CN102768195A - Method for quickly detecting moisture content of cordyceps mycelia powder - Google Patents

Method for quickly detecting moisture content of cordyceps mycelia powder Download PDF

Info

Publication number
CN102768195A
CN102768195A CN2012102277522A CN201210227752A CN102768195A CN 102768195 A CN102768195 A CN 102768195A CN 2012102277522 A CN2012102277522 A CN 2012102277522A CN 201210227752 A CN201210227752 A CN 201210227752A CN 102768195 A CN102768195 A CN 102768195A
Authority
CN
China
Prior art keywords
cordyceps
moisture
sample
near infrared
infrared spectrum
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN2012102277522A
Other languages
Chinese (zh)
Inventor
徐宁
张昀
何勇
魏萱
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Zhongmei Huadong Pharmaceutical Co Ltd
Original Assignee
Hangzhou Zhongmei Huadong Pharmaceutical Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hangzhou Zhongmei Huadong Pharmaceutical Co Ltd filed Critical Hangzhou Zhongmei Huadong Pharmaceutical Co Ltd
Priority to CN2012102277522A priority Critical patent/CN102768195A/en
Publication of CN102768195A publication Critical patent/CN102768195A/en
Pending legal-status Critical Current

Links

Images

Landscapes

  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention discloses a method for quickly detecting moisture content of cordyceps mycelia powder. The method comprises the following steps of: taking a cordyceps mycelia powder sample, and measuring the moisture content; acquiring and pre-processing a near infrared spectrum of the cordyceps mycelia powder sample, and establishing a correcting model between the moisture content of the cordyceps mycelia powder sample and the pre-processed near infrared spectrum by adopting partial least squares (PLS); acquiring and pre-processing a near infrared spectrum of a sample to be detected, and calculating the moisture content of the sample to be detected through the correcting model, wherein the spectral scanning bands for acquiring the near infrared spectrums are 4,277.63-4,316.20cm<-1>, 4,887.06-4,941.07cm<-1>, 5,056.78-5,172.50cm<-1> and 5,218.78399-5,303.64cm<-1>. By modeling with characteristic bands, the method is low in operation quantity and high in modeling speed; and the established PLS regression model is high in reliability and good in predicting effect, and can realize quick, accurate and nondestructive detection of the moisture content of the cordyceps mycelia powder.

Description

A kind of cordyceps moisture method for quick
Technical field
The present invention relates to cordyceps composition detection field, relate in particular to a kind of cordyceps moisture method for quick.
Background technology
Because the natural cs producing region has limitation, output is rare, adds that unauthorized and excessive mining for a long time causes ecological disruption serious, and wild Chinese caterpillar fungus can not satisfy the growing market demand far away.Fermentation cordyceps is to separate from the Chinese medicine protection kind of Chinese caterpillar fungus parasitical fungi through the biotechnology fermenting and producing; Also be a kind of important function health food simultaneously, be rich in adenosine, D-sweet mellow wine, several amino acids isoreactivity material, have important biomolecules such as antitumor, antifatigue and raising immunity and learn function (Pan B S; Lin C Y; Huang B M.Evid Based Complement Alternat Med, 2011,2011:750468.; Wang Z-M, Peng X, Lee K-L D, et al.Food Chemistry, 2011,125 (2): 637-643.).Cordyceps sinensis belongs to Ascomycetes, Clavicipitaceae, Cordyceps, and a kind of in the Chinese caterpillar fungus is the distinctive famous and precious strong tonic Chinese medicine material of China.The fermentation Chinese caterpillar fungus bacterium powder is for separating the fungi asexual generation bacterial classification obtain through the mycelial dried powder of liquid fermentation and culture gained from fresh Cordyceps sinensis.
At present; The main dry weight-loss method that adopts carries out detection by quantitative to the moisture of fermentation cordyceps (comprising the fermentation Chinese caterpillar fungus bacterium powder); Though do not destroy sample; But the mensuration process is very time-consuming, only can carry out sampling Detection, can't realize that enterprise requires the requirement that product quality information is obtained on a large scale.
Got into since 21 century; Overseas utilization near infrared spectrum (Near Infrared Spectroscopy; NIRS) analytical technology is increasing to the research of the detection of various compositions in food, agricultural byproducts, the oil etc. and product quality influence factor; Domestic researcher also uses the principal ingredient sweet mellow wine of NIRS analytical technology to fermentation cordyceps, Cordyceps militaris zymophyte powder gradually, and amino acid and polysaccharide carry out detection by quantitative and be used for the exploratory development of Optimizing Conditions of Fermentation (WANG Di, ZHANG Yuan-li; MENG Qin-fan.Acta Optica Sinica, 2009 (10): 2795-2799.; YANG Nan-lin, CHEN Yi-yu.analytical chemistry (analytical chemistry), 2003 (06): 664-668.; ZHAO Chen, QU Hai-bin, CHEN Yi-yu.Spectroscopy and Spectral Analysis, 2004 (01): 50-53.).But these researchs all only use NIRS to active component carry out simple quantitatively, and about the NIRS detection by quantitative of moisture in the fermentation cordyceps as yet the someone relate to, and also nobody further carries out analyses such as wavelength selection.The application for a patent for invention of publication number CN101413885A discloses a kind of near-infrared spectrum method of rapidly quantifying honey quality, comprising: collect honey sample; Obtain moisture, glucose, fructose content and the amylase value of honey sample with the conventional chemical method; Gather the near infrared light spectrogram of honey sample; Said near infrared light spectrogram is carried out pre-service, eliminate disturbing factor, chosen wavelength range; Set up moisture, glucose, fructose content and amylase value and the calibration model between the near infrared spectrum and the check of honey sample respectively; Gather the near infrared spectrum of testing sample; Moisture, glucose, fructose content and amylase value with institute's established model prediction testing sample.This method can be predicted moisture, glucose, fructose content and the amylase value in the honey simultaneously, but because what adopt is all band scanning, still undesirable to the prediction effect of moisture.
Tend to influence its quality if the moisture in the fermentation cordyceps exceeds standard, cause deterioration failure.If can solve the problem of detection by quantitative quick and precisely, the quality control in the high volume production process is had crucial meaning.
Summary of the invention
The invention provides a kind of cordyceps moisture method for quick, be used for the cordyceps moisture fast, harmless, accurately detect, solved problems such as existing detection method complex operation, consuming time, consumption power, cost height, contaminated environment.
A kind of cordyceps moisture method for quick comprises:
(1) gets the cordyceps sample, measure moisture;
(2) gather the near infrared spectrum of cordyceps sample and carry out pre-service, adopt PLS to set up the calibration model between the moisture and pretreated near infrared spectrum in the cordyceps sample;
(3) gather the near infrared spectrum of sample to be tested and carry out pre-service, calculate the moisture of sample to be tested through calibration model;
Wherein, the spectral scan wave band of collection near infrared spectrum is 4277.63-4316.20cm -1, 4887.06-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78399-5303.64cm -1
In the electromagnetic wavelength 800-2500nm scope is the frequency multiplication and the combined spectral band of material molecule vibrational spectrum, thereby has comprised the abundant information of material composition and molecular structure, can be used for the quantitative measurement of component content.Containing hydrogen group (O-H) has very strong absorption to light wave, through the near infrared spectrum of scanning samples, can obtain the characteristic information of hydrone group in the sample, is used for the quantitative of moisture.
In the step (1); Described cordyceps can (comprise separate sources bacterial classification such as cordyceps sinensis (Berk) sacc for artificial ferment cordyceps sinensis bacterium filament; Paecilomyces hepiali Chen & Dai; Cephalosporiun sinensis.Chen.Sp.nov, Mortierella SP, Cordyceps militaris kind [Cordyceps militaris (L.Fr) Link]) through powder dry, that obtain after pulverizing; Also can for natural cs through powder dry, that obtain after pulverizing.Natural cs is different Chinese caterpillar fungus Pseudomonas fungies infect bat moth (Lepidoptera Hepialidae bat Hepialus insect) through variety of way a larva; Carry out parasitic life with the organic substance in its body as the nutrient energy source; Final mycelium kink and formation stroma stretch out host's shell, the biosome of a kind of special entomogenous fungi symbiosis of formation.
When adopting artificial fermentation's method; Described cordyceps is a fermentation cordyceps; Can prepare through following method: activated Chinese caterpillar fungus strain is inoculated into carries out fermented and cultured in the fluid nutrient medium; Separate obtaining mycelium after the fermented and cultured, mycelium obtains fermentation cordyceps after drying, pulverizing; Also the commercially available prod be can be, ferment cordyceps sinensis mycelium powder raw material, capsule preparations content, Cordyceps militaris mycelium powder etc. comprised.
Cordyceps sinensis is a kind of in the Chinese caterpillar fungus, and described fermentation cordyceps is preferably the fermentation Chinese caterpillar fungus bacterium powder; The fermentation Chinese caterpillar fungus bacterium powder of Zhongmei Huadong Pharmaceutical Co., Ltd. Hangzhou's production more preferably; This product is for separating the fungi asexual generation bacterial classification obtain through the mycelial dried powder of liquid fermentation and culture gained from fresh Cordyceps sinensis; Be to prepare through special fermentation, drying process; Mycelium and fermentation liquor are more thorough discretely, the higher and steady quality of active component content, and moisture difference is less.
The particle diameter of described cordyceps is preferably the 80-100 order, and the calibration model prediction effect of structure is better.
Sample size is big more, and the reliability of constructed model is high more; But crossing conference, sample size increases working strength.The quantity of described cordyceps sample is preferably more than 162.
In order to make up calibration model and to predict, can evenly extract total sample 2/3 as calibration set, all the other are remaining 1/3 as forecast set, and guarantee that the forecast set CONCENTRATION DISTRIBUTION is even, and scope is no more than calibration set.
The fermentation Chinese caterpillar fungus bacterium powder steady quality that Zhongmei Huadong Pharmaceutical Co., Ltd. Hangzhou produces, moisture difference is less between sample, and promptly the sample gradient is narrower, is unfavorable for model construction.But adopt method of the present invention, can obtain prediction effect model preferably.The maximal value of described cordyceps sample moisture and the difference between the minimum value can be more than 1.5%.
The method of measuring moisture can be dry weight-loss method, according to the record in the Pharmacopoeia of the People's Republic of China 2000 version (two ones).
In the step (2), adopt ft-nir spectrometer when gathering near infrared spectrum; Preferably, adopt the multi-functional ft-nir spectrometer of MPA type, spectral range: liquid transmission, fibre-optical probe, integrating sphere (12800-4000cm -1), solid passes through diffusion (12800-5700cm -1), resolution: 2cm -1(0.3nm 1,250nm place), wave number accuracy: be superior to 0.05cm -1, wave number precision: be superior to 0.1cm -1, transmittance precision: be superior to 0.1%T.Adopt this near infrared spectrometer, can gather 2203 wavelength information, be beneficial to modeling.
Gathered the influence that factors such as fashionable dress appearance difference, sample is inhomogeneous are brought in order to reduce spectroscopic data, described pre-service can be adopted one or more in level and smooth (S-G the is level and smooth) processing of Savitzky-Golay convolution, normalization (normalization) processing, polynary scatter correction (MSC), baseline correction (baseline) and the standard normal variable conversion (SNV).Can simplify, strengthen model through pre-service, wherein, the S-G smoothing processing comes down to a kind of method of weighted mean, is to eliminate the most frequently used a kind of method of noise; Normalization often is used to proofread and correct the spectrum change that is caused by small light path difference.MSC is mainly used in and eliminates the diffuse transmission influence that distribution of particles is inhomogeneous and grain size produces; Baseline correction is mainly used in the deduction instrumental background or drifts about to the influence of signal; The purpose of SNV and MSC is basic identical, mainly is to be used for eliminating the influence to spectrum of solid particle size, surface scattering and change in optical path length.
Preferably, Savitzky-Golay convolution smoothing processing is adopted in described pre-service.This preprocess method is adopted in evidence, and calibration model is best to the prediction effect of moisture in the sample.
Can adopt a kind of in PLS (PLS), PCA (PCR) and the artificial neural network method (ANN) when setting up calibration model.The present invention adopts described PLS can use full spectrum or partial spectrum data; And data matrix decomposes and recurrence is combined into a step alternately; The feature value vector that obtains is relevant with tested component or character; Rather than relevant with the variable of variation maximum in the data matrix, relatively be applicable to the small sample multivariate data analysis, can be used in complicated analysis system.
Set up in the PLS calibration model process, carry out validation-cross through leaving-one method, when checking collection root-mean-square error (RMSEV) reach minimum and during the r2 maximal value employed main gene number be considered to optimum.They as input variable, are set up the PLS calibration model, predict.The foundation of calibration model can adopt Unscramber software to carry out.
Can adopt all band scanning when gathering near infrared spectrum data, the spectral scan wave band can be 12493.45-3999.91cm -1(instrument parameter is 4000-12500cm -1, be that the MPA instrument can collect full spectral limit parameter).In order effectively to select the wave band that includes characteristic information, improve arithmetic speed, can adopt related coefficient and regression coefficient combined techniques to carry out choosing of characteristic wave bands, characteristic wavelength choose the detection effect that can influence composition to be measured largely.
Correlation coefficient process is in polynary correction, and through the reflectivity vector that each wavelength is corresponding in the spectrum battle array is carried out correlation calculations with concentration vector to be measured, bigger its information of corresponding related coefficient is corresponding many more.Among the present invention, according to the PLS regression coefficient figure under all band, it is needed characteristic wavelength greater than 0.2 wavelength that moisture is set the related coefficient absolute value, and selected wave band is 5303.64-3999.91cm -1
The regression coefficient method is in the index of regretional analysis metrics dependent variable to the interdependent degree of independent variable; It is multinomial coefficient or function parameters when the function through polynomial expression or other band parameter reaches the best-fit degree to independent variable and dependent variable, can select the higher wavelength of interdependent degree in view of the above.Among the present invention,, be 6 * 10 to the moisture setting threshold according to the PLS regression coefficient figure under all band -3, selected wave band is respectively 4277.63-4316.20cm -1, 4887.07-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78-5508.07cm -1Four wave bands.
Characteristic wave bands when choosing the crossing part of these several spectrum ranges as modeling, the spectral scan wave band of promptly gathering near infrared spectrum is 4277.63-4316.20cm -1, 4887.06-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78399-5303.64cm -1Adopt this characteristic wave bands, can not only effectively improve prediction effect, and can significantly reduce the operand in the modeling, improve modeling speed, and foundation is provided for the exploitation of detecting instrument.
The quality of model performance is a standard with the accurate differentiation rate to the forecast set sample, can adopt prediction related coefficient (r), predicted root mean square error (RMSEP), remaining predicted deviation (RPD) evaluation model performance.The r value is high more, and the RMSEP value is more little, explains that model performance is good more; The RPD value is the ratio of chemical score standard deviation and RMSEP, and its value is considered to high value and the low value that model can be distinguished variable between 1.5~2.0, shows 2.0~2.5 and can carry out quantitative forecast, surpasses 2.5 and shows that good precision of prediction is arranged.
The factor number of described calibration model adopts the leaving-one method cross validation, i.e. get one of them variable in the process of modeling at every turn and be used as checking, remaining modeling.
In the step (3), described near infrared spectra collection and preprocess method can be with reference to the associative operations in the step (2).
The present invention adopts near-infrared spectrum analysis to combine stoichiometry to learn a skill moisture in the cordyceps have been carried out quantitative test, with the Savitzky-Golay smoothing processing near infrared spectrum has been carried out pre-service, and choose 4277.63-4316.20cm -1, 4887.06-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78399-5303.64cm -1Four characteristic wave bands have been set up the PLS regression model.
The inventive method is applicable to the sample that concentration gradient is narrow, carries out modeling through selected characteristic wave bands, and operand is little in the modeling process, and modeling speed is fast; And the PLS regression model validity of being set up is good, reliability is high, and is good to the prediction effect of moisture in the sample to be tested, can realize quick, accurate, Non-Destructive Testing to moisture in the cordyceps.The inventive method can realize the integrated management of analysis data and the real-time monitoring of production run through integrating with Process Control System; Spectral instrument through optical fiber joint detection probe, can be used for the real-time detection of sample in the fermentation cordyceps production run in conjunction with this method, for the near infrared online detection in the production practices lays the foundation.
Description of drawings
Fig. 1 is a full spectrum samples averaged spectrum curve map among the embodiment 1;
Fig. 2 is moisture regression coefficient and related coefficient figure among the embodiment 1, and wherein, figure (a) is moisture PLS regression coefficient figure, and figure (b) is the related coefficient figure of moisture;
Fig. 3 is a full spectrum samples averaged spectrum curve map among the embodiment 2.
Embodiment
Moisture detects in the embodiment 1 fermentation Chinese caterpillar fungus bacterium powder
1, specimen preparation
162 fermentations of random collecting Chinese caterpillar fungus bacterium powder sample is used to set up whole data set.More representative and the diversity for the sample that makes collection, the different batches that sample provides from Zhongmei Huadong Pharmaceutical Co., Ltd. Hangzhou respectively (20 batches) fermentation Chinese caterpillar fungus bacterium powder makes institute's established model have better adaptability and robustness.
Sample is preserved with the temperature of (4 ± 1 ℃); Each sample is placed in the unified plastic package bag, and whole experiment is carried out under room temperature 18-20 ℃.
2, adopt the chemical gauging moisture
According to the Pharmacopoeia of the People's Republic of China middle dry weight-loss method of describing of version (two ones) in 2000; Measured value is the content value (g) in the 100 gram samples.Measure the result and see table 1.
Sort 2/3 (amounting to 108) that the back evenly extracts gross sample as calibration set according to the moisture measurement value, all the other be left 1/3 as forecast set, guarantee that the forecast set CONCENTRATION DISTRIBUTION is even, and scope is no more than calibration set.
The moisture of table 1 modeling collection and forecast set (g/100g)
Figure BDA00001832307100071
3, based on full wave PLS (PLS) modeling
The multi-functional ft-nir spectrometer of MPA type (BRUKER, Germany), spectral range: liquid transmission, fibre-optical probe, integrating sphere (12800-4000cm -1), solid passes through diffusion (12800-5700cm -1), resolution: 2cm -1(0.3nm 1,250nm place), wave number accuracy: be superior to 0.05cm -1, wave number precision: be superior to 0.1cm -1, transmittance precision: be superior to 0.1%T.All stoichiometry analytic approachs are carried out by Unscrambler version 9.6 (CAMO PROCESS AS, Norway) and MATLAB 7.1 (The Math Works, Natick, the U.S.).OPUS 5.5 softwares (BRUKER, Germany).
(1) spectral measurement and chemometrics application
The spectrum of each sample is the mean value of continuous 32 scannings.The spectral absorption value of all samples is averaged, obtain the curve of spectrum as shown in Figure 1.Use following 5 kinds of preprocessing procedures, comprise Savitzky-Golay smoothing processing (S-G smoothing processing), normalization processing (normalization), polynary scatter correction (MSC), baseline calibration (baseline) and variable standardization (SNV).
Water is owing to the association of hydrogen bond, and its absorption peak all is a broad band.The one-level frequency multiplication of O-H stretching vibration and secondary frequency multiplication absorb and appear at 6944cm respectively -1And 10420cm -1Neighbouring (Vagnini M, Miliani C, Cartechini L, et a1.Analytical and Bioanalytical Chemistry, 2009,395 (7): 2107-2118.).Its sum of fundamental frequencies absorption band mainly contains two, and is stronger at 5155cm -1, more weak at 8197cm -1Near, but the ionic compound in the water can influence the NIRS of water with different ways.
(2) all band modeling
After pre-service, set up the PLS model of spectroscopic data and fermentation Chinese caterpillar fungus bacterium powder moisture.
The full spectrum PLS of moisture model result in the table 2 fermentation Chinese caterpillar fungus bacterium powder
Figure BDA00001832307100072
Moisture is best through the model prediction effect of setting up after the S-G smoothing processing; (WANG Di such as this and Wang Di; ZHANG Yuan-li; MENG Qin-fan.Acta Optica Sinica, 2009 (10): 2795-2799.) the observed result of content of effective is consistent in the fast measuring Cordyceps militaris.
4, based on PLS (PLS) modeling of characteristic wave bands
The MPA spectrometer is gathered altogether has 2203 wavelength information, in order effectively to select the wave band that includes characteristic information, improves arithmetic speed, adopts regression coefficient and related coefficient combined techniques, carries out choosing of characteristic wave bands.
Can obtain the PLS regression coefficient figure (see the figure a Fig. 2, v is a wave number, and Reg is a related coefficient) of moisture from all band modeling, at wave number 4107.91-3999.91cm -1And 9025.83-12493.45cm -1Noise is bigger in these two wave bands, does not choose the wavelength in these two wave bands.According to regression coefficient figure, be 6 * 10 to the moisture setting threshold -3, selected wave band is respectively: 4277.63-4316.20cm -1, 4887.07-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78-5508.07cm -1Totally four wave bands.
In Matlab, moisture is carried out correlation analysis, obtain related coefficient figure (see the figure b among Fig. 2, v is a wave number, and Rel is a related coefficient).According to result of calculation, it is needed characteristic wavelength greater than 0.2 wavelength that moisture is set the related coefficient absolute value, knows that by figure wavelength band is 5303.64-3999.91cm -1, meet this setting threshold.
Characteristic wave bands when only choosing part that these several spectrum ranges intersect and being modeling is 4277.63-4316.20cm -1, 4887.06-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78399-5303.64cm -1Four wave bands are set up the PLS model, and it is listed like table 3 to predict the outcome.
Table 3 selects wavelength to carry out moisture PLS model prediction result after the different pre-service
Figure BDA00001832307100082
Concerning the moisture original spectrum, use characteristic wavelength modeling effect is effective not as all band modeling.But the same with all band modeling is that level and smooth pre-service can obtain optimum prediction effect through the Savitzky-Golay convolution.
After utilizing moisture selected characteristic wavelength to set up the PLS model; Than all band PLS model raising is in various degree arranged all through different pretreated prediction effects: from the optimum (r=0.8683 of all band modeling; RMSEP=0.001999; RPD=1.9744) bring up to the characteristic wavelength modeling optimum (r=0.8691, RMSEP=0.001934, RPD=2.0407).
Moisture detects in embodiment 2 fermentation cordyceps
1, specimen preparation
162 fermentation cordyceps samples of random collecting are used to set up whole data set.Sample is respectively from Jiangxi Guoyao Co.,Ltd and Changxing Pharmaceutical Co., Ltd's different batches (20 batches) fermentation cordyceps.Sample experiment condition and preservation are all with reference to embodiment 1.
2, adopt the chemical gauging moisture
The mensuration of calibration set and forecast set setting, moisture is with reference to embodiment 1; Sort even 2/3 (the amounting to 60 samples) of extracting total sample in back as calibration set according to the moisture measurement value; All the other are remaining 1/3 as forecast set; Guarantee that the forecast set CONCENTRATION DISTRIBUTION is even, and scope is no more than calibration set, measures the result and see table 4.
The moisture of table 4 modeling collection and forecast set (g/100g)
Figure BDA00001832307100091
3, utilize the characteristic wave bands of selecting to carry out PLS (PLS) modeling
Near infrared spectroscopy instrument and use software are all with reference to embodiment 1.
(1) spectral measurement and chemometrics application
The spectrum of each sample is the mean value of continuous 32 scannings.The spectral absorption value of all samples is averaged, obtain the curve of spectrum as shown in Figure 3.
(2) characteristic wave bands modeling
Utilize characteristic wave bands that spectrum is not carried out pre-service and pre-service respectively and set up the PLS model, utilize same characteristic features wave band 4277.63-4316.20cm among the embodiment 1 -1, 4887.07-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78-5303.64cm -1Totally four wave bands are set up spectroscopic data and the PLS model of the Chinese caterpillar fungus bacterium powder moisture of fermenting.
Table 5 selects wavelength to carry out moisture PLS model prediction result after the different pre-service
Figure BDA00001832307100101
After utilizing the selected characteristic wavelength of moisture to set up the PLS model, no matter whether adopt pre-service, its prediction effect is all fine: optimum be (r=0.9847, RMSEP=0.01214, RPD=3.3246).Explain that this characteristic wave bands is applicable to the prediction of moisture in the fermentation cordyceps.

Claims (9)

1. cordyceps moisture method for quick comprises:
(1) gets the cordyceps sample, measure moisture;
(2) gather the near infrared spectrum of cordyceps sample and carry out pre-service, adopt PLS to set up the calibration model between the moisture and pretreated near infrared spectrum in the cordyceps sample;
(3) gather the near infrared spectrum of sample to be tested and carry out pre-service, calculate the moisture of sample to be tested through calibration model;
Wherein, the spectral scan wave band of collection near infrared spectrum is 4277.63-4316.20cm -1, 4887.06-4941.07cm -1, 5056.78-5172.50cm -1And 5218.78399-5303.64cm -1
2. cordyceps moisture method for quick according to claim 1 is characterized in that described cordyceps is a fermentation cordyceps.
3. cordyceps moisture method for quick according to claim 2 is characterized in that, described cordyceps is the fermentation Chinese caterpillar fungus bacterium powder.
4. cordyceps moisture method for quick according to claim 1 is characterized in that the particle diameter of described cordyceps is the 80-100 order.
5. cordyceps moisture method for quick according to claim 1 is characterized in that the quantity of described cordyceps sample is more than 162.
6. cordyceps moisture method for quick according to claim 5 is characterized in that the maximal value of described cordyceps sample moisture and the difference between the minimum value are more than 1.5%.
7. cordyceps moisture method for quick according to claim 1 is characterized in that, in the step (1), the method for measuring moisture is a dry weight-loss method.
8. cordyceps moisture method for quick according to claim 1; It is characterized in that; In step (2) or (3), one or more in Savitzky-Golay convolution smoothing processing, normalization processing, polynary scatter correction, baseline correction and the standard normal variable conversion are adopted in described pre-service.
9. cordyceps moisture method for quick according to claim 8 is characterized in that, Savitzky-Golay convolution smoothing processing is adopted in described pre-service.
CN2012102277522A 2012-06-29 2012-06-29 Method for quickly detecting moisture content of cordyceps mycelia powder Pending CN102768195A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2012102277522A CN102768195A (en) 2012-06-29 2012-06-29 Method for quickly detecting moisture content of cordyceps mycelia powder

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2012102277522A CN102768195A (en) 2012-06-29 2012-06-29 Method for quickly detecting moisture content of cordyceps mycelia powder

Publications (1)

Publication Number Publication Date
CN102768195A true CN102768195A (en) 2012-11-07

Family

ID=47095671

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2012102277522A Pending CN102768195A (en) 2012-06-29 2012-06-29 Method for quickly detecting moisture content of cordyceps mycelia powder

Country Status (1)

Country Link
CN (1) CN102768195A (en)

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103048278A (en) * 2012-12-25 2013-04-17 浙江工业大学 On-line measurement method for water content of mechanically-parched Longjing tea leaves
CN103175806A (en) * 2013-03-14 2013-06-26 公安部天津消防研究所 Method for detecting moisture content of dry powder extinguishing agents based on near infrared spectroscopy analysis
CN103411915A (en) * 2013-07-26 2013-11-27 江西济民可信金水宝制药有限公司 Infrared spectroscopy identification method of traditional Chinese medicine preparation-Jinshuibao capsule
CN103969211A (en) * 2013-01-28 2014-08-06 广州白云山和记黄埔中药有限公司 A method for detecting moisture content of compound salvia tablets using near infrared spectroscopy
CN103983605A (en) * 2014-05-28 2014-08-13 河北大学 Method for rapidly and nondestructively detecting sporoderm-breaking rate of reishi shell-broken spore powder
CN104390880A (en) * 2014-11-02 2015-03-04 中南林业科技大学 Method for rapidly detecting moisture content of wood
CN104949936A (en) * 2015-07-13 2015-09-30 东北大学 Sample component determination method based on optimizing partial least squares regression model
CN105548026A (en) * 2015-12-09 2016-05-04 无锡济民可信山禾药业股份有限公司 Quick detection method for quality control of radix curcumae medicinal material
WO2016155650A1 (en) * 2015-03-31 2016-10-06 山东大学 Method for rapid determination of water content in human coagulation factor viii final product
CN106716109A (en) * 2014-07-30 2017-05-24 史密斯探测公司 Estimation of water interference for spectral correction
CN109374562A (en) * 2018-10-23 2019-02-22 成都奕阳现代科技有限公司 The method of quality control of fermentation is tedded based on the lossless bean cotyledon examined fastly
CN110658157A (en) * 2019-10-21 2020-01-07 江西中医药大学 Quality control method for total polysaccharide in production process of fermented cordyceps sinensis powder by near-infrared analysis
CN112285057A (en) * 2020-11-27 2021-01-29 常州金坛江南制粉有限公司 Method for rapidly detecting water content of water-milled glutinous rice flour based on near infrared spectrum technology
CN113607681A (en) * 2021-07-19 2021-11-05 黑龙江八一农垦大学 Pleurotus eryngii mycelium detection method and device, electronic equipment and storage medium
CN113834795A (en) * 2020-06-08 2021-12-24 上海医药集团股份有限公司 Hydroxychloroquine sulfate particle moisture near infrared spectrum online quantitative model and establishing method and detection method thereof
CN114935555A (en) * 2022-06-28 2022-08-23 中国农业科学院农产品加工研究所 Rapid nondestructive testing method for flour water absorption

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19520035C1 (en) * 1995-03-31 1996-11-07 Gta Sensorik Gmbh Contactless measurement of surface moisture of objects
CN101078685A (en) * 2007-05-17 2007-11-28 常熟雷允上制药有限公司 Method for quickly on-line detection of traditional Chinese medicine Kuhuang injection effective ingredient using near infra red spectrum
CN101299022A (en) * 2008-06-20 2008-11-05 河南中医学院 Method for evaluating Chinese medicine comprehensive quality using near infrared spectra technique
CN101413885A (en) * 2008-11-28 2009-04-22 中国农业科学院蜜蜂研究所 Near-infrared spectrum method for rapidly quantifying honey quality
KR20120067809A (en) * 2010-12-16 2012-06-26 대한민국(국가기록원) Analytical method for traditional korean paper by near infrared spectroscopy

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19520035C1 (en) * 1995-03-31 1996-11-07 Gta Sensorik Gmbh Contactless measurement of surface moisture of objects
CN101078685A (en) * 2007-05-17 2007-11-28 常熟雷允上制药有限公司 Method for quickly on-line detection of traditional Chinese medicine Kuhuang injection effective ingredient using near infra red spectrum
CN101299022A (en) * 2008-06-20 2008-11-05 河南中医学院 Method for evaluating Chinese medicine comprehensive quality using near infrared spectra technique
CN101413885A (en) * 2008-11-28 2009-04-22 中国农业科学院蜜蜂研究所 Near-infrared spectrum method for rapidly quantifying honey quality
KR20120067809A (en) * 2010-12-16 2012-06-26 대한민국(국가기록원) Analytical method for traditional korean paper by near infrared spectroscopy

Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103048278A (en) * 2012-12-25 2013-04-17 浙江工业大学 On-line measurement method for water content of mechanically-parched Longjing tea leaves
CN103969211B (en) * 2013-01-28 2016-12-28 广州白云山和记黄埔中药有限公司 A kind of method using near infrared spectrum detection FUFANG DANSHEN PIAN moisture
CN103969211A (en) * 2013-01-28 2014-08-06 广州白云山和记黄埔中药有限公司 A method for detecting moisture content of compound salvia tablets using near infrared spectroscopy
CN103175806A (en) * 2013-03-14 2013-06-26 公安部天津消防研究所 Method for detecting moisture content of dry powder extinguishing agents based on near infrared spectroscopy analysis
CN103411915A (en) * 2013-07-26 2013-11-27 江西济民可信金水宝制药有限公司 Infrared spectroscopy identification method of traditional Chinese medicine preparation-Jinshuibao capsule
CN103983605A (en) * 2014-05-28 2014-08-13 河北大学 Method for rapidly and nondestructively detecting sporoderm-breaking rate of reishi shell-broken spore powder
CN103983605B (en) * 2014-05-28 2016-04-13 河北大学 A kind of method of Fast nondestructive evaluation ganoderma spove powder sporoderm-broken rate
US10942117B2 (en) 2014-07-30 2021-03-09 Smiths Detection Inc. Estimation of water interference for spectral correction
CN106716109B (en) * 2014-07-30 2019-11-05 史密斯探测公司 Water Interference Estimation for spectrum correction
CN106716109A (en) * 2014-07-30 2017-05-24 史密斯探测公司 Estimation of water interference for spectral correction
CN104390880A (en) * 2014-11-02 2015-03-04 中南林业科技大学 Method for rapidly detecting moisture content of wood
WO2016155650A1 (en) * 2015-03-31 2016-10-06 山东大学 Method for rapid determination of water content in human coagulation factor viii final product
CN104949936B (en) * 2015-07-13 2017-10-24 东北大学 Sample component assay method based on optimization Partial Least-Squares Regression Model
CN104949936A (en) * 2015-07-13 2015-09-30 东北大学 Sample component determination method based on optimizing partial least squares regression model
CN105548026A (en) * 2015-12-09 2016-05-04 无锡济民可信山禾药业股份有限公司 Quick detection method for quality control of radix curcumae medicinal material
CN109374562A (en) * 2018-10-23 2019-02-22 成都奕阳现代科技有限公司 The method of quality control of fermentation is tedded based on the lossless bean cotyledon examined fastly
CN110658157A (en) * 2019-10-21 2020-01-07 江西中医药大学 Quality control method for total polysaccharide in production process of fermented cordyceps sinensis powder by near-infrared analysis
CN113834795A (en) * 2020-06-08 2021-12-24 上海医药集团股份有限公司 Hydroxychloroquine sulfate particle moisture near infrared spectrum online quantitative model and establishing method and detection method thereof
CN112285057A (en) * 2020-11-27 2021-01-29 常州金坛江南制粉有限公司 Method for rapidly detecting water content of water-milled glutinous rice flour based on near infrared spectrum technology
CN113607681A (en) * 2021-07-19 2021-11-05 黑龙江八一农垦大学 Pleurotus eryngii mycelium detection method and device, electronic equipment and storage medium
CN114935555A (en) * 2022-06-28 2022-08-23 中国农业科学院农产品加工研究所 Rapid nondestructive testing method for flour water absorption

Similar Documents

Publication Publication Date Title
CN102768195A (en) Method for quickly detecting moisture content of cordyceps mycelia powder
CN103278473B (en) The mensuration of pipering and moisture and method for evaluating quality in white pepper
Liu et al. Discrimination of Pericarpium Citri Reticulatae in different years using Terahertz Time-Domain spectroscopy combined with convolutional neural network
Jiang et al. Rapid determination of pH in solid-state fermentation of wheat straw by FT-NIR spectroscopy and efficient wavelengths selection
CN104048941A (en) Method for quickly measuring content of multiple index components in radix ophiopogonis through near infrared spectroscopy
CN102768194A (en) Method for quickly detecting adenosine content of cordyceps mycelia powder
CN106018335A (en) Method for nondestructively determining content of phytic acid in whole cottonseed based on near infrared spectroscopy
CN107421911A (en) A kind of preprocess method of the soil nitrogen detection based on portable near infrared spectrometer
Ye et al. Application of near-infrared spectroscopy and hyperspectral imaging combined with machine learning algorithms for quality inspection of grape: a review
CN102937575B (en) Watermelon sugar degree rapid modeling method based on secondary spectrum recombination
Maraphum et al. Achieving robustness across different ages and cultivars for an NIRS-PLSR model of fresh cassava root starch and dry matter content
CN101957316A (en) Method for authenticating Xiangshui rice by near-infrared spectroscopy
Ferrara et al. Ripeness prediction in table grape cultivars by using a portable NIR device
Wang et al. Determination of polysaccharide content in shiitake mushroom beverage by NIR spectroscopy combined with machine learning: A comparative analysis
Ejaz et al. Sorghum grains grading for food, feed, and fuel using NIR spectroscopy
Chen et al. Application of infrared spectroscopy combined with chemometrics in mushroom
Ferrara et al. The prediction of ripening parameters in Primitivo wine grape cultivar using a portable NIR device
Luo et al. Quantitative detection of soluble solids content, pH, and total phenol in Cabernet Sauvignon grapes based on near infrared spectroscopy
Yang et al. Rapid detection method of Pleurotus eryngii mycelium based on near infrared spectral characteristics
Zhao et al. Chemometric development using portable molecular vibrational spectrometers for rapid evaluation of AVC (Valsa mali Miyabe et Yamada) infection of apple trees
Yu et al. Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties
Ping et al. Quality Assessment and Ripeness Prediction of Table Grapes Using Visible–Near-Infrared Spectroscopy
Mancini et al. Prediction of soluble solids content by means of NIR spectroscopy and relation with botrytis cinerea tolerance in strawberry cultivars
Zhao et al. Fast detection of the tenderness of mulberry leaves by a portable near-infrared spectrometer with variable selection
CN102608058A (en) Rapid detection method of content of threonine in Cordyceps Mycelia powder

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C12 Rejection of a patent application after its publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20121107