Beef freshness detection method and computer readable storage medium

Abstract

The invention discloses a beef freshness detection method and a computer readable storage medium. The method comprises the following steps: introducing clean air into a detection air chamber for cleaning; after the detection gas chamber is cleaned, volatile gas generated by the detected beef sample is sucked into the detection gas chamber, a gas sensor array in the detection gas chamber detects the volatile gas, and detection data is sent to a computer; the computer performs deviation heterogeneous processing on the detection data to obtain a corresponding deviation heterogeneous signal; the computer inputs the deviation heterogeneous signal into a nonlinear stochastic resonance model to obtain a corresponding signal-to-noise ratio characteristic value; and the computer judges the beef freshness according to the signal-to-noise ratio characteristic value. The detection data can be subjected to deviation heterogeneous processing, so that the input signal input into the nonlinear stochastic resonance model is matched with the nonlinear stochastic resonance model, the condition that resonance is not generated is avoided, and the detection stability and the detection precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of beef detection, and in particular to a beef freshness detection method and a computer-readable storage medium. Background Art

[0002] Beef is rich in nutrition and tastes good, and is loved by consumers. However, beef is easily contaminated and spoiled. During storage, the freshness of beef decreases with the proliferation of microorganisms and a series of physical and chemical changes. Rich nutrients such as protein, fat and carbohydrates are used by bacteria and enzymes to produce acidic gases such as ammonia, ammonium sulfide, aldehydes, ketones, ethyl mercaptan, alcohol and carboxylic acid. Aldehydes, ketones, esters and other low molecular weight compounds in the metabolites cause beef to have an odor.

[0003] At present, electronic noses are often used to detect volatile gases produced by beef to obtain detection data, and the nonlinear stochastic resonance model is used to process the detection data to judge the freshness of beef. The nonlinear stochastic resonance model induces the model to reach a resonant state by applying perturbation noise to the model, forcing the energy of the intrinsic noise to transfer to the target signal, thereby achieving the purpose of reducing noise and enhancing the target signal. However, in practical applications, the model input signal may not match the nonlinear stochastic resonance model, which may easily lead to no resonance, resulting in the failure of the nonlinear stochastic resonance model feature extraction process, and thus causing the failure of detecting the freshness of beef. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a beef freshness detection method and a computer-readable storage medium, which can perform bias isomerism processing on the detection data so that the input signal of the input nonlinear stochastic resonance model matches the nonlinear stochastic resonance model, avoiding the situation where no resonance occurs, thereby improving the detection stability and detection accuracy.

[0005] In order to solve the above problems, the present invention adopts the following technical solutions: A beef freshness detection method of the present invention comprises the following steps: S1: introduce clean air into the detection air chamber for cleaning; S2: After the detection chamber is cleaned, the volatile gas generated by the tested beef sample is sucked into the detection chamber, and the gas sensor array in the detection chamber detects the volatile gas and sends the detection data to the computer; S3: The computer performs isomerism processing on the detection data to obtain corresponding isomerism signals; S4: The computer inputs the bias isomerism signal into the nonlinear stochastic resonance model to obtain the corresponding signal-to-noise ratio characteristic value; S5: The computer determines the freshness of the beef based on the signal-to-noise ratio characteristic value.

[0006] Preferably, the gas sensor array includes 9 gas sensors, namely: a first gas sensor for detecting hydrogen sulfide, a second gas sensor for detecting hydrogen, a third gas sensor for detecting ammonia, a fourth gas sensor for detecting ethanol, a fifth gas sensor for detecting halogen gas, a sixth gas sensor for detecting methane, a seventh gas sensor for detecting propane, an eighth gas sensor for detecting nitrogen oxides, and a ninth gas sensor for detecting carbon dioxide.

[0007] Preferably, the specific steps of detecting volatile gas by the gas sensor array in the detection chamber in step S2 and sending the detection data to the computer are as follows: The first gas sensor contacts the volatile gas to generate a response signal s1(t), the second gas sensor contacts the volatile gas to generate a response signal s2(t), the third gas sensor contacts the volatile gas to generate a response signal s3(t), the fourth gas sensor contacts the volatile gas to generate a response signal s4(t), the fifth gas sensor contacts the volatile gas to generate a response signal s5(t), the sixth gas sensor contacts the volatile gas to generate a response signal s6(t), the seventh gas sensor contacts the volatile gas to generate a response signal s7(t), the eighth gas sensor contacts the volatile gas to generate a response signal s8(t), the ninth gas sensor contacts the volatile gas to generate a response signal s9(t), t is the detection time, the gas sensor array calculates the average of the response signals s1(t), s2(t), s3(t), s4(t), s5(t), s6(t), s7(t), s8(t), s9(t), and obtains the average inav(t). , The mean value inav(t) is sent to the computer as detection data.

[0008] Preferably, the formula for performing isomerization processing on the detection data in step S3 is as follows: , , Among them, inav(t) is the detection data output by the gas sensor array at the detection time t, chinp(t) is the deviation isomerism signal corresponding to inav(t), k is the adjustment parameter, A is the signal amplitude of the nonlinear stochastic resonance model, a is the real parameter of the nonlinear stochastic resonance model, and b is the real parameter of the nonlinear stochastic resonance model.

[0009] Preferably, step S4 comprises the following steps: S41: The computer inputs the deviation isomerism signal chinp(t) as an input signal into the nonlinear stochastic resonance model: , , Where x is the motion trajectory of the Brownian particle, V(x) is the standard double-well potential function, A is the signal amplitude, and f is the frequency. is the initial phase, N(t) is the intrinsic noise, D is the external noise intensity, is external noise, a and b are real parameters; S42: Nonlinear stochastic resonance model output signal-to-noise ratio curve SNR(t), , in, is the potential barrier height of the nonlinear stochastic resonance model at time t, D t is the external noise intensity when the nonlinear stochastic resonance model resonates at time t; S43: The maximum value SNR of the signal-to-noise ratio curve SNR(t) max as the signal-to-noise ratio eigenvalue.

[0010] Preferably, step S5 comprises the following steps: If W2<SNR max ≤W1, the beef is judged to be fresh cut beef; If W3<SNR max ≤W2, the beef is judged to be refrigerated for 24-48 hours; If W4<SNR max ≤W3, the beef is judged to be refrigerated for 48-72 hours; If W5<SNR max ≤W4, the beef is judged to be refrigerated for 72-96 hours; If SNR max ≤W5, the beef is judged to be beef that has been refrigerated for more than 96 hours.

[0011] Preferably, the step S1 comprises the following steps: Start preheating the gas sensor array in the detection chamber for T1 minute, and at the same time, introduce clean air into the detection chamber for cleaning. When the response signals generated by all gas sensors in the gas sensor array are stable, the cleaning is completed.

[0012] Preferably, the method of inhaling the volatile gas generated by the tested beef sample into the detection chamber in step S2 is as follows: At room temperature, place the tested beef sample in a clean and dry beaker and seal it with a sealing film. Let it stand for T2 minutes, insert the detection probe into the beaker, and the detection probe will absorb the volatile gas in the top space of the beaker into the detection gas chamber.

[0013] Preferably, the adjustment parameter k is obtained by the following method: The following relationship is established to improve the matching relationship between the input signal and the nonlinear stochastic resonance model, so that the nonlinear stochastic resonance model can resonate: , Among them, SNR is the signal-to-noise ratio output when the nonlinear stochastic resonance model generates resonance, int is the input signal, is the barrier height of the nonlinear stochastic resonance model; Solving the equation, we get: , Where, W(.) is the Lambert W function; The potential barrier height in the nonlinear stochastic resonance model needs to be less than or equal to Resonance can be generated, that is: , Solving the equation, we get: ; make , when When f(D) has a maximum value f(D) max , , f(D) max As the adjustment parameter k, that is .

[0014] A computer-readable storage medium of the present invention stores a computer program, which implements the above method when executed by a processor.

[0015] The beneficial effects of the present invention are as follows: a gas sensor array is used to detect volatile gases generated by a tested beef sample, and the detection data output by the gas sensor array is subjected to isomerism processing so that an input signal of an input nonlinear stochastic resonance model matches the nonlinear stochastic resonance model, thereby avoiding a situation where no resonance is generated, thereby improving the detection stability and detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of an embodiment. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0018] Embodiment: A beef freshness detection method of this embodiment is as follows: Figure 1 As shown, the following steps are included: S1: Start preheating the gas sensor array in the detection chamber for T1 minutes, and at the same time, introduce clean air into the detection chamber for cleaning. When the response signals generated by all gas sensors in the gas sensor array are stable, the cleaning is completed.

[0019] S2: After the detection chamber is cleaned, place the beef sample in a clean and dry beaker at room temperature and seal it with a sealing film. Let it stand for T2 minutes, insert the detection probe into the beaker, and the detection probe absorbs the volatile gas in the top space of the beaker into the detection chamber. The gas sensor array in the detection chamber detects the volatile gas and sends the detection data to the computer.

[0020] The gas sensor array includes 9 gas sensors, namely: a first gas sensor for detecting hydrogen sulfide, a second gas sensor for detecting hydrogen, a third gas sensor for detecting ammonia, a fourth gas sensor for detecting ethanol, a fifth gas sensor for detecting halogen gas, a sixth gas sensor for detecting methane, a seventh gas sensor for detecting propane, an eighth gas sensor for detecting nitrogen oxides, and a ninth gas sensor for detecting carbon dioxide.

[0021] The first gas sensor is a TGS-825 sensor, the second gas sensor is a TGS-821 sensor, the third gas sensor is a TGS-826 sensor, the fourth gas sensor is a TGS-822 sensor, the fifth gas sensor is a TGS-832 sensor, the sixth gas sensor is a TGS-813 sensor, the seventh gas sensor is a TGS-2610 sensor, the eighth gas sensor is a TGS-2201 sensor, and the ninth gas sensor is a 4N-CO2 sensor.

[0022] The specific steps of detecting volatile gases by the gas sensor array in the gas chamber and sending the detection data to the computer are as follows: The first gas sensor contacts the volatile gas to generate a response signal s1(t), the second gas sensor contacts the volatile gas to generate a response signal s2(t), the third gas sensor contacts the volatile gas to generate a response signal s3(t), the fourth gas sensor contacts the volatile gas to generate a response signal s4(t), the fifth gas sensor contacts the volatile gas to generate a response signal s5(t), the sixth gas sensor contacts the volatile gas to generate a response signal s6(t), the seventh gas sensor contacts the volatile gas to generate a response signal s7(t), the eighth gas sensor contacts the volatile gas to generate a response signal s8(t), the ninth gas sensor contacts the volatile gas to generate a response signal s9(t), t is the detection time, the gas sensor array calculates the average of the response signals s1(t), s2(t), s3(t), s4(t), s5(t), s6(t), s7(t), s8(t), s9(t), and obtains the average inav(t). , The mean value inav(t) is sent to the computer as detection data.

[0023] S3: The computer performs bias isomerism processing on the detection data to obtain corresponding bias isomerism signals.

[0024] The formula for bias isomerization processing of the detection data is as follows: , , Among them, inav(t) is the detection data output by the gas sensor array at the detection time t, chinp(t) is the deviation isomerism signal corresponding to inav(t), k is the adjustment parameter, A is the signal amplitude of the nonlinear stochastic resonance model, a is the real parameter of the nonlinear stochastic resonance model, and b is the real parameter of the nonlinear stochastic resonance model.

[0025] Under the adiabatic approximation, assuming that the signal amplitude is extremely small, the nonlinear stochastic resonance model, in the absence of sufficient energy drive, the Brownian particle is biased in one potential well, and the signal period is much longer than the relaxation time of the system in some typical potential wells. At this time, the emergence of periodic driving force causes the potential well function to tilt, and ultimately leads to the transition of the Brownian particle from one potential well to another. Since the barrier height of the nonlinear stochastic resonance model is , then the following relationship is proposed to improve the matching relationship between the input signal and the nonlinear stochastic resonance model, so as to induce the nonlinear stochastic resonance model to resonate: , Among them, SNR is the signal-to-noise ratio output when the nonlinear stochastic resonance model generates resonance, int is the input signal, is the barrier height of the nonlinear stochastic resonance model; Solving the equation, we get: , Where, W(.) is the Lambert W function; The potential barrier height in the nonlinear stochastic resonance model needs to be less than or equal to Resonance can be generated, that is: , Solving the equation, we get: , make , when When f(D) has a maximum value f(D) max， , f(D) max As the adjustment parameter k, that is .

[0026] Then the following processing is performed on the input signal int to make the input signal match the nonlinear stochastic resonance model: .

[0027] S4: The computer inputs the deviation isomerism signal chinp(t) as the input signal into the nonlinear stochastic resonance model: , , Where x is the motion trajectory of the Brownian particle, V(x) is the standard double-well potential function, A is the signal amplitude, and f is the frequency. is the initial phase, N(t) is the intrinsic noise, D is the external noise intensity, is external noise, a and b are real parameters; The nonlinear stochastic resonance model outputs the signal-to-noise ratio curve SNR(t), , in, is the potential barrier height of the nonlinear stochastic resonance model at time t, D t is the external noise intensity when the nonlinear stochastic resonance model resonates at time t; The maximum value SNR of the signal-to-noise ratio curve SNR(t) max as the signal-to-noise ratio eigenvalue.

[0028] S5: If W2<SNR max ≤W1, the beef is judged to be fresh cut beef; If W3<SNRmax ≤W2, the beef is judged to be refrigerated for 24-48 hours; If W4<SNR max ≤W3, the beef is judged to be refrigerated for 48-72 hours; If W5<SNR max ≤W4, the beef is judged to be refrigerated for 72-96 hours; If SNR max ≤W5, the beef is judged to be beef that has been refrigerated for more than 96 hours.

[0029] In this scheme, the volatile gas generated by the tested beef sample is inhaled into the detection gas chamber, and the volatile gas generated by the tested beef sample is detected by a gas sensor array. The detection data output by the gas sensor array is subjected to isomerism processing, so that the input signal of the input nonlinear stochastic resonance model matches the nonlinear stochastic resonance model, avoiding the situation where no resonance is generated, thereby improving the detection stability and detection accuracy.

[0030] For the nonlinear stochastic resonance model, the minimum values ​​of the standard double-well function V(x) are located in the two potential wells Where There is a potential barrier between the two potential wells, and the potential barrier is located at At a height of When the three elements of stochastic resonance are coordinated, Brownian particles obtain enough energy, the potential barrier is lowered, and the Brownian particles can cross the potential barrier and jump from one potential well to another. At this time, the signal-to-noise ratio presents a maximum value. At this time, the intensity of weak signals is enhanced, so they can be identified from strong background noise.

[0031] In this embodiment, A=1.9, a=8, b=4, then k=4.918 is calculated. , The computer inputs the deviation isomerism signal into the nonlinear stochastic resonance model to obtain the corresponding signal-to-noise ratio eigenvalue SNR max , If -0.31<SNR max ≤-0.24, the beef is judged to be fresh cut beef; If -0.37<SNR max ≤-0.31, the beef is judged to be refrigerated for 24-48 hours; If -0.44<SNR max ≤-0.37, the beef is judged to be refrigerated for 48-72 hours; If -0.52<SNR max ≤-0.44, the beef is judged to be refrigerated for 72-96 hours; If SNR max ≤-0.52, indicating that the beef has been refrigerated for more than 96 hours.

[0032] A computer-readable storage medium of this embodiment stores a computer program, and the computer program implements the above method when executed by a processor.

Claims

1. A method for detecting the freshness of beef, characterized in that: The following steps are involved: S1: introduce clean air into the detection air chamber for cleaning; S2: After the detection chamber is cleaned, the volatile gas generated by the tested beef sample is sucked into the detection chamber, and the gas sensor array in the detection chamber detects the volatile gas and sends the detection data to the computer; S3: The computer performs isomerism processing on the detection data to obtain corresponding isomerism signals; S4: The computer inputs the bias isomerism signal into the nonlinear stochastic resonance model to obtain the corresponding signal-to-noise ratio characteristic value; S5: The computer determines the freshness of the beef based on the signal-to-noise ratio characteristic value.

2. A beef freshness detection method according to claim 1, characterized in that: The gas sensor array includes 9 gas sensors, namely: a first gas sensor for detecting hydrogen sulfide, a second gas sensor for detecting hydrogen, a third gas sensor for detecting ammonia, a fourth gas sensor for detecting ethanol, a fifth gas sensor for detecting halogen gas, a sixth gas sensor for detecting methane, a seventh gas sensor for detecting propane, an eighth gas sensor for detecting nitrogen oxides, and a ninth gas sensor for detecting carbon dioxide.

3. A beef freshness detection method according to claim 2, characterized in that: The specific steps of detecting volatile gas by the gas sensor array in the detection chamber in step S2 and sending the detection data to the computer are as follows: The first gas sensor contacts the volatile gas to generate a response signal s1(t), the second gas sensor contacts the volatile gas to generate a response signal s2(t), the third gas sensor contacts the volatile gas to generate a response signal s3(t), the fourth gas sensor contacts the volatile gas to generate a response signal s4(t), the fifth gas sensor contacts the volatile gas to generate a response signal s5(t), the sixth gas sensor contacts the volatile gas to generate a response signal s6(t), the seventh gas sensor contacts the volatile gas to generate a response signal s7(t), the eighth gas sensor contacts the volatile gas to generate a response signal s8(t), the ninth gas sensor contacts the volatile gas to generate a response signal s9(t), t is the detection time, the gas sensor array calculates the average of the response signals s1(t), s2(t), s3(t), s4(t), s5(t), s6(t), s7(t), s8(t), s9(t), and obtains the average inav(t). , and send the mean inav(t) as detection data to the computer.

4. A beef freshness detection method according to claim 3, characterized in that: The formula for performing isomerization processing on the detection data in step S3 is as follows: ， ， Among them, inav(t) is the detection data output by the gas sensor array at the detection time t, chinp(t) is the deviation isomerism signal corresponding to inav(t), k is the adjustment parameter, A is the signal amplitude of the nonlinear stochastic resonance model, a is the real parameter of the nonlinear stochastic resonance model, and b is the real parameter of the nonlinear stochastic resonance model.

5. A beef freshness detection method according to claim 4, characterized in that: The step S4 comprises the following steps: S41: The computer inputs the deviation isomerism signal chinp(t) as an input signal into the nonlinear stochastic resonance model: ， ， Where x is the motion trajectory of the Brownian particle, V(x) is the standard double-well potential function, A is the signal amplitude, and f is the frequency. is the initial phase, N(t) is the intrinsic noise, D is the external noise intensity, is external noise, a and b are real parameters; S42: Nonlinear stochastic resonance model output signal-to-noise ratio curve SNR(t), ， in, is the potential barrier height of the nonlinear stochastic resonance model at time t, D t is the external noise intensity when the nonlinear stochastic resonance model resonates at time t; S43: The maximum value SNR of the signal-to-noise ratio curve SNR(t) max as the signal-to-noise ratio eigenvalue.

6. A beef freshness detection method according to claim 5, characterized in that: The step S5 comprises the following steps: If W2<SNR max ≤W1, the beef is judged to be fresh cut beef; If W3<SNR max ≤W2, the beef is judged to be refrigerated for 24-48 hours; If W4<SNR max ≤W3, the beef is judged to be refrigerated for 48-72 hours; If W5<SNR max ≤W4, the beef is judged to be refrigerated for 72-96 hours; If SNR max ≤W5, the beef is judged to be beef that has been refrigerated for more than 96 hours.

7. A beef freshness detection method according to claim 1, characterized in that: The step S1 comprises the following steps: Start preheating the gas sensor array in the detection chamber for T1 minute, and at the same time, introduce clean air into the detection chamber for cleaning. When the response signals generated by all gas sensors in the gas sensor array are stable, the cleaning is completed.

8. A beef freshness detection method according to claim 1, characterized in that: The method for inhaling the volatile gas generated by the tested beef sample into the detection chamber in step S2 is as follows: At room temperature, place the tested beef sample in a clean and dry beaker and seal it with a sealing film. Let it stand for T2 minutes, insert the detection probe into the beaker, and the detection probe will absorb the volatile gas in the top space of the beaker into the detection gas chamber.

9. A beef freshness detection method according to claim 5, characterized in that: The adjustment parameter k is obtained by the following method: The following relationship is established to improve the matching relationship between the input signal and the nonlinear stochastic resonance model, so that the nonlinear stochastic resonance model can resonate: ， Among them, SNR is the signal-to-noise ratio output when the nonlinear stochastic resonance model generates resonance, int is the input signal, is the barrier height of the nonlinear stochastic resonance model; Solving the equation, we get: ， Where W(.) is the Lambert W function; The potential barrier height in the nonlinear stochastic resonance model needs to be less than or equal to Resonance can be generated, that is: ， Solving the equation, we get: ， make , when When f(D) has a maximum value f(D) max， ， f(D) max As the adjustment parameter k, that is .

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

Images