System and method for judging the freshness of meat by combining CRISPR colorimetric detection system and image analysis device

Abstract

The present invention discloses a system for evaluating the freshness of meat products by combining a CRISPR colorimetric detection system with an image analysis device, which includes a liquid carrier device, a light source supplement module, an image acquisition module, and a data processing module. Based on the CRISPR colorimetric detection technology, the system adopts a self-designed image capture device and is coupled with an automatically analyzed program software for CRISPR colorimetric images programmed independently, achieving the purpose of evaluating the freshness of meat products based on CRISPR colorimetric images. The system can import the captured colorimetric images into the analysis software to realize the quantification of the B value of the images and the rapid judgment of freshness. In the actual detection of fresh meat, the significant correlation between the B value and the traditional freshness indicators and the excellent R<supgt;2< / supgt> fitting degree further indicate that the CRISPR colorimetric system can be effectively applied to the detection and evaluation of the freshness of fresh meat.

Description

Technical Field

[0001] The present invention belongs to the field of detection, and specifically relates to the development and application of a system combining CRISPR colorimetric detection and image analysis (hardware + software) to quantify dominant spoilage bacteria in meat to judge the freshness of meat. Background Art

[0002] Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) is a mechanism present in most bacteria and archaea. In particular, the CRISPR / Cas system with incidental cutting behavior has been proven to be an important tool in microbial diagnosis due to its ease of use, high cost-effectiveness and strong stability. Under the guidance of crRNA, the nuclease Cas12a can bind to crRNA to form a complex, recognize the protospacer adjacent motif site (-TTTN-) of the DNA sequence to cut the customized single-stranded DNA to produce a signal output, and the detection shows good response performance. Among them, the new CRISPR colorimetric detection has the advantages of high sensitivity, fast response speed, good selectivity and simple operation, and has been used as a simple and reliable instant analysis method. Compared with other traditional electrochemical and fluorescence detection methods, colorimetric detection can achieve rapid qualitative and color value quantification by naked eye, and the detection cost is low and no complex instruments are required. Therefore, CRISPR-based colorimetric detection also promotes the practical application of instant detection systems in various scenarios in the future.

[0003] The rapid development of the field of biological detection has gradually stimulated the research of new analytical systems. At present, colorimetric analysis has become the focus of detection, which allows all kinds of users to complete the detection and evaluation of the target to be detected efficiently, conveniently and instantly. However, colorimetric detection still has great limitations due to interference from external light sources, dependence on mobile phone software and differences between analytical methods. Colorimetric detection through image quantification has become a focus of attention in this field. The ability to intuitively perceive and analyze the analyte based on its color is an important technological breakthrough in the field of research, diagnosis and analysis. Therefore, it is crucial to develop reliable, accurate and fast analysis systems based on CRISPR colorimetric detection that are convenient for users. Summary of the invention

[0004] The purpose of the present invention is to establish a set of colorimetric detection systems based on on-site instant analysis and practical and convenient, and also suitable for quantitative detection of Pseudomonas to reflect the freshness of fresh meat. CRISPR detection system. The present invention first designs a hardware architecture for colorimetric image acquisition based on the principle of colorimetric detection and in combination with optical elements, which mainly includes the integration of three functional modules: image acquisition, light source supplementation and liquid carrier chip. The CRISPR colorimetric solution can be placed in a self-designed sample carrier tank. In a closed and dark environment, the light source is supplemented by the internal LED top light array and the backlight base plate, and the colorimetric image is captured by an image acquisition module that is stable and compatible with the computer terminal. The analysis software that is self-programmed by Matlab gui is then matched, and the real-time capture and analysis of the color image is realized by code function design, image acquisition module (industrial camera) camera parameter adjustment, and self-loading software standard curve. At the same time, the correlation analysis between the image analysis value and the traditional index is used to explore the application effect of the CRISPR analysis system in the actual evaluation of the freshness of meat.

[0005] The colorimetric principle on which the present invention is based includes the following three parts:

[0006] First, the enzyme-labeled magnetic bead probe is synthesized, the CRISPR / Cas12a-mediated cleavage process is carried out, and the colorimetric reaction of horseradish peroxidase (HRP) and 3,3',5,5'-tetramethylbenzidine is carried out. Specifically, the enzyme-labeled magnetic bead probe is connected to the magnetic nanoparticles and horseradish peroxidase through amide reaction, biotin and streptavidin reaction; the subsequent Cas12a-crRNA formed by the combination of Cas12a and crRNA can specifically recognize the target DNA and activate its own trans-cleavage activity; finally, the enzyme-labeled magnetic bead probe is non-specifically cleaved by Cas12a-crRNA to release the horseradish peroxidase, and the 3,3',5,5'-tetramethylbenzidine (TMB) colorimetric solution is introduced to produce a colorimetric reaction.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The present invention first discloses a system for judging the freshness of meat by combining a CRISPR colorimetric detection system with an image analysis device, which comprises a liquid carrier, a light source supplement module, an image acquisition module, and a data processing module, wherein:

[0009] The liquid carrier is used to carry the CRISPR colorimetric solution, which is obtained by the following steps:

[0010] ① Mix 2 volumes of Cas12a-crRNA, 4 volumes of enzyme-labeled magnetic bead probes, 1 volume of RNase inhibitor, 2 volumes of NEBuffer r2.1 (10×), 2 volumes of target DNA, and 9 volumes of Free-DNase / RNase water to make a reaction solution, and incubate at 37°C for 1 hour, and collect the supernatant after magnetic separation; wherein: Cas12a-crRNA formed by the combination of Cas12a and crRNA is 2 μM, the enzyme-labeled magnetic bead probe is 1 mg / mL, and the RNase inhibitor is 20 U / μL;

[0011] ② Add 40 volumes of TMB colorimetric solution to the collected supernatant, the solute of the colorimetric solution is 3,3',5,5'-tetramethylbenzidine, and the concentration is 0.15-0.25 mg / ml; to obtain the enzyme marker-3,3',5,5'-tetramethylbenzidine HRP-TMB reaction solution; incubate the reaction solution at 37°C for 16 minutes, and the reaction solution turns blue;

[0012] ③ Add 40 parts by volume of hydrochloric acid solution to stop the colorimetric reaction, turn yellow, and obtain CRISPR solution; wherein the concentration of the hydrochloric acid solution is 2M;

[0013] The light source supplement module includes an LED top light array and a backlight base plate arranged inside the liquid carrier device, and is used to supplement the light source in a closed and dark environment;

[0014] The image acquisition module is used to capture colorimetric images;

[0015] The data processing module outputs the meat freshness based on the colorimetric image collected by the image acquisition module through the following steps:

[0016] S1, quantize the input colorimetric image and extract each color value;

[0017] S2, input the extracted color value into the pre-constructed prediction model, and output the prediction result of the feature index;

[0018] S3. Combine the thresholds of the characteristic indicators to determine the freshness of the meat.

[0019] Specifically, the liquid carrying device includes a transparent carrier plate, which is hollow to embed two liquid carrying sheets stacked up and down, wherein the lower liquid carrying sheet is black and the upper liquid carrying sheet is white, and the top surface of the white liquid carrying sheet is lower than the top surface of the transparent carrier plate to form a groove for carrying liquid, and the lower black liquid carrying sheet can prevent the penetration of the bottom light source and provide a good image background due to the low light absorption of white.

[0020] Specifically, in S1, the image extracts various color values ​​according to the internal pixel arrangement, including R, G, B, H, S, V and Gray values; the color values ​​in the vertical direction of the image are averaged to obtain the color mean, and then the peak spectrum between the pixel value and the color intensity value is obtained according to the horizontal pixel distribution.

[0021] Specifically, the R, G, B, H, S, V, and Gray values ​​are obtained by the following formulas:

[0022] R=R / 255

[0023] G=G / 255

[0024] B=B / 255

[0025]

[0026] V=max(R,G,B)

[0027] Gray=0.299*R+0.587*G+0.114*B

[0028] Specifically, in S2, in the pre-built prediction model: the input is The characteristic index selects the number of Pseudomonas as output, and inputs There is a fitted linear relationship between the number of Pseudomonas output and the number of Pseudomonas output;

[0029] enter Obtained by the following formula:

[0030]

[0031] Among them, x 1 and x 2 are the two endpoints in the peak spectrum, and C(i) is the color value between the two endpoints.

[0032] Preferably, C(i) is a color B value between the two endpoints.

[0033] Preferably, in S3, the threshold value is 7 CFU / g, and if the output is ≥7, the meat is judged to be rotten; if the output is <7, the meat is judged to be fresh.

[0034] Preferably, the image acquisition module uses an industrial camera to capture colorimetric images, and the capture parameters are: an aperture size of 8 and a gain effect of 2.0104.

[0035] The present invention also discloses a method for judging the freshness of meat by combining a CRISPR colorimetric detection system with an image analysis device. Based on the system, the method comprises the following steps:

[0036] (1) 2 volumes of Cas12a-crRNA, 4 volumes of enzyme-labeled magnetic bead probes, 1 volume of RNase inhibitor, 2 volumes of NEBuffer r2.1 (10×), 2 volumes of target DNA, and 9 volumes of Free-DNase / RNase water were mixed to prepare a reaction solution, and incubated at 37° C. for 1 hour, and the supernatant was collected after magnetic separation; wherein: the Cas12a-crRNA formed by the combination of Cas12a and crRNA was 2 μM, the enzyme-labeled magnetic bead probe was 1 mg / mL, and the RNase inhibitor was 20 U / μL;

[0037] (2) adding 40 volumes of TMB colorimetric solution to the collected supernatant, wherein the solute of the colorimetric solution is 3,3',5,5'-tetramethylbenzidine, and the concentration is 0.15-0.25 mg / ml; to obtain an enzyme marker-3,3',5,5'-tetramethylbenzidine HRP-TMB reaction solution; incubating the reaction solution at 37° C. for 16 minutes, and the reaction solution turns blue;

[0038] (3) adding 40 parts by volume of hydrochloric acid solution to stop the colorimetric reaction, turning yellow, and obtaining a CRISPR solution; wherein the concentration of the hydrochloric acid solution is 2 M;

[0039] (4) Adding CRISPR detection colorimetric reaction solution into the liquid carrier device, pushing it into the carrier tank for fixing, and waiting for the image acquisition module to take pictures to obtain the sample image;

[0040] (5) The captured image is input into the data processing module to realize image color value extraction and meat freshness assessment.

[0041] Beneficial effects of the present invention

[0042] The present invention proposes a system and method for quantifying dominant spoilage bacteria (Pseudomonas) in meat products to judge freshness by combining a CRISPR colorimetric detection system with an image analysis device. Compared with existing detection systems, this system, as an instant analysis system, has the advantages of convenient operation, instant evaluation, and accurate quantification, ensuring the application mode of on-site instant detection, and does not require large or expensive equipment, simplifying the processing and sharing of results. The present invention has significant practical application value in the instant evaluation of meat freshness, and has the potential to be applicable to instant detection in multiple scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The diagram is a hardware device diagram, including (A) the overall hardware diagram, and (B) the self-designed liquid carrier CAD diagram and its actual picture.

[0044] Figure 2 This is a schematic diagram of the optical path, including (A) component position and size diagram, and (B) optical path display diagram.

[0045] Figure 3 It is the logic flow chart of the software.

[0046] Figure 4 This is the threshold analysis mode diagram.

[0047] Figure 5 The linear relationship between the number of Pseudomonas and the B value of Cr-SC analysis is shown in the figure.

[0048] Figure 6 This is a diagram showing the software operation steps.

[0049] Figure 7 This is a diagram showing the impact of camera parameters on image acquisition.

[0050] Where (A) image display effect and (B) signal-to-noise ratio change. S = 255-B 1 ; N = 255-B 2 , where B 1 and B 2 These are the B values ​​when there is a colorimetric image and when there is no colorimetric image, respectively.

[0051] Figure 8 The figure shows the quantification result of the colorimetric image based on the HRP concentration gradient.

[0052] Among them, (AC) R, G and B channel curves; (DF) H, S and V channel curves; (G) gray channel curve.

[0053] Fig. 9 This is the effect diagram of the B value analysis of the colorimetric image based on the HRP concentration gradient.

[0054] Wherein (A) standard curve established according to B value; (B) colorimetric image of HRP concentration gradient.

[0055] Fig.10 .Color development and OD under different reaction conditions 450 Analysis diagram.

[0056] Wherein (A): concentration of enzyme-labeled magnetic bead probe (MSH); (B): concentration of Cas12a-crRNA; (C): incubation time of horseradish peroxidase and 3,3',5,5'-tetramethylbenzidine (HRP-TMB). Positive: supernatant containing target DNA; Blank: supernatant without target DNA. Error bars represent the standard deviation of four replicate samples (n=4). Values ​​marked with different lowercase letters are significantly different (p<0.05). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001; ns not significant.

[0057] Fig.11 .Figure 2. Sensitivity study of CRISPR colorimetric solution detection.

[0058] Where (A): UV-visible absorption spectrum based on bacterial concentration (A~H: 5.1×10 8 ~5.1×10 1 CFU / mL; I: 0CFU / mL); (B): OD 450 Relationship between the logarithm of bacterial concentration; (C): UV-visible absorption spectrum based on gene concentration (A-G: 480ng / μl-480fg / μl; H: 0fg / μl); (D): OD 450 The relationship between the logarithm of gene concentration.

[0059] Fig.12 It is the Pearson correlation coefficient analysis diagram between the colorimetric image B value and the corruption index.

[0060] Fig.13 is the R between the colorimetric image B value and the corruption index 2 Correlation coefficient analysis chart.

[0061] Among them, (A) the correlation between the B value on the smartphone and the number of Pseudomonas; (B) the correlation between the B value on the computer program and the number of Pseudomonas (n=48). DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of protection defined by the claims of the present invention.

[0063] The reagents used in the present invention are as follows:

[0064] LbaCas12a (Cpf1, 100 μM) and NEB buffer 2.1 (10×) were purchased from New England Biolabs, USA. The crRNA sequence structure was 5′-UAAUUUCUACUAAGUGUAGAUCUGUGUAGCGGUGAAA UGCGUAG-3′, LbaCas12a and crRNA were mixed in the solution and combined to obtain Cas12a-crRNA for use in CRISPR colorimetric solution. Carboxyl-modified 300 nm magnetic nanoparticles were purchased from Beaver (China). Bacterial genomic DNA extraction kit was purchased from Vazyme (China). 1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide hydrochloride and N-hydroxysuccinimide were provided by Sigma-Aldrich (Shanghai, China). 3,3',5,5'-Tetramethylbenzidine substrate (TMB) (containing H 2 O 2 ) was purchased from Neobioscience Technology Co. Ltd. (Shanghai, China). Streptavidin-Horseradish peroxidase (SA-HRP) and 5% BSA blocking buffer were purchased from Solarbi (Beijing, China). Luria-Bertani (LB) broth was purchased from Qingdao Hi-Tech Source Haibo Biotechnology Co. Ltd. (Qingdao, China). Plate count agar was purchased from Landbridge (Beijing, China). DNAse / RNase-Free Water and RNase inhibitors were commercially available from Sangon Biotechnology Co. Ltd. (China). The deionized water used in the experiment was processed by the American Milli-Q system with a resistivity of ≥18.2 MΩcm. All chemical reagents were of analytical grade and used without further purification.

[0065] The strains used in the present invention were all isolated from corrupt fresh meat and stored in the National Meat Quality Safety Control Engineering Technology Research Center of Nanjing Agricultural University. The fresh chicken breast samples used in the present invention were purchased from a supermarket.

[0066] The analysis principle of the CRISPR detection system disclosed in the present invention is as follows:

[0067] The technical principle of using the CRISPR analysis system to evaluate the freshness of meat includes three parts. First, the synthesis of enzyme-labeled magnetic bead probes, the CRISPR / Cas12a-mediated cleavage process, and the colorimetric reaction of horseradish peroxidase and 3,3',5,5'-tetramethylbenzidine. Among them, in the reaction system, Cas12a and crRNA will combine to form a Cas12a-crRNA complex, and then the cleavage activity will be activated by the target DNA to produce a colorimetric reaction. Secondly, the colorimetric reaction solution is placed in a self-designed sample carrier chip. In a closed and dark environment, optical supplementation is performed through the internal adjustable upper and lower light sources, and finally the CRISPR detection image is captured using an image acquisition module (industrial camera) that is stable and compatible with the computer. Finally, with the help of the Matlabgui operating system, a software system suitable for CRISPR detection is self-written, which has three modules: importing pictures, quantifying image color values, and threshold analysis. At the same time, it can realize the color spectrum extraction and quantitative evaluation of the image data collected by the hardware device. The designed CRISPR detection and analysis system ensures the application mode of on-site instant detection, and also has the potential to be applicable to rapid freshness evaluation in multiple scenarios.

[0068] The operation of the software system includes the following steps:

[0069] (1) First, open the application. The functional panes display three main frames: image import, image quantification (color value extraction), and threshold determination (meat freshness assessment).

[0070] (2) Click the "Import" button to import the image to be analyzed in the folder selected on the computer into the preview window. The image is captured in real time from the hardware device.

[0071] (3) Click the R, G, and B buttons on the upper right corner respectively to extract the horizontal RGB color values ​​of the preview image and obtain the RGB spectrum distributed according to "color intensity-X-axis pixels".

[0072] (4) You can click the H, S, or V buttons in the middle right corner to extract the horizontal HSV color value of the preview image and obtain the HSV spectrum distributed according to "color intensity - X-axis pixels".

[0073] (5) Click the Gray button on the lower right corner to extract the horizontal Gray color value of the preview image and obtain the Gray spectrum distributed according to "color intensity-X-axis pixels".

[0074] (6) Click the "Calculation" button to obtain the analysis and processing data based on the B value, that is, to convert the intensity spectrum obtained from the preview image into a detection color value. At the same time, according to the self-loaded bacterial concentration standard curve, the sample can be evaluated in real time. According to 7 as the judgment threshold of M, the evaluation result is "M-OK" or "M-NG", where M is the calculated number of Pseudomonas.

[0075] Among them, the colorimetric image quantification mode and result interpretation method of the software are as follows:

[0076] (1) Image quantification: Quantify the color value of the colorimetric image and obtain the spectral relationship between the horizontal pixel value and the color signal intensity (the data processing software is self-written by Matlab gui). By extracting the horizontal color value of the imported image, the color value of each pixel in the image can be obtained according to the distribution of the pixel points, and then the color value of each column in the X-axis direction is averaged, so as to obtain the peak intensity spectrum of the sample colorimetric image according to the pixel distribution in the X-axis direction and the color intensity in the Y-axis;

[0077] (2) Result interpretation: The formula model of threshold analysis is as follows: By summing the edge pixel coordinates X 1 , X 2 The color values ​​between (the letter C is used here to represent the color value parameter), and the average is obtained Will By inserting the fitted linear relationship between the self-loaded Pseudomonas count and C, the contained Pseudomonas count (MCFU / g) can be calculated by the colorimetric image analysis program. The bacterial count is compared with the corruption threshold (7CFU / g). If M≥7, "M-NG" is displayed, indicating that the sample is corrupt; if M<7, "M-OK" is displayed, indicating that the sample is still fresh.

[0078]

[0079] The following examples illustrate preferred embodiments of the present invention, but the present invention is not limited thereto.

[0080] Example 1 Development of CRISPR Colorimetric Analysis System

[0081] 1. Hardware Design

[0082] The optical path components of the colorimetric detection device of the present invention are preset and mainly include: an industrial camera 1, an LED ring array 2, a liquid carrier tank 3, a closed shell 4, a control screen 5, a groove bracket 6, and a backlight source 7. The experiment can move the corresponding CRISPR colorimetric detection result to the position to be analyzed through the groove bracket 6. The bottom backlight source 7 is used to eliminate the influence of the edge background, and the top light source 2 is used to supplement the ambient light source to make the color of the corresponding sample more obvious. Finally, an industrial camera that is stable and compatible with the computer terminal can be used to complete the real-time capture of the colorimetric image after the sample to be tested is reflected. Based on the design of functional modules, the specific structure of the main components of the hardware equipment is as follows: Figure 1 As shown:

[0083] (1) The industrial camera 1 is mainly responsible for connecting to the computer terminal to achieve real-time transmission of the captured colorimetric image to the computer detection port for subsequent colorimetric analysis and processing;

[0084] (2) The closed and dark shell 4 can prevent the influence of external natural light sources, thereby improving the efficiency of contrast color image capture. The LED ring array 2 at the top and the backlight source 7 at the bottom can provide an adjustable light environment in a closed dark environment, and combined with the parameter adjustment of the industrial camera 1, a high-definition and analyzable image acquisition of contrast color results can be achieved;

[0085] (3) The sample liquid tank 3 mainly includes three parts: the transparent external support does not affect the generation of the background value; the two square slides (black and white) in the center can prevent the penetration of the bottom light source and thus affect the color saturation of the sample, and the white slide can provide a good background due to its low light absorption.

[0086] (4) The externally pull-out groove bracket 6 can facilitate the replacement of samples and the operation of the instrument. When combined with the self-designed sample carrier tank 3, it can adapt to the built-in light source environment and subsequent software processing and analysis.

[0087] 2. Optical path principle

[0088] Based on the assembly and construction of the aforementioned hardware architecture, the hardware optical path schematic diagram designed by the present invention is as follows: Figure 2 As shown, the distance between each component is optimized and adjusted to suit the effect of optical image acquisition; at the same time, the light source part is based on the light emitted by the bottom backlight plate 7 through the liquid carrier tank 3, and cooperates with the top LED ring array 2 to achieve two major functions of the liquid carrier tank 3: 1) The uniformity of the edge background, the use of a uniform light source backlight 7 can make the color of the area other than the sample tank uniform; 2) Based on the dark box environment to avoid being affected by external natural light, the color at the sample position can be clearly presented. At this point, the colorimetric solution can complete the real-time capture of the image through the industrial camera 1 compatible with the top of the device with the supplement of the top and bottom light sources.

[0089] Therefore, in order to meet the needs of on-site instant detection, CRISPR colorimetric detection is integrated and the device operation process of the present invention is as follows:

[0090] (1) Turn on the power of the industrial camera 1 and the light source. The LED ring array 2 can adjust the power adaptively, and the bottom backlight source 7 can adjust different color backgrounds according to the program software to adapt to different image backgrounds. The industrial camera is connected to the computer terminal to facilitate instant image acquisition and transmission, and adjust the shooting parameters to obtain the best image effect;

[0091] (2) Add CRISPR detection colorimetric reaction solution to the sample liquid tank 3, push it into the sample tank and fix it, then wait for the camera 1 to take a picture to obtain the sample image. Click the image acquisition button to realize real-time capture of the sample image;

[0092] (3) Save the captured image to the computer terminal, click the desktop Cr-SC colorimetric analysis software, import the image to be analyzed, and click the image analysis and threshold judgment buttons to extract the image color value and evaluate the freshness of the meat based on the sample colorimetric results;

[0093] (4) Pull out the liquid carrier tank 3. Be careful not to use excessive force to avoid spilling the sample. Rinse the sample tank with pure water and add the solution to be tested. When placing the sample tank, place the black bottom face downward. Do not touch the white bottom with your hands to avoid shooting errors.

[0094] 3. Software Development

[0095] According to the analysis requirements of colorimetric detection, this paper uses Matlab gui to write a computer-based image analysis software based on CRISPR detection. The software will have three main functions, namely, image import, color value extraction and threshold judgment. The overall logical flow structure of the software is as follows Figure 3 shown.

[0096] First, the captured images in the computer files can be imported by self-selection, so that image acquisition and colorimetric analysis can be completed only by computer operation; secondly, Figure 4 As shown in the figure, the image imported into the software window can be quantified, and the image can be extracted according to the internal pixel arrangement. Finally, the two endpoints X below 255 are set according to the peak spectrum. 1 and X 2 The color values ​​between the samples were averaged to represent the intensity of the detected color values ​​reflected by the sample. At the same time, a linear relationship between the color value and the number of Pseudomonas was established (as shown below). Figure 5 The color value intensity obtained by the software analysis can be converted into the number of Pseudomonas using this standard curve. The corruption threshold of 7log CFU / g can be set to determine whether the colorimetric reaction solution obtained by the test has reached corruption for the sample.

[0097] The operation steps of the software in the specific example are as follows Figure 6 As shown, it mainly includes opening the desktop "Cr-SC" software, clicking the "Import" button to import the captured image, RGB color value channel analysis, HSV color value channel analysis, Gray color value channel analysis and finally judging the freshness of the sample in real time according to the threshold.

[0098] Example 2 Performance Debugging of CRISPR Colorimetric Analysis System

[0099] 1. Camera parameter optimization

[0100] The equipment based on CRISPR detection system mainly relies on the detection images captured by industrial cameras to analyze samples. The parameters of key equipment are shown in Table 1 below. In order to capture colorimetric images more accurately, the image quality can be improved by setting the camera's shooting parameters. Table 1 Hardware equipment parameters (the liquid carrier is a self-developed product, see Example 1 for details)

[0101]

[0102]

[0103] Therefore, in the CRISPR Pseudomonas detection based on the colorimetric detection device of the present invention, in order to obtain the best acquisition effect and image analysis sensitivity, the experiment investigated the effects of three industrial camera shooting parameters including aperture size (F-stop), exposure time (S) and gain effect (Gain) on the detection signal-to-noise ratio (Signal / Noise ratio, S / N). Combining the above three industrial camera acquisition parameters, the aperture is set to f / 4, f / 5.6, f / 8, f / 11 and f / 16 respectively; the exposure time is: 8000, 9000, 10000, 11000 and 12000ms and the gain effect is 0, 1.0052, 2.0104, 3.0156 and 3.9849dB parameter gradient; the parameters are optimized and screened in combination with the colorimetric image and the signal-to-noise ratio S / N, where S=255-C 1 、N=255-C 2 , C 1 and C 2 The software analyzed the color values ​​for the presence and absence of the colorimetric image.

[0104] like Figure 7As shown in the figure, it is found that different aperture sizes and gain effects have a significant impact on the detection signal-to-noise ratio. When the exposure time is determined to be 10000us, the aperture and gain parameters with significant influence on the color image capture are listed. It can be seen that when the aperture size is 8 and the gain effect is 2.0104, a clearer image quality can be achieved. This is basically confirmed by the numerical comparison results of the signal-to-noise ratio S / N. A higher S / N value indicates that the colorimetric image corresponding to this condition is more obvious and clearer than the blank background value.

[0105] 2. Determination of color reference value

[0106] According to the optional hardware installation and software design and programming of the present invention, the CRISPR-based detection system has been basically constructed. In order to determine the best reference value from each color channel value, the present invention is suitable for the threshold judgment function based on system analysis. Different concentrations of HRP and TMB are used for colorimetric response, and the constructed analysis system is used for image analysis and color value extraction.

[0107] The color value spectra are as follows Figure 8 As shown, since the CRISPR colorimetric solution appears yellow, according to the principle of color complementation, the solution will absorb blue light. Figure 8 (AC) shows the intensity spectra of the three channels under the RGB color model quantization. It is found that as the HRP concentration of the B channel increases, that is, the yellow color of the colorimetric solution becomes more obvious, the color value presented becomes smaller, and the values ​​of the R and G channels do not change significantly. This may be because the chromaticity angle between red and green and yellow is small and the difference is not obvious. Figure 8 (DF) shows the intensity spectrum under HSV quantification. As mentioned above, since the sample solution is yellow and there is no obvious change in hue, the intensity value of the H channel remains basically unchanged, while the saturation (S value) represents the amount of white light components. The more white light components, the smaller the saturation. Therefore, when the yellow concentration is brighter, the S value shows a gradual downward trend. As the HRP concentration increases, that is, the color of the yellow sample accelerates, there is basically no obvious trend in the changes of the V value and Gray value channels. In summary, it is found that there is a significant correlation between the B value channel and the degree of HRP-TMB colorimetric reaction. Fig. 9 The linear fitting results also further show that the B value channel can be used as a marker value for freshness monitoring (R 2 =0.9597), and the B-value intensity was subsequently incorporated into the software program to achieve instant quantitative freshness assessment based on colorimetric images.

[0108] Example 3 Practical application of CRISPR colorimetric analysis system

[0109] 1. Construction of CRISPR colorimetric detection system

[0110] CRISPR colorimetric detection is based on the mining of Pseudomonas 16S rRNA to select the best targeting crRNA sequence, and at the same time, combined with the CRISPR / Cas12a mechanism to synthesize enzyme-labeled magnetic bead probes that can form significant colorimetric signals, and establish a set of rapid detection systems for Pseudomonas. This system has excellent specificity and sensitivity, and can accurately and quantitatively detect Pseudomonas in meat through naked eye detection, absorbance quantification, and color value analysis combined with software. In practical applications, it also demonstrates excellent recovery rate and accuracy, and lays a solid foundation for the subsequent development of a new system that combines the CRISPR colorimetric detection system with self-developed analytical equipment to quantify the dominant spoilage bacteria (Pseudomonas) in meat to judge freshness.

[0111] In order to more accurately detect the 16S rRNA gene of Pseudomonas, the concentration of enzyme-labeled magnetic bead probe, Cas12a-crRNA concentration and HRP-TMB reaction time in the detection process were further optimized to obtain the OD value of the final reaction solution. 450 The optimal conditions were determined by using the colorimetric response as an indicator. First, the concentration of the enzyme-labeled magnetic bead probe in the system was evaluated. The results showed that its increase would increase the signal value of the experimental group containing the target DNA. However, the low magnetic separation efficiency caused by high concentration may lead to an increase in the signal value of the blank control group. At the same time, the study of the cutting characteristics showed that the incidental cutting activity of Cas12a-crRNA was highly dependent on its concentration. Therefore, Fig.10 As shown, in the example, the best reaction system is 2 volume parts of Cas12a-crRNA, 4 volume parts of enzyme-labeled magnetic bead probes, 1 volume part of RNase inhibitor, 2 volume parts of NEBuffer r2.1 (10×), 2 volume parts of target DNA and 9 volume parts of Free-DNase / RNase water mixed to make a reaction solution, wherein: the Cas12a-crRNA formed by combining Cas12a with crRNA is 2 μM, the enzyme-labeled magnetic bead probe is 1 mg / mL, and the RNase inhibitor is 20 U / μL.

[0112] In order to verify the performance of the CRISPR colorimetric solution in detecting Pseudomonas in the present invention, 5 strains of Pseudomonas were screened from foodborne bacteria, including Pseudomonas fluorescens, Pseudomonas psychrophilus, Pseudomonas fragariae, Pseudomonas lundii, and Pseudomonas aeruginosa. The strain information is shown in Table 2 for evaluating the sensitivity of the method of the present invention. A mixed solution containing the above bacteria was prepared, with an initial bacterial concentration of 8.7 log CFU / mL and a DNA concentration of 480 ng / μL, and the bacterial template and DNA template were diluted 10 times in a gradient. The two Pseudomonas templates were analyzed using an optimized CRISPR colorimetric solution system to evaluate their quantitative ability. The limit of detection (LOD) was determined by the 3S / M calibration curve. S represents the standard deviation of the blank sample, and M represents the slope of the standard curve in the low concentration range.

[0113] like Fig.11 A shows that as the bacterial concentration increases, OD 450 The value also increases accordingly. 450 The linear relationship between the value and the logarithm of bacterial concentration (from 3.7 to 8.7 log CFU / ml) is as follows: y = 0.1084x - 0.2527 (x = log C Pseudomonas, R 2 =0.9939)( Fig.11 B), according to the 3S / M formula, the detection limit of Pseudomonas is 3.6logCFU / mL. Fig.11 As shown in C, the concentration of the target DNA gene was also quantified using an established method. As the DNA concentration increased, the intensity of the cleavage signal increased. In the range of 4.8 pg / μL to 480 ng / μL, the OD 450 There is a linear relationship between the value and the target DNA concentration ( Fig.11 D). The linear equation is y = 0.1334x + 0.3293 (x = log C target DNA, R 2 =0.9757). According to the 3S / M formula, the detection limit of Pseudomonas is 8.9 pg / μL. The results show that the CRISPR colorimetric solution has extremely high linear sensitivity and excellent color development effect for the 16S rRNA gene of Pseudomonas.

[0114] Table 2 Pseudomonas strains validated for CRISPR colorimetric solution performance

[0115]

[0116] 2. Evaluation of the application effect of CRISPR colorimetric system

[0117] First, based on the above optimization of the system analysis function and the screening of the reference B value of the colorimetric image, in order to further confirm that the B value of the obtained colorimetric image can directly display the freshness of the sample. The present invention collects commercially available chicken breast samples, performs total bacterial count, Pseudomonas count, odor evaluation and CRISPR colorimetric detection; the colorimetric response result analysis uses OD 450 The color value analysis was performed by using the smartphone software "Coloree" and the computer system "Cr-SC" described in the present invention. The correlation analysis between the image analysis values ​​and the traditional indicators was conducted to explore the application effect of the CRISPR colorimetric system in the actual evaluation of the freshness of meat.

[0118] 2.1 Analysis based on Pearson correlation coefficient

[0119] First, the Pearson correlation coefficient was used to analyze the correlation between the B value and the main corruption indicators. Fig.12 As shown, there is a significant correlation between the B value obtained by processing and analyzing the smartphone software "Coloree" and the computer software "Cr-SC" and the bacterial count of the sample. At the same time, based on the method of monitoring the number of Pseudomonas to evaluate the freshness of meat, the odor determination, which is mainly considered here when evaluating the freshness of fresh meat, also has a significant correlation with the Pseudomonas count. The significant correlation between the B value and the odor determination method also further proves that the sample color value (B value) obtained by the CRISPR colorimetric system of the present invention can provide a more accurate method for evaluating the freshness of meat, and the fast and convenient operating system also provides a more convenient way to instantly evaluate the freshness.

[0120] 2.2 Based on R 2 Correlation coefficient analysis

[0121] Linear regression analysis was used to further explore the correlation between the B value of the colorimetric image and the number of Pseudomonas. In the commercially available fresh meat samples collected by the present invention, the number of Pseudomonas was distributed from 3.81 to 7.33 Log CFU / g. After CRISPR colorimetric detection, two image analysis methods were used for processing, which can effectively establish a good mapping relationship between the number of Pseudomonas and the analysis B value. Fig.13 As shown, the B values ​​obtained by the smartphone software "Coloree" and the "Cr-SC" system of the present invention have a strong correlation with the number of Pseudomonas (R 2 =0.8253 and 0.8376 respectively). Therefore, based on the aforementioned CRISPR detection system, Pseudomonas was used as a spoilage indicator to evaluate the freshness of meat. The good correlation between B value and Pseudomonas also proved that it is feasible to use the image B value based on the CRISPR detection system to evaluate the freshness of fresh chicken.

[0122] In practical applications, Pearson correlation analysis and R-based 2 The correlation fitting of the CRISPR colorimetric system of the present invention has verified that the analytical B value has the ability to replace traditional parameters for application detection, and can be used to accurately reflect the number of Pseudomonas in fresh meat and be included in the actual evaluation of the freshness of fresh meat. In addition, in response to the application needs of on-site instant detection, there is a significant correlation between the B values ​​applicable to two different usage environments ("Coloree" on the smartphone and "Cr-SC" on the computer program), which also provides a wider range of application scenarios for this type of signal recognition method.

[0123] The above is only a preferred embodiment of the present invention, and therefore cannot limit the scope of the embodiments of the present invention. In other words, any non-substantial changes made according to the patent scope and the contents of the specification of the present invention shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A system for judging the freshness of meat by combining a CRISPR colorimetric detection system with an image analysis device, characterized in that It includes a liquid carrying device, a light source supplement module, an image acquisition module, and a data processing module, wherein: The liquid carrier is used to carry the CRISPR colorimetric solution, which is obtained by the following steps: ① Mix 2 volumes of Cas12a-crRNA, 4 volumes of enzyme-labeled magnetic bead probes, 1 volume of RNase inhibitor, 2 volumes of NEBuffer 2.1 (10×), 2 volumes of target DNA, and 9 volumes of Free-DNase / RNase water to make a reaction solution, and incubate at 37°C for 1 hour, and collect the supernatant after magnetic separation; wherein: the Cas12a-crRNA formed by the combination of Cas12a and crRNA is 2 μM, the enzyme-labeled magnetic bead probe is 1 mg / mL, and the RNase inhibitor is 20 U / μL; ② Add 40 volumes of TMB colorimetric solution to the collected supernatant, the solute of the colorimetric solution is 3,3',5,5'-tetramethylbenzidine, and the concentration is 0.15-0.25 mg / ml; to obtain the enzyme marker-3,3',5,5'-tetramethylbenzidine HRP-TMB reaction solution; incubate the reaction solution at 37°C for 16 minutes, and the reaction solution turns blue; ③ Add 40 parts by volume of hydrochloric acid solution to stop the colorimetric reaction, turn yellow, and obtain CRISPR solution; wherein the concentration of the hydrochloric acid solution is 2M; The light source supplement module includes an LED top light array and a backlight base plate arranged inside the liquid carrier device, and is used to supplement the light source in a closed and dark environment; The image acquisition module is used to capture colorimetric images; The data processing module outputs the meat freshness based on the colorimetric image collected by the image acquisition module through the following steps: S1, quantize the input colorimetric image and extract each color value; S2, input the extracted color value into the pre-constructed prediction model, and output the prediction result of the feature index; S3. Combine the thresholds of the characteristic indicators to determine the freshness of the meat.

2. The system according to claim 1, characterized in that The liquid carrying device includes a transparent carrier plate, which is hollow to embed two liquid carrying sheets stacked up and down, wherein the lower liquid carrying sheet is black and the upper liquid carrying sheet is white, the top surface of the white liquid carrying sheet is lower than the top surface of the transparent carrier plate to form a groove for carrying liquid, and the lower black liquid carrying sheet can prevent the penetration of the bottom light source and provide a good image background due to the small light absorption of white.

3. The system according to claim 1, characterized in that In S1, the image is subjected to color value extraction according to the internal pixel arrangement, including R, G, B, H, S, V and Gray values; the color values ​​in the vertical direction of the image are averaged to obtain the color mean, and then the peak spectrum between the pixel value and the color intensity value is obtained according to the horizontal pixel distribution.

4. The system according to claim 3, characterized in that The R, G, B, H, S, V and Gray values ​​are obtained by the following formulas: R=R / 255 G=G / 255 B=B / 255 V=max(R,G,B) Gray=0.299*R+0.587*G+0.114*B.

5. The system according to claim 3, characterized in that In S2, in the pre-built prediction model: the input is The characteristic index selects the number of Pseudomonas as output, and the input There is a fitted linear relationship between the number of Pseudomonas output and the number of Pseudomonas output; enter Obtained by the following formula: Among them, x1 and x2 are two endpoints in the peak spectrum, and C(i) is the color value between the two endpoints.

6. The system according to claim 5, characterized in that C(i) is the color B value between the two endpoints.

7. The system according to claim 1, characterized in that In S3, the threshold is 7 CFU / g. If the output is ≥7, the meat is judged to be rotten; if the output is <7, the meat is judged to be fresh.

8. The system according to claim 1, characterized in that The image acquisition module uses an industrial camera to capture colorimetric images with the following capture parameters: aperture size of 8 and gain effect of 2.0104.

9. A method for judging the freshness of meat by combining a CRISPR colorimetric detection system with an image analysis device, using the system according to any one of claims 1 to 8, characterized in that The method comprises the following steps: (1) 2 volumes of Cas12a-crRNA, 4 volumes of enzyme-labeled magnetic bead probe, 1 volume of RNase inhibitor, 2 volumes of NEBuffer r2.1 (10×), 2 volumes of target DNA and 9 volumes of Free-DNase / RNase water were mixed to prepare a reaction solution, and incubated at 37° C. for 1 hour, and the supernatant was collected after magnetic separation; wherein: the Cas12a-crRNA formed by the combination of Cas12a and crRNA was 2 μM, the enzyme-labeled magnetic bead probe was 1 mg / mL, and the RNase inhibitor was 20 U / μL; (2) adding 40 volumes of TMB colorimetric solution to the collected supernatant, wherein the solute of the colorimetric solution is 3,3',5,5'-tetramethylbenzidine, and the concentration is 0.15-0.25 mg / ml; to obtain an enzyme marker-3,3',5,5'-tetramethylbenzidine HRP-TMB reaction solution; incubating the reaction solution at 37° C. for 16 minutes, and the reaction solution turns blue; (3) adding 40 parts by volume of hydrochloric acid solution to stop the colorimetric reaction, turning yellow, and obtaining a CRISPR solution; wherein the concentration of the hydrochloric acid solution is 2 M; (4) Adding CRISPR detection colorimetric reaction solution into the liquid carrier device, pushing it into the carrier tank for fixing, and waiting for the image acquisition module to take pictures to obtain the sample image; (5) The captured image is input into the data processing module to realize image color value extraction and meat freshness assessment.

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