A method for detecting the content of active ingredients in plant callus based on microconfocal Raman spectroscopy

Abstract

The present invention belongs to the field of detection, and specifically relates to a method for detecting the active ingredient content in plant callus tissue based on confocal Raman microscopy. The present invention utilizes confocal Raman microscopy to couple a Raman spectrometer with a standard optical microscope, enabling observation of sample morphology using a high-magnification objective lens while also enabling Raman analysis using a microscopic laser spot. The present invention first plots a standard curve, then processes the plant callus tissue and determines its active ingredient content. This method, based on ultra-high-resolution confocal Raman microscopy, provides a non-destructive, rapid, and accurate method for determining the active ingredient content in plant callus tissue.

Description

Technical Field

[0001] The invention belongs to the field of detection, and in particular relates to a method for detecting the content of active components in plant callus based on microscopic confocal Raman. Background Art

[0002] Raman scattering refers to the phenomenon in which light undergoes a frequency change when it is scattered by molecules passing through a transparent medium. In the scattering spectrum of a transparent medium, the component with the same frequency as the incident light is called Rayleigh scattering. The spectral lines or bands with frequencies symmetrically distributed on either side of the incident light frequency are the Raman spectrum. The components with lower frequencies are called Stokes lines, while the components with higher frequencies are called anti-Stokes lines. Raman spectroscopy has applications in medical testing, food pesticide residue detection, environmental protection, drug analysis, and material property analysis. Each Raman peak in the Raman spectrum has specific molecular bond characteristics, and by generating a specific vibrational fingerprint, molecular identification of the sample can be achieved. Furthermore, by analyzing parameters such as the position, shape, height, width, and area of ​​the Raman peaks and combining them with different algorithms, the molecular content of the sample can be accurately determined.

[0003] Plant callus refers to the newly formed tissue that forms on the surface of a plant body after local trauma (physical injury, pathogenic microbial invasion, etc.). It is composed of parenchyma cells and can theoretically originate from any living plant cell. Currently, plant callus has been successfully applied in food, biopharmaceuticals, agriculture, and cosmetics. However, the quality of plant callus in the market varies widely, fundamentally due to the lack of effective detection methods and means. Traditional assays using reagent extraction not only damage the sample and may cause loss of the analyte during the extraction process, leading to inaccurate results, but are also cumbersome, time-consuming, and not universally applicable.

[0004] Existing methods for detecting active ingredient content using Raman spectroscopy all require extraction and preparation of the sample to be tested, which has common disadvantages such as cumbersome operations, long time consumption, and high requirements for the experimenter's skills. In addition, existing technologies also have the following disadvantages:

[0005] 1. Fluorescence Raman Technology

[0006] The disadvantages of this technology include: fluorescence interference, that is, when the fluorescent substance is excited, it will produce a strong fluorescence signal. Other fluorescent substances in the sample may mask or interfere with the Raman signal, resulting in reduced spectrum quality and affecting the analysis results; since many substances may have similar fluorescence properties, this method also has the disadvantage that substances with similar fluorescence properties cannot be distinguished.

[0007] 2. Surface Enhanced Raman (SERS) Technology

[0008] The disadvantages of this technology are: SERS technology requires the preparation of high-quality and uniform metal nanoparticles and strict control of experimental parameters, which makes the experiment difficult and the preparation cost high; currently only gold, silver, copper and a few uncommon alkali metals have a strong SERS effect, and the technology is not suitable for molecules with low vibration frequencies, so its research objects are limited; SERS signals are easily affected by factors such as the shape, size and molecular adsorption mode of nanoparticles, resulting in a low signal-to-noise ratio.

[0009] 3. UV Raman Technology

[0010] This technology uses higher-energy ultraviolet light to enhance the Raman scattering effect and improve spatial resolution. However, higher energy can easily damage samples, especially living biological samples. Secondly, the laser is more expensive and has higher requirements for filtering (optical lenses).

[0011] Therefore, based on this, the technical solution of the present invention is proposed. Summary of the Invention

[0012] To address the challenges of existing technologies, the present invention provides a method for detecting the active ingredient content in plant callus tissue using confocal Raman microscopy. This method utilizes confocal Raman microscopy to rapidly and accurately determine the active ingredient content without damaging the sample itself, and visualizes the distribution of the active ingredient content in the sample through pseudo-color imaging.

[0013] The present invention provides a method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman spectroscopy, which comprises the following steps:

[0014] (I) Drawing of standard curve:

[0015] (I-1) Select solvent and prepare gradient standard solution;

[0016] (I-2) placing a wavelength calibration standard sample on a spectrometer sample stage, focusing the sample with a microscope to clearly image the surface of the wavelength calibration standard sample, and collecting a Raman spectrum; then comparing the peak position with the standard until the peak position is accurate;

[0017] (I-3) Set the Raman spectroscopy excitation power, wavelength range, spectral range, spectral resolution, and integration time;

[0018] (I-4) measuring the gradient standard solution in sequence according to the set parameters and collecting Raman images and spectral data;

[0019] (I-5) applying a mathematical algorithm to the acquired Raman images and spectral data of the standard samples to obtain the Raman peak areas corresponding to the standard samples at different concentrations, and plotting the results into a mathematical model of concentration and peak area;

[0020] (II) Treatment of plant callus and determination of active ingredient content:

[0021] (II-1) Adding agarose to PBS buffer and stirring to dissolve to obtain an agarose solution;

[0022] (II-2) treating the plant callus with an agarose solution, and scanning the entire sample and acquiring Raman images and spectral data according to the parameters set in step (I);

[0023] (II-3) The obtained Raman image and spectral data of the plant callus are sequentially subjected to a mathematical algorithm to obtain the Raman peak area corresponding to the active ingredient to be tested in the plant callus. Then, based on the relationship between the Raman signal of the active ingredient to be tested and the sample concentration, a standard curve is established to obtain the concentration of the active ingredient to be tested in the plant callus.

[0024] The schematic flow diagram of the present invention is as follows Figure 1 shown.

[0025] Preferably, in step (I-1):

[0026] The solvent is anhydrous ethanol or methanol;

[0027] and / or, the concentration of the gradient standard solution is 0.0125 mg / mL, 0.025 mg / mL, 0.05 mg / mL, 0.1 mg / mL, 0.15 mg / mL, 0.2 mg / mL;

[0028] And / or, the concentration of the gradient standard solution is 0.01 mg / mL, 0.05 mg / mL, 0.1 mg / mL, 0.15 mg / mL, 0.20 mg / mL.

[0029] Preferably, in step (I-2), a silicon wafer is used as a wavelength calibration standard sample, the silicon wafer is placed on the sample stage of the spectrometer, and the silicon wafer surface is clearly imaged by focusing the microscope; the Raman spectrum of the silicon wafer is collected and compared with the standard peak position of 520.7 cm -1 If the peak position deviation exceeds ±0.5cm -1 , calibration is performed by adjusting parameters such as grating position and optical path delay until the peak position is accurate.

[0030] Preferably, in step (I-3), the Raman spectroscopy excitation power is set to 4-5 mW, the wavelength range is set to 530-800 nm, and the spectral range is set to 400-4000 cm -1 , the spectral resolution is set to 2~5cm -1 , the integration time is set to 8~12 seconds.

[0031] Preferably, in step (I-4), 20 µL of standard solutions of different concentrations are respectively aspirated into a 96-well ELISA plate, and three replicates are set for each concentration. The 96-well ELISA plate is placed on an automatic sample stage, and the sample stage position is adjusted by observing with a low-power microscope so that the sample is located in the center of the field of view. The microscope is then switched to a high-power microscope and the height of the sample stage is fine-tuned. The standard samples are measured sequentially according to the set parameters, and Raman images and spectral data are collected.

[0032] Preferably, in step (I-5), the mathematical algorithm includes Laida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration and spectral vector normalization.

[0033] Preferably, in step (II-1), the pH of the PBS buffer is 7-8.

[0034] Preferably, in step (II-2), the plant callus mass is divided into 3-5 mm 3 For small clumps of plant callus tissue, add 1-2 µL of agarose solution in the center of a clean glass slide, quickly place the plant callus tissue clump on the gel, and gently press to partially embed the sample into the gel, ensuring that it is firmly fixed and the sample surface is flat to facilitate subsequent focusing and spectral acquisition; then, according to the parameters set in step (I), perform an overall scan of the sample to be tested and collect Raman images and spectral data.

[0035] Preferably, in step (II-3), the mathematical algorithm includes the Laida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration and spectral vector normalization.

[0036] Preferably, the detection method further includes step (II-4), wherein step (II-4) is: using a pseudo-color imaging method to map Raman signals of different intensities into different colors, intuitively displaying the distribution of chemical components in the sample, and thereby reconstructing a distribution image of the Raman spectral data in the sample.

[0037] The beneficial effects of the present invention are:

[0038] This method utilizes microscopic confocal Raman technology, coupling a Raman spectrometer with a standard optical microscope. This allows for observation of sample morphology using a high-magnification objective lens, while also utilizing a microscopic laser spot for Raman analysis. For biological samples (plant callus), near-infrared light (785 nm wavelength) is used, as it minimizes fluorescence interference and exhibits low excitation energy, preventing destructive damage to living biological products. The active ingredient content can be determined by simply cutting the sample into small pieces and placing them in a 1.5% agar droplet. Combined with a confocal microscope, the distribution of the active ingredient within the sample can be accurately determined and visually identified.

[0039] More specific:

[0040] 1. The present invention only requires the sample to be segmented and pre-embedded in 1.5% agarose gel for measurement, which is simple to operate and greatly improves the operability of ordinary operators.

[0041] 2. Due to the interference of fluorescence signals in plant samples, the use of infrared light (785nm) for excitation can effectively suppress or eliminate the interference of fluorescence signals in the sample itself, thus achieving accurate content detection. The excitation power is set to 4.5mW, and the spectral range is 400~4000cm -1 , spectral resolution 2~5cm -1 The selected excitation power is low, which causes minimal damage to biological samples and achieves non-destructive testing; an integration time of 10 seconds can obtain the best signal intensity and the best signal-to-noise ratio, improving the reliability and accuracy of the data.

[0042] 3. The obtained Raman images and spectral data of the standard samples are sequentially subjected to mathematical algorithms such as the Raida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration, and spectral vector normalization to obtain the Raman peak areas corresponding to standard samples of different concentrations, thereby determining the mathematical model of the active ingredient concentration and Raman peak area.

[0043] 4. The obtained Raman images and spectral data of plant callus tissue were sequentially subjected to mathematical algorithms such as the Raida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration, spectral vector normalization and partial least squares-discriminant analysis (PLS-DA) to obtain the Raman peak area corresponding to rutin in the callus tissue. The accurate content of the active ingredient in the plant callus tissue was calculated using the obtained mathematical model of concentration and Raman peak area.

[0044] 5. By quickly scanning the space of 5000μm×5000μm×5000μm of plant callus tissue, spatial Raman images and spectral data are obtained. Through pseudo-color imaging, Raman signals of different intensities are mapped to different colors, which intuitively displays the distribution of chemical components in the sample, thereby reconstructing the distribution image of Raman spectral data in the sample, thereby solving the problem of inaccurate content determination caused by uneven distribution of active ingredients in plant callus tissue. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a flow chart of the method for detecting the content of active ingredients in plant callus tissue based on microscopic confocal Raman.

[0047] Figure 2 It is the standard peak of the Raman spectrum of rutin standard sample powder.

[0048] Figure 3 It is the standard peak of the Raman spectrum of the rutin standard sample solution with a concentration of 0.0125 mg / mL.

[0049] Figure 4 It is the standard peak of the Raman spectrum of the rutin standard sample solution with a concentration of 0.0250 mg / mL.

[0050] Figure 5 It is the standard peak of Raman spectrum of rutin standard sample solution with a concentration of 0.0500 mg / mL.

[0051] Figure 6 It is the standard peak of Raman spectrum of rutin standard sample solution with a concentration of 0.1000 mg / mL.

[0052] Figure 7 It is the standard peak of Raman spectrum of rutin standard sample solution with a concentration of 0.1500 mg / mL.

[0053] Figure 8 It is the standard peak of Raman spectrum of rutin standard sample solution with a concentration of 0.2000 mg / mL.

[0054] Figure 9 This is the standard curve of rutin standard sample concentration and Raman spectrum peak area.

[0055] Figure 10 This is the spatial distribution map of rutin in plant callus.

[0056] Figure 11 It is the standard peak of Raman spectrum of caffeine standard sample powder.

[0057] Figure 12 It is the standard peak of Raman spectrum of caffeine standard sample solution with a concentration of 0.01 mg / mL.

[0058] Figure 13 It is the standard peak of Raman spectrum of caffeine standard sample solution with a concentration of 0.01 mg / mL.

[0059] Figure 14 It is the standard peak of Raman spectrum of caffeine standard sample solution with a concentration of 0.01 mg / mL.

[0060] Figure 15 It is the standard peak of Raman spectrum of caffeine standard sample solution with a concentration of 0.01 mg / mL.

[0061] Figure 16 It is the standard peak of Raman spectrum of caffeine standard sample solution with a concentration of 0.01 mg / mL.

[0062] Figure 17 This is the standard curve of caffeine standard sample concentration and Raman spectrum peak area.

[0063] Figure 18 This is the spatial distribution map of caffeine in plant callus. DETAILED DESCRIPTION

[0064] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0065] Example 1

[0066] This embodiment provides a method for detecting the content of active ingredients in plant callus tissue based on microscopic confocal Raman spectroscopy, the method comprising the following steps:

[0067] (I) Drawing of standard curve:

[0068] (I-1) Selecting a suitable solvent to prepare standard solutions of different concentrations based on the physicochemical properties of the active ingredient to be tested. Taking the detection of rutin content in plant callus as an example, the present invention accurately prepared 0.0125 mg / mL, 0.025 mg / mL, 0.05 mg / mL, 0.1 mg / mL, 0.15 mg / mL, and 0.2 mg / mL rutin standard solutions using anhydrous ethanol as the solvent.

[0069] (I-2) Using a silicon wafer as a wavelength calibration standard sample, place the silicon wafer on the spectrometer sample stage and focus the microscope to clearly image the silicon wafer surface. Set the spectrometer parameters to 4.5 mW excitation power, 10 seconds integration time, and 3 scans. Collect the silicon wafer Raman spectrum and compare it with the standard peak position of 520.7 cm -1 If the peak position deviation exceeds ±0.5cm -1 , calibration is performed by adjusting parameters such as grating position and optical path delay until the peak position is accurate.

[0070] (I-3) Raman spectroscopy excitation power was set to 4.5 mW, wavelength was selected to 785 nm, and spectral range was 400–4000 cm -1 , spectral resolution 2~5cm -1 , integration time 10 seconds.

[0071] (I-4) Using rutin standard sample powder as the test sample, its Raman image and spectral data were obtained as the standard peak (556 cm -1 and 669cm -1 )(like Figure 2 20µL of standard samples of different concentrations were pipetted into a 96-well microtiter plate. Three replicates were set for each concentration. The 96-well microtiter plate was placed on the automatic sample stage. The sample was observed through a low-power microscope (5X) and the position of the sample stage was adjusted so that the sample was in the center of the field of view. The high-power microscope (50X) was switched to fine-tune the height of the sample stage. The standard samples were measured in sequence according to the parameters set above, and Raman images were collected (as shown in the figure). Figures 3 to 8 shown) and spectral data.

[0072] (I-5) The obtained Raman images and spectral data of the standard samples were sequentially processed through mathematical algorithms such as the Raida criterion (removing cosmic rays), Savitzky-Golay filtering (spectral smoothing), wavelet transform (removing background noise), baseline calibration, and spectral vector normalization to obtain the Raman peak areas corresponding to the standard samples (rutin) of different concentrations. The Raman peak areas were then plotted into a mathematical model of concentration and peak area (y = 3468.562x - 0.230, R 2 =1.000, such as Figure 9 The model is linearly related.

[0073] (II) Treatment of plant callus and determination of active ingredient content:

[0074] (II-1) Accurately weigh 1.5 g of low-melting-point agarose powder and dissolve it in 10 mL of PBS buffer (pH 7.4). Heat and stir until completely dissolved. Cool to 40–50°C and set aside to obtain an agarose solution.

[0075] (II-2) Use a scalpel to cut the plant callus into 3-5 mm pieces. 3 For small clumps of plant callus, add 1-2 µL of agarose solution to the center of a clean glass slide. Quickly place the plant callus clump on the gel and gently press to partially embed the sample into the gel. Ensure that the sample is firmly fixed and the surface is flat to facilitate subsequent focusing and spectral acquisition. Follow the parameters set for drawing the standard curve: Raman spectroscopy excitation power is set to 4.5 mW, wavelength is selected to 785 nm, and spectral range is 400-4000 cm -1 , spectral resolution 2~5cm -1 , integration time 10 seconds, repeated scanning 3 times; the sample is scanned as a whole to collect Raman images and spectral data, the scanning area has a horizontal plane range of 5000μm×5000μm, and a vertical scanning depth range of 5000μm×5000μm; the entire scanning process will last 10 minutes.

[0076] (II-3) The acquired Raman images and spectral data from the plant callus tissue were sequentially processed using mathematical algorithms such as the Raida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration, spectral vector normalization, and partial least squares-discriminant analysis (PLS-DA) to determine the Raman peak area corresponding to rutin in the plant callus tissue. Based on the relationship between the rutin Raman signal and the sample concentration, a standard curve was established to determine the rutin concentration in the plant callus tissue (0.136 mg / mL).

[0077] (II-4) Using pseudo-color imaging, Raman signals of different intensities are mapped into different colors to intuitively display the distribution of chemical components in the sample, thereby reconstructing the distribution image of Raman spectral data in the sample (e.g. Figure 10 shown).

[0078] Example 2

[0079] This embodiment provides a method for detecting the content of active ingredients in plant callus tissue based on microscopic confocal Raman spectroscopy, the method comprising the following steps:

[0080] (I) Drawing of standard curve:

[0081] (I-1) Selecting a suitable solvent to prepare standard solutions of varying concentrations based on the physicochemical properties of the active ingredient to be tested. Taking the detection of caffeine in plant callus as an example, methanol was used as the solvent to accurately prepare 0.01 mg / mL, 0.05 mg / mL, 0.10 mg / mL, 0.15 mg / mL, and 0.20 mg / mL caffeine standard solutions.

[0082] (I-2) Using a silicon wafer as a wavelength calibration standard sample, place the silicon wafer on the spectrometer sample stage and focus the microscope to clearly image the silicon wafer surface. Set the spectrometer parameters to 4.5 mW excitation power, 10 seconds integration time, and 3 scans. Collect the silicon wafer Raman spectrum and compare it with the standard peak position of 520.7 cm -1 If the peak position deviation exceeds ±0.5cm -1 , calibration is performed by adjusting parameters such as grating position and optical path delay until the peak position is accurate.

[0083] (I-3) Raman spectroscopy excitation power was set to 4.5 mW, wavelength was selected to 785 nm, and spectral range was 400–4000 cm -1 , spectral resolution 2~5cm -1 , integration time 10 seconds.

[0084] (I-4) Using caffeine standard sample powder as the test sample, obtain its Raman image and spectral data as the standard peak (3235cm -1 and 3406cm -1 )(like Figure 11 20µL of standard samples of different concentrations were pipetted into a 96-well microtiter plate. Three replicates were set for each concentration. The 96-well microtiter plate was placed on the automatic sample stage. The sample was observed through a low-power microscope (5X) and the position of the sample stage was adjusted so that the sample was in the center of the field of view. The high-power microscope (50X) was switched to fine-tune the height of the sample stage. The standard samples were measured in sequence according to the parameters set above, and Raman images were collected (as shown in the figure). Figures 12 to 16 shown) and spectral data.

[0085] (I-5) The obtained Raman images and spectral data of the standard samples were sequentially subjected to mathematical algorithms such as the Raida criterion (removing cosmic rays), Savitzky-Golay filtering (spectral smoothing), wavelet transform (removing background noise), baseline calibration, and spectral vector normalization to obtain the Raman peak areas corresponding to the standard samples (caffeine) of different concentrations. The Raman peak areas were then plotted into a mathematical model of concentration and peak area (y = 4874.7x - 6.3387, R 2 =0.9929, such as Figure 17 The model is linearly related.

[0086] (II) Treatment of plant callus and determination of active ingredient content:

[0087] (II-1) Accurately weigh 1.5 g of low-melting-point agarose powder and dissolve it in 10 mL of PBS buffer (pH 7.4). Heat and stir until completely dissolved. Cool to 40–50°C and set aside to obtain an agarose solution.

[0088] (II-2) Use a scalpel to cut the plant callus into 3-5 mm pieces. 3 For small clumps of plant callus, add 1-2 µL of agarose solution to the center of a clean glass slide. Quickly place the plant callus clump on the gel and gently press to partially embed the sample into the gel. Ensure that the sample is firmly fixed and the surface is flat to facilitate subsequent focusing and spectral acquisition. Follow the parameters set for drawing the standard curve: Raman spectroscopy excitation power is set to 4.5 mW, wavelength is selected to 785 nm, and spectral range is 400-4000 cm -1 , spectral resolution 2~5cm -1 , integration time 10 seconds, repeated scanning 3 times; the sample is scanned as a whole to collect Raman images and spectral data, the scanning area has a horizontal plane range of 5000μm×5000μm, and a vertical scanning depth range of 5000μm×5000μm; the entire scanning process will last 10 minutes.

[0089] (II-3) The acquired Raman images and spectral data from the plant callus tissue were sequentially processed using mathematical algorithms such as the Raida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration, spectral vector normalization, and partial least squares-discriminant analysis (PLS-DA) to determine the Raman peak area corresponding to rutin in the plant callus tissue. Based on the relationship between the rutin Raman signal and the sample concentration, a standard curve was established to determine the rutin concentration in the plant callus tissue (0.193 mg / mL).

[0090] (II-4) Using pseudo-color imaging, Raman signals of different intensities are mapped into different colors to intuitively display the distribution of chemical components in the sample, thereby reconstructing the distribution image of Raman spectral data in the sample (e.g. Figure 18 shown).

[0091] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman spectroscopy, characterized in that: The detection method comprises the following steps: (I) Drawing of standard curve: (I-1) Select solvent and prepare gradient standard solution; (I-2) placing a wavelength calibration standard sample on a sample stage of a spectrometer, focusing the sample through a microscope to clearly image the surface of the wavelength calibration standard sample, and collecting a Raman spectrum; Then compare the standard peak position until the peak position is accurate; (I-3) Set the Raman spectroscopy excitation power, wavelength range, spectral range, spectral resolution, and integration time; (I-4) measuring the gradient standard solution in sequence according to the set parameters and collecting Raman images and spectral data; (I-5) applying a mathematical algorithm to the acquired Raman images and spectral data of the standard samples to obtain the Raman peak areas corresponding to the standard samples at different concentrations, and plotting the results into a mathematical model of concentration and peak area; (II) Treatment of plant callus and determination of active ingredient content: (II-1) Adding agarose to PBS buffer and stirring to dissolve to obtain an agarose solution; (II-2) treating the plant callus with an agarose solution, and scanning the entire sample and acquiring Raman images and spectral data according to the parameters set in step (I); (II-3) The obtained Raman image and spectral data of the plant callus are sequentially subjected to a mathematical algorithm to obtain the Raman peak area corresponding to the active ingredient to be tested in the plant callus. Then, based on the relationship between the Raman signal of the active ingredient to be tested and the sample concentration, a standard curve is established to obtain the concentration of the active ingredient to be tested in the plant callus.

2. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (I-1): The solvent is anhydrous ethanol or methanol; and / or, the concentration of the gradient standard solution is 0.0125 mg / mL, 0.025 mg / mL, 0.05 mg / mL, 0.1 mg / mL, 0.15 mg / mL, 0.2 mg / mL; And / or, the concentration of the gradient standard solution is 0.01 mg / mL, 0.05 mg / mL, 0.1 mg / mL, 0.15 mg / mL, 0.20 mg / mL.

3. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (I-2), a silicon wafer is used as a wavelength calibration standard sample. The silicon wafer is placed on the sample stage of the spectrometer and the surface of the silicon wafer is clearly imaged by focusing the microscope. The Raman spectrum of the silicon wafer is collected and compared with the standard peak position of 520.7 cm -1 If the peak position deviation exceeds ±0.5cm -1 , calibrate by adjusting the grating position and optical path delay parameters until the peak position is accurate.

4. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (I-3), the Raman spectroscopy excitation power is set to 4-5 mW, the wavelength range is set to 530-800 nm, and the spectral range is set to 400-4000 cm -1 , the spectral resolution is set to 2~5cm -1 , the integration time is set to 8~12 seconds.

5. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (I-4), 20 µL of standard solutions of different concentrations were pipetted into a 96-well ELISA plate, with three replicates for each concentration. The 96-well ELISA plate was placed on an automated sample stage. The sample was observed through a low-power microscope and the stage position was adjusted so that the sample was in the center of the field of view. The microscope was then switched to a high-power microscope and the stage height was fine-tuned. The standard samples were measured sequentially according to the set parameters, and Raman images and spectral data were collected.

6. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (I-5), the mathematical algorithm includes the Laida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration and spectral vector normalization.

7. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (II-1), the pH of the PBS buffer is 7-8.

8. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (II-2), the plant callus mass is divided into 3~5mm 3 For small clumps of plant callus tissue, add 1-2 µL of agarose solution in the center of a clean glass slide, quickly place the plant callus tissue clump on the gel, and gently press to partially embed the sample into the gel, ensuring that it is firmly fixed and the sample surface is flat to facilitate subsequent focusing and spectral acquisition; then, according to the parameters set in step (I), perform an overall scan of the sample to be tested and collect Raman images and spectral data.

9. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: In step (II-3), the mathematical algorithm includes the Laida criterion, Savitzky-Golay filtering, wavelet transform, baseline calibration and spectral vector normalization.

10. The method for detecting the content of active ingredients in plant callus based on microscopic confocal Raman according to claim 1, characterized in that: The method further includes step (II-4), wherein the step (II-4) is: using a pseudo-color imaging method to map Raman signals of different intensities into different colors, intuitively displaying the distribution of chemical components in the sample, and thereby reconstructing a distribution image of Raman spectral data in the sample.

Images