Rapid detection method for beef freshness based on olfactory visualization chip

Abstract

The application discloses a kind of based on olfactory visualization chip beef freshness rapid detection method, comprising the following steps: selecting tetramethoxy phenyl porphyrin iron (III) chloride and phenol red as gas-sensitive material, and preparation is obtained with two sensors olfactory visualization chip;Different storage time under the representative beef sample is set, freshness index data is combined with the response characteristic matrix obtained corresponding storage time, to establish the back propagation neural network quantitative model of beef freshness.The response characteristic matrix of the olfactory visualization chip of the beef sample to be measured is input into the quantitative model established in beef, to obtain the predicted value of TVB-N content and the predicted value of TVC content of the beef sample to be measured.The application can realize rapid, simple, accurate quantitative detection of beef freshness;To solve the shortcomings of the prior art detection process, high detection cost, easy to be disturbed by environment and unable to detect visualized etc.

Description

TECHNICAL FIELD

[0001] The application relates to a method for visually detecting the freshness of meat by smell, in particular to a method for rapidly detecting the freshness of beef based on a smell visualization chip. BACKGROUND

[0002] Rapid and accurate detection of the freshness of beef is a necessary condition for reducing food safety problems and food waste. At present, the detection methods for the freshness of beef mainly include physicochemical index method, sensory evaluation method, chromatography method and mass spectrometry method. The physicochemical index method mainly adopts the methods in GB 5009.228-2016 and GB 4789.2-2010, which is complicated to operate and needs a large amount of chemical reagents; the sensory evaluation method needs professional training for the evaluators and is highly subjective; the chromatography method and the mass spectrometry method have objective and reliable detection results, but they are time-consuming, complex in sample pretreatment and expensive in instruments.

[0003] Peng Yankun et al. (patent application number CN201110376478.0) disclose a method and system for rapidly and nondestructively evaluating the freshness of fresh beef. The method combines visible / near-infrared spectrum measurement technology, computer technology, chemometrics technology and basic testing technology to establish a mathematical prediction model between the spectral information reflecting the components and state information of fresh beef and multiple indexes of the freshness of fresh beef, and to build the entire detection system. The system can simultaneously detect multiple indexes (total volatile basic nitrogen (TVB-N), pH value, total bacterial count (TVC), meat color (L*, a*)) of unknown fresh beef freshness and predict the storage time of beef, and combine multiple indexes to comprehensively grade the freshness of beef, so as to achieve the purpose of rapidly and nondestructively detecting and evaluating the freshness of fresh beef. However, the method needs a special spectrometer, the infrared spectrum detection is easily disturbed by water, and the spectral noise and baseline drift are serious.

[0004] Liu Yongfeng et al. (patent application number CN201810301930.9) disclose a method for identifying the storage time of fresh beef by DNA and PCR technology. The method separates and extracts beef DNA under different storage conditions (frozen at-20℃, refrigerated at 4℃, at moderate temperature of 10℃ and at room temperature of 20℃), determines the content of beef DNA, and determines the freshness degree (first-class fresh meat, second-class fresh meat and deteriorated meat) of the beef. Then, the DNA is used as a template for fluorescence quantitative PCR amplification, and the time of the beef placed under different storage temperatures and different freshness degrees is accurately judged according to the mathematical equation of the difference in ct value of fluorescence quantitative PCR amplification, so as to provide a new method for identifying the placing time of beef under different temperatures. However, the method is time-consuming, needs professional experimenters, and consumes a large amount of chemical reagents.

[0005] The above-mentioned existing patents are all qualitative analysis. SUMMARY

[0006] The technical problem solved by the present application is to provide a beef freshness rapid detection method based on an olfactory visualization chip.

[0007] To solve the above technical problem, the present application provides a beef freshness rapid detection method based on an olfactory visualization chip, comprising the following steps:

[0008] 1) Preparation of an olfactory visualization chip:

[0009] Select tetramethoxyphenyl porphyrin iron (III) chloride and phenol red as gas-sensitive materials to prepare an olfactory visualization chip with two sensors;

[0010] 2) Set representative beef samples under different storage times, and process each representative beef sample corresponding to each storage time as follows:

[0011] Divide one representative beef sample into two, one representative beef sample is used for step 2.1) below, and the other representative beef sample is used for step 2.2) below:

[0012] 2.1) Place the olfactory visualization chip containing the gas-sensitive material and one representative beef sample in a sealed container to react, obtain the RGB difference (R, G, B color difference) of the chip before and after the reaction, and generate the response feature matrix of the olfactory visualization chip;

[0013] 2.2) Measure the freshness indicators of the other representative beef sample, the freshness indicators being TVB-N and TVC content;

[0014] That is, according to the national standard method, measure the TVB-N and TVC content in the representative beef sample, respectively;

[0015] 2.3) Combine the freshness indicator data obtained in step 2.2) with the response feature matrix obtained in step 2.1) corresponding to the storage time, thereby establishing a back propagation neural network (BPNN) quantitative model of beef freshness.

[0016] As an improvement of the beef freshness rapid detection method based on the olfactory visualization chip of the present application, the following steps are further included:

[0017] According to the above 2.1), operate the beef sample to be tested to obtain the response feature matrix of the olfactory visualization chip of the beef sample to be tested;

[0018] The response feature matrix of the olfactory visualization chip of the beef sample to be tested is input into the quantitative model of beef established in step (2.3), so as to obtain the predicted value of the TVB-N content of the beef sample to be tested and the predicted value of the TVC content

[0019] Therefore, the method of the present application can quickly obtain the above two freshness indicators of the beef sample to be tested; and then the two freshness indicators are judged according to the conventional method.

[0020] As a further improvement of the method for rapidly detecting the freshness of beef based on the olfactory visualization chip of the present application, the beef sample for verification is repeatedly subjected to step 2), so as to verify the accuracy and robustness of the BPNN.

[0021] As a further improvement of the method for rapidly detecting the freshness of beef based on the olfactory visualization chip of the present application, step 1) is:

[0022] 8 mg of tetramethoxyphenyl porphyrin iron (III) chloride is dissolved in 4 mL of chloroform to prepare a tetramethoxyphenyl porphyrin iron (III) chloride solution;

[0023] 8 mg of phenol red is dissolved in 4 mL of anhydrous ethanol to prepare a phenol red solution;

[0024] The tetramethoxyphenyl porphyrin iron (III) chloride solution and the phenol red solution are respectively added dropwise to different positions on the same side of the polyvinylidene fluoride membrane (PVDF), so as to prepare an olfactory visualization chip with two sensors.

[0025] Note: The preparation of the above two solutions is carried out in the dark (for example, using a brown bottle) and ultrasonic oscillation, so as to ensure complete dissolution. Each solution is taken by a spotting capillary and dropped on the polyvinylidene fluoride membrane (PVDF) in an appropriate amount (about 1 μL), and the two color-sensitive response points are about 5 mm apart, so as to prepare a 1x2 (one row and two columns) olfactory visualization chip.

[0026] As a further improvement of the method for rapidly detecting the freshness of beef based on the olfactory visualization chip of the present application, step 2.1) is:

[0027] Place the representative beef sample in a closed container (e.g. a disposable plastic cup) so that the two sensors of the olfactory visualization chip are directly opposite the representative beef sample (e.g. stick the back of the olfactory visualization chip to a plastic wrap, seal the plastic cup with the plastic wrap), and take out the olfactory visualization chip after 4-8 minutes of reaction at room temperature; read the absolute value of the RGB difference (R, G, B color difference) of the two sensors of the olfactory visualization chip before and after reaction, and select the R value of tetramethoxyphenyl porphyrin iron (III) chloride and the R value of phenol red as the response feature matrix of the olfactory visualization chip.

[0028] As a further improvement of the beef freshness rapid detection method based on the olfactory visualization chip of the present application, step 2.3) is:

[0029] The TVB-N value (y tra-TVBN ) obtained in step 2.2) is combined with the response feature matrix obtained corresponding to the storage time, and a prediction model of the TVB-N content in beef is established by programming with Matlab language, and the network structure of the BPNN model is set to 2-7-1 (the number of input layer neurons is 2, the number of hidden layer neurons is 7, and the number of output layer neurons is 1).

[0030] The TVC value (y tra-TVC ) obtained in step 2.2) is combined with the response feature matrix obtained corresponding to the storage time, and a prediction model of the TVC content in beef is established by programming with Matlab language, and the network structure of the BPNN model is set to 2-3-1 (the number of input layer neurons is 2, the number of hidden layer neurons is 3, and the number of output layer neurons is 1).

[0031] As a further improvement of the beef freshness rapid detection method based on the olfactory visualization chip of the present application,

[0032] The beef samples for verification (samples not from the same batch as the "representative beef samples") are collected for their response feature matrix of the olfactory visualization chip, TVB-N and TVC content during storage, respectively, and the response feature matrix and TVB-N, and the response feature matrix and TVC are introduced into the corresponding BPNN model, respectively, for verifying the accuracy and robustness of the pre-established BPNN quantitative model;

[0033] The evaluation indexes of the above-mentioned BPNN quantitative model are the root mean square error of training (RMSEC), the determination coefficient (R c ) of the training set, the root mean square error of prediction (RMSEP), the determination coefficient (R p ) of prediction and the relative analysis error (RPD), specifically:

[0034]

[0035]

[0036]

[0037] wherein y pre and respectively represent the true value and the predicted value of the jth sample in the prediction set; represents the average value of all true value contents in the prediction set; N pre represents the number of samples in the prediction set; the calculation formula of RMSEC, R c in the training set is similar to that of RMSEP, R p in the prediction set; SD represents the standard deviation of all true values in the prediction set.

[0038] The method of the application utilizes two gas-sensitive materials to make an olfactory visualization chip, which reacts with the odor emitted by beef to present different color changes, so as to visually judge the freshness of beef, and establish a BPNN quantitative model of the freshness indicators TVB-N and TVC, so as to realize quantitative detection of the freshness of beef.

[0039] The application has the following beneficial effects:

[0040] (1) The olfactory visualization chip for beef freshness only needs two color-sensitive materials, which is simple to make, low in cost and good in portability;

[0041] (2) The olfactory visualization chip is not interfered by moisture and temperature during detection, has fast response speed and good stability, and is a simple portable detection device for beef freshness;

[0042] (3) The quantitative model of beef freshness based on BPNN can be directly input without data pre-processing, and has simple model structure and fast speed;

[0043] (4) The BPNN model can predict the contents of TVB-N and TVC in beef end to end without establishing a mathematical model, and can establish a nonlinear mapping relationship between the response value of the olfactory visualization chip and the freshness indicators of beef, and the established BPNN model has high precision and good robustness.

[0044] The application can realize rapid, simple and accurate quantitative detection of beef freshness, so as to solve the problems of complicated detection process, high detection cost, easy environmental interference and non-visual detection in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0045] The specific embodiments of the application will be further described in detail below with reference to the accompanying drawings.

[0046] Figure 1 Flow chart of the beef freshness rapid detection method based on an olfactory visualization chip of the present application.

[0047] Figure 2 Structure schematic diagram of the olfactory visualization chip for beef freshness detection used in the present application;

[0048] The left is the color-sensitive response point of tetramethoxyphenyl porphyrin iron (III) chloride, and the right is the color-sensitive response point of phenol red.

[0049] Figure 3 Color change diagram of the olfactory visualization chip before and after detecting the odor released by the stored beef used in the present application;

[0050] The left is the color difference characteristic of tetramethoxyphenyl porphyrin iron (III) chloride before and after reaction, and the right is the color difference characteristic of phenol red before and after reaction. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described in detail below, which are implemented on the premise of the technical solutions of the present application, and detailed implementation manners and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0052] Example 1, a beef freshness rapid detection method based on an olfactory visualization chip, the following steps are performed in turn:

[0053] Step 1: tetramethoxyphenyl porphyrin iron (III) chloride and phenol red are selected as gas-sensitive materials to make the olfactory visualization chip shown in Figure 2 for rapid detection of beef freshness:

[0054] (1.1) 8 mg of tetramethoxyphenyl porphyrin iron (III) chloride is weighed into a 5 mL brown bottle, 4 mL of chloroform is added to the brown bottle, the brown bottle cap is covered, and the brown bottle is taken out after being ultrasonicated in an ultrasonic cleaning chamber for 20 minutes, serving as a tetramethoxyphenyl porphyrin iron (III) chloride solution;

[0055] (1.2) 8 mg of phenol red is weighed into a 5 mL brown bottle, 4 mL of anhydrous ethanol is added to the brown bottle, the brown bottle cap is covered, and the brown bottle is taken out after being ultrasonicated in an ultrasonic cleaning chamber for 20 minutes, serving as a phenol red solution;

[0056] The ultrasonic frequency in the above steps (1.1) and (1.2) is 35 kHz, and the power density is 100 W / L.

[0057] (1.3) PVDF film with good hydrophobicity is selected as the substrate material of the olfactory visualization chip, and the PVDF film is cut into 2×3 cm 2Size, using point sample capillary respectively appropriate amount of the above prepared two kinds of solution, drop on the PVDF membrane, made into 1 x 2 arrangement of olfactory visualization chip, for subsequent detection of beef freshness.

[0058] Specifically: first take 1 μL of step (1.1) prepared tetramethoxy phenyl porphyrin iron (III) chloride solution drop on the cut PVDF membrane, and then take 1 μl of step (1.2) prepared phenol red solution drop, the drop point of two kinds of solution on a straight line, and apart about 5 mm, thereby making 1 x 2 arrangement of olfactory visualization chip on 2 x 3 cm 2 PVDF membrane, the sensing surface of the olfactory visualization chip is the side of the drop solution, which is the front of the olfactory visualization chip.

[0059] Step 2: 120 beef samples were obtained from the rib part of 10 cows, each beef sample was 6 x 3 x 2 cm 3 Size, for establishing BPNN quantitative model of beef freshness detection. Put the beef samples into 4℃ refrigerator, store for 0, 2, 3, 4, 6, 8, 10 and 12 days respectively, different storage days represent different sampling points, a total of 8 sampling points, each time randomly take 15 samples from the refrigerator, and divide the samples into 3 x 3 x 2 cm 3 Size, randomly select a small piece for the sensing experiment of the olfactory visualization chip; another small piece for the measurement of TVB-N and TVC of beef freshness indicators.

[0060] The sensing experiment process of the olfactory visualization chip is as follows:

[0061] (2.1) use the scanner to obtain the image of the olfactory visualization chip before reaction;

[0062] (2.2) at room temperature, cut the cling film into appropriate size, and paste the back of the olfactory visualization chip on the cling film, put the 3 x 3 x 2 cm 3 Size of the stored beef sample into a disposable plastic cup, seal the disposable plastic cup with the cling film with the olfactory visualization chip, make the sensing surface of the olfactory visualization chip face the beef sample, take out the olfactory visualization chip after 4-8 minutes of reaction;

[0063] (2.3) use the scanner to obtain the image of the olfactory visualization chip after reaction again;

[0064] (2.4) use the image processing toolbox in Matlab software, adopt median filtering and threshold segmentation method to process the chip images before and after reaction, Figure 3The color change chart of the olfactory visualization chip before and after detecting the odor released by the stored beef is shown. The absolute value of the R, G, B color difference before and after the reaction of the two sensor arrays in the olfactory visualization chip is obtained. From the six color characteristic variables, the R value of iron (III) chloride tetramethoxyphenyl porphyrin and the R value of phenol red are further selected as the response characteristics of the olfactory visualization chip. Since there are 120 samples in common, the size of the response characteristic matrix is 120 x 2 (120 samples, 2 color characteristic variables).

[0065] Step 3: Measure 3 x 3 x 2 cm 3 The TVB-N and TVC contents of the beef samples of different sizes are measured, and the response characteristic matrix obtained in step 2 is combined to establish a BPNN quantitative model for the freshness of beef:

[0066] (3.1) The TVB-N content in beef is detected by the method in the national standard “GB 5009.228-2016 Determination of Volatile Nitrogen in Food”;

[0067] (3.2) The TVC content in beef is detected by the method in the national standard “GB 4789.2-2010 Microbiological Examination of Foodstuffs Determination of Total Number of Colony”;

[0068] (3.3) The TVB-N values (y tra ) of the 120 beef samples (N tra-TVBN ) and the response characteristic matrix obtained in step (2.4) are combined as the training set, and the BPNN quantitative model is trained by 7-fold cross-validation method, and the BPNN network structure is optimized to 2-7-1 (the number of input layer neurons is 2, the number of hidden layer neurons is 7, and the number of output layer neurons is 1), thereby establishing a BPNN quantitative model for predicting the TVB-N content in beef;

[0069] (3.4) The TVC values (y tra ) of the 120 beef samples (N tra-TVC ) and the response characteristic matrix obtained in step (2.4) are combined as the training set, and the BPNN quantitative model is trained by 7-fold cross-validation method, and the BPNN network structure is optimized to 2-3-1 (the number of input layer neurons is 2, the number of hidden layer neurons is 3, and the number of output layer neurons is 1), thereby establishing a BPNN quantitative model for predicting the TVC content in beef.

[0070] (3.5) The evaluation indexes of the above BPNN quantitative model are the root mean square error of training (RMSEC), the determination coefficient of training (R c ), and the specific values are:

[0071]

[0072]

[0073] where y tra represents the true measured value of the i-th sample in the training set according to the national standard method, represents the training value of the i-th sample in the training set obtained according to the BPNN model; represents the average value of all true measured values of the training set. tra represents the number of samples in the prediction set.

[0074] Table 1 summarizes the fitting evaluation results of the BPNN model in the training set for the content of TVB-N and the content of TVC in the beef samples in the present application. The smaller the RMSEC is, the better the fitting effect of the BPNN model is, and the better the quantitative training of the content of TVB-N and the content of TVC in the beef is, and thus the faster the freshness of the beef can be determined (TVB-N less than 20 mg / 100 g and TVC less than 6 log CFU / g, the beef is fresh, otherwise, when any of the above conditions is not met, the beef is not fresh). c

[0075] Table 1, the fitting evaluation results of the BPNN model in the training set for the content of TVB-N and the content of TVC in the beef samples in the present application

[0076]

[0077] Generally, the RMSEC needs to be less than 5% of the index unit error (5 mg / 100 g for TVB-N; 0.5 log CFU / g for TVC), and the R c needs to be greater than 0.9, in order to indicate the effectiveness of the model training; therefore, according to the data in Table 1, it can be proved that the beef freshness rapid detection model established in the present application has the technical advantages of precision and robustness on the training set.

[0078] Example 2: 40 beef samples are purchased again as the beef samples to be measured for verification (i.e., as the prediction set); the method of step 2 in Example 1 is used to obtain a response feature matrix of 40x2, and the method of step 3 in Example 1 is used to obtain the content of TVB-N and the content of TVC in the beef samples, for verifying the precision and robustness of the two BPNN quantitative models established in step 3 of Example 1, and the specific process is as follows:

[0079] (1) the TVB-N value (y pre ) of the 40 beef samples (N pre-TVBN ​) and the 40x2 olfactory visualization chip response feature matrix obtained in step (2.4) of Reference Example 1 as a prediction set, to verify the BPNN quantitative model for predicting the TVB-N content in beef established in step (3.3) of Example 1;

[0080] (2) The TVC values (y pre ) of 40 beef samples (N pre-TVC ) and the 40x2 olfactory visualization chip response feature matrix obtained in step (2.4) of Reference Example 1 were combined as a prediction set, to verify the BPNN quantitative model for predicting the TVC content in beef established in step (3.4) of Example 1;

[0081] (3) The olfactory visualization chip response feature matrix obtained in step (1) of Example 2 was input into the BPNN quantitative model for predicting the TVB-N content in beef established in step (3.3) of Example 1, to quickly obtain the predicted value of the TVB-N content in beef The olfactory visualization chip response feature matrix obtained in step (2) of Example 2 was input into the BPNN quantitative model for predicting the TVC content in beef established in step (3.4) of Example 1, to quickly obtain the predicted value of the TVC content in beef

[0082] (4) In order to verify the precision, robustness and generalization ability of the detection of the freshness indicators (TVB-N and TVC) in beef according to the present application, the BPNN predicted values (y and ) and the true values measured according to the national standard method (the true value of TVB-N measured in step (1) of Example 2 (y pre-TVBN ), the true value of TVC measured in step (2) of Example 2 (y pre-TVC )) were statistically analyzed and compared, i.e. the performance evaluation indexes of the BPNN model on the prediction set were used for judgment. The performance evaluation indexes of the BPNN quantitative model were the root mean square error of prediction (RMSEP), the determination coefficient of prediction (R p ) and the relative analysis error (RPD), specifically:

[0083]

[0084]

[0085]

[0086] wherein y pre represents the true measurement value of the jth sample in the prediction set measured according to the national standard method, represents the predicted value of the jth sample in the prediction set obtained according to the BPNN model; represents the average of all true value contents in the prediction set. N pre represents the number of samples in the prediction set. SD represents the standard deviation of all true values in the prediction set.

[0087] Table 2 summarizes the model evaluation results of the BPNN quantitative model in the prediction set for predicting the contents of the beef freshness indicators TVB-N and TVC in the embodiment of the present application. The smaller the RMSEP, the larger the R p and the RPD, the better the prediction results of the BPNN model, which can more accurately quantitatively predict the contents of TVB-N and TVC in beef, and then quickly judge the freshness of beef; the RPD greater than 3 indicates that the established BPNN model can be used in actual life. According to the RMSEC in Example 1 and the RMSEP in Example 2, it is found that the established BPNN model has excellent robustness, and according to the RMSEP, the R p and the RPD, it is found that the established BPNN model can accurately predict the freshness of beef.

[0088] Table 2, Model evaluation results of the BPNN quantitative model in the prediction set for predicting the contents of the beef freshness indicators TVB-N and TVC

[0089]

[0090] When a brand new sample is used to verify the performance of the BPNN model established in Example 1, it is found that the RMSEP in the prediction set is less than 5% of the indicator unit error (5 mg / 100 g for TVB-N; 0.5 log CFU / g for TVC), the R p is also greater than 0.9, and the RPD is greater than 3, indicating that the BPNN model established in the present application has good accuracy and robustness, and can be used to quickly obtain the contents of the beef freshness indicators (TVB-N, TVC), and then judge the freshness of beef.

[0091] In summary, the indicators of the training set of Example 1 are mainly used to show that the content of the beef freshness physicochemical indicators (TVB-N, TVC) can be quickly obtained according to the model, i.e. the established model can replace the tedious physicochemical experiment; there is no large difference between the content of the physicochemical indicators (TVB-N, TVC) obtained according to the model and the content measured by the national standard method (for TVB-N, the average error is 2.821 mg / 100 g; for TVC, the average error is 0.357 log CFU / g), and the Rc is greater than 0.9, indicating that the model established according to the training set data is good. Example 2 verifies that the model has the same good performance when facing unknown samples, i.e. general adaptability, robustness and model generalization ability.

[0092] The beef freshness rapid detection method based on the olfactory visualization chip can quickly, simply and accurately detect the beef freshness indexes TVB-N and TVC content, the olfactory visualization chip is simple to manufacture, low in cost and has good stability.

[0093] Finally, it should also be noted that the above enumeration is only several specific embodiments of the present application. Obviously, the present application is not limited to the above embodiments, but can also have many variations. All variations that can be directly derived or inferred from the content disclosed by those skilled in the art should be considered as the protection scope of the present application.

Claims

1. A rapid detection method for beef freshness based on an olfactory visualization chip, characterized in that It comprises the following steps: 1) preparing an olfactory visualization chip: Selecting tetramethoxy phenyl porphyrin iron (III) chloride and phenol red as gas sensitive materials, an olfactory visualization chip with two sensors is prepared; Specifically as follows: (1.1) 8 mg of tetramethoxy phenyl porphyrin iron (III) chloride is weighed into a 5 mL brown bottle, 4 mL of chloroform is added to the brown bottle, the brown bottle cap is covered, and the brown bottle is taken out after being ultrasonicated in the ultrasonic cleaning chamber for 20 minutes, serving as a tetramethoxy phenyl porphyrin iron (III) chloride solution; (1.2) 8 mg of phenol red is weighed into a 5 mL brown bottle, 4 mL of anhydrous ethanol is added to the brown bottle, the brown bottle cap is covered, and the brown bottle is taken out after being ultrasonicated in the ultrasonic cleaning chamber for 20 minutes, serving as a phenol red solution; The ultrasonic frequency in the above steps (1.1) and (1.2) is 35 kHz, and the power density is 100 W / L; (1.3) PVDF film with good hydrophobic property was selected as the substrate material of the olfactory visualization chip. The PVDF film was cut into 2 3 cm 2 sized, and a point sample capillary was used to take an appropriate amount of the two solutions prepared above and drop them on the PVDF film to prepare an olfactory visualization chip with 1 2 rows for subsequent detection of beef freshness; Specifically: first, 1 μL of the tetramethoxyphenyl porphyrin iron (III) chloride solution prepared in step (1.1) is dropped on the cut PVDF membrane, and then 1 μL of the phenol red solution prepared in step (1.2) is dropped, the dropping points of the two solutions are on a straight line and are 5 mm apart, so that 1 3 cm 2 of the PVDF membrane is prepared into 1 2 rows of olfactory visualization chips, and the sensing surface of the olfactory visualization chip is the side on which the solution is dropped, which is the front surface of the olfactory visualization chip; 2) Set representative beef samples under different storage times, and process each representative beef sample corresponding to each storage time as follows: Divide one representative beef sample into two to form two representative beef sub-samples, one representative beef sub-sample is used in the following step 2.1), and the other representative beef sub-sample is used in the following step 2.2): 2.1) Place the olfactory visualization chip containing the gas sensitive material and one representative beef sub-sample in a sealed container to react, obtain the RGB difference value of the chip before and after the reaction, and generate the response feature matrix of the olfactory visualization chip; Place the representative beef sub-sample in the sealed container so that the two sensors of the olfactory visualization chip face the representative beef sub-sample, take out the olfactory visualization chip after standing at room temperature for 4-8 minutes; read the absolute value of the RGB difference value of the two sensors of the olfactory visualization chip before and after the reaction, and select the R value of tetramethoxy phenyl porphyrin iron (III) chloride and the R value of phenol red as the response feature matrix of the olfactory visualization chip; 2.2) Measure the freshness index of the other representative beef sub-sample, and the freshness index is the content of TVB-N and TVC; 2.3), the freshness index data obtained in step 2.2) is combined with the response characteristic matrix obtained with the corresponding storage time in step 2.1), thereby establishing a back propagation neural network quantitative model for beef freshness; the TVB-N value obtained in step 2.2) is combined with the response characteristic matrix obtained with the corresponding storage time, and a prediction model for the TVB-N content in beef is established by programming with Matlab language, with the network structure of the BPNN model being set as 2-7-1. The TVB-N content in beef is predicted by programming with Matlab language, with the network structure of the BPNN model being set as 2-7-1. The TVC value obtained in step 2.2) is used to calculate the response characteristic matrix of the TVC value and the corresponding storage time In combination with the response characteristic matrix obtained for the corresponding storage time, the network structure of the BPNN model is set to 2-3-1, and a prediction model for the TVC content in beef is established by programming in Matlab language.

2. The rapid detection method of beef freshness based on the olfactory visualization chip according to claim 1, characterized in that It also comprises the following steps: The measured beef sample is subjected to the above operation of 2.1) to obtain the response feature matrix of the olfactory visualization chip of the measured beef sample; The response feature matrix of the olfactory visualization chip of the beef sample to be tested is input into the quantitative model of beef established in step 2.3), so as to obtain the predicted value of the TVB-N content of the beef sample to be tested and the predicted value of the TVC content .

3. The rapid detection method of beef freshness based on the olfactory visualization chip according to claim 2, characterized in that It also comprises: The beef sample for verification is repeatedly subjected to the above step 2), so as to verify the accuracy and robustness of the BPNN.

4. The beef freshness rapid detection method based on the olfactory visualization chip according to claim 3, characterized in that: The response feature matrix, TVB-N and TVC content of the olfactory visualization chip of the beef sample for verification during storage are collected respectively, the response feature matrix and TVB-N, and the response feature matrix and TVC are introduced into the corresponding BPNN model respectively, and the accuracy and robustness of the pre-established BPNN quantitative model are verified. The evaluation indexes of the BPNN quantitative model are root mean square error of training , determination coefficient of training set , root mean square error of prediction , determination coefficient of prediction , and relative analysis error RPD, specifically: (1) (2) (3) wherein and respectively represent the true value and the predicted value of the i-th sample in the prediction set; represents the average value of all true values in the prediction set; represents the number of samples in the prediction set; the calculation formula of , is similar to the calculation formula of , in the prediction set; represents the standard deviation of all true values in the prediction set.​

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