Structural identification of compounds in the selected region was performed by library search, retention index comparison, isotope pattern verification and ion elemental composition analysis. Consider compounds detected in the sample but not in the blank sample, or compounds with a peak signal-to-noise ratio greater than ten times the corresponding peak in the blank sample. Known endogenous compounds were removed by searching the human metabolome database.

[0127] Among them, 865 compounds had a library match of >700 and a mass accuracy of <5ppm, and were classified as level 3. 62 compounds had a library match of >850, a mass accuracy of <5ppm, and a reference retention index deviation of <2%, and were classified as level 2. 43 compounds were identified by standards and classified as level 1.

[0128] Organic pollutants in breast milk are complex and diverse, but not all substances are worth monitoring. It is very important to quickly discover compounds worthy of attention from the massive data of non-target screening. This invention helps us to quickly extract and filter key information from massive data, screen out bioaccumulative compounds that will have a significant impact on human health, and provide data support for priority prevention and control of environmental pollution.

[0129] Based on the above method for screening compounds with bioaccumulation potential in a sample to be tested, the present invention also provides a device for screening compounds with bioaccumulation potential in a sample to be tested. Figure 6 The device is described in detail.

[0130] Figure 6 The structural block diagram of the device for screening compounds with bioaccumulation potential in a sample to be tested according to an embodiment of the present invention is schematically shown.

[0131] like Figure 6As shown, the device 600 of this embodiment includes an analysis module 601 , a calculation module 602 , a first prediction module 603 , a second prediction module 604 and a screening module 605 .

[0132] An analysis module 601 is used to analyze the sample to be tested by using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain multiple primary screening compounds in the sample to be tested and retention time information of each primary screening compound;

[0133] A calculation module 602 is used to calculate the first orthogonal vector of each primary screening compound in the one-dimensional gas chromatography column and the second orthogonal vector in the two-dimensional gas chromatography column according to the retention time information;

[0134] A first prediction module 603 is used to input the first orthogonal vector and the second orthogonal vector into a first target partition coefficient prediction model to obtain a first target partition coefficient of each primary screening compound;

[0135] A second prediction module 604 is used to input the first orthogonal vector and the second orthogonal vector into a second target partition coefficient prediction model to obtain a second target partition coefficient of each primary screening compound, wherein the first target partition coefficient prediction model and the second target partition coefficient prediction model have the same model structure and different model parameters;

[0136] The screening module 605 is used to screen a target compound from a plurality of primary screening compounds according to the first target partition coefficient, the second target partition coefficient and the bioaccumulation potential discrimination condition.

[0137] According to an embodiment of the present invention, any multiple modules among the analysis module 601, the calculation module 602, the first prediction module 603, the second prediction module 604 and the screening module 605 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0138] According to an embodiment of the present invention, at least one of the analysis module 601, the calculation module 602, the first prediction module 603, the second prediction module 604 and the screening module 605 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the analysis module 601, the calculation module 602, the first prediction module 603, the second prediction module 604 and the screening module 605 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0139] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination. The scope of the present invention is defined by the attached claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A method for screening a compound with bioaccumulation potential in a sample to be tested, comprising: Analyzing the sample to be tested by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain a plurality of primary screening compounds in the sample to be tested and retention time information of each of the primary screening compounds; According to the retention time information, respectively calculating the first orthogonal vector of each of the primary screening compounds in the one-dimensional gas chromatography column and the second orthogonal vector in the two-dimensional gas chromatography column; Inputting the first orthogonal vector and the second orthogonal vector into a first target partition coefficient prediction model to obtain a first target partition coefficient of each of the primary screening compounds; Inputting the first orthogonal vector and the second orthogonal vector into a second target partition coefficient prediction model to obtain a second target partition coefficient of each of the primary screening compounds, wherein the first target partition coefficient prediction model and the second target partition coefficient prediction model have different model parameters; According to the first target partition coefficient, the second target partition coefficient and the bioaccumulation potential discrimination condition, a target compound is screened out from the plurality of primary screening compounds.

2. The method according to claim 1, characterized in that According to the retention time information, respectively calculating the first orthogonal vector of each of the primary screening compounds in the one-dimensional gas chromatography column and the second orthogonal vector in the two-dimensional gas chromatography column, including: According to the retention time information, respectively calculating the first initial distribution coefficient of each of the primary screening compounds in the one-dimensional gas chromatography column and the second initial distribution coefficient in the two-dimensional gas chromatography column; Orthogonalization is performed on the first initial allocation coefficient and the second initial allocation coefficient to obtain the first orthogonal vector and the second orthogonal vector.

3. The method according to claim 2, characterized in that Orthogonalizing the first initial allocation coefficient and the second initial allocation coefficient comprises adopting the following method: in, is the first orthogonal vector, is the second orthogonal vector, is the first initial distribution coefficient, is the second initial distribution coefficient, is a constant.

4. The method according to claim 1, characterized in that: The first target allocation coefficient prediction model is constructed by the following method: Simplify multiple parameters in the multi-parameter linear relationship model to obtain a two-parameter linear relationship model; The model parameters of the two-parameter linear relationship model are fitted using the first orthogonal vector samples of multiple compound samples in a one-dimensional gas chromatography column, the second orthogonal vector samples in a two-dimensional gas chromatography column, and the logarithmic transformed octanol-water partition coefficient samples of the compound samples to obtain the first target partition coefficient prediction model.

5. The method according to claim 1, characterized in that The method further comprises: The sample to be tested is analyzed by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain an initial gas chromatogram of the sample to be tested.

6. The method according to claim 5, characterized in that Screening a target compound from the plurality of primary screening compounds according to the first target partition coefficient, the second target partition coefficient and the bioaccumulation potential discrimination condition comprises: generating a first contour line according to the first target allocation coefficient; generating a second contour line according to the second target allocation coefficient; Mapping the first contour line and the second contour line to the initial gas chromatogram to obtain a target gas chromatogram; According to the bioaccumulation potential discrimination conditions, the range of the target compound is determined based on the target gas chromatogram.

7. The method according to claim 6, characterized in that The bioaccumulation potential discrimination conditions include: the first target partition coefficient is greater than 2, the first target partition coefficient is greater than 5, and the second target partition coefficient is greater than 6 and less than 12.

8. The method according to claim 7, characterized in that According to the bioaccumulation potential discrimination conditions, the range of the target compound is determined based on the target gas chromatography, including: In the target gas chromatogram, the primary screening compound having the first target partition coefficient greater than 5 is determined as the target compound, or, In the target gas chromatogram, the primary screening compound whose first target partition coefficient is greater than 2 and whose second target partition coefficient is greater than 6 and less than 12 is determined as the target compound.

9. A device for screening a compound with bioaccumulation potential in a sample to be tested, comprising: An analysis module, used to analyze the sample to be tested by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry to obtain a plurality of primary screening compounds in the sample to be tested and retention time information of each of the primary screening compounds; A calculation module, used for respectively calculating the first orthogonal vector of each of the primary screening compounds in the one-dimensional gas chromatography column and the second orthogonal vector in the two-dimensional gas chromatography column according to the retention time information; A first prediction module, used for inputting the first orthogonal vector and the second orthogonal vector into a first target partition coefficient prediction model to obtain a first target partition coefficient of each of the primary screening compounds; A second prediction module, used for inputting the first orthogonal vector and the second orthogonal vector into a second target partition coefficient prediction model to obtain a second target partition coefficient of each of the primary screening compounds, wherein the first target partition coefficient prediction model and the second target partition coefficient prediction model have different model parameters; A screening module is used to screen the target compound from the multiple pre-screened compounds according to the first target partition coefficient, the second target partition coefficient and the bioaccumulation potential discrimination condition.

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