Food safety detection device, system and method

Abstract

The invention discloses a food safety detection device, system and method, the food safety detection device comprises a shell, a light source module is arranged in the shell, the light source output end of the light source module is sequentially provided with a cuvette support for placing a cuvette, an integrating sphere and a loading white board installed at a detection port and used for placing a loading block, one end of the integrating sphere is connected with the cuvette bracket, and the other end of the integrating sphere is connected with the detection port; a fluorescence detection module, 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 human-computer interaction interface and the operation buttons are arranged on the outer surface of the shell, and the human-computer interaction interface, the main control board, the fluorescence detection module and the light source module are all connected with the storage battery. The device has the characteristics of small size, light weight, simplicity in operation, high technicality, convenience in field use and operation and the like.

Description

Technical Field

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

[0003] Food contaminants mainly include pathogenic microorganisms, pesticide residues, veterinary drug residues, biological toxins, heavy metals and illegal additives. Common detection methods mainly include: chemical colorimetry, electrochemical method, mass spectrometry, free-radiometry, spectroscopy, etc. The detection equipment is mostly large-scale instruments in the laboratory, which have problems such as complex detection process, high professional requirements, strict operating conditions, and poor portability. In addition, the existing food safety detection equipment can only detect several pollutants of a single type, and it is difficult to simultaneously perform integrated detection of pollutants such as pathogenic microorganisms, pesticide residues, veterinary drug residues, biological toxins, heavy metals and illegal additives. There is a problem of narrow application range, and it is difficult to achieve industrial application.

[0004] In view of this, the present inventor provides a food safety detection device, system and method 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 food safety detection device, system and method. The detection device integrates the detection of pollutants such as pathogenic microorganisms, pesticide residues, veterinary drug residues, biological toxins, heavy metals, illegal additives, etc. in food safety, and realizes reflection spectrum detection of solids, transmission spectrum detection of liquids and ATP fluorescence detection of microorganisms.

[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 food safety detection device, comprising a shell, a light source module is arranged in the shell, and a cuvette holder for placing a cuvette, an integrating sphere, and a loading whiteboard installed in the detection port and used for placing a loading block are arranged in sequence at the light source output end of the light source module; a fluorescence detection module, a battery, a main control board and a spectrometer are also arranged in the shell, and 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, and the human-computer interaction interface, main control board, fluorescence detection module and light source module are all connected to the battery; the human-computer interaction interface, multiple operation buttons, fluorescence detection module and 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 control circuit includes a first control board and a boost board, and the light source is connected to the first control board via the boost board.

[0011] Furthermore, the fluorescence detection module is an ATP fluorescence measuring instrument, an experimental chamber of the ATP fluorescence measuring instrument is located in a shell, and a fluorescence detection cover covering the experimental chamber is provided on the outer surface of the shell.

[0012] 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;

[0013] 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;

[0014] The control module is used to control the opening or closing of the control circuit, the ATP fluorescence measuring instrument and the spectrometer;

[0015] The data acquisition module is used to receive signals transmitted by any input / output interface, USB interface and operation interface; specifically, the signals include spectral signals, electrical signals and command signals;

[0016] The data processing module is used to receive the signal transmitted by the data acquisition module and pre-process it;

[0017] 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;

[0018] 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 the spectral curve data of the food to be tested. The spectral curve data is 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 pollutant category of the food to be tested is obtained. Then, through the image data algorithm, based on machine deep learning, with the adaptive filtering + Fourier transform + residual connection + random forest classification 4-type mathematical model as the network model, the characteristics of the pollutants are extracted, and the extracted pollutant characteristics 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 the spectral curve data of the food to be tested, and the spectral curve data is 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 pollutant category of the food to be tested is obtained. The characteristics of the pollutants are extracted from the spectral curve data based on the convolutional neural network model (CNN), and the extracted pollutant characteristics are automatically inverted to predict the pollution content, and the content of the pollutants in the food to be tested is qualitatively and quantitatively judged. Finally, 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 test result of whether the food to be tested exceeds the standard.

[0019] The plurality of operation buttons are all connected to corresponding operation interfaces;

[0020] 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.

[0021] Furthermore, the detection device also includes a condenser, one end of which is arranged on the periphery of the light source, and the other end extends into the cuvette holder and is connected to one end of the cuvette holder, and the other end of the cuvette holder is in contact with the integrating sphere.

[0022] Furthermore, a charging socket connected to the battery is provided on the outer surface of the shell.

[0023] On the other hand, the present invention also provides a food safety detection system, based on the above-mentioned food safety detection device, including a database and a data acquisition terminal and a data analysis and calculation platform connected thereto;

[0024] 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;

[0025] The database is also used to transmit the spectral curve data, the obtained chemical composition and the pollutant category to the data analysis and calculation platform to obtain the pollutant content. The data analysis and calculation platform calculates and compares 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.

[0026] 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;

[0027] The database contains a variety of food spectrum image data.

[0028] Furthermore, the data analysis and computing platform includes a network model for measuring the content of pollutants in the food to be tested. The network model is used to extract the characteristics of pollutants in the food to be tested 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.

[0029] Specifically, the network model is the same as the network model of the data processing module described above.

[0030] In another aspect, the present invention further provides a food safety detection method, based on the above-mentioned food safety detection system, comprising the following steps:

[0031] Step 1: Using a detection device to obtain a spectral signal of the food to be detected, and preprocessing and analyzing the spectral signal in sequence to obtain spectral curve data of the food to be detected;

[0032] Step 2, establishing a database containing spectral image data of various foods;

[0033] 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;

[0034] 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;

[0035] 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.

[0036] 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 food safety detection device of the present invention.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1) The present invention provides a food safety detection device, which integrates a light source module, a spectrometer, a main control board, an integrating sphere, and modules for measuring solids, liquids, and microorganisms. It can detect six major types of pollutants in food safety, including pathogenic microorganisms, pesticide residues, veterinary drug residues, biological toxins, heavy metals, and illegal additives. Microorganisms are detected by fluorescence spectroscopy, and other pollutants are detected by reflection spectroscopy if they are solids, and by transmission spectroscopy if they are liquids (with better light transmittance). Multiple detection methods are integrated into one device to detect multiple substances. The detection device of the present invention has the characteristics of small size, light weight, simple operation, strong technicality, and convenience for field use and operation.

[0039] 2) The food safety detection system provided by the present invention is based on the detection device of the present invention, has a fast detection speed, can produce detection results in 3 minutes, and has the characteristics of non-destructive and low consumables. The detection items are not damaged during the detection process, and very few consumables are required. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] 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.

[0042] Figure 1 It is a rear view of the external structure of the food detection device of the present invention;

[0043] Figure 2 It is a front view of the external structure of the food detection device of the present invention;

[0044] Figure 3 It is a right side view of the internal structure of the food detection device of the present invention;

[0045] Figure 4 It is a left side view of the internal structure of the food detection device of the present invention;

[0046] 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;

[0047] 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;

[0048] Figure 7 is a circuit diagram of the food detection device of the present invention;

[0049] Figure 8 It is a structural schematic diagram of the food detection system of the present invention;

[0050] Fig. 9 It is a schematic diagram of the process of the food detection method of the present invention.

[0051] Among them: 1 is the shell; 2 is the cuvette holder; 3 is the integrating sphere; 4 is the loading whiteboard; 5 is the fluorescence detection module; 6 is the battery; 7 is the spectrometer; 8 is the main control board; 9 is the condenser; 11 is the power switch; 12 is the human-computer interaction interface; 13 is the operation button; 14 is the charging socket; 15 is the fluorescence detection cover; 16 is the cuvette cover; 81 is the USB interface. DETAILED DESCRIPTION

[0052] 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.

[0053] See also Figures 1 to 7 On the one hand, an embodiment of the present invention provides a food safety detection device, 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 a cuvette holder 2 for placing a cuvette, an integrating sphere 3, and a loading whiteboard 4 (such as Figure 5 , 6 As shown), one end of the integrating sphere 3 is connected to the cuvette holder 2, and the other end is in contact with the detection port; a fluorescence detection module 5, a battery 6, a main control board 8 and a spectrometer 7 are also provided in the housing 1, and the spectrometer 7 is connected to the integrating sphere 3 and the main control board 8 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 8 for processing, and a power switch 11 connected to the battery 6 is provided on the outer surface of the housing 1;

[0054] 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 8, the fluorescence detection module 5 and the light source module are all connected to the battery 6; the human-machine interaction interface 12, the three operation buttons 13, the fluorescence detection module 5 and the light source module are all connected to the main control board 8. Specifically, each operation button 13 corresponds to a text prompt on the human-machine interaction interface 12.

[0055] 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 8 uses the Raspberry Pi 4.

[0056] Furthermore, a cuvette cover 16 for covering the cuvette is provided on the top of the housing 1 and above the cuvette. In this embodiment, the cuvette is a quartz cuvette.

[0057] It should be noted that one of the main functions of the cuvette is to hold the food to be tested. It provides a container of a specific shape and volume for the food to be tested, ensuring that the food to be tested is in a stable state during the test process. Different types of cuvettes can be selected according to the properties of the food to be tested and the test requirements. For example, for liquid samples, square or round cuvettes are usually used; for solid powder samples, specially designed cuvettes can be used for tablet measurement. The thickness or optical path length of the cuvette is fixed, which enables the path length of light through the sample to be accurately controlled in spectral detection. The optical path length is very important for the measurement of absorbance. According to the Beer-Lambert law, absorbance is proportional to the sample concentration and the optical path length. By using a cuvette with a specific optical path length, the accuracy and comparability of the measurement results can be ensured.

[0058] 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).

[0059] 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.

[0060] 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 8 through the control circuit, and the control circuit is also connected to the battery 6. Specifically, the light source is a halogen lamp.

[0061] Furthermore, the detection device also includes a condenser 9 , one end of which is arranged on the periphery of the halogen lamp, and the other end of which extends into the cuvette holder 2 and is connected to one end of the cuvette holder 2 , and the other end of the cuvette holder 2 is in contact with the integrating sphere 3 .

[0062] Specifically, in the present embodiment, the cuvette holder 2 comprises a hollow cuboid and a holder connected to the hollow cuboid. The holder is arranged closer to the integrating sphere 3 than the hollow cuboid. A channel for inserting one end of the condenser 9 is provided on one side of the hollow cuboid. A tube is provided on the other side which passes through the holder and contacts the integrating sphere 3, and the tube enables the light source to pass through.

[0063] Furthermore, the fluorescence detection module 5 is an ATP fluorescence measuring instrument, the experimental chamber of the ATP fluorescence measuring instrument is located in the shell 1, and a fluorescence detection cover 15 covering the experimental chamber is provided on the outer surface of the shell 1.

[0064] It should be noted that pathogenic microorganisms are too small in size and their content is too little for the main components, so the spectral characteristics are not obvious, so the ATP fluorescence measurement method is used for measurement. The technical principle is based on the "luciferase-luciferin system" to quickly detect adenosine triphosphate (ATP). The specific method is that the ATP swab contains reagents that can lyse the cell membrane of microorganisms. Once the sample comes into contact with the ATP swab, these reagents will destroy the cell membrane of the microorganism and release the ATP in the cell into the sample. The ATP released in the sample reacts with ATPase (usually luciferase) to form a visible fluorescence signal. Since all living cells contain a constant amount of ATP, the ATP content can clearly indicate the amount of microorganisms and other biological residues in the sample. The device integrates the existing ATP fluorescence measurement technology and is controlled by the embedded computer board. When the microorganism is selected for measurement on the touch screen, it automatically switches to the ATP fluorescence measurement module.

[0065] Furthermore, the main control board 8 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;

[0066] 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 81;

[0067] The control module is used to control the opening or closing of the control circuit, the ATP fluorescence measuring instrument and the spectrometer 7; specifically, it is responsible for the overall control of the entire detection device;

[0068] The data acquisition module is used to receive signals transmitted by any input / output interface, any USB interface 81 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.

[0069] 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;

[0070] 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.

[0071] 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 of the food to be tested, and the spectral curve data is compared and matched with the stored spectral image data of multiple foods to determine the chemical composition of the food to be tested, and then the category of contaminants in the food to be tested is obtained, and the spectral curve data is 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, and 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.

[0072] The various food spectral image data in the data analysis module are the same as some food spectral image data in the database established by the system of the present invention.

[0073] The human-computer interaction interface 12 is connected to the data analysis module and is used to display the detection results.

[0074] The plurality of operation buttons 13 are all connected to the corresponding operation interface;

[0075] One of the USB interfaces 81 is disposed on the outer surface of the housing 1, so that the main control board 8 can communicate with a computer or other external devices to achieve data transmission and interaction, and complete further processing and analysis.

[0076] Inside the detection device, the main control board 8 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.

[0077] Furthermore, a charging socket 14 connected to the battery 6 is provided on the outer surface of the housing 1 .

[0078] 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.

[0079] like Figure 7As shown, the circuit connection diagram of the detection device is shown. It can be seen from the figure that the control circuit includes a first control board and a boost board. The first control board includes two relays, both of which are connected to the main control board 8, one of which is connected to the halogen lamp through the boost board, and the main control board 8 controls the switch of the halogen lamp by controlling the relay; the other relay is connected to the step-down board of the ATP fluorescence detection module; specifically, the pin IN1 of one of the relays is connected to the pin 16 of the main control board 8, the pin COM1 is connected to the pin VCC of the battery 6, and the pin NO1 is connected to the power supply end of the step-down board; the pin IN2 of the other relay is connected to the pin 18 of the main control board 8, 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 first control board is connected to the pin VCC of the battery 6, and the pin DC- is grounded. The ATP fluorescence detection module includes a second control board, a fluorescence detection board and a step-down board. The second control board includes a relay, which is connected to the fluorescence detection board. Specifically, the pin IN1 of the relay is connected to the pin 22 of the main control board 8, the pin DC+ is connected to the pin VCC of the battery 6, and the pin DC- is grounded; the positive electrode of the step-down board is connected to the positive electrode of the fluorescence detection board, and the negative electrode of the step-down board is connected to the negative electrode of the fluorescence detection board; the pin TX of the fluorescence detection board is connected to the pin 33 of the main control board 8, the pin RX is connected to the pin 32 of the main control board 8, and the pin GND 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 8, the pin RX is connected to the pin 7 of the main control board 8, and the pin GND is grounded. The spectrometer 7 is connected to the USB interface of the main control board 8. 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 8, the button 1 is connected to pin 15 of the main control board 8, the button 2 is connected to pin 37 of the main control board 8, and the button 3 is connected to pin 13 of the main control board 8. The pin SDA of the storage battery 6 is connected to pin 3 of the main control board 8, the pin SCL is connected to pin 5 of the main control board 8, and the pin GND is grounded.

[0080] On the other hand, an embodiment of the present invention further provides a food safety detection system, based on the food safety detection device described above, including a database and a data acquisition terminal and a data analysis and computing platform connected thereto;

[0081] 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;

[0082] The database is also used to transmit the spectral curve data, the obtained chemical composition and the pollutant category to the data analysis and calculation platform to obtain the pollutant content. The data analysis and calculation platform calculates and compares 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.

[0083] 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.

[0084] 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.

[0085] Specifically, in this embodiment, the data acquisition terminal is the detection device of the present invention, which 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.

[0086] 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:

[0087] 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 of the food to be detected. The detection device compares the spectral curve data with the standard data to obtain the type of pollutants in the food to be detected, and obtains the pollutant content of the food to be detected through the network model, and then corresponds to the national standard limit to determine whether the pollutant concentration in 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; the number of data collection and the short-wave integration time can be set before collecting data; the collected data can be uploaded to the main control board 8 for storage, and the stored content includes the collection location, collection time, collection number, collected data, etc. Specifically, the detection indicators include pathogenic microorganisms (≥2 types), pesticide residues (≥2 types), veterinary drug residues (≥2 types), biological toxins (≥1 type), heavy metals (≥2 types), illegal additions (≥1 type), etc.

[0088] 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; 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.

[0089] Specifically, in this embodiment, the network model is a convolutional neural network model (CNN). The convolutional neural network model (CNN) is used to obtain the pollutant content, and then the least squares 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 the pollutants in the food to be tested exceed the standard.

[0090] like Fig. 9 As shown, in another aspect, an embodiment of the present invention further provides a food safety detection method, based on the food safety detection system of the present invention, comprising the following steps:

[0091] Step 1: Using a detection device to obtain the spectral signal of the food to be detected, and preprocessing and analyzing the spectral signal;

[0092] Step 2, establishing a database containing more than 10 kinds of food spectral image data;

[0093] 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;

[0094] 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 food pollutants through the network model, and then obtain the pollutant content;

[0095] 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.

[0096] 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.

[0097] 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 is safe.

[0098] Specifically, in order to illustrate how the present invention obtains the detection results, the present invention performs the following specific experiments:

[0099] 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 (national food safety 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:

[0100]

[0101] 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:

[0102]

[0103]

[0104] 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.

[0105] 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:

[0106]

[0107]

[0108] Through least squares fitting, the spectral detection limit values ​​that meet the national standard limits are obtained as follows:

[0109]

[0110] When the food contaminant content measured 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".

[0111] 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.

[0112] 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 food safety detection device, characterized in that: The invention comprises a housing (1), wherein a light source module is arranged in the housing (1), and a cuvette holder (2) for placing a cuvette, an integrating sphere (3), and a loading whiteboard (4) installed at a detection port and used for placing a loading block are arranged in sequence at the light source output end of the light source module; a fluorescence detection module (5), a storage battery (6), a main control board (8), and a spectrometer (7) are also arranged in the housing (1); the spectrometer (7) is connected to the integrating sphere (3) and the main control board (8) respectively, and is used for receiving a spectral signal output by the integrating sphere (3) and transmitting the spectral signal to the main control board (8) for processing; and a power switch (11) connected to the storage battery (6) is arranged on the outer surface of the housing (1); It also comprises 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 (8), the fluorescence detection module (5) and the light source module are all connected to the storage battery (6); the human-machine interaction interface (12), the plurality of operation buttons (13), the fluorescence detection module (5) and the light source module are all connected to the main control board (8).

2. The food safety detection device 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 (8) via the control circuit, and the control circuit is also connected to a storage battery (6).

3. The food safety detection device according to claim 2, characterized in that: The fluorescence detection module (5) is an ATP fluorescence measuring instrument, the experimental chamber of the ATP fluorescence measuring instrument is located in the housing (1), and a fluorescence detection cover (15) covering the experimental chamber is provided on the outer surface of the housing (1).

4. The food safety detection device according to claim 3, characterized in that: The main control board (8) 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 (81); The control module is used to control the opening or closing of the control circuit, the ATP fluorescence measuring instrument and the spectrometer (7); The data acquisition module is used to receive signals transmitted by any input / output interface, USB interface (81) 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 (81) is arranged on the outer surface of the housing (1) to facilitate communication between the main control board (8) and external devices.

5. The food safety detection device according to claim 2, characterized in that: The detection device further comprises a condenser (9), one end of which is arranged on the periphery of the light source, and the other end of which extends into the cuvette holder (2) and is connected to one end of the cuvette holder (2), and the other end of the cuvette holder (2) is in contact with the integrating sphere (3).

6. The food safety detection device 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).

7. A food safety detection system, characterized in that: The food safety detection device according to any one of claims 1 to 6 comprises 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 and the pollutant category to the data analysis and calculation platform to obtain the pollutant content. The data analysis and calculation platform calculates and compares 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.

8. The food safety detection system according to claim 7, characterized in that: The data acquisition terminal comprises the detection device according to any one of claims 1 to 6, and the detection device is connected to the database via a cloud service platform; The database contains a variety of food spectrum image data.

9. The food safety detection system according to claim 7, characterized in that: The data analysis and computing platform includes a network model for measuring the content of pollutants in 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.

10. A food safety detection method, characterized in that: The food safety detection system according to claim 8 or 9 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 of the food to be detected; 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 type of pollutants to the data analysis and computing platform, extract the characteristics of the pollutants through the network model, and then obtain the pollutant content; 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.

Images