A method for nondestructive detection of beta-1,3-glucan content in ganoderma mycelium

Abstract

The application discloses a method for nondestructive detection of the content of beta-1,3-glucan in Ganoderma mycelium, and relates to the technical field of biological detection, and comprises the following steps: preparing Ganoderma mycelium samples of different varieties; determining the content of beta-1,3-glucan in the Ganoderma mycelium samples of different varieties by adopting an aniline blue fluorescence method; collecting near-infrared spectrum data of the Ganoderma mycelium of different varieties; pre-processing spectrum data; screening characteristic wavelengths; establishing a prediction model; and using the model for prediction. The prediction model for the detection of the content of beta-1,3-glucan in Ganoderma mycelium based on near-infrared spectrum technology and fluorescence quantitative technology can quickly, high-throughput and nondestructively detect the content of beta-1,3-glucan, has good prediction effect, short determination time, and the advantages of simple operation, rapidness, low cost and the like.

Description

Technical Field

[0001] This invention relates to the field of biological detection technology, specifically a non-destructive method for detecting the β-1,3-glucan content in Ganoderma mycelium. Background Technology

[0002] Traditional methods for determining β-1,3-glucan, such as the aniline blue fluorescence method and the fluorescent white method, rely on specific dye labeling, which are complex and time-consuming. High-performance liquid chromatography (HPLC), while highly sensitive, requires complex sample preparation, can damage the sample, and is also time-consuming. Near-infrared spectroscopy can rapidly determine the spectral data of a sample, but it cannot provide precise quantification.

[0003] Therefore, a semi-quantitative method for rapidly, high-throughput, and non-destructively predicting the β-1,3-glucan content in Ganoderma mycelium using a near-infrared spectroscopy prediction model is proposed. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for non-destructively detecting the β-1,3-glucan content in Ganoderma lucidum mycelium, comprising the following steps:

[0005] Step 1: Prepare mycelial samples of different Ganoderma species;

[0006] Step 2: The content of β-1,3-glucan in mycelial samples of different varieties of Ganoderma was determined by aniline blue fluorescence method to obtain chemical data on the content of β-1,3-glucan.

[0007] Step 3: Near-infrared spectral data of Ganoderma mycelia of different varieties were collected. The mycelial samples were directly subjected to non-destructive testing using a LAMBDA 1050+ long-wavelength spectrometer. After the spectrometer was preheated for 30 minutes, background scanning was performed using spectrally pure BaSO4 powder to compensate for environmental interference. The spectral acquisition parameters were set to a step size of 5 cm. -1 2300-700cm -1 The characteristic functional group intervals were continuously scanned, and the mycelial samples of each Ganoderma genus were measured three times to obtain near-infrared spectral data of different Ganoderma genus mycelia. The collected near-infrared spectral data were then standardized.

[0008] Step 4: Spectral data preprocessing. The near-infrared spectral data is denoised using the Savitzky-Golay convolutional smoothing algorithm, followed by vector normalization preprocessing to standardize the absorbance values ​​of each spectrum to the [0, 1] interval, retaining values ​​from 2300 to 700 cm⁻¹. -1 Effective spectral range;

[0009] Step 5, Feature Wavelength Screening: A competitive adaptive reweighted sampling method (CARS) is used to screen feature wavelengths. The CARS algorithm uses Monte Carlo sampling and an exponential decay function to screen key variables, setting the retention rate parameter α = 0.8. After feature band screening, wavelengths around 1800 cm⁻¹ are eliminated based on the chemical structural bonds of β-1,3-glucan. -1 1325cm -1 Two features are used to finally combine the obtained spectral data with the corresponding chemical data to form the original dataset;

[0010] Step 6: Divide the original dataset obtained in Step 5 into a training set and a validation set in a 6:4 ratio, and use partial least squares regression to establish a prediction model between near-infrared spectral data and β-1,3-glucan content.

[0011] Step 7: Use the model established in Step 6 to predict the content of β-1,3-glucan in the mycelium of unknown Ganoderma species.

[0012] Preferably, step 1 specifically includes the following steps:

[0013] Step 1.1, Preparation of solid culture medium and activation of strains: Prepare PDA solid culture medium, inoculate different varieties of Ganoderma strains in the center of the solid culture medium, and keep them at 28°C in the dark for 5 to 7 days.

[0014] Step 1.2, preparation of liquid culture medium and submerged fermentation: The liquid culture medium includes 30 g / L glucose, 5 g / L peptone, 5 g / L yeast extract, 1 g / L KH2PO4, 0.5 g / L MgSO4 and 50 mg / L VB1, and the pH is adjusted to 5.8±0.2. It is sterilized at 121℃ for 20 minutes. The activated Ganoderma lucidum mycelial blocks are inoculated into the liquid culture medium and cultured at 28℃ and 150 rpm for 5 to 7 days for submerged fermentation.

[0015] Step 1.3, preparation of mycelial samples: collect mycelia of different varieties of Ganoderma and clean them. After cleaning, place them in a -80℃ environment for quick freezing for 2 hours and then freeze-dry for 24 hours. Finally, grind them through a 60-mesh sieve to obtain homogeneous grayish-white mycelial powder, which is the mycelial sample of different varieties of Ganoderma. Store it in a sealed container at -20℃ for later use.

[0016] Preferably, step 2 specifically includes the following steps:

[0017] Step 2.1, Preparation of crude sugar samples from mycelium: 5g of mycelium samples from different varieties of Ganoderma were weighed and added to 250mL of 5% w / v NaOH solution. The samples were then extracted with ultrasonic assistance in a water bath for 2h. After centrifugation at 1000rpm for 10min at 4℃, the supernatant was collected and the pH was adjusted to neutral by adding glacial acetic acid. Then, 1L of anhydrous ethanol was added to each sample for 12h at 4℃. After centrifugation at 1000rpm for 10min at 4℃, the precipitate was collected. The collected precipitate was washed three times with deionized water and then dried in an oven at 60℃ for 6h to obtain crude sugar samples from different varieties of Ganoderma.

[0018] Step 2.2, determination of β-1,3-glucan content: 1-10 mg of crude sugar samples from different varieties were weighed and placed in centrifuge tubes. Ultrapure water was added at a mass-to-volume ratio of 0.01% for ultrasonic-assisted dissolution. The β-1,3-glucan content in the mycelial samples of different Ganoderma species was determined by aniline blue fluorescence method to obtain chemical data on β-1,3-glucan content.

[0019] Preferably, step 2.2 is as follows:

[0020] Weigh 1–10 mg of each of different types of crude sugar samples and place them in separate centrifuge tubes. Add ultrapure water at a mass-to-liquid ratio of 0.01% and dissolve using ultrasonic assistance. Mix the sample solution with 0.1% w / v aniline blue reagent at a volume ratio of 1:2. Utilize the complexation effect of polysaccharides with aniline blue... The fluorescence intensity was measured at λ. ex λ is the fluorescence excitation wavelength. em The emission wavelength was used for quantitative analysis based on the principle of specific fluorescent labeling. The crude sugar samples of each variety were measured three times in parallel and the average value was taken to determine the content of β-1,3-glucan in the mycelial samples of different Ganoderma species.

[0021] Preferably, in step 3, when performing non-destructive testing on Ganoderma mycelium samples directly using a LAMBDA 1050+ long-wavelength spectrometer, the temperature is maintained at 20±1℃ and the relative humidity at 20% throughout the process.

[0022] The present invention has the following beneficial effects:

[0023] This invention establishes a rapid, high-throughput, and non-destructive semi-quantitative system for the detection of β-1,3-glucan in Ganoderma lucidum mycelia by combining near-infrared spectroscopy data with corresponding chemical data and utilizing machine learning algorithms. The system is based on CARS screening of 845 cm⁻¹ of products that meet the target prediction. -1 The PLSR model constructed using 12 characteristic wavelengths achieved an external validation accuracy of R0. 2=0.8425, RMSEP=0.558, indicating good predictive performance; the PLSR model constructed after removing the 1800cm-1 and 1325cm-1 features by eliminating the chemical structural bonds of β-1,3-glucan achieved an external validation accuracy of R0.8425. 2 =0.8569, RMSEP=0.7628, RPD=2.5376. This method uses a LAMBDA 1050+ long-wavelength spectrometer to directly scan lyophilized mycelial powder, which is faster, higher throughput, and non-destructive than traditional chemical methods, and the measurement time is 1 / 20 of that of chemical methods; the established spectral database (2300-700cm) -1 The predictive model can rapidly, with high throughput and non-destructively detect the β-1,3-glucan content in Ganoderma mycelium, providing an innovative solution for the quality control of edible and medicinal fungi that combines high precision, non-destructiveness and industrial adaptability. It also provides a reference for the rapid and high-throughput detection of other fungal genera and other active ingredients. Attached Figure Description

[0024] Figure 1 This is a photograph of the crude sugar sample used in this invention.

[0025] Figure 2 This is a comparison chart of the preprocessing results of different algorithms for near-infrared spectral standard data in this invention;

[0026] Figure 3 This is a diagram showing the results of CARS feature extraction after SG preprocessing in this invention.

[0027] Figure 4 This is a diagram showing the PLSR modeling results in this invention;

[0028] Figure 5 This is a graph showing the correlation between the external validation predicted values ​​and the measured values ​​of the prediction model in this invention.

[0029] Figure 6 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0032] Embodiments of the present invention

[0033] To address the problems mentioned in the technical solutions, this application provides a non-destructive method for detecting the β-1,3-glucan content in Ganoderma lucidum mycelium, comprising the following steps:

[0034] Step 1: Prepare mycelial samples of different Ganoderma species, specifically including the following steps:

[0035] Step 1.1, Preparation of solid culture medium and activation of strains: Prepare PDA solid culture medium (potato 200g / L, glucose 20g / L, agar 15g / L), inoculate different varieties of Ganoderma strains in the center of the solid culture medium, and culture at 28℃ in the dark for 5-7 days. After the mycelium germinates, pick the newly grown mycelium at the edge for secondary activation.

[0036] Step 1.2, Preparation of liquid culture medium and submerged fermentation: The liquid culture medium includes 30 g / L glucose, 5 g / L peptone, 5 g / L yeast extract, 1 g / L KH2PO4, 0.5 g / L MgSO4 and 50 mg / L VB1, and the pH is adjusted to 5.8±0.2. It is sterilized at 121℃ for 20 minutes. The activated Ganoderma lucidum mycelial blocks are inoculated into the liquid culture medium at an inoculum rate of 8-10%. Submerged fermentation is carried out under constant temperature of 28℃ and shaking at 150 rpm for 5-7 days.

[0037] Step 1.3, preparation of mycelial samples: collect mycelia of different varieties of Ganoderma and clean them. After cleaning, place them in a -80℃ environment for quick freezing for 2 hours and then freeze-dry for 24 hours. Finally, grind them through a 60-mesh sieve to obtain homogeneous grayish-white mycelial powder, which is the mycelial sample of different varieties of Ganoderma. Store it in a sealed container at -20℃ for later use.

[0038] Step 2: The content of β-1,3-glucan in mycelial samples of different Ganoderma species was determined using the aniline blue fluorescence method to obtain chemical data on the β-1,3-glucan content. This step specifically includes the following steps:

[0039] Step 2.1, Preparation of crude sugar samples from mycelium: Crude sugar precipitate was obtained by alkaline extraction and alcohol precipitation. 5g of mycelium samples from different varieties of Ganoderma were weighed and 250mL of 5% w / v NaOH solution was added. The samples were then extracted with ultrasonic assistance in a water bath for 2h. After centrifugation at 1000rpm for 10min at 4℃, the supernatant was collected and the pH was adjusted to neutral by adding glacial acetic acid. Then, 1L of anhydrous ethanol at 4℃ was added to each sample for alcohol precipitation for 12h. After centrifugation under the same conditions (20℃, 1000rpm, 10min), the precipitate was collected. The collected precipitate was washed three times with deionized water and then dried in an oven at 60℃ for 6h to obtain crude sugar samples from different varieties of Ganoderma.

[0040] Step 2.2, determination of β-1,3-glucan content: Weigh 1-10 mg of each of the different varieties of crude sugar samples and place them in centrifuge tubes. Add ultrapure water at a mass-to-liquid ratio of 0.01% for ultrasonic-assisted dissolution. Mix the sample solution with 0.1% w / v aniline blue reagent (pH 9.6) at a volume ratio of 1:2. Utilize the complexation effect of polysaccharides with aniline blue... Measure fluorescence intensity (λ) ex λ is the fluorescence excitation wavelength. em The fluorescence value was measured using a Thermo Scientific 96-well black microplate and a Thermo Scientific microplate reader. The crude sugar sample of each variety was measured in triplicate (RSD < 3%), and the average value was taken to obtain the chemical data of β-1,3-glucan content in the mycelial samples of different varieties of Ganoderma.

[0041] Step 3: Near-infrared spectral data of mycelia from different Ganoderma species were collected. The mycelial powder was directly subjected to non-destructive testing using a LAMBDA 1050+ long-wavelength spectrometer. After the spectrometer was preheated for 30 minutes, background scanning was performed using spectrally pure BaSO4 powder to compensate for environmental interference. The spectral acquisition parameters were set to a step size of 5 cm. -1 2300-700cm -1 The near-infrared spectral data of mycelial powder were collected by continuous scanning of the characteristic functional group region. The mycelial powder of each variety was measured three times to obtain near-infrared spectral data of different varieties of Ganoderma. The collected near-infrared spectral data were standardized, including baseline correction, noise reduction, and normalization, to remove noise and baseline drift, so as to enhance the consistency of data and the reliability of analysis.

[0042] Step 4, spectral data preprocessing, involves using five methods for preprocessing and comparing them:

[0043] The Savitzky-Golay convolutional smoothing algorithm (SG) is used to reduce noise in near-infrared spectral data; standard normal variable transformation (SNV) is used to eliminate scattering effects caused by sample inhomogeneity; multivariate scattering correction (MSC) is used to further correct multiplicative interference caused by optical path differences and particle distribution; the first derivative of the Savitzky-Golay method is used to eliminate baseline drift and enhance the resolution of spectral characteristic peaks; the second derivative of the Savitzky-Golay method is used to further sharpen spectral characteristic peaks and improve the resolution of overlapping peaks.

[0044] refer to Figure 2 In the figure, A represents the original data after standardization, and B to F represent the results after standard normal variable transformation (SNV), multivariate scattering correction (MSC), Savitzky-Golay convolution smoothing algorithm (SG), first derivative, and second derivative, respectively. The comparison shows that the spectral data preprocessed by Savitzky-Golay convolution smoothing algorithm (SG) is better.

[0045] Finally, vector normalization preprocessing is performed to normalize the absorbance values ​​of each spectrum to the [0, 1] interval, retaining the values ​​from 2300 to 700 cm⁻¹. -1 The effective spectral range, after preprocessing, is exported as a CSV format for subsequent modeling and analysis;

[0046] Step 5: Feature wavelength screening. For the spectral data preprocessed using the Savitzky-Golay convolutional smoothing algorithm (SG) in Step 4, competitive adaptive reweighted sampling (CARS) and continuous projection algorithm (SPA) are used to screen for feature wavelengths strongly correlated with β-1,3-glucan content. Specifically, for the feature wavelengths screened using CARS, wavelengths within the 1800cm region are further removed based on the chemical structural bonds of β-1,3-glucan. -1 1325cm -1 Two extracted features: the main chemical structural bonds of β-1,3-glucan include:

[0047] 1900cm -1 ~2000cm -1 The weak peak at that point corresponds to the CH stretching vibration (from the CH bond in the glucose ring and the hydroxymethyl group);

[0048] 1400cm -1 ~1500cm -1 The broad peak at that point corresponds to the stretching vibration of the hydroxyl group (-OH), which originates from the free hydroxyl group or hydrogen bond on the glucose unit.

[0049] 1000cm -1 ~1200cm -1 The strong peak at that point corresponds to the stretching vibration of the glycosidic bond (COC), which directly reflects the presence of the β-1→3 glycosidic bond.

[0050] 800cm -1 ~900cm -1 The peak at that position corresponds to the characteristic peak of the anomeric carbon (C1) of the β-glycosidic bond, further confirming the β-1,3 linkage mode.

[0051] The CARS algorithm uses Monte Carlo sampling (50 iterations) and an exponential decay function to select key variables. The retention rate parameter α is set to 0.8. The feature extraction results after feature band selection are shown in Table 1 below.

[0052]

[0053] Table 1. CARS Feature Wavelength Screening Results

[0054] After obtaining the characteristic wavelengths through CARS screening, the 1800cm wavelength was then eliminated based on the chemical structural bonds of β-1,3-glucan. -1 1325cm -1 The two extracted features are then matched with the obtained spectral data and corresponding chemical data to form the original dataset; the spectral data and corresponding chemical data obtained by the Continuous Projection Algorithm (SPA) are also matched with the original dataset to form the original dataset.

[0055] Step 6: Divide the two sets of original datasets obtained in Step 5 (corresponding to CARS and SPA feature wavelength screening methods respectively) into training and validation sets in a 6:4 ratio. Then, use Partial Least Squares Regression (PLSR), Backpropagation (BP), Extreme Learning Machine (ELM), Support Vector Machine (SVM), and Random Forest (RF) to build prediction models between near-infrared spectral data and β-1,3-glucan content. The evaluation accuracy of the modeling results is compared in Table 2 below.

[0056] The established prediction model was validated using methods such as cross-validation and external validation to evaluate its prediction accuracy and stability. A nested cross-validation strategy was employed for system evaluation: internal validation used 5-fold cross-validation to calculate R0. 2 RMSE index; external validation calculation R 2 RMSEP and RPD values.

[0057] refer to Figure 3 , Figure 3 The image shows the optimized algorithm results for CARS feature extraction after SG preprocessing. It illustrates the changes in the number of sampled variables, root mean square error (RMSE), and regression coefficient path with the number of sampling runs. Through 50 iterations of Monte Carlo sampling (retention rate α = 0.8) and an exponential decay mechanism, key variables were dynamically screened. At this point, the RMSE reached its lowest value of 1.856, and the volatility of the regression coefficient increased to 51.95. Figure 3 Figure A in the diagram shows that, in the 33rd iteration, 13 core bands (mainly concentrated at 1100 cm⁻¹) were selected from 322 wavelengths. -1 β-glucan backbone vibration, at 845 cm -1A characteristic peak of the anomeric carbon in the β-1→3 glycosidic bond was observed nearby. This algorithm achieves data dimensionality reduction through wavelength selection, while simultaneously improving model interpretability. Figure 3 (Figure B in the diagram), and this scheme combines the chemical structural bonds of β-1,3-glucan to eliminate the 1800 cm⁻¹ bond. -1 1325cm -1 These two features successfully improved the function fit and external validation performance. Figure 4 The corresponding wavenumber point indices and their corresponding wavenumbers are shown in Table 1.

[0058] The data comparison in Table 2 shows that modeling using the results of CARS screening is more effective than modeling using the results of SPA screening. Furthermore, based on the coefficient of determination (R²),... 2 Model evaluation was performed using parameters such as root mean square error (RMSE) and relative analysis error (RPD). Without β-1,3-glucan chemical bond removal, the optimal PLSR model parameters were Rp = 0.975, RMSEP = 0.558, and R... 2 =0.946. After removing β-1,3-glucan chemical bonds, PLSR was determined to be the optimal model based on the evaluation results, with model parameters Rp = 0.978, RMSEP = 0.501, and R... 2 =0.954.

[0059] In the external validation correlation analysis, the PLSR model, established using spectral data without incorporating the chemical bond knockout features of β-1,3-glucan after CARS screening, achieved better performance (R). 2 =0.8425, RMSE=0.8025, RPD=2.4121); After CARS screening, the 1800cm group was removed by combining the chemical structural bonds of β-1,3-glucan. -1 1325cm -1 The PLSR model built from the spectral data of the two features achieved the best performance (R). 2 =0.8569, RMSE=0.7628, RPD=2.5376), the comparison results are as follows: Figure 5 As shown, Figure 5 The upper and middle figures show the modeling results without removing interfering peaks. Figure 5 The lower figure shows the modeling results after removing interfering peaks.

[0060]

[0061] Table 2 Comparison of accuracy of different models for β-1,3-glucan in Ganoderma mycelium.

[0062] Step 7: Use the model established in Step 6 to predict the content of β-1,3-glucan in the mycelium of unknown Ganoderma species.

[0063] In summary, this invention utilizes near-infrared spectral data of Ganoderma lucidum mycelia, combined with chemical data determined by the aniline blue colorimetric method of corresponding spectral samples, to provide a predictive model for the detection of β-1,3-glucan content in Ganoderma lucidum mycelia based on near-infrared spectroscopy and quantitative fluorescence techniques. A rapid, high-throughput, and non-destructive semi-quantitative method for detecting β-1,3-glucan content has been established. This method has advantages such as simple operation, speed, high throughput, and low cost, and can provide technical support for the quality control and active ingredient research of Ganoderma lucidum mycelia.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for non-destructively detecting the β-1,3-glucan content in Ganoderma lucidum mycelium, characterized in that, Includes the following steps: Step 1: Prepare mycelial samples of different varieties of Ganoderma, including collecting and washing the mycelium of different varieties of Ganoderma, then freezing it at -80℃ for 2 hours and then freeze-drying it for 24 hours. Finally, grind it through a 60-mesh sieve to obtain homogeneous grayish-white mycelial powder, which is the mycelial sample of different varieties of Ganoderma. Store it in a sealed container at -20℃ for later use. Step 2: The content of β-1,3-glucan in mycelial samples of different varieties of Ganoderma was determined by aniline blue fluorescence method to obtain chemical data on the content of β-1,3-glucan. Step 3: Near-infrared spectral data of Ganoderma mycelia of different varieties were collected. The mycelial samples were directly subjected to non-destructive testing using a LAMBDA 1050+ long-wavelength spectrometer. After the spectrometer was preheated for 30 minutes, background scanning was performed using spectrally pure BaSO4 powder to compensate for environmental interference. The spectral acquisition parameters were set to a step size of 5 cm. -1 2300-700cm -1 The characteristic functional group intervals were continuously scanned, and the mycelial samples of each Ganoderma genus were measured three times to obtain near-infrared spectral data of different Ganoderma genus mycelia. The collected near-infrared spectral data were then standardized. Step 4: Spectral data preprocessing. The near-infrared spectral data is denoised using the Savitzky-Golay convolutional smoothing algorithm, followed by vector normalization preprocessing to standardize the absorbance values ​​of each spectrum to the [0, 1] interval, retaining values ​​from 2300 to 700 cm⁻¹. -1 Effective spectral range; Step 5, Feature Wavelength Screening: A competitive adaptive reweighted sampling method (CARS) is used to screen feature wavelengths. The CARS algorithm uses Monte Carlo sampling and an exponential decay function to screen key variables, setting the retention rate parameter α=0.

8. After feature band screening, wavelengths around 1800 cm⁻¹ are eliminated based on the chemical structural bonds of β-1,3-glucan. -1 1325cm -1 Two key features of the β-1,3-glucan are that its chemical structural bonds primarily include: 1000cm -1 ~1200cm -1 The strong peak at 800 cm⁻¹ corresponds to the stretching vibration of the glycosidic bond (COC), directly reflecting the presence of the β-1→3 glycosidic bond. -1 ~900cm -1 The peak at the corresponding bond is the characteristic peak of the anomeric carbon (C1) of the β-glycosidic bond. The β-1,3 linkage mode is confirmed. Finally, the obtained spectral data and the corresponding chemical data are matched to form the original dataset. Step 6: Divide the original dataset obtained in Step 5 into a training set and a validation set in a 6:4 ratio, and use partial least squares regression to establish a prediction model between near-infrared spectral data and β-1,3-glucan content. Step 7: Use the model established in Step 6 to predict the content of β-1,3-glucan in the mycelium of unknown Ganoderma species.

2. The method for non-destructive testing of β-1,3-glucan content in Ganoderma lucidum mycelium according to claim 1, characterized in that, Step 1 specifically includes the following steps: Step 1.1, Preparation of solid culture medium and activation of strains: Prepare PDA solid culture medium, inoculate different varieties of Ganoderma strains in the center of the solid culture medium, and keep them at 28°C in the dark for 5 to 7 days. Step 1.2, preparation of liquid culture medium and submerged fermentation: The liquid culture medium includes 30 g / L glucose, 5 g / L peptone, 5 g / L yeast extract, 1 g / L KH2PO4, 0.5 g / L MgSO4 and 50 mg / L VB1, and the pH is adjusted to 5.8±0.

2. It is sterilized at 121℃ for 20 minutes. The activated Ganoderma lucidum mycelial blocks are inoculated into the liquid culture medium and cultured at 28℃ with shaking at 150 rpm for 5 to 7 days for submerged fermentation.

3. The method for non-destructively detecting the β-1,3-glucan content in Ganoderma lucidum mycelium according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1, Preparation of crude sugar samples from mycelium: 5g of mycelium samples from different varieties of Ganoderma were weighed and added to 250mL of 5%w / v NaOH solution. The samples were then extracted with ultrasonic assistance in a water bath for 2h. After centrifugation at 1000rpm for 10min at 4℃, the supernatant was collected and the pH was adjusted to neutral by adding glacial acetic acid. Then, 1L of anhydrous ethanol was added to each sample for 12h at 4℃. After centrifugation at 1000rpm for 10min at 4℃, the precipitate was collected. The collected precipitate was washed three times with deionized water and then dried in an oven at 60℃ for 6h to obtain crude sugar samples from different varieties of Ganoderma. Step 2.2, determination of β-1,3-glucan content: 1-10 mg of crude sugar samples from different varieties were weighed and placed in centrifuge tubes. Ultrapure water was added at a mass-to-volume ratio of 0.01% for ultrasonic-assisted dissolution. The β-1,3-glucan content in the mycelial samples of different Ganoderma species was determined by aniline blue fluorescence method to obtain chemical data on β-1,3-glucan content.

4. The method for non-destructive testing of β-1,3-glucan content in Ganoderma lucidum mycelium according to claim 3, characterized in that, Step 2.2 specifically involves: Weigh 1–10 mg of each of different types of crude sugar samples and place them in separate centrifuge tubes. Add ultrapure water at a mass-to-liquid ratio of 0.01% and dissolve using ultrasonic assistance. Mix the sample solution with 0.1% w / v aniline blue reagent at a volume ratio of 1:

2. Utilize the complexation effect of polysaccharides with aniline blue... The fluorescence intensity was measured at the location, where, The excitation wavelength is the fluorescence wavelength. The emission wavelength was used for quantitative analysis based on the principle of specific fluorescent labeling. The crude sugar samples of each variety were measured three times in parallel and the average value was taken to determine the content of β-1,3-glucan in the mycelial samples of different Ganoderma species.

5. The method for non-destructively detecting the β-1,3-glucan content in Ganoderma lucidum mycelium according to claim 1, characterized in that, In step 3, when performing non-destructive testing on Ganoderma mycelium samples directly using a LAMBDA 1050+ long-wavelength spectrometer, the temperature was maintained at 20±1℃ and the relative humidity at 20% throughout the process.

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