CN111929407A - Prediction model of tea selenium content and construction method and application thereof - Google Patents
Prediction model of tea selenium content and construction method and application thereof Download PDFInfo
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- 229910052711 selenium Inorganic materials 0.000 title claims abstract description 165
- 239000011669 selenium Substances 0.000 title claims abstract description 165
- BUGBHKTXTAQXES-UHFFFAOYSA-N Selenium Chemical compound [Se] BUGBHKTXTAQXES-UHFFFAOYSA-N 0.000 title claims abstract description 163
- 241001122767 Theaceae Species 0.000 title claims abstract 38
- 238000010276 construction Methods 0.000 title abstract description 7
- 239000002689 soil Substances 0.000 claims abstract description 111
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims abstract description 46
- 229910052717 sulfur Inorganic materials 0.000 claims abstract description 44
- 239000011593 sulfur Substances 0.000 claims abstract description 44
- HCHKCACWOHOZIP-UHFFFAOYSA-N Zinc Chemical compound [Zn] HCHKCACWOHOZIP-UHFFFAOYSA-N 0.000 claims abstract description 36
- 239000011701 zinc Substances 0.000 claims abstract description 36
- 229910052725 zinc Inorganic materials 0.000 claims abstract description 36
- 239000005416 organic matter Substances 0.000 claims abstract description 15
- 238000004519 manufacturing process Methods 0.000 claims abstract description 12
- 238000000034 method Methods 0.000 claims description 25
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 20
- 238000005070 sampling Methods 0.000 claims description 15
- 238000001514 detection method Methods 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 11
- 229910052757 nitrogen Inorganic materials 0.000 claims description 10
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 8
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 8
- 238000010219 correlation analysis Methods 0.000 claims description 8
- 229910052698 phosphorus Inorganic materials 0.000 claims description 8
- 239000011574 phosphorus Substances 0.000 claims description 8
- 229910052700 potassium Inorganic materials 0.000 claims description 8
- 239000011591 potassium Substances 0.000 claims description 8
- 238000012417 linear regression Methods 0.000 claims description 6
- 239000005864 Sulphur Substances 0.000 claims description 2
- 239000004016 soil organic matter Substances 0.000 abstract description 9
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- 235000009024 Ceanothus sanguineus Nutrition 0.000 description 10
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- 230000000711 cancerogenic effect Effects 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
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0098—Plants or trees
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
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- G01N33/245—
Abstract
The invention provides a prediction model of tea selenium content and a construction method and application thereof, wherein in the model, the tea selenium content is 0.0716+0.0024 multiplied by the soil organic matter content-0.8880 multiplied by the soil sulfur content +0.2973 multiplied by the soil selenium content-0.0002 multiplied by the soil zinc content; wherein, the unit of the selenium content of the tea, the selenium content of the soil and the zinc content of the soil are all mg/kg, and the unit of the organic matter content of the soil and the sulfur content of the soil are all g/kg. The invention provides a tea selenium content model which predicts the corresponding tea selenium content according to the content of organic matters in the tea garden soil and the content of elements such as sulfur, selenium and zinc, the goodness of fit is 0.51246, and the tea selenium content model reaches a very significant level; the prediction model has good reliability and stability. The prediction model of the selenium content in the tea can be applied to predicting the selenium content in the tea and improving the selenium content in the tea, and has important significance for guiding production and defining selenium-enriched tea production areas.
Description
Technical Field
The invention relates to a prediction model, in particular to a prediction model of the selenium content of tea and a construction method and application thereof.
Background
Selenium is a trace element essential to humans and animals. With intensive research on selenium, selenium is gradually recognized from toxic substances, carcinogenic substances, as an essential nutrient element for human and animals and an effective substance for treating cancer. Humans and animals ingest selenium mainly by eating selenium-containing agricultural products, and the content of selenium in plants is related to various factors in the environment.
The tea trees are acidophilic plants, the physiological metabolism is easily influenced by the environment, and the long-term cultivation measures and the environment interaction possibly influence the accumulation of the selenium in the tea trees. In the early stage, researches on selenium accumulation characteristics and influencing factors of tea trees mostly focus on aspects such as the level of environmental selenium supply and regulation and control of cultivation measures on the selenium accumulation of the tea trees. Tea tree selenium mainly comes from soil, the soil is a complicated ecological system, the interaction effect among different factors is usually larger than that of a single factor, and early researches find that the content of the tea tree selenium is possibly regulated and controlled by the interaction of multiple soil factors. Therefore, the deep research on the influence of the interaction effect of the soil factors on the selenium content of the tea has important significance on the healthy development of the selenium-enriched tea industry.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a prediction model of the selenium content of tea leaves and a construction method and application thereof.
The technical scheme adopted by the invention is as follows:
according to one aspect of the application, a prediction model of the selenium content in tea is provided, wherein the selenium content in tea is 0.0716+0.0024 x the organic content in soil-0.8880 x the sulfur content in soil +0.2973 x the selenium content in soil-0.0002 x the zinc content in soil;
wherein, the unit of the selenium content of the tea, the selenium content of the soil and the zinc content of the soil are all mg/kg, and the unit of the organic matter content of the soil and the sulfur content of the soil are all g/kg.
According to another aspect of the present application, there is provided a method for constructing a prediction model of selenium content in tea, the method comprising the following steps:
(1) collecting a sample group by adopting a random sampling method for the selenium-rich tea area, wherein the sample group consists of a tea garden soil sample and a corresponding tea sample;
(2) respectively detecting the contents of pH, organic matters, water-soluble nitrogen, available phosphorus, potassium, sulfur, available sulfur, selenium and zinc in a soil sample, and detecting the content of selenium in the tea sample;
(3) carrying out correlation analysis on the selenium content in the tea sample and the contents of pH, organic matters, water-soluble nitrogen, available phosphorus, potassium, sulfur, available sulfur, selenium and zinc in the soil sample to obtain main indexes influencing the selenium content in the tea sample, namely the soil organic matter content, the soil selenium content, the soil zinc content and the soil sulfur content;
(4) and performing multiple linear regression on the selenium content in the tea sample and the organic matter content, the selenium content, the zinc content and the sulfur content in the soil sample to obtain a prediction model of the selenium content in the tea.
In the step (1), collecting 150-200 sample groups in a selenium-rich area by adopting a random sampling method; the sampling method comprises the following steps: 1 sample group is extracted per 100 mu of tea garden, 1 mixed sample is extracted from a soil sample by adopting a five-point sampling method, and the depth of a sampling soil layer is 0-40 cm; the tea sample is dried at 80 ℃ corresponding to the new shoots with one bud and two leaves in the tea garden.
Further, correlation analysis is carried out on detection indexes in the soil sample and the tea sample by using ' cor ' and ' chart.
Further, in the step (3), the 'relweights' function is used for analyzing the relative weight of each detection index of the soil sample to the selenium content in the tea leaf sample, and the 'regsubsets' function is used for selecting the soil prediction variable of the whole set regression.
Further, in the step (4), fitting and checking of multiple linear regression are carried out by using the 'lm' function.
According to another aspect of the application, the application of the prediction model of the tea selenium content in the prediction of the tea selenium content is provided, and the organic matter content, the sulfur content, the selenium content and the zinc content in soil are respectively detected; and then inputting the organic matter content, the sulfur content, the selenium content and the zinc content of the soil into a prediction model to obtain the selenium content of the tea.
According to another aspect of the application, the application of the prediction model of the selenium content in the tea in improving the selenium content in the tea is provided. Under the condition that the selenium content of the soil is determined, increasing the organic matter content of the soil by applying organic fertilizer and other measures is an important way for improving the selenium content of the fresh tea leaves.
According to another aspect of the application, the application of the prediction model of the selenium content in the tea in the definition of selenium-enriched tea production areas is provided. The expression form of the prediction model is simply transformed, and the method can be used for estimating the reasonable soil selenium content requirement required by selenium-rich tea production after the selenium content of tea (referring to the selenium-rich tea standard) and the soil organic matter content, the sulfur content and the zinc content are assigned, and has important significance for guiding production and defining a core selenium-rich tea area.
The invention has the beneficial effects that:
(1) the invention provides a prediction model of the selenium content of tea to the organic matter content, the sulfur content, the selenium content and the zinc content of soil, the goodness of fit is 0.51246, and the most significant level is achieved; the prediction model has good reliability and stability.
(2) The prediction model of the tea selenium content can be applied to the prediction of the tea selenium content, and the soil organic matter content, the soil sulfur content, the soil selenium content and the soil zinc content are input into the prediction model to obtain the tea selenium content; in addition, the prediction model can be applied to the production of selenium-enriched tea, can be used for estimating the reasonable soil selenium content requirement required by the production of the selenium-enriched tea after the evaluation is carried out on the selenium content of the tea (referring to the selenium-enriched tea standard) and the organic matter content, the sulfur content and the zinc content of the soil, and has important significance for guiding the production and defining the selenium-enriched tea area.
Drawings
FIG. 1 is a correlation analysis of selenium content of tea samples in a selenium-rich area and various detection indexes in soil samples, wherein A to I respectively represent pH value, organic matter, hydrolyzable nitrogen, available phosphorus, available potassium, sulfur, available sulfur, total selenium and zinc of the soil detection indexes; j represents the tea tree tissue determination index selenium;
FIG. 2 is a result of analyzing the relative weights of various detection indexes in a soil sample in which selenium-rich regions affect the selenium content of a tea sample according to the present invention; a to I respectively represent the pH value of soil detection indexes, organic matters, hydrolyzable nitrogen, available phosphorus, quick-acting potassium, sulfur, available sulfur, total selenium and zinc;
FIG. 3 is an optimal variable selection of various detection indexes in a soil sample where the selenium-rich region affects the selenium content of a tea sample according to the present invention; a to I respectively represent the pH value of soil detection indexes, organic matters, hydrolyzable nitrogen, available phosphorus, quick-acting potassium, sulfur, available sulfur, total selenium and zinc;
FIG. 4 is an analysis result of outliers, leverage values, and strong impact points of various detection indexes in a soil sample where a selenium-rich region affects the selenium content of a tea sample according to the present invention;
FIG. 5 is a test diagnosis result of a model for predicting selenium content in tea obtained by multivariate regression analysis according to the present invention.
Detailed Description
The present invention is described in detail with reference to specific examples, which are provided to facilitate the understanding of the technical solutions of the present invention by those skilled in the art, and the implementation or use of the present invention is not limited by the description of the present invention.
In the present invention, the raw materials and equipment used are commercially available or commonly used in the art, if not specified. The methods in the examples are conventional in the art unless otherwise specified.
The method takes mature tea trees in tea gardens and rhizosphere soil produced in different places of Enshi, Shaanxi Ankang in Hubei of a high-selenium tea area as research objects, and compares and analyzes the numerical distribution rules of 9 important soil characteristic related factors such as pH, selenium content and the like of soil in a selenium-rich area by combining the determination of various indexes such as the total selenium content of the soil and plant samples, so as to obtain a prediction model of the selenium content of the tea.
The construction method of the prediction model of the selenium content in the tea comprises the following steps:
(1) sampling
In 2017, 5-8 months, different tea gardens in different selenium-rich tea areas, Enshi province and Ankang city, Shaanxi province, Hubei province are sampled in batches, and each sampling plot performs representative sampling on soil and epiphytic tea trees according to a 5-point sampling method. A total of 186 soil and corresponding tea samples were obtained, 53 from the selenium enriched zone samples in north huichun and 133 from the selenium enriched zone samples in ann kang city.
(2) Determination of related indexes of soil sample and tea sample
The measurement of each index of the soil sample and the tea sample is carried out according to the relevant standard. The soil determination index and the standard are pH value, LY/T1239-1999; organic matter (g.kg)-1) LY/T1237-1999; hydrolyzable Nitrogen (mg.kg)-1) LY/T1228-2015; available phosphorus (mg kg)-1) LY/T1232-2015; quick-acting potassium (mg.kg)-1) LY/T1234-2015; sulfur (mg kg)-1) LY/T1255-1999; available sulfur (mg kg)-1) LY/T1265-1999; total selenium (mg kg)-1) NY/T1104-2006 and zinc (mg kg)-1) GB/T17138-1997. The measurement index and basis standard of the tea sample is selenium (mg. kg)-1) GB 5009.268-2016. The samples are entrusted to national forestry bureau economic forest product quality inspection and detection center (Hangzhou) (http:// www.caf.ac.cn/news/kjcx/201112/2011-12-16-12-33.html) for determination.
(3) Correlation analysis of a soil factor affecting poly-selenium of tea trees and a composition matrix of partial element contents in leaves is carried out by using ' cor ' and ' chart. In regression analysis of soil detection indexes and the content of selenium in tea, a 'relweights' function is utilized to perform relative weight analysis on each detection index of soil, and the result is shown in figure 2; the soil predictor selection for the full subset regression was performed using the 'regsubsets' function, and the results are shown in fig. 3.
Correlation analysis of 186 soil samples and corresponding tea samples in sampling investigation shows that the numerical distribution of each detection index of the 186 independent soil samples and the corresponding tea samples basically conforms to normal distribution, and the method has better representativeness and reliability. The selenium content in the tea leaves has extremely obvious positive correlation (P <0.01) with the selenium content, the organic matter content, the hydrolyzable nitrogen content and the zinc content in soil, and the correlation coefficients are 0.59, 0.27, 0.25 and 0.21 respectively. The relative weight analysis result of each soil factor influencing the selenium content of the tea is similar to the correlation analysis result, the selenium content of the soil is the most important variable, and the R square percentage is about 70 percent; other soil factors have relatively small influence and are soil organic matters, zinc, hydrolyzable nitrogen, sulfur and the like in sequence. All the soil factor variables are screened and optimized by using a complete set regression method, and the result shows that the optimal multiple regression model can be obtained by using the 4 soil organic matter, sulfur, selenium and zinc factor variables.
(4) The analysis of outliers, leverage and strong influence points was plotted using the 'influeplot' function in the car package, as shown in fig. 4, and the fitting and testing of multiple linear regression using the 'lm' function, as shown in fig. 5.
According to the results, abnormal value analysis of the tea selenium content to soil organic matter, sulfur, selenium and zinc content multiple regression is carried out on the 186 observed values, wherein the abnormal value analysis comprises outliers, lever values and strong influence points. Finally, 8 abnormal observation values are deleted, and the remaining 178 observation values are used for multiple regression analysis to obtain the following regression equation: selenium content (mg/kg) in tea-1) 0.0716+0.0024 x soil organic matter content (g.kg)-1) -0.8880X soil sulphur content (g kg)-1) + 0.2973X soil selenium content (mg kg)-1) -0.0002X soil Zinc content (mg kg)-1). The test of each variable T of the equation shows that the variable T is equal to the sulfur content and the selenium content of the soil<0.01, is a significance variable; the equation goodness of fit (Multiple R2) and the corrected goodness of fit (Adjusted R2) are 0.5126 and 0.5013 respectively, and the reliability is good; f-45.49 in F test, P<0.01, indicating that the regression equation is extremely significant for the whole of the explanatory variables. The observation values are used for comprehensively diagnosing the linear regression equation of the tea in the selenium-rich area to the specific soil factor, and the residual error and the fitting chart show that the explained variable and the independent variable (soil factor) are linearly related. Normal Q-QAnalysis shows that most observed values fall on a straight line except for individual discrete observed values at two ends, which indicates that the analysis value accords with normal distribution. And the position scale analysis shows that the observed values are randomly distributed on two sides of the curve, and the random variables meet the homological difference. Residual versus lever analysis shows that there are only a very few discrete points out of a large number of observation points. The analysis shows that the data analysis meets the fitting requirement of the regression model, and the obtained regression equation has better reliability and stability.
According to the invention, based on 186 groups of samples in the entire Ankang and Enshi selenium-rich areas, the influence of 9 factors such as soil pH is considered, and a reliable tea selenium content prediction model is finally provided through soil factor variable optimization, abnormal value removal and multiple regression analysis and inspection, wherein the goodness of fit is 0.5126, and the most significant level is achieved. The prediction model optimizes 9 soil factors to 4, where the variables soil selenium and organic content are given positive coefficients, and sulfur and zinc content are given negative coefficients. The selenium content and the sulfur content of the soil in the 4 variables are two significant variables with opposite effects. This may be associated with the competitive relationship between tea plant absorption and transport of selenium mainly through the sulfur pathway.
The prediction model of the tea selenium content can predict the tea selenium content and respectively detect the soil organic matter content, the soil sulfur content, the soil selenium content and the soil zinc content; and then inputting the content of organic matters in the soil, the content of sulfur in the soil, the content of selenium in the soil and the content of zinc in the soil into a prediction model to obtain the content of selenium in the tea.
The application of the prediction model of the selenium content of the tea in improving the selenium content of the tea is provided. Under the condition that the selenium content of the soil is determined, increasing the organic matter content of the soil by applying organic fertilizer and other measures is an important way for improving the selenium content of the fresh tea leaves.
The prediction model of the selenium content of the tea is applied to the production of the selenium-enriched tea. The expression form of the prediction model is simply transformed, and the method can be used for estimating the reasonable soil selenium content requirement required by selenium-rich tea production after the selenium content of tea (referring to the selenium-rich tea standard) and the soil organic matter content, the sulfur content and the zinc content are assigned, and has important significance for guiding production and defining a core selenium-rich tea area.
The prediction model of the selenium content in the tea provided by the invention, the construction method and the application thereof are described in detail. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (9)
1. The model for predicting the selenium content of tea is characterized in that the selenium content of tea is 0.0716+0.0024 multiplied by the content of organic matters in soil, 0.8880 multiplied by the content of sulfur in soil, 0.2973 multiplied by the content of selenium in soil, and 0.0002 multiplied by the content of zinc in soil;
wherein, the unit of the selenium content of the tea, the selenium content of the soil and the zinc content of the soil are all mg/kg, and the unit of the organic matter content of the soil and the sulfur content of the soil are all g/kg.
2. The method for constructing a model for predicting the selenium content in tea leaves as claimed in claim 1, wherein the method comprises the steps of:
(1) collecting a sample group by adopting a random sampling method for the selenium-rich tea area, wherein the sample group consists of a tea garden soil sample and a corresponding tea sample;
(2) respectively detecting the contents of pH, organic matters, water-soluble nitrogen, available phosphorus, potassium, sulfur, available sulfur, selenium and zinc in a soil sample, and detecting the content of selenium in the tea sample;
(3) carrying out correlation analysis on the selenium content in the tea sample and the contents of pH, organic matters, water-soluble nitrogen, available phosphorus, potassium, sulfur, available sulfur, selenium and zinc in the soil sample to obtain main factors influencing the selenium content in the tea sample, namely the content of the soil organic matters and the contents of elements such as soil selenium, zinc and sulfur;
(4) and performing multiple linear regression on the selenium content in the tea sample and the organic matter content, the selenium content, the zinc content and the sulfur content in the soil sample to obtain a prediction model of the selenium content in the tea.
3. The method for constructing a prediction model of the selenium content in tea leaves according to claim 2, wherein the method comprises the following steps: in the step (1), collecting 150-200 sample groups in a selenium-rich area by adopting a random sampling method; the sampling method comprises the following steps: 1 sample group is extracted per 100 mu of tea garden, 1 mixed sample is extracted from a soil sample by adopting a five-point sampling method, and the depth of a sampling soil layer is 0-40 cm; the tea sample is dried at 80 ℃ corresponding to the new shoots with one bud and two leaves in the tea garden.
4. The method of claim 2, wherein correlation analysis is performed on the detection indexes in the soil sample and the tea sample by using ' cor ' and ' chart.
5. The method for constructing a model for predicting the selenium content in tea leaves as claimed in claim 2, wherein in the step (3), the 'regweights' function is used to perform the relative weight analysis of each detection index of the soil sample on the selenium content in the tea leaves, and the 'regsubsets' function is used to perform the selection of the soil prediction variables of the whole set regression.
6. The method for constructing a prediction model of the selenium content in tea leaves as claimed in claim 2, wherein in the step (4), fitting and checking of multiple linear regression is performed by using 'lm' function.
7. The use of the model for predicting the selenium content in tea leaves as claimed in claim 1, wherein the organic matter content, the sulphur content, the selenium content and the zinc content in the soil are detected separately; and then inputting the contents of organic matters, sulfur, selenium and zinc in the soil into a prediction model to obtain the selenium content of the tea.
8. The use of the predictive model for selenium content in tea as claimed in claim 1 for increasing the selenium content in tea.
9. Use of the predictive model for selenium content in tea leaves as claimed in claim 1 for the production of selenium enriched tea.
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