CN106841173A - Blade heavy metal content detection method based on K element ratiometric correction moisture content - Google Patents
Blade heavy metal content detection method based on K element ratiometric correction moisture content Download PDFInfo
- Publication number
- CN106841173A CN106841173A CN201710054865.XA CN201710054865A CN106841173A CN 106841173 A CN106841173 A CN 106841173A CN 201710054865 A CN201710054865 A CN 201710054865A CN 106841173 A CN106841173 A CN 106841173A
- Authority
- CN
- China
- Prior art keywords
- heavy metal
- sample
- detection method
- spectral
- blade
- 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
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
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
- G01N21/718—Laser microanalysis, i.e. with formation of sample plasma
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/127—Calibration; base line adjustment; drift compensation
Abstract
The present invention discloses a kind of blade heavy metal content detection method based on K element ratiometric correction moisture content, including：1) the blade sample of different content of beary metal is obtained, and sample is pre-processed；2) content of beary metal in sample is measured using standard method；3) the LIBS spectral lines of collecting sample diverse location；4) acquired spectral signal is pre-processed；5) institute's check weighing metallic element and the corresponding LIBS spectral lines peak strength of K element are extracted；6) using the content of beary metal of survey as output, the strength ratio using institute's check weighing metallic element and K element sets up calibration model as input；7) spectral line of sample to be tested is gathered, and the described calibration model of the LIBS spectral lines peak strength input of heavy metal element and K element is extracted in spectral signal after the pre-treatment, draw the content of heavy metal.The present invention eliminates the influence of water content heavy metal detection in sample, the precision of the detection of raising by moisture in calibration samples.
Description
Technical field
The present invention relates to laser spectrum tech, more particularly to a kind of blade based on K element ratiometric correction moisture content is with much money
Category detection method of content.
Background technology
LIBS (laser-induced breakdown spectroscopy, abbreviation LIBS) is
A kind of emerging atom analysis technology, the characteristics of it has simple quick, pretreatment, multielement analysis and online remote analysis.
Its detection object may include gaseous state, solid-state and liquid.The plasma that it is produced by detection and analysis by laser ablation sample
Transition spectral line, with reference to quantitative analysis method detection elements content.In theory, LIBS detectable element week
All elements on phase table.At present, LIBS applied to archaeology, biomedicine, Physicochemical, army
The fields such as thing, industry, mainly comprising heavy metal analysis, dangerous substance identification, important element analysis etc..
Yet with the presence of matrix effect, LIBS still has larger choosing for the quantitative analysis of biological specimen heavy metal
War.Under normal circumstances, researcher need to be dried to biological specimen, compressing tablet to be to reduce moisture and sample inhomogeneities brings
Influence.Additionally, for fresh plant sample, some researchers freeze blade to reduce the shadow of moisture heavy metal detection
Ring.But such preprocess method will increase the detection and analysis time, it is impossible to embody LIBS quick detection
Advantage.
The content of the invention
The invention discloses a kind of blade heavy metal content detection method based on K element ratiometric correction moisture content, eliminate
Influence of the sample moisture content to accuracy of detection, with low cost, detection is quick, the features such as simple to operate.
Concrete technical scheme of the invention is as follows：
A kind of blade heavy metal content detection method based on K element ratiometric correction moisture content, including step：
1) the blade sample of different content of beary metal is obtained, and sample is pre-processed；
2) content of beary metal in sample is measured using standard method, Y is designated as；
3) using the spectral line of laser induced breakdown spectrograph collecting sample diverse location；
4) acquired spectral signal is pre-processed；
5) the corresponding LIBS spectral lines peak strength of institute's check weighing metallic element is extracted, X is designated as；
6) the corresponding LIBS spectral lines peak strength of K element is extracted, X ' is designated as；
7) using the content of beary metal surveyed as output Y, using the strength ratio of institute's check weighing metallic element and K element as defeated
Enter, set up calibration model：Wherein a is the regression coefficient of calibration curve, and b is constant term；
Wherein, the corresponding calibration model of institute's check weighing metallic element chromium isCheck weighing metal unit of institute
The corresponding calibration model of plain manganese is
8) spectral line of sample to be tested is gathered, and heavy metal element and K element is extracted in spectral signal after the pre-treatment
The described calibration model of LIBS spectral lines peak strength input, draws the content of heavy metal.
In the application, the instrument parameter for gathering spectral signal is：Range of laser energy is 20~70mJ；Depth of focus is 2mm；Prolong
When the time be 2~8 μ s；The time of integration is 20 μ s, and detector gain is 2000.
Because plant leaf blade is relatively thin, when the laser energy for using is larger, laser will can produce sample through plant leaf blade
The signal of platform, the experiment proved that, when range of laser energy is in 20~70mJ, it is the signal from sample to be collected into signal.This
Outer depth of focus can influence the strength and stability of signal, therefore focal depth range is set into 2mm.When delay time, the integration of detector
Between and gain it is relevant with the signal to noise ratio of metal to be measured, therefore the parameter of the above is through the result of optimum experimental.
Described step 4) in pretreatment include that carrying out signal normalization and exceptional sample successively rejects.
Preferably, described signal normalization is that the average value of sample spectral line signal is normalized.
Due to the influence of environment temperature, noise of instrument and sample matrix effect, the signal of sample spectral line is produced in certain limit
The certain fluctuation of life.In order to eliminate influence in this respect, the present invention is using the average value of whole wavelength respective intensities as normalizing
Change variable, and the intensity level of each wavelength is normalized divided by normalization variable.This pretreatment can be reduced effectively a little
Spectral line with point fluctuates.
Preferably, it is the method based on median absolute deviation that described exceptional sample is rejected, i.e., replaced with median
Average in Pauta Criterion, the value that will be greater than 2.5 times of median absolute deviations is considered as exceptional value.
In the signal of diverse location collection spectral line it is not the normal distribution in standard due to LIBS, therefore using traditional base
Easily disturbed by maximum and minimum in the method for average value absolute deviation.Can effectively be eliminated due to leaf using this method
The abnormal spectral line that piece uneven surface and detection maloperation are produced, it is ensured that the reliability of detection signal.
Preferably, the variable that described exceptional sample is rejected is the corresponding peak strength of CN keys, its corresponding spectrum ripple
A length of 388.29nm.
Spectral wavelength 388.29nm correspondence CN signals are to combine the signal for producing by carbon and nitrogen, are present in organic
In the LIBS spectral lines of thing.Because general both elements are relatively stable with air in the sample therefore use the peak value of the element
Intensity as rejecting abnormalities spectrum variable.
Preferably, described abnormal spectral line is rejected follows following principle：The spectral line number of reservation should be greater than original spectrum line number
80%, and retain spectral line relative standard deviation should be less than 10~15%.In view of the integrality and detection for ensureing data
Repeatability, therefore the rejecting principle of abnormal spectral line above is set.
Described step 6) in ratio variable be the corresponding peak strength of K element, its corresponding spectral wavelength is
766.49nm.Because the moisture content of the corresponding peak strength of potassium element and sample has preferable linear relationship, therefore consideration will
The corresponding peak strength of potassium element is used as ratio variable.The method can effectively eliminate the influence of moisture content.
Described step 5) and step 6) in element-intensities extraction be choose 3 wavelength before and after reference wavelength maximum it is strong
Degree.Because there is certain drift in the wavelength location of spectrum, therefore by calculating reference wavelength front and rear 3 ripples nearby in the present invention
Intensity long, chooses peak strength of the maximum intensity as the reference wavelength.
The invention has the advantages that：
(1) quick detection of plant leaf blade content of beary metal is realized.
(2) carry out heavy metal using LIBS technologies, with simple to operate, low cost, environmental protection, it is quick the features such as；Effective gram
The characteristics of having taken traditional detection method detection time long, complex operation, it is to avoid pollution of the chemical reagent to environment.
(3) reliability of detection signal is can guarantee that using the method rejecting abnormalities spectral line based on median absolute deviation.
(4) using the corresponding peak strength of K element as ratio variable, in effectively eliminating plant leaf blade heavy metal analysis
The influence of moisture content.
(5) LIBS spectrums can effectively be solved as peak strength using 3 maximum intensitys of wavelength before and after choosing reference wavelength
The problem of line drift.
Brief description of the drawings
Fig. 1 is spectral line at 425~431nm under different water cut；
Fig. 2 corrects preceding chromium calibration curve figure not carry out moisture content；
Fig. 3 is chromium calibration curve figure after moisture content correction.
Fig. 4 corrects preceding manganese element calibration curve figure not carry out moisture content；
Fig. 5 is manganese element calibration curve figure after moisture content correction.
Specific embodiment
Below in conjunction with specific embodiments and the drawings, the present invention will be described in detail, Cr constituent contents in detection paddy rice
Comprise the following steps that：
1st, experiment rice varieties are the spring excellent 84, are the paddy rice single cropping Indica-Japonica Hybrid Rice that Zhejiang Province plants extensively.Rice paddy seed
After through NaClO solution disinfections 30min, respectively with running water and distilled water flushing several times, in the 2d that soaked seed under 25 DEG C of dark conditions, so
Vernalization 1d at a temperature of placing 35 DEG C afterwards.Rice paddy seed after sprouting is seeded in the container containing perlite, in artificial climate growth
Culture in case.Water-culturing rice condition is：Periodicity of illumination 14h/10h；30 DEG C/22 DEG C of temperature；225 μm of ol m of intensity of illumination-2s-1；
Relative humidity 85%.Paddy rice carries out Solution culture method after distilling Aquaponic 15d.Rice plant sets 5 Cr levels：0μM、
25μM、50μM、75μM、100μM.After paddy rice is processed through Cr, sampled in tillering stage, for LIBS analyses.
2nd, in order to cause certain moisture gradient, rice leaf is placed in 60 DEG C of baking ovens and carries out LIBS inspections after 30 minutes
Survey.Rice leaf is weighed rapidly after LIBS signals are obtained, and weight is designated as W1.60 DEG C are positioned over until constant weight, is designated as W2。
Shown in the computing formula of leaf water content ε such as equation (1).Measurement result is as shown in table 1.
The rice leaf moisture content overview of table 1
| Drying time (min) | Scope (%) | Average (%) | Standard deviation (%) |
| 30 | 4.3~45.6 | 20.5 | 16.0 |
3rd, the pattern that the LIBS spectra collections of rice leaf are obtained using single-point, i.e. one spectrum of each station acquisition.For
Blade is prevented to be moved position in laser shots, rice leaf is fixed on XYZ electricity driving displacement platforms surface by double faced adhesive tape,
Laser impact position for one side at vein 2mm, laser is impacted by blade bottom to vane tip with 1.5mm step-lengths,
Single blade gathers 25 spectral lines, it is ensured that each puts misaligned.To obtain preferable signal-to-background ratio and repeatability, laser impact point position
In below blade surface 2mm, laser energy is 60mJ, and the delay time of detector and the time of integration are respectively set to 2 μ s and 20 μ
S, the gain of detector is set to 2000.
4th, in order to reduce fluctuation between points, it usually needs spectral line is done into normalized, the present invention uses area
Normalized mode.Further, since there is larger fluctuation with the coupling of sample and exciting for spectral line in laser, it is thus possible to can go out
The different C constant value of now very big or minimum intensity.For rejecting abnormalities value, with median instead of average in Pauta Criterion
Number, it is exceptional value that the value that will be greater than 2.5 times of median absolute deviations is.By iterative algorithm rejecting abnormalities value, until all spectral lines
Meet criterion or remaining spectral line number is less than 80% score line number iteration stopping.
5th, chromium, the intensity of manganese element difference corresponding wavelength 425.44nm and 257.62nm and ratio element are chosen using program
The intensity of K 766.49nm.
6th, the chromium for obtaining sample by the use of ICP-MS refers to content as output, with Cr the intensity of spectral line and ratio element K
The ratio of 766.49nm sets up following formula chromium calibration model as input：
7th, the manganese element for obtaining sample by the use of ICP-MS refers to content as output, with Mn the intensity of spectral line and ratio element K
The ratio of 766.49nm sets up following formula manganese calibration model as input：
8th, gather the LIBS spectral lines of the detection sample of unknown content of beary metal, and extracted in spectral signal after the pre-treatment
LIBS spectral lines peak strength input model equation (2) and (3) of heavy metal element and K element, draws chromium in blade, manganese element
Content.
Fig. 1 is original spectrum of the identical content of beary metal different water cut sample at 425~431nm, spectral line in figure
Intensity with moisture content increase and reduces, it will be seen that moisture content heavy metal detection have disturb.Such as
Shown in Fig. 2 and Fig. 4, when moisture timing is not carried out, the prediction effect of model is poor, prediction chromium, the correlation of manganese content model
Coefficient is respectively 0.78 and 0.80.And after being corrected to intensity using ratio element K, the coefficient correlation of model has very big carrying
Rise；As shown in Figure 3 and Figure 5, prediction chromium, the coefficient correlation of manganese content model reach 0.81 and 0.87.Therefore the present invention passes through school
Moisture in positive sample, eliminates the influence of water content heavy metal detection in sample, improves the precision of detection.
The foregoing is only preferable implementation example of the invention, be not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent substitution and improvements made etc. should be included within the scope of the present invention.
Claims (9)
1. a kind of blade heavy metal content detection method based on K element ratiometric correction moisture content, it is characterised in that including step
Suddenly：
1) the blade sample of different content of beary metal is obtained, and sample is pre-processed；
2) content of beary metal in sample is measured using standard method, Y is designated as；
3) using the spectral line of laser induced breakdown spectrograph collecting sample diverse location；
4) acquired spectral signal is pre-processed；
5) the corresponding LIBS spectral lines peak strength of institute's check weighing metallic element is extracted, X is designated as；
6) the corresponding LIBS spectral lines peak strength of K element is extracted, X ' is designated as；
7) using the content of beary metal of survey as output Y, using the strength ratio of institute's check weighing metallic element and K element as input, set up
Calibration model：Wherein a is the regression coefficient of calibration curve, and b is constant term；
8) spectral line of sample to be tested is gathered, and the LIBS of heavy metal element and K element is extracted in spectral signal after the pre-treatment
The described calibration model of spectral line peak strength input, draws the content of heavy metal.
2. blade heavy metal content detection method as claimed in claim 1, it is characterised in that described step 3) in, utilize
Laser induced breakdown spectrograph collection spectral signal parameter be：Range of laser energy is 20~70mJ；Depth of focus is 2mm；Time delay
Time is 2~8 μ s；The time of integration is 20 μ s, and detector gain is 2000.
3. blade heavy metal content detection method as claimed in claim 1, it is characterised in that described step 4) in pretreatment
Rejected including carrying out signal normalization and exceptional sample successively.
4. blade heavy metal content detection method as claimed in claim 3, it is characterised in that described signal normalization is right
The average value of sample spectral line signal is normalized.
5. blade heavy metal content detection method as claimed in claim 3, it is characterised in that described exceptional sample is rejected is
Average in Pauta Criterion is replaced with median, the value that will be greater than 2.5 times of median absolute deviations is considered as exceptional value.
6. blade heavy metal content detection method as claimed in claim 5, it is characterised in that what described exceptional sample was rejected
Variable is the corresponding peak value of CN keys, and its corresponding spectral wavelength is 388.29nm.
7. blade heavy metal content detection method as claimed in claim 6, it is characterised in that described abnormal spectral line is rejected and abided by
Follow following principle：The spectral line number of reservation should be greater than the 80% of original spectrum line number, and the relative standard deviation of reservation spectral line should be less than
10~15%.
8. blade heavy metal content detection method as claimed in claim 1, it is characterised in that described step 5), step 6)
With step 8) in element-intensities extraction be choose reference wavelength before and after 3 maximum intensitys of wavelength.
9. blade heavy metal content detection method as claimed in claim 1, it is characterised in that institute's check weighing metallic element chromium is corresponding
Calibration model isThe corresponding calibration model of institute's check weighing metallic element manganese is
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710054865.XA CN106841173B (en) | 2017-01-24 | 2017-01-24 | Blade heavy metal content detection method based on K element ratiometric correction moisture content |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710054865.XA CN106841173B (en) | 2017-01-24 | 2017-01-24 | Blade heavy metal content detection method based on K element ratiometric correction moisture content |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106841173A true CN106841173A (en) | 2017-06-13 |
| CN106841173B CN106841173B (en) | 2019-09-10 |
Family
ID=59119861
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710054865.XA Active CN106841173B (en) | 2017-01-24 | 2017-01-24 | Blade heavy metal content detection method based on K element ratiometric correction moisture content |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106841173B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799195A (en) * | 2019-01-22 | 2019-05-24 | 上海交通大学 | A kind of high-precision fixed analysis method of laser induced breakdown spectroscopy |
| CN110567941A (en) * | 2019-08-19 | 2019-12-13 | 长江大学 | Rice seed moisture content grading detection method based on main element spectral intensity |
| CN111504981A (en) * | 2020-04-26 | 2020-08-07 | 上海交通大学 | Method for determining chemical components and moisture content in powder material |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5847825A (en) * | 1996-09-25 | 1998-12-08 | Board Of Regents University Of Nebraska Lincoln | Apparatus and method for detection and concentration measurement of trace metals using laser induced breakdown spectroscopy |
| WO2014175363A1 (en) * | 2013-04-24 | 2014-10-30 | 株式会社Ihi | Component-concentration measurement device and method |
| CN104374752A (en) * | 2014-11-17 | 2015-02-25 | 浙江大学 | Rapid detection method for nutrient elements of crops based on collinear laser-induced breakdown spectroscopy |
| CN105092540A (en) * | 2015-06-16 | 2015-11-25 | 江西农业大学 | Method and device for rapid and high-precision detection of content of heavy metal lead in edible oil |
-
2017
- 2017-01-24 CN CN201710054865.XA patent/CN106841173B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5847825A (en) * | 1996-09-25 | 1998-12-08 | Board Of Regents University Of Nebraska Lincoln | Apparatus and method for detection and concentration measurement of trace metals using laser induced breakdown spectroscopy |
| WO2014175363A1 (en) * | 2013-04-24 | 2014-10-30 | 株式会社Ihi | Component-concentration measurement device and method |
| CN104374752A (en) * | 2014-11-17 | 2015-02-25 | 浙江大学 | Rapid detection method for nutrient elements of crops based on collinear laser-induced breakdown spectroscopy |
| CN105092540A (en) * | 2015-06-16 | 2015-11-25 | 江西农业大学 | Method and device for rapid and high-precision detection of content of heavy metal lead in edible oil |
Non-Patent Citations (2)
| Title |
|---|
| 彭秋梅等: "激光诱导击穿光谱分析新鲜桔叶重金属元素铬", 《江西农业大学学报》 * |
| 李占锋等: "黄连、附片和茯苓内铜元素激光诱导击穿光谱分析", 《发光学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799195A (en) * | 2019-01-22 | 2019-05-24 | 上海交通大学 | A kind of high-precision fixed analysis method of laser induced breakdown spectroscopy |
| CN109799195B (en) * | 2019-01-22 | 2020-07-31 | 上海交通大学 | High-precision quantitative analysis method for laser-induced breakdown spectroscopy |
| CN110567941A (en) * | 2019-08-19 | 2019-12-13 | 长江大学 | Rice seed moisture content grading detection method based on main element spectral intensity |
| CN111504981A (en) * | 2020-04-26 | 2020-08-07 | 上海交通大学 | Method for determining chemical components and moisture content in powder material |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106841173B (en) | 2019-09-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106770195A (en) | The blade heavy metal content detection method of moisture content is corrected based on CN element ratios | |
| Ahmadi et al. | Early detection of Ganoderma basal stem rot of oil palms using artificial neural network spectral analysis | |
| Shi et al. | Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations | |
| CN106596514B (en) | Blade heavy metal content detection method based on Ca element ratios correction moisture content | |
| CN103884661A (en) | Soil total nitrogen real-time detection method based on soil visible-near infrared spectrum library | |
| Liu et al. | Estimating maize GPP using near-infrared radiance of vegetation | |
| Feng et al. | Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices | |
| CN106841173A (en) | Blade heavy metal content detection method based on K element ratiometric correction moisture content | |
| Balasundram et al. | Precision agriculture technologies for management of plant diseases | |
| CN104034689A (en) | Method and device for detecting heavy metal content of soil on basis of tabletting method | |
| Liu et al. | Quantitative analysis of cadmium in tobacco roots using laser-induced breakdown spectroscopy with variable index and chemometrics | |
| CN104062255A (en) | Method and device for detecting heavy metal content in soil based on sample box method | |
| CN108051410A (en) | A kind of tobacco root cadmium content rapid detection method based on laser induced breakdown spectroscopy | |
| CN106290292A (en) | A kind of utilize the method for carotenoid content in copolymerization Jiao's microscopic Raman detection Folium Camelliae sinensis | |
| CN112304997A (en) | Soil heavy metal content detection system and detection method based on space coupling model | |
| Zhang et al. | Estimation of corn canopy chlorophyll content using derivative spectra in the O2–A absorption band | |
| Huang et al. | A novel PCA-based calibration algorithm for classification of challenging laser-induced breakdown spectroscopy soil sample data | |
| Liu et al. | Diagnosis of citrus greening using raman spectroscopy-based pattern recognition | |
| CN106841174A (en) | The blade heavy metal content detection method of moisture content is corrected based on Mg element ratios | |
| AU2020101288A4 (en) | Method and System for Quantitatively Detecting Copper in Rice Leaves | |
| Chowdhury et al. | A rapid approach to measure extracted chlorophyll-a from lettuce leaves using electrical impedance spectroscopy | |
| Bi et al. | Hyperspectral diagnosis of nitrogen status in arbuscular mycorrhizal inoculated soybean leaves under three drought conditions | |
| Hameed et al. | Modified Vegetation Detection Index Using Different-Spectral Signature | |
| Du et al. | Wavelength selection of hyperspectral lidar based on feature weighting for estimation of leaf nitrogen content in rice | |
| Li et al. | Computer and Computing Technologies in Agriculture: 5th IFIP TC 5, SIG 5.1 International Conference, CCTA 2011, Beijing, China, October 29-31, 2011, Proceedings |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |