A volatile organic compound detection device based on olfactory visualization technology and a detection method thereof

Abstract

The application belongs to the technical field of food safety, and particularly relates to a volatile organic compound detection device based on olfactory visualization technology and a detection method thereof. The device comprises a fixed frame structure, a control module is arranged above the fixed frame structure, a detection platform is arranged below the fixed frame structure, a pipette and an image acquisition device and a sensor control device are respectively arranged above both sides of the detection platform, and the control module is connected with the pipette, the image acquisition device and the sensor control device. The control module is used for controlling the pipette, the image acquisition device and the sensor control device to operate in the detection platform area. The application can prepare a color-sensitive sensor printed with a metal porphyrin, obtain volatile gas of an organic compound, and cause a coordination reaction with the volatile gas. Image data before and after the reaction of the volatile gas with the organic compound is detected and analyzed through the image acquisition device, and corresponding chemometrics methods are combined, so that rapid quantitative analysis of the organic gas can be realized.

Description

Technical Field

[0001] This invention relates to the field of food safety technology, specifically to a volatile organic compound detection device and method based on olfactory visualization technology. Background Technology

[0002] Volatile organic compounds (VOCs) are a class of organic compounds that readily evaporate at room temperature. Food spoilage involves the action of microorganisms and enzymes, which produce and decompose a range of organic compounds, including a large number of VOCs such as formaldehyde, acetaldehyde, and acetone. These VOCs not only affect the sensory quality of food but may also pose health risks. Therefore, measures must be taken during food processing, storage, and transportation to prevent spoilage and the release of harmful substances.

[0003] Currently, there are many methods for detecting volatile organic compounds. The following are some commonly used methods:

[0004] (1) Gas chromatography (GC): GC is an analytical method based on the principle of chemical separation that can separate complex mixtures into individual compounds for qualitative and quantitative analysis. This method is suitable for the analysis of volatile organic compounds.

[0005] (2) Mass Spectrometry (MS): MS is a chemical analysis technique that converts compounds into charged ions and performs qualitative and quantitative analysis based on their mass spectrometric properties. This method can be used in conjunction with GC to improve the detection sensitivity of volatile organic compounds.

[0006] (3) Infrared spectroscopy (IR): IR is an analytical method based on molecular vibration and rotation, which can be used for qualitative and quantitative analysis of the structure of organic and inorganic compounds. This method is suitable for detecting some types of volatile organic compounds.

[0007] (4) Low-flow activated carbon adsorption tube-gas chromatography-mass spectrometry (L-LPAS-GC-MS): This technology combines three techniques: low-flow activated carbon adsorption tube, gas chromatography and mass spectrometry, and can be used for qualitative and quantitative analysis of volatile organic pollutants.

[0008] The above methods can all detect volatile organic compounds released during food quality deterioration to some extent, but they all have some drawbacks, such as cumbersome operation, long detection time, expensive instruments, and difficulty in achieving online real-time analysis.

[0009] Based on the reaction between specific color-sensitive materials and the volatile organic compounds to be detected, corresponding color-sensitive sensors for capturing volatile organic compounds can be used to achieve qualitative classification and quantitative analysis of the organic gases to be detected. Metalloporphyrins can undergo coordination reactions with organic gases, resulting in changes in their energy. This change can be characterized in the visible light spectrum, sufficient for the analysis and detection of organic gases.

[0010] Based on the above detection methods, an automated device for detecting volatile organic compounds has been developed, which has important practical significance for food quality testing. Summary of the Invention

[0011] To address the shortcomings of existing technologies, the present invention aims to provide a volatile organic compound (VOC) detection device based on olfactory visualization, enabling rapid quantitative analysis of VOCs. This invention can fabricate a color-sensitive sensor printed with metalloporphyrins to acquire the volatile gases of organic compounds, conduct coordination reactions with them, and use a 3CCD industrial camera to detect and analyze the changes in image data before and after the reaction with the volatile gases of organic compounds. Combined with appropriate chemometric methods, rapid quantitative analysis of organic gases can be achieved.

[0012] To achieve the above objectives, the present invention provides a volatile organic compound detection device based on olfactory visualization technology. The device includes a fixed frame, a control module, a detection platform, a pipette and image acquisition device, a sensor control device, and a device housing.

[0013] The internal structure of the device consists of a fixed frame, a control module, a detection platform, a pipette, an image acquisition device, and a sensor control device.

[0014] The fixed frame is used to fix the control module, which is located above the fixed frame; the detection platform is located below the fixed frame; the pipette and image acquisition device are located above one side of the detection platform, and the sensor control device is located above the other side of the detection platform; and the control module is electrically connected to the pipette and image acquisition device and the sensor control device respectively.

[0015] The control module is used to control the operation of the pipette, image acquisition device, and sensor control device in the detection platform area;

[0016] The overall structure of the device is assembled from the outer shell and the internal structure.

[0017] The control module includes a Raspberry Pi or a module with the same function, a PLC (Programmable Logic Controller), and a servo system; the Raspberry Pi or a module with the same function acts as the host computer, which is electrically connected to the PLC; the PLC acts as the slave computer and is electrically connected to the servo system, the pipette, the image acquisition device, and the sensor control device.

[0018] The Raspberry Pi acts as the host computer, issuing commands to the PLC. The PLC, as the slave computer, receives and feeds back the commands from the host computer and configures the servo system according to the commands to control the pipette, image acquisition device, and sensor control device.

[0019] The detection platform includes a support frame, with the two ends of the support frame referred to as the first end and the second end. Along the direction from the first end to the second end, a pipette box, a reagent bottle box, a reaction chamber, and a waste box are sequentially arranged on the support frame. The pipette box is used to hold pipette tips; the reagent bottle box is used to hold reagent bottles containing color-sensitive materials; and the waste box is used to hold discarded pipette tips.

[0020] The pipette and image acquisition device include an electric push rod 1, a lead screw module, an electric push rod 2, a pipette, a moving track, and an image acquisition device;

[0021] The PLC is electrically connected to electric actuator one to control its movement. Electric actuator one is connected to the pipette and is used to control the up and down movement of the pipette. The PLC is also electrically connected to electric actuator two to control its movement. Electric actuator two is used to control the pipette to draw and add color-sensitive material solution and to eject the pipette tip. The PLC is also electrically connected to the lead screw module, which is used to control the movement of pipette 4.4 and the image acquisition device along the moving track.

[0022] Furthermore, the first electric linear actuator is specifically an electric linear actuator BMNTL30, and the second electric linear actuator is an electric linear actuator BMNTL10; the image acquisition device is a 3CCD industrial camera; the moving speed range of the electric linear actuator BMNTL30 is 10-50 mm / s, and the moving speed range of the electric linear actuator BMNTL10 is 0-30 mm / s.

[0023] The sensor control device includes stepper motor one and stepper motor two, a color sensor mounting plate and a synchronous belt module; the PLC is connected to stepper motor one and stepper motor two, stepper motor one is connected to the color sensor mounting plate, and stepper motor two is connected to the synchronous belt module.

[0024] The PLC is used to control the operation of stepper motor one and stepper motor two. Stepper motor one is used to control the rotation of the color sensor mounting plate, and stepper motor two is used to control the operation of the synchronous belt module. The synchronous belt module is used to control the up and down movement of the color sensor mounting plate. The color sensor mounting plate is used to fix the color sensor.

[0025] The device housing has a viewing window and control buttons on its surface; the viewing window is used to observe the operation of the internal structure; the control buttons are connected to the Raspberry Pi and are used to control the device to turn on and off.

[0026] Furthermore, the color-sensitive sensor is composed of a color-sensitive material (which has a specific color-developing effect on the gas emitted by the sample being detected); the color-sensitive material includes, but is not limited to, porphyrin, pyrrole, and pH indicator.

[0027] Furthermore, the image acquisition device is equipped with an LED light source; the image acquisition device includes a 3CCD industrial camera.

[0028] Furthermore, when the color sensor mounting plate moves downward, it aligns with the reaction chamber to allow the reaction to proceed within the chamber.

[0029] Furthermore, when the screw module controls the pipette to move along the moving track, once it reaches above the detection platform, the pipette can be moved to the pipette box, reagent bottle box, reaction chamber, and waste box to perform corresponding operations through the control of the moving track.

[0030] Furthermore, when the image acquisition device reaches the position of the color sensor fixing plate to acquire an image, the stepper motor controls the color sensor fixing plate to rotate, ensuring that the front of the color sensor is aligned with the image acquisition device so as to acquire the image of the color sensor.

[0031] Through the cooperation of various components, the color sensor can complete the fabrication, reaction, and data acquisition within the detection device, achieving the effect of detecting volatile organic compounds.

[0032] The Raspberry Pi includes a storage module for storing detection models of volatile organic compounds and the corresponding algorithms for building these models. Once connected to a computer, the existing detection models and algorithms in the storage module can be used directly. The detection model is established by combining the RGB, HSV, or Lab data of the sample image with the physicochemical indicators of the organic compounds.

[0033] The sample image is a color sensor image captured by an image acquisition device before and after the reaction between the color sensor and the organic compound in the reaction chamber; the RGB, HSV, and Lab data are the RGB, HSV, and Lab difference data of the color sensor before and after the reaction.

[0034] The present invention also provides a method for detecting volatile organic compounds using a device based on olfactory visualization technology, the steps of which are as follows:

[0035] Step 1: Image acquisition of the sample before and after the reaction;

[0036] S1. First, fix the color sensor on the color sensor mounting plate, fill the pipette box with pipette tips, put the reagent bottle containing the color-sensitive material solution into the reagent bottle box, and put the sample to be tested into the reaction chamber.

[0037] Preferably, the color-sensitive material in step S1 includes, but is not limited to, porphyrin, pyrrole, and pH indicator, and the concentration of the color-sensitive material solution is 2 mg / mL.

[0038] S2. Press the equipment control button to start the equipment operation;

[0039] S3. After the equipment is started, the electric push rod in the PLC controls the pipette to move downwards and to the top of the detection platform. First, the pipette contacts the tip in the pipette box. The push rod pushes the pipette to install the tip.

[0040] S4. After the pipette tip is installed, the PLC controls the electric actuator one to move the pipette upwards and return it to its initial position. Then, the PLC controls the lead screw module to move, and the lead screw module controls the pipette to move along the moving track to the reagent bottle box. The PLC controls the electric actuator one to move the pipette downwards, so that the pipette tip extends into the reagent bottle containing the color-sensitive material solution in the reagent bottle box. Then, combined with the control of the electric actuator two, the color-sensitive material solution is aspirated.

[0041] S5. The PLC controls the pipette to move upward through the electric push rod one. After returning to the initial height, the screw module controls the pipette to move along the moving track to the color sensor. When the pipette tip contacts the color sensor, the PLC controls the electric push rod two to operate, so that the pipette drops the color-sensitive material solution onto the surface of the color sensor.

[0042] Preferably, the amount of color-sensitive material solution added is 10-30 μL.

[0043] S6. After the addition, the screw module controls the pipette to move along the moving track to the top of the waste box. The electric push rod 2 controlled by the PLC pushes the used pipette tip into the waste box. Then, the image acquisition device is used to acquire the image of the color sensor to obtain the initial image of the color sensor.

[0044] S7. After image acquisition, the color sensor mounting plate is rotated by stepper motor 1 so that the color sensor faces downward. The synchronous belt module controls the color sensor mounting plate to move downward so that the color sensor fits into the reaction chamber, forming a closed space, which facilitates the reaction between the sample in the reaction chamber and the color sensor.

[0045] Preferably, the reaction time in step S7 is 15-20 min.

[0046] S8. After the reaction, the synchronous belt module controls the color sensor fixing plate to move upward, and the stepper motor controls the color sensor fixing plate to rotate, so that the color sensor faces upward and returns to the initial position; the image acquisition device takes another picture of the color sensor after the reaction to obtain the reaction image.

[0047] S9. The pipette and image acquisition device return to their initial positions, and image acquisition ends.

[0048] Step 2: Near-infrared spectrum acquisition of the color-sensitive sensor

[0049] The spectral data of the color-sensitive sensor after the reaction is collected by a spectral acquisition device;

[0050] Step 3: Determine the TVB-N of the sample using the national standard method.

[0051] According to Chinese National Standard GB 5009.228-2016, the TVB-N content of the sample was first determined by the automatic Kjeldahl nitrogen determination method and expressed in mg / 100g.

[0052] Step 4: Model Building

[0053] Extract the RGB values ​​of the images before and after the color sensor reaction in step one, subtract the two to obtain the RGB difference, and then use the PCA algorithm to analyze and obtain the weight of the color-sensitive material in the RGB information of the original image. Multiply the weight value obtained from the image RGB information with the spectral data obtained in step two, and then form a new matrix from the multiplied data. Combine the TVB-N value of the corresponding sample obtained in step three, and then use the ACO, CARS, and VCPA algorithms to filter the variables in sequence to establish a PLS model. The obtained PLS model is stored in the storage module of the Raspberry Pi.

[0054] Step 5: Detection of volatile organic compounds in unknown samples

[0055] By using the device of this invention to acquire color-sensitive sensor image information of an unknown sample before and after the reaction, and to acquire near-infrared spectral information of the color-sensitive sensor after the reaction, the RGB difference of the color-sensitive sensor is automatically calculated by the algorithm stored in the Raspberry Pi. Then, the PCA algorithm is used to analyze and obtain the weight of the color-sensitive material in the RGB information of the original image. The weight value obtained from the RGB information of the image is multiplied by the spectral data obtained in step two and substituted into the PLS model constructed in step four to obtain the content of volatile organic compounds of the unknown sample.

[0056] Beneficial effects:

[0057] This invention provides a volatile organic compound (VOC) detection device based on olfactory visualization, enabling rapid quantitative analysis of VOCs. The invention can fabricate a color-sensitive sensor printed with metalloporphyrins to acquire the volatile gases of organic compounds, conduct coordination reactions with them, and use a 3CCD industrial camera to detect and analyze the changes in image data before and after the reaction with the volatile gases of organic compounds. Combined with appropriate chemometric methods, rapid quantitative analysis of organic gases can be achieved. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the volatile organic compound detection device of the olfactory visualization technology of the present invention;

[0059] Figure 2 This is a cross-sectional schematic diagram of the control module structure of the present invention;

[0060] Figure 3 This is a schematic cross-sectional view of the detection platform structure of the present invention;

[0061] Figure 4 This is a schematic cross-sectional view of the pipette and image acquisition device of the present invention;

[0062] Figure 5 This is a schematic cross-sectional view of the sensor control device of the present invention;

[0063] Figure 6 This is a schematic cross-sectional view of the outer casing structure of the device of the present invention;

[0064] Figure 7 This is a performance diagram of the detection and prediction model of the device of the present invention.

[0065] The attached diagram shows the following components: 1 - Fixed frame; 2 - Control module; 2.1 - Raspberry Pi; 2.2 - PLC; 2.3 - Servo system; 3 - Detection platform; 3.1 - Pipette box; 3.2 - Reagent bottle box; 3.3 - Reaction chamber; 3.4 - Waste box; 3.5 - Pipette tip; 3.6 - Brown reagent bottle; 3.7 - Support structure; 4 - Pipette and image acquisition device; 4.1 - Electric actuator BMNTL30; 4.2 - Lead screw module; 4.3 - Electric actuator BMNTL10; 4.4 - Pipette; 4.5 - Moving track; 4.6 - Image acquisition device; 5 - Sensor control module; 5.1 - Stepper motor one; 5.2 - Stepper motor two; 5.3 - Color sensor mounting plate; 5.4 - Synchronous belt module; 5.5 - Color sensor; 6 - Device housing; 6.1 - Viewing window; 6.2 - Control button; 7 - Internal structure; 8 - Overall architecture. Detailed Implementation

[0066] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The present invention has universal applicability for the determination of TVB-N in aquatic products, the evaluation of tea aroma, and the identification of the origin of vinegar.

[0067] In this implementation example, oysters were selected as the subject of the experiment. The chosen experimental protocol was used to detect the TVB-N content in oysters and to determine their freshness. The detection model constructed in this example is only for the detection of TVB-N in oysters. If it is necessary to detect TVB-N in other samples or other volatile organic compounds in oysters, a new detection model should be constructed.

[0068] like Figure 1-6 As shown, a volatile organic compound detection device based on olfactory visualization technology includes a fixed frame 1, a control module 2, a detection platform 3, a pipette and image acquisition device 4, a sensor control device 5, and a device housing 6.

[0069] The internal structure 7 of the device consists of a fixed frame 1, a control module 2, a detection platform 3, a pipette and image acquisition device 4, and a sensor control device 5.

[0070] The detection platform 3 is fixed below the fixed frame 1, and the control module 2 is fixed above the fixed frame 1. In the initial state, the pipette and image acquisition device 4 are located at the upper left of the detection platform, and the sensor control device 5 is located at the upper right of the detection platform 3. The control module 2 is connected to both the pipette and image acquisition device 4 and the sensor control device 5. The control module 2 controls the operation of the pipette and image acquisition device 4 and the sensor control device 5 on the detection platform 3.

[0071] The overall structure of the device 8 consists of the outer shell 6 and the internal structure 7.

[0072] The control module 2 includes a Raspberry Pi 2.1, a PLC 2.2, and a servo system 2.3. The Raspberry Pi 2.1 acts as the host computer and is electrically connected to the PLC 2.2. The PLC 2.2 acts as the slave computer and is electrically connected to the servo system 2.3, the pipette and image acquisition device 4, and the sensor control device 5. The Raspberry Pi 2.1, as the host computer, issues commands to the PLC 2.2, and the PLC 2.2, as the slave computer, receives and responds to the commands from the host computer, and configures the servo system 2.3 according to the commands to control the pipette, image acquisition device 4, and sensor control device 5. The fixed frame 1 is used to fix the control module 2.

[0073] The detection platform 3 includes a support frame 3.7, with its two ends designated as the first end and the second end. Along the direction from the first end to the second end, a pipette box 3.1, a reagent bottle box 3.2, a reaction chamber 3.3, and a waste box 3.4 are sequentially arranged on the support frame 3.7. The pipette box 3.1 is used to hold pipette tips 3.5; the reagent bottle box 3.2 is used to hold reagent bottles 3.6 containing color-sensitive materials; and the waste box 3.4 is used to hold discarded pipette tips 3.5.

[0074] The pipette and image acquisition device 4 includes an electric push rod 4.1, a lead screw module 4.2, an electric push rod 4.3, a pipette 4.4, a moving track 4.5, and an image acquisition device 4.6;

[0075] Specifically, PLC 2.2 is electrically connected to electric actuator 4.1 to control its movement, which in turn controls the vertical movement of pipette 4.4; PLC 2.2 is also electrically connected to electric actuator 4.3 to control its movement, which in turn controls the pipette 4.4 to aspirate and add color-sensitive material solution and to eject the pipette tip 3.5; and PLC 2.2 is electrically connected to lead screw module 4.2 to control the movement of pipette 4.4 and image acquisition device 4.6 along the moving track 4.5.

[0076] The sensor control device 5 includes a first stepper motor 5.1 and a second stepper motor 5.2, a color sensor mounting plate 5.3, and a synchronous belt module 5.4; the PLC is connected to the first stepper motor 5.1 and the second stepper motor 5.2, the first stepper motor 5.1 is connected to the color sensor mounting plate 5.3, and the second stepper motor 5.2 is connected to the synchronous belt module 5.4.

[0077] The PLC controls the operation of stepper motor 5.1 and stepper motor 5.2. Stepper motor 5.1 controls the rotation of the color sensor mounting plate 5.3, and stepper motor 5.2 controls the operation of the synchronous belt module 5.4. The synchronous belt module 5.4 controls the up and down movement of the color sensor mounting plate 5.3. The color sensor mounting plate 5.3 is used to fix the color sensor 5.5.

[0078] The device housing 6 has a viewing window 6.1 and a control button 6.2 on its surface; the viewing window 6.1 is used to observe the operation of the internal structure; the control button 6.2 is connected to the Raspberry Pi 2.1 and is used to control the device to turn on and off.

[0079] A method for detecting TVB-N released by oysters based on the above-mentioned device:

[0080] The volatile organic compound detection device based on olfactory visualization technology of the present invention was used to study the olfactory visualization technology of oysters at different freshness levels as follows.

[0081] This invention utilizes a color-sensitive sensor array prepared by a volatile organic compound (VOC) detection device based on olfactory visualization technology to detect oysters with different freshness levels. Simultaneously, the same sample is tested for VOCs with different concentrations using the national standard method. The data detected by the national standard method corresponds one-to-one with the data collected by the color-sensitive sensor, and a VOC detection model is established based on this.

[0082] Implementation example: Quantitative detection of TVB-N in oysters of different freshness levels (0 days, 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days and 8 days).

[0083] A novel method for quantitative detection of TVB-N in oysters, the specific operation steps are as follows:

[0084] Step 1: Oyster Sample Preparation

[0085] Fresh oysters of similar size were selected, washed evenly with distilled water to remove surface impurities, and drained for 2 hours after peeling to obtain oyster meat as experimental samples. These samples were then placed at 4°C for later use. The 170 experimental samples were divided into 5 groups: 3 groups (102 samples each) served as the training set, and 2 groups (68 samples each) served as the prediction set. The training set was used to construct the TVB-N prediction model for oysters, and the prediction set was used to test the predictive performance of the model. The following experimental steps were repeated for all 170 samples.

[0086] Step 2: Preparation of the color-sensitive sensor array, its reaction with the sample, and image acquisition.

[0087] The following operations were performed on a total of 170 samples in the training and prediction sets.

[0088] Operating instructions:

[0089] S1. Fix a 4×4cm color sensor 5.5 on the color sensor mounting plate 5.3. Fill the pipette box 3.1 with pipette tips. Place six reagent bottles filled with color-sensitive material solutions in the reagent bottle box 3.2. The six color-sensitive materials are ① (5,10,15,20-tetraphenyl-21H,23H-porphyrin iron(III) chloride), ② (5,10,15,20-tetraphenyl-21H,23H-porphyrin palladium(II)), ③ (8-(6-methoxy-2-naphthyl)-4,4-difluoroborate dipyrrolidine), ④ (cresol red), ⑤ (chlorophenol red), and ⑥ (anthocyanin). The concentration of the color-sensitive material solution is 2mg / mL. Place 10g of oyster sample into the reaction chamber 3.3.

[0090] S2. Press the equipment control button 6.2;

[0091] S3. After the equipment is started, the electric push rod 4.1 in PLC2.2 is controlled to move the pipette 4.4 downwards to the top of the detection platform 3. First, the pipette 4.4 contacts the pipette tip in the pipette box 3.1. The push rod pushes the pipette 4.4 to install the pipette tip.

[0092] S4. After the pipette tip is installed on the pipette 4.4, PLC 2.2 controls the electric actuator 4.1 to move the pipette 4.4 upwards and return it to its initial position. Then, PLC 2.2 controls the lead screw module 4.2 to move, and the lead screw module 4.2 controls the pipette 4.4 to move along the moving track 4.5 to the reagent bottle box 3.2. PLC controls the electric actuator 4.1 to move the pipette 4.4 downwards, so that the pipette tip of the pipette 4.4 extends into the reagent bottle 3.6 in the reagent bottle box 3.2 containing the color-sensitive material solution, and draws the color-sensitive material solution.

[0093] S5 and PLC2.2 control the pipette 4.4 to move upward via electric push rod 4.1. After returning to the initial height, the lead screw module 4.2 controls the pipette 4.4 to move along the moving track 4.5 to the color sensor 5.5. When the pipette tip contacts the color sensor 5.5, PLC2.2 controls the electric push rod 4.3 to operate, so that the pipette 4.4 drops the color-sensitive material solution onto the surface of the color sensor 5.5.

[0094] S6. After the addition, the lead screw module 4.2 controls the pipette 4.4 to move along the moving track 4.5 to above the waste container 3.4. The PLC 2.2 controls the electric push rod 4.3 to push the used pipette tip into the waste container 3.4. Then, the image acquisition device 4.6 is used to acquire the image of the color sensor 5.5 and obtain the initial image of the color sensor 5.5.

[0095] S7. After image acquisition, the stepper motor 5.1 controls the rotation of the color sensor fixing plate 5.3 so that the color sensor 5.5 faces downward. The synchronous belt module 5.4 controls the color sensor fixing plate 5.3 to move downward so that the color sensor 5.5 fits into the reaction chamber 3.3 to form a closed space, which facilitates the reaction between the sample in the reaction chamber 3.3 and the color sensor 5.5.

[0096] S8. After the reaction, the synchronous belt module 5.4 controls the color sensor fixing plate 5.3 to move upward, and the stepper motor 5.1 controls the color sensor fixing plate 5.3 to rotate, so that the color sensor 5.5 faces upward and returns to the initial position; the image acquisition device 4.6 takes a picture of the color sensor 5.5 after the reaction to obtain the reaction image.

[0097] S9. The pipette and image acquisition device 4 return to their initial positions, and image acquisition ends.

[0098] After the operation is completed, the images of the color sensor before and after the reaction are transmitted to the computer connected to the device.

[0099] Step 3: Near-infrared spectrum acquisition of the color-sensitive sensor

[0100] The following operations were performed on a total of 170 samples in the training and prediction sets.

[0101] The spectral data of the colorimetric sensor after the reaction were collected using a fiber optic probe of a near-infrared spectrometer. The integration time was set to 5 ms, the number of scans was 10, and the pixel smoothness was set to 5. Data was collected three times to avoid errors, and the average value represents the final spectral data.

[0102] Step 4: Determine the TVB-N of the sample using the national standard method.

[0103] The following operations were performed on a total of 170 samples in the training and prediction sets.

[0104] According to Chinese National Standard GB 5009.228-2016, the TVB-N content of 170 samples was determined by the automated Kjeldahl method.

[0105] Specifically: Place 10g of chopped oysters in a distillation tube and soak in 75mL of distilled water for 30 minutes. Then add 1g of magnesium oxide (MgO) to the distillation tube containing the treated sample and immediately connect it to the distiller. Set the concentration of the 20g / L boric acid receiving solution to 30mL. Add 10 drops of a mixed indicator solution (methyl red ethanol solution: bromocresol green ethanol solution = 1:5), and then distill for 3 minutes. Titrate the receiving solution with 0.1mol / L hydrochloric acid standard titrant. Calculate the TVB-N content and express it in mg / 100g.

[0106] Step 5: Model Building

[0107] After the experiment, the RGB values ​​of the color-sensitive sensor images of the 102 training set samples collected in step (2) were extracted from the computer connected to the device. The RGB values ​​of the images before and after the reaction were subtracted to obtain the RGB difference. The PCA algorithm was used to analyze and obtain the weight of each color-sensitive material in the RGB information of the original image. The weight values ​​obtained from the image RGB information were multiplied by the near-infrared spectral data of the corresponding color-sensitive materials of the 102 training set samples obtained in step (3), and then they were combined to form a new matrix. Combined with the TVB-N values ​​of the 102 samples obtained in step (4), and the variables were screened in sequence using the ACO, CARS, and VCPA algorithms to establish the optimal PLS model, such as Figure 7 As shown.

[0108] To verify the stability of the model, the RGB values ​​of the color-sensitive sensor images of the 68 prediction set samples collected in step (2) were extracted by the computer connected to the device. The RGB values ​​of the images before and after the reaction were subtracted to obtain the RGB difference. The PCA algorithm was used to analyze and obtain the weight of each color-sensitive material in the RGB information of the original image. The weight values ​​obtained from the image RGB information were multiplied by the near-infrared spectral data of the corresponding color-sensitive materials of the 68 prediction set samples obtained in step (3). The obtained data were substituted into the above-mentioned optimal PLS model to verify the method. The results showed that the method achieved a comprehensive recognition rate of 0.9128 for all samples, indicating that the method can quantitatively detect TVB-N released during the oyster decay process.

[0109] Step Six: Freshness Test of Unknown Oyster Samples

[0110] By using the device of this invention to measure the color-sensitive sensor image information of an unknown oyster sample before and after the reaction, and to collect the near-infrared spectral information of the color-sensitive sensor after the reaction, the computer connected to the device calculates the RGB difference of the color-sensitive sensor using the algorithm stored in the Raspberry Pi, and then uses the PCA algorithm to analyze and obtain the weight of the color-sensitive material in the RGB information of the original image. The weight value obtained from the RGB information of the image is multiplied by the spectral data obtained in step two and substituted into the model in step four to obtain the TVB-N content of the unknown oyster sample.

[0111] Note: The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Therefore, although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention. All technical solutions and improvements that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A detection method for volatile organic compounds using a device based on olfactory visualization technology, characterized in that, Includes the following steps: Step 1: Image acquisition of the sample before and after the reaction; S1. First, fix the color sensor (5.5) on the color sensor fixing plate (5.3), fill the pipette box (3.1) with pipette tips, put the reagent bottle (3.6) containing the color-sensitive material solution into the reagent bottle box (3.2), and put the sample to be tested into the reaction chamber (3.3). S2. Press the equipment control button (6.2) to start the equipment operation; S3. After the equipment is started, the electric push rod (4.1) is controlled by PLC (2.2) to move the pipette (4.4) downward to the detection platform (3). First, the pipette (4.4) contacts the pipette tip in the pipette box (3.1). The push rod pushes the pipette (4.4) to install the pipette tip. S4. After the pipette tip is installed on the pipette (4.4), the PLC (2.2) controls the electric push rod one (4.1) to move the pipette (4.4) upward and return to the initial position; then the PLC (2.2) controls the screw module (4.2) to move, and the screw module (4.2) controls the pipette (4.4) to move along the moving track (4.5) to the reagent bottle box (3.2). The PLC controls the electric push rod one (4.1) to move the pipette (4.4) downward, so that the pipette tip (4.4) extends into the reagent bottle (3.6) containing the color-sensitive material solution in the reagent bottle box (3.2), and then combines the control of the electric push rod two (4.3) to draw the color-sensitive material solution. S5, PLC (2.2) controls the pipette (4.4) to move upward through electric push rod one (4.1). After returning to the initial height, the screw module (4.2) controls the pipette (4.4) to move along the moving track (4.5) to the color sensor (5.5). When the pipette tip contacts the color sensor (5.5), PLC (2.2) controls electric push rod two (4.3) to operate, so that the pipette (4.4) drops the color-sensitive material solution onto the surface of the color sensor (5.5). S6. After the addition, the screw module (4.2) controls the pipette (4.4) to move along the moving track (4.5) to above the waste box (3.4). The electric push rod (4.3) controlled by the PLC (2.2) pushes the used pipette tip into the waste box (3.4). Then, the image acquisition device (4.6) is used to acquire the image of the color sensor (5.5) to obtain the initial image of the color sensor (5.5). S7. After image acquisition, the color sensor fixing plate (5.3) is rotated by stepper motor (5.1) so that the color sensor (5.5) faces downward. The synchronous belt module (5.4) controls the color sensor fixing plate (5.3) to move downward so that the color sensor (5.5) fits into the reaction chamber (3.3) to form a closed space, which facilitates the reaction between the sample in the reaction chamber (3.3) and the color sensor (5.5). S8. After the reaction, the synchronous belt module (5.4) controls the color sensor fixing plate (5.3) to move upward, and the stepper motor (5.1) controls the color sensor fixing plate (5.3) to rotate, so that the color sensor (5.5) faces upward and returns to the initial position; the image acquisition device (4.6) takes a picture of the color sensor (5.5) after the reaction to obtain the reaction image. S9. The pipette and image acquisition device (4) return to the initial position, and the image acquisition ends; Step 2: Near-infrared spectrum acquisition by the color-sensitive sensor; The spectral data of the color-sensitive sensor after the reaction is collected by a spectral acquisition device; Step 3: Determine the TVB-N of the sample using the national standard method; According to Chinese National Standard GB 5009.228-2016, the TVB-N content of the sample was first determined by the automatic Kjeldahl nitrogen determination method and expressed in mg / 100g. Step 4: Model Building; Extract the RGB values ​​of the images before and after the color sensor reaction in step one, subtract the two to obtain the RGB difference, and then use the PCA algorithm to analyze and obtain the weight of the color-sensitive material in the RGB information of the original image. Multiply the weight value obtained from the image RGB information with the spectral data obtained in step two, and then form a new matrix from the multiplied data. Combine the TVB-N value of the corresponding sample obtained in step three, and then use the ACO, CARS, and VCPA algorithms to filter the variables in sequence to establish a PLS model. The obtained PLS model is stored in the storage module of Raspberry Pi (2.1). Step 5: Detection of volatile organic compounds in unknown samples; By acquiring color sensor image information of the unknown sample before and after the reaction, and acquiring the near-infrared spectral information of the color sensor after the reaction, the RGB difference of the color sensor is automatically calculated by the algorithm stored in the Raspberry Pi. Then, the PCA algorithm is used to analyze and obtain the weight of the color-sensitive material in the RGB information of the original image. The weight value obtained from the image RGB information is multiplied by the spectral data obtained in step two and substituted into the PLS model constructed in step four to obtain the content of volatile organic compounds of the unknown sample. The volatile organic compound detection device of the olfactory visualization technology includes a fixed frame (1), a control module (2), a detection platform (3), a pipette and image acquisition device (4), a sensor control device (5), and a device housing (6). The internal structure of the device (7) consists of a fixed frame (1), a control module (2), a detection platform (3), a pipette and image acquisition device (4), and a sensor control device (5). The fixed frame (1) is used to fix the control module (2), and the control module (2) is located above the fixed frame (1); the detection platform (3) is located below the fixed frame (1); the pipette and image acquisition device (4) are located above one side of the detection platform (3), and the sensor control device (5) is located above the other side of the detection platform (3); and the control module (2) is electrically connected to the pipette and image acquisition device (4) and the sensor control device (5) respectively. The control module (2) is used to control the pipette and image acquisition device (4) and sensor control device (5) to operate in the detection platform (3) area; The overall structure of the device (8) is assembled from the outer shell (6) and the internal structure (7). The control module (2) includes a Raspberry Pi (2.1) or a module with the same function, a PLC (2.2) and a servo system (2.3); the Raspberry Pi (2.1) or a module with the same function serves as the host computer and is electrically connected to the PLC (2.2); the PLC (2.2) serves as the slave computer and is electrically connected to the servo system (2.3), the pipette and image acquisition device (4), and the sensor control device (5); The Raspberry Pi (2.1) acts as the host computer and issues commands to the PLC (2.2). The PLC (2.2) acts as the slave computer and receives and feeds back the commands from the host computer. It also configures the servo system (2.3) according to the commands to control the pipette, image acquisition device (4), and sensor control device (5). The detection platform (3) includes a support frame (3.7), with the two ends of the support frame (3.7) referred to as the first end and the second end. Along the direction from the first end to the second end, a pipette box (3.1), a reagent bottle box (3.2), a reaction chamber (3.3), and a waste box (3.4) are arranged sequentially on the support frame (3.7). The pipette box (3.1) is used to hold pipette tips (3.5). The reagent bottle box (3.2) is used to hold reagent bottles (3.6) containing color-sensitive materials. The waste box (3.4) is used to hold discarded pipette tips (3.5). The pipette and image acquisition device (4) includes an electric push rod one (4.1), a lead screw module (4.2), an electric push rod two (4.3), a pipette (4.4), a moving track (4.5), and an image acquisition device (4.6). The PLC (2.2) is electrically connected to the first electric actuator (4.1) to control the pushing of the first electric actuator (4.1), which is connected to the pipette (4.4) and used to control the up and down movement of the pipette (4.4); the PLC (2.2) is electrically connected to the second electric actuator (4.3) to control the pushing of the second electric actuator (4.3), which is used to control the pipette (4.4) to draw and drop the color-sensitive material solution and push out the pipette tip (3.5); the PLC (2.2) is electrically connected to the lead screw module (4.2), which is used to control the movement of the pipette (4.4) and the image acquisition device (4.6) along the moving track (4.5); The sensor control device (5) includes stepper motor one (5.1) and stepper motor two (5.2), color sensor mounting plate (5.3) and synchronous belt module (5.4); PLC (2.2) is connected to stepper motor one (5.1) and stepper motor two (5.2), stepper motor one (5.1) is connected to color sensor mounting plate (5.3), and stepper motor two (5.2) is connected to synchronous belt module (5.4); The PLC (2.2) is used to control the operation of stepper motor one (5.1) and stepper motor two (5.2). Stepper motor one (5.1) is used to control the rotation of the color sensor mounting plate (5.3), and stepper motor two (5.2) is used to control the operation of the synchronous belt module (5.4). The synchronous belt module (5.4) is used to control the up and down movement of the color sensor mounting plate (5.3). The color sensor mounting plate (5.3) is used to fix the color sensor (5.5). A viewing window (6.1) and a control button (6.2) are provided on the surface of the device housing (6); the viewing window (6.1) is used to observe the operation of the internal structure; the control button (6.2) is connected to the Raspberry Pi (2.1) and is used to control the opening and closing of the device.

2. The detection method according to claim 1, characterized in that, In step S1, the color-sensitive material includes porphyrin, pyrrole, and pH indicator, and the concentration of the color-sensitive material solution is 2 mg / mL; in step S5, the amount of color-sensitive material solution added is 10-30 μL.

3. The detection method according to claim 1, characterized in that, The reaction time in step S7 is 15-20 min.

4. The detection method according to claim 1, characterized in that, The color-sensitive sensor (5.5) is made of color-sensitive materials, including porphyrin, pyrrole, and pH indicator.

5. The detection method according to claim 1, characterized in that, When the color sensor fixing plate (5.3) moves downward, the color sensor fixing plate (5.3) and the reaction chamber (3.3) are aligned and fitted together so that the reaction can take place in the reaction chamber (3.3).

6. The detection method according to claim 1, characterized in that, When the lead screw module (4.2) controls the pipette (4.4) to move along the moving track (4.5), when it moves above the detection platform (3), the pipette (4.4) moves to the pipette box (3.1), reagent bottle box (3.2), reaction chamber (3.3), and waste box (3.4) to perform corresponding operations through the control of the moving track (4.5).

7. The detection method according to claim 1, characterized in that, When the image acquisition device (4.6) reaches the position of the color sensor fixing plate (5.3) to acquire an image, the stepper motor (5.1) controls the color sensor fixing plate (5.3) to rotate, ensuring that the front of the color sensor is aligned with the image acquisition device (4.6) so that the image of the color sensor can be acquired.

8. The detection method according to claim 1, characterized in that, The image acquisition device (4.6) is equipped with an LED light source; the image acquisition device includes a 3CCD industrial camera; the Raspberry Pi (2.1) contains a storage module.

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