CN102313708A - Method for quantitatively detecting capsaicine - Google Patents
Method for quantitatively detecting capsaicine Download PDFInfo
- Publication number
- CN102313708A CN102313708A CN2010102213019A CN201010221301A CN102313708A CN 102313708 A CN102313708 A CN 102313708A CN 2010102213019 A CN2010102213019 A CN 2010102213019A CN 201010221301 A CN201010221301 A CN 201010221301A CN 102313708 A CN102313708 A CN 102313708A
- Authority
- CN
- China
- Prior art keywords
- capsaicine
- sample
- content
- model
- infrared
- 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
Abstract
The invention provides a method for quantitatively detecting capsaicine. The method comprises the following steps of: acquiring optical data of the capsaicine in a sample by using a near-infrared spectrometer; correlating the optical data with content data of the capsaicine in the sample detected by a chemical analysis method; establishing a calibration model by a partial least squares method; and substituting the optical data of the capsaicine in a sample to be detected into the model to acquire the content of the capsaicine in the sample to be detected. A capsaicine quantitative analysis model established by the method has high accuracy, and can accurately and reliably predict the content of the capsaicine in an actual sample. The content of the capsaicine measured by a near-infrared spectrometer method and a result measured by the chemical analysis method do not have obvious differences; and by the method, the content of the capsaicine can be detected nondestructively.
Description
Technical field
The present invention relates to the analytical chemistry field, specifically, relate to the method for utilizing capsaicin content in the near-infrared spectral analysis technology test sample.
Background technology
Near infrared spectrum has been with fastest developing speed, the most noticeable spectral analysis technique since the nineties in 20th century.Near infrared light is the electromagnetic wave between visible light and mid-infrared light, and wavelength coverage is 700～2500nm, and it mainly is to contain hydrogen group (O-H, C-H, N-H, S-H, frequency multiplication that P-H) waits and sum of fundamental frequencies absorption that the near infrared spectrum of general organism in this district absorbs.Because nearly all more organic primary structures can find signal with forming in their near infrared spectrum, and spectrogram is stable, and it is easy to obtain spectrum, so near infrared spectrum (NIRS) is described as the giant of analysis in the analytical chemistry field.
Capsaicin content is bigger to whole pungent quality of capsicum and mouthfeel influence, is one of important indicator of estimating capsicum quality quality.With traditional chemical analysis method, measure capsaicin content like high performance liquid chromatography and ultraviolet spectrophotometry, belong to destructive analysis, and used experimental drug costs an arm and a leg experimental implementation complicacy, time and effort consuming etc.Near-infrared spectral analysis technology has analysis speed and soon, does not destroy sample, simple to operate, good stability, efficient advantages of higher, on the attributional analysis of fruits and vegetables series products, has obtained increasingly extensive application.
Summary of the invention
The purpose of this invention is to provide a kind of method of utilizing capsaicin content in the near-infrared spectral analysis technology test sample.
In order to realize the object of the invention; The quantitative detecting method of a kind of capsicim of the present invention; It comprises the optical data of utilizing capsicim in the near infrared spectrometer collected specimens, and carries out relatedly in the sample that records through chemical analysis method between the capsaicin content data, adopts PLS to set up calibration model; With this model of optical data substitution of testing sample capsicim, obtain the capsaicin content of testing sample.
Aforesaid detection method, wherein said chemical analysis method are high performance liquid chromatography or ultraviolet spectrophotometry etc.
The present invention utilizes the method for capsaicin content in the near-infrared spectral analysis technology test sample; Its advantage is: (1) no pre-service, nothing destructiveness, pollution-free: near infrared light has very strong penetration capacity; Can penetrate glass and plastics package directly detects sample, sample need not pre-service, also without any need for chemical reagent; Can realize the non-destructive of capsaicin content is detected; Compare with conventional method of analysis, neither can pollute, can save great amount of manpower and material resources again environment; (2) finding speed is fast: the minute of nir instrument is short, and a few minutes even a few second promptly can be accomplished mensuration; (3) the capsicim Quantitative Analysis Model precision that the present invention built is good; Can be accurately and predict the capsaicin content of actual sample reliably, adopting between the result of capsaicin content that near-infrared spectrum method of the present invention measures and chemical analysis method mensuration does not have significant difference.
Description of drawings
The capsicum sample original spectrum that Fig. 1 utilizes near infrared spectrometer to gather for the present invention;
Fig. 2 is the near infrared second derivative spectra figure of sample of the present invention;
Fig. 3 is the predicted value and the measured value correlogram of PLS calibration model of the present invention.
Embodiment
Following examples are used to explain the present invention, but are not used for limiting scope of the present invention.
Embodiment
1 materials and methods
1.1 material
121 ripe capsicum samples from the Chinese Academy of Agricultural Sciences.Wherein, 93 samples are calibration set, and 28 samples are the checking collection.Capsaicin content is at 0.406mg/g～6.71mg/g.
1.2 instrument and equipment
NIRLab N-200 type near infrared attributional analysis appearance, Switzerland Buchi company (NIRCalV4.21 software, 12cm sample cup).
1.3 method
1.3.1 sample pre-treatments
The chilli sample is connected the seed belt leather pulverize together, cross 40 mesh sieves, the capsicum powder is paved with sample cup, thickness is not less than 1cm.
1.3.2 near infrared spectra collection
At room temperature, measure the near-infrared diffuse reflection spectrum of capsicum powdered sample.During mensuration, resolution is 1cm
-1, scanning times is 3, figure spectral limit 1100～2500nm.Behind instrument preheating 20～30min, the capsicum powdered sample is placed sample rim of a cup top.
1.3.3HPLC analytical approach
Adopt the capsaicin content of the above-mentioned capsicum sample of high effective liquid chromatography for measuring.
1.4 the foundation of near infrared correction and evaluation
The modelling process will be carried out related exactly through pretreated near infrared light spectrum signature and capsaicin content data, set up correlationship.Adopt offset minimum binary (PLS) method to set up calibration model.Confirm its best major component number (N) according to inner validation-cross.
Come forecast test collection sample with this model, come the accuracy and the reliability of testing model with this.Estimate the quality of calibration model with related coefficient (R), calibration set standard deviation (SEC) and checking collection standard deviation (SEP).Coefficient R is bigger, EC is more little for the calibration set standard deviation S, and the spectral information that is extracted is good more with the correlativity of analyzing component, and the model that obtains is excellent more.
2 results and analysis
2.1 original spectrum is to the influence of PLS model
The spectrum of choosing each capsicum sample is as original spectrum.The original spectrum of 121 samples is as shown in Figure 1.
As can beappreciated from fig. 1, original diffuse reflection spectrum change is little, and spectra overlapping is not serious, and the diffuse reflection spectrum of most of capsicum samples if directly be used for quantitative test, can not covered the SPECTRAL DIVERSITY that is caused by sample composition not than evident difference.Original spectrum can be eliminated the error that measuring condition causes effectively, improves the degree of accuracy of Quantitative Analysis Model, also can reflect the actual conditions of capsicum sample itself more all sidedly.
At 4000～10000cm
-1In the spectral range, set up capsicim PLS calibration model with the original spectrum of 93 correcting samples respectively.The result is as shown in table 1 in calibration model internal chiasma checking.
Table 1 original spectrum is to the influence of PLS calibration model
Annotate: R: related coefficient; N: best major component number; SEC: calibration set standard deviation; SLOPE: slope; BIAS: deviation.
Can know from table 1; In the major component number is 15 o'clock; The PLS calibration model of capsicim that original spectrum is set up, the related coefficient of its predicted value and measured value are 0.9791, and standard deviation is 0.3602; Show spectral information that is extracted and the good relationship of analyzing component, the model that obtains is better.
2.2 (second derivative, SD) disposal route is to the influence of capsicim calibration model for second derivative
Select the second derivative disposal route that the capsicim spectroscopic data is carried out pre-service, gained spectrum is as shown in Figure 2.At 4000～10000cm
-1In the spectral range, analyze the influence of preprocess method to building LEAST SQUARES MODELS FITTING, the result is as shown in table 2.
Table 2 pre-service is to the influence of PLS calibration model
Annotate: R: related coefficient; N: best major component number; SEP: checking collection standard deviation; SLOPE: slope; BIAS: deviation.
Can know from table 2, be 15 o'clock in the major component number, the PLS calibration model of capsicim that original spectrum is set up; The related coefficient of its predicted value and measured value is 0.9244, and SEP is 0.512, and SEP/SEC is 1.42; Less than 1.6; Show that model does not have over-fitting, selecting preprocessing procedures is necessary to Optimization Model, has only the purpose of selecting suitable preprocessing procedures just can reach Optimization Model.
2.3 the foundation of calibration model
As calibration set,, select the pretreated spectrum of second dervative with 93 samples at 4000～10000cm by the analysis of front
-1Set up the calibration model of capsaicin content in the spectral region with the PLS method, collect as checking with 28 samples, in the model that the spectrum importing is built, the result as shown in Figure 3.
2.4 the check of calibration model
28 samples using near-infrared models are measured, predicted its content, and with the stability of HPLC measured value as the measured value verification model.The result is as shown in table 3.
Predicting the outcome of table 3 capsicum sample Quantitative Analysis Model
Have or not significant difference between two kinds of analytical approachs of method check of t check in pairs.For given level of significance 0.01, t
0.005(19)=2.861, the t value of calculating gained is 0.1986, less than t
0.005Therefore (19), can think that the result does not have significant difference between the capsaicin content that the capsaicin content that adopts the prediction of near-infrared analysis method and HPLC analytical approach record.
The related coefficient of checking collection sample predicted value and measured value is 0.9244, and SEP/SEC is 1.42, proves that the near infrared PLS calibration model of building has stability preferably, can satisfy the detection requirement of capsaicin content.
3 conclusions
3.1 set up the PLS Quantitative Analysis Model of capsaicin content with the original spectrum of capsicim multiposition point spectrum.
3.2 adopt the second dervative preprocess method, at 4000～10000cm
-1In the spectral region, the capsicim Quantitative Analysis Model precision of being built is good, and the coefficient correlation of its predicted value and measured value is 0.9244, and SEP/SEC is 1.42, and model is more excellent.Near-infrared spectral analysis technology can be realized the non-destructive of capsaicin content is detected.
3.3 the paired t assay to the checking collection shows do not have significant difference between the result that capsaicin content that near-infrared spectrum method is measured and chemical analysis method are measured.
Though, the present invention has been done detailed description in the preceding text with general explanation and specific embodiments, on basis of the present invention, can to some modifications of do or improvement, this will be apparent to those skilled in the art.Therefore, these modifications or the improvement on the basis of not departing from spirit of the present invention, made all belong to the scope that requirement of the present invention is protected.
List of references:
[1] Lu Wanzhen, Yuan Hongfu, Xu Guangtong, etc. near-infrared spectral analysis technology [M]. Beijing: Sinopec publishing house, 2000.
[2] Chu Xiaoli, Yuan Hongfu, Lu Wanzhen. spectrum pre-service and Wavelength selecting method progress and application [J] in the near-infrared analysis. chemical progress, 2004,16 (4): 528-542.
[3] Deng Bo. the application [M] of mathematical statistics method in analytical test. Beijing: Chemical Industry Press, 1984.
[4] Wang Yanbin. the application of artificial neural network in the near-infrared analysis method and the analysis of Dark-colored Oil [D]. Beijing: Research Institute of Petro-Chemical Engineering, 2000.
[5]PEIRIS?K?H?S，DULL?G?G，LEFFLER?R?G，et?al.Near-infrared?spectrometric?method?fornondestructive?determination?of?soluble?solids?content?in?processing?tomatoes[J].Journal?of?theAmerica?Society?for?Horticulture?Science，1998，123：1089-1093.
[6]JHYA?S，MATSUOKA?T.Non-destructive?determination?of?acerbic?ratio?of?tomato?juice?usingnear?infrared?spectroscopy[J].International?Journal?of?Food?Scienc?and?Technology，2004，39：425-430.
[7]GOULD?W?A.Tomato?production，processing?and?technology[M]，3
rd?ed.CTIPub：Baltimore，1992：15-120；236-349.
[8] Xia Junfang, Li Peiwu, Li Xiaoyu, etc. different pre-service detect the influence [J] of navel orange VC content near infrared spectrum. agricultural mechanical journal, 2007,38 (6): 107-111.
[9]PEDRO?A?M?K，FERREIRA?M?M?C.Simultaneously?calibrating?solids，sugars?and?acidity?oftomato?products?using?PLS2?and?NIR?spectroscopy[J].Analytica?Chimica?Acta，2007，595：221-227.
[10] Jin Tongming, Liu Ling. with the nutritional labeling [J] of near infrared ray Chinese cabbage. North China agronomy newspaper, 1994,9 (4): 104-110.
[11] Jin Tongming. the nondestructive analysis of saccharic acid equal size [J] in the tomato. instrument and meter and analyzing and testing, 1996,16 (1): 53-57.
[12] Liu Yande. the near infrared spectrum Non-Destructive Testing research [D] of fruit pol and acidity. Hangzhou: Zhejiang University, 2006.
[13] Wu Yuping, Shao Yan, Qin Xiyun. malic acid, citric acid and ligroin extraction [J] in the near infrared spectroscopy fast detecting tobacco. tobacco agriculture science, 2006 (1), 13-16.
[14] Jiang Jinfeng, Zhao Mingyue. total volatile acid in the near infrared spectroscopy fast measuring tobacco and total volatile alkaline [J]. tobacco science and technology, 2006 (3), 33-37.
[15] Jiang Jinfeng, Li Li, Zhao Mingyue. use near infrared detection technology fast measuring tobacco leaf main chemical compositions [J]. Chinese tobacco journal, 2006 (2), 8-11.
[16] Zhou Hanping, Wang Xinmin, Song Jizhen, etc. the near infrared spectrum prediction [J] of tobacco leaf structure and oil content. tobacco science and technology, 2006 (1), 10-14.
[17] Cao Jianguo, Dou Feng. the triacetyl glycerine [J] in the near-infrared diffuse reflectance spectrometry test cellulose acetate filter rod. tobacco science and technology, 2005 (3), 6-9.
[18] Wang Jiajun, Liang Yiceng, Wang Fan. PLS combines the Fourier transform near infrared spectrum to measure the burst size [J] of coke tar in cigarette, nicotine and carbon monoxide simultaneously. analytical chemistry, 2005 (6), 793-797.
[19] Lu Wanzhen, Yuan Hongfu, Xu Guangtong, etc. modern near-infrared spectral analysis technology [M]. Beijing: Sinopec publishing house, 2000:146-148.
[20] Yan Yanlu. near-infrared spectrum analysis basis and application [M]. Beijing: China Light Industry Press, 2005.
[21]MCCLURE?W?F.Spectral?characteristics?of?tobacco?in?the?near?infrared?region?from?0.6-2.6microns[J].Tob?Sci，1968，12：232-235.
[22] Zhang Tieqiang, Guo Shanhe, He Liqiao, etc. use NIR technology and detect moisture in cut tobacco [J]. infrared technique, 1995 (5): 45-48.
[23] Wang Wenzhen, Zhang Huaibao. utilize the total nitrogen content [J] in the IA450 near-infrared analyzer fast measuring tobacco. instrument and meter and analysis monitoring, 1995, (20): 53-55.
[24] Zhao Longlian, Min Shungeng, Yan Yanlu, etc. the Fourier near infrared spectroscopy is measured nine kinds of Q factors [J] in the tobacco. spectroscopy and spectral analysis, 1998 (4): 89-90.
[25] Zhang Jianping, Xie Wenyan, Shu Ruxin, etc. the near infrared fast quantitative analysis research [J] of tobacco components. tobacco science and technology, 1999 (3): 37-38.
[26] prince becomes, the development of the .TM55 of Yang Ze army series infrared Moisture Meter and the application in tobacco industry [J] thereof. Light Engineering Institutes Of Zhengzhou's journal (natural science edition), 2000 (4): 108-110.
[27] Wang Dongdan, Li Junhui, Wu Yuping, etc. near-infrared spectral analysis technology is in tobacco chemistry applied research [J] analytically. Yunnan University's journal (natural science edition), 2001,23 (2): 135-137.
[28] Wang Dongdan, Li Junhui, Chen Runqiong waits the .FT-NIR method to measure blue or green quality of tobacco Research on parameters [J]. Agricultural University Of Southwest's journal, 2002 (1): 64-67.
[29] Wang Fang, Chen Da, Shao Xueguang, etc. the relational model research [J] of near infrared spectrum and cigarette sample conventional ingredient. tobacco science and technology, 2002 (5): 23-25.
[30] Wang Fang, Chen Da, Shao Xueguang, etc. wavelet transformation and PLS are in tobacco conventional ingredient Application in Prediction [J]. tobacco science and technology, 2004 (3): 31-34.
[31] the pretty .FT-NIR spectral analysis technique of king family is measured total nitrogen, total reducing sugar and nicotine [J] in the tobacco. spectrographic laboratory, 2003,20 (2): 181-185.
[32] Wang Jiajun, Luo Liping, Li Hui waits the .FT-NIR spectroscopic methodology to measure nitrogen, phosphorus, chlorine and potassium [J] in tobacco root, stem, the leaf simultaneously. tobacco science and technology, 2004 (12): 24-27.
[33] Fu Qiujuan, Wang Shusheng, Dou Yuqing, etc. K, Ca, Mg Study on content [J] in the blue or green tobacco leaf of near infrared quantitative test. Chinese tobacco journal, 2006 (2), 17-19
Claims (2)
1. the quantitative detecting method of a capsicim; It is characterized in that; It comprises the optical data of utilizing capsicim in the near infrared spectrometer collected specimens, and carries out relatedly in the sample that records through chemical analysis method between the capsaicin content data, adopts PLS to set up calibration model; With this model of optical data substitution of testing sample capsicim, obtain the capsaicin content of testing sample.
2. detection method as claimed in claim 1 is characterized in that, wherein said chemical analysis method is high performance liquid chromatography or ultraviolet spectrophotometry.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010102213019A CN102313708A (en) | 2010-06-29 | 2010-06-29 | Method for quantitatively detecting capsaicine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010102213019A CN102313708A (en) | 2010-06-29 | 2010-06-29 | Method for quantitatively detecting capsaicine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102313708A true CN102313708A (en) | 2012-01-11 |
Family
ID=45427065
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010102213019A Pending CN102313708A (en) | 2010-06-29 | 2010-06-29 | Method for quantitatively detecting capsaicine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102313708A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102654487A (en) * | 2012-03-22 | 2012-09-05 | 河南羚锐制药股份有限公司 | Fingerprint measuring method of hopper pepper rheumatism paste and semi-finished product of hopper pepper rheumatism paste |
| CN102654486A (en) * | 2012-03-22 | 2012-09-05 | 河南羚锐制药股份有限公司 | Fingerprint measurement method for chilli medical material and extracts of chilli medical materal |
| CN104132720A (en) * | 2014-07-25 | 2014-11-05 | 重庆医科大学 | Method for quickly detecting tablet weight of medicine tablets through near infrared spectroscopy |
| CN111077103A (en) * | 2019-11-30 | 2020-04-28 | 贵州中烟工业有限责任公司 | Method for measuring content of glyceryl triacetate |
| CN111398212A (en) * | 2020-04-08 | 2020-07-10 | 四川虹微技术有限公司 | Method for establishing pepper detection model based on portable near-infrared spectrometer |
| CN112697888A (en) * | 2019-10-22 | 2021-04-23 | 重庆德庄农产品开发有限公司 | Method for measuring capsaicin content |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1828272A (en) * | 2006-03-30 | 2006-09-06 | 将军烟草集团有限公司 | Method for detecting tobacco leaf chemical ingredient adopting near infrared light |
| JP2006238849A (en) * | 2005-03-07 | 2006-09-14 | Iseki & Co Ltd | Method and apparatus for foreseeing component information of fruit |
| EP1746409A1 (en) * | 2004-05-10 | 2007-01-24 | Hiroshi Maeda | On-line internal quality examining method and device |
| CN1968611A (en) * | 2004-06-15 | 2007-05-23 | 味之素株式会社 | Dried capsicum containing capsinoid and method of drying therefor |
| CN101587067A (en) * | 2009-06-25 | 2009-11-25 | 上海交通大学 | Measure the method for rhizome vegetable death by poisoning tick content |
| CN101710073A (en) * | 2009-12-10 | 2010-05-19 | 云南瑞升烟草技术（集团）有限公司 | Method for detecting physical and chemical indexes of reconstituted tobacco by near infrared spectrum detection paper making method |
-
2010
- 2010-06-29 CN CN2010102213019A patent/CN102313708A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1746409A1 (en) * | 2004-05-10 | 2007-01-24 | Hiroshi Maeda | On-line internal quality examining method and device |
| CN1968611A (en) * | 2004-06-15 | 2007-05-23 | 味之素株式会社 | Dried capsicum containing capsinoid and method of drying therefor |
| JP2006238849A (en) * | 2005-03-07 | 2006-09-14 | Iseki & Co Ltd | Method and apparatus for foreseeing component information of fruit |
| CN1828272A (en) * | 2006-03-30 | 2006-09-06 | 将军烟草集团有限公司 | Method for detecting tobacco leaf chemical ingredient adopting near infrared light |
| CN101587067A (en) * | 2009-06-25 | 2009-11-25 | 上海交通大学 | Measure the method for rhizome vegetable death by poisoning tick content |
| CN101710073A (en) * | 2009-12-10 | 2010-05-19 | 云南瑞升烟草技术（集团）有限公司 | Method for detecting physical and chemical indexes of reconstituted tobacco by near infrared spectrum detection paper making method |
Non-Patent Citations (2)
| Title |
|---|
| CARLOS A. VINHA，ETC: "《Qualitative and Semiquantitative Analysis of Dried Fruits an Seasoning Products of Paprika Using Photoacoustic Spectroscopy》", 《J. AGRIC. FOOD CHEM.》 * |
| 王穗萍等: "辣椒素类物质测定方法的研究进展", 《中国食品添加剂》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102654487A (en) * | 2012-03-22 | 2012-09-05 | 河南羚锐制药股份有限公司 | Fingerprint measuring method of hopper pepper rheumatism paste and semi-finished product of hopper pepper rheumatism paste |
| CN102654486A (en) * | 2012-03-22 | 2012-09-05 | 河南羚锐制药股份有限公司 | Fingerprint measurement method for chilli medical material and extracts of chilli medical materal |
| CN104132720A (en) * | 2014-07-25 | 2014-11-05 | 重庆医科大学 | Method for quickly detecting tablet weight of medicine tablets through near infrared spectroscopy |
| CN104132720B (en) * | 2014-07-25 | 2016-08-31 | 重庆医科大学 | Near infrared spectroscopy quickly detects the tablet weight of medicinal tablet |
| CN112697888A (en) * | 2019-10-22 | 2021-04-23 | 重庆德庄农产品开发有限公司 | Method for measuring capsaicin content |
| CN111077103A (en) * | 2019-11-30 | 2020-04-28 | 贵州中烟工业有限责任公司 | Method for measuring content of glyceryl triacetate |
| CN111398212A (en) * | 2020-04-08 | 2020-07-10 | 四川虹微技术有限公司 | Method for establishing pepper detection model based on portable near-infrared spectrometer |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102313711A (en) | Method for determining hot degree of pepper | |
| Shao et al. | Visible/near infrared spectrometric technique for nondestructive assessment of tomato ‘Heatwave’(Lycopersicum esculentum) quality characteristics | |
| Ying et al. | Experiments on predicting sugar content in apples by FT-NIR technique | |
| CN101413885A (en) | Near-infrared spectrum method for rapidly quantifying honey quality | |
| CN1982872B (en) | Near-infrared diffuse reflection spectral method for fastly inspecting drop water content | |
| CN102313708A (en) | Method for quantitatively detecting capsaicine | |
| CN103487395B (en) | A kind of Paris polyphylla medicinal material multiple index quick detecting method | |
| CN103278473B (en) | The mensuration of pipering and moisture and method for evaluating quality in white pepper | |
| CN102288572A (en) | Method for quickly detecting content of index ingredient of traditional Chinese medicinal material by utilizing near infrared spectrum technique | |
| CN101393114A (en) | Rapid measuring method for physical property of flavouring essences for tobacco | |
| CN104749132A (en) | Method for measuring content of azodicarbonamide in flour | |
| CN105372200A (en) | Rapid detection method for SBS modified asphalt modifier contents | |
| CN101498658A (en) | Flue gas chemical constituents prediction method based on Fourier transform near infrared spectrum of Cambridge filter capturing flue gas particulate matter | |
| Xiong et al. | Application of the radial basis function neural networks to improve the nondestructive Vis/NIR spectrophotometric analysis of potassium in fresh lettuces | |
| CN104596979A (en) | Method for measuring cellulose of reconstituted tobacco by virtue of near infrared reflectance spectroscopy technique | |
| CN105548070A (en) | Apple soluble solid near-infrared detection part compensation method and system | |
| Zhang et al. | Use of signal to noise ratio and area change rate of spectra to evaluate the Visible/NIR spectral system for fruit internal quality detection | |
| CN104596975A (en) | Method for measuring lignin of reconstituted tobacco by paper-making process by virtue of near infrared reflectance spectroscopy technique | |
| CN105758819A (en) | Method for detecting organic components of soil by utilizing near infrared spectrum | |
| CN102313710A (en) | Method for quantitatively detecting dihydrocapsaicin | |
| CN104390946A (en) | Method for determining content of ochratoxin A in juice | |
| CN105486663A (en) | Method for detecting stable carbon isotopic ratio of soil through near infrared spectrum | |
| CN104596976A (en) | Method for determining protein of paper-making reconstituted tobacco through ear infrared reflectance spectroscopy technique | |
| Ruangratanakorn et al. | Installation design of on-line near infrared spectroscopy for the production of compound fertilizer | |
| Temma et al. | Measuring the sugar content of apples and apple juice by near infrared spectroscopy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20120111 |