Method for evaluating risk ranking of perfluorinated compounds in dairy products

Abstract

The invention discloses a method for evaluating the risk ranking of perfluorinated compounds in dairy products, which belongs to the field of risk evaluation of perfluorinated compounds in dairy products, and comprises the following steps: step 1, carrying out experimental detection on the evaluated dairy products, and taking the detection rate and concentration of each detected perfluorinated compound as exposure risk factors; step 2, evaluating the hazard potentials of different perfluorinated compounds to human health by calculating persistence data, bio-enrichment data, elimination data, ecological toxicity data, human exposure toxicity data and tolerable weekly intake data to obtain corresponding hazard potential data; and step 3, integrating the exposure risk factor and the hazard potential data by using a toxicology priority index scoring system to preferentially evaluate the risk data of each perfluorinated compound on human health, and obtaining the risk sequence of different perfluorinated compounds in the dairy product. According to the method, exposure factors and toxic factors are fused, ToxPi multi-dimensional risk integration modeling is used, and accurate sorting of risks of multiple perfluorinated compounds in dairy products is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of perfluorinated compounds assessment in dairy products, and in particular to a method for assessing the risk ranking of perfluorinated compounds in dairy products. Background Art

[0002] Perfluorinated compounds (PFAS) are a new class of pollutants that have attracted global attention in recent years due to their environmental persistence, bioaccumulation, and high biological toxicity. In 2021, based on the latest toxicological data, the European Food Safety Authority set the weekly intake limit for four PFAS, including PFOS, PFOA, PFHxS, and PFNA, at 4.4 ng / kg per week. -1 bw week -1 . However, there are currently many types of perfluorinated compounds, and reports of perfluorinated compounds found in food are endless. For example, there are more than 1 million perfluorinated compounds registered on PubChem, and there is a lack of information on the specific categories that are put into production and use. The monomer properties of various perfluorinated compounds vary greatly. Some are highly toxic, some have long half-lives, and their risks to humans and the ecological environment vary greatly. my country is a major consumer of dairy products. According to my country's latest "Dietary Guidelines for Chinese Residents (2022)", the recommended daily milk intake has increased from 200mL / day to 300-500mL / day, far exceeding the 120-200g / day intake of other animal-derived foods. Therefore, milk is an important source of perfluorinated compounds for the human body. However, there is currently no method to comprehensively evaluate the risks of perfluorinated compounds in dairy products and to rank the risks of different perfluorinated compound monomers.

[0003] Currently, there is no customized method for ranking the risks of perfluorinated compounds in dairy products. The main difficulties are as follows: (1) International organizations and Western countries have set daily intake limits for only 4 to 5 perfluorinated compounds. However, there is a lack of methods to compare the toxicity of dozens of perfluorinated compound monomers that are frequently detected. (2) Perfluorinated compounds have many toxic effects, such as endocrine disruption, reproductive toxicity, and developmental toxicity. It is difficult to define which toxic endpoints should be used to conduct risk assessment and ranking. (3) When examining the risks of perfluorinated compounds, their potential metabolic capacity and persistence must be considered. (4) Exposure level and toxicity risk are synergistic and intertwined factors. When ranking risks, how to balance them uniformly is a problem that needs to be solved.

[0004] In view of this, the present invention is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for evaluating the risk ranking of perfluorinated compounds in dairy products, which can use multi-dimensional data of exposure levels and toxicity risks to evaluate the risks of perfluorinated compounds in dairy products, and achieve priority ranking of the risks to the human body of dozens to hundreds of perfluorinated compounds, thereby solving the above-mentioned technical problems existing in the prior art.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] A method for assessing the risk ranking of perfluorinated compounds in dairy products includes:

[0008] Step 1: Conduct laboratory tests on the assessed dairy products, and use the detection rates and concentrations of each perfluorinated compound obtained as exposure risk factors for the corresponding perfluorinated compounds;

[0009] Step 2: Evaluate the potential harm of different perfluorinated compounds to human health by calculating persistence data, bioaccumulation data, elimination data, ecotoxicity data, human exposure toxicity data, and tolerable weekly intake data to obtain corresponding potential harm data;

[0010] Step 3: Use the toxicology priority index scoring system to integrate exposure risk factors and hazard potential data to prioritize the risk data of each perfluorinated compound to human health, and derive the risk ranking of different perfluorinated compounds in dairy products based on the risk data.

[0011] Compared with the prior art, the method for assessing the risk ranking of perfluorinated compounds in dairy products provided by the present invention has the following beneficial effects:

[0012] The detection rate and concentration of each perfluorinated compound in dairy products obtained through experimental testing are used as exposure risk factors. Persistence data, bioaccumulation data, elimination data, ecotoxicity data, human exposure toxicity data, and tolerable weekly intake data are calculated to obtain the potential harm of different perfluorinated compounds to human health. These data are used to prioritize the human health risks of each perfluorinated compound from multiple dimensions, and an accurate risk ranking of different perfluorinated compounds in dairy products is obtained based on the risk data. The method of the present invention is characterized by high flexibility, strong visualization, and wide adaptability. Its risk assessment results are closer to actual dietary exposure scenarios and can be extended to the risk ranking research of pollutants in other foods and environmental media. It has broad application prospects and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A flow chart of a method for assessing the risk ranking of perfluorinated compounds in dairy products provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the specific content of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments, and do not constitute a limitation of the present invention. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0016] First, the following terms may be used in this article:

[0017] The term “and / or” means that either or both of them can be realized at the same time. For example, X and / or Y includes both “X” or “Y” and “X and Y”.

[0018] The terms "include," "comprises," "contains," "has," or other similar expressions should be interpreted as non-exclusive. For example, "including certain technical features (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, procedures, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products, or manufactured articles, etc.) should be interpreted as including not only the technical features explicitly listed, but also other technical features known in the art that are not explicitly listed.

[0019] The term "consisting of" excludes any technical features not explicitly listed. If used in a claim, this term renders the claim closed, excluding any technical features other than those explicitly listed, except for conventional impurities associated with them. If this term appears only in a clause of a claim, it limits only the elements explicitly listed in that clause; elements listed in other clauses are not excluded from the claim as a whole.

[0020] The term "parts by mass" refers to the mass ratio of multiple components. For example, if component X is x parts by mass and component Y is y parts by mass, then the mass ratio of component X to component Y is x:y. One part by mass can represent any mass, for example, 1 kg or 3.1415926 kg. The sum of the parts by mass of all components is not necessarily 100 parts; it can be greater than, less than, or equal to 100 parts. Unless otherwise specified, parts, ratios, and percentages herein are by mass.

[0021] When concentration, temperature, pressure, size or other parameters are expressed in the form of a numerical range, the numerical range should be understood to specifically disclose all ranges formed by the pairing of any upper limit, lower limit, or preferred value within the numerical range, regardless of whether the range is explicitly stated. For example, if a numerical range of "2 to 8" is stated, the numerical range should be interpreted as including ranges of "2 to 7," "2 to 6," "5 to 7," "3 to 4 and 6 to 7," "3 to 5 and 7," "2 and 5 to 7," etc. Unless otherwise specified, the numerical ranges stated herein include both their endpoints and all integers and fractions within the numerical range.

[0022] The scheme provided by the present invention is described in detail below. The contents not described in detail in the examples of the present invention belong to the prior art known to professionals in this field. If specific conditions are not specified in the examples of the present invention, they are carried out according to conventional conditions in the field or conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments used in the examples of the present invention is not specified, they are all conventional products that can be purchased commercially.

[0023] like Figure 1 As shown, an embodiment of the present invention provides a method for assessing the risk ranking of perfluorinated compounds in dairy products, comprising:

[0024] Step 1: Conduct laboratory tests on the assessed dairy products, and use the detection rates and concentrations of each perfluorinated compound obtained as exposure risk factors for the corresponding perfluorinated compounds;

[0025] Step 2: Evaluate the potential harm of different perfluorinated compounds to human health by calculating persistence data, bioaccumulation data, elimination data, ecotoxicity data, human exposure toxicity data, and tolerable weekly intake data to obtain corresponding potential harm data;

[0026] Step 3: Use the toxicology priority index scoring system to integrate exposure risk factors and hazard potential data to prioritize the risk data of each perfluorinated compound to human health, and derive the risk ranking of different perfluorinated compounds in dairy products based on the risk data.

[0027] Preferably, in step 2 of the above method, the persistence data is calculated in the following manner:

[0028] The BIOWIN3 parameters obtained from the BIOWIN module in EPI Suite v4.1 were used to evaluate the ultimate biodegradation time of different perfluorinated compounds as persistence data.

[0029] Preferably, in step 2 of the above method, the bioaccumulation data is calculated in the following manner:

[0030] ADMETlab 3.0 was used to calculate the physicochemical parameter log D, which characterizes the partition coefficient, to evaluate the absorption, distribution, metabolism, excretion, and toxicity of different perfluorinated compounds as bioconcentration data.

[0031] Preferably, in step 2 of the above method, the elimination data is calculated in the following manner:

[0032] The half-life prediction module in ADMETlab 3.0 was used to calculate the half-life values of different perfluorinated compounds. Perfluorinated compounds with a half-life value of >3 were classified as T1 / 2-, i.e., category 0, and perfluorinated compounds with a half-life value of ≤3 were classified as T1 / 2+, i.e., category 1. The output value ranging from 0 to 1 representing the probability that the perfluorinated compound belongs to category 1 was used as the elimination data.

[0033] Preferably, in step 2 of the above method, the ecotoxicity data is calculated in the following manner:

[0034] The risk quotient RQ of different perfluorinated compounds to aquatic organisms is calculated according to formula (1):

[0035]

[0036] Where RQ is the risk quotient of perfluorinated compounds; MEC is the measured concentration of perfluorinated compounds; PNEC is the predicted no-effect concentration of perfluorinated compounds for aquatic organisms, which is predicted by the eco-structure activity relationship prediction model, and the most sensitive effect endpoint is selected for further calculation. Among them, the eco-structure activity relationship prediction model is ECOSAR v2.2, and the most sensitive effect endpoints are lethality, reproductive toxicity, and developmental / growth toxicity;

[0037] PNEC is calculated according to the following formula (2):

[0038]

[0039] Wherein, LC50 is the median lethal concentration, which is provided by the ecological structure-activity relationship prediction model; AF is the assessment factor, which is set to 1000; PNECaquaticbiota is PNEC;

[0040] The risk quotients (RQs) of different perfluorinated compounds to aquatic organisms were used as ecotoxicity data.

[0041] Preferably, in step 2 of the above method, the human exposure toxicity data is calculated in the following manner:

[0042] The AutodockVina program was used to simulate the binding affinities of 14 key human proteins with different perfluorinated compounds as human exposure toxicity data.

[0043] Preferably, the 14 key human proteins of the above method include:

[0044] Seven transporters: human serum albumin (HSA, PDB ID: 7AAI), liver fatty acid binding protein (L-FABP, PDB ID: 2LKK), organic anion transporter 4 (OAT4, PDB ID: 8WJH-SWISSMODEL), apical sodium-dependent bile acid transporter (ASBT, PDB ID: AF), sodium taurocholate cotransporting polypeptide (NTCP, PDB ID: 8HRY), prethyroxine albumin (TTR, PDB ID: 5JIM), and thyroxine-binding globulin (TBG, PDB ID: 2XN6);

[0045] Seven nuclear receptors: peroxisome proliferator-activated receptor α (PPARα, PDB ID: 2ZNN), PPARβ (PDB ID: 3GZ9), PPARγ (PDB ID: 8U57), thyroid hormone receptor α (TRα, PDB ID: 3JZB), TRβ (PDB ID: 3JZC), estrogen receptor α (ERα, PDB ID: 2YJA) and ERβ (PDB ID: 4J26).

[0046] Preferably, in step 2 of the above method, the tolerable weekly intake data is determined in the following manner: the tolerable weekly intake of PFOS, PFOA, PFHxS and PFNA set by the European Food Safety Authority in 2020 and the oral reference dose set by the U.S. Environmental Protection Agency for the emerging PFAS compound HFPO-DA are used as the tolerable weekly intake data.

[0047] Preferably, in step 3 of the above method, the toxicological priority index scoring system is used to integrate exposure risk factors and hazard potential data in the following manner to prioritize the risk data of each perfluorinated compound to human health, and the risk ranking of different perfluorinated compounds in dairy products is obtained based on the risk data, including:

[0048] The risk data of each perfluorinated compound to human health are evaluated in priority from eight dimensions. The risk data of each dimension are normalized by formula (3), which is:

[0049]

[0050] Among them, x Norm represents the normalized value; x represents the actual detection value of a certain perfluorinated compound monomer; x max is the maximum value of the perfluorinated compound among all perfluorinated compounds; x minis the minimum value for this perfluorinated compound among all perfluorinated compounds;

[0051] The final toxicological priority index ToxPiscore was calculated according to the following formula (4): i , formula (4) is:

[0052] ToxPi score i =w Q ·RQ i +w F DF i +w L PS i +w C CT i +w P ·CP i +w E EL i +w H ·RfD i +w T ·PT i (4);

[0053] Among them, RQ i represents the normalized value of the risk quotient of the i-th perfluorinated compound; DF i represents the normalized value of the detection frequency of the i-th perfluorinated compound; PS i represents the normalized value of the persistence of the i-th perfluorinated compound; CT i represents the normalized value of the concentration of the i-th perfluorinated compound; CP i represents the normalized value of the i-th perfluorinated compound on logD; EL i RfD represents the normalized value of the biological elimination rate of the i-th perfluorinated compound; i represents the normalized value of the i-th perfluorinated compound on the perfluorinated compound with the reference dose; PT i W represents the normalized value of the binding affinity of the i-th perfluorinated compound to human proteins: Q is the weight of the corresponding risk quotient; W F Represents the weight corresponding to the detection frequency; W L represents the weight corresponding to persistence; W C Indicates the weight of the corresponding concentration; W P Indicates the weight corresponding to logD; W E represents the weight of the corresponding biological elimination rate; W H W represents the weight of the perfluorinated compound with the reference dose; T The weights correspond to the binding affinity to human proteins, and each weight is equally distributed.

[0054] In summary, the method according to the embodiment of the present invention has significant technical advantages because it integrates exposure factors and toxicity factors and adopts ToxPi multidimensional risk integration modeling to conduct a systematic risk priority assessment for various perfluorinated compounds in dairy products, thereby accurately ranking the risks of various perfluorinated compounds in dairy products.

[0055] In order to more clearly demonstrate the technical solution and technical effects provided by the present invention, the solution provided by the embodiment of the present invention is described in detail with reference to specific embodiments below.

[0056] Example 1

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the risk ranking of perfluorinated compounds in dairy products, comprising:

[0058] Step 1: Conduct laboratory tests on the assessed dairy products, and use the detection rates and concentrations of each perfluorinated compound obtained as exposure risk factors for the corresponding perfluorinated compounds;

[0059] Step 2: Combine the exposure risk factors obtained in step 1 to evaluate the potential harm of different perfluorinated compounds to human health by calculating persistence data, bioaccumulation data, elimination data, ecotoxicity data, human exposure toxicity data, and tolerable weekly intake data, and obtain the corresponding potential harm data;

[0060] Step 3: Use the toxicology priority index scoring system to integrate exposure risk factors and hazard potential data to prioritize the risk data of each perfluorinated compound to human health, and derive the risk ranking of different perfluorinated compounds in dairy products based on the risk data.

[0061] Specifically, the risk assessment of perfluorinated compounds (PFAS) in dairy products is prioritized through calculation and integration of the following eight dimensions, including:

[0062] (1) Determine exposure risk:

[0063] Calculating PFAS exposure risk factors involves two factors: detection rate and concentration (concentration is the detection value). These two data can be obtained based on the experimental results of specific laboratories.

[0064] (II) Determine toxicity risk:

[0065] Six factors are involved when assessing the potential for PFAS hazards.

[0066] (21) In terms of persistence, the BIOWIN3 parameters obtained from the BIOWIN module (v4.11) in the EPI Suite v4.1 (https: / / www.epa.gov / tsca-screening-tools / epi-suitetm-estimation-programinterface#models) were used to evaluate the ultimate biodegradation time of chemicals.

[0067] (22) In terms of bioconcentration, the physicochemical parameter log D, which characterizes the partition coefficient, was calculated using ADMETlab 3.0 (https: / / admetlab3.scbdd.com / ). This platform is specifically designed to evaluate the absorption, distribution, metabolism, excretion, and toxicity of chemical substances.

[0068] (23) In terms of elimination, the half-life (T1 / 2) of a chemical substance is a complex concept involving clearance and distribution volume, so it is more appropriate to use methods that provide reliable estimates of these two properties. To this end, we used a module in ADMETlab 3.0 to calculate the T1 / 2 values of PFAS. Based on the data description, compounds with T1 / 2>3 were classified as T1 / 2- (category 0), while compounds with T1 / 2≤3 were classified as T1 / 2+ (category 1). The output value represents the probability that the compound is T1 / 2+, ranging from 0 to 1.

[0069] (24) In terms of ecotoxicity, the risk quotient (RQ) for aquatic organisms was calculated according to Equation 1. The predicted no-effect concentration (PNEC) was predicted using the Ecological Structure Activity Relationships (ECOSAR) Predictive Model | USEPA (ECOSAR v2.2). The most sensitive effect endpoints (e.g., lethality, reproductive toxicity, and developmental / growth toxicity) were selected for further calculations. The median lethal concentration (LC50) was provided by ECOSAR v2.2. Subsequently, the PNEC aquaticbiota was calculated using Equation 2, with the assessment factor (AF) set to 1000.

[0070]

[0071] Among them, RQ represents the risk quotient of PFAS; MEC is the measured concentration of PFAS; PNECaquaticbiota is PNEC, which represents the predicted no-effect concentration value of PFAS on aquatic organisms (fish); LC50 is the median lethal concentration; AF is the assessment factor, which is generally set to 1000.

[0072] (25) In terms of human exposure toxicity, the AutodockVina program was used to simulate the binding affinity of 14 key human proteins to PFAS. These human proteins were selected based on their toxic effects on human health, including 7 transporters and 7 nuclear receptors:

[0073] (251) Seven transporters: human serum albumin (HSA, PDB ID: 7AAI), liver fatty acid binding protein (L-FABP, PDB ID: 2LKK), organic anion transporter 4 (OAT4, PDB ID: 8WJH-SWISSMODEL), apical sodium-dependent bile acid transporter (ASBT, PDB ID: AF), sodium taurocholate cotransporting polypeptide (NTCP, PDB ID: 8HRY), prethyroxine albumin (TTR, PDB ID: 5JIM), and thyroxine-binding globulin (TBG, PDB ID: 2XN6);

[0074] (252) Seven nuclear receptors: peroxisome proliferator-activated receptor α (PPARα, PDB ID: 2ZNN), PPARβ (PDB ID: 3GZ9), PPARγ (PDB ID: 8U57), thyroid hormone receptor α (TRα, PDB ID: 3JZB), TRβ (PDB ID: 3JZC), estrogen receptor α (ERα, PDB ID: 2YJA), and ERβ (PDB ID: 4J26).

[0075] The crystal structures of these proteins were obtained from the Protein Data Bank (PDB). The docking conformation with the highest score was selected as the optimal configuration.

[0076] (26) In addition, the European Food Safety Authority (EFSA) set a tolerable weekly intake (TWI) of 4.4 ng / kg body weight / week for PFOS, PFOA, PFHxS, and PFNA in 2020 (EFSA, 2020). According to known information, only the United States Environmental Protection Agency (USEPA) has set an oral reference dose (RfD) for the emerging PFAS compound HFPO-DA, which is 3 ng / kg body weight / day (USEPA, 2021). Therefore, these PFAS with existing reference doses are treated separately as a field because they pose a universally recognized threat to human health.

[0077] 3. Conduct risk integration assessment:

[0078] The present invention uses the Toxicology Priority Index (ToxPi) scoring system to integrate these multi-dimensional data to prioritize the risk assessment of PFAS to humans. A total of eight areas are involved, and each area is assigned equal weight. The data from all areas are normalized using formula (3), and the ToxPiscore is i The total score is calculated according to formula (4).

[0079]

[0080] Among them, x Norm represents the normalized value; x represents the actual detection value of a certain perfluorinated compound monomer; x max is the maximum value of the perfluorinated compound among all perfluorinated compounds; x min is the minimum value for this perfluorinated compound among all perfluorinated compounds;

[0081] ToxPiscore i =w Q ·RQ i +w F DF i +w L PS i +w C CT i +w P ·CP i +w E EL i +w H ·RfD i +w T ·PT i (4);

[0082] Among them, RQ i DF i 、PS i , CT i 、CP i EL i , RfD i PT i Respectively represent the normalized values of the i-th PFAS in the following eight dimensions: risk quotient (RQ), detection frequency, persistence, concentration, log D, bioelimination rate, PFAS with reference dose, and binding affinity to human proteins; W Q 、W F 、W L 、W C 、W P 、W E , W H 、W TThe weights for each of the eight dimensions are given equal weight. The PNEC and log D data for example PFAS are listed in Table 1 below.

[0083] Table 1 shows the calculated PFAS information for each perfluorinated compound

[0084]

[0085]

[0086] From the above, it can be seen that the method of the embodiment of the present invention adopts ToxPi multidimensional risk integration modeling to conduct a systematic risk priority assessment of various perfluorinated compounds in dairy products from the perspective of exposure factors and their toxicity risks, which has significant technical advantages. Different from the traditional single indicator evaluation method, the present invention integrates key toxicological and exposure parameters of multiple dimensions, including physicochemical properties (such as logKow), environmental behavior (such as half-life, bioaccumulation), human health hazards (such as carcinogenicity, reproductive toxicity), exposure levels (such as actual monitoring concentrations), etc., which are conveniently integrated into a unified graphical platform after standardized weighting, and can form an intuitive and comparable risk ranking of perfluorinated compounds. This method not only improves the integration efficiency of multi-source heterogeneous data, but also can scientifically rank the risk priority of different perfluorinated compounds on the basis of retaining the relative contribution of each factor, and clarify the key controlled pollutants. This method is particularly suitable for the analysis of perfluorinated compound pollution characteristics in complex food matrices such as dairy products. It can accurately identify high-priority substances that pose a greater threat to public health, and provide a quantitative basis for the formulation of regulatory limits and pollution source control. Furthermore, the present invention incorporates dairy-specific exposure factors (such as binding capacity for key human proteins, content levels, and detection rates) into the ToxPi framework and optimizes the weighting strategy to make the risk assessment more responsive to actual dietary exposure scenarios. This method, characterized by high flexibility, strong visualization, and broad adaptability, can be extended to risk ranking studies of pollutants in other foods and environmental media, demonstrating broad application prospects and practical value.

[0087] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims. The information disclosed in the background technology section of this article is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as an admission or any form of implication that the information constitutes prior art already known to those skilled in the art.

Claims

1. A method for assessing the risk ranking of perfluorinated compounds in dairy products, characterized in that: include: Step 1: Conduct laboratory tests on the assessed dairy products, and use the detection rates and concentrations of each perfluorinated compound obtained as exposure risk factors for the corresponding perfluorinated compounds; Step 2: Evaluate the potential harm of different perfluorinated compounds to human health by calculating persistence data, bioaccumulation data, elimination data, ecotoxicity data, human exposure toxicity data, and tolerable weekly intake data to obtain corresponding potential harm data; Step 3: Use the toxicology priority index scoring system to integrate exposure risk factors and hazard potential data to prioritize the risk data of each perfluorinated compound to human health, and derive the risk ranking of different perfluorinated compounds in dairy products based on the risk data.

2. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 2, the persistence data is calculated as follows: The BIOWIN3 parameters obtained from the BIOWIN module in EPI Suite v4.1 were used to evaluate the ultimate biodegradation time of different perfluorinated compounds as persistence data.

3. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 2, the bioaccumulation data is calculated as follows: ADMETlab 3.0 was used to calculate the physicochemical parameter log D, which characterizes the partition coefficient, to evaluate the absorption, distribution, metabolism, excretion, and toxicity of different perfluorinated compounds as bioconcentration data.

4. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 2, the elimination data is calculated in the following manner: The half-life module in ADMETlab 3.0 was used to calculate the half-life values of different perfluorinated compounds. Perfluorinated compounds with a half-life value of >3 were classified as T1 / 2-, i.e., category 0, and perfluorinated compounds with a half-life value of ≤3 were classified as T1 / 2+, i.e., category 1. The output value ranging from 0 to 1 representing the probability that the perfluorinated compound belongs to category 1 was used as the elimination data.

5. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 2, the ecotoxicity data is calculated as follows: The risk quotient RQ of different perfluorinated compounds to aquatic organisms is calculated according to formula (1): Where RQ is the risk quotient of perfluorinated compounds; MEC is the measured concentration of perfluorinated compounds; PNEC is the predicted no-effect concentration of perfluorinated compounds for aquatic organisms, which is predicted by the eco-structure activity relationship prediction model, and the most sensitive effect endpoint is selected for further calculation. Among them, the eco-structure activity relationship prediction model is ECOSAR v2.2, and the most sensitive effect endpoints are lethality, reproductive toxicity, and developmental / growth toxicity; PNEC is calculated according to the following formula (2): Among them, LC50 is the median lethal concentration, which is provided by the ecological structure-activity relationship prediction model; AF is the assessment factor, which is set to 1000; The risk quotients (RQs) of different perfluorinated compounds to aquatic organisms were used as ecotoxicity data.

6. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 2, the human exposure toxicity data is calculated as follows: The AutodockVina program was used to simulate the binding affinities of 14 key human proteins with different perfluorinated compounds as human exposure toxicity data.

7. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 6, characterized in that: The 14 key human proteins include: Seven transporters: human serum albumin, liver fatty acid binding protein, organic anion transporter 4, apical sodium-dependent bile acid transporter, sodium taurocholate co-transporting polypeptide, prethyroxine albumin, and thyroxine-binding globulin; Seven nuclear receptors: peroxisome proliferator-activated receptor α, PPARβ, PPARγ, thyroid hormone receptor α, TRβ, estrogen receptor α and ERβ.

8. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 2, the tolerable weekly intake data is determined in the following manner: The tolerable weekly intake (TWI) for PFOS, PFOA, PFHxS and PFNA set by the European Food Safety Authority in 2020 was compared with the oral reference dose (ORD) set by the U.S. Environmental Protection Agency for the emerging PFAS compound HFPO-DA as TWI data.

9. The method for assessing the risk ranking of perfluorinated compounds in dairy products according to claim 1, characterized in that: In step 3, the toxicology priority index scoring system is used to integrate exposure risk factors and hazard potential data in the following manner to prioritize the risk data of each perfluorinated compound to human health, and the risk ranking of different perfluorinated compounds in dairy products is derived based on the risk data, including: The risk data of each perfluorinated compound to human health are evaluated in priority from eight dimensions. The risk data of each dimension are normalized by formula (3), which is: Among them, x Norm represents the normalized value of the risk data of a certain perfluorinated compound to human health; x represents the actual detection value of a certain perfluorinated compound monomer; x max is the maximum value of the perfluorinated compound among all perfluorinated compounds; x min is the minimum value for this perfluorinated compound among all perfluorinated compounds; The final toxicological priority index ToxPiscore was calculated according to the following formula (4): i , formula (4) is: ToxPiscore i =w Q ·RQ i +w P ·DF i +w L ·PS i +w C ·CT i +w P ·CP i +w E ·EL i +w H ·RfD i +w T ·PT i (4); Among them, RQ i represents the normalized value of the risk quotient of the i-th perfluorinated compound; DF i represents the normalized value of the detection frequency of the i-th perfluorinated compound; PS i represents the normalized value of the persistence of the i-th perfluorinated compound; CT i represents the normalized value of the concentration of the i-th perfluorinated compound; CP i represents the normalized value of the i-th perfluorinated compound on logD; EL i RfD represents the normalized value of the biological elimination rate of the i-th perfluorinated compound; i represents the normalized value of the i-th perfluorinated compound on the perfluorinated compound with the reference dose; PT i W represents the normalized value of the binding affinity of the i-th perfluorinated compound to human proteins: Q is the weight of the corresponding risk quotient; W F is the weight corresponding to the detection frequency; W L is the weight corresponding to persistence; W C is the weight of the corresponding concentration; W P is the weight corresponding to logD; W E is the weight corresponding to the biological elimination rate; W H is the weight of the perfluorinated compound corresponding to the reference dose; W T is the weight corresponding to the binding affinity with human protein, each weight W Q 、W F 、W L 、W C 、W P 、W E 、W H 、W T All are equal.

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