CN116413245A - Method and system for rapidly detecting pesticide residues in food based on cloud model resource library - Google Patents
Method and system for rapidly detecting pesticide residues in food based on cloud model resource library Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
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- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
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Abstract
The invention discloses a method and a system for rapidly detecting pesticide residues in food based on a cloud model resource library, wherein the detection system comprises a portable detection terminal, a man-machine interaction terminal and a cloud data platform, and the portable detection terminal comprises a protective shell, a Raman probe, a spectrum acquisition module, a light source module, a power supply module, a control processing system, a sensor module, a communication module and a sample loading unit; and acquiring Raman spectrum signals of the sample to be detected in the portable detection terminal, storing the acquired data cloud by utilizing the man-machine interaction terminal and the cloud data platform, matching the acquired data cloud with a database in the cloud data platform, identifying the types of the dangerous substances, and carrying out data analysis and calculation according to the cloud built-in model. The device and the method can enable the surface enhanced Raman spectrum data of the hazardous substances in the food to be processed quickly in the cloud database, and can quickly obtain the detection result of the residual content of the hazardous substances in the food.
Description
Technical Field
The invention belongs to the technical field of food safety detection, and particularly relates to a method and a system for rapidly detecting pesticide residues in food based on a cloud model resource library.
Background
Food safety problems are all the problems of great concern of national departments, the direct relationship of the food safety problems is directly related to the physical health and safety of people, and the unstable social development of China is easily caused, thus preventing the social development. In the planting process of crops such as tea, wheat, rice and the like, chemical fertilizers and pesticides with stronger toxicity are frequently used, and the purposes of improving the growth vigor of crops and eliminating pests can be achieved, but along with the flushing of rainwater, the chemical fertilizers and pesticides flow into underground water and rivers, so that serious environmental pollution is easily caused; meanwhile, as the food chain is transmitted, some harmful substances are accumulated, and when the crops or foods containing a large amount of pesticide residues are ingested by human bodies, the life and health of people can be seriously endangered. In recent years, although relevant departments in China strengthen the construction of food safety, the scale of food markets is continuously enlarged, the workload of food safety detection is very huge, and compared with other countries, the detection technology in China still has a certain gap, and many food safety problems cannot be effectively solved due to the imperfect detection technology, so that the occurrence of the food safety problems cannot be effectively avoided.
Traditional artificial food safety detection technology can not meet the related requirements of food industry safety rapid detection work, and although the methods have high precision, the methods rely on high-precision large-scale instruments, the instruments have high manufacturing cost, long detection time and complex operation, professional staff sample preparation detection is often required, and the requirements of on-site rapid detection are difficult to realize, and are not suitable for on-site rapid detection.
In addition, the existing device and method for rapidly detecting pesticide residues in food cannot interact with cloud data.
Disclosure of Invention
In order to solve the defects in the prior art, the application provides a method and a system for rapidly detecting pesticide residues in food based on a cloud model resource library, which can provide a detection system of a portable detection terminal combined with a mobile terminal on one hand, and can rapidly process spectrum data energy at a mobile terminal by utilizing the cloud model resource library on the other hand, so that a device can rapidly obtain a detection result of the residual content of the hazard in the food on site.
The technical scheme adopted by the invention is as follows:
a quick detection system of pesticide residue in food based on high in the clouds model resource storehouse includes:
the portable detection terminal comprises a protective shell, a Raman probe, a spectrum acquisition module, a light source module, a power supply module, a control processing system, a sensor module, a communication module and a sample loading unit, wherein the Raman probe, the spectrum acquisition module, the light source module, the power supply module, the control processing system, the sensor module and the communication module are arranged in the protective shell; the sample loading unit comprises a detection table and a detection plate, wherein a sample pool is arranged on the detection plate and is used for placing a sample to be detected and a Raman signal enhancement substrate; the Raman probe is arranged towards the position of the sample cell, and the central axis of the Raman probe and the central axis of the sample cell are on the same axis; the light source module, the spectrum acquisition module and the Raman probe are connected through optical fibers to form a complete optical loop; the power supply module is used for providing electric energy for each power utilization unit; the sensor module and the communication module are integrated on the control processing system, and the sensor module, the communication module, the LED display screen, the light source module, the power supply module and the spectrum acquisition module are all in signal connection with the control processing system;
the human-computer interaction terminal is in information interaction with the portable detection terminal and the cloud data platform;
the cloud data platform is internally provided with a hazard type standard sample spectrum library, a hazard type detection model library and a spectrum data algorithm library;
raman spectrum data corresponding to different concentrations of various dangerous substances are arranged in the spectrum library of the standard sample of the dangerous species and are used for matching the spectrum of the object to be detected collected by the portable detection terminal, and identifying the type of the object to be detected;
the dangerous substance type detection model library is used for carrying out model calculation on the spectrum of the object to be detected and bringing the spectrum data of the object to be detected into a model; obtaining the content of the object to be detected through model calculation; the detection model is a quantitative model, the detection model method is a set mathematical equation, y=a0+a1 h1+a2 h2+a3 h3+a4 h4+a5 h5+ … +an Hn; wherein Y is a detection result, a0 and a1 … an are model coefficients, and H1 and H2 … Hn are spectral variables;
the spectrum data algorithm library is internally provided with a plurality of chemometric algorithms for updating and correcting the model.
Further, the detection table is provided with a clamping groove matched with the detection plate, and the clamping groove is used for fixing the detection plate.
Furthermore, slots are formed in the side walls of the detection table and the protective shell, and the detection plate is inserted into the clamping groove from the outside of the protective shell from the slots.
Further, a sample cell is arranged on the detection plate, and a glass cover plate is arranged on the sample cell to seal the sample in the sample cell.
Further, the surface of the detection plate is uniformly covered by tin, and a plurality of layers of anti-skid patterns are arranged on the side edges of the holding part of the detection plate.
Further, the raman probe is a dual fiber probe structure having a dual channel for receiving laser light from a laser and transmitting raman scattered light.
Further, the control processing system is an embedded system board, control instructions of all the circuits are written in the main control chip, and the control processing system is integrated with a sensor module, a communication module, a USB interface circuit, a power supply voltage stabilizing circuit, a signal amplifying circuit and an analog-to-digital conversion circuit.
Further, the raman signal enhanced substrate adopts gold nanorods.
Further, the preparation of the raman signal enhancing substrate: preparing nano materials by using CTAB, HAuCl4, ascorbic acid and the like as main raw materials in two steps, wherein the first step is to prepare seed solution, and the second step is to prepare growth solution to assist in growing a final product AuNR; in the preparation of the nano-substrate AuNR seed solution, CTAB: HAuCl4: n (N)aBH4 the volume ratio is 25:25:3, concentration ratio of 0.2M:0.5mM:0.1M; in the preparation of the nano-substrate AuNR growth solution, agNO 3 ：HAuCl 4 : the volume ratio of AA is 1.25:75:1.05.
a method for rapidly detecting pesticide residues in food based on cloud model resource library comprises the following steps:
enhancement of hazard raman signal: uniformly mixing the hazard extracting solution and the nano signal reinforcing material, sampling into a sample cell of the detection plate, sealing the sample in the sample cell by using a glass cover plate, and mounting the sealed detection plate to a detection table;
acquisition of hazard raman signals: the method comprises the steps of emitting laser by controlling a light source module, transmitting the laser to a Raman probe to irradiate a nano signal substrate by utilizing an optical fiber, obtaining a Raman spectrum signal endangered by an object to be detected, converting the spectrum signal into a digital signal by a power amplification circuit and a digital-to-analog conversion circuit which are arranged in the portable detection terminal, and transmitting the processed spectrum data to a man-machine interaction terminal;
processing and calculating a hazard Raman signal: the human-computer interaction terminal receives the spectrum data, matches the collected spectrum data with a hazard type standard spectrum database in the cloud data platform, identifies the hazard type, displays the matching result on the human-computer interaction terminal, calculates according to a preprocessing algorithm selected by the human-computer interaction terminal and a detection model, and displays the calculation result on the human-computer interaction terminal, thereby realizing qualitative and quantitative detection of the hazard.
Further, the data preprocessing algorithm is a baseline correction, wavelet analysis, adaptive filtering, orthogonal signal correction method, differential processing data smoothing and noise filtering method, and is used for preprocessing the acquired spectrum information.
The invention has the beneficial effects that:
the gold nanorod substrate material provided by the invention is relatively simple and convenient in preparation process, has a strong enhancement factor, and can be applied to SERS spectrum acquisition of most pesticides.
The detection plate provided by the invention is inserted into the appointed clamping groove on the shell, so that the surface enhanced Raman spectrum of the sample can be quickly acquired by combining the portable Raman spectrometer, the focal length of the sample is ensured not to be changed when the sample is acquired each time, and the error caused by the change of the measurement distance is greatly reduced.
The portable detection terminal and the detection system of the mobile terminal provided by the invention can enable the spectrum data to be rapidly processed at the mobile terminal, so that the device can rapidly obtain the detection result of the residual content of the hazard in the food on site.
Drawings
Fig. 1 is an internal structural view of a portable detection terminal of the present invention;
fig. 2 is an external structural view of the portable sensing terminal of the present invention;
FIG. 3 is a schematic diagram of a test plate configuration;
FIG. 4 is a schematic diagram of a human-machine interaction terminal;
FIG. 5 is a schematic diagram of a test board and socket configuration
FIG. 6 is a schematic diagram of a detection system
In the figure, 1, a protective housing, 2, a Raman probe, 3, a spectrum acquisition module, 4, a fixed support, 5, a slot, 6, a light source module, 7, a power module, 8, a control processing system, 9, a sensor module, 10 and a communication module are formed, 11, SMA905 optical fibers, 12, an LED display screen, 13, a switch button, 14, a charging port, 15, a glass cover plate, 16, anti-skid patterns, 17, a detection plate, 18, a sample cell, 19, a man-machine interaction terminal, 20 and a detection table.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The application provides a quick detection of pesticide residue in food based on high in the clouds model resource storehouse, include: the device comprises a portable detection terminal, a man-machine interaction terminal and a Raman signal enhancement substrate. The method comprises the following steps:
1. portable detection terminal
The mechanism of the portable detection terminal is combined with the structure shown in fig. 1-6, and comprises a protective shell 1, a Raman probe 2, a spectrum acquisition module 3, a light source module 6, a power supply module 7, a control processing system 8, a sensor module 9, a communication module component 10 and a sample loading unit, wherein the Raman probe 2, the spectrum acquisition module 3, the light source module 6, the power supply module 7, the control processing system 8, the sensor module 9 and the communication module component 10 are arranged in the protective shell 1.
The sample loading unit comprises a detection table 20 and a detection plate 17, wherein the detection table 20 is fixed at the bottom of the protective shell 1, and a clamping groove matched with the detection plate 17 is arranged on the detection table 20 and used for fixing the detection plate 17. The side walls of the detection table 20 and the protective shell 1 are provided with slots 5, and the size of the slots 5 is matched with the size of the detection plate 17. The slot 5 is used for inserting the detection plate 17 into the card slot from the outside of the protective case 1. In this embodiment, as shown in fig. 2, the length-to-width ratio of the detection plate 17 is 10:5:1; one end of the device is provided with a circular sample cell 18, and the sample cell 18 is provided with a glass cover plate 15 to seal the sample in the sample cell 18; the surface of the detection plate 17 is uniformly covered by tin, light is not transmitted, and multiple layers of anti-skid patterns 16 are arranged on two side edges of the other end, so that the detection plate is convenient to hold.
The raman probe 2 is set towards the detection stage 20 at the cuvette 18, i.e. the detection plate 17 is inserted to a depth such that the central axis of the raman probe, the central axis of the circular cuvette, are just on the same axis. In this embodiment, the raman probe 2 is a dual fiber probe structure having a dual channel for receiving laser light from a laser and transmitting raman scattered light, and the raman probe 2 is suspended directly above the sample cell 18 by the fixed support 4 at a focal length of one time so that the focal point is located on the sample.
The light source module 6, the spectrum acquisition module 3 and the Raman probe 2 are connected through an SMA905 optical fiber 11 to form a complete optical loop, the LED display screen 12, the light source module 6, the power supply module 7 and the switch button 13 are all connected to the control processing system 8 through flat cables, the spectrum acquisition module 3 is connected with the control processing system 8 through USB data, and the sensor module 9 and the communication module 10 are all connected to the control processing system 8 through welding modes.
The upper part of the protective shell 1 is provided with a clamping groove matched with the LED display screen 12, and is used for placing the LED display screen 12, and the display screen 12 is used for displaying the system electric quantity, the internal temperature of the portable detection device, the state of the portable detection device and the like.
The control processing system 8 is an embedded system board, control instructions of all the circuits are written in the main control chip, and the control processing system 8 is integrated with a sensor module 9, a communication module 10, a USB interface circuit, a power supply voltage stabilizing circuit, a signal amplifying circuit, an analog-to-digital conversion circuit and the like. The sensor module 9 is a temperature and humidity sensor and is used for collecting the temperature inside the portable detection terminal. The communication module 10 is a bluetooth module, and is used for wireless data transmission between the portable detection terminal and the man-machine interaction terminal, and when the internal temperature of the portable detection terminal is too high, bluetooth is automatically disconnected.
The power module 7 provides power to each power unit and is provided with a power supply voltage stabilizing circuit for providing a substantially constant operating voltage to the system.
In this embodiment, the protective casing 1 is not longer than 200mm, not longer than 10mm, and not more than 80mm in height, ensuring portability of the portable detection device.
In this embodiment, the USB interface circuit is configured to charge the power supply, and may also implement an expansion function, for example, exchange spectral data with a computer.
2. Man-machine interaction terminal
In this embodiment, the man-machine interaction terminal is a smart phone with an operating system of more than 5.0 android.
Furthermore, the man-machine interaction terminal is also used for storing data and analyzing the number table under batch data.
3. Cloud data platform
And a hazard type standard spectrum database, a hazard type detection model database and a spectrum data algorithm database are built in the database of the cloud data platform. Wherein, raman spectrum data corresponding to different concentrations of various hazardous substances are arranged in the standard spectrum database of the hazardous species, the hazardous species comprises chlorpyrifos, carbendazim, paraquat, glyphosate and graminium, 2, 4-dichlorophenoxyacetic acid, staphylococcus aureus, escherichia coli, salmonella and the like, and obtaining Raman spectra corresponding to the standard substances at different concentrations of the dangerous substances to form a standard sample spectrum library. And comparing the spectrum of the object to be detected acquired by the portable detection terminal with Raman spectrum data pre-stored in a standard spectrum database of the hazard species, so as to identify the type of the object to be detected.
The dangerous substance type detection model library is used for carrying out model calculation on the spectrum of the object to be detected, and bringing the spectrum data of the object to be detected into a model (according to the dangerous substance type, the selection is carried out at a man-machine interaction end); obtaining the content of the object to be detected through model calculation; in this embodiment, the detection model is a quantitative model, and the detection model method is a set mathematical equation, expressed as:
Y＝a0+a1*V1+a2*V2+a3*V3+a4*V4+a5*V5+…+an*Vn
wherein Y is the detection result, a0 and a1 … an are model coefficients, and V1 and V2 … Vn are spectral variables.
The spectrum data algorithm library is internally provided with a plurality of chemometric algorithms for updating and correcting the model. Chemometric algorithms include baseline correction, wavelet analysis, adaptive filtering, quadrature signal correction, differential processed data smoothing, PLS, SVM, BOSS, etc.
For example, the baseline correction method is a discrete state transition algorithm, which includes the steps of:
(1) A binary vector v. of length consistent with the raman spectrum is defined and its elements are randomly initialized to 0 and 1. The wavelength point whose vector V corresponds to an element value of 1 is assumed to be a point on the true base line, while the wavelength point whose element value is 0. Let (Xi, yi) i=1..n be a set of points on the base line, where Xi represents the wavenumber value at the ith wavelength point, Y represents the raman spectrum intensity value corresponding to the ith wavelength point, and a cubic spline curve is used to achieve raman spectrum baseline fitting
Wherein: a. b, c and d are different order coefficients of a cubic spline function
(2) Adding penalty term in the function expression of cubic spline curve to transplant the influence of noise
Wherein: lambda is a penalty coefficient;
obtaining a predicted baseline estimation result by setting a penalty coefficient lambda and calculating the minimum value of the formula (2) for a second derivative function of a cubic spline function S (x)
(3) Fitting spectral baseline estimation results
And obtaining a predicted baseline estimation result based on a smooth spline curve fitting algorithm according to a formula 1. And judging whether an optimal result of the baseline estimation is obtained. The calculation method is that
Wherein the method comprises the steps of
And->
Respectively representing intensity values of an ith point in the fitted baseline and the Raman original spectrum of the t-th iterative process; n is the total wavelength number in the Raman spectrum; />
Representing the accumulated sum of the element values in the binary vector V of the t-th iteration process.
(4) Judging whether the cycle end condition is satisfied
|d t -d t-1 |＜10 -3 T is more than or equal to 2. The optimal baseline estimation result is considered to have been obtained, ending the iterative process.
(5) The baseline data is subtracted from the original raman spectral data to obtain corrected spectral data.
In this embodiment, the cloud service is configured to be more than "2-core CPU/4G memory/3M public network bandwidth", so as to ensure rapid analysis and processing of data.
4. Raman signal enhancement substrate
The application adopts gold nanorods as a Raman signal enhancement substrate,
the preparation method of the Raman signal enhancement substrate comprises the following steps: first, 5mL cetyltrimethylammonium bromide solution (CTAB) and tetrachloroauric acid (HAuCl) were mixed in equal volumes at room temperature 4 ) And mixing and stirring uniformly to prepare the gold nanorod seed solution. Subsequently, the stirring speed was increased to 1200rpm, and 0.6mL of 0.1M sodium borohydride (NaBH) at 4℃was added dropwise to the mixed solution 4 ) Standing for half an hour. Then preparing a growth solution: 1.25mL AgNO 3 And 75mL of HAuCl 4 75mL of a 0.2M CTAB solution was added and mixed. Then 1.05mL of ascorbic acid was added to the mixed solution, and the solution turned from yellow to colorless. Simultaneously, 180 mu L of seed solution is taken by using a liquid-transfering gun and added into the growth solution, the solution changes from colorless to red within 15 minutes, and the AuNR is obtained after aging and stabilization for 24 hours.
Example 2
Based on the device for rapidly detecting the pesticide residue in the food designed by the application, the invention also provides a method for rapidly detecting the pesticide residue in the food, which comprises the following steps:
enhancement of hazard raman signal: the centrifuge tube is used as a material reaction place, the hazard extracting solution and the nano signal enhancement material are uniformly mixed, 3 drops of the mixture are taken and transferred to the circular sample cell 18, a sample is sealed in the sample cell 18 by the glass cover plate 15, the sample enters the inside of the shell from the slot 5 of the shell, the insertion depth is just the center shaft of the Raman probe and the center shaft of the circular sample cell are on the same axis, and the hazard Raman signal enhancement is realized.
Acquisition of hazard raman signals: the portable detection terminal emits laser through controlling the light source module 6, the laser is transmitted to the Raman probe 2 to irradiate the nano signal substrate by utilizing the optical fiber, a Raman spectrum signal endangered by an object to be detected is obtained, the spectrum signal is converted into a digital signal through a power amplification circuit and a digital-to-analog conversion circuit which are arranged in the portable detection terminal, and the data is transmitted to the human-computer interaction terminal through the Bluetooth communication module.
Processing and calculating a hazard Raman signal: and the man-machine interaction terminal matches the received spectrum data with a hazard species standard spectrum database in a database, identifies the hazard species and displays the matching result. And selecting a data preprocessing method and a detection model to finish data inversion calculation of the content of the target substance of the object to be detected, and returning the calculation result to a man-machine interaction terminal for display so as to realize qualitative and quantitative detection of the dangerous substances. The data preprocessing algorithm is a baseline correction method, a wavelet analysis method, a self-adaptive filtering method, an orthogonal signal correction method, a differential processing data smoothing and noise filtering method and is used for preprocessing the acquired spectrum information.
The baseline correction method is a discrete state transfer algorithm and comprises the following steps:
(1) A binary vector v. of length consistent with the raman spectrum is defined and its elements are randomly initialized to 0 and 1. The wavelength point whose vector V corresponds to an element value of 1 is assumed to be a point on the true base line, while the wavelength point whose element value is 0. Let (Xi, yi) i=1..n be a set of points on the base line, where Xi represents the wavenumber value at the ith wavelength point, Y represents the raman spectrum intensity value corresponding to the ith wavelength point, and a cubic spline curve is used to achieve raman spectrum baseline fitting
Wherein: a. b, c and d are different order coefficients of a cubic spline function
(2) Adding penalty term in the function expression of cubic spline curve to transplant the influence of noise
Wherein: lambda is a penalty coefficient;
obtaining a predicted baseline estimation result by setting a penalty coefficient lambda and calculating the minimum value of the formula (2) for a second derivative function of a cubic spline function S (x)
(3) Fitting spectral baseline estimation results
And obtaining a predicted baseline estimation result based on a smooth spline curve fitting algorithm according to a formula 1. And judging whether an optimal result of the baseline estimation is obtained. The calculation method is that
Wherein the method comprises the steps of
And->
Respectively representing intensity values of an ith point in the fitted baseline and the Raman original spectrum of the t-th iterative process; n is the total wavelength number in the Raman spectrum; />
Representing the accumulated sum of the element values in the binary vector V of the t-th iteration process.
(4) Judging whether the cycle end condition is satisfied
|d t -d t-1 |＜10 -3 T is more than or equal to 2. The optimal baseline estimation result is considered to have been obtained, ending the iterative process.
(5) The baseline data is subtracted from the original raman spectral data to obtain corrected spectral data.
In this embodiment, the hazardous substance extract is obtained by soaking in an organic solution, centrifuging at high speed, and filtering by solid phase extraction.
Example 3
Taking chlorpyrifos detection as an example, the detection device and the detection method of the invention are described:
s1, preparing a gold nanorod by a seed growth method to serve as a Raman signal enhancement substrate, enhancing a surface plasma effect due to the fact that a rod-shaped structure of the gold nanorod promotes an electromagnetic field, and enabling a SERS signal of an object to be detected to be remarkably enhanced, wherein the specific preparation process is as follows: first, 5mL of a 0.2M cetyltrimethylammonium bromide solution (CTAB) was mixed with an equal volume of 0.5mM tetrachloroauric acid (HAuCl 4) at room temperature and stirred uniformly to prepare a gold nanorod seed solution. Subsequently, the stirring speed was increased to 1200rpm, 0.6mL of 0.1M sodium borohydride (NaBH 4) at 4℃was added dropwise to the mixed solution, and the mixture was allowed to stand for half an hour. Then preparing a growth solution: 1.25mL of 4mM AgNO3 and 75mL of 1mM HAuCl4 were added to 75mL of 0.2M CTAB solution and mixed. Then 1.05mL of 0.1M ascorbic acid was added to the mixed solution, and the solution turned from yellow to colorless. Simultaneously, 180 mu L of seed solution is taken by using a liquid-transfering gun and added into the growth solution, the solution changes from colorless to red within 15 minutes, and the AuNR is obtained after aging and stabilization for 24 hours.
S2, uniformly mixing 5-7 mu L of chlorpyrifos with different concentrations and SERS response media in a centrifuge tube to obtain a mixture of pesticide residue molecules with different concentrations and the SERS response media; 1ml is removed through a liquid-transferring gun, transferred into a detection pool on a detection plate, covered by a glass plate to prevent liquid from being spilled, then the detection plate is inserted into a clamping groove of portable detection equipment, and spectrum acquisition is carried out on the detection plate by utilizing a Raman spectrometer.
And opening a switch to supply power to the system, starting the device, clicking operation software on the mobile terminal, sending a spectrum acquisition command through Bluetooth, and acquiring the current state parameters of the equipment. One detection cycle includes the steps of turning on the laser, receiving data, turning off the laser, and returning the data. Finally, the mobile terminal obtains the acquired surface enhanced Raman spectrum data.
The human-computer interaction terminal receives the spectrum data, matches the collected spectrum data with a hazard type standard spectrum database in the cloud data platform, identifies the hazard type, displays the matching result on the human-computer interaction terminal, calculates according to a preprocessing algorithm and a detection model selected by the human-computer interaction terminal, displays the calculation result on the human-computer interaction terminal, realizes qualitative and quantitative detection of the hazard, stores the spectrum data and the result, and enables a user to analyze the data more efficiently.
The above embodiments are merely for illustrating the design concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, the scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications according to the principles and design ideas of the present invention are within the scope of the present invention.
Claims (10)
1. Quick detecting system of pesticide residue in food based on high in clouds model resource storehouse, its characterized in that includes:
the portable detection terminal comprises a protective shell (1), a Raman probe (2), a spectrum acquisition module (3), a light source module (6), a power supply module (7), a control processing system (8), a sensor module (9), a communication module (10) and a sample loading unit, wherein the Raman probe (2), the spectrum acquisition module (3), the light source module (6), the power supply module (7), the control processing system (8) and the sensor module (9) are arranged in the protective shell (1); the sample loading unit comprises a detection table (20) and a detection plate (17), wherein a sample pool (18) is arranged on the detection plate (17) and is used for placing a detected sample and a Raman signal enhancement substrate; the Raman probe (2) is arranged towards the position of the sample cell (18), and the central axis of the Raman probe (2) and the central axis of the sample cell (18) are on the same axis; the light source module (6), the spectrum acquisition module (3) and the Raman probe (2) are connected through optical fibers to form a complete optical loop; the power supply module (7) is used for providing electric energy for each power utilization unit; the sensor module (9) and the communication module (10) are integrated on the control processing system (8), and the sensor module (9), the communication module (10), the LED display screen (12), the light source module (6), the power supply module (7) and the spectrum acquisition module (3) are all in signal connection with the control processing system (8);
the human-computer interaction terminal is in information interaction with the portable detection terminal and the cloud data platform;
the cloud data platform is internally provided with a hazard type standard sample spectrum library, a hazard type detection model library and a spectrum data algorithm library;
raman spectrum data corresponding to different concentrations of various dangerous substances are stored in the spectrum library of the standard sample of the dangerous species, and the raman spectrum data are used for matching the spectrum of the object to be detected collected by the portable detection terminal and identifying the type of the object to be detected;
the dangerous substance type detection model library is used for carrying out model calculation on the spectrum of the object to be detected and bringing the spectrum data of the object to be detected into a model; obtaining the content of the object to be detected through model calculation; the detection model is a quantitative model, the detection model method is a set mathematical equation,
Y＝a0+a1*H1+a2*H 2+a3*H3+a4*H4+a5*H5+…+an*Hn
wherein Y is a detection result, a0 and a1 … an are model coefficients, and H1 and H2 … Hn are spectral variables;
the spectrum data algorithm library is internally provided with a plurality of chemometric algorithms for updating and correcting the model.
2. The rapid detection system for pesticide residues in food based on the cloud model resource library as claimed in claim 1, wherein the detection table (20) is provided with a clamping groove matched with the detection plate (17), and the clamping groove is used for fixing the detection plate (17).
3. The rapid detection system for pesticide residues in food based on the cloud model resource library according to claim 2, wherein the detection table (20) and the side wall of the protective shell (1) are provided with slots (5), and the detection plate (17) is inserted into the clamping groove from the outside of the protective shell (1) from the slots (5).
4. A rapid detection system for pesticide residues in food based on cloud model resource library according to claim 1, 2 or 3, wherein a sample cell (18) is arranged on the detection plate (17), a glass cover plate (15) is arranged on the sample cell (18), and a sample is sealed in the sample cell (18).
5. The rapid detection system for pesticide residues in food based on the cloud model resource library as claimed in claim 4, wherein the surface of the detection plate (17) is uniformly covered with tin, and a plurality of layers of anti-skid patterns (16) are arranged on the side edges of the holding part of the detection plate (17).
6. The rapid detection system for pesticide residues in food based on the cloud model resource library of claim 5, wherein the raman probe (2) is of a dual-fiber probe structure and has a dual channel for receiving laser light emitted by a laser and transmitting raman scattered light.
7. The rapid detection system for pesticide residues in food based on the cloud model resource library according to claim 5, wherein the control processing system (8) is an embedded system board, control instructions of all the partial circuits are written in the main control chip, and the control processing system (8) is integrated with a sensor module (9), a communication module (10), a USB interface circuit, a power supply voltage stabilizing circuit, a signal amplifying circuit and an analog-to-digital conversion circuit.
8. The rapid detection system for pesticide residues in food based on cloud model resource library of claim 5, wherein the raman signal enhancement substrate is a gold nanorod.
9. The rapid detection system for pesticide residues in food based on cloud model resource library as set forth in claim 8, wherein the preparation of the raman signal enhancing substrate comprises: preparing nano materials by using CTAB, HAuCl4, ascorbic acid and the like as main raw materials in two steps, wherein the first step is to prepare seed solution, and the second step is to prepare growth solution to assist in growing a final product AuNR; in the preparation of the nano-substrate AuNR seed solution, CTAB: HAuCl4: the volume ratio of NaBH4 is 25:25:3, concentration ratio of 0.2M:0.5mM:0.1M; in the preparation of the nano-substrate AuNR growth solution, agNO 3 ：HAuCl 4 : the volume ratio of AA is 1.25:75:1.05.
10. the method for rapidly detecting pesticide residues in food based on the cloud model resource library based on the system of claim 1 is characterized by comprising the following steps:
enhancement of hazard raman signal: uniformly mixing the hazard extracting solution and the nano signal enhancement material, sampling into a sample cell (18) of a detection plate (17), sealing the sample in the sample cell (18) by using a glass cover plate (15), and mounting the sealed detection plate (17) to a detection table (20);
acquisition of hazard raman signals: the method comprises the steps of emitting laser by controlling a light source module (6), transmitting the laser to a Raman probe (2) to irradiate a nano signal substrate by utilizing an optical fiber, obtaining a Raman spectrum signal endangered by an object to be detected, converting the spectrum signal into a digital signal by a power amplification circuit and a digital-to-analog conversion circuit which are arranged in a portable detection terminal, and transmitting the processed spectrum data to a human-computer interaction terminal;
processing and calculating a hazard Raman signal: the human-computer interaction terminal receives the spectrum data, matches the collected spectrum data with a hazard type standard spectrum database in the cloud data platform, identifies the hazard type, displays the matching result on the human-computer interaction terminal, calculates according to a preprocessing algorithm selected by the human-computer interaction terminal and a detection model, and displays the calculation result on the human-computer interaction terminal, thereby realizing qualitative and quantitative detection of the hazard.
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