Multi-parameter food safety rapid detection system

Abstract

The invention relates to the technical field of food detection, and discloses a multi-parameter food safety rapid detection system, a liquid adding unit is used for sending a to-be-detected liquid sample into a detection unit, the detection unit detects the liquid sample, an image unit collects image data of a reaction result in the detection unit, and the image data of the reaction result is stored in a storage unit. The central unit controls the operation of other units, the processing unit processes image data acquired by the display unit, and the result unit receives the processed image data and calculates the concentration of a target object in a liquid sample; according to the invention, multiple detection functions are integrated in one system, one machine for multiple detection is realized, the detection efficiency is greatly improved, the system is suitable for rapid screening of complex samples, except hardware units in the liquid adding unit, the quality control unit and the detection unit, other functions are integrated in a notebook computer, field use and data management are facilitated, and the detection efficiency is improved. Therefore, accurate detection can be carried out without being carried out in a laboratory when a detection position is selected.

Description

Technical Field

[0001] This invention relates to the field of food testing technology, and more specifically to a multi-parameter rapid food safety testing system. Background Technology

[0002] Food safety is a major issue concerning the national economy and people's livelihood. With the increasing awareness of health and the increasingly frequent global food trade, the need to monitor various harmful pollutants in food has become more extensive and urgent. These pollutants mainly include pesticide residues, veterinary drug residues, biotoxins, illegal additives, pathogenic microorganisms, and heavy metal ions. Their sources are complex and diverse, which puts extremely high demands on the breadth, speed and accuracy of detection technology. Currently, food safety testing mainly relies on two major technical approaches: one is laboratory precision instrument analysis, and the other is on-site rapid screening. Laboratory methods are represented by high performance liquid chromatography-mass spectrometry, which has the advantages of high sensitivity, high accuracy and the ability to analyze multiple components simultaneously. On-site rapid screening methods are represented by immunochromatographic test strips and enzyme-linked immunosorbent assay kits, which are convenient to use and can detect results on-site. While laboratory methods offer high accuracy and strong multi-parameter capabilities, their equipment is complex and time-consuming, requiring specialized technicians in fixed locations, thus failing to meet the needs of on-site, real-time, and large-scale screening. On-site rapid testing methods, on the other hand, typically only detect one or a few indicators at a time, resulting in low throughput. Furthermore, they cannot accurately calculate the concentration of the detected target analyte during testing, thus hindering precise judgment. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a multi-parameter food safety rapid detection system to solve the technical problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a multi-parameter food safety rapid detection system, comprising a liquid addition unit, a detection unit, a quality control unit, a judgment unit, a display unit, an image unit, a central unit, a processing unit, and a result unit. The liquid addition unit is used to deliver the liquid sample to be tested into the detection unit. The detection unit performs liquid sample detection. The quality control unit detects the status of the detection unit. The judgment unit judges the detection results of the quality control unit. The display unit displays data and results. The image unit acquires image data of the reaction results within the detection unit. The central unit controls the operation of the other units. The processing unit processes the image data acquired by the display unit. The result unit receives the processed image data and calculates the concentration of the target substance in the liquid sample. The display unit is displayed via a laptop computer. Except for the liquid addition unit, quality control unit, and detection unit, all other units are integrated into the laptop computer, and all units are connected to a wireless network. The detection unit is equipped with ten detection chambers, including a detection chamber for organophosphates, carbamates, and pyrethroids for pesticide residues; a detection chamber for antibiotics and β-adrenergic agonists for veterinary drug residues; a detection chamber for pathogenic bacteria, virulence, and biotoxins; a detection chamber for additives; and a detection chamber for heavy metals.

[0005] In a preferred embodiment, the quality control unit includes a positive module and a negative module. The positive module fuses the liquid sample with all the detection reagents of the detection unit in its internal chamber. The negative module fuses the inert protein with all the detection reagents of the detection unit in its internal chamber. The image unit acquires image data information from the positive and negative modules and sends it to the processing unit. After receiving the image data information, the processing unit extracts the image data inside the chamber, performs grayscale processing on it, and calculates the average grayscale values ​​H1 and H2 of the image data inside the chambers of the positive and negative modules. H1 is the average grayscale value of the positive module, and H2 is the average grayscale value of the negative module. The processing unit sends the acquired average grayscale values ​​H1 and H2 to the judgment unit.

[0006] In a preferred embodiment, the determination unit compares the average gray value H1 and the average gray value H2 with its internal positive threshold Y1 and negative threshold Y2. When the average gray value H1 ≥ positive threshold Y1 and the average gray value H2 ≤ negative threshold Y2, the determination unit sends a detection command to the central unit. When the average gray value H1 ≥ positive threshold Y1 and the average gray value H2 > negative threshold Y2, the determination unit sends a reagent command to the central unit. When the average gray value H1 < positive threshold Y1 and the average gray value H2 ≤ negative threshold Y2, the determination unit sends a sample command to the central unit. When the average gray value H1 < positive threshold Y1 and the average gray value H2 > negative threshold Y2, the determination unit sends a total loss command to the central unit.

[0007] In a preferred embodiment, when the central unit receives a detection command, it displays the text "Detection in progress" in the display unit while simultaneously controlling the addition of reagents to the detection unit and the image acquisition unit to perform image acquisition. When the central unit receives a reagent command, it displays the text "Reagent damaged" in the display unit and controls the detection unit and image unit to cease operation. When the central unit receives a sample command, it displays the text "Sample damaged" in the display unit and controls the detection unit and image unit to cease operation. When the central unit receives a total loss command, it displays the text "Both sample and reagent damaged" in the display unit and controls the detection unit and image unit to cease operation.

[0008] In a preferred embodiment, when the display unit displays the text indicating that a test is being performed, a liquid sample is added to the liquid addition unit. The liquid addition unit is provided with ten flow channels, and the liquid addition unit delivers the liquid sample into the ten detection chambers of the detection unit through the ten flow channels. When the central unit controls the operation of the detection unit, the corresponding detection reagent flows out of the ten detection chambers of the detection unit, and the liquid sample entering the detection chamber reacts with the reagent.

[0009] In a preferred embodiment, when the image unit acquires images of the reaction occurring within the detection unit, the image unit includes an LED light source and a CCD image sensor, both of which are directly facing each detection chamber within the detection unit. After the liquid sample is added for ten minutes, the LED light source exposes the sample while the CCD image sensor captures image data from each detection chamber within the detection unit, thereby obtaining an optical image of the reaction occurring within the detection unit. Simultaneously, the image unit acquires image data from the quality control unit to obtain the optical image.

[0010] In a preferred embodiment, the image unit sends the optical images acquired from the detection unit and the quality control unit to the processing unit. The processing unit receives the optical images and processes them. The processing unit extracts the image data inside the detection chamber of the detection unit. Then, the processing unit performs grayscale processing on each base image and calculates the average grayscale value of each region, which is the average grayscale value H1 to the average grayscale value H10. The processing unit subtracts the average grayscale value H2 from the average grayscale value H1 to the average grayscale value H10 in sequence, and at this time, the net grayscale value J1 to the net grayscale value J10 are generated. The processing unit sends the calculated net grayscale value J1 to the net grayscale value J10 to the result unit.

[0011] In a preferred embodiment, the result unit uses a four-parameter logistic curve to combine the net grayscale values ​​J1 to J10 with the target concentration values ​​N1-N10 corresponding to the ten detection chambers within the detection unit. The formula for the four-parameter logistic curve is as follows: In the formula, n takes values ​​from 1 to 10, corresponding to ten detection chambers respectively; B is the slope factor of the curve; Jn is the net gray value from J1 to J10; A is the net gray value calculated when the sample does not contain the target substance; D is the net gray value when the target substance is saturated; C is the target substance concentration; C0 is the net gray value of the target substance when the saturation is 50%; and B is the slope factor.

[0012] In a preferred embodiment, the result unit calculates the target concentration using a four-parameter logistic curve, and sends the calculated target concentration to the display unit for display. The result unit has ten parameters in the four-parameter logistic curve, each corresponding to a target in one of the ten detection chambers in the detection unit.

[0013] The technical effects and advantages of this invention are as follows: This invention integrates multiple detection functions into one system, enabling multi-detection with a single device, significantly improving detection efficiency. It is suitable for rapid screening of complex samples. Except for the liquid addition unit, quality control unit, and hardware units within the detection unit, the remaining functions are integrated into a laptop computer, facilitating on-site use and data management. This allows for accurate detection without the need for a laboratory when selecting a detection location. The wireless network connection enables on-site connection of hardware and software, making it sufficiently convenient to use. This invention uses a combination of LED light source and CCD sensor to acquire optical images of the reaction inside the detection chamber. The CCD sensor has high imaging quality and is suitable for capturing weak signals, while the LED light source has uniform brightness, avoiding uneven illumination from affecting the results. It can acquire images of multiple chambers simultaneously. After acquiring the image data, the acquired images are processed to grayscale, the average grayscale value of each detection chamber is calculated, and the net grayscale value is obtained by subtracting the grayscale value of the negative control, thus eliminating background interference and improving accuracy. This invention employs a four-parameter logistic model to mathematically fit net grayscale values ​​to target analyte concentrations, achieving the conversion from optical image data to concentration values. The four-parameter model effectively describes the signal-concentration relationship in immunological or biochemical reactions, resulting in more reliable results and enabling the analysis of more precise target analyte concentration values. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall system composition of the present invention. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The multi-parameter food safety rapid detection system involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Reference Figure 1 This invention provides a multi-parameter rapid food safety detection system, including a liquid addition unit, a detection unit, a quality control unit, a judgment unit, a display unit, an image unit, a central unit, a processing unit, and a result unit. The liquid addition unit is used to deliver the liquid sample to be tested into the detection unit. The detection unit performs liquid sample detection. The quality control unit monitors the status of the detection unit. The judgment unit judges the detection results of the quality control unit. The display unit displays data and results. The image unit acquires image data of the reaction results within the detection unit. The central unit controls the operation of the other units. The processing unit processes the image data acquired by the display unit. The result unit receives the processed image data and calculates the concentration of the target substance in the liquid sample. The display unit displays the data via a laptop computer. Except for the liquid addition unit, the quality control unit, and the detection unit, all other units are integrated into the laptop computer, and all units are wirelessly connected.

[0017] In this embodiment, multiple detection functions are integrated into one system to achieve multi-detection with one machine, greatly improving detection efficiency. It is suitable for rapid screening of complex samples. Except for the liquid addition unit, quality control unit, and hardware units within the detection unit, the remaining functions are integrated into a laptop computer, which is convenient for on-site use and data management. This allows the present application to perform accurate detection without being in a laboratory when selecting a detection location. It is connected via wireless network, so the hardware and software can be connected on-site, making it convenient to use.

[0018] Reference Figure 1 The detection unit is equipped with ten detection chambers, including a detection chamber for organophosphates, carbamates, and pyrethroids for pesticide residues; a detection chamber for antibiotics and β-receptor agonists for veterinary drug residues; a detection chamber for pathogenic bacteria, virulence, and biotoxins; an additive detection chamber; and a heavy metal detection chamber.

[0019] In this embodiment, the detection unit includes ten detection chambers, covering multiple food safety indicators such as pesticide residues, veterinary drug residues, pathogens, toxins, additives, and heavy metals. Therefore, when conducting detection, this application can simultaneously analyze multiple components, taking into account the advantages of laboratory testing, and also has the advantages of being mobile and capable of on-site testing.

[0020] Reference Figure 1 The quality control unit includes a positive module and a negative module. The positive module fuses the liquid sample with all the detection reagents of the detection unit in its internal chamber. The negative module fuses the inert protein with all the detection reagents of the detection unit in its internal chamber. The image unit acquires image data from the positive and negative modules and sends it to the processing unit. The processing unit receives the image data, extracts the image data from the chambers, performs grayscale processing, and calculates the average grayscale values ​​H1 and H2 of the image data from the positive and negative modules. H1 is the average grayscale value of the positive module, and H2 is the average grayscale value of the negative module. The processing unit sends the acquired average grayscale values ​​H1 and H2 to the judgment form. The determination unit compares the average gray values ​​H1 and H2 with its internal positive threshold Y1 and negative threshold Y2. When the average gray value H1 ≥ positive threshold Y1 and the average gray value H2 ≤ negative threshold Y2, the determination unit sends a detection command to the central unit. When the average gray value H1 ≥ positive threshold Y1 and the average gray value H2 > negative threshold Y2, the determination unit sends a reagent command to the central unit. When the average gray value H1 < positive threshold Y1 and the average gray value H2 ≤ negative threshold Y2, the determination unit sends a sample command to the central unit. When the average gray value H1 < positive threshold Y1 and the average gray value H2 > negative threshold Y2, the determination unit sends a total loss command to the central unit.

[0021] In this embodiment, the positive and negative modules simulate ideal and non-reactive states, respectively. After simulating the states, the average gray values ​​H1 and H2 under the two states are calculated. These are then compared with the positive threshold Y1 and the negative threshold Y2. When the average gray value H1 ≥ the positive threshold Y1, it indicates that the sample being tested is in a normal state. If the sample is abnormal, the positive threshold Y1 will be abnormal, resulting in the average gray value H1 < the positive threshold Y1. When the average gray value H2 ≤ the negative threshold Y2, the reagent being tested is in a normal state. If the reagent is abnormal, the average gray value H2 will be greater than the negative threshold Y2. Therefore, this application performs the test only when both the sample and the reagent are in a normal state to avoid problems with the test results.

[0022] Reference Figure 1When the central unit receives a detection command, it displays the text "Detection in Progress" on the display unit while simultaneously controlling the addition of reagents to the detection unit and the image acquisition unit to perform image acquisition. When the central unit receives a reagent command, it displays the text "Reagent Damaged" on the display unit and controls the detection unit and image unit to cease operation. When the central unit receives a sample command, it displays the text "Sample Damaged" on the display unit and controls the detection unit and image unit to cease operation. When the central unit receives a total loss command, it displays the text "Both Sample and Reagent Damaged" on the display unit and controls the detection unit and image unit to cease operation. When the display unit displays the text "Detection in Progress," a liquid sample is added to the liquid addition unit. The liquid addition unit has ten flow channels, and the liquid addition unit delivers the liquid sample into the ten detection chambers of the detection unit through these ten flow channels. When the central unit controls the detection unit to operate, the corresponding detection reagents flow out of the ten detection chambers of the detection unit, and the liquid sample and reagents entering the detection chambers react with each other.

[0023] In this embodiment, the display unit will show where the problem occurs, so that it can be dealt with in a timely manner and the detection speed can be improved. During the detection, the liquid sample is sent into the ten detection chambers of the detection unit through ten flow channels. Ten types of indicators can be detected simultaneously with one sample addition, which greatly improves the detection efficiency.

[0024] Reference Figure 1 When the image unit acquires images of the reaction occurring within the detection unit, the image unit includes an LED light source and a CCD image sensor, both of which are directly facing each detection chamber within the detection unit. After ten minutes of liquid sample addition, the LED light source exposes the sample while the CCD image sensor captures image data from each detection chamber, thus obtaining an optical image of the reaction occurring within the detection unit. Simultaneously, the image unit acquires image data from the quality control unit to obtain optical images. The image unit will then collect image data from both the detection unit and the quality control unit. The acquired optical image is sent to the processing unit. The processing unit receives the optical image and processes it. The processing unit extracts the image data inside the detection chamber of the detection unit. Then, the processing unit performs grayscale processing on each basic image and calculates the average grayscale value of each region, which is the average grayscale value H1 to the average grayscale value H10. The processing unit subtracts the average grayscale value H2 from the average grayscale value H1 to the average grayscale value H10 in sequence. At this time, the net grayscale value J1 to the net grayscale value J10 are generated. The processing unit sends the calculated net grayscale value J1 to the net grayscale value J10 to the result unit.

[0025] In this embodiment, a combination of LED light source and CCD sensor is used to acquire optical images of the reaction in the detection chamber. The CCD sensor has high imaging quality and is suitable for capturing weak signals. The LED light source has uniform brightness, avoiding uneven illumination from affecting the results. It can acquire images of multiple chambers simultaneously. After acquiring the image data, the acquired images are processed to grayscale, the average grayscale value of each detection chamber is calculated, and the net grayscale value is obtained by subtracting the grayscale value of the negative control, thus eliminating background interference and improving subsequent accuracy.

[0026] Reference Figure 1 The result unit uses a four-parameter logistic curve to combine the net grayscale values ​​J1 to J10 with the target concentration values ​​N1-N10 corresponding to the ten detection chambers within the detection unit. The formula for the four-parameter logistic curve is as follows: In the formula, n takes values ​​from 1 to 10, corresponding to ten detection chambers respectively; B is the slope factor of the curve; Jn is the net gray value from J1 to J10; A is the net gray value calculated when the sample does not contain the target substance; D is the net gray value when the target substance is saturated; C is the target substance concentration to be determined; C0 is the net gray value of the target substance when the saturation is 50%; and B is the slope factor. The result unit calculates the target substance concentration through a four-parameter logistic curve, and the result unit sends the extreme target value concentration to the display unit for display. Each parameter in the four-parameter logistic curve in the result unit has ten values, corresponding to the target substances in the ten detection chambers of the detection unit.

[0027] In this embodiment, a four-parameter logistic model is used to mathematically fit the net grayscale value with the target concentration, thereby realizing the conversion from optical image data to concentration value. The four-parameter model can well describe the signal-concentration relationship in immune or biochemical reactions, and is especially suitable for nonlinear response ranges. Compared with simple linear fitting, it can better reflect the actual reaction kinetics and the results are more reliable. In this way, more accurate target concentration values ​​can be analyzed. Then, the values ​​can be displayed through the display unit, and the detection results are more direct.

[0028] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. The units and algorithm steps of the various examples described in the embodiments can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0029] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0030] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0031] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-parameter rapid food safety detection system, characterized in that: The system includes a liquid addition unit, a detection unit, a quality control unit, a judgment unit, a display unit, an image unit, a central unit, a processing unit, and a result unit. The liquid addition unit is used to deliver the liquid sample to be tested into the detection unit. The detection unit performs liquid sample testing. The quality control unit monitors the status of the detection unit. The judgment unit judges the detection results of the quality control unit. The display unit displays data and results. The image unit acquires image data of the reaction results within the detection unit. The central unit controls the operation of the other units. The processing unit processes the image data acquired by the display unit. The result unit receives the processed image data and calculates the concentration of the target substance in the liquid sample. The display unit is displayed via a laptop computer. Except for the liquid addition unit, quality control unit, and detection unit, all other units are integrated into the laptop computer, and all units are connected to a wireless network. The detection unit is equipped with ten detection chambers, including a detection chamber for organophosphates, carbamates, and pyrethroids for pesticide residues; a detection chamber for antibiotics and β-adrenergic agonists for veterinary drug residues; a detection chamber for pathogenic bacteria, virulence, and biotoxins; a detection chamber for additives; and a detection chamber for heavy metals.

2. The multi-parameter rapid food safety detection system according to claim 1, characterized in that: The quality control unit includes a positive module and a negative module. The positive module fuses the liquid sample with all the detection reagents of the detection unit in its internal chamber. The negative module fuses the inert protein with all the detection reagents of the detection unit in its internal chamber. The image unit acquires image data information from the positive and negative modules and sends it to the processing unit. After receiving the image data information, the processing unit extracts the image data inside the chamber, performs grayscale processing on it, and calculates the average grayscale values ​​H1 and H2 of the image data inside the chambers of the positive and negative modules. H1 is the average grayscale value of the positive module, and H2 is the average grayscale value of the negative module. The processing unit sends the acquired average grayscale values ​​H1 and H2 to the judgment unit.

3. The multi-parameter rapid food safety detection system according to claim 2, characterized in that: The determination unit compares the average gray values ​​H1 and H2 with its internal positive threshold Y1 and negative threshold Y2. When the average gray value H1 ≥ positive threshold Y1 and the average gray value H2 ≤ negative threshold Y2, the determination unit sends a detection command to the central unit. When the average gray value H1 ≥ positive threshold Y1 and the average gray value H2 > negative threshold Y2, the determination unit sends a reagent command to the central unit. When the average gray value H1 < positive threshold Y1 and the average gray value H2 ≤ negative threshold Y2, the determination unit sends a sample command to the central unit. When the average gray value H1 < positive threshold Y1 and the average gray value H2 > negative threshold Y2, the determination unit sends a total loss command to the central unit.

4. The multi-parameter rapid food safety detection system according to claim 1, characterized in that: When the central unit receives a detection command, it displays the text "Detection in progress" in the display unit while simultaneously controlling the addition of reagents in the detection unit and the image acquisition unit to perform image acquisition. When the central unit receives a reagent command, it displays the text "Reagent damaged" in the display unit and controls the detection unit and image unit to cease operation. When the central unit receives a sample command, it displays the text "Sample damaged" in the display unit and controls the detection unit and image unit to cease operation. When the central unit receives a total loss command, it displays the text "Both sample and reagent damaged" in the display unit and controls the detection unit and image unit to cease operation.

5. The multi-parameter rapid food safety detection system according to claim 4, characterized in that: When the display unit displays the text indicating that a test is being performed, a liquid sample is added to the liquid addition unit. The liquid addition unit has ten flow channels, and the liquid addition unit delivers the liquid sample into the ten detection chambers of the detection unit through the ten flow channels. When the central unit controls the operation of the detection unit, the corresponding detection reagents flow out of the ten detection chambers of the detection unit, and the liquid sample entering the detection chamber reacts with the reagents.

6. The multi-parameter rapid food safety detection system according to claim 1, characterized in that: When the image unit acquires images of the reaction within the detection unit, the image unit includes an LED light source and a CCD image sensor, both of which are directly facing each detection chamber within the detection unit. After the liquid sample is added for ten minutes, the LED light source exposes the sample while the CCD image sensor captures image data from each detection chamber within the detection unit, thereby obtaining an optical image of the reaction within the detection unit. Simultaneously, the image unit acquires image data from the quality control unit to obtain an optical image.

7. The multi-parameter rapid food safety detection system according to claim 6, characterized in that: The image unit sends the optical images acquired from the detection unit and the quality control unit to the processing unit. The processing unit receives the optical images and processes them. The processing unit extracts the image data inside the detection chamber of the detection unit. Then, the processing unit performs grayscale processing on each basic image and calculates the average grayscale value of each region, which is the average grayscale value H1 to the average grayscale value H10. The processing unit subtracts the average grayscale value H2 from the average grayscale value H1 to the average grayscale value H10 in sequence, and at this time, the net grayscale value J1 to the net grayscale value J10 are generated. The processing unit sends the calculated net grayscale value J1 to the net grayscale value J10 to the result unit.

8. The multi-parameter rapid food safety detection system according to claim 1, characterized in that: The result unit uses a four-parameter logistic curve to combine the net grayscale values ​​J1 to J10 with the target concentration values ​​N1-N10 corresponding to the ten detection chambers within the detection unit. The formula for the four-parameter logistic curve is: In the formula, n takes values ​​from 1 to 10, corresponding to ten detection chambers respectively; B is the slope factor of the curve; Jn is the net gray value from J1 to J10; A is the net gray value calculated when the sample does not contain the target substance; D is the net gray value when the target substance is saturated; C is the target substance concentration; C0 is the net gray value of the target substance when the saturation is 50%; and B is the slope factor.

9. A multi-parameter rapid food safety detection system according to claim 8, characterized in that: The result unit calculates the target concentration using a four-parameter logistic curve, and sends the calculated target concentration value to the display unit for display. The result unit has ten parameters in the four-parameter logistic curve, each corresponding to a target in one of the ten detection chambers in the detection unit.

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