Fresh meat quality detection system based on flexible multimode sensor

Abstract

The invention belongs to the technical field of fresh meat quality detection, and provides a fresh meat quality detection system based on a flexible multi-mode sensor, which comprises a flexible multi-mode sensor, a fungus concentration sensor and a rear-end subsystem, the flexible multimode sensor comprises an impedance sensor and an ammonia gas sensor. Through the fungus concentration sensor and the flexible multi-mode sensor integrated with the impedance sensor and the ammonia gas sensor, ammonia gas, impedance and bacterium concentration can be detected in real time, and reliable information of food deterioration can be effectively sensed.

Description

Technical Field

[0001] The present invention relates to the technical field of fresh meat quality detection, and in particular to a fresh meat quality detection system based on a flexible multi-mode sensor. Background Art

[0002] Graphene is abbreviated as LIG. In the detection of the freshness of cold fresh meat, gas sensors can be used to detect ammonia produced during the meat spoilage process. Researchers verified the reliability of the developed sensor by correlating the freshness of meat with the concentration of ammonia released during the meat spoilage process. In addition, in the agricultural field, impedance detection technology is an effective method for evaluating food quality. Laser-induced LIG is widely used in the field of food detection due to its fast responsiveness and long-term stability. It can be used for impedance characteristic detection of cold fresh meat through effective pattern design.

[0003] There are few existing methods to effectively combine graphene materials with MXene materials to build flexible ammonia sensors. In addition, dripping, as a simple process, has not received much attention, which is particularly important for large-scale, low-cost applications. In addition, no researchers have yet effectively combined ammonia with impedance measurements. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the object of the present invention is to provide a fresh meat quality detection system based on a flexible multi-mode sensor, reduce the cost of detection equipment and realize the combination of ammonia and impedance.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A fresh meat quality detection system based on a flexible multi-mode sensor, comprising: a flexible multi-mode sensor, a fungus concentration sensor and a back-end subsystem; the flexible multi-mode sensor comprises: an impedance sensor and an ammonia sensor;

[0007] The flexible multi-mode sensor and the fungus concentration sensor are respectively connected to the rear subsystem; the ammonia sensor is located on the upper side of the impedance sensor;

[0008] The impedance sensor is used to detect the impedance of the target fresh meat surface; the ammonia sensor is used to detect the ammonia concentration of the target fresh meat surface; the fungus concentration sensor is used to detect the bacteria concentration of the target fresh meat surface; the back-end subsystem is used to predict the shelf life based on the collected information of the impedance sensor, the ammonia sensor and the fungus concentration sensor to obtain a shelf life prediction value.

[0009] Preferably, the preparation process of the flexible multi-mode sensor includes:

[0010] A laser engraving machine is used to engrave two symmetrical interdigitated graphene electrode patterns on a pre-prepared kapton tape, and the engraved kapton tape is folded in half to obtain the flexible multi-mode sensor; the upper layer of the flexible multi-mode sensor is the ammonia sensor; and the lower layer of the flexible multi-mode sensor is the impedance sensor.

[0011] Preferably, the raw materials for preparing the ammonia sensor include: an ammonia response solution; and the preparation process of the ammonia response solution includes:

[0012] The prepared chitosan was dissolved in 2% acetic acid solution and stirred by magnetic force at room temperature to obtain a uniform chitosan solution;

[0013] adding graphene oxide nanosheets to the chitosan solution and stirring the mixture to obtain a mixed solution;

[0014] The mixed solution is placed in an ice-water bath for ultrasonic treatment and oscillation treatment;

[0015] uniformly adding a silver nitrate solution to the mixed solution after the oscillation, reducing the silver nitrate solution to generate silver nanoparticles by light, and controlling the reaction temperature within a target temperature range to obtain a composite solution;

[0016] The composite solution is centrifuged, and the supernatant of the composite solution after centrifugation is collected, fixed to volume and stored in a light-proof environment to obtain the ammonia response solution.

[0017] Preferably, the preparation process of the ammonia sensor includes:

[0018] The ammonia response solution is placed in a constant temperature water bath and continuously stirred using a magnetic stirrer;

[0019] The prepared potassium persulfate solution is uniformly added to the ammonia response solution using a pipette, and a polymerization reaction is carried out at 23° C. under stirring for 1 hour to obtain a polymerization solution;

[0020] placing the polymerization solution in an ultracentrifuge for centrifugal treatment, and collecting the colloidal precipitate in the ultracentrifuge;

[0021] After washing the colloidal precipitate with deionized water for three times, the colloidal precipitate is dispersed in deionized water and stirred with a glass rod to obtain a final response solution;

[0022] After the surface of the graphene electrode prepared in advance is cleaned by using peroxide plasma, the final response solution is evenly dripped onto the surface of the graphene electrode using a pipette, and then heated and dried on a hot stage to obtain the ammonia sensor.

[0023] Preferably, the response formula of the ammonia sensor is:

[0024]

[0025] Wherein, Response is the response value of the ammonia sensor; L a is the resistance value of the ammonia sensor in ambient air; L g is the resistance value of the ammonia sensor in ammonia gas; k is the temperature correction coefficient; ΔT is the difference between the test environment temperature and the reference temperature; α is the humidity sensitivity coefficient; and C is the ambient humidity.

[0026] Preferably, the electrodes of the impedance sensor and the ammonia sensor are made of graphene materials; the electrodes of the impedance sensor and the ammonia sensor are both interdigitated; the substrate of the ammonia sensor is a PI film; a PDMS buffer layer is spin-coated under the PI film; the electrodes of the ammonia sensor are packaged with a waterproof and breathable membrane; the surface of the electrodes of the ammonia sensor is modified with a carbon nanotube coating or graphene oxide; the response correction formula of the ammonia sensor is:

[0027]

[0028] Among them, R comp k is the response value of the ammonia sensor after humidity and temperature compensation; h is the first-order compensation coefficient of humidity; k h2 is the second-order compensation coefficient of humidity; H is the ambient humidity; k t is the temperature compensation coefficient; k ht is the interaction compensation coefficient between humidity and temperature.

[0029] Preferably, the preparation materials of the electrode and the sensitive layer of the fungus concentration sensor are Dimatix deposition material and McFarland standard solution with a density of 0.6 respectively; the calculation formula of the sensitivity of the fungus concentration sensor is:

[0030]

[0031] Among them, S comp is the sensitivity after environmental compensation; L Bac is the impedance amplitude of the fungus concentration sensor; L Ref Concentration of sterile saline.

[0032] Preferably, the complex impedance calculation formula of the fungus concentration sensor is:

[0033]

[0034] in,L (ω) is the complex impedance of the fungus concentration sensor; L is the impedance of the fungus concentration sensor for collecting signals; R is the impedance phase of the signal collected by the fungus concentration sensor; s is the solution resistance; R ct is the charge transfer resistance; C d l is the double layer capacitance; ω is the angular frequency; α1 is a dimensionless parameter.

[0035] Preferably, the back-end subsystem has a built-in conversion module, an ADC module, a DSP module and an HC-08 module;

[0036] The conversion module is used to convert the collected signals of the fungus concentration sensor, the impedance sensor and the ammonia sensor into voltage signals; the ADC module is used to convert the voltage signals into digital signals; the DSP module is used to perform discrete Fourier transform on the digital signals; and the HC-08 module is used to transmit the output of the DSP module to a preset terminal.

[0037] Preferably, the prediction model formula of the shelf life prediction value is:

[0038]

[0039] Wherein, S is the predicted shelf life value; α2 is a constant term; I is the impedance amplitude; P is the impedance phase; A is the ammonia concentration; T is the temperature; x i is the i-th combination of the impedance amplitude, the impedance phase, the ammonia concentration, the temperature and the humidity; NonLinear(·) represents a nonlinear feature extraction layer.

[0040] The present invention discloses the following technical effects:

[0041] The present invention provides a fresh meat quality detection system based on a flexible multi-mode sensor. Through a fungus concentration sensor and a flexible multi-mode sensor integrated with an impedance sensor and an ammonia sensor, the system solves the problems of complex manufacturing process and high cost of conventional detection equipment, and realizes real-time detection of ammonia, impedance and bacterial concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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 will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1A schematic diagram of a fresh meat quality detection system based on a flexible multi-mode sensor provided in an embodiment of the present invention;

[0044] Figure 2 The electrode structure diagram of the ammonia and impedance dual-mode bacteria concentration sensor provided by the embodiment of the present invention;

[0045] Figure 3 A diagram of the electrode structure of a fungus concentration sensor provided by an embodiment of the present invention;

[0046] Figure 4 A flow chart of fresh meat quality detection based on a flexible multi-mode sensor provided in an embodiment of the present invention;

[0047] Description of reference numerals:

[0048] 1-Flexible multi-mode sensor, 11-Ammonia sensor, 12-Impedance sensor, 2-Fungus concentration sensor, 3-Back-end subsystem. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The purpose of the present invention is to provide a fresh meat quality detection system based on a flexible multi-mode sensor, reduce the cost of detection equipment and realize the combination of ammonia and impedance.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Figure 1 Schematic diagram of a fresh meat quality detection system based on a flexible multi-mode sensor 1 provided in an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the present invention provides a fresh meat quality detection system based on a flexible multi-mode sensor 1, comprising: a flexible multi-mode sensor 1, a fungus concentration sensor 2 and a back-end subsystem 3; the flexible multi-mode sensor 1 comprises: an impedance sensor 12 and an ammonia sensor 11;

[0053] The flexible multi-mode sensor 1 and the fungus concentration sensor 2 are respectively connected to the rear subsystem 3; the ammonia sensor 11 is located on the upper side of the impedance sensor 12;

[0054] The impedance sensor 12 is used to detect the impedance of the target fresh meat surface; the ammonia sensor 11 is used to detect the ammonia concentration of the target fresh meat surface; the fungus concentration sensor 2 is used to detect the bacteria concentration of the target fresh meat surface; the back-end subsystem 3 is used to predict the shelf life based on the collected information of the impedance sensor 12, the ammonia sensor 11 and the fungus concentration sensor 2 to obtain a shelf life prediction value.

[0055] Preferably, the preparation process of the flexible multi-mode sensor 1 includes:

[0056] Use a laser engraving machine to engrave two symmetrical interdigitated graphene electrode patterns on a pre-prepared kapton tape, and fold the engraved kapton tape in half to obtain the flexible multi-mode sensor 1; the upper layer of the flexible multi-mode sensor 1 is the ammonia sensor 11; the lower layer of the flexible multi-mode sensor 1 is the impedance sensor 12.

[0057] Furthermore, the raw materials for preparing the ammonia sensor 11 include: an ammonia response solution; and the preparation process of the ammonia response solution includes:

[0058] The prepared chitosan was dissolved in 2% acetic acid solution and stirred by magnetic force at room temperature to obtain a uniform chitosan solution;

[0059] adding graphene oxide nanosheets to the chitosan solution and stirring the mixture to obtain a mixed solution;

[0060] The mixed solution is placed in an ice-water bath for ultrasonic treatment and oscillation treatment;

[0061] uniformly adding a silver nitrate solution to the mixed solution after the oscillation, reducing the silver nitrate solution to generate silver nanoparticles by light, and controlling the reaction temperature within a target temperature range to obtain a composite solution;

[0062] The composite solution is centrifuged, and the supernatant of the composite solution after centrifugation is collected, fixed to volume and stored in a light-proof environment to obtain the ammonia response solution.

[0063] Preferably, the preparation process of the ammonia sensor 11 includes:

[0064] The ammonia response solution is placed in a constant temperature water bath and continuously stirred using a magnetic stirrer;

[0065] The prepared potassium persulfate solution is uniformly added to the ammonia response solution using a pipette, and a polymerization reaction is carried out at 23° C. under stirring for 1 hour to obtain a polymerization solution;

[0066] placing the polymerization solution in an ultracentrifuge for centrifugal treatment, and collecting the colloidal precipitate in the ultracentrifuge;

[0067] After washing the colloidal precipitate with deionized water for three times, the colloidal precipitate is dispersed in deionized water and stirred with a glass rod to obtain a final response solution;

[0068] After the surface of the pre-prepared graphene electrode is cleaned by using peroxide plasma, the final response solution is evenly dripped onto the surface of the graphene electrode using a pipette, and then heated and dried on a hot stage to obtain the ammonia sensor 11.

[0069] Specifically, the response formula of the ammonia sensor 11 is:

[0070]

[0071] Wherein, Response is the response value of the ammonia sensor 11; L a L is the resistance value of the ammonia sensor 11 in ambient air; g is the resistance value of the ammonia sensor 11 in ammonia gas; k is the temperature correction coefficient; ΔT is the difference between the test environment temperature and the reference temperature; α is the humidity sensitivity coefficient; and C is the ambient humidity.

[0072] Furthermore, the electrodes of the impedance sensor 12 and the ammonia sensor 11 are made of graphene materials; the electrodes of the impedance sensor 12 and the ammonia sensor 11 are both in the shape of interdigital; the substrate of the ammonia sensor 11 is a PI film; a PDMS buffer layer is spin-coated under the PI film; the electrodes of the ammonia sensor 11 are packaged with a waterproof and breathable membrane; the surface of the electrodes of the ammonia sensor 11 is modified with a carbon nanotube coating or graphene oxide; the response correction formula of the ammonia sensor 11 is:

[0073]

[0074] Among them, R comp k is the response value of the ammonia sensor 11 after humidity and temperature compensation; h is the first-order compensation coefficient of humidity; k h2 is the second-order compensation coefficient of humidity; H is the ambient humidity; k t is the temperature compensation coefficient; k ht is the interaction compensation coefficient between humidity and temperature.

[0075] Specifically, the preparation materials of the electrode and the sensitive layer of the fungus concentration sensor 2 are Dimatix deposition material and McFarland standard solution with a density of 0.6 respectively; the calculation formula of the sensitivity of the fungus concentration sensor 2 is:

[0076]

[0077] Among them, S comp is the sensitivity after environmental compensation; L Bac is the impedance amplitude of the fungus concentration sensor 2; L Ref Concentration of sterile saline.

[0078] Furthermore, the complex impedance calculation formula of the fungus concentration sensor 2 is:

[0079]

[0080] in, L (ω) is the complex impedance of the fungus concentration sensor 2; L is the impedance of the fungus concentration sensor 2 collecting signals; R is the impedance phase of the signal collected by the fungus concentration sensor 2; s is the solution resistance; R ct is the charge transfer resistance; C d l is the double layer capacitance; ω is the angular frequency; α1 is a dimensionless parameter.

[0081] Preferably, the back-end subsystem 3 has a built-in conversion module, an ADC module, a DSP module and an HC-08 module;

[0082] The conversion module is used to convert the collected signals of the fungus concentration sensor 2, the impedance sensor 12 and the ammonia sensor 11 into voltage signals; the ADC module is used to convert the voltage signals into digital signals; the DSP module is used to perform discrete Fourier transform on the digital signals; and the HC-08 module is used to transmit the output of the DSP module to a preset terminal.

[0083] Specifically, the prediction model formula of the shelf life prediction value is:

[0084]

[0085] Wherein, S is the predicted shelf life value; α2 is a constant term; I is the impedance amplitude; P is the impedance phase; A is the ammonia concentration; T is the temperature; x i is the i-th combination of the impedance amplitude, the impedance phase, the ammonia concentration, the temperature and the humidity; NonLinear(·) represents a nonlinear feature extraction layer.

[0086] Preferably, the fresh meat quality detection system includes a flexible multi-mode sensor 1 and a flexible sensor for detecting fungus concentration, which is used for real-time monitoring of NH3 gas concentration, specific bacteria concentration and electrical impedance characteristics of fresh meat, and a back-end subsystem 3 for detecting gas concentration, fresh meat surface bacteria concentration and fresh meat electrical impedance characteristics.

[0087] refer to Figure 2 and Figure 3 The flexible sensing patch consists of two sides. Using a laser engraving machine, two symmetrical interdigitated graphene electrode patterns are engraved on a certain area of ​​kapton tape, and after folding it in half, a flexible multi-modal device is realized. The lower layer is the impedance sensor 12, and the upper layer is the electrode of the ammonia sensor 11, which effectively combines the measurement of ammonia and impedance. Moreover, both modes in the back-end system have good sensitivity and stability without any crosstalk behavior.

[0088] Further, the preparation of the ammonia response (PTM) solution of the ammonia sensor 11 electrode of the upper flexible sensing patch, after multiple experimental comparisons, obtained this specific PTM solution preparation method, first, an appropriate amount of chitosan was dissolved in a 2% acetic acid solution, and magnetically stirred at room temperature for 1 hour to form a uniform chitosan solution. Subsequently, graphene oxide (GO) nanosheets were added and stirred for 30 minutes to promote dispersion. The mixed solution was placed in an ice water bath (4°C) and oscillated by ultrasonic treatment for 30 minutes to further enhance the uniform composite effect of chitosan and graphene oxide. Then, silver nitrate (AgNO3) solution was slowly added to the above solution, and silver nanoparticles were reduced by light (ultraviolet light wavelength of 365nm), while the reaction temperature was controlled to be maintained at 10°C, and the reaction time was 2 hours. Finally, the resulting composite solution was centrifuged to remove large particle impurities and the supernatant was collected, fixed to volume and stored in a dark environment at 4°C, which was a PTM solution.

[0089] Preferably, the upper ammonia sensor 11 of the flexible sensing patch is prepared by plasma treating another graphene electrode, then using a pipette to suck 25 microliters of the PTM solution prepared in the previous step and dripping it into the center of the electrode, and heating it on a heating table at 40°C for 30 minutes to form a flexible ammonia sensor 11. The sample is recorded as PTM-1. Repeating this step, FDP electrodes with different ammonia sensitive layer thicknesses can be obtained, and the samples are recorded as PTM-2 (50 microliters), PTM-3 (75 microliters), and PTM-4 (100 microliters).

[0090] Furthermore, experiments have shown that after 1200 bends, the resistance response of FDP to 10ppm ammonia decreased by 8.54%. After 14 days, the resistance response to 10ppm ammonia decreased by 3.14%, indicating the excellent stability and repeatability of FDP. In addition, FDP shows a positive resistance response to ammonia gas. The resistance response of FDP has a trend of first increasing and then decreasing with the increase of its thickness. When 50 microliters of PTM solution (2 layers of thickness) is dripped, the ammonia response of FDP is the highest. The response / recovery time of PTM-2 is 69 / 651s, while the response / recovery time of other samples is 68 / 698s (PTM-1), 67 / 741s (PTM-3), and 65 / 745s (PTM-4). This shows that the stability and repeatability of PTM-2 are more outstanding.

[0091] Specifically, during the operation of the upper ammonia sensor 11 of the flexible sensing patch, ammonia is adsorbed and reacted with the FDP electrode of the ammonia sensitive layer: NH3(g)+2H2O(l)→NH4 + (aq)+OH - (aq); In this reaction, ammonia (NH3) is adsorbed on the ammonia sensitive layer and reacts with water (H2O) to form ammonium ions (NH4 + ) and hydroxide ions (OH - ). This reaction will cause the resistivity of the semiconductor to change, which is used to measure the ammonia sensor 11. In addition, the response formula of the ammonia sensor 11 can be expressed as:

[0092]

[0093] Preferably, the ammonia sensing electrode and the impedance sensing electrode of the flexible sensing patch are both based on graphene (LIG) material and designed to be interdigitated. In addition, each layer structure of the FDP uses a PI film as a substrate, which effectively reduces the interference of the external environment on the electrical properties of the electrode relative to PDMS, silicone and other elastomer substrates. In order to avoid the wrinkling of the film during the laser direct writing process, a layer of PDMS is spin-coated under the PI film as a buffer layer. Finally, the prepared sensor is lead-processed with silver wire and the ammonia sensing electrode is encapsulated with a waterproof and breathable membrane to achieve a certain waterproof function, which can ensure the stability and reliability of the sensor in a humid working environment.

[0094] Specifically, the electrodes for flexible sensing of fungus concentration were printed on a paper substrate by a Dimatix deposition material printer DMP3000 located in an ISO5 / ISO7 clean room. The sensitive layer was prepared in a standard solution at a density of 0.6 McFarland, corresponding to 2.5·10 8CFU / mL, prepared in 5mL of saline. When the bacterial concentration increases during sensor operation, the impedance amplitude increases, and the sensitivity of the sensor can be expressed as follows:

[0095]

[0096] Temperature compensation coefficient k T , used to correct the effect of temperature on impedance. Humidity compensation coefficient k H , which is used to correct the effect of humidity on impedance.

[0097] Furthermore, the sensitivity of the sensor must be better than the overall error of the measurement equipment used. Considering that the maximum error of the impedance amplitude measurement of HP4194A is 0.17%, the sensitivity of the sensor was studied at a concentration of 1.5·10 2 CFU / mL, 1.5·10 5 CFU / mL and 1.5·10 8 The change in sensor impedance amplitude observed when the CFU / m was 0.11. 2 CFU / mL), from which it can be concluded that the change in sensor impedance is related to the change in bacterial concentration. From the sensor sensitivity value, it can be seen that the shape of the sensing electrode and the overall size of the sensor need to be further analyzed to improve the sensitivity of the sensor to low concentrations of Pseudomonas aeruginosa. In addition, the sensor impedance amplitude (δL(f)) and phase angle For different bacterial concentrations of P. aeruginosa and S. aureus, the following expression can be used for calculation:

[0098]

[0099] Among them, L est (f): estimated impedance magnitude (predicted value at frequency f); L meas (f): measured impedance amplitude (actual measured value at frequency f); δL(f): relative error of impedance amplitude (in %), which is the difference between the estimated value and the measured value; Estimated impedance phase angle (predicted value at frequency f); The measured impedance phase angle (actual measured value at frequency f); Relative error of the impedance phase angle (in %).

[0100] Specifically, the flexible sensor used to detect fungus concentration consists of multiple finger-like structures. When this sensor is working, its internal response can be regarded as an equivalent circuit consisting of two resistors and a capacitor, and its complex impedanceL At some angular frequencies ω can be expressed as follows:

[0101]

[0102] When the bacterial concentration increases during sensor operation, the solution resistance, charge transfer resistance and α increase, while the double layer capacitance decreases. The dimensionless parameter α has no physical interpretation. The changes in solution resistance, charge transfer resistance and double layer capacitance can be related to the physical processes occurring in the electrolyte and electrode surfaces.

[0103] Furthermore, the core of the ammonia and two impedance detection-based back-end subsystems 3 is the microcontroller. In addition, the sensed ammonia resistance signal is converted into a voltage signal through a conversion module, and then the voltage signal is converted into a digital signal through the ADC module in the MCU. In the impedance sensing part, the chip is used to scan the impedance amplitude and phase angle in the range of 1Hz to 100kHz, and it is connected to the FDP impedance electrode. The DSP module in the chip performs a discrete Fourier transform on the impedance data, which is read by the I2C protocol and transmitted to the terminal by the HC-08 module. The entire system is powered by a 5V lithium battery.

[0104] Specifically, the ammonia and two impedance detection-based rear subsystems 3 collect the resistance response value of FDP and combine it with the impedance sensing data set of the previous step, with ammonia concentration, impedance amplitude and impedance phase as independent variables and shelf life as dependent variable to form a four-dimensional space, which can be used to predict its shelf life. The shelf life prediction model formula is as follows:

[0105]

[0106] x i It reflects the interaction effects and polynomial terms between variables. NonLinear(·) is used to capture the nonlinear relationship between complex input data.

[0107] Furthermore, the CNLS method was used to estimate the model parameters of different bacterial concentrations of Pseudomonas aeruginosa and Staphylococcus aureus. The quality of the proposed model was evaluated by calculating the root mean square error (RMSE) of the impedance magnitude and phase angle. Experiments have shown that the RMSE values ​​of the accessories for both bacteria are lower than 0.072Ω, the impedance amplitude is 0.157°, and the impedance phase angle is 0.157°. According to multiple experiments, at a frequency of 37599.25Hz, the fitting errors of the impedance magnitude and phase angle of all concentrations of Pseudomonas aeruginosa and Staphylococcus aureus are less than 3%. The calculation formula for the comprehensive model error is:

[0108]

[0109] RMSEI,P are the root mean square errors of impedance magnitude and impedance phase angle, respectively. I is the relative fitting error of the impedance amplitude. P is the relative fitting error of the impedance phase angle. i is the frequency.

[0110] refer to Figure 4 , the complete process of the test is as follows: At the beginning, the sample to be tested is placed in the temperature control device, and the previously calibrated ammonia concentration data and the predicted fungus concentration are considered. During the test, the sensor monitors the ammonia concentration and compares it with a specific standard. If the ammonia concentration exceeds the set threshold (such as 1ppm), the next step of judgment is continued. If the ammonia concentration remains above 50ppm within 12 hours and the fresh meat has begun to rot, the complete result judgment process is entered. If the ammonia concentration is lower than 50ppm, the sensor will continue to analyze the ammonia concentration. Next, the sensor will collect the impedance signal and process the data according to the electrical impedance characteristics. If the data meets the preset standard, it can be judged whether the fresh meat has begun to deteriorate or rot. Finally, all the data is output through the corresponding conversion process to determine the quality changes of the fresh meat. This process also involves multiple links such as calibration of the sensor system, signal conversion and data processing to ensure the accuracy and effectiveness of the data.

[0111] The beneficial effects of the present invention are as follows:

[0112] The fresh meat quality detection system proposed in the present invention can be deployed on the surface of the film-coated tray cold fresh meat packaging, and is used for non-destructive perception of the quality of fresh meat such as mutton and fish. Both modes of the system have good sensitivity and stability, without any crosstalk behavior. The performance of the system has been verified on fresh meat such as mutton and fish, providing a feasible method for rapid non-destructive detection of food quality.

[0113] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0114] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A fresh meat quality detection system based on flexible multi-mode sensor, characterized in that: include: Flexible multi-mode sensors, fungus concentration sensors, and back-end subsystems; The flexible multi-mode sensor comprises: an impedance sensor and an ammonia sensor; The flexible multi-mode sensor and the fungus concentration sensor are respectively connected to the rear subsystem; the ammonia sensor is located on the upper side of the impedance sensor; The impedance sensor is used to detect the impedance of the target fresh meat surface; the ammonia sensor is used to detect the ammonia concentration of the target fresh meat surface; the fungus concentration sensor is used to detect the bacteria concentration of the target fresh meat surface; the back-end subsystem is used to predict the shelf life based on the collected information of the impedance sensor, the ammonia sensor and the fungus concentration sensor to obtain a shelf life prediction value.

2. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 1, characterized in that: The preparation process of the flexible multi-mode sensor comprises: A laser engraving machine is used to engrave two symmetrical interdigitated graphene electrode patterns on a pre-prepared kapton tape, and the engraved kapton tape is folded in half to obtain the flexible multi-mode sensor; the upper layer of the flexible multi-mode sensor is the ammonia sensor; and the lower layer of the flexible multi-mode sensor is the impedance sensor.

3. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 1, characterized in that: The raw materials for preparing the ammonia sensor include: an ammonia response solution; and the preparation process of the ammonia response solution includes: The prepared chitosan was dissolved in 2% acetic acid solution and stirred by magnetic force at room temperature to obtain a uniform chitosan solution; adding graphene oxide nanosheets to the chitosan solution and stirring the mixture to obtain a mixed solution; The mixed solution is placed in an ice-water bath for ultrasonic treatment and oscillation treatment; uniformly adding a silver nitrate solution to the mixed solution after the oscillation, reducing the silver nitrate solution to generate silver nanoparticles by light, and controlling the reaction temperature within a target temperature range to obtain a composite solution; The composite solution is centrifuged, and the supernatant of the composite solution after centrifugation is collected, fixed to volume and stored in a light-proof environment to obtain the ammonia response solution.

4. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 3, characterized in that: The preparation process of the ammonia sensor includes: The ammonia response solution is placed in a constant temperature water bath and continuously stirred using a magnetic stirrer; The prepared potassium persulfate solution is uniformly added to the ammonia response solution using a pipette, and a polymerization reaction is carried out at 23° C. under stirring for 1 hour to obtain a polymerization solution; placing the polymerization solution in an ultracentrifuge for centrifugal treatment, and collecting the colloidal precipitate in the ultracentrifuge; After washing the colloidal precipitate with deionized water for three times, the colloidal precipitate is dispersed in deionized water and stirred with a glass rod to obtain a final response solution; After the surface of the graphene electrode prepared in advance is cleaned by using peroxide plasma, the final response solution is evenly dripped onto the surface of the graphene electrode using a pipette, and then heated and dried on a hot stage to obtain the ammonia sensor.

5. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 4, characterized in that: The response formula of the ammonia sensor is: Wherein, Response is the response value of the ammonia sensor; L a is the resistance value of the ammonia sensor in ambient air; L g is the resistance value of the ammonia sensor in ammonia gas; k is the temperature correction coefficient; ΔT is the difference between the test environment temperature and the reference temperature; α is the humidity sensitivity coefficient; and C is the ambient humidity.

6. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 5, characterized in that: The electrodes of the impedance sensor and the ammonia sensor are made of graphene materials; the electrodes of the impedance sensor and the ammonia sensor are both in the shape of interdigital fingers; the substrate of the ammonia sensor is a PI film; a PDMS buffer layer is spin-coated under the PI film; the electrodes of the ammonia sensor are packaged with a waterproof and breathable film; the surface of the electrodes of the ammonia sensor is modified with a carbon nanotube coating or graphene oxide; the response correction formula of the ammonia sensor is: Among them, R comp k is the response value of the ammonia sensor after humidity and temperature compensation; h is the first-order compensation coefficient of humidity; k h2 is the second-order compensation coefficient of humidity; H is the ambient humidity; k t is the temperature compensation coefficient; k ht is the interaction compensation coefficient between humidity and temperature.

7. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 6, characterized in that: The preparation materials of the electrode and the sensitive layer of the fungus concentration sensor are Dimatix deposition material and McFarland standard solution with a density of 0.6 respectively; the calculation formula of the sensitivity of the fungus concentration sensor is: Among them, S comp is the sensitivity after environmental compensation; L Bac is the impedance amplitude of the fungus concentration sensor; L Ref Concentration of sterile saline.

8. A fresh meat quality detection system based on a flexible multi-mode sensor according to claim 7, characterized in that: The complex impedance calculation formula of the fungus concentration sensor is: in, L (ω) is the complex impedance of the fungus concentration sensor; L is the impedance of the fungus concentration sensor for collecting signals; R is the impedance phase of the signal collected by the fungus concentration sensor; s is the solution resistance; R ct is the charge transfer resistance; C d l is the double layer capacitance; ω is the angular frequency; α1 is a dimensionless parameter.

9. The fresh meat quality detection system based on flexible multi-mode sensor according to claim 1, characterized in that: The back-end subsystem has built-in conversion module, ADC module, DSP module and HC-08 module; The conversion module is used to convert the collected signals of the fungus concentration sensor, the impedance sensor and the ammonia sensor into voltage signals; the ADC module is used to convert the voltage signals into digital signals; the DSP module is used to perform discrete Fourier transform on the digital signals; and the HC-08 module is used to transmit the output of the DSP module to a preset terminal.

10. The fresh meat quality detection system based on flexible multi-mode sensor according to claim 1, characterized in that: The prediction model formula of the shelf life prediction value is: Wherein, S is the predicted shelf life value; α2 is a constant term; I is the impedance amplitude; P is the impedance phase; A is the ammonia concentration; T is the temperature; x i is the i-th combination of the impedance amplitude, the impedance phase, the ammonia concentration, the temperature and the humidity; NonLinear(·) represents a nonlinear feature extraction layer.

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