CN113029990A - Food safety detection system and method with sensor network - Google Patents

Abstract

The embodiment of the application discloses food safety inspection system and method with sensor network, the system includes that piezoelectric sensor, biosensor, spectrophotometer, sampling control module, preprocessing circuit, controller, signal recording device and PC constitute, piezoelectric sensor with biosensor constitutes sensor network, piezoelectric sensor, biosensor and spectrophotometer all are connected with sampling control module, biosensor still with signal recording device connects, and sampling control module and preprocessing circuit are connected, and preprocessing circuit is connected with the controller, and controller and PC are connected mutually, and preprocessing circuit and PC are connected mutually.

Description

Food safety detection system and method with sensor network

Technical Field

The specification relates to the technical field of food safety, in particular to a food safety detection system and method with a sensor network.

Background

In the technical field of food safety, photoelectric colorimetric analysis is a commonly used technology, however, the existing colorimetric system is often accompanied with the existence of measurement errors, a test element often only comprises an emitting plate, a detection chamber and a photoelectric sensor, and no specific design for reducing errors is provided, only simple correction of an A/D signal exists, the wavelength of an LED light path is fixed in a single instrument, few single instruments contain devices with multiple LED light wavelengths, in addition, in the aspect of structural design, a light hole c which is communicated is arranged on one side of a colorimetric groove a and used for inserting an LED lamp tightly, and a detection hole b for placing the photoelectric sensor is arranged on the other side of the colorimetric groove a. The light emitted by the LED light source on the lamp hole c is received by the photoelectric sensor after passing through the liquid sample in the colorimetric cylinder a, and the photoelectric sensor converts the light energy into corresponding electric energy to be transmitted to a specific chip. In addition, the lampshade in the prior art is only arranged on the upper part of the LED lamp, and is also influenced by the surrounding environment during collection. This simple structure, the low price, but the accuracy of test result is very poor: each colorimetric groove a only has one lamp hole c and only can be plugged with an LED lamp with a certain specific wavelength, so that each colorimetric groove a is limited to only measure a sample to be measured corresponding to the wavelength of the LED lamp, the detection efficiency is low, and the detection of a plurality of samples cannot be simultaneously carried out. And the central shaft and the diameter are unreasonable, so that the light emitted by the LED lamp is relatively dispersed, the detection precision is not high, and no corresponding judgment system which can automatically calibrate and correspond to different safety levels according to different detection values exists in the prior art.

Disclosure of Invention

One of the embodiments of this specification provides a food safety inspection system with sensor network, the system includes that piezoelectric sensor, biosensor, spectrophotometer, sampling control module, preprocessing circuit, controller, signal recording device and PC constitute, piezoelectric sensor with biosensor constitutes sensor network, piezoelectric sensor, biosensor and spectrophotometer all are connected with sampling control module, biosensor still with signal recording device connects, and sampling control module and preprocessing circuit are connected, and preprocessing circuit is connected with the controller, and controller and PC are connected, and preprocessing circuit and PC are connected.

One of the embodiments of the present specification provides a food safety detection method with a sensor network, which is performed by the food safety detection system with a sensor network, and the method includes: extracting a food sample, preparing a plurality of same sample solutions, and respectively placing the same sample solutions in different cuvettes; performing pathogen detection on the food sample based on the sensor network; performing antibiotic residue detection on the food sample based on the sensor network; performing a detection of a toxin on the food sample based on the sensor network; and detecting pesticide residues of the food sample based on the sensor network.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a block diagram of a food safety detection system with a sensor network according to some embodiments described herein;

FIG. 2 is a schematic structural diagram of a biosensor according to some embodiments of the present description;

fig. 3 is an exemplary flow diagram of a method for food safety detection with a sensor network, according to some embodiments described herein.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is a block diagram of a food safety detection system having a sensor network according to some embodiments of the present disclosure, and fig. 2 is a schematic structural diagram of a biosensor according to some embodiments of the present disclosure.

As shown in fig. 1 and 2, a food safety detection method mainly comprises a piezoelectric sensor 1, a biosensor 2, a spectrophotometer 3, a sampling control module 4, a preprocessing circuit 5, a controller 6 and a PC 7, and is characterized in that: piezoelectric sensor 1, biosensor 2 and spectrophotometer 3 all are connected with sampling control module 4, and sampling control module 4 is connected with preprocessing circuit 5, and preprocessing circuit 5 is connected with controller 6, and controller 6 is connected with PC 7, and preprocessing circuit 5 is connected with PC 7.

After the piezoelectric sensor 1 outputs a signal, the signal is immediately sent to an oscillating circuit, the oscillating circuit consists of a standard crystal oscillator and 74AHC04, a counter circuit needs to use a direct frequency measurement method to measure the frequency of the signal, and a timer adopts a 555 timing circuit.

The preprocessing circuit 5 adopts an STC89C51RC singlechip, is provided with an external reset circuit, has a power supply voltage stabilization block front end power-off detection function, needs to pay attention to the fact that before the STC89C51RC is powered on and reset, a control system of a PC (personal computer) needs to send a downloading command stream, and when the system starts to run a system programmable bootstrap program, the system is made to judge whether the PC sends a legal downloading command stream, and if the system does not send the legal downloading command stream, the user program needs to be executed immediately. When the watchdog is reset or the external manual reset operation is performed, the singlechip does not need to run a system programmable bootstrap program.

When the device works, the piezoelectric sensor, the biosensor and the spectrophotometer collect signals such as formaldehyde, heavy metal lead, pesticide and the like in videos, and after the signals are converted by the multi-sensor system, a large amount of various data related to food quality safety can be generated, and the data can enter the data acquisition system. Because the sensor is only responsible for the conversion of signals, the output signals are very weak, and therefore the signals need to be amplified through an amplifying circuit, the original signals are amplified through an AM401, the signals are filtered and purified through an MAX274, the analog signals are converted into digital signals through an analog-to-digital conversion circuit, the EMP240 is used as a processor for data processing, and the EMP is provided with a reset circuit, a crystal oscillator circuit and an independent power supply system and finally transmits information to a controller and a PC.

The biosensor is an analysis device that closely binds a biological recognition element and a signal conversion element to detect a target compound. The biological recognition element in the biosensor comprises enzyme, antibody (antigen), microorganism, cell, animal and plant tissue, gene, etc., and the signal conversion element in the biosensor comprises electrochemical electrode, semiconductor, optical element (such as optical fiber, surface plasma resonance), thermosensitive element, piezoelectric device (such as quartz crystal microbalance, surface acoustic wave), etc. Different biometric elements and signal conversion elements constitute different biosensors, and their nomenclature follows from this. The basic principle is that the substance to be detected is specifically combined with the molecular recognition element to generate biochemical reaction, the generated biological information is converted into electric and optical signals which can be quantitatively processed through a signal converter, and the electric and optical signals are amplified and output through an instrument, so that the purpose of analysis and detection is achieved.

Fig. 3 is an exemplary flow diagram of a method for food safety detection with a sensor network, according to some embodiments described herein.

The process 300 may include the following steps: step 310, extracting a food sample, preparing a plurality of identical sample solutions, and placing the same sample solutions in different cuvettes respectively.

The detection of pathogenic bacteria in food is always carried out by using a traditional plate counting method, and the method is complicated and time-consuming. The advent of biosensors has revolutionized bacteriology and has made possible the automated online detection of pathogenic bacteria in the production and packaging processes of the food industry. In the united states, 6700 million food-borne diseases are associated with bacterial contamination in meat, poultry, eggs each year, and result in 4500 deaths. Food-borne diseases in the united states are estimated to be lost annually to $ 140 billion. The optical immunosensor is used for realizing the rapid detection of the salmonella typhimurium. The method uses magnetic microbeads coated with anti-salmonella, separates out salmonella in a solution to be detected through the combination of antibody and antigen, and then adds a secondary antibody marked by alkaline phosphatase to form a sandwich structure of antibody-salmonella-enzyme-labeled antibody. After magnetic separation, the substrate p-nitrophosphate was enzymatically hydrolyzed to produce p-nitrophenol, and the total number of Salmonella was measured by measuring the absorbance of p-nitrophenol at 404 nm. The research shows that the linear relation exists under 2.2X 104 to 2.2X 106 CFU/m L, and the whole detection process can be completed within 2 h.

Coli in vegetables was detected by detecting the change in pH caused by N H3 due to the urease action of e.coli. They washed lettuce, carrot slices, lettuce and the like with peptone water solution, separated bacterial cells and then activated in liquid culture medium. Compared with the traditional colony-unit Counting (CFU) method, the method is sensitive and rapid, can detect the concentration of 10 cells/mL within 1.5 h, and takes 10-20 times of CFU time. A metal-clad leaky waveguide sensor device (LWD) was set up to detect Bacillus subtilis var niger with a detection limit of 104 spores/mL. Two different microorganisms are detected simultaneously with a two-channel Surface Acoustic Wave (SAW) biosensor: legionella and Escherichia coli. Unlike conventional methods, they first coat bacteria on the surface of SAW and then bind specific antibodies. The detection limit achieved by this method is better than that achieved by the traditional method (antibody immobilization first). Love wave (Love wave) is a type of surface acoustic wave, which is a surface shear transverse wave propagating in a thin layer acoustic waveguide deposited on the surface of a piezoelectric substrate. The application of a love acoustic wave device in virus and bacteria detection in drinking water or liquid food is researched, and the result shows that the sensor is an effective immunoassay method and can be widely applied to the food industry. The electrochemical biosensor capable of rapidly measuring the content of bacteria in the dairy products is developed, and experimental results show that the biosensor can effectively measure the content of microorganisms in fresh milk. An electrochemical biosensor is developed for responding to colibacillus, beer yeast, vibrio cholerae and bacillus calmette guerin vaccine, and the total number of beer yeast in a fermentation tank and the total number of bacteria in sterilized fresh milk are actually measured. In short, the application of biosensors in this field is mature, and some commercialized instruments are available, but the detection limit is still high, and false positive and false negative results are easy to occur in actual detection.

And 320, detecting pathogenic bacteria of the food sample based on the sensor network. Antibiotics are chemical substances produced by certain microorganisms during their metabolism that inhibit or kill other pathogenic microorganisms. If the medicine is used or abused in a large amount, the metabolite of the medicine can be excessively accumulated and stored in cells, tissues and organs of animals, and people can cause lesions in vivo by eating or contacting the medicine. The detection methods commonly used include physical and chemical detection methods such as microbiological methods and immunological methods. Beta-lactam antibiotics (including penicillin) are commonly used to treat cow mastitis, and are therefore the most common antibiotic residues in milk. In 2002, Gustav sson et al designed experiments to detect beta-lactam antibiotics in milk using biosensor-based Surface Plasmon Resonance (SPR). They use as probe molecules microbial receptor proteins with carboxypeptidase activity, which have the advantage over antibodies of being able to distinguish only active, intact beta-lactam structures. In the presence of beta-lactam antibiotics, stable complexes are formed between the receptor protein and the antibiotics, the activity of protease is inhibited, and penicillin G in milk can be qualitatively detected through the reduction value of the enzyme activity. The detection limit of this method is 2.6. mu.g/kg, which is below the Maximum Residual Limit (MRL) of 4. mu.g/kg in Europe.

Penicillin and cephalosporins in milk were detected by SPR optical biosensor. The basic principle is that a derivative of penicillin binding protein 2x (PBP 2 x) is added to the sample and the β -lactam in the positive sample is bound to PBP2x by a covalent bond. After incubation, digoxigenin-labeled ampicillin (DIG-AMPI) and unbound PBP2x were added to form a DIGAMPI/PBP2x complex. If no β -lactam is present in the sample, all PBP2x molecules bind to DIG-AM PI and the complex formed is captured by the digoxigenin antibody immobilized on the sensor surface. If the sample contains β -lactam, relatively less DIG-AMPI/PBP 2x complex will be formed, binding to the immobilized antibody. The negative samples produced a greater signal than the positive samples because the mass density on the sensor chip varied with the difference in molecular weight between the DIG-AMPI/PBP 2x complexes and DIG-AM PI.

Step 330, performing antibiotic residue detection on the food sample based on the sensor network. Nicarbazin (nicarbazin) is often used as a feed additive to prevent coccidiosis in broiler chickens. It is an equimolecular complex of 4, 4' -dinitro-symmetric Dibenzoylnithine (DNC) and 2-hydroxy-4, 6-Dimethyl (DHP). In 1998, the FAO/W HO Joint food additives expert Committee (JEC FA) stipulated that the MRL value of DN C in broiler chickens was 200. mu.g/kg. McCarney detects DNC with a BiacoreR SPR biosensor with detection limits of 17 ng/g and 19 ng/g in chicken and eggs. The analysis result of the immunosensor has good correlation (R2 = 0.88) compared with LC-MS, and can be used for qualitative and quantitative detection. SPR biosensors have also been used to detect chloramphenicol and the metabolite chloramphenicol-glucuronide in honey, shrimp and pig kidneys at detection limits below 0.1. mu.g/kg.

Step 340, detecting the toxin of the food sample based on the sensor network.

Biotoxins are bacterial metabolites and are a wide variety of species. The food may be contaminated in the links of prenatal, transportation, processing and sale, and has high toxicity and teratogenic and carcinogenic effects. The determination of biotoxins in food, one of the most promising areas for the breakthrough application of biosensors, has been reported.

Detecting Staphylococcal Enterotoxin A (SEA) in food in real time with an evanescent wave biosensor. This "sandwich" structure of biosensors utilizes two antibodies, a "primary antibody" and a "secondary antibody". The toxin is first bound to a "primary antibody" covalently immobilized on the biosensor detector, and then a "secondary antibody" is added to bind to the captured toxin. They were able to detect SEA in food with little or no background interference. The results demonstrate that this biosensor is not only feasible for pure SEA, but also effective for complex food substrates such as hot dogs, potato salads, milk and mushrooms. The analysis sensitivity is 10-100 ng/g according to different experimental materials. Staphylococcal enterotoxin b (seb) is a major cause of frequent food poisoning in humans. It can contaminate various foods. Nedelkov et al used biomolecular interaction mass spectrometry (BIA-MS) to detect SEB in food samples. The method utilizes SPR to detect the combination of toxin and an antibody on the surface of a sensor chip, and then matrix-assisted laser desorption/ionization time-of-flight mass spectrometry is used for identifying the bound toxin, so that SEB with the concentration of 1 ng/mL in milk and mushroom samples can be easily detected. They analyzed the presence of SEB and toxic shock syndrome toxin (toxin-shock syndrome), demonstrating the suitability of BIA-MS in the analysis of the presence of multiple residual components. In 2002, Homo la et al established wavelength modulation based SPR biosensors to detect SEB in milk. They compared two detection modes, direct and "sandwich" detection. The results show that the lowest detection limit in the direct detection mode is 5 ng/mL in buffer, whereas the detection limit in the "sandwich" detection mode is 0.5 ng/mL in buffer and milk.

Fumonisins (fumonisins) are a class of mycotoxins produced primarily by Fusarium moniliforme and are associated with human and animal diseases such as malachite leukomalacia, porcine pulmonary edema syndrome, and human esophageal cancer. Wherein the fumonisin B1 (FB 1) is the main fumonisin component of a natural polluted corn sample and feed. Mullet et al used a Surface Plasmon Resonance (SPR) immunosensor to detect FB1 concentrations in corn extracts, and a polyclonal antibody against FB1 was adsorbed onto the gold film of a glass prism, and the light beam emitted by the diode was focused through the prism onto the surface of the gold film to excite SPR. When the sample was added, the reflected light was sensitively changed in an angle proportional to the concentration of FB1, with a lower detection limit of 50 ng/mL and an analysis time of 10 min. Aflatoxins (aflatoxins) are the strongest group of biotoxins found to contaminate agricultural products to date, and are also strong carcinogens. The aflatoxin can occur in any links of crop growth, harvesting, processing and storage, so that agricultural products such as peanuts, corns, rice and the like are easily polluted, and directly enter a food chain from the agricultural products, and the chain pollution of foods is caused. An immunofluorescent biosensor was developed to detect aflatoxins in agricultural products. The sensor can continuously measure 100 times and can detect the concentration of 0.1-50 ppb within 2 min by using the volume of 1 m L.

And 350, detecting pesticide residues of the food sample based on the sensor network. In recent years, scholars at home and abroad make some beneficial researches on the application of biosensors in the field of pesticide residue detection. 274 enzyme sensors most commonly used in pesticide residue detection in the journal of agro-engineering 2007. The mechanism of pesticide residue detection by different enzyme sensors is different, and the signal generated by enzyme reaction is detected by utilizing the specific inhibition effect of the residue on the enzyme activity (such as acetylcholinesterase), so as to indirectly determine the content of the residue. However, some of them utilize the hydrolyzing ability of an enzyme (e.g., organophosphorus hydrolase) to a target substance.

Biosensors based on the immunological principle also have many applications in the field of pesticide residues. Priby l et al immobilized atrazine (a tra zine) monoclonal antibody on the surface of gold electrode on piezoelectric crystal by protein a method, and the atrazine in the sample adsorbed the load mass on the quartz crystal, thereby changing the oscillation frequency of the crystal to determine the concentration of the test substance, and they tested another determination method, in which atrazine was immobilized on the surface of piezoelectric crystal by self-assembly, and atrazine was determined by indirect method, with the limit of detection being 0.025 ng/mL. In 2003, Co rry et al used adsorptive and covalent immobilization of atrazine monoclonal antibodies on gold deposited quartz crystal (GQC) electrodes and indium doped tin oxide (ITO) electrodes, and the binding of atrazine and its antibodies was delineated by Electrochemical Impedance Spectroscopy (EIS) and Quartz Crystal Microbalance (QCM). The electrochemical immunosensor can be used for detecting pesticides with ppm level.

A flow injection calorimetric biosensor based on immobilized chicken liver enzyme is developed for detecting the dichlorvos residue. Such sensors include peristaltic pumps, injection valves, thermo-electric regulators, enzyme reaction chambers, reference chambers, thermocouples and computers. They used chicken liver enzyme as a biological recognition element instead of acetylcholinesterase, and the reaction temperature was fixed at 40 ℃. When the substrate is injected into the system through the injection valve, a catalytic reaction takes place in the enzymatic reaction chamber and heat generated by the non-enzymatic reaction is removed through the reference chamber. The temperature change of the two reaction chambers was measured with the sensors of the thermocouples. In the case of 1 mg/L and 10 mg/L of DDVP, the inhibition rates of the enzyme reactions were 30.7% and 41.8%, respectively. Experiments prove that the calorimetric biosensor can be used for rapid detection of pesticides. However, in the current practical application, due to the problems of detection limit, sensitivity, repeatability and the like, the biosensor has many limitations in the practical application of pesticide residue detection, and is mostly only used as a method and a means for rapidly screening a large amount of samples. Therefore, the potential of the application of biosensors in this field is still to be further explored.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (8)

1. The utility model provides a food safety inspection system with sensor network, its characterized in that, the system includes that piezoelectric sensor, biosensor, spectrophotometer, sampling control module, preprocessing circuit, controller, signal recording device and PC constitute, piezoelectric sensor with biosensor constitutes sensor network, piezoelectric sensor, biosensor and spectrophotometer all are connected with sampling control module, biosensor still with signal recording device connects, and sampling control module and preprocessing circuit are connected, and preprocessing circuit is connected with the controller, and controller and PC are connected mutually, and preprocessing circuit and PC are connected mutually.

2. The food safety detection system with a sensor network of claim 1, wherein the biosensor comprises an identification element and a conversion element, wherein the identification element comprises one or more of an enzyme, an antibody, a microorganism, a cell, animal and plant tissue, a gene, and the conversion element comprises one or more of an electrochemical electrode, a semiconductor, an optical element, a thermosensitive element, a piezoelectric device.

3. The food safety detection system with sensor network of claim 1, wherein the piezoelectric sensor is configured to: immediately after the signal is output, the signal is sent to an oscillating circuit, wherein the oscillating circuit consists of a standard crystal oscillator and 74AHC04, a counter circuit needs to use a direct frequency measurement method to measure the frequency of the signal, and a timer adopts a 555 timing circuit.

4. The food safety detection system with the sensor network according to claim 1, wherein the preprocessing circuit adopts an STC89C51RC single chip microcomputer, is provided with an external reset circuit, and has a power supply voltage stabilizing block front end power-off detection function, and the preprocessing circuit is used for: before the STC89C51RC is powered on and reset, the control system of the PC must first send a download command stream, and when the system starts to run the system programmable bootstrap program, the system first determines whether the PC sends a legal download command stream, if not, the user program needs to be executed immediately, and when the watchdog reset or the external manual reset operation is performed, the single chip microcomputer does not run the system programmable bootstrap program.

5. A food safety detection method with a sensor network, characterized in that the method is executed by the food safety detection system with a sensor network of any one of claims 1-4, and the method comprises:

extracting a food sample, preparing a plurality of same sample solutions, and respectively placing the same sample solutions in different cuvettes;

performing pathogen detection on the food sample based on the sensor network;

performing antibiotic residue detection on the food sample based on the sensor network;

performing a detection of a toxin on the food sample based on the sensor network;

and detecting pesticide residues of the food sample based on the sensor network.

6. The method for detecting the safety of the food with the sensor network as claimed in claim 5, wherein the step of detecting the pathogenic bacteria of the food sample based on the sensor network comprises the following steps:

separating out salmonella in a solution to be detected by combining antibody and antigen by using magnetic microbeads coated with anti-salmonella, adding a secondary antibody marked by alkaline phosphatase to form a three-layer structure with an antibody, salmonella and an enzyme-labeled antibody, generating p-nitrophenol by substrate p-nitrophenol under the hydrolysis action of enzyme after magnetic separation, and measuring the total number of the salmonella by measuring the absorbance of the p-nitrophenol at 404 nm.

7. The method for detecting food safety with sensor network according to claim 5, wherein the detecting antibiotic residue on the food sample based on the sensor network comprises:

a derivative of penicillin binding protein 2x was added to the sample.

8. The method for detecting food safety with sensor network according to claim 5, wherein the detecting the toxin based on the sensor network for the food sample comprises:

the food sample was continuously measured 100 times based on the sensor network and the concentration of 0.1 to 50 ppb was detected with a volume of 1 m L within 2 min.

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