CN105486663A - Method for detecting stable carbon isotopic ratio of soil through near infrared spectrum - Google Patents
Method for detecting stable carbon isotopic ratio of soil through near infrared spectrum Download PDFInfo
- Publication number
- CN105486663A CN105486663A CN201610113351.2A CN201610113351A CN105486663A CN 105486663 A CN105486663 A CN 105486663A CN 201610113351 A CN201610113351 A CN 201610113351A CN 105486663 A CN105486663 A CN 105486663A
- Authority
- CN
- China
- Prior art keywords
- soil
- calibration
- stable carbon
- pedotheque
- sample
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 100
- 239000002689 soil Substances 0.000 title claims abstract description 90
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 26
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 26
- 238000002329 infrared spectrum Methods 0.000 title claims abstract description 25
- 230000000155 isotopic effect Effects 0.000 title abstract description 10
- 238000009499 grossing Methods 0.000 claims abstract description 9
- 230000003595 spectral effect Effects 0.000 claims description 39
- 238000012937 correction Methods 0.000 claims description 20
- 238000001914 filtration Methods 0.000 claims description 18
- 230000008569 process Effects 0.000 claims description 10
- 238000012795 verification Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 6
- 238000001546 stable isotope ratio mass spectrometry Methods 0.000 claims description 6
- 238000002360 preparation method Methods 0.000 claims description 5
- 230000001419 dependent effect Effects 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 238000002835 absorbance Methods 0.000 claims description 2
- 238000002790 cross-validation Methods 0.000 claims description 2
- 238000001228 spectrum Methods 0.000 description 24
- 238000012360 testing method Methods 0.000 description 17
- 238000001514 detection method Methods 0.000 description 10
- 230000000694 effects Effects 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 10
- 238000004458 analytical method Methods 0.000 description 8
- 239000002245 particle Substances 0.000 description 7
- 240000008289 Quercus suber Species 0.000 description 6
- 235000016977 Quercus suber Nutrition 0.000 description 6
- 238000010521 absorption reaction Methods 0.000 description 6
- 230000008859 change Effects 0.000 description 6
- 229910052500 inorganic mineral Inorganic materials 0.000 description 6
- 239000011707 mineral Substances 0.000 description 6
- 238000007781 pre-processing Methods 0.000 description 6
- 238000004611 spectroscopical analysis Methods 0.000 description 6
- 238000000862 absorption spectrum Methods 0.000 description 4
- 238000013459 approach Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 238000010298 pulverizing process Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 239000007787 solid Substances 0.000 description 4
- 229910004261 CaF 2 Inorganic materials 0.000 description 3
- 229910000530 Gallium indium arsenide Inorganic materials 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008030 elimination Effects 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 239000011521 glass Substances 0.000 description 3
- 230000008676 import Effects 0.000 description 3
- 239000012535 impurity Substances 0.000 description 3
- 238000002307 isotope ratio mass spectrometry Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 238000002203 pretreatment Methods 0.000 description 3
- 238000000513 principal component analysis Methods 0.000 description 3
- 238000012628 principal component regression Methods 0.000 description 3
- 229910001220 stainless steel Inorganic materials 0.000 description 3
- 239000010935 stainless steel Substances 0.000 description 3
- 238000012706 support-vector machine Methods 0.000 description 3
- 230000001629 suppression Effects 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000013480 data collection Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000010884 ion-beam technique Methods 0.000 description 2
- 239000011777 magnesium Substances 0.000 description 2
- 239000011572 manganese Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000010238 partial least squares regression Methods 0.000 description 2
- 238000002922 simulated annealing Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 238000011282 treatment Methods 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
- PWHULOQIROXLJO-UHFFFAOYSA-N Manganese Chemical compound [Mn] PWHULOQIROXLJO-UHFFFAOYSA-N 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 239000011575 calcium Substances 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 125000000524 functional group Chemical group 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003455 independent Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 229910052749 magnesium Inorganic materials 0.000 description 1
- 229910052748 manganese Inorganic materials 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 150000002739 metals Chemical class 0.000 description 1
- 230000000813 microbial effect Effects 0.000 description 1
- 239000011859 microparticle Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 239000011591 potassium Substances 0.000 description 1
- 229910052700 potassium Inorganic materials 0.000 description 1
- 238000007430 reference method Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000007873 sieving Methods 0.000 description 1
- 238000004856 soil analysis Methods 0.000 description 1
- 238000001179 sorption measurement Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 230000031068 symbiosis, encompassing mutualism through parasitism Effects 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N2021/3595—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using FTIR
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention provides a method for detecting a stable carbon isotopic ratio of soil through a near infrared spectrum. The method comprises the following steps that 1, stable carbon isotopic ratios of multiple calibrated soil samples are detected; 2, diffuse reflection spectrograms of the near infrared wave sections of the calibrated soil samples are collected to obtain original spectrograms; 3, smoothing pretreatment is performed on the original spectrograms to obtain treated spectrograms; 4, a quantitative relationship model between the treated spectrograms and the stable carbon isotopic ratios of the calibrated soil samples is built by adopting a partial least squares method; 5, a diffuse reflection spectrogram of the near infrared wave section of a to-be-detected soil sample is collected, and the stable carbon isotopic ratio of the to-be-detected soil sample is calculated according to the quantitative relationship model. According to the method, the stable carbon isotopic ratio of the soil can be quickly detected through the near infrared spectrum.
Description
Technical field
The present invention relates to field of ecology, relate to a kind of method detecting the stable carbon isotope ratio of soil particularly.
Background technology
Stable carbon isotope ratio (δ
13c) analytical approach can be used to show the source of the soil organism, the research soil organism and component thereof degree of decomposition and turnover, reappear C
3/ C
4the change histories of vegetation, just day by day becomes a strong instrument in soil organism research and ecology.
Measure soil δ
13c generally adopts stable isotope ratio mass spectroscopy (IRMS), and its principle of work is: soil is by high-temp combustion, and organic carbon is wherein transformed into gaseous state CO
2; After chromatographic column or adsorption column are separated with other gases, be ionized in an ion source; Ion beam line focus and accelerate after, enter mass analyzer; Under magnetic fields, ion current deflects by certain specific charge (m/z), and because various isotopic quality (m) is different, the degree of ion current deflection is also different; Often kind of isotopic ion beam arrives ion acceptor by the track of oneself, records often kind of isotopic ion current intensity, measure isotopic ratio after amplifying.Stable isotope ratio mass spectrometer is expensive, and need special messenger to operate, test period is long, and corresponding testing cost is also high.Therefore, isotope ratio mass spectrometry (IRMS) measures soil δ 13C following deficiency: 1, expensive equipment, and testing cost is high; 2, technical requirement is high, not easy to operate; 3, test period is long; 4, not easily promote.
The application of infrared spectrum technology in soil analysis is risen in the eighties in last century.Utilize near infrared spectrum (NIR) and middle infrared spectrum (MIR) technology at present, in conjunction with Chemical Measurement means, be widely used in the analysis of the various physicochemical property of soil, result is satisfactory.As: the content, Microbial etc. of the metals such as total carbon content, total nitrogen content, content of tatal phosphorus, moisture, the soil texture, potassium (K), calcium (Ga), iron (Fe), manganese (Mn), magnesium (Mg).NIR, MIR spectroscopic analysis methods is a kind of indirect analysis method, needs first to measure with the physicochemical property of reference method to a large amount of representative pedotheque, builds calibration model by association sample spectra and its physicochemical property; Then calibration model is used to predict composition and the character of unknown pedotheque.Therefore, tested pedotheque will comprise the type of predicted pedotheque and the scope of physicochemical property as far as possible, and carries out Accurate Determining to the physicochemical property of its each component.
Near infrared (NIR) SPECTRAL REGION refers to the electromagnetic wave of wavelength within the scope of 780 ~ 2500nm, its spectral information derives from frequency multiplication and the sum of fundamental frequencies of intramolecule vibration, and hydric group is (as C-H in main reflection molecule, N-H, O-H, S-H etc.) frequency multiplication and sum of fundamental frequencies absorption of vibrations.Many organism have characteristic absorption in this SPECTRAL REGION, and the molecular structure of the absorption intensity of different-waveband and this material and concentration exist corresponding relation.In infrared (MIR) SPECTRAL REGION be the electromagnetic wave of wavelength within the scope of 2500 ~ 25000nm, material is that fundamental frequency, frequency multiplication and sum of fundamental frequencies absorb at the absorption peak of this scope.The infrared absorption spectrum that different compound has it special, the intensity of its bands of a spectrum, position, shape and number are all relevant with compound and state thereof.The difference that MIR and NIR light are composed is, near infrared spectrum is the frequency multiplication of material molecule internal vibration and the absorption of sum of fundamental frequencies, and the bands of a spectrum of different component and functional group are comparatively easily overlapping and information strength is more weak, cause spectrum elucidation relative difficulty, institute's established model is subject to the impact of extraneous factor, poor stability; And the fundamental frequency that middle infrared spectrum is intramolecule vibration absorbs, its information strength is comparatively strong, and information extraction is relatively easy.
Diffuse reflection is a kind of conventional near infrared acquisition method, its ultimate principle is: when illumination is mapped to loose solid sample surperficial, except some is reflected except (being called mirror reflected light) by sample surfaces immediately, remaining incident light produces unrestrained transmitting at sample surfaces, or toss about in bed reflection decay gradually between sample particulate, or the scattering for turning back again after penetrating internal layer.The light being diffusely reflected or scattering out after these contact sample microparticle surfaces has absorption-attenuation characteristic, and diffuse reflection that Here it is produces the fundamental cause of spectrum.The effect of diffuse device be exactly maximum intensity these diffusions, the luminous energy pinching that scatters out are got up to send into detecting device, make the spectral signal obtaining having good signal-to noise ratio.The spectral technology that diffuses is a kind of detection method developed rapidly over nearly 20 years, the method is easy and simple to handle, quick, can analyze fast, accurately various sample to non-demolition, in addition the digitizing of analytical instrument and the development of chemometrics method, use chemometrics method can solve the extraction of spectral information and the impact of background interference aspect well, give play to vital role in making it in a lot of fields, and achieve good Social and economic benef@.
No matter be NIR or MIR spectrum, in the spectral information collected, comprise some can produce interference information to From Spectral Signal, thus affect foundation and the prediction of model, therefore need to carry out Pretreated spectra.Conventional preprocessing procedures has the conbined usage etc. of data smoothing, baseline correction, centralization, multiplicative scatter correction, standardization, derivative, Fourier transform and above several method.In addition, spectrogram compression and information extraction can improve the effective information rate in analytic signal, and its main method has principal component analysis (PCA) (PCA), wavelet analysis, simulated annealing (SAA), genetic algorithm (GA), moving window (MWPLS) etc.
One of core technology of NIR and MIR spectral analysis sets up funtcional relationship between spectral information and component physicochemical property, namely sets up calibration model.The analytical approach that spectrum regretional analysis is commonly used has: multiple linear regression (MLR), principal component regression (PCR), partial least square method return (PLSR), artificial neural network (ANN), support vector machine (SVM) etc.MLR, PCR and PLSR are used for solving linear correction problem, ANN and SVM is used for solving gamma correction problem.
Yet there are no and utilize near infrared (NIR) spectral detection soil δ
13the report of C value.
Summary of the invention
Because the above-mentioned defect of prior art, the invention provides a kind of detection soil δ newly
13the method of C value, the technical matters that solve utilizes near infrared (NIR) spectrum to detect soil δ fast
13c value.
For solving the problem, the technical scheme that the present invention takes is: a kind of method utilizing near infrared spectrum to detect the stable carbon isotope ratio of soil, described method comprises the steps:
1) the stable carbon isotope ratio of multiple calibration pedotheque is recorded;
2) gather the diffuse reflection spectrogram of the near-infrared band of calibration pedotheque, obtain original spectrogram;
3) by smoothing for original spectrogram pre-service, obtain processing rear spectrogram;
4) partial least square method is adopted to set up causes between spectrogram and stable carbon isotope ratio after the process of calibration pedotheque;
5) gather the diffuse reflection spectrogram of the near-infrared band of pedotheque to be measured, calculate the stable carbon isotope ratio of pedotheque to be measured according to causes.
Preferably, in described step 1) in, the method recording the stable carbon isotope ratio of calibration pedotheque is stable isotope ratio mass spectroscopy.
Preferably, in described step 1) in, the concrete steps of preparation calibration pedotheque comprise: after soil sample being dewatered, levigate, cross 60 mesh sieves.
Preferably, in described step 1) in, described multiple calibration pedotheques comprise the sample of Oe and Oa layer soil.
Preferably, in described step 3) in, smoothing for original spectrogram pretreated concrete steps comprised: the atmospheric background suppresses, absorbance is changed, automatic baseline correction and the process of Norris first order derivative filtering.
Preferably, in described step 4) in, the concrete steps setting up causes comprise: respectively spectral information and stable carbon isotope ratio are divided into calibration set and checking collection by SPXY method; Adopt partial least square method, in calibration set spectral information, extract major component, and choose best number of principal components with 20 folding cross verifications, with the spectral information of calibration set for independent variable, with calibration set stable carbon isotope ratio for dependent variable, set up regression model; And utilize the precision of checking collection inspection regression model.More preferably, calibration set and verify that the ratio of sample number integrated is as 3:1.More preferably, the concrete steps of 20 described folding cross verifications comprise: by number of principal components f successively from 1 value to 20, for getting a fixed number of principal components, calibration set is divided into 20 subsets, each subset data is respectively used to do one-time authentication, and other 19 subset data are used for training simultaneously, and cross validation repeats 20 times, the result of average 20 times, finally obtains the validation-cross root-mean-square error of a described number of principal components of correspondence.More preferably, comprise by the concrete steps of the precision of checking collection inspection calibration model: by the correction coefficient of determination, validation-cross root-mean-square error and prediction related coefficient, predicted root mean square error four parameters, calibration model is evaluated.
Preferably, in described step 1) in, the concrete steps preparing pedotheque to be measured comprise: after soil sample being dewatered, levigate, cross 60 mesh sieves.
Beneficial effect of the present invention is: 1, this method can quick, accurate, easy, mensuration soil δ 13C value at a low price.2, easy and simple to handle, popularization is strong, applied range.3, the various ecosystems such as forest, farmland, meadow are applicable to.
Be described further below with reference to the technique effect of accompanying drawing to design of the present invention, concrete structure and generation, to understand object of the present invention, characteristic sum effect fully.
Accompanying drawing explanation
Fig. 1 is the near infrared absorption spectrogram of 6 the different depth soil in sample Pinggu, ground in the embodiment of the present invention 1 and 2.
Fig. 2 is the schematic diagram of the RMSECV value that in the embodiment of the present invention 1, the different number of principal components of spectral model (comprising Oe with Oa) validation-cross is corresponding.
Fig. 3 is the schematic diagram of spectral model (comprising Oe and Oa) predicted value and actual value correlativity in the embodiment of the present invention 1.
Fig. 4 is the RMSECV value that in the embodiment of the present invention 2, the different number of principal components of spectral model (not comprising Oe with Oa) validation-cross is corresponding.
Fig. 5 is the schematic diagram of spectral model (not comprising Oe and Oa) predicted value and actual value correlativity in the embodiment of the present invention 2.
Embodiment
As the preferred embodiment of one, the method utilizing near infrared spectrum to detect the stable carbon isotope ratio of soil fast provided by the present invention comprises the steps.
(1) standby soil sample to be checked
By the impurity elimination of mineral layer soil, air-dry, pulverizing, cross 60 mesh sieves, exsiccator saves backup; Oe and Oa layer soil (two kinds of Litter leaf do not decomposed completely in various degree) is dried 48h to eliminate moisture in 60 DEG C, pulverizes, cross 60 mesh sieves, exsiccator saves backup.Totally 200 samples come from the pedotheque of different depth (Oe, Oa, 0-2,2-5,5-10,10-20cm) in the cork oak forest in seven different regions (Pinggu, Hong Yashan, Bai An, Huang Zangyu, Xinyang, Mount Huang, the Yunshan Mountain).
Sample preparation details: forest soil sample is solid particle in irregular shape, the size of particle, shape and degree of uniformity all have a huge impact spectroscopic data; By pulverizing and sieving, less soil particle degree can be obtained, increase the uniformity coefficient of sample, reducing the impact that particle scattering effect causes spectra collection as far as possible.
Two innovative points of the present invention: one, the degree of depth, general soil test all can not add Oe and Oa layer, but increasing research shows, Oe and Oa layer, because containing a large amount of soil organism, is also the important component part of forest soil; Two, sample area, the sample of collection of the present invention comes from 5 provinces on a latitudinal gradient, at a distance of 1500 kilometers from north to south, is transitioned into the subtropics in south from the warm temperate zone in the north gradually.
(2) stable isotope ratio mass spectroscopy is utilized to record the δ of soil sample to be checked
13c value
(3) spectra collection
The xsect that low-hydroxy-group squartz glass is placed an internal diameter 11mm is the stainless steel cylinder of annular, its white light penetrated up bottom is unobstructed, the 200mg soil sample accurately taken is placed in it, be 4g, diameter again by a weight be also the bottle of 11mm, be placed on gently in soil sample, its can make thickness of sample homogeneous, have enough dress sample degree of depth and can not press and too tightly produce mirror-reflection.Utilize designed, designed of the present invention and the sample stage built, gather the diffuse reflection spectrogram of middle-infrared band, instrument configuration is: Fourier transformation infrared spectrometer, and annex is near infrared integrating sphere, white light source, CaF
2beam splitter, InGaAs detecting device; Acquisition parameter is: take Jin Jing as background, sweep limit 10000-4000cm
-1, resolution 4cm
-1, scan 64 times.
Innovation about harvester: general extensive collection soil sample has the automatic sampling apparatus of fixed measure, but it can improve instrument price and testing cost, and use underaction.Based on the angle of practicality, designed, designed of the present invention has also built the irreflexive sample stage of pedotheque, its object mainly keep thickness of sample homogeneous, have enough dress sample degree of depth and can not press and too tightly produce mirror-reflection.
(4) data prediction
Software Omnic8.2 is carried by whole for original spectrogram wave band (i.e. 10000-4000cm with instrument
-1) carry out the atmospheric background suppression, change into absorption spectrum, then carry out automatic baseline correction.After spectroscopic data being imported software matlab7.8, with Norris (7,7,1) differentiate smoothing method process, in bracket, first 7 represents at 7 smoothly, and second 7 represents 7 differential width, and 1 represents first order derivative.Norris differentiate smoothing processing can stress release treatment, removes irrelevant information variable as far as possible.
The pre-service of pedotheque near infrared spectrum, can reduce system deviation effectively, and noise, granularity and crest cross the impact of point etc.In the preprocessing procedures that some are common, baseline correction mainly eliminates baseline wander; Smoothing processing is stress release treatment information mainly; Derivative processing (first derivation or second order differentiate) effectively can eliminate base-line shift, reduces peak overlapping with peak-to-peak, obtains more effective information; Multiplicative scatter correction is to eliminate the impact on solid diffuse reflection spectrum of solid particle size, surface scattering and change in optical path length.
The cross-reference of preprocess method not of the same race has different improvement and impact to its corresponding forecast model, so, compare the modelling effect of Norris first order derivative filtering and Norris second derivative filtering herein, also compare the modelling effect of Norris first order derivative filtering when adding multiplicative scatter correction and do not add multiplicative scatter correction.
(5) partial least square method is adopted to set up soil near infrared spectrum and δ
13quantitative relationship between C value
By soil δ
13c value also imports in matlab software.With SPXY algorithm by test sample in the ratio of 3:1 respectively by spectral information and δ
13c value is divided into calibration set and checking collection, is respectively used to model and sets up and checking.
The innovative point that the present invention divides about sample set: during modeling, much research is all around how choosing best preprocessing procedures, the less division methods comparing calibration set and checking collection, but the selection of calibration set and checking collection sample is most important to spectrum Multivariate Calibration.At present conventional Method of Sample Selection mainly comprises random approach (RS) and K-S (Kennard ?Stone) method and SPXY (samplesetpartitioningbasedonjointx-ydistance) method.Random approach randomness is large, can not ensure that selected sample has enough representativenesses; Sample large for SPECTRAL DIVERSITY is selected into calibration set by K-S method, and all the other samples are included into checking collection, but low for content or that concentration is low scope, and between sample, spectrum change is very little, and the sample often selected is not representative yet; SPXY algorithm is a kind of sample set system of selection of Corpus--based Method basis, at utmost characterizes sample distribution, to improve model stability by spectrum-physics and chemistry value symbiosis distance as according to guarantee.The present invention is based on the spectrum of Norris first order derivative filtering process, compare the effect of SPXY method and K-S method institute established model.
1. the single dependent variable (δ in calibration set is utilized
13c value) offset minimum binary (partialleastsquaresmethod is carried out to many independents variable (mid-infrared light spectrum information), PLS) regression modeling, its basic process is: in spectral information, extract major component t1 (t1 is the linear combination of spectral information), t1 should carry the variation information in spectroscopic data as much as possible, and t1 and δ
13the degree of correlation of C value can reach maximum.After first major component t1 is extracted, partial least squares regression implements spectral information to the recurrence of t1, if regression equation has reached satisfied precision, then algorithm stops; Otherwise, the residual, information after being explained utilizing spectral information by t1 and δ
13c value explained by t1 after residual, information carry out the second Principle component extraction of taking turns.And so forth, until a precision be comparatively satisfied with can be reached.If be finally extracted m major component t1, t2 altogether to spectral information ..., tm, partial least squares regression will by implementing δ
13c value to t1, t2 ..., the recurrence of tm, and then be expressed as δ
13c value is about the regression equation of former variable (i.e. spectral information).
2., in analytic process, the present invention's 20 folding cross verifications choose best number of principal components (namely ensureing the quantity of the major component needed for the better precision of model).The optimum configurations of " 20 folding method " is: to a certain number of principal components f (getting 20 from 1 successively), calibration set is divided into 20 groups (generally dividing equally), independent subsample is retained the data as verification model, and other 19 samples are used for training.Validation-cross repeats 20 times, each subsample checking and only verifying once, the result of average 20 times or use other combination, finally obtains the validation-cross root-mean-square error (RMSECV) of this number of principal components corresponding.RMSECV value is less, illustrates that the predictive ability of model is better.General use RMSECV value establishes best number of principal components to the method that major component number is mapped, and number of principal components corresponding to RMSECV minimum point is generally best number of principal components.
3. using the infrared spectrum in calibration set as independent variable, by partial least square method extract much more as far as possible comprise spectral information simultaneously again with δ
13the major component (this number is determined by above-mentioned validation-cross) of the some that C value is closely related, with δ
13c value is dependent variable, sets up soil middle infrared spectrum and δ
13calibration model between C value.And the spectral information of individual authentication collection is substituted into this model calculating δ
13c value, surveys δ by collecting with checking
13c value compares, testing model precision of prediction.
Model prediction ability and stability are by correcting the coefficient of determination (R
2), validation-cross root-mean-square error (RMSECV) and prediction related coefficient (R), predicted root mean square error (RMSEP) 4 parameters evaluate, good model should possess the feature that two coefficients are high and two errors are low.In addition, also deep evaluation can be carried out to model by usage forecastings relation analysis error (Residualpredictivedeviation, RPD); Think that when RPD value is greater than 3 model has good predictive ability, the fixing quantity of index can be carried out.The infrared information of unknown, that character is similar soil can be substituted in this model after model establishes, calculate its δ
13c value.
About the determination of best number of principal components.Not conventionally choose the minimum point of RMSECV, but test one by one, consider 4 parameters (paying close attention to RMSEP value).
The present invention gathers the near infrared light spectrogram of cork oak forest soil (from the different local and degree of depth), after series of preprocessing is carried out to raw data, in conjunction with the PLSR in Chemical Measurement, the δ in various soil can be detected accurate, quick, easy, at low cost
13c value.
Advantage of the present invention is:
(1) accurate.Correct coefficient of determination R
2all be greater than 0.96 with prediction related coefficient R, correction root-mean-square error and predicted root mean square error are all less than 1.05, RPD and are greater than 3.
(2) quick.A sample collection only needs 3min, and within one day, can gather at least 200 infrared spectrums, follow-up data processing can complete in 1h.
(3) simple to operate.Sample pre-treatments is simple, and instrumentation is simple.
(4) testing cost is low.Compare stable isotope ratio mass spectrometer, infrared spectrometer low price.
(5) popularization is strong, is easy to promote.Instrumentation is simple, and price is relatively cheap.
(6) applied widely, not only can be used for the detection of forest soil, also can be used for other ecosystem such as farmland, grassland.
Embodiment 1 comprises the soil of Oe and Oa
(1) soil sample to be checked is prepared.By the impurity elimination of mineral layer soil, air-dry, pulverizing, cross 60 mesh sieves, exsiccator saves backup; Oe and Oa layer soil is dried 48h to eliminate moisture in 60 DEG C, pulverizes, cross 60 mesh sieves, exsiccator saves backup.Totally 199 samples come from the pedotheque of different depth (Oe, Oa, 0-2,2-5,5-10,10-20cm) in the cork oak forest in seven different regions (Pinggu, Hong Yashan, Bai An, Huang Zangyu, Xinyang, Mount Huang, the Yunshan Mountain), wherein 139, mineral layer soil, totally 60, Oe and Oa layer soil.
After adopting multiplicative scatter correction pre-service by table 1, spectral model precision is not improved, and proves that sample preparation obtains better, reduces the impact that particle scattering effect causes spectra collection as far as possible.
(2) stable isotope ratio mass spectroscopy is utilized to record the δ of soil sample to be checked
13c value.
(3) spectra collection.Take 200mg soil sample, the xsect being placed in 11mm is that in the stainless steel cylinder of annular, bottom is low-hydroxy-group squartz glass, and sample top flattens.Gather the diffuse reflection spectrogram of near-infrared band, instrument configuration is: Fourier transformation infrared spectrometer, and annex is near infrared integrating sphere, white light source, CaF
2beam splitter, InGaAs detecting device; Acquisition parameter is: take Jin Jing as background, sweep limit 10000-4000cm
-1, resolution 4cm
-1, scan 64 times.For Pinggu, sample ground, figure 1 shows the near infrared spectrogram of 6 depth of soil.
(4) data prediction.Software Omnic8.2 is carried by whole for original spectrogram wave band (i.e. 10000-4000cm with instrument
-1) carry out the atmospheric background suppression, change into absorption spectrum, then carry out automatic baseline correction.After the spectroscopic data of 199 samples is imported software matlab7.8, carry out the process such as 7 level and smooth, first order derivatives by Norris method.
The near infrared spectrum of this embodiment to pedotheque has attempted 4 kinds of common pre-service: 1. Norris first order derivative filtering+SPXY method divides calibration set and checking collection; 2. multiplicative scatter correction+Norris first order derivative filtering+SPXY method divides calibration set and checking collection; 3. Norris second derivative filtering+SPXY method divides calibration set and checking collection; 4. Norris first order derivative filtering+K-S method divides calibration set and checking collection, then applies offset minimum binary (PLS) method and set up soil δ
13the quantitative estimation model of C value.Result (table 1) shows, and different pretreatments method is to built soil δ
13the precision of prediction of C value estimation models has certain influence, and the spectrum modeling accuracy of the preprocess method (Norris first order derivative filtering+SPXY method divides calibration set and checking collection) adopted with this patent is the highest.
This model preprocessing of table 1 method and the common disposal route of partial least square model predict soil δ
13the precision comparison of C (comprising Oe and Oa)
(5) partial least square method is adopted to set up soil near infrared spectrum and δ
13quantitative relationship between C value.By 199 of correspondence soil δ
13c value also imports in matlab software.With SPXY algorithm by test sample in the ratio of 3:1 respectively by spectral information and δ
13c value is divided into calibration set and checking collection, and calibration set is containing 150 samples, and checking collection is containing 49 samples.Adopt partial least square method, choosing best number of principal components with 20 folding cross verifications is that 11, RMSECV value is shown in accompanying drawing 2 to the mapping of major component number.Soil near infrared spectrum and δ is set up with training set in all-wave spectral limit
13regression model between C value, and the spectral information of individual authentication collection is substituted into this model calculating δ
13c value, surveys δ by collecting with checking
13c value compares, testing model precision of prediction.Correct the coefficient of determination (R
2) be 0.9684, validation-cross root-mean-square error (RMSECV) is 0.9311; Prediction related coefficient (R) is 0.9631 (accompanying drawing 3), and predicted root mean square error (RMSEP) is 0.5965.Prediction relation analysis error (RPD) is 3.74.
Attention: from accompanying drawing 2, when number of principal components is 12, verifying that root-mean-square error (RMSECV) is minimum mutually, is 0.9276; But corresponding prediction related coefficient (R) is 0.9536, predicted root mean square error (RMSEP) is 0.6691.And the present invention is through the one by one test of number of principal components from 1 to 20, consider and choose 11 for best number of principal components, its RMSECV rank in all major components is second from the bottom, only large than 12, is 0.9311; But corresponding R is 0.9631, be maximum, and RMSEP is 0.5965, is minimum in all major components in all major components.
This result shows, the method is suitable for δ in forest soil different depth (Oe, Oa, 0-2cm, 2-5cm, 5-10cm, 10-20cm)
13the detection of C value, can detect soil δ within a short period of time fast
13c value, and satisfied accuracy of detection can be reached.Meanwhile, the preprocess method such as Norris first order derivative filtering and SPXY method dividing data collection and how to choose best number of principal components all to utilizing near-infrared diffuse reflectance technology harmless quantitative to detect soil δ in PLS modeling
13c plays an important role.Nearly 200 samples in test model are from the cork oak forest soil in leap two climate zones and five provinces, and different weather conditions and soil property all can have an impact to spectrum.But the model set up at such complex condition just just has the wider scope of application, and therefore, the near-infrared spectrum technique based on partial least square method is applicable soil δ
13the efficient detection technology that C detects.
Embodiment 2 does not comprise the mineral layer soil of Oe and Oa
(1) soil sample to be checked is prepared.By the impurity elimination of mineral layer soil, air-dry, pulverizing, cross 60 mesh sieves, exsiccator saves backup.Totally 139 samples come from the mineral layer pedotheque of different depth (0-2,2-5,5-10,10-20cm) in the cork oak forest in seven different regions (Pinggu, Hong Yashan, Bai An, Huang Zangyu, Xinyang, Mount Huang, the Yunshan Mountain).
After adopting multiplicative scatter correction pre-service by table 2, spectral model precision is not improved, and proves that sample preparation obtains better, reduces the impact that particle scattering effect causes spectra collection as far as possible.
(2) stable isotope ratio mass spectroscopy is utilized to record the δ of soil sample to be checked
13c value.
(3) spectra collection.Take 200mg soil sample, the xsect being placed in 11mm is that in the stainless steel cylinder of annular, bottom is low-hydroxy-group squartz glass, and sample top flattens.Gather the diffuse reflection spectrogram of near-infrared band, instrument configuration is: Fourier transformation infrared spectrometer, and annex is near infrared integrating sphere, white light source, CaF
2beam splitter, InGaAs detecting device; Acquisition parameter is: take Jin Jing as background, sweep limit 10000-4000cm
-1, resolution 4cm
-1, scan 64 times.For Pinggu, sample ground, figure 1 shows the near infrared spectrogram of 4 depth of soil.
(4) data prediction.Software Omnic8.2 is carried by whole for original spectrogram wave band (i.e. 10000-4000cm with instrument
-1) carry out the atmospheric background suppression, change into absorption spectrum, then carry out automatic baseline correction.After the spectroscopic data of 139 samples is imported software matlab7.8, carry out the process such as 7 level and smooth, first order derivatives by Norris method.
The near infrared spectrum of this embodiment to pedotheque has attempted 4 kinds of common pre-service: 1. Norris first order derivative filtering+SPXY method divides calibration set and checking collection, 2. multiplicative scatter correction+Norris first order derivative filtering+SPXY method divide calibration set and checking collection, 3. Norris second derivative filtering+SPXY method divide calibration set and checking collection, 4. Norris first order derivative filtering+K-S method divide calibration set and checking collection, then applies offset minimum binary (PLS) method and set up soil δ
13the quantitative estimation model of C value.Result (table 2) shows, and different pretreatments method is to built soil δ
13the precision of prediction of C value estimation models has certain influence, and the spectrum modeling accuracy of the preprocess method (Norris first order derivative filtering+SPXY method divides calibration set and checking collection) adopted with this patent is the highest.
This model preprocessing of table 2 method and the common disposal route of partial least square model predict soil δ
13the precision comparison of C (not comprising Oe and Oa)
(5) partial least square method is adopted to set up soil near infrared spectrum and δ
13quantitative relationship between C value.By 139 of correspondence soil δ
13c value also imports in matlab software.With SPXY algorithm by test sample in the ratio of 3:1 respectively by spectral information and δ
13c value is divided into calibration set and checking collection, and calibration set is containing 105 samples, and checking collection is containing 34 samples.Adopt partial least square method, choosing best number of principal components with 20 folding cross verifications is that 12, RMSECV value is shown in accompanying drawing 4 to the mapping of major component number.Soil near infrared spectrum and δ is set up with calibration set in all-wave spectral limit
13regression model between C value, and the spectral information of individual authentication collection is substituted into this model calculating δ
13c value, surveys δ by collecting with checking
13c value compares, testing model precision of prediction.Correct the coefficient of determination (R
2) be 0.9866, validation-cross root-mean-square error (RMSECV) is 1.0213; Prediction related coefficient (R) is 0.9753 (accompanying drawing 5), and predicted root mean square error (RMSEP) is 0.5705.Prediction relation analysis error (RPD) is 4.38.
This result shows, the method is suitable for δ in the forest soil of different depth (0-2cm, 2-5cm, 5-10cm, 10-20cm)
13the detection of C value, can detect soil δ within a short period of time fast
13c value, and satisfied accuracy of detection can be reached.The present invention acquires near-infrared band modeling by homemade sample stage, achieves extraordinary effect.Meanwhile, the preprocess method such as Norris first order derivative filtering and SPXY method dividing data collection and how to choose best number of principal components all to utilizing near-infrared diffuse reflectance technology harmless quantitative to detect soil δ in PLS modeling
13c plays an important role.139 samples in test model are from the cork oak forest soil in leap two climate zones and five provinces, and different weather conditions and soil property all can have an impact to spectrum.But the model set up at such complex condition just just has the wider scope of application, and therefore, the near-infrared spectrum technique based on partial least square method is applicable soil δ
13the efficient detection technology that C detects.
More than describe preferred embodiment of the present invention in detail.Should be appreciated that those of ordinary skill in the art just design according to the present invention can make many modifications and variations without the need to creative work.Therefore, all technician in the art, all should by the determined protection domain of claims under this invention's idea on the basis of existing technology by the available technical scheme of logical analysis, reasoning, or a limited experiment.
Claims (10)
1. utilize near infrared spectrum to detect a method for the stable carbon isotope ratio of soil, it is characterized in that, described method comprises the steps:
1) the stable carbon isotope ratio of multiple calibration pedotheque is recorded;
2) gather the diffuse reflection spectrogram of the near-infrared band of calibration pedotheque, obtain original spectrogram;
3) by smoothing for original spectrogram pre-service, obtain processing rear spectrogram;
4) partial least square method is adopted to set up causes between spectrogram and stable carbon isotope ratio after the process of calibration pedotheque;
5) gather the diffuse reflection spectrogram of the near-infrared band of pedotheque to be measured, calculate the stable carbon isotope ratio of pedotheque to be measured according to causes.
2. the method for claim 1, is characterized in that, in described step 1) in, the method recording the stable carbon isotope ratio of calibration pedotheque is stable isotope ratio mass spectroscopy.
3. the method for claim 1, is characterized in that, in described step 1) in, the concrete steps of preparation calibration pedotheque comprise: after soil sample being dewatered, levigate, cross 60 mesh sieves.
4. the method for claim 1, is characterized in that, in described step 1) in, described multiple calibration pedotheques comprise the sample of Oe and Oa layer soil.
5. the method for claim 1, it is characterized in that, in described step 3) in, smoothing for original spectrogram pretreated concrete steps are comprised: the atmospheric background suppresses, absorbance is changed, automatic baseline correction and the process of Norris first order derivative filtering.
6. the method for claim 1, is characterized in that, in described step 4) in, the concrete steps setting up causes comprise: respectively spectral information and stable carbon isotope ratio are divided into calibration set and checking collection by SPXY method; Adopt partial least square method, in calibration set spectral information, extract major component, and choose best number of principal components with 20 folding cross verifications, with the spectral information of calibration set for independent variable, with calibration set stable carbon isotope ratio for dependent variable, set up regression model; And utilize the precision of checking collection inspection regression model.
7. method as claimed in claim 6, is characterized in that, the ratio of the sample number that calibration set and checking integrate is as 3:1.
8. method as claimed in claim 6, it is characterized in that, the concrete steps of 20 described folding cross verifications comprise: by number of principal components f successively from 1 value to 20, for getting a fixed number of principal components, calibration set is divided into 20 subsets, each subset data is respectively used to do one-time authentication, other 19 subset data are used for training simultaneously, cross validation repeats 20 times, the result of average 20 times, finally obtains the validation-cross root-mean-square error of a described number of principal components of correspondence.
9. method as claimed in claim 6, it is characterized in that, comprise by the concrete steps of the precision of checking collection inspection calibration model: by the correction coefficient of determination, validation-cross root-mean-square error and prediction related coefficient, predicted root mean square error four parameters, calibration model is evaluated.
10. the method for claim 1, is characterized in that, in described step 1) in, the concrete steps preparing pedotheque to be measured comprise: after soil sample being dewatered, levigate, cross 60 mesh sieves.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610113351.2A CN105486663B (en) | 2016-02-29 | 2016-02-29 | A method of detecting the stable carbon isotope ratio of soil using near infrared spectrum |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610113351.2A CN105486663B (en) | 2016-02-29 | 2016-02-29 | A method of detecting the stable carbon isotope ratio of soil using near infrared spectrum |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105486663A true CN105486663A (en) | 2016-04-13 |
| CN105486663B CN105486663B (en) | 2018-10-19 |
Family
ID=55673787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610113351.2A Expired - Fee Related CN105486663B (en) | 2016-02-29 | 2016-02-29 | A method of detecting the stable carbon isotope ratio of soil using near infrared spectrum |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105486663B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106770005A (en) * | 2016-11-25 | 2017-05-31 | 山东大学 | A kind of division methods of the calibration set for near-infrared spectrum analysis and checking collection |
| CN110646497A (en) * | 2018-06-27 | 2020-01-03 | 中国科学院沈阳应用生态研究所 | Method for identifying organic agricultural products through stable nitrogen isotope ratio |
| CN111781163A (en) * | 2020-07-22 | 2020-10-16 | 中国农业大学 | Method for eliminating influence of soil granularity on soil parameter detection of discrete near-infrared band |
| CN112666038A (en) * | 2021-01-22 | 2021-04-16 | 山东大学 | Method for representing moisture absorption process based on near infrared spectrum |
| WO2021073541A1 (en) * | 2019-10-17 | 2021-04-22 | 山东大学 | Spectral similarity-based correction set and validation set selecting and modeling method |
| CN115615951A (en) * | 2022-09-14 | 2023-01-17 | 上海市农业科学院 | Near infrared spectrum rapid detection method for light stable isotope ratios of coix seeds in different producing areas |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100304426A1 (en) * | 2009-05-27 | 2010-12-02 | David Osborne | Analytical Methods for Measuring Synthetic Progesterone |
| US20120298868A1 (en) * | 2011-05-26 | 2012-11-29 | Southwest Sciences Incorporated | Laser Based, Temperature Insensitive, Carbon Dioxide Isotope Ratio Measurement |
| CN103105366A (en) * | 2013-01-22 | 2013-05-15 | 中国科学院安徽光学精密机械研究所 | Method and device for detecting CO2 carbon isotope by infrared spectrum |
| CN103728327A (en) * | 2013-12-18 | 2014-04-16 | 中国原子能科学研究院 | Method for measuring uranium isotope ratio in single particles |
-
2016
- 2016-02-29 CN CN201610113351.2A patent/CN105486663B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100304426A1 (en) * | 2009-05-27 | 2010-12-02 | David Osborne | Analytical Methods for Measuring Synthetic Progesterone |
| US20120298868A1 (en) * | 2011-05-26 | 2012-11-29 | Southwest Sciences Incorporated | Laser Based, Temperature Insensitive, Carbon Dioxide Isotope Ratio Measurement |
| CN103105366A (en) * | 2013-01-22 | 2013-05-15 | 中国科学院安徽光学精密机械研究所 | Method and device for detecting CO2 carbon isotope by infrared spectrum |
| CN103728327A (en) * | 2013-12-18 | 2014-04-16 | 中国原子能科学研究院 | Method for measuring uranium isotope ratio in single particles |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106770005A (en) * | 2016-11-25 | 2017-05-31 | 山东大学 | A kind of division methods of the calibration set for near-infrared spectrum analysis and checking collection |
| CN110646497A (en) * | 2018-06-27 | 2020-01-03 | 中国科学院沈阳应用生态研究所 | Method for identifying organic agricultural products through stable nitrogen isotope ratio |
| CN110646497B (en) * | 2018-06-27 | 2021-10-29 | 中国科学院沈阳应用生态研究所 | Method for identifying organic agricultural products through stable nitrogen isotope ratio |
| WO2021073541A1 (en) * | 2019-10-17 | 2021-04-22 | 山东大学 | Spectral similarity-based correction set and validation set selecting and modeling method |
| CN111781163A (en) * | 2020-07-22 | 2020-10-16 | 中国农业大学 | Method for eliminating influence of soil granularity on soil parameter detection of discrete near-infrared band |
| CN111781163B (en) * | 2020-07-22 | 2021-05-14 | 中国农业大学 | Method for eliminating influence of soil granularity on soil parameter detection of discrete near-infrared band |
| CN112666038A (en) * | 2021-01-22 | 2021-04-16 | 山东大学 | Method for representing moisture absorption process based on near infrared spectrum |
| CN112666038B (en) * | 2021-01-22 | 2023-02-28 | 山东大学 | Method for representing moisture absorption process based on near infrared spectrum |
| CN115615951A (en) * | 2022-09-14 | 2023-01-17 | 上海市农业科学院 | Near infrared spectrum rapid detection method for light stable isotope ratios of coix seeds in different producing areas |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105486663B (en) | 2018-10-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105486663A (en) | Method for detecting stable carbon isotopic ratio of soil through near infrared spectrum | |
| Patz et al. | Application of FT-MIR spectrometry in wine analysis | |
| CN105784628A (en) | Method for detecting chemical composition of soil organic matter with mid-infrared spectra | |
| CN105758819A (en) | Method for detecting organic components of soil by utilizing near infrared spectrum | |
| CN1982872B (en) | Near-infrared diffuse reflection spectral method for fastly inspecting drop water content | |
| CN103235095B (en) | Water-injected meat detection method and device | |
| CN103278473B (en) | The mensuration of pipering and moisture and method for evaluating quality in white pepper | |
| CN104020129A (en) | Method for discriminating fermentation quality of congou black tea based on near-infrared-spectroscopy-combined amino acid analysis technology | |
| CN101231274B (en) | Method for rapid measuring allantoin content in yam using near infrared spectrum | |
| CN104849233B (en) | A kind of method and device of detection cereal new-old degree | |
| CN104749132A (en) | Method for measuring content of azodicarbonamide in flour | |
| CN106950192A (en) | A kind of method of Contents of Main Components quick detection in vegetable protein beverage based on near-infrared spectral analysis technology | |
| CN110749565A (en) | Method for rapidly identifying storage years of Pu' er tea | |
| CN102937575B (en) | Watermelon sugar degree rapid modeling method based on secondary spectrum recombination | |
| CN103940767A (en) | Gas concentration inversion method based on multi-manifold learning | |
| CN104297203A (en) | Rapid discriminant method for fermentation quality of congou black tea on basis of near-infrared spectrum analysis technology | |
| CN105784672A (en) | Drug detector standardization method based on dual-tree complex wavelet algorithm | |
| CN105699314B (en) | A method of detecting soil stabilization carbon isotope ratio using middle infrared spectrum | |
| CN102313711A (en) | Method for determining hot degree of pepper | |
| CN109540832A (en) | A kind of detection method based on total nitrogen in the near-to-mid infrared spectrum large-scale milch cow farms liquid manure of fusion | |
| CN105784629B (en) | The method that the stable carbon isotope ratio of soil is quickly detected using middle infrared spectrum | |
| CN102042967A (en) | Glucose aqueous solution quick identification method based on near infrared spectrum technology | |
| CN103353443A (en) | Near infrared spectrum based discrimination method for Zhongning fructus lycii | |
| CN104155262A (en) | Method for selecting spectrum scope in tobacco water-soluble sugar near infrared quantification model | |
| Liu et al. | Research on the online rapid sensing method of moisture content in famous green tea spreading |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181019 Termination date: 20210228 |