CN102539375A - Straw solid-state fermentation process parameter soft measurement method and device based on near infrared spectrum - Google Patents
Straw solid-state fermentation process parameter soft measurement method and device based on near infrared spectrum Download PDFInfo
- Publication number
- CN102539375A CN102539375A CN2012100048317A CN201210004831A CN102539375A CN 102539375 A CN102539375 A CN 102539375A CN 2012100048317 A CN2012100048317 A CN 2012100048317A CN 201210004831 A CN201210004831 A CN 201210004831A CN 102539375 A CN102539375 A CN 102539375A
- Authority
- CN
- China
- Prior art keywords
- sample
- near infrared
- ferment process
- solid ferment
- state fermentation
- 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
Links
Images
Landscapes
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a straw solid-state fermentation process parameter soft measurement method and a device based on near infrared spectrum. Firstly, a physical and chemical analysis method is adopted for obtaining solid-state fermentation process product sample reference measurement values to form a database, a near infrared spectrometer is used for acquiring spectral data, the acquired spectral data is transmitted to a computer, the computer conducts principal component analysis to the preprocessed spectral data to obtain the eigenvalue information of a principal component score matrix and a spectrum covariance matrix, cumulative variance contribution rate is calculated through an eigenvalue matrix, and first few principal component score vectors of the score matrix with the cumulative variance contribution rate being above 90 percent are extracted as the characteristic variables of a solid-state fermentation process product sample; then the characteristic variables of a solid-state fermentation process product sample are correlated with the database and a partial least square method is adopted for building a multi-parameter soft measurement model; and finally the obtained characteristic variables of the sample to be detected are input into the model for detection to obtain the predicted value of the process parameter index of the sample to be detected. The straw solid-state fermentation process parameter soft measurement method and the device based on near infrared spectrum have the advantages of simplicity and convenience in operation, high detection speed and good repeatability.
Description
Technical field
The present invention relates to solid ferment process control field, specifically, is a kind of stalk solid ferment process parameter flexible measurement method and device based near infrared spectrum.
Background technology
Agricultural crop straw is the fourth-largest energy that is only second to coal, oil and natural gas in the world today.According to the relevent statistics, China can produce nearly 700,000,000 tons of stalks every year, and is main with corn, wheat and paddy stalk, account for 80% of stalk total production, but utilization factor is very low, is most ofly directly burned, and fraction machinery is the field also.Burning not only wastes resource, also natural resources is caused greatly and destroys.Development along with agricultural biotechnologies; Particularly handle stalk with biotechnology through the microorganism solid fermentation means; Make it become green resources such as biological feedstuff, bio-feritlizer and ethanol, not only can improve the utilization value and the utilization factor of stalk, but also can improve agroecological environment; Realize making full use of of resource, turn waste into wealth.
(solid-state fermentation SSF) is meant the process of cultivating microorganism in not containing or contain hardly the wet solid material of free water to solid state fermentation.In the stalk solid ferment process, the water cut in the material is very limited, but it is very important, if consumption is too high or too low then very big to the productive rate influence; Simultaneously, water also has complicated influence to the physicochemical property of material, and further influences productive rate.PH is another the important factor in the sweat, and each microorganism all has one to be fit to its growth and the active pH scope of performance.The control of pH still is a problem that waits to solve in the solid state fermentation at present, and the heterogeneity in the sweat constantly changes pH on the one hand, is not have suitable instrument detecting to confirm the pH in the solid-state material on the other hand.PH in many solid ferment process has distinctive variation, says that just water cut lower in the material makes the pH detection method of routine property be difficult to prove effective, thereby has limited the feasibility of pH as the important control parameter.In addition, also all contact is closely arranged as parameters such as biomass concentration and purpose product content with this important procedure parameter of pH.
At present, all adopt off-line chemical experiment method the detection of solid ferment process parameter (like humidity, pH, biomass concentration).Though the result of chemical detection method is objective credible, step is loaded down with trivial details, detection time is long, testing cost is high, and off-line measurement has brought a lot of inconvenience for the control and the optimization of Fermentation Engineering.Therefore, be unfavorable for realizing optimal control to whole fermentation process status information variable.
Near infrared spectrum (Near Infrared Spectroscopy; NIR) analytical technology has fast, can't harm, accurately; Advantages such as polycomponent detects simultaneously; Be one of mature technology that is suitable for implementation in most line analysis and control in real time, obtained widespread use in fields such as oil, chemical industry, food, pharmacy and tobaccos.
Summary of the invention
The objective of the invention is to be to overcome the deficiency of existing agricultural crop straw solid ferment process parameter detection method; A kind of stalk solid ferment process parameter flexible measurement method and device based near infrared spectrum is provided; Near-infrared spectrum technique is applied to the online soft sensor of solid ferment process parameter; The real-time online that satisfies multi-target ingredient simultaneously detects, and realization is monitored in real time the stalk solid ferment process and diagnosed, and guarantees the quality of final fermented product.
The technical scheme that the present invention is based on the stalk solid ferment process parameter flexible measurement method of near infrared spectrum is to adopt following four steps:
choose the stalk solid ferment process product sample of different fermentations batch, different fermentations time; Utilize conventional physico-chemical analysis method to obtain the reference measurement values of humidity in the stalk solid ferment process product sample, pH, biomass concentration, these procedure parameter indexs of purpose product content, form a database;
utilizes near infrared spectrometer to gather the spectroscopic data of said stalk solid ferment process product sample and imports computing machine into; Computing machine is to the spectroscopic data pre-service; Pretreated spectroscopic data is carried out principal component analysis (PCA); Obtain the characteristic value information of principal component scores matrix and spectrum covariance matrix; Calculate the accumulative total variance contribution ratio through eigenvalue matrix, extract the accumulative total variance contribution ratio reach get 90% or more sub matrix before several principal component scores vectors as the characteristic variable of solid ferment process product sample;
carries out the characteristic variable and the said database of said solid ferment process product sample related, adopts PLS to set up the multiparameter soft-sensing model of stalk solid ferment process;
adopts the described method of step
to obtain the characteristic variable of sample to be tested sample to be tested; The characteristic variable of this sample to be tested is imported said multiparameter soft-sensing model, detect through the multiparameter soft-sensing model and obtain sample to be tested procedure parameter index prediction value.
The technical scheme that the present invention is based on the device employing of near infrared spectrum stalk solid ferment process parameter flexible measurement method is: comprise near infrared spectrometer; Halogen lamp LED is arranged in the near infrared spectrometer; Near infrared spectrometer connects computing machine and connects sample cup through y-type optical fiber through data line, and sample cup is placed on the objective table, is placed with stalk solid ferment process product sample or sample to be tested in the sample cup; The light that Halogen lamp LED sends shines on the sample through y-type optical fiber; In the inner formation of sample diffuse reflection, diffusing gets near infrared spectrometer through y-type optical fiber again, and the spectral signal after the spectrometer analysis conversion imports in the computing machine through data line.
The invention has the beneficial effects as follows: the present invention compares with the traditional chemical analysis means; Fast and the favorable reproducibility of simple to operation, detection speed; Can be used for the on-line monitoring of stalk solid ferment process product quality; Defective such as solve in the stalk solid state fermentation production run that conventional off-line physics and chemistry detection method cost is high, length consuming time and efficient are low provides strong technical guarantee for the quality of stalk solid state fermentation Related product simultaneously.
Description of drawings
Fig. 1 is the schematic flow sheet that the present invention is based near infrared spectrum stalk solid ferment process parameter flexible measurement method;
Fig. 2 is that the structure that the present invention is based on the soft measurement mechanism of near infrared spectrum stalk solid ferment process parameter connects synoptic diagram;
Among the figure: 1. sample cup; 2. objective table; 3.Y type optical fiber; 4. computing machine; 5. data line; 6. near infrared spectrometer.
Embodiment
The present invention at first utilizes diffuse reflection type near infrared spectra collection device to obtain the near infrared spectrum data of stalk solid ferment process product, and the spectral signal of being gathered imports computing machine into through data line after near infrared spectrometer analysis conversion; Then; Original spectrum data to obtaining are carried out pre-service; Use principal component analysis (PCA) from these spectral informations, to extract the major component characteristic variable again; And the actual measurement reference value (being measured by conventional physico-chemical analysis method) of these proper vectors and stalk solid ferment process product parameter index is carried out related, adopt PLS to set up the soft-sensing model of sweat product parameter index.For unknown sample to be tested; Extract through corresponding spectrum data gathering and characteristic information; And then utilize the relevant mathematical model of having set up to predict the property value of this sweat product sample parameter index; To accomplish the online detection of stalk solid ferment process product parameter, help to realize the process of stalk solid state fermentation is monitored in real time and diagnosed.Specifically describe as follows:
As shown in Figure 2; Near infrared spectrometer 6 connects computing machine 4 through data line 5; Near infrared spectrometer 6 also connects sample cup 1 through y-type optical fiber 3 simultaneously, and sample cup 1 is placed on the objective table 2, is placed with stalk solid ferment process product sample or sample to be tested in the sample cup 1.In near infrared spectrometer 6, be provided with Halogen lamp LED; The light that Halogen lamp LED sends shines on stalk solid ferment process product sample or the sample to be tested through y-type optical fiber 3; In stalk solid ferment process product sample or the inner formation of sample to be tested diffuse reflection; Irreflexive light gets near infrared spectrometer 6 through y-type optical fiber 3 again, and the spectral signal after spectrometer analysis 6 conversions imports in the computing machine 4 through data line 5.Infrared spectrometer 6 is used to gather the near infrared spectrum data of sample; Computing machine 4 is to be used for the receiving spectrum signal; And the original spectrum signal that receives is carried out pre-service, major component characteristic variable extract and set up soft-sensing model; The soft-sensing model that the characteristic variable substitution of extracting has been set up, the property value of corresponding key parameter index that just can the fast prediction sample to be tested.
As shown in Figure 1; The representative stalk solid ferment process product sample of extensively collecting different fermentations batch, different fermentations time is used for carrying out model tuning; General stalk solid ferment process product sample is greater than 80; Each sample can take by weighing and put into sample cup 1 about 40g, and sample cup 1 is placed on the objective table 2; Near infrared spectrometer 6 is connected with sample cup 1 through y-type optical fiber 3, and the spectral signal that near infrared spectrometer 6 is gathered imports near infrared spectrometer 6 into again by y-type optical fiber 3, is reached in the computing machine 4 by the data line 5 that is connected between computing machine 4 and the infrared spectrometer 6 again.
With reference to the concerned countries standard; Physico-chemical analysis method through routine records stalk solid ferment process product parameter index; Process reference measurement values like biomass content, purpose product content, humidity, PH; Wherein, purpose product content is protein content etc. for example, and these process reference measurement values are formed a database.
Utilize the spectroscopic data of near infrared spectrometer 6 collection stalk solid ferment process product samples and import computing machine 4 into; In order to eliminate inconsistent etc. the influence of background interference, grain size and uniformity coefficient; Improve the quality of spectrum, computing machine 4 needs carry out pre-service to the original spectrum data of gathering, and the preprocess method of spectrum mainly contains standard normal variable conversion, level and smooth, centralization, differentiate, normalization and small echo filter and makes an uproar etc.; In these preprocessing procedures of practical application; Can use separately, also can make up utilization, more pretreated spectroscopic data carried out principal component analysis (PCA); Obtain the information such as eigenwert of principal component scores matrix and spectrum covariance matrix; Calculate the accumulative total variance contribution ratio through eigenvalue matrix, and according to the accumulative total variance contribution ratio reach 90% or more extract sub matrix before several principal component scores vectors as the proper vector of the information of spectrum, the i.e. characteristic variable of solid ferment process product sample entirely.
Carry out related with the database that the process reference measurement values of stalk solid ferment process product parameter index is formed the characteristic variable of the solid ferment process product sample that extracts; The utilization PLS is set up the multiparameter soft-sensing model of stalk solid ferment process product parameter index; The method that the utilization PLS is set up the multiparameter soft-sensing model is a linear correction method comparatively ripe in the near-infrared spectrum analysis, and has obtained using comparatively widely.
For the unknown stalk solid ferment process to be measured product sample; The tunning that equally at every turn takes by weighing about 40g is put into sample cup 1; The light that sends with the Halogen lamp LED in the near infrared spectrometer 6 then shines on the sweat product sample through y-type optical fiber 3; And in the inner formation of this sample diffuse reflection, the light that diffuse reflection is come out gets near infrared spectrometers 6 through y-type optical fiber 3 again, and the spectral signal that obtains imports in the computing machine 4 through data line 5 after spectrometer 6 is analyzed conversion.In computing machine 4, accomplishing the pre-service and the major component characteristic variable of original spectrum data extracts; And the multiparameter soft-sensing model that the characteristic variable input of the sample to be tested that extracts has been set up; The property value of corresponding key parameter index that just can the fast prediction sample to be tested; And be presented on the interface of computing machine 4; The multiparameter soft-sensing model of utilize setting up is accomplished the real-time detection of sample to be tested key parameter index attribute value, and key parameter index attribute value that so far should the unknown sweat product to be measured sample is measured and finished.
The present invention has versatility to the fast detecting of stalk solid ferment process product parameter index, utilizes the stalk solid state fermentation can produce the plurality of target product, for example is used for stalk protein feed solid ferment process product parameter index fast detecting etc.
Claims (2)
1. one kind based near infrared spectrum stalk solid ferment process key parameter flexible measurement method, it is characterized in that adopting following four steps:
2. realize the described device of claim 1 for one kind based near infrared spectrum stalk solid ferment process parameter flexible measurement method; Comprise near infrared spectrometer; Be provided with Halogen lamp LED in the near infrared spectrometer, near infrared spectrometer connects computing machine through data line, it is characterized in that: near infrared spectrometer connects sample cup through y-type optical fiber; Sample cup is placed on the objective table; Be placed with stalk solid ferment process product sample or sample to be tested in the sample cup, the light that Halogen lamp LED sends shines on the sample through y-type optical fiber, in the inner formation of sample diffuse reflection; Diffusing gets near infrared spectrometer through y-type optical fiber again, and the spectral signal after the spectrometer analysis conversion imports in the computing machine through data line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100048317A CN102539375A (en) | 2012-01-10 | 2012-01-10 | Straw solid-state fermentation process parameter soft measurement method and device based on near infrared spectrum |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100048317A CN102539375A (en) | 2012-01-10 | 2012-01-10 | Straw solid-state fermentation process parameter soft measurement method and device based on near infrared spectrum |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102539375A true CN102539375A (en) | 2012-07-04 |
Family
ID=46346804
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012100048317A Pending CN102539375A (en) | 2012-01-10 | 2012-01-10 | Straw solid-state fermentation process parameter soft measurement method and device based on near infrared spectrum |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102539375A (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103018181A (en) * | 2012-12-14 | 2013-04-03 | 江苏大学 | Soft measurement method based on correlation analysis and ELM neural network |
| CN103048275A (en) * | 2012-12-12 | 2013-04-17 | 江苏大学 | Adaptive soft-instrument device and adaptive soft-instrument construction method based on near infrared spectrum |
| CN103234935A (en) * | 2013-03-29 | 2013-08-07 | 江苏康缘药业股份有限公司 | Detection method of cortex moutan |
| CN103267740A (en) * | 2012-12-20 | 2013-08-28 | 江苏大学 | Straw fermentation process characteristic wave number soft instrument apparatus and construction method thereof |
| CN103487398A (en) * | 2013-09-30 | 2014-01-01 | 中粮生物化学（安徽）股份有限公司 | Analysis method of lysine fermentation liquid |
| CN103575680A (en) * | 2013-11-22 | 2014-02-12 | 南京农业大学 | Spectroscopic method for evaluating quality indexes of organic fertilizer |
| CN104111234A (en) * | 2014-07-29 | 2014-10-22 | 中国农业大学 | Method and device for online detection of biomass basic characteristics based on near infrared spectroscopy |
| CN104990893A (en) * | 2015-06-24 | 2015-10-21 | 南京富岛信息工程有限公司 | Gasoline octane number detecting method based on similar discriminance |
| CN105092524A (en) * | 2015-08-31 | 2015-11-25 | 浙江大学 | System for detecting total solid content of fermentation liquid in mixed continuous anaerobic fermentation process of water hyacinths and rice straws |
| CN105181635A (en) * | 2015-08-31 | 2015-12-23 | 浙江大学 | Detection system for volatile solid content in fermentation broth during Eichhornia crassipes and rice straw mixing continuous anaerobic fermentation process |
| CN105911019A (en) * | 2016-07-01 | 2016-08-31 | 内蒙古自治区纤维检验局 | Method and equipment for testing net wool rates of raw cashmere and washed cashmere |
| CN105987886A (en) * | 2015-02-03 | 2016-10-05 | 中国石油化工股份有限公司 | Method for determining hydrocracking tail oil property by near-infrared spectroscopy |
| CN106841101A (en) * | 2017-01-17 | 2017-06-13 | 安徽莱姆佳生物科技股份有限公司 | The method of near-infrared quick detection wheat stalk rotten degree |
| CN107515204A (en) * | 2017-10-19 | 2017-12-26 | 西华大学 | Detection method using NIR to bean paste sweet tea valve fermenting-ripening degree |
| CN110296956A (en) * | 2019-07-12 | 2019-10-01 | 上海交通大学 | The method of the content of organic matter in a kind of fermentation of near infrared ray rice straw |
| CN110749555A (en) * | 2019-10-30 | 2020-02-04 | 宜宾五粮液股份有限公司 | Hyperspectral technology-based device and method for detecting internal fermentation state of white spirit koji |
| CN115304410A (en) * | 2022-10-12 | 2022-11-08 | 广东省农业科学院动物科学研究所 | Method for treating Chinese herbal medicine solid waste |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339186A (en) * | 2008-08-07 | 2009-01-07 | 中国科学院过程工程研究所 | Method for on-line detection for solid-state biomass bioconversion procedure |
| CN101419166A (en) * | 2008-11-18 | 2009-04-29 | 江苏大学 | Tea quality nondestructive detecting method and device based on near-infrared spectrum and machine vision technology |
-
2012
- 2012-01-10 CN CN2012100048317A patent/CN102539375A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339186A (en) * | 2008-08-07 | 2009-01-07 | 中国科学院过程工程研究所 | Method for on-line detection for solid-state biomass bioconversion procedure |
| CN101419166A (en) * | 2008-11-18 | 2009-04-29 | 江苏大学 | Tea quality nondestructive detecting method and device based on near-infrared spectrum and machine vision technology |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103048275A (en) * | 2012-12-12 | 2013-04-17 | 江苏大学 | Adaptive soft-instrument device and adaptive soft-instrument construction method based on near infrared spectrum |
| CN103018181A (en) * | 2012-12-14 | 2013-04-03 | 江苏大学 | Soft measurement method based on correlation analysis and ELM neural network |
| CN103267740A (en) * | 2012-12-20 | 2013-08-28 | 江苏大学 | Straw fermentation process characteristic wave number soft instrument apparatus and construction method thereof |
| CN103234935A (en) * | 2013-03-29 | 2013-08-07 | 江苏康缘药业股份有限公司 | Detection method of cortex moutan |
| CN103487398B (en) * | 2013-09-30 | 2016-05-25 | 中粮生物化学（安徽）股份有限公司 | A kind of analytical method of lysine fermentation liquor |
| CN103487398A (en) * | 2013-09-30 | 2014-01-01 | 中粮生物化学（安徽）股份有限公司 | Analysis method of lysine fermentation liquid |
| CN103575680A (en) * | 2013-11-22 | 2014-02-12 | 南京农业大学 | Spectroscopic method for evaluating quality indexes of organic fertilizer |
| CN104111234A (en) * | 2014-07-29 | 2014-10-22 | 中国农业大学 | Method and device for online detection of biomass basic characteristics based on near infrared spectroscopy |
| CN105987886A (en) * | 2015-02-03 | 2016-10-05 | 中国石油化工股份有限公司 | Method for determining hydrocracking tail oil property by near-infrared spectroscopy |
| CN105987886B (en) * | 2015-02-03 | 2019-11-15 | 中国石油化工股份有限公司 | The method of near infrared ray hydrocracking tail oil property |
| CN104990893B (en) * | 2015-06-24 | 2017-12-29 | 南京富岛信息工程有限公司 | A kind of gasoline octane value detection method based on similar differentiation |
| CN104990893A (en) * | 2015-06-24 | 2015-10-21 | 南京富岛信息工程有限公司 | Gasoline octane number detecting method based on similar discriminance |
| CN105092524A (en) * | 2015-08-31 | 2015-11-25 | 浙江大学 | System for detecting total solid content of fermentation liquid in mixed continuous anaerobic fermentation process of water hyacinths and rice straws |
| CN105181635A (en) * | 2015-08-31 | 2015-12-23 | 浙江大学 | Detection system for volatile solid content in fermentation broth during Eichhornia crassipes and rice straw mixing continuous anaerobic fermentation process |
| CN105911019A (en) * | 2016-07-01 | 2016-08-31 | 内蒙古自治区纤维检验局 | Method and equipment for testing net wool rates of raw cashmere and washed cashmere |
| CN106841101A (en) * | 2017-01-17 | 2017-06-13 | 安徽莱姆佳生物科技股份有限公司 | The method of near-infrared quick detection wheat stalk rotten degree |
| CN107515204A (en) * | 2017-10-19 | 2017-12-26 | 西华大学 | Detection method using NIR to bean paste sweet tea valve fermenting-ripening degree |
| CN110296956A (en) * | 2019-07-12 | 2019-10-01 | 上海交通大学 | The method of the content of organic matter in a kind of fermentation of near infrared ray rice straw |
| CN110749555A (en) * | 2019-10-30 | 2020-02-04 | 宜宾五粮液股份有限公司 | Hyperspectral technology-based device and method for detecting internal fermentation state of white spirit koji |
| CN110749555B (en) * | 2019-10-30 | 2022-05-31 | 宜宾五粮液股份有限公司 | Hyperspectral technology-based device and method for detecting internal fermentation state of white spirit koji |
| CN115304410A (en) * | 2022-10-12 | 2022-11-08 | 广东省农业科学院动物科学研究所 | Method for treating Chinese herbal medicine solid waste |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102539375A (en) | Straw solid-state fermentation process parameter soft measurement method and device based on near infrared spectrum | |
| CN101339186A (en) | Method for on-line detection for solid-state biomass bioconversion procedure | |
| CN102879353B (en) | The method of content of protein components near infrared detection peanut | |
| CN102590129B (en) | Method for detecting content of amino acid in peanuts by near infrared method | |
| CN103278473B (en) | The mensuration of pipering and moisture and method for evaluating quality in white pepper | |
| CN103018181A (en) | Soft measurement method based on correlation analysis and ELM neural network | |
| CN102879340A (en) | Method for quickly detecting nutritional quality of root/stem crops on basis of near-infrared spectrum | |
| Chen et al. | Determining sucrose and glucose levels in dual‐purpose sorghum stalks by Fourier transform near infrared (FT‐NIR) spectroscopy | |
| Pang et al. | Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis | |
| CN102768195A (en) | Method for quickly detecting moisture content of cordyceps mycelia powder | |
| CN103278503B (en) | Multi-sensor technology-based grape water stress diagnosis method and system therefor | |
| CN102313712B (en) | Correction method of difference between near-infrared spectrums with different light-splitting modes based on fiber material | |
| CN106018335A (en) | Method for nondestructively determining content of phytic acid in whole cottonseed based on near infrared spectroscopy | |
| CN105628644A (en) | Device and method for on-line monitoring of protein enzymolysis process based on in-situ real-time spectrum | |
| CN105486662A (en) | Cottonseed gossypol content non-destructive measurement method based on near-infrared spectrum technology | |
| CN105866050A (en) | Low-cost lossless and fast detecting equipment for apple moldy core | |
| CN106290240A (en) | A kind of method based on near-infrared spectral analysis technology to Yeast Growth curve determination | |
| CN104034691A (en) | Rapid detection method for beta vulgaris quality | |
| CN111537469A (en) | Apple quality rapid nondestructive testing method based on near-infrared technology | |
| CN111795943A (en) | Method for nondestructive detection of exogenous doped sucrose in tea based on near infrared spectrum technology | |
| Wang et al. | Development of near‐infrared online grading device for long jujube | |
| CN109142238B (en) | Cotton phosphorus nutrition rapid diagnosis method | |
| CN108169168A (en) | Test and analyze rice grain protein content mathematical model and construction method and application | |
| CN106338488A (en) | Method for fast undamaged determination of transgenic soybean milk powder | |
| CN103425091B (en) | The online lossless detection method of near infrared spectrum of simple grain rapeseed quality and device |
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: 20120704 |