Methods, apparatus, storage media and electronic devices for obtaining key components of mixtures

Abstract

This invention relates to a method, apparatus, storage medium, and electronic device for obtaining key components of a mixture. The method includes: obtaining each component in a multi-component mixture system in a target region; determining the number of factor levels corresponding to each component; constructing trajectory starting points based on the number of factor levels; determining that the currently constructed trajectory starting points are not repeated based on a previously constructed set of trajectory starting points; determining a preferred set of trajectory starting points based on the uniformity deviation of each constructed set of trajectory starting points; for each preferred trajectory starting point in the preferred set of trajectory starting points, performing a transformation according to the factor order corresponding to that preferred trajectory starting point, with each transformation targeting the factor level of one factor, to obtain a mixture sample; conducting a mixture toxicity test based on the mixture sample to obtain mixture toxicity data; performing a global sensitivity analysis on the mixture toxicity data; and determining key components based on the results of the global sensitivity analysis. This can improve the reliability of mixture toxicity assessment results.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method, apparatus, storage medium, and electronic device for obtaining key components of a mixture. Background Technology

[0002] With the continuous development of industry, a large number of multi-component mixtures have been generated. These mixtures commonly contain low doses of various chemical components. Therefore, toxicity assessment of mixtures in the target environment is a crucial foundation for ensuring sustainable environmental quality goals. Conducting toxicity assessments of mixtures requires obtaining the specific chemical components and their concentrations. However, since multi-component mixtures in the environment constitute a mixture system—a collection of various mixtures with certain chemical components but different concentrations—there are numerous mixtures with varying component concentration ratios. For the same toxicity endpoint and the same exposure time, the toxicity of a mixture is closely related to the concentration ratio and concentration level of each component in the multi-component mixture. Comprehensively assessing the toxicity of mixtures with all different concentration ratios or levels is practically impractical. Therefore, it is necessary to obtain representative mixtures from various mixture collections for toxicity assessment to make chemical mixture toxicity assessment feasible. Currently, most mixture toxicity studies mainly use methods with fixed concentration ratios, such as the half-maximum effective ratio (WAP). However, this method cannot obtain the toxicity of mixtures at other different mixing ratios. To obtain the toxicity of mixtures at different mixing ratios, an improved approach employs trajectory-based Morris sampling. This involves designing samples for multivariate mixtures at different mixing ratios: r trajectories (typically between 10 and 50) are constructed in a p-dimensional space composed of p factors (chemicals or components). A random starting point is generated for each trajectory. Then, the factor level (OAT) of one factor is changed sequentially to expand the trajectory, resulting in a complete trajectory. Each starting point and each sample point obtained from the expansion corresponds to a component and its concentration, respectively. All sample points on the trajectory constitute a complete mixture sample. Different trajectories correspond to component concentrations at different mixing ratios. Compared to fixed concentration ratio design methods, trajectory-based methods can obtain more comprehensive and diverse mixture samples dispersed in a multidimensional space, enabling the acquisition of mixture samples at different mixing ratios for corresponding mixture toxicity assessments. Based on the mixture toxicity assessment results, key components can be extracted. However, the method of generating mixture samples with different mixing ratios based on trajectories and determining key components based on the mixture toxicity assessment results of each mixture sample has problems such as uneven distribution of the level of each factor and duplicate mixture samples, which reduces the reliability of the mixture toxicity assessment results. Summary of the Invention

[0003] In view of this, the present invention provides a method, apparatus, storage medium, and electronic device for obtaining key components of a mixture.

[0004] Specifically, the present invention is achieved through the following technical solution:

[0005] According to a first aspect of the present invention, a method for obtaining a key component of a mixture is provided, the method comprising:

[0006] Obtain each component in the multi-component mixture system in the target region, and determine the factor level number of the factor corresponding to each component based on the concentration range of each component in the target region;

[0007] Based on the number of factor levels, a trajectory starting point is constructed. The existing trajectory starting point group is queried. If the current trajectory starting point is the same as any trajectory starting point in the trajectory starting point group, the trajectory is reconstructed. A trajectory starting point group is formed based on the constructed trajectory starting point.

[0008] For each constructed trajectory starting point group, the uniformity deviation of the trajectory starting point group is obtained, and the preferred trajectory starting point group is determined based on the uniformity deviation.

[0009] For each preferred trajectory starting point in the preferred trajectory starting point group, a pre-set factor level transformation strategy is used to transform the sample according to the factor order corresponding to the preferred trajectory starting point. Each transformation targets the factor level of one factor, resulting in a mixture sample containing the preferred trajectory starting point.

[0010] For each mixture subsample in the mixture sample, a corresponding mixture subsample solution is prepared, and the mixture toxicity test is performed on the test organism to obtain mixture toxicity data. A global sensitivity analysis is then performed on the mixture toxicity data to determine the key components based on the results of the global sensitivity analysis.

[0011] According to a second aspect of the present invention, an apparatus for obtaining a key component of a mixture is provided, the apparatus comprising:

[0012] The level number determination module is used to obtain the number of factors of each component in the multi-component mixture system in the target region, and determine the factor level number of the factor corresponding to each component based on the concentration range of each component in the target region.

[0013] The trajectory reconstruction module is used to construct trajectory starting points based on the number of factor levels, query the already constructed trajectory starting point group, determine that the currently constructed trajectory starting point is the same as any trajectory starting point in the trajectory starting point group, reconstruct the trajectory, and form a trajectory starting point group based on the constructed trajectory starting points.

[0014] The deviation calculation module is used to obtain the uniformity deviation of each constructed trajectory starting point group and determine the preferred trajectory starting point group based on the uniformity deviation.

[0015] The trajectory expansion module is used to perform a conversion on each preferred trajectory starting point in the preferred trajectory starting point group according to a pre-set factor level conversion strategy and the factor order corresponding to the preferred trajectory starting point. Each conversion is performed on the factor level of one factor, resulting in a mixture sample containing the preferred trajectory starting point.

[0016] The key component identification module is used to prepare corresponding mixture sub-sample solutions based on each mixture sub-sample in the mixture sample, conduct mixture toxicity tests on the test organisms, obtain mixture toxicity data, perform global sensitivity analysis on the mixture toxicity data, and identify key components based on the results of the global sensitivity analysis.

[0017] According to a third aspect of the invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for obtaining key components of a mixture in any possible implementation of the first aspect.

[0018] According to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for obtaining key components of a mixture in any possible implementation of the first aspect. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for obtaining key components of a mixture according to an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of an apparatus for obtaining key components of a mixture provided in an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In related technologies, the Morris trajectory-based sampling method is employed. This method utilizes p factors (chemicals or components) to construct a p-dimensional space. A random starting point is generated within this p-dimensional space as the starting point of the trajectory. Then, by changing the level of one factor at a time in a random order (OAT), the trajectory is expanded to obtain a complete trajectory. Each trajectory corresponds to a mixture sample composed of key components with a fixed mixing ratio. Multiple trajectories correspond to mixture samples with different mixing ratios, allowing for corresponding mixture toxicity assessments based on these samples. However, this trajectory-based method for generating mixture samples with different mixing ratios suffers from uneven distribution of factor levels across multiple trajectories and duplicate mixture samples. Since the toxicity of a mixture (mixture sample) is closely related to the concentration ratio and concentration level of each key component, performing mixture toxicity assessments under conditions of uneven factor (key component) level distribution and duplicate mixture samples reduces the reliability of the assessment results.

[0026] In this embodiment, for the method of generating trajectory starting points in a random manner, samples generated by the Latin Hypercube Sampling (LHS) method are used as the starting points of multiple trajectories to optimize the OAT method with varying factor levels. This effectively solves the problems of uneven distribution of factor levels and the generation of duplicate mixture samples. However, the randomness of the LHS method may lead to the generation of identical trajectory starting points, thus reducing the diversity of mixture samples. Furthermore, random samples generated by the LHS method may have significant deviations in uniformity, affecting the uniformity and representativeness of mixture sample points. This embodiment improves upon the LHS-OAT method in terms of randomness and uniformity by optimizing the design method to enhance the scientific rigor, flexibility, and practicality of the obtained mixture samples. This provides more representative mixture samples for mixture toxicity assessment, enabling accurate evaluation of the combined toxicity of mixtures. Based on the combined toxicity assessment results, key components affecting mixture toxicity can be accurately identified, providing a scientific basis for determining key pollutants in mixture risk assessment.

[0027] See Figure 1 This invention provides a method for obtaining key components of a mixture, which may include the following steps:

[0028] S101. Obtain each component in the multi-component mixture system in the target region, and determine the factor level number of the factor corresponding to each component based on the concentration range of each component in the target region.

[0029] In this embodiment, as an optional embodiment, the factor level number of the factor corresponding to each component is determined based on the concentration range of each component in the target region, including:

[0030] For each component, obtain the upper and lower limit concentration difference of the corresponding concentration range for that component;

[0031] Based on the maximum upper and lower limit concentration difference and the pre-set factor level number setting strategy, the factor level number is determined. Based on the factor level number, the concentration range corresponding to each component is divided into concentration sub-ranges, and each concentration sub-range corresponds to a factor level.

[0032] In this embodiment, as an optional embodiment, for each component (factor), the concentration sub-ranges obtained by dividing each factor according to the number of factor levels are equally probable non-overlapping intervals.

[0033] S102. Construct trajectory starting points based on the number of factor levels, query the already constructed trajectory starting point group, determine that the currently constructed trajectory starting point is the same as any trajectory starting point in the trajectory starting point group, reconstruct, and form a trajectory starting point group based on the constructed trajectory starting points;

[0034] In this embodiment, for the multi-component mixture system in the target region, the number of components (factors) contained therein is clear, and each component has a clear concentration range in the target region. Based on the components and their concentration ranges, a factor (mixture component) - factor level (concentration) table is constructed, which is the mapping relationship between components and concentrations.

[0035] In this embodiment, as an optional implementation, constructing the trajectory starting point based on the number of factor levels includes:

[0036] A component sequence is constructed based on each component. For each factor corresponding to a component in the component sequence, a factor level is randomly selected from the factor levels corresponding to that factor. The trajectory starting point is obtained based on the factor level randomly selected for each component in the component sequence.

[0037] In this embodiment, as an optional implementation, a trajectory starting point group is formed based on the constructed trajectory starting points, including:

[0038] Based on the number of factor levels and the pre-set factor level coefficients, determine the number of trajectory starting points included in the trajectory starting point group;

[0039] Multiple trajectory starting points are obtained by traversing each component in the component sequence multiple times. Based on the multiple trajectory starting points, the trajectory starting point group is constructed. Each traversal of the component sequence corresponds to one trajectory starting point, and the multiple traversals are equal to the number of trajectory starting points.

[0040] In this embodiment, if the starting point of each constructed trajectory is different from any of the previously constructed starting points, then the starting points of the trajectories whose number equals the number of the trajectories are grouped into a trajectory starting point group. As an optional embodiment, the constructed trajectory starting point group includes the constructed trajectory starting points in the current trajectory starting point group that has not yet been completed, as well as the trajectory starting points in each of the completed trajectory starting point groups.

[0041] In this embodiment, as an optional implementation, the LHS method is used to generate the trajectory starting points. When determining the number of trajectory starting points contained in the trajectory starting point group, the number of factors (p) and the number of factor levels (l) are used. The number of factors determines the number of components contained in the trajectory starting point group, i.e., the number of factors. The number of factor levels (l) is used to calculate the minimum value of the number of trajectory starting points (r) contained in the trajectory starting point group. As an optional implementation, r = m × l ≥ 10, where m represents an integer multiple, such as 1, 2, 3, etc.

[0042] In this embodiment, for each factor in the component sequence, a factor level is randomly selected from each factor level, and a trajectory starting point is constructed based on the randomly selected factor level.

[0043] In this embodiment, for a multivariate mixture system containing p factors and l factor levels, the constructed trajectory starting point group is a Latin hypercube sample group, which can be represented in matrix form L = (x i,j ), where i=1, 2, …, l; j=1,2, …, p. Each row of matrix L represents a sequence of mixtures of different factors obtained by Latin hypercube sampling, that is, each row corresponds to a trajectory starting point, and each column represents the factor being sampled l times in l equally probable disjoint intervals (factor levels).

[0044] In this embodiment, for the case of the number of factors p and the number of factor levels l, the number of trajectory starting points (the number of trajectory starting points) is a multiple of the number of factor levels (l), and r (r = m × l ≥ 10, where m represents an integer multiple, such as 1, 2, 3, etc.) trajectory starting points can be obtained.

[0045] In this embodiment, the Latin hypercube sample group (trajectory starting point group) composed of multiple trajectory starting points is illustrated as follows:

[0046]

[0047] In this embodiment, taking a multi-component mixture system with 12 factors and 3 factor levels as an example, corresponding to p=12 and l=3, the factor-level table containing 12 factors is shown in Table 1, where the value corresponding to the factor level is the identifier of the corresponding concentration subrange.

[0048]

[0049] In Table 1, each factor (chemical) includes 3 factor levels, which are represented by level numbers 1, 2, and 3 respectively. The actual concentration (concentration sub-range) represented by level numbers 1, 2, and 3 is different for different factor levels.

[0050] In this embodiment, m=4, and based on the number of levels l=3, the minimum value of the number of trajectory starting points r is 12. Thus, using the LHS method, three layers can be generated, with each factor having four identical factor levels in each layer. Four LHS iterations generate a total of 12 trajectory starting points, forming a trajectory starting point group, where the frequency of each factor level is uniform across all trajectory starting points. As an optional embodiment, the generated trajectory starting point groups (samples) are shown in Table 2.

[0051]

[0052] In Table 2, Ci represents the i-th factor, and rk represents the k-th trajectory starting point.

[0053] In this embodiment, although the LHS method can improve the flexibility of sample design, its randomness may lead to the generation of the same trajectory starting point, thereby reducing sample diversity. Therefore, deduplication of trajectory starting points is performed as an optional embodiment. If the currently constructed trajectory starting point is found to be the same as any trajectory starting point in the trajectory starting point group, reconstruction is performed, including:

[0054] For each duplicate factor to be removed in the current trajectory starting point, obtain the duplicate factor level of that duplicate factor;

[0055] Traverse each constructed trajectory starting point in the constructed trajectory starting point group to obtain the level of the deduplicated factor of the to be deduplicated factor at each constructed trajectory starting point. The constructed trajectory starting point group includes the currently constructed trajectory starting point group.

[0056] If the level of the repeated factor to be removed in the current trajectory starting point is the same as the level of the repeated factor to be removed in any existing trajectory starting point, delete the current trajectory starting point and reconstruct a new trajectory starting point.

[0057] In this embodiment, after generating the trajectory starting point, the generated sample set (with established trajectory starting points) is queried. If a trajectory starting point identical to the generated starting point exists in the sample set, it is deleted, and the trajectory starting point generation process is repeated. This adds a loop-based check mechanism to the trajectory starting point generation process using the LHS method, ensuring that duplicate starting points exist. If duplicate starting points are found, the LHS method is used again to generate the trajectory starting point until a unique starting point is obtained. This effectively avoids the possibility of duplicate starting points arising from the randomness of multiple LHS method iterations.

[0058] S103. For each constructed trajectory starting point group, obtain the uniformity deviation of the trajectory starting point group, and determine the preferred trajectory starting point group based on the uniformity deviation.

[0059] In this embodiment, the trajectory starting point group generated by the LHS method has a large deviation in uniformity, which may affect the representativeness of the trajectory starting points and the reliability of the toxicity assessment results.

[0060] In this embodiment, to address the issue of large uniformity deviation in the samples (trajectory starting point group) generated by LHS, as an optional implementation, five groups of samples are randomly generated using the LHS method. Each group of samples covers multiple factors and levels in the space, and the uniformity deviation of each group of samples is calculated. The smaller the calculated uniformity deviation, the more uniform the distribution of the samples in space. By comparing the uniformity deviations of each group of samples, the group with the smallest uniformity deviation is selected as the optimal result, i.e., the preferred trajectory starting point group.

[0061] In this embodiment, as an optional implementation, uniformity can be measured by the uniformity deviation of the column (factor or component) combination. The formula for uniformity deviation is as follows:

[0062] f ( x 1 , x 2 , ⋯ , x p ) = 1 r × ∑ i = 1 r ∏ j = x 1 x p ( 1 − 2 π × ln[ 2 × sin( π × u ( i , j ) r + 1 )])

[0063] in, This represents the factor level of the factor in the i-th row and j-th column of the trajectory starting point group.

[0064] S104. For each preferred trajectory starting point in the preferred trajectory starting point group, according to the pre-set factor level conversion strategy, the conversion is performed according to the factor order corresponding to the preferred trajectory starting point. Each conversion is performed on the factor level of one factor, and a mixture sample containing the preferred trajectory starting point is obtained.

[0065] In this embodiment, as an optional embodiment, for each preferred trajectory starting point in the preferred trajectory starting point group, a pre-set factor level transformation strategy is used to transform the sample according to the factor order corresponding to the preferred trajectory starting point. Each transformation targets the factor level of one factor, resulting in a mixture sample containing the preferred trajectory starting point, including:

[0066] Based on the number of factor levels contained in the factors of the preferred trajectory starting point group, query the mapping relationship between the number of factor levels and the factor level transformation strategy, and obtain the factor level target transformation strategy mapped by the number of factor levels;

[0067] Extract the first preferred trajectory starting point from the preferred trajectory starting point group, obtain the first factor from the first preferred trajectory starting point according to the factor order, and place the first preferred trajectory starting point in the mixture sample;

[0068] According to the factor level target conversion strategy, the factor level of the first factor is converted. Based on the converted factor level and the factor levels of each factor after the first factor, a second sub-sample of the mixture is obtained. The second sub-sample of the mixture is placed after the starting point of the first preferred trajectory in the mixture sample.

[0069] According to the factor level target conversion strategy, the factor level of the second factor in the second sub-sample of the mixture is converted. The second factor is located after the first factor. Based on the first factor, the converted factor level, and the factor levels of each factor after the second factor, the third sub-sample of the mixture is obtained. The third sub-sample of the mixture is placed after the second sub-sample of the mixture in the mixture sample until all factors at the starting point of the first preferred trajectory are traversed, and a mixture sample containing the starting point of the first preferred trajectory is obtained.

[0070] In this embodiment, for each trajectory starting point in the trajectory starting point group, according to the factor order, the current factor level j of the first factor (component) of the trajectory starting point is converted to factor level jt according to the factor level target conversion strategy, while the factor levels of other factors remain unchanged. Then, the converted component and other unconverted components are used as new trajectory starting points. The current level j of the second factor in the new trajectory starting point is converted to level jt according to the factor level target conversion strategy, while the factor levels of other factors remain unchanged, resulting in another new trajectory starting point. This process continues until all factors in the trajectory starting point have completed factor level conversion, resulting in a mixture sample containing the trajectory starting point and each new trajectory starting point.

[0071] In this embodiment, each trajectory starting point in the trajectory starting point group corresponds to a mixture sample.

[0072] In this embodiment, based on the optimized set of trajectory starting points, the optimized OAT method is used to expand the mixtures of different concentrations at each trajectory starting point, and finally all mixture samples are obtained.

[0073] In this embodiment, as an optional embodiment, when the number of factor levels is even, the corresponding factor level target transformation strategy is as follows, which is called the factor level even transformation strategy or factor level even transformation formula, that is, factor level j is transformed into factor level jt according to the following formula:

[0074]

[0075] Here, mod(a, b) means taking the remainder of a with respect to b.

[0076] When the number of factor levels is odd, the corresponding factor level target transformation strategy is the factor level odd transformation strategy or the factor level odd transformation formula, that is, factor level j is transformed into factor level jt according to the following formula:

[0077]

[0078] In this embodiment, as an optional embodiment, after obtaining the second sub-sample of the mixture and before placing the second sub-sample of the mixture after the first preferred trajectory starting point in the mixture sample, the method further includes:

[0079] Determine whether the second subsample of the mixture is the same as any subsample in the mixture sample;

[0080] If they are not the same, proceed with the step of placing the second sub-sample of the mixture after the starting point of the first preferred trajectory in the mixture sample;

[0081] If they are the same, the factor levels in the second sub-sample of the mixture are transformed again according to the pre-set factor level and transformation strategy to update the second sub-sample of the mixture, and the step of placing the second sub-sample of the mixture after the first preferred trajectory starting point in the mixture sample is executed.

[0082] In this embodiment, if a subsample containing the transformed factor level has the same subsample in the mixture sample, then for the same factor level obtained in this transformation, the transformed factor level j is converted to factor level jt according to the following factor level conversion strategy or factor level conversion formula:

[0083]

[0084] In this embodiment, taking the first trajectory starting point r1 in Table 2 as an example, the trajectory starting point r1 is taken as the first subsample M1 of the mixture. The level number of factor C1 is 2, and the number of factor levels l=3 is odd. Therefore, according to the factor level odd conversion formula, the current level number 2 of factor C1 is converted to level number 1. The level numbers of the other factors do not change, and the second subsample M2 of the mixture is obtained, as shown in Table 3.

[0085] In this embodiment, after obtaining the second subsample M2 of the mixture, according to the factor order, the factor after factor C1 is factor C2, and the level number of factor C2 is 3. Then, according to the factor level conversion formula, the level number of factor C2 is converted from 3 to 1, and the level numbers of the other factors remain unchanged, thus generating the third subsample M3 of the mixture, as shown in Table 3.

[0086] Following the same method described above, analogous extensions are made to obtain the fourth to eleventh sub-samples of the mixture, i.e., M4 to M11, as shown in Table 3. In this embodiment, for the same factor, the same level number will be randomly transformed according to the factor level odd transformation formula or the factor level same transformation formula, but the number of transformations according to the factor level odd transformation formula or the factor level same transformation formula is the same. In this way, by analogy extending the starting points of each trajectory, it is possible to finally obtain N=r The mixture sample consists of (p+1) mixture subsamples, i.e., 12. (12+1)=156. Table 3 is a schematic table of each mixture subsample obtained after expanding the starting point of the first trajectory, including the first subsample of the mixture to the thirteenth subsample of the mixture.

[0087]

[0088] S105. Based on each mixture subsample in the mixture sample, prepare a corresponding mixture subsample solution, conduct a mixture toxicity test on the test organism, obtain mixture toxicity data, perform a global sensitivity analysis on the mixture toxicity data, and determine the key components based on the results of the global sensitivity analysis.

[0089] In this embodiment, a mixture subsample solution is prepared, and a mixture toxicity test is performed on the test organism. A global sensitivity analysis is performed on the mixture toxicity data, and key components are determined based on the results of the global sensitivity analysis. For details, please refer to relevant technical literature, which will not be elaborated here.

[0090] In this embodiment, when constructing trajectory starting points, repeatability testing avoids generating duplicate mixture samples, thereby improving the diversity of mixture samples. By constructing multiple sets of trajectory starting point groups and calculating uniformity deviation, a preferred trajectory starting point group is determined, ensuring a uniform distribution of factor levels. Simultaneously, for each preferred trajectory starting point in the preferred trajectory starting point group, a pre-set factor level transformation strategy is used to transform the mixture according to the factor order corresponding to that preferred trajectory starting point, resulting in a mixture sample. This expands the preferred trajectory starting points, further improving the uniformity of factor level distribution. Mixture toxicity tests are conducted based on the mixture samples, and global sensitivity analysis is performed based on the mixture toxicity test data. Key components are identified based on the results of the global sensitivity analysis. Thus, due to the uniform distribution of factor levels and the absence of duplicate mixture samples, the mixture samples exhibit diversity, providing more representative mixture samples for mixture toxicity assessment. This enables accurate assessment of the combined toxicity of mixtures, effectively improving the reliability of mixture toxicity assessment results. Furthermore, based on the combined toxicity assessment results, key components affecting mixture toxicity are accurately identified, providing a scientific basis for determining key pollutants in mixture risk assessment.

[0091] The following specific example will be used to illustrate the embodiments of the present invention in detail.

[0092] (1) Constructing a factor-factor level table

[0093] In this embodiment, for a multi-component mixture system containing 12 factors and 3 factor levels, i.e., p=12, l=3, the factor-level table is constructed as shown in Table 1. In Table 1, each factor (chemical) contains 3 factor levels, which are represented by level numbers 1, 2, and 3 respectively. As an optional embodiment, the actual concentration range represented by the same level number for different factors is different.

[0094] (2) The starting point of the trajectory with the smallest uniformity deviation

[0095] In this embodiment, based on the factor level number l=3, the minimum value of the trajectory starting point number r is set to 12. Using the LHS method, 12 trajectory starting points can be generated, forming a trajectory starting point group. Five groups of multi-trajectory starting points are generated using the LHS method, as shown in Table 4. The uniformity deviations of these five groups are calculated, and the obtained uniformity deviations are 3.84, 4.17, 4.02, 3.30, and 2.64, respectively. Among them, the fifth group has the smallest uniformity deviation; therefore, the fifth group is selected as the optimal trajectory starting point group. Table 4 shows the level number and uniformity deviation of each factor in the 12 trajectory starting points of each trajectory starting point group generated using Latin hypercube sampling.

[0096]

[0097]

[0098]

[0099] In Table 4, Ci represents the i-th factor, and rk represents the k-th trajectory starting point of the trajectory starting point group.

[0100] (3) Generate mixture samples based on trajectory starting point expansion

[0101] In this embodiment, taking the first trajectory starting point of group 5 in Table 4 as an example, trajectory starting point r1 is the first sub-sample M1 of the mixture. The level number of factor C1 is 1, and the number of factor levels l=3 is odd. Therefore, it is converted according to the factor level odd conversion formula to obtain level number 2. The level numbers of the other factors do not change, generating the second sub-sample M2 of the mixture, as shown in Table 5. The conversion is performed sequentially according to the factor order. The level number of factor C2 is 2. The level number of factor C2 is converted to level number 1. The level numbers of the other factors do not change, generating the third sub-sample M3 of the mixture, as shown in Table 5. And so on, the level numbers are transformed one by one according to the factor order to obtain the sub-samples M4~M13 of the mixture, and the mixture sample corresponding to the trajectory starting point r1 is obtained.

[0102] In this embodiment, for the same factor, the same level number will be randomly transformed according to either the factor level odd transformation formula or the factor level same transformation formula, but the number of transformations according to each formula is the same. For example, for factor C1, in the 12 trajectories, the number of times level number 1 is transformed according to the factor level odd transformation formula (level number 1 to level number 2) is the same as the number of times it is transformed according to the factor level same transformation formula (level number 1 to level number 3). This process is repeated to expand the starting points of each trajectory. Each trajectory starting point, through expansion, can obtain (p+1), that is, 13 mixed subsamples. The 12 trajectory starting points can ultimately obtain N=r. (p+1) mixed subsamples, i.e., 12 (12+1)=156. Table 5 is a schematic table of the level numbers of each factor in each mixture in the mixture subsample obtained after expanding each optimal trajectory starting point in the optimal trajectory starting point group.

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] (4) Toxicity test of mixture

[0110] In Table 5, the level number actually represents the relative concentration value corresponding to each factor (chemical). Therefore, by consulting the pre-set mapping table between factor level numbers and concentration values, the concentration value mapped to each factor's level number can be obtained. After determining the concentration value corresponding to each level number of each factor, the level numbers in the mixture samples in Table 5 are replaced with the corresponding concentration values. The replaced mixture samples are recorded as matrix X. Based on the replaced mixture samples, mixture sample solutions are prepared, and mixture toxicity tests are conducted on the test organisms (e.g., luminescent bacteria). The obtained mixture toxicity data are recorded as matrix Y. Table 6 shows the mapping table between factor level numbers and concentration values. In the table, the unit of concentration values ​​is ng / L.

[0111]

[0112] (5) Identification of key components for the toxicity of mixtures

[0113] In this embodiment, a global sensitivity analysis is performed based on the mixture samples (X matrix) and experimentally determined mixture toxicity data (Y matrix). As an optional embodiment, the initial state is the obtained mixture trajectory starting point and its corresponding toxicity, and the final state is the other mixture sub-samples and their toxicities. The change in mixture toxicity caused by the change of each factor (each chemical) is calculated to obtain the corresponding basic effect (EEi) value. The basic effect value is calculated using the following formula:

[0114]

[0115] In the formula, the basic effect value is the change in concentration of the factor (xi, i=1, 2, 3, ..., p) (each chemical). This can lead to changes in the toxicity of the mixture.

[0116] In this embodiment, for each trajectory (mixture subsample), each factor in that trajectory undergoes a level change (xi,k (i=1, 2, ..., p; k=1, 2, ..., r), allowing the calculation of a basic effect value for that factor. Therefore, for r trajectories in the X-matrix, r EEs for each factor can be calculated. As an optional embodiment, to balance the differences in the level values ​​of each factor, the X-matrix is ​​standardized column-wise with a mean of 0 and a variance of 1 before calculating the basic effect value.

[0117] In this embodiment, the arithmetic mean of the r EE values ​​of each factor, the arithmetic mean of the absolute values ​​of the EE values, and the standard deviation of the EE values ​​are used as sensitivity indicators:

[0118]

[0119]

[0120]

[0121] In the formula, μ and μ μ is used to evaluate the direct or main effect of a factor. The sign of μ indicates whether it has a positive or negative impact on the effect. This method more accurately reflects the importance of factors, and using absolute values ​​avoids the influence of positive and negative effects canceling each other out. σ is used to evaluate the dispersion of the factor's basic effect; a larger σ value indicates a stronger correlation with other factors and the existence of potential interactions. Therefore, μ and μ Together, they are used to assess which factors have the greatest impact on the toxicity of mixtures, while σ is used to reveal potential interactions. This is achieved by constructing two-dimensional µ-σ and µ-σ pairs. -σ diagrams were used to further analyze the key components affecting the toxicity of the mixture and the strength of their interactions.

[0122] In this embodiment, by optimizing the LHS-OAT method, more representative samples are provided for the toxicity assessment of mixtures, thereby accurately identifying the key components affecting the toxicity of mixtures and providing a scientific basis for prioritizing the control of which key pollutants in the risk assessment of complex mixtures.

[0123] Based on the same inventive concept, such as Figure 2 As shown, this embodiment of the invention also provides an apparatus for obtaining key components of a mixture, the apparatus comprising:

[0124] The level number determination module 201 is used to obtain each component in the multi-component mixture system in the target region, and determine the factor level number of the factor corresponding to the component based on the concentration range of each component in the target region.

[0125] In this embodiment, as an optional embodiment, the level number determination module 201 is specifically used for:

[0126] For each component, obtain the upper and lower limit concentration difference of the corresponding concentration range for that component;

[0127] Based on the maximum upper and lower limit concentration difference and the pre-set factor level number setting strategy, the factor level number is determined. Based on the factor level number, the concentration range corresponding to each component is divided into concentration sub-ranges, and each concentration sub-range corresponds to a factor level.

[0128] The trajectory reconstruction module 202 is used to construct trajectory starting points based on the number of factor levels, query the already constructed trajectory starting point group, determine that the currently constructed trajectory starting point is the same as any trajectory starting point in the trajectory starting point group, reconstruct the trajectory, and form a trajectory starting point group based on the constructed trajectory starting points.

[0129] In this embodiment, as an optional embodiment, the trajectory reconstruction module 202 is specifically used for:

[0130] For each duplicate factor to be removed in the current trajectory starting point, obtain the duplicate factor level of that duplicate factor;

[0131] Traverse each constructed trajectory starting point in the constructed trajectory starting point group to obtain the level of the deduplicated factor of the to be deduplicated factor at each constructed trajectory starting point. The constructed trajectory starting point group includes the currently constructed trajectory starting point group.

[0132] If the level of the repeated factor to be removed in the current trajectory starting point is the same as the level of the repeated factor to be removed in any existing trajectory starting point, delete the current trajectory starting point and reconstruct a new trajectory starting point.

[0133] In this embodiment, as another optional embodiment, the trajectory reconstruction module 202 is also specifically used for:

[0134] A component sequence is constructed based on each component. For each factor corresponding to a component in the component sequence, a factor level is randomly selected from the factor levels corresponding to that factor. The trajectory starting point is obtained based on the factor level randomly selected for each component in the component sequence.

[0135] In this embodiment, as another optional embodiment, the trajectory reconstruction module 202 is further specifically used for:

[0136] Based on the number of factor levels and the pre-set factor level coefficients, determine the number of trajectory starting points included in the trajectory starting point group;

[0137] Multiple trajectory starting points are obtained by traversing each component in the component sequence multiple times. Based on the multiple trajectory starting points, the trajectory starting point group is constructed. Each traversal of the component sequence corresponds to one trajectory starting point, and the multiple traversals are equal to the number of trajectory starting points.

[0138] The deviation calculation module 203 is used to obtain the uniformity deviation of each constructed trajectory starting point group and determine the preferred trajectory starting point group based on the uniformity deviation.

[0139] The trajectory extension module 204 is used to perform a conversion on each preferred trajectory starting point in the preferred trajectory starting point group according to a pre-set factor level conversion strategy and the factor order corresponding to the preferred trajectory starting point. Each conversion is performed on the factor level of one factor, resulting in a mixture sample containing the preferred trajectory starting point.

[0140] In this embodiment, as an optional embodiment, the trajectory extension module 204 is specifically used for:

[0141] Based on the number of factor levels contained in the factors of the preferred trajectory starting point group, query the mapping relationship between the number of factor levels and the factor level transformation strategy, and obtain the factor level target transformation strategy mapped by the number of factor levels;

[0142] Extract the first preferred trajectory starting point from the preferred trajectory starting point group, obtain the first factor from the first preferred trajectory starting point according to the factor order, and place the first preferred trajectory starting point in the mixture sample;

[0143] According to the factor level target conversion strategy, the factor level of the first factor is converted. Based on the converted factor level and the factor levels of each factor after the first factor, a second sub-sample of the mixture is obtained. The second sub-sample of the mixture is placed after the starting point of the first preferred trajectory in the mixture sample.

[0144] According to the factor level target conversion strategy, the factor level of the second factor in the second sub-sample of the mixture is converted. The second factor is located after the first factor. Based on the first factor, the converted factor level, and the factor levels of each factor after the second factor, the third sub-sample of the mixture is obtained. The third sub-sample of the mixture is placed after the second sub-sample of the mixture in the mixture sample until all factors at the starting point of the first preferred trajectory are traversed, and a mixture sample containing the starting point of the first preferred trajectory is obtained.

[0145] In this embodiment, as another optional embodiment, the trajectory extension module 204 is also specifically used for:

[0146] Determine whether the second subsample of the mixture is the same as any subsample in the mixture sample;

[0147] If they are not the same, proceed with the step of placing the second sub-sample of the mixture after the starting point of the first preferred trajectory in the mixture sample;

[0148] If they are the same, the factor levels in the second sub-sample of the mixture are transformed again according to the pre-set factor level and transformation strategy to update the second sub-sample of the mixture, and the step of placing the second sub-sample of the mixture after the first preferred trajectory starting point in the mixture sample is executed.

[0149] The key component determination module 205 is used to prepare a corresponding mixture sub-sample solution based on each mixture sub-sample in the mixture sample, conduct mixture toxicity tests on the test organism, obtain mixture toxicity data, perform global sensitivity analysis on the mixture toxicity data, and determine key components based on the results of the global sensitivity analysis.

[0150] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for obtaining key components of a mixture in any of the above possible implementations.

[0151] Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0152] Based on the same inventive concept, see [link to inventive concept] Figure 3 This invention also provides an electronic device, including a memory 101 (e.g., non-volatile memory), a processor 102, and a computer program stored on the memory 101 and executable on the processor 102. When the processor 102 executes the program, it implements the steps of the method for obtaining key components of a mixture in any of the above possible implementations, which can be equivalent to the aforementioned apparatus for obtaining key components of a mixture. Of course, the processor can also be used to process other data or perform calculations. This electronic device can be a PC, server, terminal, or other similar device.

[0153] like Figure 3 As shown, the electronic device may also include: memory 103, network interface 104, and internal bus 105. In addition to these components, other hardware may also be included, which will not be described in detail here.

[0154] It should be noted that the above-mentioned device for obtaining key components of the mixture can be implemented by software. As a device in a logical sense, it is formed by the processor 102 of the electronic device in which it is located reading the computer program instructions stored in the non-volatile memory into the memory 103 for execution.

[0155] The embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0156] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by special-purpose logic circuitry—such as FPGA (Field Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit), and the device can also be implemented as special-purpose logic circuitry.

[0157] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0158] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0159] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily used to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0160] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0161] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0162] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0163] The above are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for obtaining key components of a mixture, characterized in that, include: Obtain each component in the multi-component mixture system in the target region, and determine the factor level number of the factor corresponding to each component based on the concentration range of each component in the target region; Based on the number of factor levels, a trajectory starting point is constructed. The existing trajectory starting point group is queried. If the current trajectory starting point is the same as any trajectory starting point in the trajectory starting point group, the trajectory is reconstructed. A trajectory starting point group is formed based on the constructed trajectory starting point. For each constructed trajectory starting point group, the uniformity deviation of the trajectory starting point group is obtained, and the preferred trajectory starting point group is determined based on the uniformity deviation. For each preferred trajectory starting point in the preferred trajectory starting point group, a pre-set factor level transformation strategy is used to transform the sample according to the factor order corresponding to the preferred trajectory starting point. Each transformation targets the factor level of one factor, resulting in a mixture sample containing the preferred trajectory starting point. For each mixture subsample in the mixture sample, a corresponding mixture subsample solution is prepared, and the mixture toxicity test is performed on the test organism to obtain mixture toxicity data. A global sensitivity analysis is then performed on the mixture toxicity data to determine the key components based on the results of the global sensitivity analysis.

2. The method for obtaining key components of a mixture according to claim 1, characterized in that, For each preferred trajectory starting point in the preferred trajectory starting point group, a pre-set factor level transformation strategy is used to transform the sample according to the factor order corresponding to that preferred trajectory starting point. Each transformation targets the factor level of one factor, resulting in a mixture sample containing that preferred trajectory starting point, including: Based on the number of factor levels contained in the factors of the preferred trajectory starting point group, query the mapping relationship between the number of factor levels and the factor level transformation strategy, and obtain the factor level target transformation strategy mapped by the number of factor levels; Extract the first preferred trajectory starting point from the preferred trajectory starting point group, obtain the first factor from the first preferred trajectory starting point according to the factor order, and place the first preferred trajectory starting point in the mixture sample; According to the factor level target conversion strategy, the factor level of the first factor is converted. Based on the converted factor level and the factor levels of each factor after the first factor, a second sub-sample of the mixture is obtained. The second sub-sample of the mixture is placed after the starting point of the first preferred trajectory in the mixture sample. According to the factor level target conversion strategy, the factor level of the second factor in the second sub-sample of the mixture is converted. The second factor is located after the first factor. Based on the first factor, the converted factor level, and the factor levels of each factor after the second factor, the third sub-sample of the mixture is obtained. The third sub-sample of the mixture is placed after the second sub-sample of the mixture in the mixture sample until all factors at the starting point of the first preferred trajectory are traversed, and a mixture sample containing the starting point of the first preferred trajectory is obtained.

3. The method for obtaining key components of a mixture according to claim 2, characterized in that, The method further includes placing the second sub-sample of the mixture after the first preferred trajectory starting point in the mixture sample and before: Determine whether the second subsample of the mixture is the same as any subsample in the mixture sample; If they are not the same, proceed with the step of placing the second sub-sample of the mixture after the starting point of the first preferred trajectory in the mixture sample; If they are the same, the factor levels in the second sub-sample of the mixture are transformed again according to the pre-set factor level and transformation strategy to update the second sub-sample of the mixture, and the step of placing the second sub-sample of the mixture after the first preferred trajectory starting point in the mixture sample is executed.

4. The method for obtaining key components of a mixture according to any one of claims 1 to 3, characterized in that, The step of determining that the starting point of the currently constructed trajectory is the same as any trajectory starting point in the trajectory starting point group, and then reconstructing it, includes: For each duplicate factor to be removed in the current trajectory starting point, obtain the duplicate factor level of that duplicate factor; Traverse each constructed trajectory starting point in the constructed trajectory starting point group to obtain the level of the deduplicated factor of the to be deduplicated factor at each constructed trajectory starting point. The constructed trajectory starting point group includes the currently constructed trajectory starting point group. If the level of the repeated factor to be removed in the current trajectory starting point is the same as the level of the repeated factor to be removed in any existing trajectory starting point, delete the current trajectory starting point and reconstruct a new trajectory starting point.

5. The method for obtaining key components of a mixture according to claim 4, characterized in that, The construction of the trajectory starting point based on the factor level number includes: A component sequence is constructed based on each component. For each factor corresponding to a component in the component sequence, a factor level is randomly selected from the factor levels corresponding to that factor. The trajectory starting point is obtained based on the factor level randomly selected for each component in the component sequence.

6. The method for obtaining key components of a mixture according to claim 5, characterized in that, The formation of a trajectory starting point group based on the constructed trajectory starting points includes: Based on the number of factor levels and the pre-set factor level coefficients, determine the number of trajectory starting points included in the trajectory starting point group; Multiple trajectory starting points are obtained by traversing each component in the component sequence multiple times. Based on the multiple trajectory starting points, the trajectory starting point group is constructed. Each traversal of the component sequence corresponds to one trajectory starting point, and the multiple traversals are equal to the number of trajectory starting points.

7. The method for obtaining key components of a mixture according to any one of claims 1 to 3, characterized in that, The step of determining the number of factor levels corresponding to each component based on the concentration range of each component in the target region includes: For each component, obtain the upper and lower limit concentration difference of the corresponding concentration range for that component; Based on the maximum upper and lower limit concentration difference and the pre-set factor level number setting strategy, the factor level number is determined. Based on the factor level number, the concentration range corresponding to each component is divided into concentration sub-ranges, and each concentration sub-range corresponds to a factor level.

8. An apparatus for obtaining key components of a mixture, characterized in that, The apparatus for obtaining key components of the mixture includes: The level number determination module is used to obtain the number of factors of each component in the multi-component mixture system in the target region, and determine the factor level number of the factor corresponding to each component based on the concentration range of each component in the target region. The trajectory reconstruction module is used to construct trajectory starting points based on the number of factor levels, query the already constructed trajectory starting point group, determine that the currently constructed trajectory starting point is the same as any trajectory starting point in the trajectory starting point group, reconstruct the trajectory, and form a trajectory starting point group based on the constructed trajectory starting points. The deviation calculation module is used to obtain the uniformity deviation of each constructed trajectory starting point group and determine the preferred trajectory starting point group based on the uniformity deviation. The trajectory expansion module is used to perform a conversion on each preferred trajectory starting point in the preferred trajectory starting point group according to a pre-set factor level conversion strategy and the factor order corresponding to the preferred trajectory starting point. Each conversion is performed on the factor level of one factor, resulting in a mixture sample containing the preferred trajectory starting point. The key component identification module is used to prepare corresponding mixture sub-sample solutions based on each mixture sub-sample in the mixture sample, conduct mixture toxicity tests on the test organism, obtain mixture toxicity data, perform global sensitivity analysis on the mixture toxicity data, and identify key components based on the results of the global sensitivity analysis.

9. A storage medium, characterized in that, A program or instructions are stored on a storage medium, and the program or instructions are executed by a processor to implement the steps of the method for obtaining key components of a mixture as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for obtaining key components of a mixture as described in any one of claims 1 to 7.

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