Safety detection device, system and method for solid food

Abstract

The invention discloses a safety detection device, system and method for solid food, and the device comprises a housing, a light source module is arranged in the housing, the light source output end of the light source module is sequentially provided with an integrating sphere and a loading white board which is installed at a detection port and is used for placing a loading block, and the periphery of the light source module is provided with a light gathering cover. One end of the integrating sphere is in contact with the light gathering cover, and the other end is connected with the detection port; a storage battery, a main control board and a spectrograph are further arranged in the shell, the spectrograph is connected with the integrating sphere and the main control board, and a power switch connected with the storage battery is arranged on the outer surface of the shell; the man-machine interaction interface and the operation buttons are arranged on the outer surface of the shell, and the man-machine interaction interface, the main control board and the light source module are all connected with the storage battery; the man-machine interaction interface, the plurality of operation buttons and the light source module are all connected with the main control board. The device has the characteristics of small size, light weight, simplicity in operation, high technicality and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety detection, and in particular to a safety detection device, system and method for solid food. Background Art

[0002] Food security is of paramount importance, and food safety must be given top priority. In recent years, with the continuous improvement of people's living standards, the eating pattern has basically achieved a revolutionary change from "eating enough" to "eating well", and consumers are paying more and more attention to food quality and safety control.

[0003] Common detection methods mainly include: chemical colorimetry, electrochemical method, mass spectrometry, free-radiometry, spectroscopy, etc. Most of the detection equipment is large-scale laboratory instruments, which have problems such as complex detection process, high professional requirements, strict operating conditions, and poor portability. In addition, existing food safety detection devices can only detect several pollutants of a single type, and it is difficult to simultaneously perform integrated detection of pollutants such as pesticide residues, veterinary drug residues, biological toxins, heavy metals and illegal additives. There is a problem of narrow application scope, which is difficult to achieve industrial application and cannot meet the daily needs of ordinary people.

[0004] In view of this, the present inventor provides a safety detection device, system and method for solid food to solve the above technical problems. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and to propose a safety detection device, system and method for solid food. The detection device integrates the detection of pollutants such as pesticide residues, veterinary drug residues, biological toxins, heavy metals and illegal additives in food safety to realize reflectance spectrum detection of solids.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] On the one hand, the present invention provides a safety detection device for solid food, comprising a shell, a light source module is arranged in the shell, an integrating sphere and a loading whiteboard installed in the detection port and used to place a loading block are arranged in sequence at the light source output end of the light source module, and a condenser is arranged on the periphery of the light source module, one end of the integrating sphere is in contact with the condenser, and the other end is in contact with the detection port; a battery, a main control board and a spectrometer are also arranged in the shell, the spectrometer is connected to the integrating sphere and the main control board respectively, and is used to receive the spectral signal output by the integrating sphere and transmit the spectral signal to the main control board for processing, and a power switch connected to the battery is arranged on the outer surface of the shell;

[0008] It also includes a human-computer interaction interface and multiple operation buttons arranged on the outer surface of the shell, wherein the human-computer interaction interface, the main control board and the light source module are all connected to the battery; the human-computer interaction interface, the multiple operation buttons and the light source module are all connected to the main control board.

[0009] Furthermore, the light source module includes a light source and a control circuit, the light source is connected to a main control board via the control circuit, and the control circuit is also connected to a battery.

[0010] Furthermore, the main control board includes a control module, a data acquisition module, a data processing module, a data analysis module, a communication module, a storage module, a plurality of operation interfaces and a plurality of input / output interfaces;

[0011] The communication module includes wireless and / or wired communication modes, the wireless communication mode includes WiFi and Bluetooth, and the wired communication mode includes multiple USB interfaces;

[0012] The control module is used to control the opening or closing of the control circuit and the spectrometer;

[0013] The data acquisition module is used to receive signals transmitted by any input / output interface, USB interface and operation interface;

[0014] The data processing module is used to receive the signal transmitted by the data acquisition module and pre-process it;

[0015] The data analysis module is used to store a variety of food spectral image data, analyze the pre-processed signals, obtain spectral curve data, compare and match the spectral image data of a variety of foods with the spectral curve data, and obtain the pollutant content of the food to be tested through a network model, calculate and compare the pollutant content based on the obtained content, and obtain the test result;

[0016] Specifically, the pre-processed spectral signals are compared, and characteristic parameters such as the intensity and wavelength range of the spectrum are calculated to obtain spectral curve data, which are compared and matched with the stored spectral image data of various foods to determine the chemical composition of the food to be tested, and then the category of pollutants in the food to be tested is obtained. Then, through the image data algorithm, based on machine deep learning, with the four-type mathematical model of adaptive filtering + Fourier transform + residual connection + random forest classification as the network model, the characteristics of the pollutants are extracted, and the extracted characteristics of the pollutants are automatically inverted to predict the pollution content, and the content of pollutants in the food to be tested is qualitatively and quantitatively judged; or the pre-processed spectral signals are compared, and characteristic parameters such as the intensity and wavelength range of the spectrum are calculated to obtain spectral curve data, which are compared and matched with the stored spectral image data of various foods to determine the chemical composition of the food to be tested, and then the category of pollutants in the food to be tested is obtained, and the characteristics of the pollutants are extracted from the spectral curve data based on the convolutional neural network model (CNN), and the extracted characteristics of the pollutants are automatically inverted to predict the pollution content, and the content of pollutants in the food to be tested is qualitatively and quantitatively judged. Finally, the least square method is used to fit the content of pollutants in the food to be tested with the national food safety standard limit to obtain the spectral detection limit. Finally, the spectral detection limit is compared with the national food safety standard limit to obtain the test result of whether it exceeds the standard;

[0017] The plurality of operation buttons are all connected to corresponding operation interfaces;

[0018] Any of the USB interfaces is arranged on the outer surface of the shell, so as to facilitate the communication between the main control board and the external device.

[0019] Furthermore, a charging socket connected to the battery is provided on the outer surface of the shell.

[0020] On the other hand, the present invention also provides a safety detection system for solid food, based on the above-mentioned safety detection device for solid food, including a database and a data acquisition terminal and a data analysis and calculation platform connected thereto;

[0021] The database is used to receive the spectral curve data sent by the data acquisition terminal, and compare and match the received spectral curve data with the food spectral image data in the pre-established database to determine the chemical composition of the food to be tested, and then obtain the category of the contaminants in the food to be tested;

[0022] The database is also used to transmit the spectral curve data, the obtained chemical composition of the food to be tested, and the pollutant category to the data analysis and calculation platform to obtain the pollutant content of the food to be tested. The data analysis and calculation platform calculates and compares the pollutant content based on the obtained pollutant content to obtain the test results, and returns the obtained pollutant category and test results to the data acquisition terminal for display.

[0023] Furthermore, the data collection terminal includes the detection device described above, and the detection device is connected to the database via a cloud service platform;

[0024] The database contains a variety of food spectrum image data.

[0025] Furthermore, the data analysis and computing platform includes a network model for measuring the pollutant content of the food to be tested. The pollutant characteristics of the food to be tested are extracted through the network model to obtain the pollutant content of the food to be tested; the obtained pollutant content is calculated and compared to obtain the spectral detection limit, and the spectral detection limit is compared with the national food safety standard limit to obtain the test result.

[0026] Specifically, the network model is the same as the network model of the data processing module described above.

[0027] In another aspect, the present invention further provides a safety detection method for solid food, based on the above-mentioned safety detection system for solid food, comprising the following steps:

[0028] Step 1: Using a detection device to obtain a spectral signal of the food to be detected, and preprocessing and analyzing the spectral signal to obtain spectral curve data;

[0029] Step 2, establishing a database containing spectral image data of various foods;

[0030] Step 3, transmitting the spectral curve data obtained in step 1 to a database for comparison and matching with a plurality of food spectral image data, determining the chemical composition of the food to be tested, and then obtaining the contaminant category of the food to be tested;

[0031] Step 4: Establish a network model for analyzing the pollutant content of the food to be tested on the data analysis and computing platform, transmit the spectral curve data, the chemical composition of the food to be tested obtained in step 3, and the type of pollutants to the data analysis and computing platform, extract the characteristics of the pollutants in the food to be tested through the network model, and then obtain the pollutant content of the food to be tested;

[0032] Step 5: Calculate the corresponding spectral detection limit for the pollutant content obtained in step 4 according to the national food safety standard limit, and then compare the spectral detection limit with the national food safety standard limit to obtain the test result of whether the food to be tested is safe.

[0033] It should be noted that the detection device of the present invention has two modes: online analysis and offline analysis. The offline analysis mode is mainly used in special occasions, such as field work without network signals. The detection device stores the spectral curve data on the device and compares it with the spectral image data of various foods stored in the device to obtain the detection results. The online analysis mode can be used in normal areas with network signals, and the latest database information can be obtained through the Internet of Things, thereby expanding the types of detection substances of the "food safety detection device based on hyperspectral technology", that is, the safety detection device of the present invention.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1) The present invention provides a safety detection device for solid food. The device integrates a light source module, a spectrometer, a main control board, an integrating sphere, and a solid measurement module, and can detect several major types of pollutants in food safety, such as pesticide residues, veterinary drug residues, biological toxins, heavy metals, and illegal additives. The detection device of the present invention has the characteristics of small size, light weight, simple operation, strong technicality, and easy field use and operation.

[0036] 2) The present invention provides a safety detection system for solid food. Based on the detection device of the present invention, the detection speed is fast and the detection result can be obtained in 3 minutes. It is non-destructive and has low consumables. The detection items are not damaged during the detection process and very few consumables are required. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are incorporated in and constitute a part of this specification and, together with the description, serve to explain the principles of the present invention.

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 It is a rear view of the external structure of the safety detection device for solid food of the present invention;

[0040] Figure 2 It is a front view of the external structure of the safety detection device for solid food of the present invention;

[0041] Figure 3 It is a right side view of the internal structure of the safety detection device for solid food of the present invention;

[0042] Figure 4 It is a left side view of the internal structure of the safety detection device for solid food of the present invention;

[0043] Figure 5 It is a schematic diagram of the structure after the meat and vegetable loading block and the loading white board of the present invention are installed;

[0044] Figure 6 It is a schematic diagram of the structure after the particle loading block and the loading whiteboard of the present invention are installed;

[0045] Figure 7 A circuit diagram of a safety detection device for solid food according to the present invention;

[0046] Figure 8 It is a schematic structural diagram of a safety detection system for solid food of the present invention;

[0047] Fig. 9 It is a schematic diagram of the process of the safety detection method for solid food of the present invention.

[0048] Among them: 1 is the shell; 2 is the condenser; 3 is the integrating sphere; 4 is the whiteboard; 5 is the main control board; 6 is the battery; 7 is the spectrometer; 11 is the power switch; 12 is the human-computer interaction interface; 13 is the operation button; 14 is the charging socket; 51 is the USB interface. DETAILED DESCRIPTION

[0049] Here, exemplary embodiments will be described in detail, and the embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are only examples consistent with some aspects of the present invention described in detail in the appended claims.

[0050] See also Figures 1 to 7 On the one hand, the embodiment of the present invention provides a safety detection device for solid food, including a housing 1, in which a light source module ( Figure 3 and 4 The light source output end of the light source module is sequentially provided with an integrating sphere 3 and a loading whiteboard 4 (such as Figure 5 , 6As shown), a condenser 2 is arranged on the periphery of the light source module, one end of the integrating sphere 3 is in contact with the condenser 2, and the other end is in contact with the detection port; a battery 6, a main control board 5 and a spectrometer 7 are also arranged in the housing 1, and the spectrometer 7 is connected to the integrating sphere 3 and the main control board 5 respectively, and is used to receive the spectral signal output by the integrating sphere 3 and transmit the spectral signal to the main control board 5 for processing, and a power switch 11 connected to the battery 6 is arranged on the outer surface of the housing 1;

[0051] The housing 1 also includes a human-machine interface 12 and three operation buttons 13 disposed on the outer surface of the housing 1. The human-machine interface 12, the main control board 5, and the light source module are all connected to the battery 6; the human-machine interface 12, the three operation buttons 13, and the light source module are all connected to the main control board 5. Specifically, each operation button 13 corresponds to a text prompt on the human-machine interface 12.

[0052] In this embodiment, the spectrometer 7 uses the GPJC-10-001 model, with a resolution of 0.1nm to 5nm; a spectral range of 300nm to 1100nm; a wavelength accuracy of 0.5nm; a detector: a linear array 2048-pixel CMOS; a signal-to-noise ratio of 500:1; and the main control board 5 uses the Raspberry Pi 4B raspberry pi 4.

[0053] Specifically, the size of the detection device (L×W×H): 282mm×72mm×212mm, weight ≤1500g; detection response time ≤150s; operating environment temperature: -10°C~46°C; battery 6 is a lithium battery (8600mAh).

[0054] The loading block includes two types, both of which have the same external structure but different internal structures; specifically, Figure 5 As shown, the loading block is a meat and vegetable loading block, such as Figure 6 As shown, the loading block is a particle loading block. Each type of loading block can be covered with a cover glass, such as when the food to be tested is meat or particle matter, a cover glass needs to be covered.

[0055] Furthermore, the light source module can be set to a built-in mode and an external mode, and the built-in mode is applied in this embodiment. The light source module includes a light source and a control circuit, and the light source is connected to the main control board 5 through the control circuit, and the control circuit is also connected to the battery 6. Specifically, the light source is a halogen lamp.

[0056] Furthermore, the main control board 5 includes a control module, a data acquisition module, a data processing module, a data analysis module, a communication module, a storage module, a plurality of operation interfaces and a plurality of input / output interfaces;

[0057] The communication module includes wireless and / or wired communication modes, the wireless communication mode includes WiFi and Bluetooth, and the wired communication mode includes three USB interfaces 51;

[0058] The control module is used to control the opening or closing of the control circuit and the spectrometer 7; specifically, it is responsible for overall control of the entire detection device;

[0059] The data acquisition module is used to receive signals transmitted by any input / output interface, any USB interface 51 and any operation interface, and the signals include spectral signals, electrical signals and command signals, etc. Specifically, it coordinates the detector of the spectrometer 7 to collect data, determines the parameters such as the time, frequency and method of collection, and ensures accurate acquisition of signals.

[0060] The data processing module is used to receive the signal transmitted by the data acquisition module and pre-process it; specifically, pre-process the spectral signal collected by the detector of the spectrometer 7, such as removing noise, amplifying weak signals, etc., to improve the quality of the spectral signal;

[0061] The data analysis module is used to store a variety of food spectral image data, and analyze the preprocessed signals to obtain spectral curve data, compare and match the spectral image data of a variety of foods with the spectral curve data, and obtain the pollutant content of the food to be tested through a network model, calculate and compare the pollutant content based on the obtained content, and obtain the test results.

[0062] Specifically, in this embodiment, the preprocessed spectral signals are compared, and characteristic parameters such as the intensity and wavelength range of the spectrum are calculated to obtain spectral curve data, which are compared and matched with stored spectral image data of multiple foods to determine the chemical composition of the food to be tested, and then the type of pollutants in the food to be tested is obtained. The spectral curve data are analyzed based on a convolutional neural network model (CNN) to obtain the pollutant content of the food to be tested, and the content of pollutants in the food to be tested is fitted with the national food safety standard limit using the least squares method to obtain the spectral detection limit of the pollutants in the food to be tested. Finally, the spectral detection limit is compared with the national food safety standard limit to obtain a test result of whether the standard is exceeded.

[0063] The food spectrum image data is the same as some food spectrum image data in the database established by the system of the present invention.

[0064] The human-computer interaction interface 12 is connected to the data analysis module and is used to display the detection results.

[0065] The plurality of operation buttons 13 are all connected to the corresponding operation interface;

[0066] One of the USB interfaces 51 is disposed on the outer surface of the housing 1 , so that the main control board 5 can communicate with a computer or other external devices to achieve data transmission and interaction, and complete further processing and analysis.

[0067] Inside the detection device, the main control board 5 is responsible for coordinating the communication between the various modules to ensure that the various parts can work together; it also provides status display, such as working mode, operation progress, fault prompts and other information, so that users can understand the operation of the device; it also provides some indicator lights to prompt users.

[0068] Furthermore, a charging socket 14 connected to the battery 6 is provided on the outer surface of the housing 1 .

[0069] In addition, the housing 1 is provided with a hand-held handle integrally formed therewith, which is convenient for human hands to hold and operate components.

[0070] like Figure 7 As shown, the circuit connection diagram of the detection device is shown. It can be seen from the figure that the control circuit includes a control board and a boost board. The control board includes two relays, both of which are connected to the main control board 5, one of which is connected to the halogen lamp through the boost board, and the main control board 5 controls the switch of the halogen lamp by controlling the relay; specifically, the IN1 pin of one of the relays is connected to the pin 16 of the main control board 5, and the pin COM1 is connected to the pin VCC of the battery 6; the pin IN2 of the other relay is connected to the pin 18 of the main control board 5, the pin COM2 is connected to the pin VCC of the battery 6, the pin NO2 is connected to one end of the boost board, the other end of the boost board is grounded, and the halogen lamp is connected to the boost board; the pin DC+ of the control board is connected to the pin VCC of the battery 6, and the pin DC- is grounded. The human-computer interaction interface 12 is a display screen, the power supply end of the display screen is connected to the pin VCC of the battery 6, the pin TX is connected to the pin 29 of the main control board 5, the pin RX is connected to the pin 7 of the main control board 5, and the pin GND is grounded. The spectrometer 7 is connected to the USB interface of the main control board 5. The operation button 13 includes button 1, button 2 and button 3, the power supply end of the operation button 13 is connected to pin 1 of the main control board 5, the button 1 is connected to pin 15 of the main control board 5, the button 2 is connected to pin 37 of the main control board 5, and the button 3 is connected to pin 13 of the main control board 5. The pin SDA of the battery 6 is connected to pin 3 of the main control board 5, the pin SCL is connected to pin 5 of the main control board 5, and the pin GND is grounded.

[0071] On the other hand, an embodiment of the present invention further provides a safety detection system for solid food, based on the above-mentioned safety detection device for solid food, including a database and a data acquisition terminal and a data analysis and computing platform connected thereto;

[0072] The database is used to receive the spectral curve data of the food to be tested sent by the data acquisition terminal, and compare and match the received spectral curve data with the food spectral image data in the pre-established database to determine the chemical composition of the food to be tested, and then obtain the contaminant category of the food to be tested;

[0073] The database is also used to transmit the spectral curve data, the obtained chemical composition of the food to be tested, and the pollutant category to the data analysis and calculation platform to obtain the pollutant content of the food to be tested. The data analysis and calculation platform calculates and compares the pollutant content based on the obtained pollutant content to obtain the test results, and returns the obtained pollutant category and test results to the data acquisition terminal for display.

[0074] There are many ways to connect the database and the data analysis and computing platform, such as a cloud service platform, a direct connection, and an interface connection. The present embodiment uses a cloud service platform.

[0075] like Figure 8 As shown, the data acquisition terminal includes the detection device of the present invention, the user operation terminal and the experimental terminal, and its function is to provide the spectrum curve data obtained by processing and analysis and upload it to the cloud; the cloud device includes a cloud service platform, an experimental data management platform, a data analysis and calculation platform and a database. The cloud device uploads the spectrum curve data of the food to be detected sent by the data acquisition terminal to the database, and after data comparison and matching, the chemical composition of the food to be detected is determined, and then the pollutant category of the food to be detected is obtained. The database transmits the spectrum curve data, the obtained chemical composition of the food to be detected and the pollutant category to the data analysis and calculation platform, and after analysis and processing, the test results are transmitted to the data acquisition terminal, and the final test results are displayed on the data acquisition terminal. Among them, the detection device and the user operation terminal are connected to the cloud service platform, and the experimental terminal is connected to the experimental data management platform.

[0076] Specifically, in this embodiment, the data acquisition terminal is the detection device of the present invention, and the detection device is connected to the database through a cloud service platform; and the database contains spectral image data of various types of food such as vegetables, fruits, meat and eggs, grains and oils.

[0077] Specifically, a standard spectral library of various foods, such as vegetables, fruits, meat and eggs, grains and oils, is established. Standard food samples are tested to obtain the corresponding food spectral image data, and the food spectral image data is stored in the database to obtain standard food spectral image data. The database also includes spectral image data of food contaminants such as pesticide residues and heavy metal residues in vegetables and fruits, veterinary drug residues in meat and eggs, and biological toxins and illegal additives in grains and oils. At the same time, as industry standards change, the standard food spectral image database can be continuously upgraded and support secondary development, and data settings can be re-customized; the uses of the database are as follows:

[0078] The detection device is used to collect the spectral signal of the food to be detected, and the spectral signal is preprocessed and analyzed in turn to obtain the spectral curve data. The detection device compares the spectral curve data with the standard data to determine the type of pollutants in the food to be detected, and obtains the pollutant content through the network model, and then corresponds to the national standard limit to determine whether the pollutant concentration of the food to be detected exceeds the standard, whether the food to be detected is safe, and if not safe, which specific types of pollutants exceed the standard; before collecting data, the number of data collection and the short-wave integration time can be set; the collected data can be uploaded to the main control board 5 for storage, and the stored content includes the collection location, collection time, collection number, collected data, etc. Specifically, the detection indicators include pesticide residues (≥2 types), veterinary drug residues (≥2 types), biological toxins (≥1 type), heavy metals (≥2 types), illegal additions (≥1 type), etc.

[0079] Furthermore, the data analysis and computing platform includes a network model for measuring the pollutant content of the food to be tested. The characteristics of the pollutants in the food to be tested are extracted through the network model to obtain the pollutant content of the food to be tested; the obtained pollutant content is calculated and compared to obtain the spectral detection limit, and the spectral detection limit is compared with the national food safety standard limit to obtain the test result.

[0080] Specifically, in this embodiment, the network model is a convolutional neural network model (CNN). The pollutant content is obtained through the convolutional neural network model (CNN), and then the content of pollutants in the food to be tested is fitted with the national food safety standard limit using the least squares method to obtain the spectral detection limit. Finally, the spectral detection limit is compared with the national food safety standard limit to obtain the detection result of whether the pollutant exceeds the standard.

[0081] like Fig. 9 As shown, in another aspect, an embodiment of the present invention further provides a safety detection method for solid food, based on the safety detection system for solid food of the present invention, comprising the following steps:

[0082] Step 1: Using a detection device to obtain the spectral signal of the food to be detected, and preprocessing and analyzing the spectral signal;

[0083] Step 2, establishing a database containing more than 10 kinds of food spectral image data;

[0084] Step 3, transmitting the spectral curve data obtained in step 1 to a database for comparison and matching with a plurality of food spectral image data, determining the chemical composition of the food to be tested, and then obtaining the contaminant category of the food to be tested;

[0085] Step 4: Establish a network model for analyzing the pollutant content of the food to be tested on the data analysis and computing platform, transmit the spectral curve data, the chemical composition of the food to be tested obtained in step 3, and the pollutant category to the data analysis and computing platform, extract the pollutant characteristics of the food to be tested through the network model, and then obtain the pollutant content of the food to be tested;

[0086] Step 5: Calculate the corresponding spectral detection limit for the pollutant content obtained in step 4 according to the national food safety standard limit, and then compare the spectral detection limit with the national food safety standard limit to obtain the test result of whether the food to be tested is safe.

[0087] Specifically, when the spectral detection limit is less than the national food safety standard limit, the food to be tested meets the national food safety limit standard and is a safe food; otherwise, the pollutant exceeds the standard.

[0088] Among them, the pollutant content obtained in step 4 is fitted with the national food safety standard limit using the least squares method to obtain the spectral detection limit of the pollutant, and then the spectral detection limit is compared with the national food safety standard limit to obtain the test result of whether the food to be tested is safe.

[0089] Specifically, in order to illustrate how the present invention obtains the detection results, the present invention performs the following specific experiments:

[0090] The national standard has a "limit" indicator for pollutants in food. The unit of "limit" is mg / kg, while the detection device of the present invention measures spectral curve data. The detection device will obtain different spectral curves when measuring different food pollutants. In order to match the obtained spectral curve data of food and pollutants with the national standard limit, we artificially pollute the food, prepare a series of concentration solutions of each pollutant, and soak the food in the solution. The surface of the food will contain pollutants, as shown in Table 1:

[0091]

[0092]

[0093] Three samples of different concentrations were taken from each category of the above items and sent to Xi'an Product Quality Supervision and Inspection Institute for comparative testing according to relevant national standards. The test results are as follows:

[0094] Serial number Vector samples Parameter name Sample volume recommendations Content (ppm) result 1 spinach Dichlorvos 200g 10 1.04mg / kg 2 spinach Dichlorvos 200g 2 0.23mg / kg 3 spinach Dichlorvos 200g 0.5 0.038mg / kg 4 apple Imidacloprid 200g 10 7.2mg / kg 5 apple Imidacloprid 200g 5 3.6mg / kg 6 apple Imidacloprid 200g 2 1.4mg / kg 7 Fish Florfenicol 200g 12.8 54.673mg / kg 8 Fish Florfenicol 200g 3.2 14.943mg / kg 9 Fish Florfenicol 200g 1 4.534mg / kg 10 beef Doxycycline 200g 6.4 32.517mg / kg 11 beef Doxycycline 200g 0.8 3.604mg / kg 12 beef Doxycycline 200g 0.1 0.5911mg / kg 13 spinach Lead 200g 5 5.46mg / kg 14 spinach Lead 200g 1 1.63mg / kg 15 spinach Lead 200g 0.2 0.409mg / kg 16 spinach Arsenic Metal 200g 20 7.1mg / kg 17 spinach Arsenic Metal 200g 5 1.7mg / kg 18 spinach Arsenic Metal 200g 1 0.21mg / kg 19 beef Chromium 200g 10 10.0mg / kg 20 beef Chromium 200g 2 1.73mg / kg 21 beef Chromium 200g 0.5 0.311mg / kg

[0095] Finally, the least squares method is used to match the pollutant content obtained above with the national standard limit. The least squares method can easily obtain unknown data by minimizing the square of the error and finding the best function match for the data, and minimize the sum of the squares of the errors between the obtained data and the actual data to obtain the most accurate value.

[0096] Assuming the concentration of the sample pollutant solution is x, and the spectral detection limit is y, the following expression is obtained by least squares fitting:

[0097]

[0098] Through least squares fitting, the spectral detection limit values ​​that meet the national standard limits are obtained as shown in the following table:

[0099]

[0100] When the food contaminant content detected by the detection device is less than the national standard limit, the food is judged to be "qualified" and the contaminants do not exceed the national standard. When the contaminant content is greater than the national standard limit, the contaminants are judged to be "exceeding the national standard".

[0101] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention.

[0102] It should be understood that the present invention is not limited to what has been described above and that various modifications and changes may be made without departing from its scope. The scope of the present invention is limited only by the appended claims.

Claims

1. A safety detection device for solid food, characterized in that: The invention comprises a shell (1), wherein a light source module is arranged in the shell (1), an integrating sphere (3) and a loading whiteboard (4) installed at a detection port and used to place a loading block are arranged in sequence at the light source output end of the light source module, and a condenser (2) is arranged on the outer periphery of the light source module, one end of the integrating sphere (3) is in contact with the condenser (2), and the other end is in contact with the detection port; a storage battery (6), a main control board (5) and a spectrometer (7) are also arranged in the shell (1), and the spectrometer (7) is connected to the integrating sphere (3) and the main control board (5) respectively, and is used to receive a spectral signal output by the integrating sphere (3) and transmit the spectral signal to the main control board (5) for processing; and a power switch (11) connected to the storage battery (6) is arranged on the outer surface of the shell (1); It also includes a human-machine interaction interface (12) and a plurality of operation buttons (13) arranged on the outer surface of the housing (1); the human-machine interaction interface (12), the main control board (5) and the light source module are all connected to the storage battery (6); and the human-machine interaction interface (12), the plurality of operation buttons (13) and the light source module are all connected to the main control board (5).

2. The safety detection device for solid food according to claim 1, characterized in that: The light source module comprises a light source and a control circuit, the light source is connected to a main control board (5) via the control circuit, and the control circuit is also connected to a storage battery (6).

3. The safety detection device for solid food according to claim 2, characterized in that: The main control board (5) comprises a control module, a data acquisition module, a data processing module, a data analysis module, a communication module, a storage module, a plurality of operation interfaces and a plurality of input / output interfaces; The communication module includes wireless and / or wired communication modes, the wireless communication mode includes WiFi and Bluetooth, and the wired communication mode includes multiple USB interfaces (51); The control module is used to control the opening or closing of the control circuit and the spectrometer (7); The data acquisition module is used to receive signals transmitted by any input / output interface, USB interface (51) and operation interface; The data processing module is used to receive the signal transmitted by the data acquisition module and pre-process it; The data analysis module is used to store a variety of food spectral image data and analyze the pre-processed signals to obtain detection results; The plurality of operation buttons (13) are all connected to corresponding operation interfaces; Any of the USB interfaces (51) is arranged on the outer surface of the housing (1) to facilitate communication between the main control board (5) and external devices.

4. The safety detection device for solid food according to claim 1, characterized in that: A charging socket (14) connected to the storage battery (6) is provided on the outer surface of the housing (1).

5. A safety detection system for solid food, characterized in that: A safety detection device for solid food according to any one of claims 1 to 4, comprising a database and a data acquisition terminal and a data analysis and computing platform connected thereto; The database is used to receive the spectral curve data of the food to be tested sent by the data acquisition terminal, and compare and match the received spectral curve data with the food spectral image data in the pre-established database to determine the chemical composition of the food to be tested, and then obtain the contaminant category of the food to be tested; The database is also used to transmit the spectral curve data, the obtained chemical composition of the food to be tested, and the pollutant category to the data analysis and calculation platform to obtain the pollutant content of the food to be tested. The data analysis and calculation platform calculates and compares the pollutant content based on the obtained pollutant content to obtain the test results, and returns the pollutant category and the test results to the data acquisition terminal for display.

6. The solid food safety detection system according to claim 5, characterized in that: The data acquisition terminal comprises the detection device according to any one of claims 1 to 4, and the detection device is connected to the database via a cloud service platform; The database contains a variety of food spectrum image data.

7. The solid food safety detection system according to claim 6, characterized in that: The data analysis and computing platform includes a network model for measuring the pollutant content of the food to be tested. The characteristics of the pollutants in the food to be tested are extracted through the network model to obtain the pollutant content; the obtained pollutant content is calculated and compared to obtain the spectral detection limit, and the spectral detection limit is compared with the national food safety standard limit to obtain the test result.

8. A safety detection method for solid food, characterized in that: The solid food safety detection system according to claim 6 or 7 comprises the following steps: Step 1: Using a detection device to obtain a spectral signal of the food to be detected, and preprocessing and analyzing the spectral signal to obtain spectral curve data; Step 2, establishing a database containing spectral image data of various foods; Step 3, transmitting the spectral curve data obtained in step 1 to a database for comparison and matching with a plurality of food spectral image data, determining the chemical composition of the food to be tested, and then obtaining the contaminant category of the food to be tested; Step 4: Establish a network model for analyzing the pollutant content of the food to be tested on the data analysis and computing platform, transmit the spectral curve data, the chemical composition of the food to be tested obtained in step 3, and the pollutant category to the data analysis and computing platform, extract the pollutant characteristics of the food to be tested through the network model, and then obtain the pollutant content of the food to be tested; Step 5: Calculate the corresponding spectral detection limit for the pollutant content obtained in step 4 according to the national food safety standard limit, and then compare the spectral detection limit with the national food safety standard limit to obtain the test result of whether the food to be tested is safe.

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