Method for automatically positioning Raman characteristic peak position based on Raman equipment

Abstract

The invention provides a method for automatically positioning a Raman characteristic peak position based on Raman equipment, and belongs to the technical field of Raman spectrum detection of microbial colonies, a microscopic imaging camera and Raman confocal equipment are adopted and are located in the same light path, the direction of the light path is controlled through a beam splitter, and when the beam splitter is in a position 1 state, the Raman characteristic peak position is automatically positioned; light returned by the Raman laser through the sample is dispersed into the microscopic imaging camera and the spectrometer CCD according to a certain proportion; and when the beam splitter is in a position 2 state, light returned by the Raman laser through the sample is completely transmitted to the spectrometer CCD, analysis and feature extraction are performed on an image acquired by the microscopic confocal Raman equipment by adopting originally designed coarse positioning, fine positioning and positioning correction, and on the premise that other external equipment is not used, the detection accuracy is greatly improved. The problems of automation and rapidness of positioning the Raman characteristic peak position of the microbial colony are solved, and the time cost and the hardware cost of positioning the Raman characteristic peak position are reduced.

Description

Technical Field

[0001] This invention belongs to the field of microbial colony Raman spectroscopy detection technology, and in particular, it is a method for automatically locating Raman characteristic peaks based on Raman equipment. It utilizes laser imaging data provided by microscopic confocal Raman equipment and combines it with digital image processing technology to complete the automatic location of Raman characteristic peaks in the process of microbial colony Raman spectroscopy detection. Background Technology

[0002] Acquiring Raman spectra of microbial colonies using a microconfocal Raman microscope is an important step in the detection and analysis of microbial colonies. Positioning the microconfocal microscope at the Raman characteristic peak position by changing the distance between the objective lens and the sample is a prerequisite for Raman detection. Generally, there are two methods for finding Raman characteristic peaks: manual and automatic.

[0003] The manual search method involves manually adjusting the distance between the objective lens and the sample, acquiring Raman spectra in real time, and judging by manually comparing the characteristic peaks of the spectra. This method is time-consuming, complex to operate, and requires a high level of expertise from personnel. It cannot automatically acquire Raman spectra of microbial colonies in large quantities. Moreover, there are significant errors in the data recorded by different operators.

[0004] The automatic search method involves integrating additional laser rangefinders to calculate the distance between the objective lens and the sample, thereby automating the acquisition of spectra. However, this method requires the integration of additional equipment, which is costly, and the results from Raman confocal equipment are too complex.

[0005] To overcome the shortcomings of the above-mentioned technical solutions, existing technologies also use the method of real-time acquisition of Raman spectra and real-time calculation of the signal-to-noise ratio of each spectrum to find the maximum value of the spectral signal-to-noise ratio to complete the positioning. Since the Raman laser integration time for acquiring microbial colonies is usually between 1 and 15 seconds, this method will cause a lot of time consumption for acquiring Raman spectra at each location, which is not conducive to the automated acquisition of Raman spectra of large batches of microbial colonies. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for automatically locating Raman characteristic peaks based on Raman equipment. Using only a microscopic confocal Raman instrument, it rapidly and automatically locates the Raman characteristic peaks of microbial colonies, reflecting these peaks as the distance D between the objective lens and the sample in the Raman spectrum. This provides a fundamental guarantee for obtaining high-quality microbial spectra. No additional equipment is required, and the method is highly efficient, reliable, and suitable for widespread application.

[0007] A method for automatically locating Raman feature peaks based on a Raman device comprises a three-part architecture, specifically:

[0008] Part 1: Coarse Positioning;

[0009] ① Simultaneously turn on the Raman laser and the microscopic imaging camera, and set the beam splitter to position 1. At this time, the microscopic imaging camera receives the imaging spot of the Raman laser at the sample position in real time, and the automatic stage moves down from the starting position P by a fixed distance D1 each time. Each time it moves, the imaging data Data of the coarse positioning of the laser can be obtained.

[0010] ② Using digital image processing algorithms, the imaging data Data of the coarse localization of the Raman laser is converted into HSV format, and the V channel is separated to obtain the imaging brightness data Data of the coarse localization of the Raman laser. v For the imaging brightness data Data v Perform brightness and grayscale distribution analysis, and extract the corresponding feature values;

[0011] As an example, the corresponding feature values ​​include:

[0012] Brightness mean Brightness variance std Brightness concentration distribution Brightness at the center of the bright spot point wait.

[0013] ③ The extracted corresponding feature values ​​are used to form the first feature vector;

[0014] As an example, the first feature vector includes:

[0015] x = {Brightness} mean Brightness std Brightness distribution Brightness point , ...}.

[0016] ④ Calculate the imaging brightness data Data using a feature dimensionality reduction function. v The eigenvalues ​​Feature = f(x);

[0017] Where: x is the image brightness data Data v f(x) is the feature transformation process function;

[0018] When the distance D between the objective lens and the sample is far from the optimal Raman characteristic peak position of the colony, the calculated imaging brightness data Data v The feature value is close to 0;

[0019] When the feature value of the imaging brightness data gradually increases and exceeds a certain threshold, the distance D between the objective lens and the sample is close to the optimal Raman feature peak of the colony. At this time, the position of the automatic stage is the coarse positioning success position P1, and the coarse positioning is completed.

[0020] Part Two: Detailed Positioning;

[0021] After coarse positioning is completed, position P1 still needs fine positioning for further position optimization.

[0022] ① Control the automatic stage to move downward from position P1 by a fixed distance D2 each time. Each time it moves, the imaging data Data2 of the fine positioning of the Raman laser can be obtained.

[0023] As an example, in order to further refine the positioning operation, it is required that the fixed distance D2 < the fixed distance D1.

[0024] ② Using the digital image processing algorithm, the imaging data Data2 is converted to HSV format, and the V channel is separated to obtain the imaging brightness data Data for fine positioning of the Raman laser. v2 For the imaging brightness data Data v2 Perform brightness and grayscale distribution analysis to extract the corresponding feature values;

[0025] As an example, the corresponding feature values ​​include:

[0026] Brightness mean2 Brightness variance std2 Brightness concentration distrbution2 Brightness at the center of the bright spot point2 Bright spot area area2 wait.

[0027] ③ The extracted feature values ​​are used to form a second feature vector;

[0028] As an example, the second feature vector includes:

[0029] x = {Brightness} mean Brightness std Brightness distribution2 Brightness point2 , ...}.

[0030] ④ Calculate the imaging brightness data Data using the feature dimensionality reduction function. v2 The eigenvalues ​​feature2 = f2(x);

[0031] The imaging brightness data at position P1 is Data v2 The feature value Feature2 will first increase and then decrease during the fine-tuning scanning process;

[0032] Finally, the fine-tuned positioning was successful, with position P2 located within the imaging brightness data Data. v2 The first minimum position of the feature value curve P2 is reflected in the fine-positioned imaging data Data2 of the Raman laser. The image formed by the Raman laser will gradually converge into a spot, and after forming the smallest spot at the position P2, it will gradually diverge.

[0033] Part Three: Positioning Correction;

[0034] After successfully locating the fine-tuning position P2, the distance D between the objective lens and the sample is close to the focal length of the objective lens. The Raman laser collects the signal on the surface of the colony. If you want to collect the best Raman characteristic peak position of the colony, you still need to continue to probe into the interior of the colony.

[0035] ① The optimal Raman characteristic peak position of the colony is a fixed downward probe distance D3 from the colony surface; the downward probe distance D3 is obtained by comparing the signal-to-noise ratio at various positions in the previous test;

[0036] ② Position the spectrometer at position 2, turn off the microscopic imaging camera, and after the automatic stage extends down from the surface of the colony to a distance D3, obtain the optimal Raman characteristic peak position P3 of the colony;

[0037] As an example, since microbial colonies have strong fluorescence signals, which can reduce the quality of the acquired Raman signal, the spectrometer needs to be positioned at position 2. At this time, the microscope imaging camera is turned off, and all the energy of the Raman laser is irradiated onto the sample. After irradiation for a fixed fluorescence quenching time T, the optimal Raman characteristic peak position P3 of the colony is automatically located.

[0038] Furthermore, it also includes a device for automatically locating Raman characteristic peaks based on a Raman apparatus, comprising:

[0039] The camera and Raman confocal device for microscopic imaging are both in the same optical path. The direction of the optical path is controlled by a beam splitter. When the beam splitter is in position 1, the light returned by the Raman laser after passing through the sample will be dispersed into the microscopic imaging camera and the spectrometer CCD in a certain proportion. When the beam splitter is in position 2, all the light returned by the Raman laser after passing through the sample is transmitted to the spectrometer CCD.

[0040] The beneficial effects of this invention are:

[0041] This invention utilizes digital image processing technology to analyze and extract features from images acquired by a microscopic confocal Raman spectroscopy device. Without relying on external equipment, it solves the problem of automating and accelerating the localization of Raman characteristic peaks in microbial colonies through three steps: coarse localization, fine localization, and localization correction. This reduces the time and hardware costs associated with Raman characteristic peak localization and provides convenient conditions for the automated acquisition of Raman spectra from a large number of microbial colonies. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall structure of a device for automatically locating Raman characteristic peaks based on a Raman device according to the present invention.

[0043] Figure 2 This is a schematic diagram of the imaging data when a certain threshold of 240 is used in the coarse localization process of the method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0044] Figure 3 This invention relates to a method for automatically locating Raman feature peaks using a Raman device, specifically focusing on imaging brightness data during the fine-tuning process. v2 Schematic diagram a.

[0045] Figure 4 This is a schematic diagram (b) of the imaging brightness data Datav2 during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0046] Figure 5 This is a schematic diagram (c) of the imaging brightness data Datav2 during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0047] Figure 6 This is a schematic diagram d of the imaging brightness data Datav2 during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0048] Figure 7 This is a schematic diagram of the imaging brightness data Datav2e during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0049] Figure 8 This is a schematic diagram (f) of the imaging brightness data Datav2 during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0050] Figure 9 This is a schematic diagram g of the imaging brightness data Datav2 during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention.

[0051] Figure 10 This is a schematic diagram h of the imaging brightness data Datav2 during the fine positioning process of a method for automatically locating Raman feature peaks based on a Raman device according to the present invention. Figures 3-10 (Detailed description of imaging change trends)

[0052] Figure 11 The image brightness data of position P1 in the method for automatically locating Raman feature peaks based on a Raman device according to the present invention is Data. v2 The trend of Feature2 value changing with the fine-positioning scanning process. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Figures 1 to 11 As shown.

[0054] As an example, the Raman laser used is a laser with a wavelength of 785nm.

[0055] Part 1: Coarse Positioning;

[0056] ① Simultaneously turn on the Raman laser and the microscopic imaging camera, and set the beam splitter to position 1. At this time, the microscopic imaging camera receives the imaging spot of the Raman laser at the sample position in real time, and the automatic stage moves down from the starting position P by a fixed distance D1 each time. Each time it moves, the imaging data Data of the coarse positioning of the laser can be obtained.

[0057] ② Using digital image processing algorithms, the imaging data Data of the coarse localization of the Raman laser is converted into HSV format, and the V channel is separated to obtain the imaging brightness data Data of the coarse localization of the Raman laser. v For the imaging brightness data Data v Perform brightness and grayscale distribution analysis, and extract the corresponding feature values;

[0058] As an example, the corresponding feature values ​​include:

[0059] Brightness mean Brightness variance std Brightness concentration distribution Brightness at the center of the bright spot point wait.

[0060] ③ The extracted corresponding feature values ​​are used to form the first feature vector;

[0061] As an example, the first feature vector includes:

[0062] x = {Brightness} mean Brightness std Brightness distribution Brightness point , ...}.

[0063] ④ Calculate the imaging brightness data Data using a feature dimensionality reduction function. v The eigenvalues ​​Feature = f(x);

[0064] Where: x is the image brightness data Data v f(x) is the feature transformation process function;

[0065] When the distance D between the objective lens and the sample is far from the optimal Raman characteristic peak position of the colony, the calculated imaging brightness data Data v The feature value is close to 0;

[0066] When the feature value of the imaging brightness data gradually increases and exceeds a certain threshold, the distance D between the objective lens and the sample is close to the optimal Raman feature peak of the colony. At this time, the position of the automatic stage is the coarse positioning success position P1, and the coarse positioning is completed.

[0067] As an example, the threshold value is 240 (the feature value has no unit), and the acquired imaging data Data at this time is as follows: Figure 1 As shown, some white light spots appear; the calculated imaging brightness data at this time is Data. v The feature value is 251.0. At this time, the distance D between the objective lens and the sample is close to the optimal Raman characteristic peak position of the colony. The position at this time is the coarse localization successful position P1, and the coarse localization is completed.

[0068] Part Two: Detailed Positioning;

[0069] After coarse positioning is completed, position P1 still needs fine positioning for further position optimization.

[0070] ① Control the automatic stage to move downward from position P1 by a fixed distance D2 each time. Each time it moves, the imaging data Data2 of the fine positioning of the Raman laser can be obtained.

[0071] As an example, in order to further refine the positioning operation, it is required that the fixed distance D2 < the fixed distance D1.

[0072] ② Using the digital image processing algorithm, the imaging data Data2 is converted to HSV format, and the V channel is separated to obtain the imaging brightness data Data for fine positioning of the Raman laser. v2 For the imaging brightness data Data v2 Perform brightness and grayscale distribution analysis to extract the corresponding feature values;

[0073] As an example, the corresponding feature values ​​include:

[0074] Brightness mean2 Brightness variance std2 Brightness concentration distribution2 Brightness at the center of the bright spot point2 Bright spot area area2 wait.

[0075] ③ The extracted feature values ​​are used to form a second feature vector;

[0076] As an example, the second feature vector includes:

[0077] x = {Brightness} mean Brightness std Brightness distribution2 Brightness point2 , ...}.

[0078] ④ Calculate the imaging brightness data Data using the feature dimensionality reduction function. v2 The eigenvalues ​​Feature2 = f2(x);

[0079] The imaging brightness data at position P1 is Data v2 The feature value Feature2 will first increase and then decrease during the fine-tuning scanning process;

[0080] Finally, the fine-tuned positioning was successful, with position P2 located within the imaging brightness data Data. v2 The first minimum position of the feature value curve (Feature2) is reflected in the fine-positioned imaging data (Datav2) of the Raman laser. The image formed by the Raman laser will gradually converge into a light spot, and after forming the smallest light spot at the position P2, it will gradually diverge.

[0081] As an example, the imaging brightness data of the Raman laser during fine positioning is shown in the data. v2 Reference Figures 3 to 10 As shown.

[0082] As an example, based on extensive research and testing, the imaging brightness data Data at position P1 is... v2 The feature value Feature2 will first increase and then decrease during the fine-tuning scanning process, and its trend is as follows: Figure 10 As shown.

[0083] Part Three: Positioning Correction;

[0084] After successfully locating the fine-tuning position P2, the distance D between the objective lens and the sample is close to the focal length of the objective lens. The Raman laser collects the signal on the surface of the colony. If you want to collect the best Raman characteristic peak position of the colony, you still need to continue to probe into the interior of the colony.

[0085] ① The optimal Raman characteristic peak position of the colony is a fixed downward probe distance D3 from the colony surface; the downward probe distance D3 is obtained by comparing the signal-to-noise ratio at various positions in the previous test;

[0086] ② Position the spectrometer at position 2, turn off the microscopic imaging camera, and after the automatic stage extends down from the surface of the colony to a distance D3, obtain the optimal Raman characteristic peak position P3 of the colony;

[0087] As an example, since microbial colonies have strong fluorescence signals, which can reduce the quality of the acquired Raman signal, the spectrometer needs to be positioned at position 2. At this time, the microscope imaging camera is turned off, and all the energy of the Raman laser is irradiated onto the sample. After irradiation for a fixed fluorescence quenching time T, the optimal Raman characteristic peak position P3 of the colony is automatically located.

[0088] Furthermore, it also includes a device for automatically locating Raman characteristic peaks based on a Raman apparatus, as described above. Figure 1 As shown, it includes:

[0089] The camera and Raman confocal device for microscopic imaging are both in the same optical path. The direction of the optical path is controlled by a beam splitter. When the beam splitter is in position 1, the light returned by the Raman laser after passing through the sample will be dispersed into the microscopic imaging camera and the spectrometer CCD in a certain proportion. When the beam splitter is in position 2, all the light returned by the Raman laser after passing through the sample is transmitted to the spectrometer CCD.

[0090] As an example, when the beam splitter is in position 1, it means that: Figure 1 The beam splitter, which is installed at a 45° angle directly above the culture dish, is movable and removable. When the beam splitter is in position 2, it means that the beam splitter is removed and no longer participates in the optical path structure.

[0091] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0092] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0093] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0094] The above description is only a preferred embodiment of the present invention. It should be understood that the above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention, and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, etc. made within the idea and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for automatically locating Raman characteristic peaks based on a Raman device, characterized in that, It includes a three-part architecture: Part 1: Coarse Positioning; ① Simultaneously turn on the Raman laser and the microscopic imaging camera, and set the beam splitter to position 1. At this time, the microscopic imaging camera receives the imaging spot of the Raman laser at the sample position in real time, and the automatic stage moves down from the starting position P by a fixed distance D1 each time. Each time it moves, the imaging data Data of the coarse positioning of the laser can be obtained. ② Using digital image processing algorithms, the imaging data Data of the coarse localization of the Raman laser is converted into HSV format, and the V channel is separated to obtain the imaging brightness data Data of the coarse localization of the Raman laser. v For the imaging brightness data Data v Perform brightness and grayscale distribution analysis, and extract the corresponding feature values; ③ The extracted corresponding feature values ​​are used to form the first feature vector; ④ Calculate the imaging brightness data Data using a feature dimensionality reduction function. v The eigenvalues ​​Feature = f(x); Where: x is the image brightness data Data v f(x) is the feature transformation process function; When the distance D between the objective lens and the sample is far from the optimal Raman characteristic peak position of the colony, the calculated imaging brightness data Data v The feature value is close to 0; When the feature value of the imaging brightness data gradually increases and exceeds a certain threshold, the distance D between the objective lens and the sample approaches the optimal Raman feature peak position of the colony. At this time, the position of the automatic stage is the coarse positioning success position P1, and the coarse positioning is completed. Part Two: Detailed Positioning; After coarse positioning is completed, position P1 still needs fine positioning for further position optimization. ① Control the automatic stage to move downward from position P1 by a fixed distance D2 each time. Each time it moves, the imaging data Data2 of the fine positioning of the Raman laser can be obtained. ② Using the digital image processing algorithm, the imaging data Data2 is converted to HSV format, and the V channel is separated to obtain the imaging brightness data Data for fine positioning of the Raman laser. v2 For the imaging brightness data Data v2 Perform brightness and grayscale distribution analysis to extract the corresponding feature values; ③ The extracted feature values ​​are used to form a second feature vector; ④ Calculate the imaging brightness data Data using the feature dimensionality reduction function. v2 The eigenvalues ​​Feature2 = f2(x); The imaging brightness data at position P1 is Data v2 The feature value Feature2 will first increase and then decrease during the fine-tuning scanning process; Finally, the fine-tuned positioning was successful, with position P2 located within the imaging brightness data Data. v2 The first minimum value of the feature value curve is reflected in the fine-positioned imaging data Data2 of the Raman laser. The image formed by the Raman laser will gradually converge into a light spot, and after forming the smallest light spot at the position P2, it will gradually diverge. Part Three: Positioning Correction; After successfully locating the fine-tuning position P2, the distance D between the objective lens and the sample is close to the focal length of the objective lens. The Raman laser collects the signal on the surface of the colony. If you want to collect the best Raman characteristic peak position of the colony, you still need to continue to probe into the interior of the colony. ① The optimal Raman characteristic peak position of the colony is a fixed downward probe distance D3 from the colony surface; the downward probe distance D3 is obtained by comparing the signal-to-noise ratio at various positions in the previous test; ② Position the spectrometer at position 2, turn off the microscope camera, and after the automatic stage descends from the surface of the colony to a distance D3, obtain the optimal Raman characteristic peak position P3 of the colony.

2. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, The corresponding feature values ​​include: Brightness mean Brightness variance std Brightness concentration distribution Brightness at the center of the bright spot point .

3. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, The first feature vector includes: X=Brightness mean ,Brightness std ,Brightness distribution ,Brightness point }。 4. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, For further fine-tuning of the positioning operation, the fixed distance D2 is less than the fixed distance D1.

5. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, The corresponding feature values ​​include: Brightness mean2 Brightness variance std2 Brightness concentration distribution2 Brightness at the center of the bright spot point2 Bright spot area area2 .

6. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, The second feature vector includes: X={Brightness mean ,Brightness std ,Brightness distribution2 ,Brightness point2 }。 7. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, Since microbial colonies have strong fluorescence signals, which can reduce the quality of the acquired Raman signal, the spectrometer needs to be positioned at position 2. At this time, the microscope imaging camera is turned off, and all the energy of the Raman laser is irradiated onto the sample. After irradiation for a fixed fluorescence quenching time T, the optimal Raman characteristic peak position P3 of the colony is automatically located.

8. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, It also includes a device for automatically locating Raman characteristic peaks based on a Raman device, comprising: a camera for microscopic imaging and a Raman confocal device, both located in the same optical path. The direction of the optical path is controlled by a beam splitter. When the beam splitter is in position 1, the light returned by the Raman laser from the sample is dispersed into the camera for microscopic imaging and the CCD of the spectrometer in a certain proportion. When the beam splitter is in position 2, all the light returned by the Raman laser from the sample is transmitted to the CCD of the spectrometer.

9. The method for automatically locating Raman characteristic peaks based on a Raman device according to claim 1, characterized in that, When the beam splitter is in position 1, it means that the RemovingBeam Splitter is installed at a 45° angle directly above the culture dish; when the beam splitter is in position 2, it means that the RemovingBeamSplitter is removed and no longer participates in the optical path architecture.

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