A method for detecting heavy metals in food processing

Abstract

The present invention discloses a method for detecting heavy metals in food processing, which relates to the technical field of heavy metal detection. The core framework of the method for detecting heavy metals in food processing is formed by the mutual cooperation of three algorithm units. The method comprehensively considers multiple influencing factors such as the basic heavy metal value influence value Hb, the detection environment influence value De and the detection time Dt, and calculates the final heavy metal content index M. est It can accurately reflect the heavy metal content in food under different environmental conditions, and provide scientific and reliable data support for the safety risk assessment in food processing and production. est By comparing with the qualified threshold value Y, it is possible to quickly determine whether the heavy metal content in the tested food is qualified. Compared with the traditional method of uniformly testing heavy metal elements in food through absorption spectroscopy and then analyzing them one by one, data analysis and recording are faster, which improves the efficiency of heavy metal detection in food processing.

Description

Technical Field

[0001] The invention relates to the technical field of heavy metal detection, in particular to a method for detecting heavy metals in food processing. Background Art

[0002] Due to the intensification of industrial activities, heavy metals such as lead, cadmium, mercury, and arsenic are enriched in the human environment. These heavy metal elements enter the human body through food and accumulate in certain organs of the human body, causing chronic poisoning and endangering human health. Therefore, food safety issues are taken into consideration in food processing. The detection of heavy metals in food is of great significance to ensuring food safety and maintaining human health.

[0003] In the prior art, heavy metal detection in food processing generally uses absorption spectroscopy to detect heavy metal elements in food at the same time and then analyze them one by one. On the one hand, this detection method cannot take into account the impact of ambient temperature and humidity on the detection results during the heavy metal detection process, which is prone to detection errors. On the other hand, the heavy metal elements in food are detected and analyzed one by one at the same time, and the detection efficiency is also low. Summary of the invention

[0004] The purpose of the present invention is to provide a method for detecting heavy metals in food processing to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for detecting heavy metals in food processing, comprising the following steps:

[0006] Food sample collection and testing: The food sample testing module extracts samples of food to be tested in the food processing factory, and uses the spectral detection method to detect the heavy metal content in the food samples in the order of lead, cadmium, mercury and arsenic;

[0007] Data collection: obtaining the standard values ​​of heavy metal content of different metal elements in the tested food, as well as the standard pH value P0 of food and the standard value S0 of salt content of food from the food safety standard information published in public data through the data collection module;

[0008] Data preprocessing: the collected data information is transmitted to the data processing module for decoding preprocessing to obtain important parameters involved in the calculation in the calculation processing module;

[0009] Calculation and evaluation: the parameter values ​​obtained after decoding and processing by the data processing module are substituted into the basic heavy metal value impact value algorithm unit, the detection environment impact value algorithm unit and the final heavy metal content index algorithm unit in the calculation processing module through the calculation processing and analysis module to calculate the basic heavy metal value impact value Hb and the final heavy metal content index M of the detected food. est and transmitted to the database;

[0010] Set the final heavy metal content index M of food in the database est The qualified threshold Y is 25%, and the final heavy metal content index M of the tested food transmitted to the database est Compare with the qualified threshold Y;

[0011] When the final heavy metal content index of food is M est When it is higher than Y, the heavy metal content in the food is deemed unqualified and food production is stopped immediately;

[0012] When the final heavy metal content index of food is M est When it is lower than Y, the heavy metal content in the food is deemed to be qualified and production is allowed.

[0013] Optionally, the food processing heavy metal detection method is operated using a food sample detection module, a data collection module, a data processing module, and a calculation processing and analysis module.

[0014] Optionally, the food sample detection module includes an atomic absorption spectrometer, and the data processing module includes data cleaning and data standardization.

[0015] Optionally, the calculation processing and analysis module includes a basic heavy metal value impact value algorithm unit, a detection environment impact value algorithm unit and a final heavy metal content index algorithm unit.

[0016] Optionally, the basic heavy metal value impact value algorithm unit is as follows:

[0017]

[0018] in:

[0019] Hb represents the basic heavy metal value impact value;

[0020] F(Hmc) represents the heavy metal content in food;

[0021] P stands for food pH, which is obtained by measuring the acidity or alkalinity of a food sample with pH test paper;

[0022] P0 represents the food standard pH value, which is obtained from the food safety standard information published in public data;

[0023] S represents the salt content of food, which is obtained by measuring the salt content in food samples through the salt determination method;

[0024] S0 represents the standard value of salt content in food, which is obtained from food safety standard information published in public data;

[0025] α and β represent the weight coefficients of pH value and salt content in the calculation of the basic heavy metal value impact value Hb, respectively. The values ​​of α and β determine the contribution of food pH value P and salt content S to the basic heavy metal value impact value Hb.

[0026] In the calculation formula:

[0027] The part "[1+α×(P-P0)]" represents the adjustment factor of food pH to heavy metal content. When the food pH value P is higher than the standard pH value P0, "P-P0" is positive, and the adjustment factor will be greater than 1, thereby increasing the value of the basic heavy metal value impact value Hb, indicating that in a high pH environment, the activity of heavy metals increases, posing a greater threat to food safety;

[0028] When the food pH value P is lower than the standard pH value P0, "P-P0" is negative, and the adjustment factor will be less than 1 but greater than 0, thereby reducing the value of the basic heavy metal value impact value Hb, indicating that the activity of heavy metals is reduced in a low pH environment;

[0029] This part represents the adjustment factor of food salt content S to heavy metal content. When the food salt content S is higher than the salt content standard value S0, If it is less than 1, the adjustment factor will be less than 1 but greater than 0, thereby reducing the value of the calculated basic heavy metal value impact value Hb, which means that in a high-salinity environment, the activity of heavy metals is inhibited and reduced;

[0030] When the salt content S of food is lower than the standard value S0, If it is greater than 1, the adjustment factor will be greater than 1, thereby increasing the value of the basic heavy metal value impact value Hb, indicating that the activity of heavy metals increases in a low-salinity environment.

[0031] Optionally, the function of F(Hmc) is as follows:

[0032]

[0033] in:

[0034] Pb con It stands for the detection of the content of heavy metal lead in food by absorption spectroscopy;

[0035] Pb stan Represents the standard value of the heavy metal lead content in food safety standards;

[0036] Cd con It stands for the detection of the heavy metal cadmium content in food by absorption spectroscopy;

[0037] Cd stanRepresents the standard value of the heavy metal element cadmium in the food safety standard;

[0038] Hg con It stands for the detection of the content of heavy metal element mercury in food by absorption spectroscopy;

[0039] Hg stan Represents the standard value of the content of heavy metal element mercury in food safety standards;

[0040] As con It stands for the detection of the content of heavy metal element arsenic in food by absorption spectroscopy;

[0041] As stan Represents the standard value of heavy metal arsenic content in food safety standards;

[0042] k1, k2, k3, k4 represent the weight coefficients of lead, cadmium, mercury and arsenic in the calculation of heavy metal content in food;

[0043] In piecewise function calculation:

[0044] When the actual content of any heavy metal such as lead, cadmium, mercury or arsenic in food exceeds the allowable content standard, the calculated value of F(Hmc) is directly 0, which means that as long as the content of any heavy metal in the food exceeds the standard, it does not meet the food safety requirements and the production of the food shall be stopped immediately.

[0045] Optionally, the detection environment impact value algorithm unit is as follows:

[0046]

[0047] in:

[0048] De represents the detection environmental impact value,

[0049] T stands for actual ambient temperature, which indicates the actual temperature in the laboratory when the food samples are tested;

[0050] T ref The reference value of the ambient temperature when testing food samples is obtained from the food safety testing standard information published in public data;

[0051] H stands for actual ambient humidity, which indicates the actual humidity in the laboratory when the food samples are tested;

[0052] H ref The reference value representing the ambient humidity is obtained from the food safety testing standard information published in public data;

[0053] γ and θ represent the weight coefficients of the actual ambient temperature T and the actual ambient humidity H in the calculation of the detection environment impact value De respectively;

[0054] In the calculation formula:

[0055] “[1+γ×(TT ref ) 2 ]" This part indicates the ambient temperature T relative to the standard temperature T ref The square of the deviation of ref ) 2 "This square term is used to amplify the effect of temperature deviation. Adding 1 to the calculation formula ensures that the adjustment factor is always positive. The value 1 represents a benchmark reference value for the temperature impact value in food testing.

[0056] Similarly, “[1+θ×(HH ref ) 2 ]" This part indicates the ambient humidity H relative to the standard humidity H ref square of deviation;

[0057] This "[1+γ×(TT ref ) 2 ]” and “[1+θ×(HH ref ) 2 The detection environment impact value De calculated by multiplying the results of the two parts and taking the square root can map the two influencing factors of temperature and humidity in the food detection environment into a numerical range. The calculated detection environment impact value De can more smoothly reflect the actual situation of the temperature and humidity in the food detection environment.

[0058] Optionally, the final heavy metal content index algorithm unit is as follows:

[0059]

[0060] in:

[0061] M est Represents the final heavy metal content index;

[0062] Hb represents the basic heavy metal value impact value;

[0063] De represents the detection environmental impact value;

[0064] Dt represents the detection time;

[0065] μ represents the detection time in the final heavy metal content index M est Weight coefficients in calculations;

[0066] In the calculation formula:

[0067] The part “[1+μ×log(Dt+1)]” indicates the effect of the detection time Dt on the final heavy metal content index Mest The logarithmic function is used to smooth the impact of the detection time Dt on the result, and the detection time Dt is used to determine the final heavy metal content index M. est The impact is converted into a value on a logarithmic scale. As the detection time Dt increases, the growth rate of the value of "log(Dt+1)" will gradually slow down;

[0068] This part represents the arithmetic square root of the basic heavy metal value impact value Hb and the detection environment impact value De. The square root operation has the characteristics of smooth transition, making the final heavy metal content index M est It is more sensitive to changes in the basic heavy metal value impact value Hb and the detection environment impact value De, but the changes are smoother.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. The present invention forms the core framework of the food processing heavy metal detection method through the cooperation of three algorithm units, comprehensively considering multiple influencing factors such as the basic heavy metal value impact value Hb, the detection environment impact value De and the detection time Dt, and calculates the final heavy metal content index M est It can more accurately reflect the heavy metal content in food under different environmental conditions, and provide scientific and reliable data support for the safety risk assessment in food processing and production. est By comparing with the qualified threshold value Y, it is possible to intuitively judge whether the heavy metal content in the tested food is qualified. Compared with the traditional detection of heavy metal content in food by only a single absorption spectroscopy method, data analysis and recording are faster, thereby improving the efficiency of heavy metal detection in food processing.

[0071] 2. The present invention sets the F(Hmc) function in a piecewise calculation mode in the basic heavy metal value influence value algorithm unit, comprehensively considers the actual content of the four heavy metals that are most harmful to the human body in food, the allowable content standard, the weighting coefficient and other factors. When any heavy metal content exceeds the standard, the food is directly judged as unqualified and the subsequent testing of the food is stopped immediately. Compared with the traditional method of detecting heavy metal elements in food at the same time by absorption spectroscopy and then analyzing them one by one, the efficiency of heavy metal detection in food processing is further improved, which is worthy of promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of a method for detecting heavy metals in food processing;

[0073] Figure 2 This is a schematic diagram of the overall structure of a method for detecting heavy metals in food processing. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] For example, see Figure 1 to Figure 2 The present invention provides a method for detecting heavy metals in food processing, comprising the following steps:

[0076] Food sample collection and testing: The food sample testing module extracts samples of food to be tested in the food processing factory, and uses the spectral detection method to detect the heavy metal content in the food samples in the order of lead, cadmium, mercury and arsenic;

[0077] Data collection: obtaining the standard values ​​of heavy metal content of different metal elements in the tested food, as well as the standard pH value P0 of food and the standard value S0 of salt content of food from the food safety standard information published in public data through the data collection module;

[0078] Data preprocessing: the collected data information is transmitted to the data processing module for decoding preprocessing to obtain important parameters involved in the calculation in the calculation processing module;

[0079] Calculation and evaluation: the parameter values ​​obtained after decoding and processing by the data processing module are substituted into the basic heavy metal value impact value algorithm unit, the detection environment impact value algorithm unit and the final heavy metal content index algorithm unit in the calculation processing module through the calculation processing and analysis module to calculate the basic heavy metal value impact value Hb and the final heavy metal content index M of the detected food. est and transmitted to the database;

[0080] Set the final heavy metal content index M of food in the database est The qualified threshold Y is 25%, and the final heavy metal content index M of the tested food transmitted to the database est Compare with the qualified threshold Y;

[0081] When the final heavy metal content index of food is M est When it is higher than Y, the heavy metal content in the food is deemed unqualified and food production is stopped immediately;

[0082] When the final heavy metal content index of food is M est When it is lower than Y, the heavy metal content in the food is deemed to be qualified and production is allowed.

[0083] In this embodiment:

[0084] The present invention cooperates with the three algorithm units to form the core framework of the food processing heavy metal detection method. The final heavy metal content index M is calculated by comprehensively considering multiple influencing factors such as the basic heavy metal value influence value Hb, the detection environment influence value De and the detection time Dt. est It can more accurately reflect the heavy metal content in food under different environmental conditions and calculate the final heavy metal content index M est It has higher accuracy and reliability, providing scientific and reliable data support for safety risk assessment in food processing and production. The final heavy metal content index M est By comparing with the qualified threshold value Y, it is possible to intuitively judge whether the heavy metal content in the tested food is qualified. Compared with the traditional detection of heavy metal content in food by only a single absorption spectroscopy method, data analysis and recording are faster, which improves the efficiency of heavy metal detection in food processing.

[0085] Moreover, by setting the F(Hmc) function segmented calculation method in the basic heavy metal value influence value algorithm unit, the actual content of the four heavy metals that are most harmful to the human body in food, the allowable content standard and the weighting coefficient and other factors are comprehensively considered. When any heavy metal content exceeds the standard, the food is directly judged as unqualified and the subsequent testing of the food is stopped immediately. Compared with the traditional method of detecting heavy metal elements in food at the same time through absorption spectroscopy and then analyzing them one by one, the efficiency of heavy metal detection in food processing is further improved.

[0086] See also Figure 1 to Figure 2 , the basic heavy metal value impact value algorithm unit is as follows:

[0087]

[0088] in:

[0089] Hb represents the basic heavy metal value impact value;

[0090] F(Hmc) represents the heavy metal content in food;

[0091] P stands for food pH value; it is obtained by measuring the acidity or alkalinity of food samples with pH test paper;

[0092] P0 represents the food standard pH value, which is obtained from the food safety standard information published in public data;

[0093] S represents the salt content of food; it is obtained by measuring the salt content in food samples through the salt determination method;

[0094] S0 represents the standard value of salt content in food, which is obtained from food safety standard information published in public data;

[0095] α and β represent the weight coefficients of pH value and salt content in the calculation of basic heavy metal value influence value Hb, respectively. The values ​​of α and β determine the contribution of food pH value P and salt content S to basic heavy metal value influence value Hb, and can be self-adjusted in the basic heavy metal value influence value algorithm unit;

[0096] For example, the production reports of some foods indicate that the activity of heavy metals will increase significantly within a specific pH range, that is, under highly acidic or alkaline conditions, heavy metals in food are more easily absorbed by the human body. In this case, it is necessary to increase the value of α to increase the contribution of the pH value of the food to the basic heavy metal value Hb to reflect this effect.

[0097] In the calculation formula:

[0098] The part "[1+α×(P-P0)]" represents the adjustment factor of food pH value to heavy metal content. When the food pH value P is higher than the standard pH value P0, "P-P0" is positive, and the adjustment factor will be greater than 1, thereby increasing the value of the basic heavy metal value impact value Hb, indicating that in a higher pH environment, the activity of heavy metals increases, posing a greater threat to food safety;

[0099] When the pH value P of food is lower than the standard pH value P0, "P-P0" is negative, and the adjustment factor will be less than 1 but greater than 0, thereby reducing the value of the basic heavy metal value impact value Hb, indicating that the activity of heavy metals is reduced in a lower pH environment;

[0100] Adding 1 to the calculation formula can ensure that the adjustment factor is always a positive number. The value 1 represents a baseline reference value of the pH value in the food, which represents the basic level of heavy metal content when there is no influence of the pH value (i.e., when P=P0). When the pH value of the food deviates from this baseline value, the adjustment factor will increase or decrease accordingly, reflecting the impact of this deviation on the basic heavy metal value Hb;

[0101] This part represents the adjustment factor of food salt content S to heavy metal content. When the salt content S of food is higher than the standard value of salt content S0, If the value is less than 1, the adjustment factor will be less than 1 but greater than 0, thereby reducing the value of the calculated basic heavy metal value impact value Hb, which means that in a higher salt environment, the activity of heavy metals is inhibited and reduced. When the salt content S of the food is lower than the salt content standard value S0, If it is greater than 1, the adjustment factor will be greater than 1, thereby increasing the value of the basic heavy metal value impact value Hb, indicating that the activity of heavy metals increases in a lower salt environment;

[0102] The function of F(Hmc) is as follows:

[0103]

[0104] in:

[0105] Pb con It stands for the detection of the content of heavy metal lead in food by absorption spectroscopy;

[0106] Pb stan Represents the standard value of the heavy metal lead content in food safety standards;

[0107] Cd con It stands for the detection of the heavy metal cadmium content in food by absorption spectroscopy;

[0108] Cd stan Represents the standard value of the heavy metal element cadmium in the food safety standard;

[0109] Hg con It stands for the detection of the content of heavy metal element mercury in food by absorption spectroscopy;

[0110] Hg stan Represents the standard value of the content of heavy metal element mercury in food safety standards;

[0111] As con It stands for the detection of the content of heavy metal element arsenic in food by absorption spectroscopy;

[0112] As stan Represents the standard value of heavy metal arsenic content in food safety standards;

[0113] k1, k2, k3, k4 represent the weight coefficients of lead, cadmium, mercury and arsenic in the calculation of heavy metal content in food, and k1+k2+k3+k4=1, which is used to reflect the relative importance of different heavy metals in food safety. For example, for canned food and puffed food, special attention is paid to whether the lead content exceeds the standard during their production and processing. Therefore, in the calculation, the value of k1 needs to be increased;

[0114] In piecewise function calculation:

[0115] When the actual content of any heavy metal such as lead, cadmium, mercury or arsenic in food exceeds the allowable content standard, the calculated value of F(Hmc) is directly 0, which means that as long as the content of any heavy metal in the food exceeds the standard, it does not meet the food safety requirements and the production of the food should be stopped immediately.

[0116] This part indicates that when the actual content of all heavy metals in food does not exceed their allowable content standards, the calculated value of F(Hmc) is calculated through a weighted formula. Specifically, the actual content of each heavy metal is divided by its allowable content standard to obtain the relative value of the pollution degree of the heavy metal, and then the relative value of the pollution degree of each heavy metal is multiplied by its corresponding weighting coefficient, and the results are added to obtain the heavy metal content value in the food.

[0117] In this embodiment:

[0118] The vulnerability risk assessment value algorithm unit comprehensively considers multiple factors such as the heavy metal content value F (Hmc) in food, the pH value P of food, and the salt content S, and jointly calculates the basic heavy metal value impact value Hb, which more accurately reflects the actual situation of heavy metals in food and improves the accuracy of detection. At the same time, because it takes into account the impact of different heavy metal contents absorbed by the human body on the basic heavy metal value impact value Hb when the pH value P and salt content S of the food are different under the same heavy metal content value F (Hmc), the detection result is more targeted and can more accurately assess the risk of heavy metals in food.

[0119] Moreover, through the piecewise calculation method of F(Hmc) function, the actual content of four heavy metals in food, the allowable content standard and the weighting coefficient are comprehensively considered to make a comprehensive evaluation of the heavy metal content in food. When the content of any heavy metal exceeds the standard, the food is directly judged as unqualified. Compared with the traditional method of detecting and analyzing multiple heavy metal elements in food one by one through absorption spectroscopy, the efficiency of heavy metal detection in food processing is improved.

[0120] See also Figure 1 to Figure 2 , the algorithm unit for detecting environmental impact value is as follows:

[0121]

[0122] in:

[0123] De represents the detection environmental impact value,

[0124] T represents the actual ambient temperature, which means the actual temperature in the laboratory when the food sample is tested, and is measured in real time by a thermometer;

[0125] T ref Represents the reference value of the ambient temperature when testing food samples. It is obtained from the food safety testing standard information published in public data. Depending on the season, the reference value of the ambient temperature T ref There will be some adjustments to compare with the actual ambient temperature T and calculate the impact of temperature differences on heavy metal detection results;

[0126] H represents the actual ambient humidity, which indicates the actual humidity in the laboratory when the food samples are tested, measured in real time by a hygrometer;

[0127] H ref Represents the benchmark reference value of ambient humidity, which is obtained from the food safety testing standard information published in public data. Depending on the season, the benchmark reference value of ambient humidity H ref There will be some adjustments to compare with the actual ambient humidity H and calculate the impact of humidity differences on heavy metal detection results;

[0128] γ and θ represent the weight coefficients of the actual ambient temperature T and the actual ambient humidity H in the calculation of the detection environment impact value De, respectively. The values ​​of γ and θ determine the contribution of the actual ambient temperature T and the actual ambient humidity H to the detection environment impact value De in the formula calculation, and can be self-adjusted on the environmental impact value algorithm unit dial;

[0129] For example, the production reports of some foods indicate that as the actual ambient temperature T of the laboratory during testing increases, the activity of heavy metals in the food will increase significantly. Accordingly, the value of γ will be adjusted higher to increase the weight of the actual ambient temperature T in the calculation of the test environment impact value De, thereby more accurately reflecting the impact of ambient temperature on heavy metal test results.

[0130] In the calculation formula:

[0131] “[1+γ×(TT ref ) 2 ]" This part indicates the ambient temperature T relative to the standard temperature T ref The square of the deviation of ref ) 2 "This square term is used to amplify the impact of temperature deviation, because in the actual environment, the change in temperature will have a nonlinear relationship with the change in the activity of heavy metals; adding 1 to the calculation formula can ensure that the adjustment factor is always a positive number, and the value 1 represents a benchmark reference value for the temperature impact value in food testing;

[0132] Similarly, “[1+θ×(HH ref ) 2 ]" This part indicates the ambient humidity H relative to the standard humidity H ref square of deviation;

[0133] This "[1+γ×(TT ref ) 2 ]” and “]1+θ×(HH ref ) 2The detection environment impact value De calculated by multiplying the results of the two parts and taking the square root can map the two influencing factors of temperature and humidity in the food detection environment to a more reasonable numerical range, so that the calculated detection environment impact value De can more smoothly reflect the actual situation of the temperature and humidity in the food detection environment.

[0134] In this embodiment:

[0135] During the food processing process, changes in the temperature and humidity of the detection environment will cause changes in the form, solubility and mobility of heavy metals in the food, thereby affecting the heavy metal detection results. The detection environment impact value De calculated by the detection environment impact value algorithm unit comprehensively considers the impact of ambient temperature T and ambient humidity H on the heavy metal detection results, and can more accurately reflect the impact of these environmental factors on the heavy metal content, thereby improving the accuracy of heavy metal detection results in food processing. For example, in a high temperature and high humidity environment, certain heavy metals may be more easily released from food, resulting in high detection results. In the calculation of the detection environment impact value De in the detection environment impact value algorithm unit, the detection results can be adjusted according to the actual ambient temperature and humidity, so that the stability and accuracy of the food heavy metal detection results can be maintained under different environments, thereby enhancing the applicability and flexibility of the detection method.

[0136] In summary, by calculating the detection environment impact value De in the detection environment impact value algorithm unit, the results of heavy metal detection in food processing under different detection environments can be made more accurate and reliable, which provides strong support for subsequent food safety risk assessment. By comparing the test results under different environments, it can be determined which environmental factors (such as high humidity or high temperature) may have a significant impact on the heavy metal content, so as to take corresponding risk control measures, providing scientific and reliable data support for food safety management.

[0137] See also Figure 1 to Figure 2 , the final heavy metal content index algorithm unit is as follows:

[0138]

[0139] in:

[0140] M est Represents the final heavy metal content index;

[0141] Hb represents the basic heavy metal value impact value;

[0142] De represents the detection environmental impact value;

[0143] Dt represents the detection time, which is the time from sample collection to the completion of the test, in hours;

[0144] μ represents the detection time in the final heavy metal content index M est The weight coefficient in the calculation can be self-adjusted in the final heavy metal content index algorithm unit;

[0145] As the heavy metal detection time in food processing increases, the heavy metal content in food samples will also change. This is because the stability of heavy metals in the environment is affected by environmental factors such as temperature and humidity, which causes changes in the form, solubility and mobility of heavy metals. Therefore, the detection time Dt is also an important factor affecting the detection of heavy metals in food.

[0146] Since the effect of detection time Dt on heavy metal content is not linear, but presents a trend of gradually slowing down, in the formula calculation:

[0147] The “[1+μ×log(Dt+1)]” part is used to express the effect of the detection time Dt on the final heavy metal content index M. est The degree of influence of the detection time Dt on the result is smoothed by using a logarithmic function, and the detection time Dt is used to determine the final heavy metal content index M. est The influence of is converted into a value on a logarithmic scale. As the detection time Dt increases, the growth rate of the value of "log(Dt+1)" will gradually slow down. Since the domain of the logarithmic function is a positive number, adding 1 in the calculation can avoid mathematical errors when the Dt value is too low.

[0148] This part represents the arithmetic square root of the basic heavy metal value impact value Hb and the detection environment impact value De. The square root operation has the characteristics of smooth transition, making the final heavy metal content index M est It is more sensitive to the changes of the basic heavy metal value impact value Hb and the detection environment impact value De, but the change is more gentle, that is, when the basic heavy metal value impact value Hb and the detection environment impact value De change slightly, the final heavy metal content index M est The changes in the heavy metal content index M are more obvious, but when they change greatly, the final heavy metal content index M est The change is relatively slow, so that it can more smoothly reflect the influence of the basic heavy metal value Hb and the detection environment impact value D on the final heavy metal content index M est impact.

[0149] In this embodiment:

[0150] The final heavy metal content index M is calculated by the final heavy metal content index algorithm unit by comprehensively considering multiple influencing factors such as the basic heavy metal value impact value Hb, the detection environment impact value De, and the detection time Dt. estIt can more accurately reflect the heavy metal content in food under different environmental conditions. Compared with the traditional method of detecting the heavy metal content in food by a single absorption spectrum method, it can calculate the final heavy metal content index M. est It has higher accuracy and reliability, and also provides an important basis for safety risk assessment in food processing and production. By comparing the final heavy metal content index M under different environmental conditions est , we can determine which environmental factors may have a significant impact on the heavy metal content, so as to take corresponding risk control measures. In addition, by using the final heavy metal content index M of different foods est The data can be uploaded to the database for big data analysis and can also be used to evaluate the changing trends of heavy metal content in different food processing processes, providing scientific and reliable data support for food safety management.

[0151] And by introducing the detection time Dt into the calculation of the final heavy metal content index algorithm unit to quantify the impact of the detection time on the heavy metal content, it helps to optimize the detection process and ensure that the detection is completed within the optimal detection time, thereby further improving the detection efficiency and accuracy.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting heavy metals in food processing, characterized in that: The following steps are involved: Food sample collection and testing: The food sample testing module extracts samples of food to be tested in the food processing factory, and uses the spectral detection method to detect the heavy metal content in the food samples in the order of lead, cadmium, mercury and arsenic; Data collection: obtaining the standard values ​​of heavy metal content of different metal elements in the tested food, as well as the standard pH value P0 of the food and the standard value S0 of the salt content of the food through the data collection module; Data preprocessing: the collected data information is transmitted to the data processing module for decoding preprocessing to obtain important parameters involved in the calculation in the calculation processing module; Calculation and evaluation: the parameter values ​​obtained after decoding and processing by the data processing module are substituted into the basic heavy metal value impact value algorithm unit, the detection environment impact value algorithm unit and the final heavy metal content index algorithm unit in the calculation processing module through the calculation processing and analysis module to calculate the basic heavy metal value impact value Hb and the final heavy metal content index M of the detected food. est and transmitted to the database; Set the final heavy metal content index M of food in the database est The qualified threshold Y is 25%, and the final heavy metal content index M of the tested food transmitted to the database est Compare with the qualified threshold Y; When the final heavy metal content index of food is M est When it is higher than Y, the heavy metal content in the food is deemed unqualified and food production is stopped immediately; When the final heavy metal content index of food is M est When it is lower than Y, the heavy metal content in the food is deemed to be qualified and production is allowed.

2. A method for detecting heavy metals in food processing according to claim 1, characterized in that: The food processing heavy metal detection method is operated by using a food sample detection module, a data collection module, a data processing module, and a calculation processing and analysis module.

3. A method for detecting heavy metals in food processing according to claim 2, characterized in that: The food sample detection module includes an atomic absorption spectrometer, and the data processing module includes data cleaning and data standardization.

4. A method for detecting heavy metals in food processing according to claim 2, characterized in that: The calculation processing and analysis module includes a basic heavy metal value impact value algorithm unit, a detection environment impact value algorithm unit and a final heavy metal content index algorithm unit.

5. A method for detecting heavy metals in food processing according to claim 4, characterized in that: The basic heavy metal value impact value algorithm unit is as follows: in: Hb represents the basic heavy metal value impact value; F(Hmc) represents the heavy metal content in food; P stands for food pH, which is obtained by measuring the acidity or alkalinity of a food sample with pH test paper; P0 represents the standard pH value of food; S represents the salt content of food, which is obtained by measuring the salt content in food samples through the salt determination method; S0 represents the standard value of salt content in food; α and β represent the weight coefficients of pH value and salt content in the calculation of the basic heavy metal value impact value Hb, respectively. The values ​​of α and β determine the contribution of food pH value P and salt content S to the basic heavy metal value impact value Hb. In the calculation formula: The "[1+α×(P-P0)]" part represents the adjustment factor of food pH to heavy metal content. When the food pH value P is higher than the standard pH value P0, "P-P0" is positive, and the adjustment factor will be greater than 1, thereby increasing the value of the basic heavy metal value impact value Hb, indicating that in a high pH environment, the activity of heavy metals increases, posing a greater threat to food safety; When the food pH value P is lower than the standard pH value P0, "P-P0" is negative, and the adjustment factor will be less than 1 but greater than 0, thereby reducing the value of the basic heavy metal value impact value Hb, indicating that the activity of heavy metals is reduced in a low pH environment; This part represents the adjustment factor of food salt content S to heavy metal content. When the food salt content S is higher than the salt content standard value S0, Less than 1, the adjustment factor is less than 1 but greater than 0, thereby reducing the value of the calculated basic heavy metal value impact value Hb, which means that in a high-salinity environment, the activity of heavy metals is inhibited and reduced; When the salt content S of food is lower than the standard value S0, Greater than 1, the adjustment factor is greater than 1, thereby increasing the value of the basic heavy metal value impact value Hb, indicating that the activity of heavy metals increases in a low-salinity environment.

6. A method for detecting heavy metals in food processing according to claim 5, characterized in that: The function of F(Hmc) is as follows: in: Pb con It stands for the detection of the content of heavy metal lead in food by absorption spectroscopy; Pb stan Represents the standard value of the heavy metal lead content in food safety standards; Cd con It stands for the detection of the heavy metal cadmium content in food by absorption spectroscopy; Cd stan Represents the standard value of the heavy metal element cadmium in the food safety standard; Hg con It stands for the detection of the content of heavy metal element mercury in food by absorption spectroscopy; Hg stan Represents the standard value of the content of heavy metal element mercury in food safety standards; As con It stands for the detection of the content of heavy metal element arsenic in food by absorption spectroscopy; As stan Represents the standard value of heavy metal arsenic content in food safety standards; k1, k2, k3, k4 represent the weight coefficients of lead, cadmium, mercury and arsenic in the calculation of heavy metal content in food; In piecewise function calculation: When the actual content of any of the heavy metals lead, cadmium, mercury and arsenic in food exceeds the allowable content standard, the calculated value of F(Hmc) is directly 0, which means that as long as the content of any one heavy metal in the food exceeds the standard, it does not meet the food safety requirements.

7. A method for detecting heavy metals in food processing according to claim 6, characterized in that: The detection environment impact value algorithm unit is as follows: in: De represents the detection environmental impact value, T stands for actual ambient temperature, which indicates the actual temperature in the laboratory when the food samples are tested; T ref Represents the baseline reference value of the ambient temperature when testing food samples; H stands for actual ambient humidity, which indicates the actual humidity in the laboratory when the food samples are tested; H ref Represents the baseline reference value of ambient humidity; γ and θ represent the weight coefficients of the actual ambient temperature T and the actual ambient humidity H in the calculation of the detection environment impact value De respectively; In the calculation formula: "[1+γ×(TT ref ) 2 ]" This part indicates the ambient temperature T relative to the standard temperature T ref The square of the deviation of ref ) 2 "This square term is used to amplify the effect of temperature deviation. Adding 1 to the calculation formula ensures that the adjustment factor is always positive. The value 1 represents a benchmark reference value for the temperature impact value in food testing. Similarly, "[1+θ×(HH ref ) 2 ]" This part indicates the ambient humidity H relative to the standard humidity H ref square of deviation; This "[1+γ×(TT ref ) 2 ]” and “[1+θ×(HH ref ) 2 The test environment impact value De is calculated by multiplying the results of the two parts and taking the square root. The two influencing factors of temperature and humidity in the food testing environment are mapped into a numerical range. The calculated test environment impact value De reflects the actual situation of the temperature and humidity in the food testing environment more smoothly.

8. A method for detecting heavy metals in food processing according to claim 7, characterized in that: The final heavy metal content index algorithm unit is as follows: in: M est Represents the final heavy metal content index; Hb represents the basic heavy metal value impact value; De represents the detection environmental impact value; Dt represents the detection time; μ represents the detection time in the final heavy metal content index M est Weight coefficients in calculations; In the calculation formula: "[1+μ×log(Dt+1)]" indicates the effect of the detection time Dt on the final heavy metal content index M est The logarithmic function is used to smooth the impact of the detection time Dt on the result, and the detection time Dt is used to determine the final heavy metal content index M. est The influence of is converted into a value on a logarithmic scale. As the detection time Dt increases, the growth rate of the value of "log(Dt+1)" gradually slows down; This part represents the arithmetic square root of the basic heavy metal value impact value Hb and the detection environment impact value De. The square root operation has the characteristics of smooth transition, making the final heavy metal content index M est It is more sensitive to changes in the basic heavy metal value impact value Hb and the detection environment impact value De, but the changes are smoother.

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