CN103983605B - A kind of method of Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate - Google Patents

Abstract

The invention provides a method for rapidly and non-destructively detecting the wall breaking rate of the broken ganoderma lucidum spore powder. The method includes the following steps: using a near-infrared spectrometer to collect near-infrared spectral data of the sample to be tested; performing multivariate scattering correction preprocessing on the near-infrared spectral data of the sample to be tested; selecting 9411.6-5446.4cm ^-1 and 4613.2 -4243cm ^-1 data in two spectral intervals, under the condition that the factor number is 10, the wall breaking rate prediction model established based on the partial least squares regression method is used to detect the wall breaking rate of the sample to be tested. The invention has the advantages of being fast, accurate, non-destructive and low in cost, is beneficial to popularization, and can effectively solve the problem of quality monitoring of the wall breaking rate of the broken ganoderma lucidum spore powder.

Description

A method for rapid and nondestructive detection of wall breaking rate of broken Ganoderma lucidum spore powder

technical field

The invention relates to a method for detecting the wall-breaking rate of fungal spores, in particular to a method for rapidly and non-destructively detecting the wall-breaking rate of wall-broken ganoderma lucidum spore powder.

Background technique

Ganoderma lucidum spore powder is the extremely tiny oval reproductive cells ejected from the gills of Ganoderma lucidum during the growth and maturity stage, that is, the seeds of Ganoderma lucidum. It condenses the essence of Ganoderma lucidum, and has all the genetic material and health care functions of Ganoderma lucidum. The shell of Ganoderma lucidum spore powder includes two layers of spore walls made of very hard chitin cellulose; This makes it difficult for the effective substances in the Ganoderma lucidum spore powder to be absorbed by the human body. After the spore wall of Ganoderma lucidum spore powder is broken or eliminated through artificial mechanical, physical and chemical methods or biological enzymatic methods, the active ingredients tightly wrapped by the spore wall can be directly absorbed by the human stomach to the greatest extent. Some data show that the broken Ganoderma lucidum spore powder is conducive to the dissolution of triterpenes, crude polysaccharides, crude fat and other components, and the content of crude polysaccharides is 70% higher than that of unbroken Ganoderma lucidum spore powder. If the unbroken Ganoderma lucidum spore powder is eaten directly, the human body can only utilize about 12% of the active ingredients, while taking the broken Ganoderma lucidum spore powder, the utilization rate of the active ingredients can reach more than 95%. Therefore, the wall breaking rate of the broken ganoderma lucidum spore powder is the key to utilizing the active ingredients of the ganoderma lucidum spore powder to the greatest extent. For numerous broken ganoderma spore powders on the market, the broken wall rate is often used as an important indicator to measure the quality of this product.

At present, the determination of the wall breaking rate of the broken Ganoderma lucidum spore powder mainly refers to the standard of the Ministry of Agriculture "NY/T1677-2008 Determination of the wall breaking rate of the broken Ganoderma lucidum spore powder" and the national standard "GB/T29344-2012 Harvesting and processing of Ganoderma lucidum spore powder Appendix A of "Technical Specifications" "Determination of wall breaking rate of broken Ganoderma lucidum spore powder". In these two standards, the principle of determination of the breaking rate is to count the unbroken spores by the hemocytometer, and calculate the number of intact spores per unit mass of unbroken Ganoderma lucidum spore powder and the unit mass of broken Ganoderma lucidum spores The number of complete spores in the powder, thereby obtaining the wall-breaking rate of the broken-wall ganoderma spore powder. The operation process of this analysis method is complicated, the measurement time is long, the sample preparation and counting process of the hemocytometer is prone to errors, and the operator's proficiency is required to be high.

In addition to the hemocytometer method, currently reported methods for the determination of wall breaking rate include water-mounted slices combined with microscopic technology detection methods, suspension method combined with physical technology detection methods, and chemical fingerprint detection methods. Among them, the chemical fingerprint detection method broke through the limitation of artificial counting, and redefined the detection method of the wall breaking rate of the broken Ganoderma lucidum spore powder from the composition. Due to the current development of chromatographic technology, it is not a problem to use the analysis method of component content to determine the wall breaking rate, and its accuracy can be fully guaranteed; however, the detection steps of the component analysis method are complicated, the equipment used is expensive, the sample is destroyed, and the detection cost higher.

Contents of the invention

The purpose of the present invention is to provide a method for rapid and non-destructive detection of the wall-breaking rate of the broken ganoderma lucidum spore powder, so as to solve the problems of complicated steps, high cost and easy damage to samples in the existing detection method.

The present invention is achieved in the following way: a method for rapidly and non-destructively detecting the wall breaking rate of the broken ganoderma lucidum spore powder comprises the following steps:

a. A near-infrared spectrometer is used to collect near-infrared spectral data of the sample to be tested;

b. Performing multivariate scattering correction preprocessing on the near-infrared spectral data of the sample to be measured;

c. Select the data of the two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 after pretreatment, and use the prediction model of wall breaking rate established by the partial least squares regression method under the condition that the factor number is 10 To detect the wall breaking rate of the sample to be tested.

The prediction model of the wall breaking rate in the step c is:

Y＝A ₀ +A ₁ ×X ₁ +A ₂ ×X ₂ +A ₃ ×X ₃ +A ₄ ×X ₄ +A ₅ ×X ₅ +A ₆ ×X ₆ +A ₇ ×X ₇ +A ₈ × X ₈ +A ₉ ×X ₉ +A ₁₀ ×X ₁₀

In the formula: Y is the wall breaking rate, A ₀ , A ₁ ,..., A ₁₀ are constants, X ₁ is the score of the first factor after the dimensionality reduction of the spectral data through principal component analysis, and X ₂ is the spectral data through principal component analysis The score of the second factor after dimensionality reduction, X ₃ is the score of the third factor after dimensionality reduction of spectral data through principal component analysis, X ₄ is the score of the fourth factor after dimensionality reduction of spectral data through principal component analysis, X ₅ is the score of spectral data through principal component analysis Analysis of the score of the fifth factor after dimensionality reduction, X ₆ is the score of the sixth factor after the dimensionality reduction of the spectral data through principal component analysis, X ₇ is the score of the seventh factor after the dimensionality reduction of the spectral data through the principal component analysis, X ₈ is the score of the spectral data through the principal component analysis The score of the eighth factor after component analysis dimensionality reduction, X ₉ is the score of the ninth factor after the dimensionality reduction of the spectral data through the principal component analysis, and X ₁₀ is the score of the tenth factor after the dimensionality reduction of the spectral data through the principal component analysis; the spectral data is nearly The data of two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 after preprocessing of infrared spectrum data.

The prediction model of the wall breaking rate in the step c is:

Y＝0.06866×X ₁ +0.046809×X ₂ +0.019892×X ₃ +0.125289×X ₄ +0.06593×X ₅ +0.019621×X ₆ +0.053336×X ₇ +0.018258×X ₈ +0.094993×X ₉ +0.079206×X ₁₀

In the formula: Y is the breaking rate, X ₁ is the score of the first factor after the dimensionality reduction of the spectral data through principal component analysis, X ₂ is the score of the second factor after the dimensionality reduction of the spectral data through the principal component analysis, X ₃ is the score of the spectral data through the principal component analysis The score of the third factor after dimension reduction by component analysis, X ₄ is the score of the fourth factor after dimensionality reduction of spectral data by principal component analysis, X ₅ is the score of fifth factor after dimensionality reduction of spectral data by principal component analysis, and X ₆ is the score of factor 5 after dimensionality reduction of spectral data by principal component analysis. The score of the sixth factor after principal component analysis dimension reduction, X ₇ is the score of the seventh factor after the dimensionality reduction of the spectral data by the principal component analysis, X ₈ is the score of the eighth factor after the dimensionality reduction of the spectral data by the principal component analysis, X ₉ is the spectral data The score of the ninth factor after dimensionality reduction by principal component analysis, X ₁₀ is the score of the tenth factor after the dimensionality reduction of spectral data by principal component analysis; the spectral data is 9411.6-5446.4cm ^-1 and 4613.2 -4243cm ^-1 data in two spectral intervals.

Described step a specifically comprises the following steps:

a1. Dry the sample to be tested at 30°C for 48 hours, and then put it into a sample cup;

a2, using a Fourier near-infrared spectrometer to scan the sample to be tested in a diffuse reflection mode, and obtain spectral data of the sample to be tested in the range of 12500-4000cm ^-1 ; the scanning resolution of the Fourier near-infrared spectrometer is 8cm ^-1 ;

a3. Take the average value after scanning the sample to be tested 32 times.

The specific construction method of the wall breaking rate prediction model described in the step c is as follows:

c1, select some broken Ganoderma lucidum spore powders with different wall breaking rates as training samples required for setting up the wall breaking rate prediction model;

c2, using a near-infrared spectrometer to collect near-infrared spectral data of the training sample;

c3, using the hemocytometer method to detect the wall breaking rate of the training sample, as a reference value for the training sample wall breaking rate required to establish the wall breaking rate prediction model;

c4. Perform multivariate scattering correction preprocessing on the near-infrared spectral data of the training sample, select data in two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 after preprocessing, under the condition that the factor number is 10 , combined with the reference value of the wall breaking rate of the training samples, a partial least squares regression method is used to establish a wall breaking rate prediction model.

The selection process of the two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 in the step c4 is as follows:

According to the loading diagram of the first three factors of the principal component analysis of the near-infrared spectral data, select several spectral intervals with larger loading values, and then obtain two 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 through optimization selection spectral range.

The step c2 specifically includes the following steps:

c21, drying the training sample at 30°C for 48 hours, and then putting it into a sample cup;

c22, using a Fourier near-infrared spectrometer to scan the training samples in diffuse reflectance mode, and obtain spectral data of each training sample within the range of 12500-4000cm ^-1 ; the scanning resolution of the Fourier near-infrared spectrometer is 8cm ^-1 ;

c23. Take the average value after scanning each training sample 32 times.

The step c3 specifically includes the following steps:

c31. Weigh respectively 0.01g, 0.02g, 0.03g, 0.04g and 0.05g of unbroken Ganoderma lucidum spore powder after drying at 60°C for 5 hours, and then put them into 5 colorimetric tubes respectively;

c32. Weigh 1.25g of 5 parts of sucrose powder after being ground and screened through a 100-mesh sieve respectively, and add 5 parts of sucrose powder to the 5 colorimetric tubes in the step c31 respectively, so that the added sucrose powder and The Ganoderma lucidum spore powder in the corresponding colorimetric tube is mixed until the color is uniform;

c33. Add distilled water to each colorimetric tube to dissolve the sucrose powder and Ganoderma lucidum spore powder in the corresponding colorimetric tube, then add 0.2mL Tween 80 to each colorimetric tube, and then distill each colorimetric tube to 25mL with distilled water ;

c34. Make each colorimetric tube ultrasonically vibrate for 30 minutes at room temperature, during which each colorimetric tube is manually shaken several times every 10 minutes, so that the ganoderma lucidum spore powder in each colorimetric tube is fully dispersed;

c35. Pipette 8.0 μL of Ganoderma lucidum spore powder suspension from each colorimetric tube onto the edges of 5 different coverslips, and use the siphon phenomenon to fill the corresponding grids of the hemocytometer under the corresponding coverslips with corresponding Ganoderma spores powder suspension;

c36. After standing still for 30s, use an optical microscope with a magnification of 400 times to count the number of complete Ganoderma lucidum spore powder in the 4 top corners and the 5 middle grids in the center of each blood cell counting board, and count each blood cell Observation and statistics of the plate take the average value after 5 times;

c37, taking the mass of 5 parts of unbroken ganoderma lucidum spore powder weighed in the step c1 as the abscissa, taking the 5 middle grids on each hemocytometer plate counted in the step c36 to contain complete ganoderma lucidum spore powder The number is the ordinate, making the standard curve of the unbroken Ganoderma lucidum spore powder quality and number;

c38. Weigh 0.04 g of each training sample after drying at 60°C for 5 hours, and put them into different colorimetric tubes; follow the steps c32 to c36 to make statistics on the hemocytometer plate corresponding to each training sample. The number of complete Ganoderma lucidum spore powder contained in each grid;

c39. From the standard curve drawn in the step c37, search for the number N _A of the whole Ganoderma lucidum spore powder corresponding to the quality of 0.04g unbroken Ganoderma lucidum spore powder, according to the formula

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; \%</span>$

Calculate the wall-breaking rate of each training sample; In the formula: X is the wall-breaking rate of the training sample, and N _B is that the training sample corresponding to the training sample counted in the step c38 contains complete The number of Ganoderma lucidum spore powder.

When adopting the present invention to detect the wall breaking rate of the broken ganoderma lucidum spore powder, since the time for the near-infrared spectrometer to collect the spectral data is very short, the time for preprocessing the spectral data and using the partial least squares regression method for calculation and processing is also very short. Therefore, the wall breaking rate of the sample to be tested can be detected conveniently and quickly without damaging the sample to be tested, which solves the complicated operation process and sample It is easy to be destroyed, there are many artificial unstable factors, and the repeatability is poor.

For the sample to be tested after the detection by the present invention, the traditional method of blood counting plate is used to detect the wall breaking rate, and the detection results of the two are compared. The results show that the wall breaking rate measured by the two methods is very close , the relative deviation is between 0.7-8.6%, indicating that the non-destructive testing method established by the present invention is accurate and reliable.

Ganoderma lucidum spore powder is a high-end health care product, and its price has remained high. The invention uses the near-infrared spectrum technology and the multivariate statistical method to quickly and non-destructively detect the wall-breaking rate of the wall-broken ganoderma lucidum spore powder, which can greatly reduce the detection cost, improve the detection speed, and facilitate product quality monitoring.

Description of drawings

Fig. 1 is the near-infrared spectrogram of the training sample of the present invention.

Fig. 2 is a photomicrograph of unbroken ganoderma spore powder in an embodiment of the present invention.

Fig. 3 is the standard curve of quality and number of unbroken Ganoderma lucidum spore powder in the present invention.

Fig. 4 is a photomicrograph of a training sample in an embodiment of the present invention.

Fig. 5 is a loading diagram of the first three factors of the principal component of the near-infrared spectrum of the training sample in the embodiment of the present invention.

Fig. 6 is a correlation diagram between the predicted value obtained by using the present invention to detect the training samples and the reference value obtained by using the hemocytometer method to detect the training samples.

Fig. 7 is a spectrogram of the preprocessed near-infrared spectral data of the sample to be tested in the embodiment of the present invention.

detailed description

When the present invention detects the wall breaking rate of the ganoderma lucidum spore powder to be tested, a near-infrared spectrometer is first used to collect the near-infrared spectrum data of the sample to be tested; After processing the data in the two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 , under the condition that the factor number is 10, the wall breaking rate prediction model established based on the partial least squares regression method is used to detect the tested The breaking rate of the sample.

The prediction model of the broken wall rate established is:

Y＝A ₀ +A ₁ ×X ₁ +A ₂ ×X ₂ +A ₃ ×X ₃ +A ₄ ×X ₄ +A ₅ ×X ₅ +A ₆ ×X ₆ +A ₇ ×X ₇ +A ₈ × X ₈ +A ₉ ×X ₉ +A ₁₀ ×X ₁₀ (1)

In formula (1): Y is the breaking rate, A ₀ , A ₁ ,..., A ₁₀ are all constants, X ₁ is the score of the first factor after dimension reduction of spectral data by principal component analysis, and X ₂ is the spectral data after dimension reduction. The score of the second factor after principal component analysis dimension reduction, X ₃ is the score of the third factor after the dimension reduction of spectral data by principal component analysis, X ₄ is the score of the fourth factor after dimension reduction of spectral data by principal component analysis, X ₅ is the spectral data The score of the fifth factor after dimensionality reduction by principal component analysis, X ₆ is the score of the sixth factor after dimensionality reduction of spectral data by principal component analysis, X ₇ is the score of the seventh factor after dimensionality reduction of spectral data by principal component analysis, X ₈ is the spectrum The score of the eighth factor after the dimensionality reduction of the data through principal component analysis, _X9 is the score of the ninth factor after the dimensionality reduction of the spectral data through the principal component analysis, and X10 is the score of the _tenth factor after the dimensionality reduction of the spectral data through the principal component analysis; The data are the data of the two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 after the preprocessing of the near-infrared spectral data.

The establishment of the prediction model of the wall breaking rate in the present invention, the specific form of the established wall breaking rate prediction model, and the collection of near-infrared spectral data will be described in detail below.

The partial least squares regression method was used to establish the prediction model of the wall breaking rate, which specifically included the following steps:

a. Select several broken Ganoderma lucidum spore powders with different wall breaking rates as the training samples required for establishing the breaking rate prediction model.

b. A near-infrared spectrometer is used to collect near-infrared spectral data of the training samples.

Firstly, several training samples selected in step a were dried at 30°C for 48 hours, and then each training sample was put into a sample cup respectively. Afterwards, the training samples in each sample cup were scanned by the MPA Fourier near-infrared spectrometer of German BRUKER company in diffuse reflectance mode, and the spectral data of each training sample in the range of 12500-4000 cm ^-1 were obtained. Before each scan, the noise, wavelength accuracy and reproducibility of the Fourier near-infrared spectrometer should be diagnosed; the scanning resolution of the Fourier near-infrared spectrometer is 8cm ^-1 , and the average is taken after scanning 32 times for each training sample value. The near-infrared spectrum data (average value) of each training sample collected is processed by relevant software, and the near-infrared spectrum graph of each training sample can be obtained. Referring to FIG. 1 , FIG. 1 shows the near-infrared spectrograms of each training sample. It can be seen from Figure 1 that the near-infrared spectra of the broken ganoderma lucidum spore powders with different wall-breaking rates seem to have little difference, so it is not possible to analyze directly from the spectra, but it is necessary to analyze the collected near-infrared spectral data. After pretreatment, the partial least squares regression method can be used to establish the wall breaking rate prediction model based on the pretreated spectral data to realize the nondestructive detection of the wall breaking rate of the broken ganoderma lucidum spore powder. For the specific pretreatment method and the partial least squares regression method to establish the prediction model of the wall breaking rate, please refer to step d.

c. Using the hemocytometer method to detect the wall breaking rate of the training samples as a reference value for the wall breaking rate of the training samples needed to establish the wall breaking rate prediction model.

This step can comprise again sample (unbroken ganoderma spore powder) several steps of dilution, preparation, counting, drawing standard curve and the calculation of training sample wall breaking rate, specifically as follows:

c1. Use AR1140 electronic balance (accuracy: 0.1 mg) of Mettler-Toledo Instruments (Shanghai) Co., Ltd. to accurately weigh the unbroken Ganoderma lucidum spore powder after drying at 60°C (±1°C) for 5 hours. 0.01g, 0.02g, 0.03g, 0.04g and 0.05g, put the 5 parts of unbroken Ganoderma lucidum spore powder into 5 25mL colorimetric tubes respectively. The instrument used for drying is Beijing Hongda Tianju DHG-9023A constant temperature blast drying oven.

c2. Use the electronic balance used in step c1 to weigh 1.25 g each of 5 parts of sucrose powder after being ground and then screened through a 100-mesh sieve, and add 5 parts of sucrose powder to the 5 colorimetric tubes in step c1 respectively, so that The added sucrose powder is mixed with the Ganoderma lucidum spore powder in the corresponding colorimetric tube until the color is uniform.

c3. Add distilled water to each colorimetric tube to dissolve the sucrose powder and Ganoderma lucidum spore powder in the corresponding colorimetric tube, then add 0.2mL Tween 80 to each colorimetric tube, and then distill each colorimetric tube to 25mL with distilled water .

c4. Use the YQ-220D ultrasonic cleaner from Shanghai Yijing Ultrasonic Instrument Co., Ltd. to ultrasonically vibrate each colorimetric tube for 30 minutes at room temperature. During this period, manually shake each colorimetric tube several times every 10 minutes to make each colorimetric tube The Ganoderma lucidum spore powder in the tube is fully dispersed.

c5. Take 8.0 μL of Ganoderma lucidum spore powder suspension from each colorimetric tube, respectively drop 5 parts of 8.0 μL of Ganoderma lucidum spore powder suspension on the edge of 5 coverslips, and make use of the siphon phenomenon to make the bottom of the corresponding coverslips The hemocytometer grid is filled with the corresponding Ganoderma lucidum spore powder suspension.

Before drawing the suspension of Ganoderma lucidum spore powder from each colorimetric tube, shake the colorimetric tube by hand to disperse the Ganoderma lucidum spore powder in the colorimetric tube evenly, and immediately draw 8.0 μL of the suspension of Ganoderma lucidum spore powder. Drop different Ganoderma lucidum spore powder suspensions drawn from 5 colorimetric tubes on the edges of 5 cover slips respectively, and a hemocytometer is arranged under each cover slip, that is: the cover slip is covered on the hemocytometer Above the counting board; the grid of each blood counting board can be filled with the corresponding suspension of Ganoderma lucidum spore powder through the siphon phenomenon. The grid on the blood counting board includes 25 middle grids, each of which is divided into 16 small grids.

c6, make each ganoderma lucidum spore powder suspension stand in the corresponding hemacytometer grid for 30s, then adopt the BX51 system optical microscope of OLYMPUS company, under 400 times magnification, check the 4 top angles and The number of complete Ganoderma lucidum spore powder in the central 5 grids was observed and counted. With reference to Fig. 2, Fig. 2 has shown the photomicrograph of the unbroken Ganoderma lucidum spore powder (corresponding to 0.04g unbroken Ganoderma spore powder) on one of the hemocytometer plates, as can be seen from the figure, each Ganoderma lucidum spore powder It is oval granular, that is, unbroken Ganoderma lucidum spore powder.

When counting the number of complete Ganoderma lucidum spore powder contained in the 5 middle grids on the blood cell counting board, if some Ganoderma lucidum spore powder is on the sidelines of the middle grid, two of the four sides of the middle grid should be counted when counting. The number of Ganoderma lucidum spore powder on adjacent borders. During the statistical process, the larger outlier values should be removed, and the number of intact Ganoderma lucidum spore powder contained in the 5 middle grids on each hemocytometer board should be effectively observed and counted for 5 times, and then the average value should be calculated.

c7, taking the quality of 5 parts of unbroken ganoderma lucidum spore powder taken in step c1 as the abscissa, the number of complete ganoderma lucidum spore powder in the 5 middle grids on each hemacytometer plate counted in step c6 is On the ordinate, make a standard curve of the quality and number of unbroken Ganoderma lucidum spore powder, see Figure 3 for details, the fitting equation of the standard curve shown in Figure 3 is y=1706.6x-2.0173, and the linear correlation coefficient is R ² =0.999; It can be seen from Figure 3 that the quality of the unbroken ganoderma lucidum spore powder is directly proportional to the number, the greater the mass, the more the number of intact ganoderma spore powder it contains.

c8. Make all the training samples dry at 60° C. for 5 hours, then weigh 0.04 g of each dried training sample, and put the different training samples weighed into different colorimetric tubes; According to steps c2-c6, count the number of whole Ganoderma lucidum spore powder in the 5 middle grids on the hemocytometer corresponding to each training sample. With reference to Fig. 4, the photomicrograph of wherein a training sample under the optical microscope is shown among the figure, can find out from the figure, many Ganoderma lucidum spore powders in the training sample have been broken, thereby forming the powder granular structure of different sizes, By counting the number of remaining intact ganoderma spore powder, the wall breaking rate of the training samples can be calculated.

c9. From the standard curve drawn in step c7, search for the number N _A of the complete Ganoderma lucidum spore powder corresponding to the unbroken Ganoderma lucidum spore powder with a quality of 0.04g, according to the formula

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; B</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}<span\; class="notranslate">\; \%---</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Calculate the wall breaking rate of each training sample; In the formula (2): X is the wall breaking rate of the training sample, and N _B is the number contained in the 5 middle grids on the blood count plate corresponding to the training sample counted in step c8. The number of whole Ganoderma lucidum spore powder.

The calculated wall breaking rate values of all the training samples are used as the reference value of the training sample wall breaking rate required to establish the wall breaking rate prediction model.

d. Perform multivariate scattering correction preprocessing on the near-infrared spectral data of the training samples, select the data in the two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 after preprocessing, and combine them under the condition that the factor number is 10 The reference value of the broken wall rate of the training samples was used to establish a prediction model for the broken wall rate by partial least squares regression method.

Commonly used near-infrared spectral data preprocessing methods include multivariate scattering correction method, first-order derivative method, vector normalization method, second-order derivative method, subtracting a straight line method, and eliminating Changshu offset method. In order to establish the best prediction model for wall breaking rate, an optimal preprocessing method should be selected; the optimal preprocessing method should be able to retain the original information of each training sample at most, and can also reduce the number of training samples for spectral data. The influence of factors such as inhomogeneity, light scattering and random noise of the instrument can improve the prediction accuracy and stability of the model.

In addition to selecting the optimal preprocessing method, the characteristic spectral interval and the number of factors should also be optimized.

The near-infrared spectrum is the spectral range of 12500-4000cm ^-1 , which mainly reflects the double frequency and combined frequency absorption information of hydrogen-containing groups (such as CH, NH, OH, etc.). It is an effective way to improve the accuracy of the prediction model to select the characteristic spectral interval closely related to the wall breaking rate of the broken Ganoderma lucidum spore powder.

The selection of the characteristic spectral interval of the present invention abandons the method of substituting segment by segment in the whole wave band, according to the loading diagram (see Fig. 5) of the first three factors of principal component analysis, several spectral intervals (9411.6-5446.4cm) with larger loading values are selected ^-1 , 4613.2-4243cm ^-1 , 7513.9-6094.4cm ^-1 , 5461.8-4243cm ^-1 , 7436.7-5446.3cm ^-1 , 7513.9-5446.4cm ^-1 ) for optimal selection, thus greatly reducing the work of selecting the characteristic spectrum interval quantity.

In partial least squares regression analysis, the huge data matrix will be reduced to just a few factors. If the number of factors is too small, more information will be lost, and if the number of factors is too large, it will be overfitting. Therefore, it is very necessary to select an appropriate number of factors.

Through orthogonal experiments, different near-infrared spectral data preprocessing methods, characteristic spectral intervals and factor numbers were compared. The comparison results are shown in Table 1. 5446.4cm ^-1 and 4613.2-4243cm ^-1 are selected in the two spectral intervals, and when the factor number is 10, the root mean square error (RMSECV) of cross-validation is the smallest, that is, the partial least squares regression method is used to establish the The performance of the model for predicting the wall breaking rate of the broken Ganoderma lucidum spore powder is the best, and the specific expression of the best model is:

A ₀ =0;

Y＝0.06866×X ₁ +0.046809×X ₂ +0.019892×X ₃ +0.125289×X ₄ +0.06593×X ₅ +0.019621×X ₆ +0.053336×X ₇ +0.018258×X ₈ +0.094993×X ₉ +0.079206×X ₁₀ (3)

In formula (3): Y is the wall breaking rate, X1 is the score of the _first factor after the dimensionality reduction of the spectral data by principal component analysis, X2 is the score of the _second factor after the dimensionality reduction of the spectral data by principal component analysis, and _X3 is the spectrum The score of the third factor after the dimensionality reduction of the data by principal component analysis, X ₄ is the score of the fourth factor after the dimensionality reduction of the spectral data by the principal component analysis, X ₅ is the score of the fifth factor after the dimensionality reduction of the spectral data by the principal component analysis, X ₆ is The score of the sixth factor after the dimensionality reduction of spectral data by principal component analysis, X ₇ is the score of the seventh factor after dimensionality reduction of spectral data by principal component analysis, X ₈ is the score of the eighth factor after dimensionality reduction of spectral data by principal component analysis, X ₉ is the score of the ninth factor after the dimensionality reduction of the spectral data by principal component analysis, X ₁₀ is the score of the tenth factor after the dimensionality reduction of the spectral data by the principal component analysis; the spectral data is ^{9411.6-5446.4cm- 1} and 4613.2-4243cm ^-1 data in two spectral intervals.

Under this condition, when the wall breaking rate of Ganoderma lucidum spore powder is in the range of 20.0-99.0%, the correlation between the predicted value of wall breaking rate and the reference value is R ² =99.8, RMSECV=1.39 (see Figure 6).

Table 1 Condition optimization results of partial least squares regression method to detect the wall breaking rate of broken Ganoderma lucidum spore powder

After the successful construction of the wall breaking rate prediction model, when it is necessary to detect the wall breaking rate of the sample to be tested, it is only necessary to use a near-infrared spectrometer to collect the near-infrared spectral data of the sample to be tested, and perform multiple scattering correction on the near-infrared spectral data of the sample to be tested Preprocessing, select the data of the two spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 after preprocessing, under the condition that the factor number is 10, use the above-mentioned established wall breaking rate prediction model to detect the sample to be tested Broken rate.

In this embodiment, 14 samples to be tested are selected, and all 14 samples to be tested are dried at 30°C for 48 hours, and then put into different sample cups respectively; For the samples to be tested in the sample cup, the spectral data of each sample to be tested in the range of 12500-4000cm ^-1 is obtained, and the scanning resolution of the Fourier transform near-infrared spectrometer is 8cm ^-1 ; after scanning 32 times for each sample to be tested take the average.

The near-infrared spectrum data (mean value) of 14 samples to be measured is first carried out multivariate scattering correction pretreatment, with reference to Fig. 7, Fig. 7 shows that the near-infrared spectrum data of the sample to be measured that sample number is P01 is preprocessed by multivariate scattering correction Spectrum after processing; interval A in the figure is the spectral interval of 9411.6-5446.4cm ^-1 , interval B is the spectral interval of 4613.2-4243cm ^-1 . After preprocessing the near-infrared spectral data of the sample to be tested, the data located in the spectral intervals of 9411.6-5446.4cm ^-1 and 4613.2-4243cm ^-1 were selected from the preprocessed data, and the selection factor was 10, using partial least squares The multiplicative regression method is used to obtain the scores of the first ten factors of the selected spectral data after principal component analysis, that is, X ₁ , X ₂ , ..., X ₁₀ , and X ₁ , X ₂ , ..., X ₁₀ Substituting into formula (3), the wall breaking rate of the sample to be tested can be detected.

In order to verify the accuracy of the detection of the wall breaking rate of the present invention, after the wall breaking rate of the 14 samples to be tested is detected by the present invention, the traditional blood counting plate method is used to detect the wall breaking rate of the 14 samples to be tested. The test result is the reference value of the wall breaking rate, and the test results of the two methods are shown in Table 2. The results show that the wall breaking rates measured by the two methods are very close, and the relative deviation is between 0.7-8.6%, which shows that the non-destructive testing method established by the invention is accurate and reliable.

Table 2 Analysis results of wall breaking rate of samples to be tested

Claims (6)

1. a method for Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate, is characterized in that, comprises the steps:

A, employing near infrared spectrometer gather the near infrared spectrum data of sample to be tested;

B, multiplicative scatter correction pre-service is carried out to the near infrared spectrum data of described sample to be tested;

C, choose 9411.6-5446.4cm after pre-service ^-1and 4613.2-4243cm ^-1the data of two spectrum ranges, under being the condition of 10 because of subnumber, adopt the sporoderm-broken rate forecast model set up according to partial least-squares regression method to detect the sporoderm-broken rate of described sample to be tested;

Described sporoderm-broken rate forecast model in described step c is:

Y＝0.06866×X ₁+0.046809×X ₂+0.019892×X ₃+0.125289×X ₄+0.06593×X ₅+0.019621×X ₆+0.053336×X ₇+0.018258×X ₈+0.094993×X ₉+0.079206×X ₁₀

In formula: Y is sporoderm-broken rate, X ₁for spectroscopic data factor I score after principal component analysis (PCA) dimensionality reduction, X ₂for spectroscopic data factor Ⅱ score after principal component analysis (PCA) dimensionality reduction, X ₃for spectroscopic data factor III score after principal component analysis (PCA) dimensionality reduction, X ₄for spectroscopic data CA++ score after principal component analysis (PCA) dimensionality reduction, X ₅for spectroscopic data accelerator factor score after principal component analysis (PCA) dimensionality reduction, X ₆for spectroscopic data factor Va score after principal component analysis (PCA) dimensionality reduction, X ₇for spectroscopic data factor VII score after principal component analysis (PCA) dimensionality reduction, X ₈for spectroscopic data Factor VIII score after principal component analysis (PCA) dimensionality reduction, X ₉for spectroscopic data factor IX score after principal component analysis (PCA) dimensionality reduction, X ₁₀for spectroscopic data factor X score after principal component analysis (PCA) dimensionality reduction; Described spectroscopic data is near infrared spectrum data 9411.6-5446.4cm after pretreatment ^-1and 4613.2-4243cm ^-1the data of two spectrum ranges.

2. the method for Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate according to claim 1, it is characterized in that, described step a specifically comprises the steps:

A1, described sample to be tested to be dried 48 hours at 30 DEG C, then load in sample cup;

A2, employing Fourier transform near infrared instrument scan sample to be tested with diffuse reflectance mode, obtain sample to be tested at 12500-4000cm ^-1spectroscopic data in scope; The scanning resolution of described Fourier transform near infrared instrument is 8cm ^-1;

Average after a3, scanning testing sample 32 times.

3. the method for Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate according to claim 1, is characterized in that, the concrete construction method of sporoderm-broken rate forecast model described in described step c is as follows:

C1, choose some ganoderma spove powders with different sporoderm-broken rate as the training sample set up needed for sporoderm-broken rate forecast model;

C2, employing near infrared spectrometer gather the near infrared spectrum data of described training sample;

C3, blood counting chamber method is adopted to detect the sporoderm-broken rate of described training sample, using as the training sample sporoderm-broken rate reference value set up needed for sporoderm-broken rate forecast model;

C4, multiplicative scatter correction pre-service is carried out to the near infrared spectrum data of described training sample, choose 9411.6-5446.4cm after pre-service ^-1and 4613.2-4243cm ^-1the data of two spectrum ranges, under being the condition of 10 because of subnumber, in conjunction with described training sample sporoderm-broken rate reference value, adopt partial least-squares regression method to set up sporoderm-broken rate forecast model.

4. the method for Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate according to claim 3, is characterized in that, 9411.6-5446.4cm in described step c4 ^-1and 4613.2-4243cm ^-1two spectrum ranges to choose process specific as follows:

According to the load diagram of first three factor of described near infrared spectrum data principal component analysis (PCA), select several spectrum ranges that load value is larger, draw 9411.6-5446.4cm by optimum choice afterwards ^-1and 4613.2-4243cm ^-1two spectrum ranges.

5. the method for Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate according to claim 3, it is characterized in that, described step c2 specifically comprises the steps:

C21, described training sample to be dried 48 hours at 30 DEG C, then load in sample cup;

C22, employing Fourier transform near infrared instrument scan training sample with diffuse reflectance mode, obtain each training sample at 12500-4000cm ^-1spectroscopic data in scope; The scanning resolution of described Fourier transform near infrared instrument is 8cm ^-1;

C23, to average to after each training Sample Scan 32 times.

6. the method for Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate according to claim 3, it is characterized in that, described step c3 specifically comprises the steps:

C31, take non-ganoderma spove powder 0.01g, 0.02g, 0.03g, 0.04g and 0.05g dry 5h at 60 DEG C after respectively, be then respectively charged in 5 color comparison tubes;

C32, respectively take 5 parts through grinding after again through 100 mesh sieves screening after each 1.25g of sucrose powder, 5 parts of sucrose powder are added respectively in 5 color comparison tubes in described step c31, make the lucidum spore powder in the sucrose powder that adds and corresponding color comparison tube be mixed to color and luster homogeneous;

C33, in each color comparison tube, add distilled water to dissolve sucrose powder in corresponding color comparison tube and lucidum spore powder, backward each color comparison tube in add 0.2mL Tween 80, then with distilled water, each color comparison tube is settled to 25mL;

C34, at room temperature make each color comparison tube ultrasonic vibration 30min, period carries out manually rocking of several times every 10min to each color comparison tube, and the lucidum spore powder in each color comparison tube is fully disperseed;

C35, in each color comparison tube, draw 8.0 μ L lucidum spore powder suspending liquid respectively in the edge of 5 different cover glasses, utilize and inhale rainbow phenomenon and makes in the blood counting chamber grid below corresponding lid slide, to be full of corresponding lucidum spore powder suspending liquid;

C36, to leave standstill after 30s, adopt the optical microscope of 400 times of enlargement factors to add up the number containing complete lucidum spore powder in 4 drift angles and central authorities on each blood counting chamber totally 5 middle lattice respectively, average after statistics 5 times is observed to each blood counting chamber;

C37, with the quality of take in described step c31 5 parts of non-ganoderma spove powders for horizontal ordinate, to contain the number of complete lucidum spore powder in 5 middle lattice on each blood counting chamber added up in described step c36 for ordinate, make the typical curve of non-ganoderma spove powder quality and number;

C38, respectively take each at 60 DEG C, dry 5h after training sample 0.04g, put into different color comparison tubes; The number containing complete lucidum spore powder in 5 middle lattice on blood counting chamber corresponding to each training sample is added up respectively according to described step c32 ~ c36;

The number N of the complete lucidum spore powder of search quality corresponding to the non-ganoderma spove powder of 0.04g in c39, the typical curve drawn from described step c37 _a, according to formula

$X=(1-\frac{N_B}{N_A})\&times;100\%$

Calculate the sporoderm-broken rate of each training sample; In formula: X is the sporoderm-broken rate of training sample, N _bfor the number containing complete lucidum spore powder in 5 middle lattice on the blood counting chamber that the training sample added up in described step c38 is corresponding.