Rapid microbiological detection system and method

Abstract

The invention belongs to the technical field of biological detection, and particularly discloses a rapid microbiological detection system and method. The method comprises the following steps: acquiring a morphology image of a microbial sample by adopting the laser scanning subsystem; identifying a microbial target from the morphology image of the microbial sample by adopting an image segmentation method, and extracting position coordinates of the microbial target; based on the position coordinates of the identified microbial target, calculating the shortest path starting from the initial position to access all microbial target points as a scanning track; controlling a laser scanning subsystem and a spectrum detection subsystem to perform spectrum detection on each target position according to the scanning track; and carrying out feature extraction on spectral data including but not limited to Raman spectrum, comparing the spectral data with a standard spectral database, identifying the category of a microbial target, and judging the activity and other characteristics of microorganisms. According to the invention, the species, activity and other characteristics of microorganisms can be rapidly identified.

Description

Technical Field

[0001] This application belongs to the field of biological detection technology, and more specifically, relates to a rapid microbial detection system and method. Background Technology

[0002] Microbial detection is of great significance in fields such as medical diagnostics, food safety, environmental monitoring, and biopharmaceuticals. Traditional microbial detection methods, such as culture methods, can take several days, immunological detection methods suffer from cross-reactivity issues, and PCR methods require complex sample pretreatment.

[0003] Raman spectroscopy can provide molecular fingerprint information of microorganisms, and has advantages such as being non-destructive, rapid, and highly specific. However, existing Raman microbial detection techniques have the following shortcomings: It requires scanning the entire sample area point by point, which results in low detection efficiency and a large amount of data. Microorganisms are unevenly distributed in the sample, and full-area scanning generates a large amount of invalid data; The lack of effective methods for locating microorganisms makes accurate detection difficult. The scanning path is not optimized, resulting in lengthy detection times. The existing system lacks flexibility and is difficult to adapt to the detection needs of different types of biological targets.

[0004] Therefore, there is an urgent need for a method that can quickly locate microorganisms and perform efficient spectral detection. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this application is to provide a rapid microbial detection system and method, which aims to solve the problem of low detection efficiency caused by the fact that the existing Raman spectroscopy technology for microbial detection adopts full-area scanning and scans point by point.

[0006] The first aspect of this application relates to a rapid microbial detection system, comprising: a laser scanning subsystem, a first optical subsystem, a second optical subsystem, a spectral detection subsystem, and a signal detection subsystem; The first optical subsystem is located between the laser scanning subsystem and the microbial sample to be detected; the signal detection subsystem and the spectral detection subsystem are located in the output direction of the signal light through the laser scanning subsystem; the second optical subsystem is connected to the laser scanning subsystem, the spectral detection subsystem, and the signal detection subsystem. The laser scanning subsystem controls the deflection of the laser beam in a two-dimensional direction, enabling the laser beam to perform a two-dimensional scan on the microbial sample; the signal detection subsystem measures the signal light intensity at the laser beam scanning position; the second optical subsystem acquires the morphology of the microbial sample and locates the microbial target based on the laser beam scanning position coordinates and the signal light intensity at the laser beam scanning position; wherein, the signal light is the beam generated by the laser beam reflected by the microbial sample. The second optical subsystem is used to obtain the shortest path from the starting position to all microbial target points based on the coordinates of the microbial target location as the scanning trajectory, and then control the laser scanning subsystem and the spectral detection subsystem to perform spectral detection on each biological target location. The spectral detection subsystem is used to acquire the spectral information of the microbial target; the second optical subsystem is used to identify the currently located microbial category based on the spectral information of the microbial target.

[0007] In some implementations, the laser scanning subsystem includes a laser scanner and an optical scanning module; the optical scanning module is located between the laser scanner and the microbial sample; the laser scanner is used to control the laser beam to perform two-dimensional scanning on the microbial sample; and the optical scanning module is used to focus the scanning beam output by the laser scanner.

[0008] In some embodiments, the laser scanner is at least one or a combination of a galvanometer scanner, an acousto-optic polarizer, a piezoelectric galvanometer, a galvanometer galvanometer, an electro-optic deflector, a rotating mirror, and a polygonal mirror; the optical scanning module includes at least one of a scanning lens group, a relay lens group, and a scene group.

[0009] In some embodiments, the rapid microbial detection system further includes a bright-field imaging subsystem, which includes a Kohler illumination module and an imaging camera; a first optical subsystem includes a beam splitter and an objective lens, or a beam splitter and a lens; the beam splitter is used to allow the scanning beam to pass through while reflecting the signal light into the Kohler illumination module; the objective lens or lens is used to focus the scanning beam onto the microbial sample; the Kohler illumination module is located in front of the imaging camera and is used to provide uniform bright-field illumination for the imaging camera; the imaging camera is used to acquire the distribution of microorganisms.

[0010] In some embodiments, the spectral detection subsystem includes a spectral detector, which is at least one of a Raman spectrometer, a fluorescence spectrometer, or an absorption spectrometer.

[0011] The second aspect of this application relates to a rapid microbial detection method, specifically comprising the following steps: Step 1: The laser is sequentially passed through the laser scanning subsystem and the first optical subsystem. The scanning laser is focused onto the microbial sample and reflected, generating signal light that returns to the detector along the original optical path to obtain a morphological image of the microbial sample. Step 2: For the morphological images of the microbial samples, image segmentation methods are used to identify the microbial targets and extract their location coordinates; Step 3: Based on the location coordinates of the identified microbial targets, calculate the shortest path from the starting position to all microbial target points as the scanning trajectory; Step 4: Control the laser scanning subsystem and the spectral detection subsystem to perform spectral detection at each target location according to the scanning trajectory; Step 5: Extract features from the spectral data and compare them with a standard spectral database to identify the category of microbial targets.

[0012] In some implementations, step one involves using a Kohler illumination module and an imaging camera to acquire morphological images of the microbial sample.

[0013] In some embodiments, the microorganisms include at least one of the following: bacteria, fungi, cells, yeast, viruses, protozoa, algae, and spores.

[0014] In some implementations, step two involves preprocessing the morphological image of the microbial sample, including at least one of the following operations: denoising, enhancement, filtering, thresholding, and connected component analysis, before using an image segmentation method to identify the microbial target.

[0015] In some implementations, the method for obtaining the scanning trajectory in step three includes at least one of the following algorithms: nearest neighbor algorithm, genetic algorithm, simulated annealing algorithm, ant colony algorithm, dynamic programming algorithm, greedy algorithm, and divide-and-conquer algorithm; the acquisition of the scanning trajectory needs to consider at least one of the following factors: distance, detection time, and turning angle.

[0016] In some implementations, multiple wavelength lasers are simultaneously excited in step one to enhance the contrast in the identification of different microbial targets.

[0017] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: This application provides a rapid microbial detection system and method, which employs a laser scanner and an optical scanning module to work together. The laser beam is deflected in the X and Y directions, allowing the laser point to quickly scan the microbial sample. The microbial sample emits signal light, which returns along the original path. The laser performs a descan on the signal light, turning it into a stable beam. Simultaneously, the signal light intensity can be detected by the detector. The laser scanner determines the target location of the microorganism, avoiding invalid scans, while the detector acquires the light signal intensity. This allows for accurate and rapid imaging of the microbial morphology. Combined with a spectral detector, the types of microorganisms can be quickly identified.

[0018] Based on the laser scanner and optical scanning module, the deflection of the laser beam can be precisely controlled. This application proposes to optimize the scanning path, obtain the optimal scanning path through multiple algorithms, and optimize the intelligent algorithm to significantly reduce the microbial detection time.

[0019] The laser scanner used in this application supports various scanners such as galvanometers, acousto-optic deflectors, and piezoelectric galvanometers, making it highly versatile in practical applications.

[0020] The rapid microbial detection system and method provided in this application have a spectral detector that can establish standard spectral data for various biological targets and can identify different types of biological targets, including bacteria, fungi, cells, yeast, etc. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the rapid microbial detection system provided in the embodiments of this application.

[0022] Figure 2 These are bright-field images or laser scan images provided in the embodiments of this application.

[0023] Figure 3 This is a schematic diagram of obtaining the location coordinates of microorganisms from an image, provided in an embodiment of this application.

[0024] Figure 4 This is a schematic diagram of calculating the scanning trajectory based on position coordinates provided in an embodiment of this application.

[0025] Figure 5 This is a schematic diagram of the path optimization algorithm provided in the embodiments of this application.

[0026] Figure 6 This is a schematic diagram of microbial classification and identification provided in the embodiments of this application.

[0027] In all the accompanying drawings, the same reference numerals are used to denote the same elements or structures, wherein: 1 is a laser; 2 is a beam splitter or dichroic mirror; 3 is a laser scanner; 4 is an optical scanning module; 5 is a beam splitter or optical path switching module; 6 is an objective lens or lens; 7 is a microbial sample; 8 is a Kohler illumination module; 9 is an imaging camera; 10 is a beam splitter or optical path switching module; 11 is a detector; 12 is a spectral detector. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] In this application, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A existing alone, A and B existing simultaneously, and B existing alone. In this application, the symbol " / " indicates that the related objects are in an "or" relationship, for example, A / B means A or B.

[0030] In this application, the terms “first” and “second” are used to distinguish different objects, rather than to describe a specific order of objects.

[0031] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0032] In the description of the embodiments in this application, unless otherwise stated, "multiple" means two or more.

[0033] The embodiments of this application are described below with reference to the accompanying drawings.

[0034] This application provides a rapid microbial detection system that quickly locates microorganisms using laser scanning imaging and combines path optimization algorithms with Raman spectroscopy, fluorescence spectroscopy, or other spectra to achieve rapid, accurate, and efficient detection of various biological targets, including microorganisms, bacteria, fungi, cells, yeasts, and tiny organisms.

[0035] On the one hand, such as Figure 1 As shown, this application provides a rapid microbial detection system, comprising: A laser illumination subsystem includes a laser 1 and a beam splitter or dichroic mirror 2; the beam splitter or dichroic mirror 2 is located in the laser emission direction of the laser 1; the laser 1 is at least one of a solid-state laser, a semiconductor laser, a gas laser, and a fiber laser, with operating wavelengths including but not limited to 405nm, 488nm, 532nm, 633nm, 785nm, and 1064nm; the laser is used to emit a laser beam as an illumination source; the beam splitter or dichroic mirror 2 is used to separate and combine the laser beam while allowing the return signal light to pass through; The laser scanning subsystem includes a laser scanner 3 and an optical scanning module 4. The optical scanning module 4 is located between the laser scanner 3 and the microbial sample 7. The laser scanner 3 is at least one or a combination of a galvanometer scanner, an acousto-optic polarizer, a piezoelectric galvanometer, a galvanometer galvanometer, an electro-optic deflector, a rotating mirror, and a polygonal mirror. The laser scanner 3 is used to control the laser beam to perform two-dimensional scanning on the microbial sample. The optical scanning module 4 includes at least one of a scanning lens group, a relay lens group, and a field lens group, which is used to focus the scanning beam of the laser scanner 3 and work together with the objective lens or lens 6 to project the scanning beam onto the sample plane. The first optical subsystem includes a beam splitter or optical path switching module 5 and an objective lens or lens 6. The beam splitter or optical path switching module 5 is used to allow the scanning beam to pass through while reflecting the signal light into the Kohler illumination module. The objective lens or lens 6 is used to focus the laser transmitted and output by the beam splitter or optical path switching module onto the microbial sample and to receive the signal light reflected back from the microbial sample. The bright-field imaging subsystem is located in the direction of the signal light reflected by the beam splitter or optical path switching module 5; it includes a Kohler illumination module 8 and an imaging camera 9; the Kohler illumination module 8 is located in front of the imaging camera 9 and is used to provide uniform bright-field illumination for the imaging camera; the imaging camera 9 can be at least one of a CCD camera, a CMOS camera, or a SCOMS camera, and is used to acquire the distribution of microorganisms in order to locate the position coordinates of the microorganisms. The signal detection subsystem includes a beam splitter or optical path switching module 10 and a detector 11. The beam splitter or optical path switching module 10 is located in the direction in which the signal light is transmitted through the dichroic mirror or beam splitter 2. The detector 11 is located in the direction in which the signal light is reflected through the beam splitter or optical path switching module 10, and is used to collect fluorescence or other signals and convert the optical signal into an electrical signal to generate a laser scanning image. It should be noted that the laser emitted by laser 1 is guided to laser scanner 3 after passing through beam splitter / dichroic mirror 2. Laser scanner 3 works in conjunction with optical scanning module 4 to control the laser beam to deflect in the X and Y directions, so that the laser point can quickly scan the microbial sample. Objective lens / lens 6 is used to focus the laser on a tiny point on the microbial sample. When a laser irradiates a microbial sample, the sample emits a signal (e.g., fluorescence, reflected light, or scattered light). This signal light is collected by the objective lens 6 and returns along the original optical path. In the return path, the laser scanner 3 performs a descan on the signal light, transforming it into a stable beam that is no longer scanned. Therefore, regardless of the laser point's scanning position, the returning signal light propagates along the same path. When the returning signal light passes through the beam splitter / dichroic mirror 2, due to its beam splitting characteristics, it is transmitted into the beam mirror / optical path switching module 10, further guiding the signal light to the detector 11. The detector 11 synchronously measures the signal light intensity at each laser point. The scanning position of the laser scanner 3 is synchronized with the output signal of the detector 11. Based on the laser scanner's position coordinates and the detector's signal intensity, a two-dimensional image is constructed point by point. By scanning the entire sample area, the signal value of each pixel is obtained, thus forming a morphological image of the sample, such as... Figure 2 As shown; The spectral detection subsystem includes a spectral detector 12, which includes at least one of a Raman spectrometer, a fluorescence spectrometer, and an absorption spectrometer, for acquiring spectral information of biological targets; The second optical subsystem includes: an image processing module, a target recognition module, a path optimization module, a spectral analysis module, and a system control module; the image processing module is used to process bright-field images and laser scanning images; the target recognition module is used to identify and locate biological targets; the path optimization module is used to calculate the optimal scanning path; the spectral analysis module is used for the classification and identification of biological targets; and the system control module is used to coordinate the work of each subsystem. It should be noted that in practical applications, microbial sample 7 is located on the sample stage. Microbial sample 7 includes, but is not limited to, biological targets such as bacteria, fungi, cells, yeasts, viruses, protozoa, algae, spores, and microorganisms. The sample stage is used to support and fix the microbial sample.

[0036] On the other hand, this application provides a rapid microbial detection method, specifically including the following steps: Step 1: Image Acquisition. Acquire a bright-field image of the sample using bright-field imaging, and / or acquire a laser scanning image using a laser scanner, such as... Figure 2 As shown; Step Two: Localization of Microbial Targets. Preprocessing of the acquired bright-field and / or laser scan images includes at least one operation: denoising, enhancement, filtering, thresholding, and connected component analysis. Then, image segmentation methods are used to identify potential biological targets, extract their location coordinates, and generate a coordinate list, such as... Figure 3 As shown; Step 3: Path Optimization: Based on the identified biological target location coordinates, calculate the shortest path from the starting position to all target points as the scanning trajectory; apply path optimization algorithms, including but not limited to nearest neighbor algorithm, genetic algorithm, simulated annealing algorithm, ant colony algorithm, dynamic programming algorithm, greedy algorithm, and divide-and-conquer algorithm; among them, the calculation of the optimal scanning trajectory considers at least one factor among distance, detection time, and turning angle, such as... Figure 4 As shown and Figure 5 As shown; Step 4: Precise Detection: Following the optimized path, the laser scanner and spectral detector are controlled to perform spectral detection at each target location, acquiring Raman spectra, fluorescence spectra, or other spectral information; it should be noted that multi-wavelength laser excitation is supported to enhance the contrast in the identification of different biological targets; Step 5: Classification and Identification: The collected spectral data is preprocessed and features are extracted. It is then compared with a standard spectral database to identify and classify biological targets, outputting a detection result report. Classification and identification are based on spectral feature matching and its learning algorithms or artificial intelligence algorithms, such as... Figure 6 As shown.

[0037] It should be noted that the system can simultaneously detect and classify multiple different types of biological targets.

[0038] It should be noted that rapid and accurate classification and identification can be achieved by establishing a standard spectral database of various biological targets.

[0039] More specifically, the following introduces several path optimization algorithms, including but not limited to the following types: Nearest neighbor algorithm implementation: The algorithm goal is to find the nearest neighbor path starting from the starting position; the input is the starting coordinates ( ), target point set The output is the access order and the total path length. Specifically, starting from the current position, all remaining target points are accessed. The access method is as follows: first, calculate the Euclidean distance from the current position to all remaining target points, select the closest target point as the next target, move to the next target, and remove the point from the remaining target points. Update the current position, and so on, until the access path and total distance are obtained.

[0040] Genetic Algorithm Implementation: The algorithm aims to optimize paths based on genetic algorithms. It sets the population size, crossover probability, mutation probability, and number of generations, randomly generates N access sequences, calculates the total path length for each individual, obtains the individual's fitness, updates the population using roulette wheel selection, crossover, and mutation operations, and selects the optimal individual as the path.

[0041] Ant colony algorithm implementation: The algorithm aims to optimize path planning for ant colonies; it sets the number of ants, pheromone importance, heuristic factor, pheromone volatility coefficient, and pheromone enhancement coefficient; it constructs a path from the starting point, selects the next target point based on the state transition probability, and continues until all target points are visited; and then obtains the globally optimal path by updating the pheromone.

[0042] Example 1 Embodiment 1 of this application provides a bacterial detection method based on galvanometer scanning; The microbial detection system includes: laser 1, beam splitter 2, laser scanner 3, optical scanning system 4, objective lens 6, imaging camera 9, detector 11, and spectral detector 12; Among them, laser 1 is a 532nm solid-state laser with a power of 20mW; the beam splitter is a 532nm dichroic mirror; laser scanner 3 is a two-dimensional galvanometer scanner with a scanning angle of [missing information]. The optical scanning system 4 is a 4f scanning lens system, focusing at f1=100mm and f2=200mm; the objective lens 6 is a 40mm lens. Water immersion objective lens, numerical aperture NA=0.8; imaging camera 9, 2048. 2048-pixel sCMOS camera; detector 11 is a PMT photomultiplier tube; spectral detector 12 is a Raman spectrometer with a wavelength range of 200~4000 nm. .

[0043] Microbial detection methods include the following steps: Step 1: Image acquisition. The Kohler lighting system 8 provides uniform white light illumination, and the camera 9 acquires the image. Bright-field image of the field of view, with a resolution of 0.25. / pixel; simultaneously, a 532nm laser performs a grating-shaped scan via a galvanometer scanner; a PMT detector collects fluorescence or other signals to generate a laser scan image; Step 2: Target localization. The Otsu threshold segmentation algorithm is applied to the bright field image or laser scan image to identify bacterial targets. The recognition accuracy is enhanced by combining the fluorescence signal of the laser scan image. The centroid coordinates of the bacteria are extracted by connected component analysis. In the experiment, 23 E. coli targets were identified, with coordinates ranging from (50, 80) to (450, 420) pixels. Step 3: Path optimization, using an improved nearest neighbor algorithm: Starting at position (50, 50), calculate the distance matrix. Nearest neighbor selection is applied, taking into account the steering angle penalty factor. =0.1; the total path length after optimization is 1247.3. This represents a 42.6% reduction compared to random access. Step 4: Precise detection, the galvanometer guides the Raman laser (10mW power) to each target location according to the optimized path; Step 5: Classification and identification: Extract characteristic peaks of E. coli for identification.

[0044] Example 2 Embodiment 2 of this application provides a yeast detection system based on an acousto-optic deflector; the difference from the system in Embodiment 1 is that the laser scanner 3 is an acousto-optic deflector (AOD) with a response time <10. Laser 1 is a 785nm semiconductor laser to reduce phototoxicity; detector 11 is a SiPM array to improve detection efficiency.

[0045] Its advantages include: no moving mechanical parts, resulting in faster scanning speed; strong random access capability, making it suitable for sparsely distributed samples; and almost zero path switching time.

[0046] The test results showed that the detection of yeast samples targeting multiple targets was completed in a shorter time and with improved detection efficiency.

[0047] Example 3 This application provides a method for mixed detection of multiple biological targets, including Escherichia coli (bacteria), Candida albicans (fungi), Saccharomyces cerevisiae (yeast), and HeLa (mammalian cells). The detection method is as follows: multi-wavelength excitation: 488nm + 633nm dual-wavelength contrast enhancement; the spectral feature library is a Raman feature spectrum database of four biological targets; the machine learning classification is a support vector machine (SVM) multi-classification algorithm.

[0048] Path optimization and improvement: Considering the detection time requirements of different targets: bacteria: 3 seconds, fungi: 5 seconds, yeast: 4 seconds, bacteria: 8 seconds; applying the weighted TSP algorithm, the weight is reduced by 35.7% in detection time. The identification result is: different microorganisms are identified.

[0049] In summary, compared with traditional full-area scanning methods, this application reduces the detection time by 30-80%.

[0050] It should be understood that expressions such as “comprising” and “may include” used in this application indicate the existence of the disclosed functions, operations, or constituent elements, and do not limit one or more additional functions, operations, and constituent elements. In this application, terms such as “comprising” and / or “having” are to be interpreted as indicating a particular characteristic, number, operation, constituent element, component, or combination thereof, but not to exclude the existence or possibility of adding one or more other characteristics, numbers, operations, constituent elements, components, or combinations thereof.

[0051] Furthermore, in this application, the expression "and / or" includes any and all combinations of the associated listed words. For example, the expression "A and / or B" may include A, may include B, or may include both A and B.

[0052] In the description of the embodiments of this application, it should be noted that, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a detachable connection or a non-detachable connection; it can be a direct connection or an indirect connection through an intermediate medium. "Fixed connection" refers to a connection where the relative positional relationship remains unchanged after connection. "Rotary connection" refers to a connection where the components can rotate relative to each other after connection. "Sliding connection" refers to a connection where the components can slide relative to each other after connection. The directional terms mentioned in the embodiments of this application, such as "top," "bottom," "inner," "outer," "left," and "right," are only for reference to the directions in the accompanying drawings. Therefore, the directional terms used are for better and clearer explanation and understanding of the embodiments of this application, and are not intended to indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0053] Furthermore, the mathematical concepts mentioned in the embodiments of this application, such as symmetry, equality, parallelism, and perpendicularity, are limitations specific to the current technological level, rather than absolute and strict mathematical definitions. Slight deviations are permissible; approximations of symmetry, equality, parallelism, and perpendicularity are all acceptable. For example, "A and B are parallel" means that A and B are parallel or approximately parallel, and the angle between A and B can be between 0 and 10 degrees. "A and B are perpendicular" means that A and B are perpendicular or approximately perpendicular, and the angle between A and B can be between 80 and 100 degrees.

[0054] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A rapid microbial detection system, characterized in that, include: The system comprises a laser scanning subsystem, a first optical subsystem, a second optical subsystem, a spectral detection subsystem, and a signal detection subsystem. The first optical subsystem is located between the laser scanning subsystem and the microbial sample to be detected; the signal detection subsystem and the spectral detection subsystem are located in the output direction of the signal light through the laser scanning subsystem; the second optical subsystem is connected to the laser scanning subsystem, the spectral detection subsystem, and the signal detection subsystem. The laser scanning subsystem controls the deflection of the laser beam in a two-dimensional direction, enabling the laser beam to perform a two-dimensional scan on the microbial sample; the signal detection subsystem measures the signal light intensity at the laser beam scanning position; the second optical subsystem acquires the morphology of the microbial sample and locates the microbial target based on the laser beam scanning position coordinates and the signal light intensity at the laser beam scanning position; wherein, the signal light is the beam generated by the laser beam emitted by the microbial sample. The second optical subsystem is used to obtain the shortest path from the starting position to all microbial target points based on the coordinates of the microbial target location as the scanning trajectory, and then control the laser scanning subsystem and the spectral detection subsystem to perform spectral detection on each biological target location. The spectral detection subsystem is used to acquire the spectral information of the microbial target; the second optical subsystem is used to identify the currently located microbial category based on the spectral information of the microbial target.

2. The rapid microbial detection system according to claim 1, characterized in that, The laser scanning subsystem includes a laser scanner and an optical scanning module; the optical scanning module is located between the laser scanner and the microbial sample; the laser scanner is used to control the laser beam to perform two-dimensional scanning on the microbial sample; the optical scanning module is used to focus the scanning beam output by the laser scanner.

3. The rapid microbial detection system according to claim 2, characterized in that, The laser scanner is at least one or a combination of a galvanometer scanner, an acousto-optic polarizer, a piezoelectric galvanometer, a galvanometer galvanometer, an electro-optic deflector, a rotating mirror, and a polygonal mirror; the optical scanning module includes at least one of a scanning lens group, a relay lens group, and a scene group.

4. The rapid microbial detection system according to any one of claims 1 to 3, characterized in that, It also includes a bright-field imaging subsystem, which includes a Kohler illumination module and an imaging camera; the first optical subsystem includes a beam splitter and an objective lens, or a beam splitter and a lens; the beam splitter is used to allow the scanning beam to pass through while reflecting the signal light into the Kohler illumination module; Objective lenses or lenses are used to focus the scanning beam onto the microbial sample; the Kohler illumination module is located in front of the imaging camera and is used to provide uniform bright-field illumination for the imaging camera; the imaging camera is used to acquire the distribution of microorganisms.

5. The rapid microbial detection system according to claim 1, characterized in that, The spectral detection subsystem includes a spectral detector, which is at least one of a Raman spectrometer, a fluorescence spectrometer, or an absorption spectrometer.

6. A rapid microbial detection method based on the rapid microbial detection system according to any one of claims 1 to 5, characterized in that, Includes the following steps: Step 1: The laser is sequentially passed through the laser scanning subsystem and the first optical subsystem. The scanning laser is focused onto the microbial sample and reflected, generating signal light that returns to the detector along the original optical path to obtain a morphological image of the microbial sample. Step 2: For the morphological images of the microbial samples, image segmentation methods are used to identify the microbial targets and extract their location coordinates; Step 3: Based on the location coordinates of the identified microbial targets, calculate the shortest path from the starting position to all microbial target points as the scanning trajectory; Step 4: Control the laser scanning subsystem and the spectral detection subsystem to perform spectral detection at each target location according to the scanning trajectory; Step 5: Extract features from the spectral data and compare them with a standard spectral database to identify the category of microbial targets.

7. The rapid microbial detection method according to claim 6, characterized in that, The method for obtaining the scanning trajectory in step three includes at least one of the following algorithms: nearest neighbor algorithm, genetic algorithm, simulated annealing algorithm, ant colony algorithm, dynamic programming algorithm, greedy algorithm, and divide-and-conquer algorithm; the acquisition of the scanning trajectory needs to consider at least one of the following factors: distance, detection time, and turning angle.

8. The rapid microbial detection method according to claim 6, characterized in that, In step one, multiple wavelength lasers are simultaneously excited to enhance the contrast in the identification of different microbial targets.

9. The rapid microbial detection method according to claim 6 or 8, characterized in that, In step two, the morphological images of the microbial samples are preprocessed, including at least one of the following operations: denoising, enhancement, filtering, thresholding, and connected component analysis. Then, image segmentation methods are used to identify the microbial targets.

10. The rapid microbial detection method according to claim 6, characterized in that, Microorganisms include at least one of the following: bacteria, fungi, cells, yeasts, viruses, protozoa, algae, and spores.

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