Method for nondestructively detecting content of beta-1, 3-glucan in ganoderma mycelium

Abstract

The invention discloses a method for nondestructively detecting the content of beta-1, 3-glucan in ganoderma mycelium, and relates to the technical field of biological detection, and the method comprises the following steps: preparing different varieties of ganoderma mycelium samples; determining the content of beta-1, 3-glucan in different varieties of ganoderma mycelium samples by adopting an aniline blue fluorescence method; acquiring near infrared spectrum data of different varieties of ganoderma mycelia; preprocessing the spectral data; screening characteristic wavelengths; establishing a prediction model; and predicting by using the model. According to the prediction model for detecting the content of the beta-1, 3-glucan in the ganoderma mycelium based on the near infrared spectrum technology and the fluorescent quantitative technology, the content of the beta-1, 3-glucan can be rapidly and nondestructively detected at high flux, the prediction effect is good, the determination time is short, and the prediction model has the advantages of being easy and convenient to operate, rapid, low in cost and the like.

Description

Technical Field

[0001] The present invention relates to the field of biological detection technology, in particular to a method for non-destructive detection of beta-1,3-glucan content in Ganoderma mycelium. Background Art

[0002] Traditional methods for measuring β-1,3-glucan, such as aniline blue fluorescence and fluorescent white, rely on specific dye labeling, are complex and time-consuming. High-performance liquid chromatography (HPLC), while highly sensitive, requires complex pretreatment, damages the sample, and is time-consuming. Near-infrared spectroscopy can rapidly measure sample spectral data, but it cannot accurately quantify it.

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

[0004] To achieve the above object, the present invention provides the following technical solution: a method for non-destructive detection of β-1,3-glucan content in Ganoderma mycelium, comprising the following steps:

[0005] Step 1, preparing mycelium samples of different varieties of Ganoderma lucidum;

[0006] Step 2, using an aniline blue fluorescence method to determine the content of β-1,3-glucan in mycelial samples of different varieties of Ganoderma lucidum to obtain chemical data of the β-1,3-glucan content;

[0007] Step 3: Acquisition of near-infrared spectral data of different varieties of Ganoderma mycelium. Non-destructive testing of Ganoderma mycelium samples was performed directly using a LAMBDA 1050+ long-wavelength spectrometer. After the spectrometer was preheated for 30 minutes, a background scan 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 , at 2300~700cm -1 The characteristic functional group interval was continuously scanned, and the mycelium samples of each species of Ganoderma lucidum were measured three times to collect the near-infrared spectral data of the mycelium of different species of Ganoderma lucidum, and the collected near-infrared spectral data were standardized.

[0008] Step 4: Spectral data preprocessing: Savitzky-Golay convolution smoothing algorithm was used to reduce noise of near-infrared spectral data, and then vector normalization preprocessing was performed to standardize the absorbance value of each spectrum to the range of [0, 1], retaining the 2300-700 cm -1 Effective spectral range;

[0009] Step 5: Characteristic wavelength screening. Competitive adaptive reweighted sampling (CARS) is used to screen characteristic wavelengths. The CARS algorithm screens key variables through Monte Carlo sampling and exponential decay function. The retention rate parameter α is set to 0.8. After the characteristic band screening, the 1800 cm-1 wavelength is eliminated based on the chemical structure bond of β-1,3-glucan. -1 、1325cm -1 Two features, finally the obtained spectral data and the corresponding chemical data are matched to form the original data set;

[0010] Step 6, the original data set obtained in step 5 is divided into a training set and a validation set in a ratio of 6:4, and a prediction model between near-infrared spectral data and β-1,3-glucan content is established using partial least squares regression method;

[0011] Step 7: using the model established in step 6, predict the content of β-1,3-glucan in the mycelium of the unknown variety of Ganoderma lucidum.

[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 strains of Ganoderma lucidum in the center of the solid culture medium, maintain incubation at 28°C in the dark for 5-7 days;

[0014] Step 1.2, preparation of liquid culture medium and submerged fermentation: the liquid culture medium comprises 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. The culture medium is sterilized at 121°C for 20 minutes, and the activated Ganoderma mycelium mass is inoculated into the liquid culture medium. The culture medium is shaken at 28°C and 150 rpm for 5-7 days for submerged fermentation.

[0015] Step 1.3, preparation of mycelium samples: collect mycelium of different varieties of Ganoderma lucidum and wash them. After washing, place them in a -80°C environment for quick freezing for 2 hours and then freeze-dry them for 24 hours. Finally, grind them through a 60-mesh sieve to obtain homogeneous off-white mycelium powder, i.e., mycelium samples of different varieties of Ganoderma lucidum, and store them in a sealed container at -20°C for future use.

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

[0017] Step 2.1, preparation of mycelial crude sugar samples, weighing 5 g of each mycelial sample of different varieties of Ganoderma lucidum and adding them to 250 mL of 5% w / v NaOH solution, then performing water bath ultrasonic-assisted extraction for 2 h, centrifuging at 4°C and 1000 rpm for 10 min, taking the supernatant and adding glacial acetic acid dropwise to adjust the pH to neutral, then adding 1 L of anhydrous ethanol at 4°C and precipitating for 12 h, centrifuging at 4°C and 1000 rpm for 10 min, collecting the precipitate, washing the collected precipitate three times with deionized water, and drying it in an oven at 60°C for 6 h to obtain crude sugar samples of different varieties of Ganoderma lucidum mycelium;

[0018] Step 2.2, determination of β-1,3-glucan content: 1-10 mg of each crude sugar sample of different varieties was weighed and placed in a centrifuge tube. 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 mycelium samples of different varieties of Ganoderma lucidum was determined by aniline blue fluorescence method to obtain chemical data of the β-1,3-glucan content.

[0019] Preferably, step 2.2 is specifically as follows:

[0020] 1-10 mg of different varieties of raw sugar samples were weighed and placed in centrifuge tubes respectively. Ultrapure water was added at a mass-to-volume ratio of 0.01% for ultrasonic-assisted dissolution. The sample solution was mixed with 0.1% w / v aniline blue reagent at a volume ratio of 1:2. The complexation between polysaccharide and aniline blue was used to The fluorescence intensity is measured at ex is the fluorescence excitation wavelength, λ em The emission wavelength was used for quantitative analysis by the principle of specific fluorescence labeling. The crude sugar sample of each variety was measured in parallel three times and the average value was taken to determine the content of β-1,3-glucan in the mycelium samples of different varieties of Ganoderma lucidum.

[0021] Preferably, in step 3, when the LAMBDA 1050+ long-wavelength spectrometer is used to directly perform non-destructive testing on the Ganoderma mycelium sample, the temperature is maintained at 20±1° C. and the relative humidity is maintained at 20% throughout the process.

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

[0023] The present invention combines near-infrared spectroscopy data with corresponding chemical data and uses machine learning algorithms to establish a rapid, high-throughput, non-destructive semi-quantitative detection system for β-1,3-glucan in Ganoderma mycelium. -1 The external verification accuracy of the constructed PLSR model reached R 2=0.8425, RMSEP=0.558, which has a good prediction effect; the external verification accuracy of the PLSR model constructed after removing the two features of 1800cm-1 and 1325cm-1 of the chemical structure bond of β-1,3-glucan is R 2 =0.8569, RMSEP=0.7628, RPD=2.5376. This method uses a LAMBDA 1050+ long-wavelength spectrometer to directly scan freeze-dried mycelium powder. Compared with traditional chemical methods, this method is faster, more high-throughput, and non-destructive, with a measurement time of 1 / 20 of that of chemical methods. The spectral database (2300-700cm -1 ) and the prediction model can rapidly, 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 is both highly precise, non-destructive and industrially adaptable, while also providing a reference for the rapid and high-throughput detection of other fungal genera and other active ingredients. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a physical picture of the raw sugar sample of the present invention;

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

[0026] Figure 3 This is the result diagram of CARS feature extraction after SG preprocessing in the present invention;

[0027] Figure 4 This is the PLSR modeling result diagram in the present 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 of the present invention;

[0029] Figure 6 Schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

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

[0032] Embodiments of the present invention

[0033] To solve the problems mentioned in the technical solution, the present invention provides a method for non-destructively detecting the content of β-1,3-glucan in Ganoderma mycelium, comprising the following steps:

[0034] Step 1, preparing mycelium samples of different varieties of Ganoderma lucidum, specifically comprising the following steps:

[0035] Step 1.1, Preparation of Solid Culture Medium and Activation of Strain: Prepare PDA solid culture medium (200 g / L potato, 20 g / L glucose, 15 g / L agar), inoculate different strains of Ganoderma lucidum in the center of the solid culture medium, incubate at 28°C in the dark for 5-7 days, and after mycelium germination, select new mycelium at the edge for secondary activation;

[0036] Step 1.2, preparation of liquid culture medium and submerged fermentation, the liquid culture medium comprising 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, with the pH adjusted to 5.8±0.2, sterilized at 121°C for 20 minutes, inoculated with activated Ganoderma mycelial mass at an inoculum rate of 8-10%, and submerged fermentation carried out at a constant temperature of 28°C and agitation at 150 rpm for 5-7 days;

[0037] Step 1.3, preparation of mycelium samples: collect mycelium of different varieties of Ganoderma lucidum and wash them, then quickly freeze them at -80°C for 2 hours and freeze-dry them for 24 hours, and finally grind them through a 60-mesh sieve to obtain homogeneous off-white mycelium powder, i.e., mycelium samples of different varieties of Ganoderma lucidum, and store them sealed at -20°C for later use;

[0038] Step 2, using aniline blue fluorescence method to determine the content of β-1,3-glucan in mycelium samples of different varieties of Ganoderma lucidum to obtain chemical data of the β-1,3-glucan content, specifically comprises the following steps:

[0039] Step 2.1, preparation of mycelial crude sugar sample, obtaining crude sugar precipitate by alkali extraction and alcohol precipitation, weighing 5 g of each of the mycelial samples of different varieties of Ganoderma lucidum, and adding 250 mL of 5% w / v NaOH solution, then performing water bath ultrasonic-assisted extraction for 2 h, maintaining the temperature at 4°C and centrifuging at 1000 rpm for 10 min, taking the supernatant and adding glacial acetic acid dropwise to adjust the pH to neutral, then adding 1 L of anhydrous ethanol at 4°C and precipitating for 12 h, centrifuging under the same conditions (20°C, 1000 rpm, 10 min), collecting the precipitate, washing the collected precipitate three times with deionized water, and drying it in an oven at 60°C for 6 h to obtain crude sugar samples of different varieties of Ganoderma lucidum mycelium;

[0040] Step 2.2, determination of β-1,3-glucan content, 1-10 mg of each crude sugar sample of different varieties was weighed and placed in a centrifuge tube, and ultrapure water was added according to a mass volume ratio of 0.01% for ultrasonic-assisted dissolution. The sample solution was mixed with 0.1% w / v aniline blue reagent (pH 9.6) at a volume ratio of 1:2, and the complexation of polysaccharide and aniline blue was used to obtain the β-1,3-glucan content. The fluorescence intensity (λ ex is the fluorescence excitation wavelength, λ em The fluorescence value was measured by a Thermo Scientific 96-well black microplate reader (with an emission wavelength of 20 nm). The fluorescence value was determined by the specific fluorescence labeling principle. The crude sugar sample of each variety was measured in triplicate (RSD < 3%), and the average value was taken to determine the β-1,3-glucan content in the mycelial samples of different varieties of Ganoderma lucidum. The chemical data of the β-1,3-glucan content were obtained.

[0041] Step 3: Acquisition of near-infrared spectral data of mycelia of different varieties of Ganoderma lucidum. Non-destructive testing of mycelial powder was performed directly using a LAMBDA 1050+ long-wavelength spectrometer. After the spectrometer was preheated for 30 minutes, a background scan 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 , at 2300~700cm -1 The characteristic functional group intervals were continuously scanned to collect near-infrared spectral data of the mycelium powder. The mycelium powder of each variety was measured three times to obtain near-infrared spectral data of different varieties of Ganoderma mycelium. The collected near-infrared spectral data were standardized, including baseline correction, denoising, and normalization, to remove noise and baseline drift to enhance data consistency and analytical reliability.

[0042] Step 4: Spectral data preprocessing. Five methods were used for preprocessing and compared, namely:

[0043] The Savitzky-Golay convolution smoothing algorithm (SG) was used to reduce noise in near-infrared spectral data. The standard normal variate transformation (SNV) was used to eliminate scattering effects caused by sample inhomogeneity. Multivariate scattering correction (MSC) was used to further correct for multiplicative interference caused by optical pathlength differences and particle distribution. The first-order derivative processing based on the Savitzky-Golay method was used to eliminate baseline drift and enhance the resolution of spectral characteristic peaks. The second-order derivative transformation of the Savitzky-Golay method was used to further sharpen spectral characteristic peaks and improve the resolution of overlapping peaks.

[0044] refer to Figure 2 , where A is the original data after standardization, B to F are the results after standard normal variable transformation (SNV), multivariate scatter correction (MSC), Savitzky-Golay convolution smoothing algorithm (SG), first-order derivative (1stDerivative) and second-order derivative (2ndDerivative) processing, respectively. The comparison shows that the spectral data preprocessed by Savitzky-Golay convolution smoothing algorithm (SG) is better;

[0045] Finally, vector normalization preprocessing was performed to standardize the absorbance value of each spectrum to the range of [0, 1], retaining the 2300-700 cm -1 Effective spectral range, after preprocessing, the data is exported to CSV format for subsequent modeling analysis;

[0046] Step 5, characteristic wavelength screening, the spectral data preprocessed by Savitzky-Golay convolution smoothing algorithm (SG) in step 4 were respectively screened by competitive adaptive reweighted sampling (CARS) and continuous projection algorithm (SPA) to screen characteristic wavelengths with strong correlation with β-1,3-glucan content. Among them, for the characteristic wavelengths screened by competitive adaptive reweighted sampling (CARS), the 1800cm was eliminated in combination with the chemical structure bond of β-1,3-glucan. -1 、1325cm -1 Two extracted features, the chemical structure bonds of β-1,3-glucan mainly include:

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

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

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

[0050] 800cm -1 ~900cm -1 The peak at , corresponding to the bond: the characteristic peak of the anomeric carbon (C1) configuration of the β-glycosidic bond, further confirms the β-1,3 connection mode.

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

[0052]

[0053] Table 1 CARS characteristic wavelength screening results

[0054] After the characteristic wavelengths were obtained by CARS screening, the 1800 cm-1 wavelength was eliminated based on the chemical structure of β-1,3-glucan. -1 、1325cm -1 The two extracted features are finally matched with the obtained spectral data and the corresponding chemical data to form the original data set; the spectral data and the corresponding chemical data obtained by the continuous projection algorithm (SPA) are also matched to form the original data set;

[0055] In step 6, the two sets of original data sets obtained in step 5 (corresponding to the two characteristic wavelength screening methods of CARS and SPA, respectively) were divided into training set and validation set in a ratio of 6:4, and the prediction models between near-infrared spectral data and β-1,3-glucan content were established using partial least squares regression (PLSR), back propagation algorithm (BP), extreme learning machine (ELM), support vector machine (SVM), and random forest (RF), respectively. The evaluation accuracy comparison of the modeling results is shown in Table 2 below.

[0056] Cross-validation and external validation were used to validate the established prediction model and evaluate the prediction accuracy and stability of the model. A nested cross-validation strategy was used for system evaluation: internal validation used 5-fold cross-validation to calculate R 2 , RMSE indicator; external validation calculation R 2 , RMSEP and RPD values.

[0057] refer to Figure 3 , Figure 3 The results of the optimal algorithm for CARS feature extraction after SG preprocessing are shown, showing 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 Monte Carlo iterative sampling (retention rate α = 0.8) and exponential decay mechanism to dynamically screen key variables, the RMSE reaches the lowest value of 1.856 at this time, and the regression coefficient volatility increases to 51.95 ( Figure 3 The results show that 13 core wavelengths (mainly concentrated at 1100 cm) were selected from 322 wavelengths in the 33rd iteration. -1 β-glucan backbone vibration, at 845 cm -1The characteristic peak of the anomeric carbon in the β-1→3 glycosidic bond was observed near the wavelength. This algorithm achieves data dimensionality reduction through wavelength selection and improves model interpretability. Figure 3 B in Figure 3), and this scheme removes the 1800cm -1 、1325cm -1 Two features successfully improved the function fitting and external verification effect ( Figure 4 ). The corresponding wave number point index and the corresponding wave number are shown in Table 1.

[0058] The data comparison in Table 2 shows that the modeling effect of the results of CARS screening is better than that of SPA screening. 2 ), root mean square error (RMSE), relative analytical error (RPD) and other model evaluation parameters were used for model evaluation; without removing the chemical bond of β-1,3-glucan, the optimal model parameters of PLSR were Rp=0.975, RMSEP=0.558, R 2 =0.946. After removing the chemical bond of β-1,3-glucan, PLSR was determined to be the optimal model according to the evaluation results. Its model parameters were Rp=0.978, RMSEP=0.501, R 2 =0.954.

[0059] In the external validation correlation analysis, the PLSR model established by using the spectral data of the chemical structure bond elimination features that did not bind to β-1,3-glucan after CARS screening achieved better performance (R 2 =0.8425, RMSE=0.8025, RPD=2.4121); after CARS screening, the 1800 cm -1 、1325cm -1 The PLSR model established with the spectral data of 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 middle figure shows the modeling result without removing the interference peak. Figure 5 The lower middle figure shows the modeling result after removing the interfering peaks.

[0060]

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

[0062] Step 7: using the model established in step 6, predict the content of β-1,3-glucan in the mycelium of the unknown variety of Ganoderma lucidum.

[0063] In summary, the present invention utilizes near-infrared spectral data of Ganoderma mycelium, combined with chemical data determined by the aniline blue colorimetry method of the corresponding spectral samples, to provide a prediction model for detecting the β-1,3-glucan content in Ganoderma mycelium based on near-infrared spectroscopy technology and fluorescence quantitative technology, and establishes a rapid, high-throughput, non-destructive semi-quantitative method for detecting β-1,3-glucan content. This method has the advantages of simple operation, rapidity, high throughput, and low cost, and can provide technical support for quality control of Ganoderma mycelium and research on active ingredients.

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

Claims

1. A method for non-destructive detection of β-1,3-glucan content in Ganoderma mycelium, characterized in that: The steps include: Step 1, preparing mycelium samples of different varieties of Ganoderma lucidum; Step 2, using an aniline blue fluorescence method to determine the content of β-1,3-glucan in mycelial samples of different varieties of Ganoderma lucidum to obtain chemical data of the β-1,3-glucan content; Step 3: Acquisition of near-infrared spectral data of different varieties of Ganoderma mycelium. Non-destructive testing of Ganoderma mycelium samples was performed directly using a LAMBDA 1050+ long-wavelength spectrometer. After the spectrometer was preheated for 30 minutes, a background scan 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 , at 2300~700cm -1 The characteristic functional group interval was continuously scanned, and the mycelium samples of each species of Ganoderma lucidum were measured three times to collect the near-infrared spectral data of the mycelium of different species of Ganoderma lucidum, and the collected near-infrared spectral data were standardized. Step 4: Spectral data preprocessing: Savitzky-Golay convolution smoothing algorithm was used to reduce noise of near-infrared spectral data, and then vector normalization preprocessing was performed to standardize the absorbance value of each spectrum to the range of [0, 1], retaining the 2300-700 cm -1 Effective spectral range; Step 5: Characteristic wavelength screening. Competitive adaptive reweighted sampling (CARS) is used to screen characteristic wavelengths. The CARS algorithm screens key variables through Monte Carlo sampling and exponential decay function. The retention rate parameter α is set to 0.

8. After the characteristic band screening, the 1800 cm-1 wavelength is eliminated based on the chemical structure bond of β-1,3-glucan. -1 、1325cm -1 Two features, finally the obtained spectral data and the corresponding chemical data are matched to form the original data set; Step 6, the original data set obtained in step 5 is divided into a training set and a validation set in a ratio of 6:4, and a prediction model between near-infrared spectral data and β-1,3-glucan content is established using partial least squares regression method; Step 7: using the model established in step 6, predict the content of β-1,3-glucan in the mycelium of the unknown variety of Ganoderma lucidum.

2. The method for non-destructive detection of β-1,3-glucan content in Ganoderma 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 strains of Ganoderma lucidum in the center of the solid culture medium, maintain incubation at 28°C in the dark for 5-7 days; Step 1.2, preparation of liquid culture medium and submerged fermentation: the liquid culture medium comprises 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. The culture medium is sterilized at 121°C for 20 minutes, and the activated Ganoderma mycelium mass is inoculated into the liquid culture medium. The culture medium is shaken at 28°C and 150 rpm for 5-7 days for submerged fermentation. Step 1.3, preparation of mycelium samples: collect mycelium of different varieties of Ganoderma lucidum and wash them. After washing, place them in a -80°C environment for quick freezing for 2 hours and then freeze-dry them for 24 hours. Finally, grind them through a 60-mesh sieve to obtain homogeneous off-white mycelium powder, i.e., mycelium samples of different varieties of Ganoderma lucidum, and store them in a sealed container at -20°C for future use.

3. The method for non-destructive detection of β-1,3-glucan content in Ganoderma mycelium according to claim 2, characterized in that: Step 2 specifically includes the following steps: Step 2.1, preparation of mycelial crude sugar samples, weighing 5 g of each mycelial sample of different varieties of Ganoderma lucidum and adding them to 250 mL of 5% w / v NaOH solution, then performing water bath ultrasonic-assisted extraction for 2 h, centrifuging at 4°C and 1000 rpm for 10 min, taking the supernatant and adding glacial acetic acid dropwise to adjust the pH to neutral, then adding 1 L of anhydrous ethanol at 4°C and precipitating for 12 h, centrifuging at 4°C and 1000 rpm for 10 min, collecting the precipitate, washing the collected precipitate three times with deionized water, and drying it in an oven at 60°C for 6 h to obtain crude sugar samples of different varieties of Ganoderma lucidum mycelium; Step 2.2, determination of β-1,3-glucan content: 1-10 mg of each crude sugar sample of different varieties was weighed and placed in a centrifuge tube. 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 mycelium samples of different varieties of Ganoderma lucidum was determined by aniline blue fluorescence method to obtain chemical data of the β-1,3-glucan content.

4. The method for non-destructive detection of β-1,3-glucan content in Ganoderma mycelium according to claim 3, characterized in that: Step 2.2 is as follows: 1-10 mg of different varieties of raw sugar samples were weighed and placed in centrifuge tubes respectively. Ultrapure water was added at a mass-to-volume ratio of 0.01% for ultrasonic-assisted dissolution. The sample solution was mixed with 0.1% w / v aniline blue reagent at a volume ratio of 1:

2. The complexation between polysaccharide and aniline blue was used to The fluorescence intensity is measured at ex is the fluorescence excitation wavelength, λ em The emission wavelength was used for quantitative analysis by the principle of specific fluorescence labeling. The crude sugar sample of each variety was measured in parallel three times and the average value was taken to determine the content of β-1,3-glucan in the mycelium samples of different varieties of Ganoderma lucidum.

5. The method for non-destructive detection of β-1,3-glucan content in Ganoderma mycelium according to claim 1, characterized in that: In step 3, when the LAMBDA 1050+ long-wavelength spectrometer is used to directly perform non-destructive testing on the Ganoderma mycelium sample, the temperature is maintained at 20±1° C. and the relative humidity is maintained at 20% throughout the process.

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