CN109142650A - A kind of modeling method and its application of Cadmium in Vegetables content prediction model - Google Patents
A kind of modeling method and its application of Cadmium in Vegetables content prediction model Download PDFInfo
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- 235000013311 vegetables Nutrition 0.000 title claims abstract description 82
- 229910052793 cadmium Inorganic materials 0.000 title claims abstract description 72
- BDOSMKKIYDKNTQ-UHFFFAOYSA-N cadmium atom Chemical compound [Cd] BDOSMKKIYDKNTQ-UHFFFAOYSA-N 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 49
- 239000002689 soil Substances 0.000 claims abstract description 193
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 22
- QTBSBXVTEAMEQO-UHFFFAOYSA-N Acetic acid Chemical compound CC(O)=O QTBSBXVTEAMEQO-UHFFFAOYSA-N 0.000 claims abstract description 17
- MUBZPKHOEPUJKR-UHFFFAOYSA-N Oxalic acid Chemical compound OC(=O)C(O)=O MUBZPKHOEPUJKR-UHFFFAOYSA-N 0.000 claims abstract description 15
- 239000000284 extract Substances 0.000 claims abstract description 12
- VMHLLURERBWHNL-UHFFFAOYSA-M Sodium acetate Chemical compound [Na+].CC([O-])=O VMHLLURERBWHNL-UHFFFAOYSA-M 0.000 claims abstract description 11
- 229910052742 iron Inorganic materials 0.000 claims abstract description 9
- 239000004411 aluminium Substances 0.000 claims abstract description 7
- 229910052782 aluminium Inorganic materials 0.000 claims abstract description 7
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims abstract description 7
- VBIXEXWLHSRNKB-UHFFFAOYSA-N ammonium oxalate Chemical compound [NH4+].[NH4+].[O-]C(=O)C([O-])=O VBIXEXWLHSRNKB-UHFFFAOYSA-N 0.000 claims abstract description 6
- 239000001632 sodium acetate Substances 0.000 claims abstract description 6
- 235000017281 sodium acetate Nutrition 0.000 claims abstract description 6
- HKLSJXLTTHYVNJ-UHFFFAOYSA-I C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.C(=O)(O)[O-].OS(=O)S(=O)[O-].[Na+].[Na+].[Na+].[Na+].[Na+] Chemical compound C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.C(=O)(O)[O-].OS(=O)S(=O)[O-].[Na+].[Na+].[Na+].[Na+].[Na+] HKLSJXLTTHYVNJ-UHFFFAOYSA-I 0.000 claims abstract 2
- 240000007124 Brassica oleracea Species 0.000 claims description 15
- 235000003899 Brassica oleracea var acephala Nutrition 0.000 claims description 15
- 235000011301 Brassica oleracea var capitata Nutrition 0.000 claims description 15
- 235000001169 Brassica oleracea var oleracea Nutrition 0.000 claims description 15
- 235000021384 green leafy vegetables Nutrition 0.000 claims description 12
- 244000106835 Bindesalat Species 0.000 claims description 10
- 235000000318 Bindesalat Nutrition 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 8
- 238000007689 inspection Methods 0.000 claims description 4
- XEEVLJKYYUVTRC-UHFFFAOYSA-N oxomalonic acid Chemical compound OC(=O)C(=O)C(O)=O XEEVLJKYYUVTRC-UHFFFAOYSA-N 0.000 claims description 2
- 230000004069 differentiation Effects 0.000 claims 1
- 238000000605 extraction Methods 0.000 abstract description 8
- 230000007613 environmental effect Effects 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 4
- 235000006408 oxalic acid Nutrition 0.000 abstract description 4
- 229910001385 heavy metal Inorganic materials 0.000 description 35
- 239000000523 sample Substances 0.000 description 32
- 238000004458 analytical method Methods 0.000 description 18
- 238000012360 testing method Methods 0.000 description 15
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- 230000001419 dependent effect Effects 0.000 description 9
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 6
- 239000000047 product Substances 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 235000013305 food Nutrition 0.000 description 5
- 230000009466 transformation Effects 0.000 description 5
- 244000221633 Brassica rapa subsp chinensis Species 0.000 description 4
- 241000196324 Embryophyta Species 0.000 description 4
- 238000009825 accumulation Methods 0.000 description 4
- 229960000583 acetic acid Drugs 0.000 description 4
- 238000010219 correlation analysis Methods 0.000 description 4
- 238000002354 inductively-coupled plasma atomic emission spectroscopy Methods 0.000 description 4
- 238000003908 quality control method Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- PNEYBMLMFCGWSK-UHFFFAOYSA-N Alumina Chemical compound [O-2].[O-2].[O-2].[Al+3].[Al+3] PNEYBMLMFCGWSK-UHFFFAOYSA-N 0.000 description 3
- 239000002253 acid Substances 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
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- 235000010149 Brassica rapa subsp chinensis Nutrition 0.000 description 2
- 235000000536 Brassica rapa subsp pekinensis Nutrition 0.000 description 2
- MHAJPDPJQMAIIY-UHFFFAOYSA-N Hydrogen peroxide Chemical compound OO MHAJPDPJQMAIIY-UHFFFAOYSA-N 0.000 description 2
- UQSXHKLRYXJYBZ-UHFFFAOYSA-N Iron oxide Chemical compound [Fe]=O UQSXHKLRYXJYBZ-UHFFFAOYSA-N 0.000 description 2
- 235000003228 Lactuca sativa Nutrition 0.000 description 2
- 240000008415 Lactuca sativa Species 0.000 description 2
- QPCDCPDFJACHGM-UHFFFAOYSA-N N,N-bis{2-[bis(carboxymethyl)amino]ethyl}glycine Chemical compound OC(=O)CN(CC(O)=O)CCN(CC(=O)O)CCN(CC(O)=O)CC(O)=O QPCDCPDFJACHGM-UHFFFAOYSA-N 0.000 description 2
- 230000009418 agronomic effect Effects 0.000 description 2
- 238000007605 air drying Methods 0.000 description 2
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- 239000004927 clay Substances 0.000 description 2
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- 239000008367 deionised water Substances 0.000 description 2
- 229910021641 deionized water Inorganic materials 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- AMWRITDGCCNYAT-UHFFFAOYSA-L hydroxy(oxo)manganese;manganese Chemical compound [Mn].O[Mn]=O.O[Mn]=O AMWRITDGCCNYAT-UHFFFAOYSA-L 0.000 description 2
- 238000001727 in vivo Methods 0.000 description 2
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- 229910052751 metal Inorganic materials 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
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- 239000002344 surface layer Substances 0.000 description 2
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 2
- 239000002351 wastewater Substances 0.000 description 2
- 239000011701 zinc Substances 0.000 description 2
- 244000025254 Cannabis sativa Species 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 241001237160 Kallima inachus Species 0.000 description 1
- PWHULOQIROXLJO-UHFFFAOYSA-N Manganese Chemical compound [Mn] PWHULOQIROXLJO-UHFFFAOYSA-N 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 238000010220 Pearson correlation analysis Methods 0.000 description 1
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 1
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 210000001361 achilles tendon Anatomy 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 239000012496 blank sample Substances 0.000 description 1
- OJIJEKBXJYRIBZ-UHFFFAOYSA-N cadmium nickel Chemical compound [Ni].[Cd] OJIJEKBXJYRIBZ-UHFFFAOYSA-N 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000005341 cation exchange Methods 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 230000008859 change Effects 0.000 description 1
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- 239000012362 glacial acetic acid Substances 0.000 description 1
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- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 229910052748 manganese Inorganic materials 0.000 description 1
- 239000011572 manganese Substances 0.000 description 1
- 238000004949 mass spectrometry Methods 0.000 description 1
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- 239000002686 phosphate fertilizer Substances 0.000 description 1
- 239000000049 pigment Substances 0.000 description 1
- 239000004069 plant analysis Substances 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
- 229920003023 plastic Polymers 0.000 description 1
- 238000007747 plating Methods 0.000 description 1
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- 239000010865 sewage Substances 0.000 description 1
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- 239000010703 silicon Substances 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000000377 silicon dioxide Substances 0.000 description 1
- BHZOKUMUHVTPBX-UHFFFAOYSA-M sodium acetic acid acetate Chemical class [Na+].CC(O)=O.CC([O-])=O BHZOKUMUHVTPBX-UHFFFAOYSA-M 0.000 description 1
- CDBYLPFSWZWCQE-UHFFFAOYSA-L sodium carbonate Substances [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 description 1
- 229910000029 sodium carbonate Inorganic materials 0.000 description 1
- JVBXVOWTABLYPX-UHFFFAOYSA-L sodium dithionite Chemical compound [Na+].[Na+].[O-]S(=O)S([O-])=O JVBXVOWTABLYPX-UHFFFAOYSA-L 0.000 description 1
- CLJTZNIHUYFUMR-UHFFFAOYSA-M sodium;hydrogen carbonate;2-hydroxypropane-1,2,3-tricarboxylic acid Chemical compound [Na+].OC([O-])=O.OC(=O)CC(O)(C(O)=O)CC(O)=O CLJTZNIHUYFUMR-UHFFFAOYSA-M 0.000 description 1
- 238000004856 soil analysis Methods 0.000 description 1
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- 238000003900 soil pollution Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 239000003381 stabilizer Substances 0.000 description 1
- -1 state Chemical compound 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 231100000331 toxic Toxicity 0.000 description 1
- 230000002588 toxic effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
- 229910052725 zinc Inorganic materials 0.000 description 1
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- 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
- G01N21/73—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited using plasma burners or torches
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- 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
- G01N21/74—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited using flameless atomising, e.g. graphite furnaces
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- G01N33/245—Earth materials for agricultural purposes
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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Abstract
The invention belongs to agricultural and environmental areas, are related to the modeling method and its application of a kind of Cadmium in Vegetables content prediction model.The modeling method comprises the steps of: acquisition pedotheque, measures sodium dithionite-trisodium citrate-sodium bicarbonate (DCB) in soil and extracts state iron (FeDCB‑soil) content, oxalic acid/ammonium oxalate (Oxalate) extraction state cadmium (Cdoxalate‑soil) content, acetic acid/sodium acetate (NaOAc) extracts state aluminium (Al in soilNaoac‑soil) content, establish FeDCB‑soi、Cdoxalate‑soil、AlNaoac‑soilContent and vegetables cadmium (Cdplant) content relationship model.The model of foundation are as follows: logCdplant=alogCdOxalate‑soil‑bFeDCB‑soil+cAlNaoAc‑soil+d.The prediction of model vegetables cadmium content suitable for red soil, can be improved the forecasting accuracy of vegetables cadmium content, offer reference for vegetable safety production distribution.
Description
Technical field
The invention belongs to agricultural and environmental areas, are related to a kind of modeling method of Cadmium in Vegetables content prediction model and its answer
With.
Background technique
Cadmium is a kind of nonessential toxic heavy metal, and can be accumulated in vivo, and zinc ore and phosphate fertilizer are primarily present in
In equal substances, the industries such as plating, pigment, stabilizer for plastics and nickel-cadmium cell are widely used in, and useless with sewage, sludge or municipal administration
The forms such as gurry are discharged into environment, cause serious pollution to city suburbs and agricultural soil.Vegetables are as southern area
Second largest diet of the resident in addition to rice, in the soil for being grown on cadmium pollution, excessive cadmium will be accumulated in vegetables body,
Both the growth and quality for having influenced vegetables, are eaten for a long time this vegetables, and great accumulative risk can be also caused to human body.On the one hand,
Southern area weather is damp and hot, and soil acidification is serious, and heavy metal activity is strong, and soil weathering leaching intensity is big, soil desiliconization richness iron aluminium
Change degree is high, and such weather and edaphic condition are more advantageous to transfer of the cadmium from soil to vegetables.On the other hand, for big face
Product, low pollution, pollution of area source agricultural land soil, be unpractical with engineering restorative procedure, such as soil improvement by importing soil from other places, deep-cut, fill work
Cheng Fangfa, therefore, it is necessary to adaptation to local conditions, science determines the relationship model of cadmium content in soil and crop, pointedly takes and arrange
It applies, effectively inhibits heavy metal to enter food chain, and then improve agricultural product quality.
Current existing soil-crop heavy metal accumulation prediction model, is mostly by heavy metal-polluted soil full dose, soil property
(pH, quality, organic matter, CEC etc.), crop condition (crop species, plantation etc.), atmospheric environment and other factors etc. directly or
The factor integration for influencing heavy metal transformation indirectly forms the basis of Removed In Soil-crop System enrichment model, few by soil
Rawore object (iron oxide, manganese oxide, aluminium oxide and silica etc.) is studied as enrichment driven factor.And it is red in Pearl River Delta
Territory area, weather is damp and hot, and soil acidification is serious, and soil weathering leaching intensity is big, and soil desiliconization richness iron calorize degree is high, this
The migration and conversion of the stronger influence heavy metal cadmium of the condition energy of sample, need to pay close attention to.In addition, work more mature at present
Object heavy metal accumulation model focuses mostly in European countries such as Holland, Britain, and soil is mostly the constant charge soil and stone in temperate zone
Ash soil has biggish difference with the characteristic of acid red soil chemical property of the earth of South China.Therefore, from geochemical angle,
The relationship model for establishing soil Yu crop heavy metal is Accurate Prediction Heavy Metals Quantity of Crop in Wastewater in effective means.
Technology based on heavy metal available state contaminated soil risk management and control is more weak.Heavy metal is mainly derived from crop
Soil, but the total amount of heavy metal in soil cannot function as judging the index to crop supply capacity, and only biologically effective state is to work
Object is effective.Largely studies have shown that heavy metal-polluted soil total amount and the relationship of the content of beary metal in crop plant tissue be not close
It cuts, or even has and some not there is correlativity.And the existing soil environment quality relevant criterion in current China, prediction mould
Type is subject to heavy metal more, this not can accurately reflect the corresponding relationship of soil and crop, it is more difficult to the quality of accurate representation agricultural product
Safety, and then lead to the deviation of associated contamination soil risk management and control measures, there are biggish potential risks.
Correlation study shows that typical regional pollution feature is presented in Pearl River Delta Soils In The Region, and soil environment quality is overall not
It is prominent to hold optimism, especially cadmium pollution Quality Safety Problems of Agricultural Products more outstanding.It is existing to be related to Crop-soil system
Heavy metal be largely confined in the finite region of mining area film micro area or pot experiment, to the Producing Area Soil under the environment of crop field
Relationship and its Mechanism Study between environmental quality and crop heavy metal lack globality and systematic research, to place of production ring
The Transport And Transformation of border heavy metal in soil remains in the research of single or limited influence factor mostly, lacks to a soil huge sum of money
Belong to the accurate judgement and quantitative resolution of migration, it is difficult to which Removed In Soil-crop System enrichment model of the building based on heavy metal pollution closes
System.Therefore, the present invention is accurately to analyze Cadmium in Soil in home environment based on Crop-soil system further investigation on regional scale
Distribution and occurrence status, verify its pollution behavior, establish Removed In Soil-crop System cadmium enrichment relationship model.
Based on considerations above, the present invention further investigation heavy metal-polluted soil cadmium content and its available state, basic physical and chemical,
In the feature base that secondary mineral, Weathering indices etc. are enriched with vegetables, the earth of heavy metal cadmium in leaf vegetables (Cd) enrichment is constructed
Chemical model improves the forecasting accuracy of Heavy Metals Quantity of Crop in Wastewater.The invention can enrich the conversion of khoai heavy metal transformation
Theory provides technical support for the reparation and management of China's South China Regional heavy-metal contaminated soil, or heavy metal-polluted soil
Pollution and crop safety production distribution are offered reference, and the safe utilization rate in pollution arable land, General Promotion crop quality are effectively improved
The integral level of safety and Regional environmental quality.
Summary of the invention
The purpose of the present invention is to provide a kind of modeling methods of Cadmium in Vegetables content prediction model, in particular, provide one
The prediction technique of cadmium, improves the pre- of leaf vegetables cadmium content in the leaf vegetables of chemical property of the earth of the kind based on Subtropical Red Soil specificity
Survey accuracy.
The object of the invention is also to provide a kind of methods for utilizing prediction model, predicting Cadmium in Vegetables content.
The purpose of the present invention is realized by following technological means:
The present invention provides a kind of modeling methods of Cadmium in Vegetables content prediction model, and the method includes the steps of:
S1. soil, modeling vegetable sample are acquired；
S2. the Cd in soil is measuredoxalate-soil、FeDCB-soil、AlNaoac-soilContent；
S3. Cd in measurement modeling vegetablesplantContent；
S4. Cadmium in Vegetables content Cd is establishedplantWith the Cd in soiloxalate-soil、FeDCB-soil、AlNaoac-soilContent
Relationship model.
Wherein, step S2 and S3 are interchangeable, can also carry out simultaneously, as long as completing before modeling.
Wherein, in step S2, Cdoxalate-soilRefer to that soil mesoxalic acid/ammonium oxalate (Oxalate) extracts the content of state cadmium.
As an alternative embodiment, the Cdoxalate-soilIt is measured using ICP-OES.As a kind of exemplary implementation
Mode, Cdoxalate-soilSpecific measuring method are as follows: 0.2M ammonium oxalate, 0.1M oxalic acid are settled to 1L, and adjustment pH is 3.2, shake
It is filtered after swinging, 300DV ICP-OES on supernatant is taken to detect.
Wherein, in step S2, FeDCB-soilRefer to sodium dithionite in soil-trisodium citrate-sodium bicarbonate (DCB)
State iron content is extracted, i.e. soil has free state iron content.As an alternative embodiment, measuring method can refer to: Lu Ru
It is female, soil agrochemistry analysis, the analysis correlation analysis method (pp60-62) of free state oxide.
Wherein, in step S2, AlNaoac-soilRefer to the content of acetic acid/sodium acetate (NaOAc) extraction state aluminium in soil.Make
For a kind of optional embodiment, the AlNaoac-soilIt is measured using ICP-OES.As a kind of exemplary embodiment party
Formula, AlNaoac-soilSpecific measuring method be measuring method: glacial acetic acid 49.2ml, sodium acetate 14.0g are diluted to 1L, pH
4.0, it is filtered after concussion, 3300DV ICP-OES is detected on supernatant.
Wherein, in step S3, CdplantRefer to the content of Cadmium in Vegetables.As an alternative embodiment, electricity can be used
Feel Coupled Plasma-Mass Spectroscopy (ICP-MS) or graphite furnace atomic absorption spectrometry (GF-AAS) measurement.As a kind of exemplary reality
Mode is applied, measuring method specifically refers to: " measurement of multielement in national food safety standard food " (GB 5009.268-
2016) inductively coupled plasma mass spectrometry in；Or " measurement of national food safety standard cadmium in foods " (GB
5009.15-2014) in graphite furnace atomic absorption spectrometry.
Wherein, in step S4, the step of modeling are as follows:
The first step constructs model.If y is that (in the present invention, dependent variable y is Cd to dependent variableplant), x1,x2,x3,...xk
For independent variable (in the present invention, independent variable Cdoxalate-soil、FeDCB-soil、AlNaoac-soil), and independent variable and dependent variable
Between when being linear relationship, then multiple linear regression model are as follows:
Y=bo+b1x1+b2x2+...+bkxk+e
Wherein, b0For constant term b1, b2... bkFor regression coefficient, b1For x2, x3…xkWhen fixed, x1One list of every increase
Effect of the position to y, i.e. x1To the partial regression coefficient of y；Similarly b2For x1, x3…xkWhen fixed, x2One unit of every increase is to y's
Effect, i.e. x2To the partial regression coefficient of y, and so on.The parameter Estimation of multiple linear regression model, it is desirable that error sum of squares (∑
e2) be it is the smallest under the premise of, with least square method solve parameter.
After model construction, model need to be verified.
Second step, model-fitting degree (R2) examine.After model foundation, in order to determine whether model can be used, needing to carry out must
The inspection and evaluation wanted.R2Refer in total variation of dependent variable, ratio shared by the variation (regression sum of square) explained as equation
Weight, R2Bigger, the degree that equation is fitted sample number strong point is stronger, and the relationship of all independents variable and dependent variable is closer.It calculates
Formula are as follows:
Wherein,
Third step, model criteria error (SE) estimation.Standard error (SE), the i.e. actual value of dependent variable y and constructed side
The estimated value that journey is found outBetween standard error, standard error estimate is smaller, and equation model degree is higher.Calculation formula are as follows:
Wherein, k is the number of the independent variable in multiple linear regression equations (k is 3 in the present invention).
Wherein n is sample size.
4th step, the significance test of model.The conspicuousness of entire regression equation is examined in model significance test, or
Person says whether the linear relationship for evaluating all independents variable and dependent variable is close.It is examined frequently with F, the calculation formula of F statistic
Are as follows:
Wherein, R2For model-fitting degree.
According to given level of signifiance a, freedom degree (k, n-k-1) looks into F distribution table, obtains corresponding critical value Fa, if F >
Fa, then regression equation has significant meaning, and regression effect is significant；F < Fa, then regression equation is without significant meaning, and regression effect is not
Significantly.Generally tested in statistical software using p value.
5th step, multicollinearity differentiate.Multicollinearity refers in equation with many unknowns there is stronger line between independent variable
Sexual intercourse, if this relationship has been more than the linear relationship of dependent variable and independent variable, the stability of model is destroyed, model
Coefficient estimation inaccuracy.It discriminates whether that the correlation between every two independent variable can be calculated separately there are serious multicollinearity
r2If r2>R2Or close to R2, then should try the influence for reducing multilinear.The method for reducing multicollinearity mainly turns
The value of independent variable is changed, becomes absolute number such as relative number or average, or replaces other independents variable.It should be pointed out that
In multivariate regression models, multicollinearity is difficult to avoid that, as long as multicollinearity is less serious.
Above-mentioned modeling process and inspection can be completed by software reduction.
Finally, the model of foundation are as follows:
logCdplant=alogCdOxalate-soil-bFeDCB-soil+cAlNaoAc-soil+d；
The a is Cd in soiloxalate-soilModel coefficient, value range 0.85-0.95；
The b is Fe in soilDCB-soilModel coefficient, value range 0.15-0.35；
The c is Al in soilNaoac-soilModel coefficient, value range 0.2-0.35；
The d is constant term, value range 0.85-0.95.
As preferred embodiment, the model of building are as follows:
logCdplant=0.911logCdOxalate-soil-0.336FeDCB-soil+0.272AlNaoAc-soil+0.948。
In the modeling method, the vegetables can be selected from arbitrary leaf vegetables, as preferred embodiment, institute
The vegetables stated are selected from leaf vegetables, more preferable cabbage heart Flowering Chinese cabbage (Brassica campestris
L.ssp.chinensis var.Utilis Tsen et Lee), romaine lettuce Lettuce (Lactuca sativa
One of L.var.Romana Hort)；Even more preferably from being cabbage heart.
It is understood that according to the type of the quantity of the modeling sample taken and selected vegetables difference,
The coefficient of the independent variable for the model that the present invention establishes can fluctuate in a certain range.
In modeling method of the invention, the soil is selected from red soil；The more preferably red soil of South China.
The present invention also provides a kind of methods for predicting Cadmium in Vegetables content, and the method includes the steps of:
1) soil of vegetable sample growing location to be measured is acquired.
2) Cd in soil is measuredoxalate-soil、FeDCB-soil、AlNaoac-soilContent.
3) model prediction brings the value measured in step 2) into modeling side by above-mentioned Cadmium in Vegetables content prediction model
In the model that method is established, by calculating, prediction result is obtained:
As a preferred embodiment, the model is；
logCdplant=alogCdOxalate-soil-bFeDCB-soil+cAlNaoAc-soil+d
In the model, a is Cd in soiloxalate-soilModel coefficient, value range 0.85-0.95；
The b is Fe in soilDCB-soilModel coefficient, value range 0.15-0.35；
The c is Al in soilNaoac-soilModel coefficient, value range 0.2-0.35；
The d is constant term, value range 0.85-0.95.
In the prediction technique, the vegetables can be selected from arbitrary leaf vegetables, as preferred embodiment, institute
The vegetables stated are selected from leaf vegetables, more preferable cabbage heart Flowering Chinese cabbage (Brassica campestris
L.ssp.chinensis var.Utilis Tsen et Lee), romaine lettuce Lettuce (Lactuca sativa
One of L.var.Romana Hort).
In a preferred embodiment of the invention, the vegetables are selected from cabbage heart, the cadmium content prediction model of the vegetables are as follows:
logCdplant=0.911logCdOxalate-soil-0.336FeDCB-soil+0.272AlNaoAc-soil+0.948
In prediction technique of the invention, the soil is selected from red soil；It is more highly preferred to the red soil of South China.
It is that the present invention obtains the utility model has the advantages that
(1) prediction model established can predict the content situation of Typical Vegetable heavy metal cadmium, establish on regional scale
The science of soil and crop cadmium content contacts, and a huge sum of money in soil labile organic matter prediction crop may be implemented in the prediction model of building
Belong to pollution situation, early warning crop cadmium risk.
(2) technology will more targetedly instruct peasant household to take control measure in advance, reduce vegetables heavy metal accumulation, mention
Agricultural product quality is risen, provides scientific guidance using technology implementation for pollution achilles tendon tear, effectively guidance is repaired compared with contaminated soil engineering
Recovering technology greatly reduces cost, has biggish market potential.
(3) technology can enrich khoai heavy metal transformation Transformation Theory, be China's South China Regional heavy metal cadmium
The reparation and management of soil provide technical support, or heavy metal pollution of soil and the offer of crop safety production distribution are borrowed
Mirror effectively improves the safe utilization in pollution arable land, the integral level of General Promotion crop quality safety and Regional environmental quality.
Detailed description of the invention
Fig. 1 is Cadmium in Vegetables content prediction model modelling approach flow diagram of the present invention；
Fig. 2 is the sampling cloth point diagram of institute of embodiment of the present invention measured data；
Fig. 3 is the surveyed Cadmium in Soil total amount of the embodiment of the present invention and available state normal distribution；
Fig. 4 is the surveyed vegetables cadmium content normal distribution of the embodiment of the present invention；
Fig. 5 is the correlation analysis figure that the surveyed vegetables cadmium content of the embodiment of the present invention and soil difference extract state cadmium concentration
(A grass acid extractable, B amorphous state, C Acetic acid extraction).
Specific embodiment
Technical solution of the present invention is further illustrated below by way of specific embodiment, and specific embodiment does not represent to this hair
The limitation of bright protection scope.Other people still fall within this at theory is made according to the present invention some nonessential modifications and adjustment
The protection scope of invention.
It should be understood that
" available heavy metal ", the available state for the heavy metal for using chemical leaching test to define refers to can be fast for plant in soil
Speed absorbs the heavy metal with assimilation part.
" extracting state ", which refers to, to be mentioned after certain duration of oscillation using the extractant of certain type with certain native liquor ratio
Resulting amount is taken to determine, applied bio-available Zn concentration generally has certain correlativity between plant in-vivo content.With
The content for stating the obtained heavy metal of method belongs to " the extracting state " of certain extractant.
In many cases, " extracting state " cannot be equal completely between true " plant effective state ", but have certain
Instruction, the characterization effect of kind degree.
The modeling method of 1 Cadmium in Vegetables content prediction model of embodiment
As shown in Figure 1, the modeling method of vegetables content of beary metal prediction model provided by the present invention.Specifically:
S1. soil, vegetable sample are acquired.
For the interference for avoiding man-made pollution source, sampled point is generally off-site from city, industrial area, and the base of generally larger area
With GPS positioning longitude and latitude when this farmland protection area or Vegetable Base sample (Fig. 2 is shown in sampling point distribution).Total acquisition soil and vegetable
It is right that dish corresponds sample 112.
Pedotheque is the rhizosphere soil sample of surface layer 0-20cm, and vegetables (cabbage heart) sample is the vegetables sample in maturity period
Product, and corresponded with pedotheque.Soil and vegetable sample after new acquisition are placed in valve bag, have been handled in 6 hours.
Pedotheque need to remove residual branch therein and lose the sundries such as leaf, rhizome, be placed in shady and cool, dry, ventilation, without special odor and dust
Natural air drying is carried out in the environment of pollution.Pedotheque after air-drying, a part retains undisturbed soil, spare；In addition, selection is suitable
Amount pedotheque is ground in the agate mortar, crosses 80 meshes, spare.Vegetable sample removes dead leaf, yellow leaf, rotten leaf and root
Afterwards, vegetables edible portion is taken, sludge is washed off with water, and rinsed 2 times with deionized water, is placed in 60 degree of drying in oven, agate
Mortar crushes, pack, spare.
S2. heavy metal in soil content and the measurement in relation to parameter
The lab analysis being related in this patent include Analysis On Physical And Chemical Property, Soil oxidation object analysis, soil it is total
Cadmium and its morphological analysis, the total cadmium analysis of vegetables.Physiochemical properties of soil analysis has soil pH (saliferous is mentioned to be mentioned with water), soil
Organic matter (OM), soil total organic carbon (TOC), cation exchange capacity (CEC) (CEC) and the soil texture sand grains (Sand), powder
(Silt) and clay (Clay) content.The analysis of Soil oxidation object includes soil iron, aluminium, silicon and manganese complete analysis and different extractions
State analysis, extracting state mainly includes complex state (only iron and aluminium), oxalic acid/ammonium oxalate extraction state (Oxalate state), DCB state (DCB
State), acetic acid/sodium acetate extract state (NaOAc state), DTPA state (Chelating state).The total cadmium of soil and its morphological analysis include that soil is total
Cadmium, and its different extraction state analyses mainly include that network oxalic acid/ammonium oxalate extracts state (Oxalate state), DCB state (DCB state), second
Acid/sodium acetate extracts state (NaOAc state), DTPA state (Chelating state), Na2CO3Extract state.Specific detection method is shown in Table 1.
Quality control in analysis test.Respective country standard substance, and stochastic analysis are added during testing and analyzing
10% or so repeat samples and blank sample, to guarantee the accuracy of sample analysis.The standard substance used is purchased from country
There are standard sample of soil GBW07423 (GSS-9), GBW07428 (GSS-15), GBW07429 (GSS-15) in standard substance research center
To control the accuracy of detection data.Test result meets quality control requirement, and the standard specimen rate of recovery is greater than 90%, relative standard
Deviation is less than 10%.
1 soil index of correlation determination method of the embodiment of the present invention of table
S3. the measurement of Heavy Metal Content in Vegetables
The total cadmium Cd of vegetablesplantAnalysis refers to that vegetables edible portion after deionized water is cleaned, is dried through 60 DEG C, is ground into
Powder weighs 0.3000g and boils in pipe to disappearing, and 5ml HNO is added3-HClO4(87:13v·v-1), 5ml hydrogen peroxide is added, passes through
After 8 hours cold nitrifications, disappear boil in furnace respectively by 80 DEG C 1 hour, 100 DEG C 1 hour, 120 DEG C 1 hour, 130 DEG C 2 hours
Disappear and boil, after cooling, add 5ml 20%HCl and disappear through 80 DEG C and boil 1 hour, shift and constant volume is into 50ml volumetric flask, constant volume is standby
With being detected using ICP-MS.Quality control in analysis test.Respective country standard substance is added during testing and analyzing, plants
Object standard specimen GBW10021 and GBW10020, to control the accuracy of detection data.Test result meets quality control requirement, mark
The sample rate of recovery is greater than 90%, and relative standard deviation is less than 10%.
Measurement result is as follows:
The surveyed soil correlation physicochemical property of 2 embodiment of the present invention of table and oxide content statistical analysis
The total cadmium of the surveyed vegetables of 3 embodiment of the present invention of table (Cd) statisticallys analyze (mg/kg) from different extraction state soil Cd contents
S4 establishes the relationship model of vegetables cadmium content and soil
1) data prediction
Before either carrying out Basic Statistics and Tables or Geostatistics analysis, there is certain want to the basic law of data
It asks.For the reliability for ensureing result, it is often necessary to data are pre-processed, such as excluding outlier, normal distribution analysis,
Normal distribution-test and conversion are carried out to processed data.Specifically: a) excluding outlier.In this research, sample size is big
In 100, exceptional value is examined using threshold method, i.e., is examined with average value plus-minus three times standard deviation [X ± 3s], in the range
In addition be considered as exceptional value, exception is replaced with the maximum of regime values or minimum value, do not influence thus study area's sample
Point quantity.B) normal distribution-test and conversion.After abnormality processing, need to carry out normal distribution-test to data, if disobeying just
State distribution needs to carry out normal distribution conversion.In this research, normal distribution is examined using the kurtosis degree of bias, as a result, it has been found that heavy metal
Constituent content does not meet normal distribution, to do not meet normal distribution heavy metal data carry out it is logarithmic transformed after, kurtosis and partially
Angle value significantly reduces, and by normal distribution-test, they better conform to normal state or approximate normal distribution, can be used in next step
Statistical analysis.
2) quantitative model is established.
A) correlation analysis.To primarily determine the factor relevant to Cadmium in Vegetables content, carrying out the total cadmium of soil first and its having
The correlation analysis for imitating the factors and vegetables cadmium such as state, soil property, the results are shown in Table 4.
Between the total cadmium of the surveyed vegetables of 4 embodiment of the present invention of table (Cd) and different extraction state soil Cd and soil property
Pearson correlation analysis
*Correlation is significant at the 0.05level(2-tailed).
**Correlation is significant at the 0.01level(2-tailed).
B) model and inspection are established.Contained using statistical analysis software using the relevant factor as independent variable with Cadmium in Vegetables
The logarithm of amount carries out regression analysis as dependent variable.Table 5 lists the parameter situation of every step model of fit.R2For definite
Reflection model the goodness of fit, numerical value illustrates that equation model degree is better closer to 1；S.E. be illustrate actual value with
The index of relative depature degree between its estimated value, for measuring the representativeness of fit equation, standard error value is smaller, then estimates
It measures smaller with the approximate error of its true value；P is exactly model significance test as a result, showing that model includes in the present embodiment
When different independent variable, significance probability value is respectively less than 0.01, i.e. refusal regression coefficient is zero null hypothesis, illustrates 6
Equation all has preferable fitting effect.About Problems of Multiple Synteny, due in model construction, every one kind independent variable we
Only factor is selected to enter equation, thus substantially reduce synteny the problem of.Compare 6 equations, the R of equation 62Value
Larger, S.E. numerical value is smaller, is best fit equation, can be tentatively to the content for predicting Cadmium in Vegetables.
The model of cadmium content in 5 vegetables of table-soil system
Note: only presented in 0.05 and 0.01 level with Cadmium in Vegetables content correlation the extremely significant factor be included in model into
Row stepwise regression analysis.
2 Cadmium in Vegetables content prediction of embodiment
The present invention also provides a kind of methods for predicting Cadmium in Vegetables content, and the method includes the steps of:
1) soil of vegetable sample growing location to be measured is acquired；
Pedotheque is the rhizosphere soil sample of surface layer 0-20cm.For Cadmium in Vegetables content prediction in the present embodiment
Sample, cabbage heart are 14 pairs, and romaine lettuce is 12 pairs.Specific sampling step and sample pre-treatments are referring to embodiment 1.
2) vegetable sample needed for collection effect verifying.Required vegetables (cabbage heart and romaine lettuce) sample is the vegetables in maturity period
Sample, and corresponded with pedotheque, collecting quantity is consistent with the pedotheque acquired in step 1).Specific sampling step
Rapid and sample pre-treatments are referring to embodiment 1.
3) Cd in soil is measuredoxalate-soil、FeDCB-soil、AlNaoac-soilContent；
Specific detection method is referring to embodiment 1.Testing result is shown in Table 6.
6 vegetables cadmium content of table predicts the content of the parameters such as heavy metal in soil in embodiment
Note: " S " is represented as pedotheque in sample number into spectrum.
4) model prediction brings the value measured in step 3) into model:
logCdplant=0.911logCdOxalate-soil-0.336FeDCB-soil+0.272AlNaoAc-soil+0.948
By calculating, prediction result Cd is obtainedplant,。
5) prediction effect is verified.Measure the Cd in vegetable sampleplant, detection method is the same as referring to embodiment 1.Cadmium in Vegetables
Predicted value, measured value and its deviation ratio are shown in Table 7.Deviation ratio refers to the departure degree between predicted value and actual value, calculating side
Method is that predicted value subtracts actual value divided by actual value, and negative value indicates that predicted value is lower than the deviation of actual value, and positive value indicates prediction
Value is higher than the deviation of actual value.Obviously, deviation ratio is to measure that prediction effect is the most direct, effective method, and deviation ratio is closer
Zero, illustrate that the accuracy of prediction is higher, prediction effect is also better.The absolute value of deviation ratio is bigger, it is meant that the prediction energy of model
Power decline, needs to be adjusted prediction model.
Table 7 is as can be seen that the deviation ratio variation floating of each sample is larger, and the mean value of deviation ratio is -13.7%, standard deviation
Be 28.8, deviation ratio waving interval be -49.2% to 48% between, predicted value and measured value deviation it is the smallest be -1.5%.Than
Prediction case compared with cabbage heart and romaine lettuce sees that cabbage heart deviation ratio mean value is -5.6%, and romaine lettuce deviation ratio mean value is -23.1%, cabbage heart
Lower compared with romaine lettuce deviation ratio, this is more with the influence factor of vegetables cadmium, and plantation agronomic measures difference etc. is closely related.It is comprehensive
On, obtained prediction model has important guiding effect to the cadmium content prediction of the leaf vegetables such as cabbage heart, romaine lettuce, especially for
Vegetable cultivation relatively broad region in South China's is especially prominent.Meanwhile according to prediction result, can targetedly adopt in advance
It takes certain agronomic measures to reduce the validity of cadmium in soil to reduce its accumulation in vegetables, improves the ring of produced vegetables
Border quality realizes the safe utilization in contaminated arable land.
Cadmium in Vegetables content prediction Contrast on effect in 7 vegetables cadmium content embodiment of table
Note: " V " is represented as vegetable sample in sample number into spectrum.
Claims (10)
1. a kind of modeling method of Cadmium in Vegetables content prediction model, which is characterized in that comprise the steps of:
S1. soil, modeling vegetable sample are acquired；
S2. the Cd in soil is measuredoxalate-soil、FeDCB-soil、AlNaoac-soilContent；
S3. Cd in measurement modeling vegetablesplantContent；
S4. Cadmium in Vegetables content Cd is establishedplantWith the Cd in soiloxalate-soil、FeDCB-soil、AlNaoac-soilThe model of content
Relationship；
Wherein, step S2 and S3 are interchangeable, can also carry out simultaneously.
2. the method according to claim 1, wherein in step S4, the model of foundation are as follows:
logCdplant=alogCdOxalate-soil-bFeDCB-soil+cAlNaoAc-soil+d；
The a is Cd in soiloxalate-soilModel coefficient, value range 0.85-0.95；
The b is Fe in soilDCB-soilModel coefficient, value range 0.15-0.35；
The c is Al in soilNaoac-soilModel coefficient, value range 0.2-0.35；
The d is constant term, value range 0.85-0.95.
3. the method according to requiring 2, which is characterized in that the model are as follows:
logCdplant=0.911logCdOxalate-soil-0.336FeDCB-soil+0.272AlNaoAc-soil+0.948。
4. the method according to claim 1, wherein the vegetables are leaf vegetables；
Preferably, the leaf vegetables is cabbage heart, romaine lettuce；
It is highly preferred that the leaf vegetables is cabbage heart.
5. the method according to claim 1, wherein the soil is red soil；
It preferably, is the red soil of South China.
6. according to right to go 1 described in method, which is characterized in that the Cdoxalate-soilTo extract containing for state cadmium in soil
Amount；Preferably, the content of state cadmium is extracted for soil mesoxalic acid/ammonium oxalate (Oxalate).
7. according to right to go 1 described in method, which is characterized in that the FeDCB-soilTo extract state iron content in soil；It is excellent
Selection of land is that sodium dithionite-trisodium citrate-sodium bicarbonate (DCB) extracts state iron content.
8. according to right to go 1 described in method, which is characterized in that the AlNaoac-soilTo extract state aluminium content in soil；
Preferably, the content of state aluminium is extracted for acetic acid/sodium acetate (NaOAc).
9. the method according to claim 1, wherein further including being verified to model in step S4；
Preferably, model verifying includes: model-fitting degree R2It examines, model criteria error SE estimates, the conspicuousness of model
At least one of inspection, multicollinearity differentiation.
10. a kind of method using model prediction Cadmium in Vegetables content described in claim 1-9, which is characterized in that including with
Lower step:
1) with acquiring vegetable growth to be measured soil；
2) Cd in soil is measuredoxalate-soil、FeDCB-soil、AlNaoac-soilContent；
3) it brings the measured value in step 2) into model that claim 1-9 is established, calculates the content of Cadmium in Vegetables；
Preferably, the model is
logCdplant=alogCdOxalate-soil-bFeDCB-soil+cAlNaoAc-soil+d；
The a is Cd in soiloxalate-soilModel coefficient, value range 0.85-0.95；
The b is Fe in soilDCB-soilModel coefficient, value range 0.15-0.35；
The c is Al in soilNaoac-soilModel coefficient, value range 0.2-0.35；
The d is constant term, value range 0.85-0.95；
It is highly preferred that the model are as follows:
logCdplant=0.911logCdOxalate-soil-0.336FeDCB-soil+0.272AlNaoAc-soil+0.948；
Preferably, the vegetables are leaf vegetables；More preferably cabbage heart, romaine lettuce；Still more preferably cabbage heart；
Preferably, the soil is red soil；More preferably South China's red soil.
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