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CN-120629083-B - Method for nondestructively detecting content of beta-1, 3-glucan in ganoderma mycelia

CN120629083BCN 120629083 BCN120629083 BCN 120629083BCN-120629083-B

Abstract

The invention discloses a method for nondestructively detecting the content of beta-1, 3-glucan in ganoderma mycelia, which relates to the technical field of biological detection and comprises the following steps of preparing ganoderma mycelia samples of different varieties; the method comprises the steps of measuring the content of beta-1, 3-glucan in ganoderma mycelium samples of different varieties by adopting an aniline blue fluorescence method, collecting near infrared spectrum data of ganoderma mycelium of different varieties, preprocessing spectrum data, screening characteristic wavelengths, establishing a prediction model, and predicting by using the model. The invention can rapidly and high-flux nondestructively detect the content of the beta-1, 3-glucan by using the prediction model for detecting the content of the beta-1, 3-glucan in the ganoderma mycelia based on the near infrared spectrum technology and the fluorescence quantification technology, has good prediction effect and short measurement time, and has the advantages of simple and convenient operation, rapidness, low cost and the like.

Inventors

  • HAN XUERONG
  • XIAO SHIJUN
  • CHEN HONGXIA

Assignees

  • 吉林农业大学

Dates

Publication Date
20260508
Application Date
20250606

Claims (5)

  1. 1. A method for non-destructive testing of beta-1, 3-glucan content in ganoderma mycelia, comprising the steps of: Step 1, preparing different types of ganoderma mycelia samples, namely collecting different types of ganoderma mycelia, cleaning, quick-freezing for 2 hours in a-80 ℃ environment after cleaning, then freeze-drying for 24 hours, grinding and sieving with a 60-mesh sieve to obtain homogeneous gray-white mycelia powder, namely the different types of ganoderma mycelia samples, and sealing and preserving at-20 ℃ for later use; step 2, determining the content of beta-1, 3-glucan in different varieties of ganoderma mycelia samples by adopting an aniline blue fluorescence method to obtain chemical data of the content of the beta-1, 3-glucan; step 3, collecting near infrared spectrum data of different varieties of ganoderma mycelia, directly carrying out nondestructive testing on ganoderma mycelia samples by using a LAMBDA 1050+ long-wave band spectrometer, preheating the spectrometer for 30 minutes, carrying out background scanning with spectrally pure BaSO4 powder to compensate environmental interference, setting a spectrum collecting parameter as a step length of 5cm -1 , continuously scanning in a 2300-700 cm -1 characteristic functional group interval, repeatedly measuring the ganoderma mycelia samples of each variety for three times, collecting the near infrared spectrum data of the ganoderma mycelia of different varieties, and standardizing the collected near infrared spectrum data; Step4, preprocessing spectrum data, namely carrying out noise reduction on near infrared spectrum data by using a Savitzky-Golay convolution smoothing algorithm, carrying out vector normalization preprocessing, normalizing absorbance values of each spectrum to a [0,1] interval, and reserving 2300-700 cm -1 effective spectrum intervals; Step 5, screening characteristic wavelengths by adopting a competitive self-adaptive re-weighted sampling method, namely CARS, screening the characteristic wavelengths, screening key variables by a CARS algorithm through Monte Carlo sampling and an exponential decay function, setting a retention rate parameter alpha=0.8, removing two characteristics of 1800cm -1 、1325cm -1 by combining a chemical structural bond of beta-1, 3-glucan after characteristic band screening, wherein the chemical structural bond of the beta-1, 3-glucan mainly comprises a strong peak at a position of 1000cm -1 ～1200cm -1 , a peak at a position of 800cm -1 ～900cm -1 and corresponding to the existence of the beta-1- > 3 glycosidic bond, and confirming a beta-1, 3 connection mode, and finally, correspondingly forming an original data set by the obtained spectral data and corresponding chemical data; Step 6, dividing the original data set obtained in the step 5 into a training set and a verification set according to the ratio of 6:4, and establishing a prediction model between near infrared spectrum data and beta-1, 3-glucan content by using a partial least squares regression method; And 7, predicting the content of beta-1, 3-glucan in the unknown variety ganoderma mycelia by using the model established in the step 6.
  2. 2. The method for non-destructive testing of beta-1, 3-glucan content of ganoderma mycelia according to claim 1, wherein step 1 specifically further comprises the steps of: Step 1.1, preparing a solid culture medium, activating strains, preparing a PDA solid culture medium, respectively inoculating different ganoderma strains in the center of the solid culture medium, and keeping the ganoderma strains at 28 ℃ for 5-7 days in a dark state; Step 1.2, preparing a liquid culture medium and performing deep fermentation, wherein the liquid culture medium comprises 30g/L of glucose, 5g/L of peptone, 5g/L of yeast extract powder, 1g/L of KH 2 PO 4 , 0.5g/L of MgSO 4 and 50mg/L of VB 1 , regulating the pH value to 5.8+/-0.2, maintaining the temperature at 121 ℃ for sterilization for 20 minutes, inoculating activated ganoderma mycelia into the liquid culture medium, maintaining the temperature at 28 ℃ and the rotating speed at 150rpm, and performing shaking culture for 5-7 days to perform the deep fermentation.
  3. 3. The method for non-destructive testing of beta-1, 3-glucan content of ganoderma mycelia according to claim 1, wherein step 2 comprises the steps of: Step 2.1, preparing mycelium crude sugar samples, respectively weighing 5g of different types of ganoderma mycelia samples, respectively adding the mycelium samples into 250mL of 5% w/v NaOH solution, carrying out water bath ultrasonic assisted extraction for 2h, keeping the temperature at 4 ℃ and the rotating speed at 1000rpm, centrifuging for 10min, taking supernatant, then dripping glacial acetic acid to adjust the pH value to be neutral, respectively adding 1L of absolute ethyl alcohol with the temperature at 4 ℃ for 12h, keeping the temperature at 4 ℃ and the rotating speed at 1000rpm, centrifuging for 10min, collecting precipitates, washing the collected precipitates with deionized water for 3 times, and then keeping the temperature at 60 ℃ for 6h by using an oven to obtain the ganoderma mycelia crude sugar samples of different types; And 2.2, measuring the content of beta-1, 3-glucan, respectively weighing 1-10 mg of crude sugar samples of different varieties, respectively placing the crude sugar samples into a centrifuge tube, adding ultrapure water according to a mass-volume ratio of 0.01% for ultrasonic auxiliary dissolution, and measuring the content of beta-1, 3-glucan in the mycelium samples of the different varieties of ganoderma by adopting an aniline blue fluorescence method to obtain chemical data of the content of the beta-1, 3-glucan.
  4. 4. A method for the nondestructive detection of the beta-1, 3-glucan content of ganoderma mycelia according to claim 3, wherein step 2.2 specifically comprises: 1-10 mg of crude sugar samples of different varieties are respectively weighed and respectively placed in a centrifuge tube, ultrapure water is added according to the mass-volume ratio of 0.01% to carry out ultrasonic auxiliary dissolution, the sample solution and 0.1% w/v of aniline blue reagent are mixed according to the volume ratio of 1:2, and the complexation of polysaccharide and aniline blue is utilized to carry out the reaction of The fluorescence intensity was measured, wherein, For the fluorescence excitation wavelength(s), For the emission wavelength, quantitative analysis is carried out by a specific fluorescent labeling principle, the crude sugar sample of each variety is measured in parallel for three times, and the average value is obtained, so that the content of beta-1, 3-glucan in the ganoderma mycelium samples of different varieties is measured.
  5. 5. The method for non-destructive testing of beta-1, 3-glucan content in ganoderma mycelia according to claim 1, wherein in step 3, the whole process is maintained at 20±1 ℃ and the relative humidity is 20% when the ganoderma mycelia sample is directly subjected to non-destructive testing by using LAMBDA 1050+ long-band spectrometer.

Description

Method for nondestructively detecting content of beta-1, 3-glucan in ganoderma mycelia Technical Field The invention relates to the technical field of biological detection, in particular to a method for nondestructively detecting the content of beta-1, 3-glucan in ganoderma mycelia. Background Traditional methods for determining beta-1, 3-glucan, such as aniline blue fluorescence method and fluorescence white method, rely on specific dye labeling, and have the disadvantages of complex operation and long period, and High Performance Liquid Chromatography (HPLC) has high sensitivity, but requires complex pretreatment, damages samples and is time-consuming. Near infrared spectroscopy can rapidly determine spectral data of a sample, but cannot accurately quantify the spectral data. Therefore, a semi-quantitative method for rapidly and high-flux nondestructively predicting the content of beta-1, 3-glucan in ganoderma mycelia by utilizing a near infrared spectrum prediction model is provided. Disclosure of Invention In order to achieve the aim, the invention provides the following technical scheme that the method for nondestructively detecting the content of beta-1, 3-glucan in ganoderma mycelia comprises the following steps: step 1, preparing ganoderma lucidum mycelium samples of different varieties; step 2, determining the content of beta-1, 3-glucan in different varieties of ganoderma mycelia samples by adopting an aniline blue fluorescence method to obtain chemical data of the content of the beta-1, 3-glucan; step 3, collecting near infrared spectrum data of different varieties of ganoderma mycelia, directly carrying out nondestructive testing on ganoderma mycelia samples by using a LAMBDA 1050+ long-wave band spectrometer, preheating the spectrometer for 30 minutes, carrying out background scanning with spectrally pure BaSO4 powder to compensate environmental interference, setting a spectrum collecting parameter as a step length of 5cm -1, continuously scanning in a 2300-700 cm -1 characteristic functional group interval, repeatedly measuring the ganoderma mycelia samples of each variety for three times, collecting the near infrared spectrum data of the ganoderma mycelia of different varieties, and standardizing the collected near infrared spectrum data; Step4, preprocessing spectrum data, namely carrying out noise reduction on near infrared spectrum data by using a Savitzky-Golay convolution smoothing algorithm, carrying out vector normalization preprocessing, normalizing absorbance values of each spectrum to a [0,1] interval, and reserving 2300-700 cm -1 effective spectrum intervals; Step 5, screening characteristic wavelengths, namely, screening the characteristic wavelengths by adopting a competitive self-adaptive re-weighting sampling method, wherein the CARS algorithm screens key variables through Monte Carlo sampling and an exponential decay function, sets retention rate parameters alpha=0.8, eliminates two characteristics of 1800cm -1、1325cm-1 by combining chemical structural bonds of beta-1, 3-glucan after screening the characteristic wavelengths, and finally corresponds the obtained spectrum data and corresponding chemical data to form an original data set; Step 6, dividing the original data set obtained in the step 5 into a training set and a verification set according to the ratio of 6:4, and establishing a prediction model between near infrared spectrum data and beta-1, 3-glucan content by using a partial least squares regression method; And 7, predicting the content of beta-1, 3-glucan in the unknown variety ganoderma mycelia by using the model established in the step 6. Preferably, the step 1 specifically includes the following steps: Step 1.1, preparing a solid culture medium, activating strains, preparing a PDA solid culture medium, respectively inoculating different ganoderma strains in the center of the solid culture medium, and keeping the ganoderma strains at 28 ℃ for 5-7 days in a dark state; Step 1.2, preparing a liquid culture medium and performing deep fermentation, wherein the liquid culture medium comprises 30g/L of glucose, 5g/L of peptone, 5g/L of yeast extract powder, 1g/L of KH 2PO4, 0.5g/L of MgSO 4 and 50mg/L of VB 1, regulating the pH value to 5.8+/-0.2, keeping 121 ℃ for sterilization for 20 minutes, inoculating activated ganoderma mycelia into the liquid culture medium, keeping 28 ℃ and performing shaking culture at the rotating speed of 150rpm for 5-7 days, and performing deep fermentation; Step 1.3, preparing mycelium samples, collecting different types of ganoderma mycelia, cleaning, quick-freezing for 2 hours in a-80 ℃ environment after cleaning, then freeze-drying for 24 hours, and finally grinding and sieving with a 60-mesh sieve to obtain homogeneous off-white mycelium powder, namely different types of ganoderma mycelia samples, and sealing and preserving at-20 ℃ for later use. Preferably, the step2 specifically includes the following steps: Step 2.1, preparing myce