Bursaphelenchus xylophilus infection disease detector based on Raman scattering spectrum analysis technology
By optimizing the optical path design and algorithm, and combining lasers, beam splitters, and filter modules, the sensitivity and accuracy issues of pine wood nematode detection have been resolved, enabling efficient and portable early detection while reducing cost and size.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-11
- Publication Date
- 2026-03-31
AI Technical Summary
Existing pine wilt disease detection technologies suffer from low sensitivity and insufficient accuracy, making it difficult to detect early infection quickly and portablely. Furthermore, existing devices are susceptible to stray light interference, have low signal-to-noise ratios, and limited applicability.
The design employs a 532nm semiconductor laser, a beam splitter prism assembly, and a filter module. Combined with a Raman spectroscopy imaging module, a data acquisition module, and a CNN algorithm, the optical path design is optimized. A temperature control device stabilizes the detection environment, achieving spatial separation of incident and scattered light and suppression of background noise.
It improves the sensitivity and accuracy of detection, increases the signal-to-noise ratio by 30%, and achieves a classification accuracy of 97.2%. It can detect trace metabolite changes in the early stage of infection within 7 days. It is suitable for continuous monitoring of live pine wood. The device volume is reduced by 60% and the cost is reduced by 45%.
Smart Images
Figure CN224066624U_ABST
Abstract
Description
Technical Field
[0001] This utility model relates to a detection instrument for pine wilt disease based on Raman scattering spectroscopy analysis technology, specifically belonging to the field of infectious disease detection technology. Background Technology
[0002] Pine wilt disease is a devastating disease of pine trees caused by the pine wood nematode. It spreads rapidly and is difficult to control, posing a serious threat to global pine forest ecosystems and forestry economies. Current detection technologies for pine wilt disease mainly include hyperspectral imaging, multispectral imaging, and molecular biological detection methods (such as PCR amplification).
[0003] In recent years, systematic research on pine wood nematode in China has formed a multi-dimensional technical system. Through cross-regional sample collection and biological characteristic analysis, researchers have revealed the genetic diversity of pine wood nematode populations and its pathogenic mechanisms, especially its transmission pathway mediated by the pine sawyer beetle. Regarding control strategies, the combined application of chemical agents (such as abamectin) and biological control (such as Beauveria bassiana) has been proven to reduce nematode population density, but the long-term ecological risks still need to be assessed.
[0004] Existing spectral detection technologies rely on large-scale equipment and professional operators, and are only suitable for large-scale pine forest monitoring. They are difficult to use for rapid on-site screening of circulating pine trees. Although hyperspectral and multispectral technologies can obtain rich spatial-spectral information, they are insufficient in responding to weak biomarkers in the early infection stage (incubation period), resulting in a high false negative rate. Traditional machine learning algorithms (such as support vector machines and random forests) are easily affected by noise when processing high-dimensional, nonlinear spectral data, and their model generalization ability is poor. At the same time, existing Raman spectroscopy detection devices are prone to stray light interference due to their complex optical path design (such as multiple mirror groups and redundant spectroscopic systems), which affects the spectral signal-to-noise ratio (SNR) and data reliability. Utility Model Content
[0005] The purpose of this invention is to provide a pine wilt disease detection instrument based on Raman scattering spectroscopy analysis technology, so that the detection has the characteristics of high sensitivity, high accuracy, portability and low cost.
[0006] To solve the above-mentioned technical problems, the technical solution adopted by this utility model is: the new laser fixing bracket and beam splitter assembly also includes a filter module and a sample chamber;
[0007] The laser mounting bracket has four corner clips on its inner side, which are used to install the laser. A beam splitter assembly is installed on one side of the laser, and a filter module is installed on the other side of the beam splitter assembly. The laser mounting bracket, beam splitter assembly, and filter module are installed in an "L" shape. A sample chamber is installed on the other side of the filter module. The laser is a 532 nm semiconductor laser, and the laser mounting bracket ensures that the laser offset is less than 0.1 mm.
[0008] Furthermore, the laser is fixed by a laser mounting bracket to ensure the collimation stability of the laser path, the incident light and the scattered light are spatially separated by a beam splitter assembly to reduce crosstalk, the background noise is suppressed by a filter module, low-frequency impurity light is filtered out during beam return, and the sample chamber is used to stabilize the detection environment.
[0009] The beam splitter assembly includes a frame, a beam splitter body, a convex lens, and a filter. The frame is fixedly installed inside the beam splitter assembly, and the beam splitter body is set inside the frame. A convex lens is installed at the front end of the beam splitter body, and a filter is installed at the rear end of the beam splitter body. The convex lens is a cemented doublet convex lens, the filter is a 532 nm cutoff filter, and the filter module is an 800 nm low-pass filter with a transition band slope greater than 40 dB / oct.
[0010] Furthermore, a beam splitter body is mounted on the frame to convert the vertical optical path to a horizontal one. A convex lens placed at the front end is used to focus the light onto the objective lens. When the Raman scattered beam returns, a filter is used to filter the reflected laser light, and a convex lens is used to focus it onto the four-dimensional fiber optic adjustment frame SMA905.
[0011] The sample chamber is equipped with a temperature control device, and the outside of the sample chamber is covered with a soft mirror.
[0012] Furthermore, the temperature is stabilized at ±0.5℃ by a temperature control device, and the Raman scattered beam is reflected back by a soft mirror.
[0013] The beneficial effects of this utility model are:
[0014] 1. The core module of the device consists of a Raman spectroscopy imaging module, a data acquisition module, a data comparison module, and a result output module. This optimizes the optical path design and CNN algorithm, resulting in a 30% improvement in the signal-to-noise ratio and a classification accuracy of 97.2%. Furthermore, it can detect changes in trace metabolites in the early stages of infection, reducing the incubation period to less than 7 days, thus giving the device high sensitivity and specificity.
[0015] 2. By setting up the detection of collected resin, the sample tissue structure can be maintained without damaging it, making it suitable for continuous monitoring of live pine wood. This improves detection efficiency and accuracy. Through professional software for data processing and analysis, the system automatically identifies pine wilt disease infection, reduces human error, and enhances the stability and reliability of the detection.
[0016] 3. By adopting miniaturized hardware such as lasers, beam splitters, convex lenses, and filters, the device size is reduced by 60%, the overall cost is reduced by 45%, and the device can be designed and operated as a single unit with a detection cycle of less than 3 minutes. Attached Figure Description
[0017] Figure 1 This is a schematic diagram of the overall structure of this utility model;
[0018] Figure 2 This is a top view schematic diagram of the beam-splitting prism assembly of this utility model;
[0019] Figure 3 This is a schematic diagram of the overall process of this utility model;
[0020] Figure 4 This is a schematic diagram comparing the characteristic peaks of the Raman spectrum of this utility model;
[0021] Figure 5 This is a schematic diagram of the testing process of this utility model.
[0022] 1. Laser mounting bracket; 2. Beam splitter assembly; 3. Filter module; 4. Sample chamber; 5. Frame; 6. Beam splitter body; 7. Convex lens; 8. Filter. Detailed Implementation
[0023] The following will be combined with the appendix Figure 1-5 The technical solutions in the embodiments are described clearly and completely.
[0024] Specific implementation method one: as follows Figures 1-4 As shown, the detector architecture consists of four core modules: a Raman spectroscopy imaging module, a data acquisition module, a data comparison module, and a result output module. The Raman spectroscopy imaging module uses a miniature fiber optic spectrometer with a wavelength range of 500 nm to 1000 nm and a resolution of 0.5 nm. The Raman spectroscopy imaging module is equipped with a laser, which is a 532 nm semiconductor laser with an output power of 150 mW and a beam divergence angle of less than 0.5 mrad. The laser is installed in the four corner clips inside the laser mounting bracket 1. The laser mounting bracket 1 ensures that the laser offset is less than 0.1 mm, thus ensuring the optical path collimation stability of the laser.
[0025] The Raman spectroscopy imaging module includes a beam-splitting prism assembly 2 and a filter module 3. The beam-splitting prism assembly 2 includes a beam-splitting prism body 6 and a convex lens 7. The beam-splitting prism body 6 has a beam splitting ratio of 50:50. A frame 5 is fixedly mounted inside the beam-splitting prism assembly 2, and the beam-splitting prism body 6 is positioned inside the frame 5. The beam-splitting prism body 6 is mounted through the frame 5 to convert the vertical optical path to a horizontal one, and the convex lens 7 at the front end is used to focus the light onto the objective lens. The convex lens 7 is mounted at the front end of the beam-splitting prism body 6, and a filter 8 is mounted at the rear end of the beam-splitting prism body 6. The convex lens 7 is a cemented doublet convex lens with a focal length f of 150mm. The filter 8 is a 532nm cutoff filter. The filter module 3... An 800nm low-pass filter is used, with a transition band slope greater than 40dB / oct. During the Raman scattered beam return, the reflected laser light is filtered by filter 8 and focused onto the four-dimensional fiber optic adjustment frame SMA905 by convex lens 7. The incident light and scattered light are spatially separated by beam splitter prism assembly 2 to reduce optical path crosstalk. Background noise is suppressed by filter module 3, which filters out low-frequency impurity light during beam return. A sample chamber 4 is installed on the other side of filter module 3. A temperature control device is installed inside the sample chamber 4, and a soft mirror is wrapped around the outside of the sample chamber 4. The temperature is stabilized at ±0.5℃ by the temperature control device, and the Raman scattered beam is reflected back by the soft mirror.
[0026] By adopting miniaturized hardware such as laser, beam splitter prism body 6, convex lens 7 and filter 8, the device volume is reduced by 60%, the overall cost is reduced by 45%, and the device can be integrated into a single design, with a detection cycle of less than 3 minutes.
[0027] The data acquisition module integrates a high-speed ADC (1MHz sampling rate) and an FPGA controller to achieve real-time acquisition and preprocessing of spectral data (baseline correction, noise filtering). The data comparison module can build a classification model based on a convolutional neural network (CNN), with normalized spectral feature vectors as input and disease probability values (0-1 interval) as output. The result output module can display the detection results synchronously on an LCD screen and a VGA.
[0028] The core module of the device consists of a Raman spectroscopy imaging module, a data acquisition module, a data comparison module, and a result output module. This optimizes the optical path design and CNN algorithm, resulting in a 30% improvement in the signal-to-noise ratio and a classification accuracy of 97.2%. Furthermore, it can detect changes in trace metabolites in the early stages of infection, reducing the incubation period to less than 7 days, thus giving the device high sensitivity and specificity.
[0029] Specific implementation method two: such as Figure 5 As shown, the detection method process includes the following steps:
[0030] Step 1: Sample Preparation
[0031] The collected resin was placed in a quartz sample tank.
[0032] Step 2, Spectral Acquisition:
[0033] After the laser beam is split into 6 beams by the beam splitter body, it is focused onto the sample surface by the microscope objective (spot diameter ≤10μm); the Raman scattered light returns through the reflector and passes sequentially through the 532nm filter 8 (to filter out the laser part) and the 800nm (12500cm-1) low-pass filter (to suppress fluorescence background), and is finally received by the spectrometer.
[0034] Step 3: Data Analysis
[0035] Extract characteristic peaks (such as 1600 cm⁻¹ and 3000 cm⁻¹ corresponding to terpenoid compounds); perform pattern recognition using a CNN model (ResNet-18 architecture, with a training set containing 5000 sets of labeled spectra) and output disease diagnosis results;
[0036] By setting up tests on the collected resin, the sample tissue structure can be preserved without damage, making it suitable for continuous monitoring of live pine wood. This improves detection efficiency and accuracy. Through professional software for data processing and analysis, the system automatically identifies pine wilt disease infection, reduces human error, and enhances the stability and reliability of the test.
[0037] The above description is merely a preferred embodiment of the present utility model and is not intended to limit the present utility model in any way. Although the present utility model has been disclosed above with reference to a preferred embodiment, it is not intended to limit the present utility model. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present utility model's technical solution. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present utility model's technical solution, based on the technical essence of the present utility model and within the spirit and principles of the present utility model, shall still fall within the protection scope of the present utility model's technical solution.
Claims
1. A device for detecting pine wilt disease based on Raman scattering spectroscopy, a laser fixing support (1) and a spectrometer prism assembly (2), characterized in that, The filter module (3) and the sample bin (4) are also included. The laser fixing support (1) is internally provided with a four-corner buckle, and the laser fixing support (1) is used for mounting the laser through the buckle. The laser is provided on one side with a light splitting prism assembly (2), and the light splitting prism assembly (2) is provided on the other side with a filter module (3). The laser fixing support (1), the light splitting prism assembly (2) and the filter module (3) are in an L-shaped structure. The filter module (3) is provided on the other side with a sample bin (4).
2. The apparatus for detecting pine wilt disease based on Raman scattering spectroscopy according to claim 1, wherein The light splitting prism assembly (2) comprises a frame (5), a light splitting prism body (6), a convex lens (7) and a filter lens (8). The frame (5) is fixedly installed on the inner side of the light splitting prism assembly (2), and the frame (5) is internally provided with the light splitting prism body (6). The light splitting prism body (6) is provided at the front end with the convex lens (7), and the light splitting prism body (6) is provided at the rear end with the filter lens (8).
3. The apparatus for detecting pine wilt disease based on Raman scattering spectroscopy according to claim 2, wherein The convex lens (7) is a double-cement convex lens, the filter lens (8) is a 532-nanometer cutoff filter lens, the filter module (3) is an 800-nanometer low-pass filter, and the transition band slope of the 800-nanometer low-pass filter is greater than 40 dB / oct.
4. The apparatus for detecting pine wilt disease based on Raman scattering spectroscopy according to claim 3, wherein The sample bin (4) is internally provided with a temperature control device, and the sample bin (4) is externally wrapped with a soft mirror.
5. The apparatus for detecting pine wilt disease based on Raman scattering spectroscopy according to claim 3, wherein The laser is a 532-nanometer semiconductor laser, and the laser fixing support (1) makes the laser offset less than 0.1 millimeter.