Dacheng Wang

Hongfeng Wu

Xiaodongyang

Wei Guo

Weihong Cui

STUDY ON AUTOMATIC COMPOSITION OF SEMANTIC GEOSPATIAL WEB SERVICE

Keywords: Semantic, Geographic information service, Automatic services composition, Enterprise geographic information integration

we designed and implemented an integration framework of enterprise business information and multi-source heterogeneous geospatial information based on composition of semantic geospatial Web service. It can integrate business information and geospatial information seamlessly and effectively in enterprise workflow. An example of tobacco planting spatial analysis this framework was described to verify the practicability and feasibility of this framework.

to promote the development of semantic Web technology. Among these project teams, W3C, DAML,SDK WSMO, LSDIS lab in Georgia University and the European Union IST research projects are representative in the semantic Web research. In 2002, the semantic Web technology was listed in the National 863 Program as a key support project. The NSF has also supported many research programs about the geospatial services on the semantic level. Which mainly includes "Digital Government: Geospatial Knowledge Ontology" research programs developed by Findler and Malyankar, "Spatial thinking and inference" research program hosted by Reginald G. Golledge, and "IGERT: Geographic Information Science Training Scheme" hosted by David M. Mark and Barry Smith. Werner Kuhn, a researcher in the institute of geographic information science in Munster University, Germany, led the semantic interoperability laboratory and enhanced the availability of geospatial information through the implementation of semantic interoperability. It provided a semantic reference body for geospatial information. In addition, the application of geographic body in the transportation, environment and emergency systems was also studied. The CSISS Center at George Mason University studied the knowledge discovery of heterogeneous remote sensing images based on intelligent Web services under the support of NGA. The state key laboratory of remote sensing information engineering in Wuhan University carried out spatial information grid research based on geographic ontology.

THE BASIS OF SEMANTIC GEOSPATIAL SERVICES TECHNOLOGY

WEB SERVICES

Web services are software systems which are used to support the network interoperability of the software interaction, whose interface can be described by the format (usually is WSDL) being automatically processed by machine. The other systems use SOAP message mechanism to communicate with it to the specified network services. Usually, they use the HTTP protocol and the combination of the XML serialization objects and other network standards to communicate message.

Web services have the following characteristics [START_REF] Snell | Implementing Web Services with the WSTK[END_REF]:

1) Good encapsulation. Web services are the objects which are deployed on the Web. So they have good encapsulation. The user can only need and see the function list provided for objects.

2) Loosely coupled. This feature stems from the object/component technology. As long as the call interfaces of Web services do not change, any changes in the implementation of Web services are all transparent. Even when the implementation platform of Web services migrates from J2EE to .NET or in the opposite direction, the user may know nothing about it.

SEMANTIC WEB SERVICES

Tim Berners-Lee presented the concept and the architecture of semantic Web in 2000. The extensible markup language (XML), resource description framework (RDF) and ontology are at the core of this system [START_REF] Yang | Research on Geo Web Services Based on Ontology[END_REF].

The ontology as semantic layer helps human and machine to communicate clearly and supports exchange on the semantic level, not just on the syntax level. This layer describes the relation among various resources, and the ontology reveals more complex and richer semantic information of resource itself as well as among resources.

Thus, it can separate structure from content of information and describe information formally to make the information online have computer-readable semantics. So the ontology plays an important role in the semantic Web. Meanwhile, it is the basis of solving the sharing and exchange of Web information on the semantic level.

The semantic Web services provide machine-readable description by adding semantic annotations for Web services. It can make automatic discovery, composition, invocation and monitoring services better in open, complex and chaotic network environment [START_REF] Henriksson | Semantic Web Service Composition[END_REF]. The Web Ontology Language for Service (OWL-S) is recommended by the World Wide Web Consortium (W3C) to describe the inputs and outputs of Web services, predefined conditions and the ontology layer in function and processing model. The OWL-S is based on the ontology Web language (OWL) which is a W3C recommended standard and presented by the Web-Ontology work group. The OWL is a markup language which can distribute and share data on the Internet by the ontology.

Through OWL, ontology library can be created for any field so the instantiated ontology description can be provided to sites in any field. In addition, the OWL supports logical inference so it can ensure the logical relation and logical order of the ontology expression by automated inference. The W3C does not give a specific definition of OWL-S, and just gives its main task to achieve [START_REF] Owl-S | Semantic Markup for Web Services[END_REF]:

1) Automatic Web services discovery: The automatic Web service discovery can automatically position and provide the specified functions. Meanwhile, it can meet the constraints of the client. Through the services utilizing the OWL-S semantic markup, it can standard out the information required by Web service using computer-readable semantic markup at Web services site. Thus, the enhanced search engine of service registry center or the ontology can mark up automatic positioning services by these semantic markups. In addition, service providers can also utilize the OWL-S to pre-advertising its services at registry center (that is middle agent). The service requester can find these services when they query the registry center. Therefore, the OWL-S supports the service properties of automatic service discovery and statement advertising of functions.

2) Automatic Web services invoking: The automatic Web service call just needs a declarative description of service. The application program or agent can automatically call the service and does not need to bind a particular service in pre-programmed way. The execution of Web service is similar to a series of remote call procedures. The OWL-S markup of Web services provides a declarative computer-readable API. It includes parameters and the semantics of the information returned after execution which is required during executing call.

Software agents should be able to explain the inputs and the information returned required by calling service.

The OWL-S combines with the domain ontology written in OWL and provides declarative API to automatically execute Web services.

3) Automatic Web services composition and interoperability: The automatic Web services composition and interoperability is to implement the automatic selection, composition and interoperability of Web services involved in a complex task after given abstract description of a task. Through the semantic markup of Web services, the information required by the selection and composition services can be encoded at the Web services site. Software can automatically process the description of task and complete the task. In order to support these functions, the OWL-S not only provides the prerequisite required by each atomic services and declarative definition of result information, but also provides a kind of descriptive services combination and data flow interaction language.

More and more companies begin to transform some their business processes into Web services which makes the company"s business relatively independent and more conducive to maintenance. At the same time, the needs of enterprises become more and more complex. A single atomic Web services often can not meet the complicated and individualized requirements of enterprises. So the requirements of the Web services composition emerge in response to. the situation requires. The Web services composition aims to let the seamless integration of cross-enterprise processes and the life cycle of transaction have same type, and to be used for many Web services.

At present, business process execution language for Web services (BPEL4WS), Web services modeling ontology, OWL-S, Petri nets and pacification [START_REF] Charif | An Overview of Semantic Web Services Composition Approaches[END_REF] are the commonly used methods in the Web services composition. The BPEL4WS is a static services composition mode. It executes the pre-binding workflow file using workflow processing engine. The advantage of this mode is the stability of the services composition, and its disadvantage is the poor adaptability. The WSMO, OWL-S, Petri nets and pacification all belong to the dynamic services composition mode. This mode dynamically searches and discovers services during the execution. Besides, it handles business processes using dynamic binding and composite services. It has good flexibility and adaptability. Among these methods, the OWL-S is the Web services composition standard recommended by the W3C. In the paper, this mode is also utilized.

2.3GEOSPATIAL SERVICES

At present, the geospatial services are the easiest way to access geographic data by using common network protocol. It is due to that geospatial services shield the differences between data formats and interfaces, and do not need to utilize traditional data distribution model [START_REF] Breitman | Semantic Web: Concepts,Technologies and Applications[END_REF]. The geospatial services have the potential to change the development, access and use of GIS, and make the sharing of geographic information data and functions easier [START_REF] Tang | Connecting Our World: GIS Web Services[END_REF]. Using the semantic Web technology, the geospatial services can be understood by other programs or software agents to achieve automatic composition of geographic services according to demand. It can provide more flexible and powerful functions. The ISO/TC211, OGC, INSPIRE and FGDC are all committed to research and develop the standards and norms of the geospatial services. The most important basis of the implementation of the semantic geospatial services is to establish a formal unified geographic ontology.

INTEGRATION FRAMEWORK DESIGN OF SEMANTIC GEOSPATIAL SERVICES

The semantic geospatial services will have many advantages comparing with the existing geospatial services in many aspects. It will provide real-time and interactive geographic information services which also support spatial reasoning. The semantic geospatial services will support the seamless integration with general business management system. So the spatial information can be seamlessly managed as a part of a larger enterprise management system. This completely changes the island status of the geographic information in enterprise application.

The basis of achieving the semantic geospatial services integration is to establish the ontology library which meets the application requirements. According to the level of production, the ontology can be divided into three layers: Top-level ontology, domain ontology and application ontology. The top-level ontology usually describes the general concepts independent of any fields, such as event and object. The domain ontology is usually a concept in a specific field, such as the geographic information field and business logistics field. The application ontology is usually an application project oriented concept developed for a specific field and application.

Figure 1 shows the overall framework of the designed geospatial services integration in this paper. The basis of integration is to establish application ontology library according to the application requirements of project. This ontology library consists of agriculture, tobacco, logistics, commercial and much other ontology.

Figure 2 shows the tobacco-growing geographic information application ontology object. The geospatial services and enterprise services achieve the sharing and interoperability of information using the semantics provided by this ontology library.

In figure 1, the service manager is implemented by a Web service. It responses to the inputs of user and parses the user"s request into a service list. This service list contains the name and parameters of input and output of the required service, and receives the result data returned from the service composition manger. The service composition manger is also a Web service and implemented by Jena + OWL-S. Its main function is to combine service list sent by service manger into concept-level service composition chain. The service chain repository is used to save the executed service chain information which includes the composition state of service chain and the reference state of result data generated by executing service chain. The function of inference service and matching service is to change concept-level service composition chain into executive instance-level service composition chain through the semantic integration service. The semantic integration service provides the unified entrance for accessing geospatial services and enterprise application services.

A typical user request and response process is as follows:

1) User submits a request to the service manager by client;

2) Service manger parses the request into service list;

3) Service manger sends the service list to service composition manger. Service composition manger generates a concept-level service composition chain according to the input parameters. 4) Service composition manager looks for the instance of this service chain from instance service chain library. If it is found, directly return data reference address to the service composition manager. Service composition manager returns the address to service manager. Service manager executes this reference and return the result data to user. 5) If the instance of service chain can not be found, service composition manager calls the matching service and inference service. Through semantic integration services, the two services call the corresponding geospatial services and enterprise services to generate the instance of this service chain according to according to semantic information provided by the ontology library. After the instance service chain executes, the instance is returned to the service composition manager and is stored to service chain library. The service composition manager returns the address reference of result data generated by the execution of the instance service chain to service manager.

Before the execution of each atomic service of the instance service chain generated by inference service and matching service, the instance service chain library is checked first. If the instance of this atomic service is found, the result address reference of the instance is directly called. Otherwise, execute the specific service process after finding this instance service.

AN EXAMPLE OF APPLICATION

Since China has joined the international "Framework Convention on Tobacco Control" in 2005, the State Tobacco Monopoly Bureau has begun to implement the strict "dual control" policy gradually, so the rational tobacco farming planning has become very important to tobacco manufactures. In the last two years, tobacco manufacturers in every district have begun to implement basic tobacco farming planning. Most of the basic data have been collected manually. The spatial data have been stored in paper maps and the attribute data have been stored in ordinary relational databases.

Due to the subjectivity and arbitrariness of the manual planning, companies now need to determine whether tobacco farm is suitable for growing tobacco. That is the "whether a land suits to grow tobacco" problem. Assume that the user selects a piece of land from the client (usually a map page, also can be a data report) and then it is submitted to services manager for querying whether this land is suitable to grow tobacco. The process flow of the service request handled by the service manager is as follows: The existing service manager divides user request into atomic service list. The atomic service lists are combined into service list included two sub-service chain by the service composition manager. A sub-service chain is to determine whether the land conforms to the related spatial information conditions. Another sub-service chain is to determine whether the township where the land locates conforms to the related statistical conditions. Figure 3 shows the details of service chain process. Finally, the return value of the whole service(ie, appropriate level) is calculated according to the results returned by the two sub-service chains. The appropriate level can be calculated according to the following equation.

Q Q R q k Q q k Q
Where ki ,qi , kj ,qj represent the relevance degree and weights returned using the queried land as input parameters in a Web service, respectively; R is the appropriate level of growing tobacco in the queried land.

CONCLUSIONS

In this paper, the seamless integration and interoperability of the geospatial services in tobacco-growing enterprise application system is implemented under the framework of the geospatial services theory and technology. It promotes the application of the geospatial services in enterprise decision-making and meets the requirements of the enterprise geographic information application well.

The semantic geospatial services have wide prospect in the enterprise application. It is the development trend of the geospatial services enterprise integration. But it still has few mature applications at present. The further research and practice in this aspect will be conducted.

3 )

 3 Using standard protocol specifications. All public Web services protocols use the open standard protocols. In general, the majority of protocol norms will eventually use W3C or OASIS as the publisher and maintenance side of the final version. 4) Highly integrated capability. Web services use simple and intelligible standard protocols as the norms of the component interface description and collaboration description. It shields the differences of different software platforms. So CORBA, DCOM or EJB can use a standard protocol for interaction operation to achieve highly integrated in the current environment.

Fig. 1 Fig. 2

 12 Fig.1 Geospatial Service Integration Framework

Figure 4 Fig. 3 Fig. 4 A

 434 Figure4shows the GUI of the system.

Table 1 .

 1 The temperature, humidity, sunshine, rainfall and other climatic conditions of tobacco growing have certain requirements. Meanwhile, tobacco growing needs appropriate soil type, land cover, slope, elevation and other geographic conditions. The climate data are often the statistical data of each administrative area issued by meteorological department. The geographic data often derives from the raster of vector map data issued by territory department and surveying and mapping department.Whether a farm is suitable for the cultivation of tobacco depends not only on the elevation, slope, soil type, soil PH, water conservancy facilities and other geographic data, but also on sunshine, rainfall, temperature and other statistical data. Table1shows the details. Conditions for Tobacco PlantingThe elevation and slope data are generated from the DEM data. The soil type distribution maps, PH value of soil distribution maps and land use distribution maps are obtained from the relevant departments, respectively.The sunshine, rainfall and temperature statistical data of each administrative area can be obtained from the meteorology bureau. Because the study area in this paper does not provide online data services, the data obtained in this paper are the original data files. In actual, the corresponding data services are established in the internal network. Table2shows the service list.

	Project	Description of the appropriate
		conditions
	Altitude	1200-1900 meters
	Slope	less than 15 degree
	Soil type	Red soil, yellow soil, red and yellow
		soil
	The type of	Land, land on a field whose slope is
	land use	less than 15 degree
	Soil PH	5.5-7
	Sunlight	To large land, Sunlight duration is
		up to 500-700 hours; sunshine
		rate is greater than or equal to
		40% ; Mining grilled period
		needs 280-400 hours; sunshine
		rate is greater than or equal to
		30%。
	Rainfall	Lager land requires that the monthly
		average rainfall is 100-130 mm
	Temperature	The average daily temperature is
		less than 20 centigrade from July to
		September. The optimal temperature
		is	20-25	centigrade.	The
		accumulated temperature is less than
		10 centigrade in growth period

Table 2

 2 Services used in the Application

ACKNOWLEDGEMENTS

The work was supported by National Natural Science Foundation of China (Grant No.41101395 ) and Special Fund for Agro-scientific Research in the Public Interest (200903010).


Linsheng Huang
email: linsheng0808@163.com

Qinying Yang

Dong Liang

Yansheng Dong

Xingang Xu

Wenjiang Huang
email: huangwj@nercita.org.cn

The Estimation of Winter Wheat Yield Based on MODIS Remote Sensing Data

Keywords: LAI, remote sensing, yield estimation, regression model, ArcGIS Server

A yield estimation method by remote sensing was used to estimate the yield of winter wheat in Jiangsu province, China. The first step of this study was to extract the planting area of winter wheat from environmental satellite images and land -use map of Jiangsu province, meanwhile, correlation analyses were performed by using 8-day of composite Leaf Area Index (LAI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and statistical yield of corresponding counties. Secondly, the average LAI was calculated at the optimal growth period, and the statistical yields of wheat for all counties were collected, in which the former was chosen as the independent variable and the latter was the dependent variable, and the regression model was established. Finally, the accuracy and stability of the regression model were validated using the data of another year. The results indicated that the yield estimation model at provincial level is reliable, the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) of the model was 12.1% and 9.7%, respectively. In addition, the yield estimation system of winter wheat in Jiangsu province was constructed and published based on ArcMap and ArcGIS Server.

Introduction

With the increase of population and the reduction of arable land, food security has become a very serious and prominent issue around the world, especially in China. Winter wheat is one of the major crops in China, its planting area accounts for one-fifth of the total crops area nationwide. Timely and accurate estimation of the winter wheat yield has an important significance for a country to enhance the risk management of food security as well as to develop appropriate market policies [START_REF] Li | Development of an Intelligent Yield Monitor for Grain Combine Harvester[END_REF][START_REF] Huang | Inversion of Foliar Biochemical Parameters at Various Physiological Stages and Grain Quality Indicators of Winter Wheat With Canopy Reflectance[END_REF][START_REF] Xu | Research of Crop Yield Models[END_REF]. Currently, a range of methods have been used for estimating crop yield [START_REF] Liu | Improving Winter Wheat Yield Prediction by Novel Spectral Index[END_REF][START_REF] Li | Active Solar Heating System With Soil Heat Storage for Plastic Film Greenhouse and Its Effects[END_REF][START_REF] Li | A Model of Estimating Winter Wheat Yield Based on TM Image and Yield Formation[END_REF][START_REF] Tang | Yield Estimation by Hyperserctral Data of Rice Canopies in Mature Stages[END_REF][START_REF] Wang | Study on NPLS Model with Three-Dimensional Hyperspectrum for Assessing Per-Mu-Yield of Winter Wheat[END_REF]. These methods, however, have their own drawbacks respectively. The statistical surveys methods are time-consuming and require a lot of manpower and material resources; agricultural weather forecasting methods and agricultural forecasting methods are not suitable for large-scale crop yield estimation.

As satellite technologies improved significantly in recent years, remote sensing has become an important method to monitor and gather agricultural information in practice. Compared with these methods mentioned above, remote sensing yield estimation methods, by means of particular advantages such as large coverage, short monitoring cycle, strong instantaneity as well as low cost, have provided new technological means for the realization of large-scale crop yield estimation. Remote sensing yield estimation methods include crop growth model method, semiempirical and semi-mechanistic model method and empirical model method. They have their own advantages and disadvantages, crop growth model method requires a lot of input parameters (such as meteorological parameters, management parameters and crop genetic parameters, etc.), which limits the use in large regions because some of the parameters are not easy to obtain in these areas. Also, it is difficult to simulate some parameters in semi-empirical and semimechanistic model method. Empirical model method though has some limitations, is a simple and suitable method for large-scale crop yield estimation under certain accuracy conditions. By using this method, many scholars at home and abroad had made outstanding achievements in crop yield estimation [START_REF] Ren | LAI-based Regional Winter Wheat Yield Estimation by Remote Sensing[END_REF][START_REF] Becker-Reshef | A Generalized Regression-Based Model for Forecasting Winter Wheat Yields in Kansas and Ukraine Using Modis Data[END_REF]. Ren et al. (2010) estimated regional winter wheat yield based on the inversion of LAI, the yield of winter wheat could be accurately forecasted 20-30 days prior to harvest, the results indicated that LAI and crop yield have a good correlation [START_REF] Ren | LAI-based Regional Winter Wheat Yield Estimation by Remote Sensing[END_REF]. Becker-Reshef et al. (2010) utilized the maximum of Normalized Difference Vegetation Index (NDVI) in crop growing season to estimate yield, the error was less than 10% [START_REF] Becker-Reshef | A Generalized Regression-Based Model for Forecasting Winter Wheat Yields in Kansas and Ukraine Using Modis Data[END_REF]. [START_REF] Mkhabela | Crop Yield Forecasting on the Canadian Prairies Using MODIS NDVI Data[END_REF] developed a production-NDVI model to estimate crop yield, in which the NDVI values were selected at the optimal estimation period (the period when correlation coefficient of NDVI values and the yields is largest) [START_REF] Mkhabela | Crop Yield Forecasting on the Canadian Prairies Using MODIS NDVI Data[END_REF]. Based on previous studies, this study estimated the winter wheat yield with LAI at the optimal estimation period in Jiangsu province.

Study area and data processing 1.Study areas

Jiangsu province is located in eastern coast of China mainland which is the transition zone of subtropical to tropical region, between longitude 116°18'-121°57 ' and north latitude 30°45' -35°20',the geographic range shown in Figure 1(a). Its average temperature is 13°C -16°C and annual rainfall is about 1000 ml, the area of arable land is 4,902 million hectares, accounting for 3.97% of nationwide. It is one of the most important food sources in China, growing areas of winter wheat shown in Figure 1 Winter wheat in Jiangsu Province was sowed at the period of early to mid October, matured at about late May to early June, the phenophase of winter wheat is shown in Table 1.

Table1. Phenophase of winter wheat in Jiangsu province

Month

Data processing

This study utilized the data of environmental satellite and the land use map of 2009 to extract the information of winter wheat growing areas and got the average LAI of county scale from MODIS products. By using the remote sensing images of environmental satellite, the information of winter wheat growing areas was extracted in 16 April and 18 April, 2009. The process of preprocessing included radiometric calibration, atmospheric correction as well as geometric correction. The first step was using the land use map of Jiangsu province to mask remote sensing images which had been preprocessed by which environmental satellite images within the scope of cultivated land obtained, and then through the process of supervised classification for satellite images (which had been masked) to extract the winter wheat growing areas. The principle of winter wheat extraction is: there were almost no other crops grown in Jiangsu province in April, therefore the green vegetation in arable land can be regarded as winter wheat. The next step was stitching the winter wheat growing areas of the images, which captured in April 16 and April 18, 2009 to form an integrated image of Jiangsu Province. In order to match the pixels of MODIS-LAI data, this study resampled the spatial resolution of 30m to that of 1km in these areas. Also, it assumed that the wheat growing areas varied slightly at the county scale on the condition that the spatial resolution was 1km. Meanwhile, the wheat growing areas of 2009 could be used as that of 2006 and 2007, as shown in Figure 1(b). LAI data used in this study came from MOD15A2 which is a product of MODIS. The advantages of MODIS data as below: short repetition period, large coverage area and strong access ability. After the process of smoothing and denoising, MOD15A2 data was reprojected with MODIS Reprojection Tool (MRT), and the projection types were geographic latitude-longitude projection and WGS84 coordinates system. In order to get data of wheat growing areas in Jiangsu province, h27v05 and h28v05 (track number) were stitched in ENVI software in this study. Next, the LAI data in wheat growing areas would be reached by utilizing the wheat areas to mask the stitched images. A total of 64 scenes of MOD15A2 data were adopted in this study, the time range covered the seven critical growth stages of winter wheat (turning green, erecting, jointing , booting, heading, flowering and milky mature) from 2006 to 2007.

Estimation methods and results analysis 2.1 Estimation methods

As different crops have different spectral information, firstly satellite sensors are used to record information of the surface and identify the type of crops, then extract various vegetation indices to establish the relationship between vegetation index and crop yield, and finally use vegetation indices for estimation, this method is called yield estimation [START_REF] Deng | Research on Yield Estimation of Rice Based on Remote Sensing Using Moderate-Resolution Imaging Spectroradiometer(MODIS) Data:A Case Study of Jiangsu Province, China[END_REF]. LAI is defined as the ratio the projected area of the leaves to that of the total area when the sunlight is direct. Crop LAI is an important parameter in the process of material exchange with the environment, and it directly relate to photosynthesis, respiration, transpiration and biomass accumulation of crop and has significant impact on the yield of crop [START_REF] Chen | Rice Yield Estimation by Assimilation Remote Sensing Into Crop Growth Model[END_REF][START_REF] Bai | Study State and Developing Trends of Hyperspectral Remote Sensing Technology in Estimating Crop Yield[END_REF]. LAI used in this study was the average number in the county scale, which was got through the statistical computation of MOD15A2 product in wheat growing areas. Also, the LAI of winter wheat growing areas in each county and the number of pixels corresponding to these areas were counted in ArcMap. Total number of LAI in winter wheat growing areas divided by the number of pixels will get the average LAI of the winter wheat in this county. Due to the correlation between LAI at different periods and crop production is different, firstly through the correlation analysis of average LAI at county-scale and statistical yield in a county in 2006, then selected the period which correlation coefficient was largest as the optimal estimation period. Furthermore, this study utilized the average LAI of this period and statistical yield for linear regression to obtain a provincial-scale estimation model. Estimated yield of 2007 would be obtained when substituted the average LAI of each county at the optimal estimation period in this year into estimation model. Finally, accuracy of the model would be got through the comparison and validation of estimated yield with statistical yield.

RMSE and MAE are the two indicators for validate the accuracy of yield estimation model. RMSE and MAE are defined as follow:

    n i i i M P n RMSE 1 2 ) ( 1 (1)
    n i i i M P n MAE 1 | | 1 (2)
where n is the number of counties for estimation in Jiangsu Province, Pi is the estimated yield and Mi is the statistical data. The percentage values of RMSE and MAE can be obtained when the values of them divided by the average value of statistical yield. RMSE can well reflect the accuracy of predicted values, when RMSE is less than 10% indicates that the consistency of predicted and actual values is very good, when the number is less than 10% and greater than 20% shows the predicted results are good, between 20% and 30% means predicted results are general, greater than 30% indicates large deviations [START_REF] Mkhabela | Crop Yield Forecasting on the Canadian Prairies Using MODIS NDVI Data[END_REF].

Result analysis

At the period from turning green to maturity stage of winter wheat in 2006, through the correlation analysis on the average LAI every 8 days and the statistical yields in each county, the results shown in Figure 2(a). The correlation coefficient of winter wheat LAI and yield was maximum in the first 97 to 121 days of the year, up to 0.6. It also showed that the booting to heading stage was the optimal yield estimation period for winter wheat in Jiangsu province because this period was very important in the process of reproductive growth and yield formation. In this study, through the linear regression of the LAI at heading stage and statistical yield of winter wheat in each county in 2006, the yield estimation model will be reached, as shown in Figure 2(b). The regression equation is:

  (b).

Fig. 1 .

 1 Fig. 1. the study area (a) and growing areas of winter wheat (b) selected in this study

Fig. 2 .

 2 Fig.2. The correlation analysis (a) and relationship between wheat yield and LAI (b)

Acknowledgements. This study was subsidized by the National Natural Science Foundation of China (41001244,41071276) and Key Research Project of Education Department of Anhui Province(KJ2010A020, KJ2010A021).

. 3348 29 . 878  

x y [START_REF] Xu | Research of Crop Yield Models[END_REF] where y is the yield of winter wheat, x is the average LAI at heading stage. As can be illustrated from Figure 2(b) that LAI and yield are correlated remarkably.

When substituted the average LAI at heading stage in 2007 into regression model, the yield estimation of each county and the spatial distribution of winter wheat yield in 2007 will be reached, as shown in Figure 3(a). In order to validate the results, this study compared the estimated yield with statistical yield in Figure 3

Implementation of Web publishing system

Web services make it possible for service providers to sell their services by publishing their availability over the Internet. They provide a standard means of interoperating between different software applications, running on a variety of platforms or frameworks. ArcGIS Server is developed by ESRI based on ArcObjects (AO) components; the aim of it is to build enterprise-class Geographic Information system (GIS). AO components provide a wealth of features, including access to GIS data, maps performance and spatial analysis, etc [START_REF] Zhao | A Method to Develope WebGIS Based on ArcIMS and Application[END_REF]. Map Service function of ArcGIS Servers are mainly used in this study. Before calling the estimation data, the data must first be published as the configuration information of map layer. The map layer is designed in ArcMap, the basic functions of it including zoom, layer stack as well as inserting data to the layer, the output map is saved as mxd format. A service object will be created in the interface of ArcGIS Server Manage by double-clicking the Add New Service, entering the name of the service object and the folder where the published map located, and the published map is a active layer [START_REF] Yang | Research on integration method of GIS-based application model based on web service composition[END_REF]. This study uses the JavaScript language and calls the components of ArcGIS through API to build the published system. The system is developed in the .net framework of Microsoft Visual Studio 2008, where js file (a script file of JavaScript) can be directly executed. The function modules of the system including basic map functions, layer management as well as search functions. The function menus and the operation interface of the system showed in Table 2 and Figure 4, respectively. Winter wheat yield can be estimated one to two months prior to harvest, which provide the scientific basis for the local government to make appropriate food policies. In addition, for the purpose of information sharing, this study designed a Web publishing system to publish the data of wheat yield. Despite these good results, there are still some inadequacies in this study: 1, the current model was developed based on the 2 years of data available and the accuracy of the model was validated only by 1 year of data, the results were lack of universality because of the small amount of data;

Table 2. function menus of the system

2, the constructed environment of the model was relatively simple in which the impact of natural disasters and other factors on yield were not carefully considered;

3, the functions of the Web publishing system were not perfect and needed to further improvement.

Consequently, further studies are needed to resolve these drawbacks mentioned above.


Haiguang Wang
email: wanghaiguang@cau.edu.cn

Zhanhong Ma

Prediction of Wheat Stripe Rust Based on Neural Networks

Keywords: Wheat stripe rust, prediction, neural networks, epidemics

Stripe rust caused by Puccinia striiformis f. sp. tritici, is a devastating wheat disease in the world. The prediction of this disease is very important to make control strategies. In order to figure out suitable prediction methods based on neural networks that could provide accurate prediction information with high stability, the predictions of wheat stripe rust by using backpropagation networks with different transfer functions, training functions and learning functions, radial basis networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were conducted in this study. The results indicated that suitable backpropagation networks, radial basis networks and GRNNs could be used for the prediction of wheat stripe rust. Good fitting accuracy and prediction accuracy could be obtained by using backpropagation networks with trainlm, trainrp or trainbfg as training function. Radial basis networks had more power than backpropagation networks and GRNNs to predict wheat stripe rust. GRNNs were easier to be used than backpropagation networks. New methods based on neural networks were provided for the prediction of wheat stripe rust.

Introduction

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici, is a very important wheat disease in the world [START_REF] Li | Wheat Rusts in China[END_REF]. This disease is widely distributed in eastern and central Asia, western Europe and the pacific coast of North America [START_REF] Li | Wheat Rusts in China[END_REF], [START_REF] Chen | Epidemiology and Control of Stripe Rust [Puccinia striiformis f. sp. tritici] on Wheat[END_REF], [START_REF] Line | Stripe Rust of Wheat and Barley in North America: a Retrospective Historical Review[END_REF]. The epidemics of this disease could also occur in Oceania, northern and eastern Africa, and South America [START_REF] Li | Wheat Rusts in China[END_REF]. In China, in terms of distribution range and destructivity, stripe rust is the most important among the three wheat rusts including leaf rust and stem rust. Stripe rust could occur in all main wheat areas and could cause severe losses in wheat production in China. It is critical to the national food security of China. The epidemics of wheat stripe rust are related to the long distance transports of the pathogen driven by winds. Accurate prediction of this disease is the key to make strategies for effective control.

The prediction of plant disease is to estimate the prevalence of plant disease after a certain time using expert experience or system simulation methods based on epidemic law of the disease. There have been many reports about the prediction of wheat stripe rust. The methods that were usually used included regression analysis [START_REF] Zeng | Interregional Spread of Wheat Yellow Rust in China (in Chinese)[END_REF], [START_REF] Yang | Dinamic Forecasting of Stripe Rust of Winter Wheat (in Chinese)[END_REF], [START_REF] Hu | Studies on the Prediction of Wheat Stripe Rust Epidemics in Hanzhong District of Shaanxi Province (in Chinese)[END_REF], [START_REF] Fan | Forecast Model for Prevalent Stripe Rust in Winter Wheat in Shanxi Province (in Chinese)[END_REF], discrimination analysis [START_REF] Chen | Forecasting Wheat Stripe Rust by Discrimination Analysis (in Chinese)[END_REF], [START_REF] Chen | Preliminary Research on the Regional Relationship of Epidemic of Puccinia striiformis in China (in Chinese)[END_REF], [START_REF] Yun | Forecast of Wheat Stripe Rust by Upper-air Wind (in Chinese)[END_REF], Markov forecast method [START_REF] Qiang | Markov Forecast of Wheat Stripe Rust in Qinghai in 1998[END_REF], principal component analysis [START_REF] Yuan | Application of Principal Component Analysis of Wheat Stripe Rust (in Chinese)[END_REF], grey model forecast method [START_REF] Pu | On Periodic Epidemic Regular Pattern and Prediction of Wheat Stripe Rust in Gansu Province (in Chinese)[END_REF], [START_REF] Liu | The Prediction Methodology of Wheat Stripe Rust Using Combination Model Based on GM(1,l) (in Chinese)[END_REF] and neural networks [START_REF] Hu | Prediction of Wheat Stripe Rust in Hanzhong Area by BP Neural Network (in Chinese)[END_REF], [START_REF] Zhang | Research on Chaotic Characteristics of the Disaster Rate of Crops and Its GA-BPNN Forecasting Model (in Chinese)[END_REF], [START_REF] Jin | Long-term Meteorological Prediction of Country Wide Wheat Stripe Rust by Genetic Neural Network (in Chinese)[END_REF]. Wheat stripe rust is influenced by various factors, including the amount of pathogen, variety resistance and meteorological conditions. The relationship between wheat stripe rust and these factors is usually nonlinear. Neural networks have good advantages of solving the nonlinear problems. They are composed of interconnecting artificial neurons. They have good adaptive learning ability and nonlinear mapping ability. Neural networks have been successfully applied in various fields, including pattern recognition, image analysis and control systems. Although neural networks have been used for the prediction of wheat stripe rust [START_REF] Hu | Prediction of Wheat Stripe Rust in Hanzhong Area by BP Neural Network (in Chinese)[END_REF], [START_REF] Zhang | Research on Chaotic Characteristics of the Disaster Rate of Crops and Its GA-BPNN Forecasting Model (in Chinese)[END_REF], [START_REF] Jin | Long-term Meteorological Prediction of Country Wide Wheat Stripe Rust by Genetic Neural Network (in Chinese)[END_REF], individual backpropagation network or individual function combination was be used in most cases. To the authors' knowledge, there is not any report about the application of radial basis networks to the prediction of wheat stripe rust. Furthermore, the comparison between prediction performances of different neural networks and the comparison between prediction performances of backpropagation networks combined with different transfer functions, training functions and learning functions have not been reported yet.

In order to select prediction methods based on neural networks with good prediction performance and prediction accuracy and provide disease information for making control strategies and taking control measures, the predictions of wheat stripe rust based on backpropagation networks with different transfer functions, training functions and learning functions, radial basis networks, and two variants of radial basis networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs), were conducted and their prediction effects were compared in this study.

2

Materials and Methods

Materials

The data used for analysis in this study were colleted from [START_REF] Ma | Plant Disease Epidemiology[END_REF]. They consisted of the data of disease prevalence of wheat stripe rust and related meteorological data in Hanzhong in Shannxi Province during the period from 1974 to 1997. In detail, they included the amount of stripe rust pathogen in autumn, the amount of stripe rust pathogen in spring, average temperature in January, average temperature in February, average temperature in March, average temperature in April, average temperature in November, average temperature in December, precipitation in January, precipitation in February, precipitation in March, precipitation in April, precipitation in November, precipitation in December, the proportion of susceptible variety area and disease prevalence in adult stage of wheat. Among these factors, the amount of stripe rust pathogen in autumn and related data in November and December referred to the data in the preceding year. The fifteen influencing factors were normalized. The data set contained the fifteen normalized factors and disease prevalence of wheat stripe rust was regarded as Data set 1. In the data set, disease prevalence of wheat stripe rust was divided into five classes represented by 1, 2, 3, 4 and 5, respectively. The data set was regarded as Data set 2 while Class 1, 2, 3, 4 and 5 were expressed as (0 0 0 0 1), (0 0 0 1 0), (0 0 1 0 0), (0 1 0 0 0) and (1 0 0 0 0), respectively. In the literature [START_REF] Ma | Plant Disease Epidemiology[END_REF], the amount of stripe rust pathogen in spring, the amount of stripe rust pathogen in autumn, the proportion of susceptible variety area, precipitation in April and average temperature in April were selected as the main factors influencing disease prevalence from the fifteen factors by using stepwise regression analysis. The five selected factors were normalized. The data set contained the five normalized factors and disease prevalence of wheat stripe rust represented by 1, 2, 3, 4 or 5, was regarded as Data set 3. The data set was regarded as Data set 4 while the disease prevalence of wheat stripe rust was represented by (0 0 0 0 1), (0 0 0 1 0), (0 0 1 0 0), (0 1 0 0 0) or (1 0 0 0 0).

Methods

Under MATLAB environment, backpropagation networks were used to process Data set 2 and Data set 4, radial basis networks and GRNNs were used to process Data set 1, Data set 2, Data set 3 and Data set 4, and PNNs were used to process Data set 1 and Data set 3. During data processing, disease prevalences were used as outputs and the other variables were used as inputs. For the data sets, the data in 1974-1993 were considered as the training sets and the data in 1994-1997 were considered as the testing sets. The prevalences of wheat stripe rust in 1974-1993 in Hanzhong in Shannxi Province were fitted and that in 1994-1997 were predicted. Firstly, the training sets were trained to acquire optimized neural networks. And then the neural networks were used to predict output values of the testing sets, and meanwhile the training sets were also fitted. The fitting and prediction results obtained were compared with actual disease prevalences and then fitting accuracy and prediction accuracy were obtained, respectively.

One-hidden-layer backpropagation networks were constructed. Three common transfer functions including logsig, tansig and purelin were used in the networks. Since the outputs of the networks were between 0 and 1, the output layer used the logsigmoid transfer function (logsig). The networks used trainlm, traingd, traingdm, trainrp and trainbfg for training. Learngd and learngdm were used as learning functions. Different combinations of transfer functions, training functions and learning functions were recorded as BP1, BP2, BP3, … , BP30, and were shown as in Table 1. For Data set 2, fifteen neurons were used in the input layer and five neurons were used in the output layer. For Data set 4, five neurons were used in the input layer and five neurons were used in the output layer. Maximum number of epochs to train for the backpropagation networks was 5000. The neuron number in the hidden layer was assumed to be 1 to 20 with step size 1.

Radial basis networks, GRNNs and PNNs were used to fit the training sets and predict the disease prevalences using the testing sets. Spreads of radial basis functions for radial basis networks, GRNNs and PNNs were assumed to be 0.1 to 2.0 with step size 0.1.

Results and Analysis

The data sets were processed using the methods as shown above. The results of the neural networks with fitting accuracy ≥75% and prediction accuracy ≥50% were picked out and were shown in Table 2, Table 3, Table 4, Table 5, Table 6 and Table 7, respectively.

Using the backpropagation networks, for either Data set 2 or Data set 4, the selected combinations with good fitting accuracy and prediction accuracy were BP1, BP2, BP3, BP4, BP5, BP6, BP19, BP20, BP21, BP22, BP23, BP24, BP25, BP28, BP29 and BP30. Among these combinations, the training functions of BP1, BP2, BP3, BP4, BP5 and BP6 were trainlm, that of BP19, BP20, BP21, BP22, BP23, BP24 and BP25 were trainrp, and that of BP28, BP29 and BP30 were trainbfg. When the training function was traingd or traingdm, good fitting accuracy and prediction accuracy could not be obtained using the backpropagation networks. So it is important to choose suitable training function for backpropagation networks.

As Table 2 shown, for Data set 2, the fitting accuracies were all 100% and the prediction accuracies were all 75% while using BP1 with the number of neurons in the hidden layer equal to 3 or 11, BP3 with the number of neurons in the hidden layer equal to 10, 12 or 17, BP6 with the number of neurons in the hidden layer equal to 16, BP19 with the number of neurons in the hidden layer equal to 4, BP21 with the number of neurons in the hidden layer equal to 5, BP24 with the number of neurons in the hidden layer equal to 10 or 19, and BP28 with the number of neurons in the hidden layer equal to 5.

For Data set 4, the fitting results and the prediction results of the backpropagation networks were shown as in Table 3. Fitting accuracies were all 100% and prediction accuracies were all 75% while using BP4 with the number of neurons in the hidden layer equal to 9 and BP22 with the number of neurons in the hidden layer equal to 8 besides the combinations as the same as those with the same number of neurons in the hidden layer for Data set 2.

The results indicated that backpropagation networks could be used for the prediction of wheat stripe rust. When using this method, the inputs could be the data of all the factors obtained in the investigations or be the data of selected main factors. Otherwise, it would take a longer time to build suitable networks when using the data of all the influencing factors obtained.

The fitting results and the prediction results of radial basis networks were shown as in Table 4 and Table 5. The fitting accuracies were all 100% for the selected networks. The results implied that radial basis networks have good adaptive learning ability and stable prediction ability. For Data set 1, the prediction accuracy was 75% when the value of spread was 0.8, 0.9, 1.0 or 1.1. For Data set 2, the prediction accuracies were all 50% while using the selected networks. For Data set 3, the lowest prediction accuracy was 50% and the highest prediction accuracy was 100%. The prediction accuracy was 75% when the value of spread was 0.3, 0.4, 0.5, 1.5, 1.6, 1.7, 1.8, 1.9 or 2.0. The prediction accuracy was 100% when the value of spread was 0.6 to 1.4. For Data set 4, the prediction accuracy was 75% when the value of spread was 0.3, 1.2, 1.3, 1.4 or 1.5. The results indicated that radial basis networks have high prediction ability for the disease. In practical applications, it is better to choose the key influencing factors as the inputs of the networks to improve the run speed of the programs, prediction accuracy, and performance stability. The fitting results and the prediction results of GRNNs were shown as in Table 6. For Data set 1 including the fifteen influencing factors, the fitting accuracy was 100% and the prediction accuracy was 75% while the value of spread was equal to 0.3. For Data set 3 including the five selected factors, the fitting accuracy was 100% and the prediction accuracy was 75% while the value of spread was equal to 0.1 or 0.2. And for Data set 3, there was one exception that the fitting accuracy was only 80%, but the prediction accuracy was 75% while the value of spread was equal to 0.4. For Data set 2 including the fifteen influencing factors, the fitting accuracy was 100% and the prediction accuracy was 50% while the value of spread was equal to 0.2, 0.3 or 0.4. For Data set 4 including the five selected factors, the fitting accuracy was 100% and the prediction accuracy was 50% only when the value of spread was 0.1. The results showed that GRNNs could be used as a method to predict wheat stripe rust and that the prediction ability when disease prevalence of wheat stripe rust was expressed as 1, 2, 3, 4 or 5 was higher than that when disease prevalence was expressed as (0 0 0 0 1), (0 0 0 1 0), (0 0 1 0 0), (0 1 0 0 0) or (1 0 0 0 0). Therefore, disease prevalence could be expressed as 1, 2, 3, 4 or 5 when the prediction of wheat stripe rust is conducted using GRNNs.

When PNNs were used to process the data of wheat stripe rust, the highest fitting accuracy was 100% and the highest prediction accuracy was 50% (as shown in Table 7). PNNs showed relatively low prediction ability. For Data set 1, the fitting accuracy was 100% and the prediction accuracy was 50% when the value of spread was 0.1, 0.2, 0.3 or 0.4. For Data set 3, the fitting accuracy was 100% and the prediction accuracy was 50% when the value of spread was 0.1. The fitting accuracy had a tendency to decrease with the value of spread increasing.

Conclusion and Discussion

In this study, backpropagation networks with different transfer functions, training functions and learning functions, radial basis networks, GRNNs and PNNs were applied to predict wheat stripe rust, and comparison of prediction effects of these methods were also conducted. The results showed that good fitting accuracy and prediction accuracy could be achieved using suitable backpropagation networks, radial basis networks and GRNNs, and that the methods based on these three algorithms of neural networks could be used for the prediction of wheat stripe rust. In contrast, radial basis networks had more power to predict wheat stripe rust than backpropagation networks and GRNNs, and the programs of GRNNs were simpler than backpropagation networks and needed less time to run. The backpropagation networks using traingd or traingdm as the training function could not fit the data of wheat stripe rust well and could not make accurate predictions. The studies on using neural networks to predict wheat stripe rust reported before mainly used backpropagation networks [START_REF] Hu | Prediction of Wheat Stripe Rust in Hanzhong Area by BP Neural Network (in Chinese)[END_REF], [START_REF] Zhang | Research on Chaotic Characteristics of the Disaster Rate of Crops and Its GA-BPNN Forecasting Model (in Chinese)[END_REF], [START_REF] Jin | Long-term Meteorological Prediction of Country Wide Wheat Stripe Rust by Genetic Neural Network (in Chinese)[END_REF]. This study provides new approaches based neural networks for the prediction of this disease. Many studies on the prediction of wheat stripe rust have been conducted and many methods have been applied for the prediction of this disease. However, some kinds of problems such as small sample size are often met in the process of making disease prediction and could not be solved very well. Support vector machine (SVM), a new kind of machine learning method proposed by Vapnik [START_REF] Vapnik | The Nature of Statistical Learning Theory[END_REF], could solve these problems with many advantages. SVM is an algorithm based on VC dimension theory and structural risk minimization principle. It could solve the small sample, nonlinear problems, high dimension and local minimum points and other practical issues. SVMs are currently being used in many fields, such as text classification [START_REF] Bao | Text Categorization Method Based on Fuzzy Support Vector Machine (in Chinese)[END_REF], [START_REF] Liu | Appropriateness in Applying SVMs to Text Classification (in Chinese)[END_REF], image recognition [START_REF] Liu | Image Recognition Based on SVM Information Fusion and DSP Parallel Realization (in Chinese)[END_REF], remote sensing information analysis and processing [START_REF] Wang | Application of Hyperspectral Data to the Classification and Identification of Severity of Wheat Stripe Rust (in Chinese)[END_REF], [START_REF] Fu | Support Vector Machine Based Groundwater Level Monitoring Model by Using Remote Sensing Images (in Chinese)[END_REF], disastrous weather forecast [START_REF] Wang | Support Vector Machines Based Algorithm for the Disastrous Weather Forecast (in Chinese)[END_REF], and so on. The prediction models based on SVMs to predict wheat stripe rust timely and accurately could be established.

In the studies on the prediction of wheat stripe rust, many prediction models have been established. However, each model generally has some certain application conditions. Therefore, suitable model should be selected for application according to the actual conditions. Prediction model system for wheat stripe rust could be built to collect the prediction models into one computer system through programming the models. It could make easier to choose suitable model. Integrated prediction model could be established using different models and methods according to the needs in the future.

Table 1 .

 1 Different combinations of transfer functions, training functions and learning functions for backpropagation networks.

	Tag	Transfer function Hidden layer Output layer	Training function	Learning function
	BP1	tansig	logsig	trainlm	learngdm
	BP2	purelin	logsig	trainlm	learngdm
	BP3	logsig	logsig	trainlm	learngdm
	BP4	tansig	logsig	trainlm	learngd
	BP5	purelin	logsig	trainlm	learngd
	BP6	logsig	logsig	trainlm	learngd
	BP7	tansig	logsig	traingd	learngdm
	BP8	purelin	logsig	traingd	learngdm
	BP9	logsig	logsig	traingd	learngdm
	BP10	tansig	logsig	traingd	learngd
	BP11	purelin	logsig	traingd	learngd
	BP12	logsig	logsig	traingd	learngd
	BP13	tansig	logsig	traingdm	learngdm
	BP14	purelin	logsig	traingdm	learngdm
	BP15	logsig	logsig	traingdm	learngdm
	BP16	tansig	logsig	traingdm	learngd
	BP17	purelin	logsig	traingdm	learngd
	BP18	logsig	logsig	traingdm	learngd
	BP19	tansig	logsig	trainrp	learngdm
	BP20	purelin	logsig	trainrp	learngdm
	BP21	logsig	logsig	trainrp	learngdm
	BP22	tansig	logsig	trainrp	learngd
	BP23	purelin	logsig	trainrp	learngd
	BP24	logsig	logsig	trainrp	learngd
	BP25	tansig	logsig	trainbfg	learngdm
	BP26	purelin	logsig	trainbfg	learngdm
	BP27	logsig	logsig	trainbfg	learngdm
	BP28	tansig	logsig	trainbfg	learngd
	BP29	purelin	logsig	trainbfg	learngd
	BP30	logsig	logsig	trainbfg	learngd

Table 2 .

 2 The fitting results and the prediction results of the backpropagation networks for Data set 2.

	Tag	Number of neurons in the hidden layer	Fitting accuracy	Prediction accuracy	Tag	Number of neurons in the hidden layer	Fitting accuracy	Prediction accuracy
	BP1	1	80%	50% BP19	4	100%	75%
	BP1	4, 5	85%	50% BP20	10, 11, 13, 15, 16, 17, 18	100%	50%
	BP1	6	95%	50% BP21	2, 4, 14, 15, 16, 17, 19, 20	95%	50%
	BP1	8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20	100%	50% BP21	3, 6, 7, 8, 9, 10, 11, 12, 13, 18	100%	50%
	BP1	7	95%	75% BP21	5	100%	75%
	BP1	3, 11	100%	75% BP22	2, 4, 5, 10, 12, 17, 18	95%	50%
	BP2	1, 2, 4, 13	85%	50% BP22	6, 7, 9, 11, 13, 14, 15, 16, 19, 20	100%	50%
	BP2	3	95%	50% BP22	8	100%	75%
	BP2	6	100%	50% BP23	10	90%	50%
	BP3	1, 7	85%	50% BP23	5, 7, 12, 13, 14, 15, 17	95%	50%
	BP3	9	90%	50% BP23	4, 6, 8, 9, 11, 16, 18, 19, 20	100%	50%
	BP3	3, 4, 11, 13, 16	95%	50% BP24	1	85%	50%
	BP3	2, 5, 8, 14, 15, 18, 19, 20	100%	50% BP24	2, 3, 4, 9, 15, 16, 20	95%	50%
	BP3	10, 12, 17	100%	75% BP24	6, 8, 11, 12, 13, 14, 17, 18	100%	50%
	BP4	7	75%	50% BP24	10, 19	100%	75%
	BP4	3, 4, 5, 17	85%	50% BP25	15	80%	75%
	BP4	2, 6, 8, 9, 10, 11, 16, 18, 19, 20	100%	50% BP25	7	85%	50%
	BP5	1	75%	50% BP25	4, 18	95%	50%
	BP5	2, 8	80%	50% BP25 9, 11, 14, 17, 20	100%	50%
	BP5	3, 5, 9	85%	50% BP28	7, 11, 19	85%	50%
	BP5	4, 20	90%	50% BP28	1	90%	75%
	BP5	6, 13, 15	95%	50% BP28	4, 15	95%	50%
	BP5	7, 10, 11, 17, 19	100%	50% BP28 12, 13, 14, 17	100%	50%
	BP6	11, 19	90%	50% BP28	5	100%	75%
	BP6	13	90%	75% BP29	5	85%	50%
	BP6	4	95%	50% BP29	10	100%	50%
	BP6	3, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18	100%	50% BP30	20	85%	50%
	BP6	16	100%	75% BP30	7, 13, 16	90%	50%
	BP19	2	90%	50% BP30	8	95%	50%
	BP19	3, 8, 13	95%	50% BP30	3	100%	50%
	BP19	5, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 20	100%	50%

Table 3 .

 3 The fitting results and the prediction results of the backpropagation networks for Data set 4.

	Tag	Number of neurons in the hidden layer	Fitting accuracy	Prediction accuracy	Tag	Number of neurons in the hidden layer	Fitting accuracy Prediction accuracy
	BP1	1	80%	50% BP19	4	100%	75%
	BP1	4, 5	85%	50% BP19	5, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 20	100%	50%
	BP1	6	95%	50% BP20	10, 11, 13, 15, 16, 17, 18	100%	50%
	BP1	7	95%	75% BP21	2, 4, 14, 15, 16, 17, 19, 20	95%	50%
	BP1	8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, 20	100%	50% BP21	3, 6, 7, 8, 9, 10, 11, 12, 13, 18	100%	50%
	BP1	3, 11	100%	75% BP21	5	100%	75%
	BP2	1, 2, 4, 13	85%	50% BP22	2, 4, 5, 10, 12, 17, 18	95%	50%
	BP2	3	95%	50% BP22	6, 7, 9, 11, 13, 14, 15, 16, 19, 20	100%	50%
	BP2	6	100%	50% BP22	8	100%	75%
	BP3	1, 7	85%	50% BP23	10	80%	50%
	BP3	2, 5, 8, 14, 15, 18, 19, 20	100%	50% BP23	5, 7, 12, 13, 14, 15, 17	95%	50%
	BP3	9	90%	50% BP23	4, 6, 8, 9, 11, 16, 18, 19, 20	100%	50%
	BP3	3, 4, 11, 13, 16	95%	50% BP24	1	85%	50%
	BP3	10, 12, 17	100%	75% BP24 2, 3, 4, 9, 15, 16, 20 95%	50%
	BP4	2, 6, 8, 10, 11, 16, 18, 19, 20	100%	50% BP24	6, 8, 11, 12, 13, 14, 17, 18	100%	50%
	BP4	7	75%	50% BP24	10, 19	100%	75%
	BP4	3, 4, 5, 17	85%	50% BP25	15	80%	75%
	BP4	9	100%	75% BP25	7	85%	50%
	BP5	1	75%	50% BP25	4, 18	95%	50%
	BP5	2, 8	80%	50% BP25	9, 11, 14, 17, 20	100%	50%
	BP5	3, 5, 9	85%	50% BP28	7, 11, 19	85%	50%
	BP5	4, 20	90%	50% BP28	1	90%	75%
	BP5	6, 13, 15	95%	50% BP28	4, 15	95%	50%
	BP5	7, 10, 11, 17, 19	100%	50% BP28	12, 13, 14, 17	100%	50%
	BP6	11, 19	90%	50% BP28	5	100%	75%
	BP6	13	90%	75% BP29	5	85%	50%
	BP6	4	95%	50% BP29	10	100%	50%
	BP6	3, 5, 6, 7, 8, 9, 10, 12, 14, 15, 17, 18	100%	50% BP30	20	85%	50%
	BP6	16	100%	75% BP30	7, 13, 16	90%	50%
	BP19	2	90%	50% BP30	8	95%	50%
	BP19	3, 8, 13	95%	50% BP30	3	100%	50%

Table 4 .

 4 The fitting results and the prediction results of radial basis networks for Data set 1 and Data set 2.

	Data set	Spread	Fitting accuracy	Prediction accuracy
	Data set 1	0.1	100%	50%
	Data set 1	0.2	100%	50%
	Data set 1	0.3	100%	50%
	Data set 1	0.4	100%	50%
	Data set 1	0.6	100%	50%
	Data set 1	0.8	100%	75%
	Data set 1	0.9	100%	75%
	Data set 1	1.0	100%	75%
	Data set 1	1.1	100%	75%
	Data set 1	1.2	100%	50%
	Data set 1	1.3	100%	50%
	Data set 1	1.4	100%	50%
	Data set 1	1.5	100%	50%
	Data set 1	1.6	100%	50%
	Data set 1	1.7	100%	50%
	Data set 1	1.8	100%	50%
	Data set 1	1.9	100%	50%
	Data set 1	2.0	100%	50%
	Data set 2	0.1	100%	50%
	Data set 2	0.2	100%	50%
	Data set 2	0.3	100%	50%
	Data set 2	0.4	100%	50%
	Data set 2	0.6	100%	50%
	Data set 2	1.9	100%	50%
	Data set 2	2.0	100%	50%

Table 5 .

 5 The fitting results and the prediction results of radial basis networks for Data set 3 and Data set 4.

	Data set	Spread	Fitting accuracy	Prediction accuracy	Data set	Spread	Fitting accuracy	Prediction accuracy
	Data set 3	0.1	100%	50%	Data set 4	0.1	100%	50%
	Data set 3	0.2	100%	50%	Data set 4	0.2	100%	50%
	Data set 3	0.3	100%	75%	Data set 4	0.3	100%	75%
	Data set 3	0.4	100%	75%	Data set 4	0.4	100%	50%
	Data set 3	0.5	100%	75%	Data set 4	0.5	100%	50%
	Data set 3	0.6	100%	100%	Data set 4	0.6	100%	50%
	Data set 3	0.7	100%	100%	Data set 4	0.7	100%	50%
	Data set 3	0.8	100%	100%	Data set 4	0.8	100%	50%
	Data set 3	0.9	100%	100%	Data set 4	0.9	100%	50%
	Data set 3	1.0	100%	100%	Data set 4	1.0	100%	50%
	Data set 3	1.1	100%	100%	Data set 4	1.1	100%	50%
	Data set 3	1.2	100%	100%	Data set 4	1.2	100%	75%
	Data set 3	1.3	100%	100%	Data set 4	1.3	100%	75%
	Data set 3	1.4	100%	100%	Data set 4	1.4	100%	75%
	Data set 3	1.5	100%	75%	Data set 4	1.5	100%	75%
	Data set 3	1.6	100%	75%	Data set 4	1.6	100%	50%
	Data set 3	1.7	100%	75%	Data set 4	1.7	100%	50%
	Data set 3	1.8	100%	75%	Data set 4	1.8	100%	50%
	Data set 3	1.9	100%	75%	Data set 4	1.9	100%	50%
	Data set 3	2.0	100%	75%	Data set 4	2.0	100%	50%

Table 6 .

 6 The fitting results and the prediction results of GRNNs.

	Data set	Spread	Fitting accuracy	Prediction accuracy
	Data set 1	0.2	100%	50%
	Data set 1	0.3	100%	75%
	Data set 1	0.4	100%	50%
	Data set 1	0.5	95%	75%
	Data set 1	0.6	95%	50%
	Data set 2	0.2	100%	50%
	Data set 2	0.3	100%	50%
	Data set 2	0.4	100%	50%
	Data set 2	0.5	95%	50%
	Data set 2	0.6	95%	50%
	Data set 2	0.7	85%	50%
	Data set 2	0.8	85%	50%
	Data set 3	0.1	100%	75%
	Data set 3	0.2	100%	75%
	Data set 3	0.3	90%	75%
	Data set 3	0.4	80%	100%
	Data set 4	0.1	100%	50%
	Data set 4	0.2	95%	50%
	Data set 4	0.3	95%	50%
	Data set 4	0.4	80%	50%
	Data set 4	0.5	80%	50%

Table 7 .

 7 The fitting results and the prediction results of PNNs for Data set 1 and Data set 3.

	Data set	Spread	Fitting accuracy	Prediction accuracy
	Data set 1	0.1	100%	50%
	Data set 1	0.2	100%	50%
	Data set 1	0.3	100%	50%
	Data set 1	0.4	100%	50%
	Data set 1	0.5	95%	50%
	Data set 1	0.6	95%	50%
	Data set 1	0.7	85%	50%
	Data set 1	0.8	80%	50%
	Data set 3	0.1	100%	50%
	Data set 3	0.2	95%	50%
	Data set 3	0.3	95%	50%
	Data set 3	0.4	80%	50%
	Data set 3	0.5	80%	50%

Acknowledgments

. This work was supported in part by Special Fund for Agroscientific Research in the Public Interest (200903035).


Yunxia Luan
email: yunxialuan@nercita.org.cn

Hua Ping

Zhihong Ma

Ligang Pan

Research on surface sampling for determination of pesticide residues in pome fruit

Keywords: Poem fruit, Pesticide residues, Phoxim, Surface sampling

A surface sampling method for analysis of pesticide residues in pome fruit was studied by layer-by-layer scan from pericarp to kernel. Phoxim residues of apples, pears and papayas was determined by GC-MS, respectively, in core, different layers of pulp, peel ,as well as the whole fruit. Phoxim residue content was decreased when depth of the layer increased. It was inferred from experimental data the the appropriate depth of surface sampling is 2.5mm, 2mm and 3.5mm in apples, pears and papayas. Although the ratio of the pesticide in pericarp and the total (Cp/Ct) ranged from 6.241 to 9.262 in apple, 4.254 to 5.069 in pears, and 4.39 to 5.037 in papayas in different groups, the ratio of Cp/Ct and quality of specific group is essentially unchanged for a certain kind of fruit. According to the results, a conversion formula of the content of pesticide residues in whole fruit and pericarp was set, based on surface sampling method.

Introduction

One of the major roles of public health agencies is to ensure safe products for consumers through analysis of residual agricultural chemicals and veterinary drugs in foods. The use of agricultural chemicals and veterinary drugs in agriculture is necessary to improve the quality of the food produced.

They play a beneficial role in providing a plentiful, low-cost supply of high-quality food. On the other hand, as a consequence of this use, the presence of residues in food that is a critical element of overall public health is unavoidable, and residues in food are of great importance in the evaluation of food quality. Organophosphorus (OP) pesticides are the most widely used agricultural pesticide. More than 70% of that are hyper-toxic or high-toxic, and normally are forbidden to use in planting fruits and vegetables. OP insecticides are routinely applied to fruit crops (e.g., apple, peach, pear) that are subject to consumption as single servings [START_REF] Hamers | Estrogenic and esterase-inhibiting potency in rainwater in relation to pesticide concentrations, sampling season and location[END_REF]. Consumption of individual fruits containing high OP insecticide residues may result in high exposure over a short period of time. Residue testing for the presence of pesticides, however, is routinely performed on composites of 5 to 10 individual or unit samples rather than samples of individual fruit [START_REF] Fenske | Lessons learned for the assessment of children's pesticide exposure: critical sampling and analytical issues for future studies[END_REF].

Experimental study of the fruit for pesticide residues in agricultural products exist in the form, that is attached to the fruit into the fruit surface and the distribution of pesticides within the fruit. It is necessary to provide a representative sample to the laboratory for analysis [START_REF] Wang | Flexibility of solid-phase microextraction for passive sampling of atmospheric pesticides[END_REF]. No matter how carefully laboratory analyses are carried out, the results will be of limited value unless the sample provided is truly representative of the sample being tested [START_REF] Wang | Sequential sampling plans for western flower thrips (Thysanoptera: Thripidae) on greenhouse cucumbers[END_REF]. The uneven distribution of pesticide residues among the treated objects leads to an inevitable variability of pesticide residue levels measured in the samples, which may significantly contribute to the combined uncertainty of the analytical results. Data generated in the UK have indicated that pesticide residue levels can be highly variable between the individual fruit from the same crop or lot in trade. Many fruit or vegetables in trade are mixed after harvest to form combined lots [START_REF] Ambrus | Estimation of sampling uncertainty for determination of pesticide residues in plant commodities[END_REF]. Analysis of composite samples provides a good indication of average residues but, where the lot has been mixed, such average values are potentially misleading [START_REF] Yusa | Sampling and analysis of pesticides in ambient air[END_REF]. Residues monitoring is the best means available for general control of pesticide use and consumer exposure, but new strategies for sampling and analysis are required to address the combined effects of residues variability and mixing of lots.

Development of analytical methods for pesticides in agri-food requires precise method of sampling.

Pome fruit peel is the main remaining parts, parts of the peel residue is a measure of its ability to meet the safety standard limit fruit key factor. The apple pesticide residue question has become the chief obstacle in affecting the apple industry internationalization of our country [START_REF] Neumann | A qualitative sampling method for monitoring water quality in temporary channels or point sources and its application to pesticide contamination[END_REF]. At present, samples of pome fruit pesticide residue mainly from the entire apple or some edible parts,because the results from these have the generalization. However, the pesticide residue mainly exists in the pericarp. Therefore, the careful research to pesticide residue of the pericarp provides the theory instruction and the design basis to the pesticide residue and the elimination research.

However, limited data have been reported that directly compare the sampling procedures against one another. Even less information is published regarding the most appropriate part of the material to use in conjunction with a sampling procedure for pesticides detection, which is particularly important to the portable rapid detector of contamination in fruits and vegetables [START_REF] Shen | Determination of carbendazim residue in orange and soil using high performance liquid chromatography[END_REF]. In our Previous studies, the temporal and spatial variations of phoxim residue in apples were studied. The results show that half-life of phoxim in apples was 1.64 days. The phoxim residue order in different position of apple is: pericarp >entire apple> sarcocarp> kernel and the ratio of residue in pericarp and the entire apple ranged from 6.241 to 9.262 in groups with different specific surface area [START_REF] Luan | Residue dynamics of phoxim in pericarp, sarcocarp and kernel of apple[END_REF]. The results indicate that, there are significant differences in the contents of residues between the pericarp and entire apple, which provide evidence in the theory instruction and design basis to the sampling method of pesticide residue based on pericarp. In this paper, further research was carried out on a surface sampling method for analysis of pesticide residues in pome fruit was studied by layer-by-layer scan from pericarp to kernel, and to explore the relationship between the content of pesticide in pericarp and the whole fruit.

Materials and methods

Chemicals and Reagents

Phoxim standard was purchased from National Institute of Metrology P.R.China. High-purity solvents cyclohexane, acetone, and acetonitrile were all purchased from Fisher Chemicals [START_REF] Shen | Determination of carbendazim residue in orange and soil using high performance liquid chromatography[END_REF]. Analytical reagents magnesium sulfate and sodium chloride were purchased from Beijing Chemical Reagents Company (China). High-purity solvents acetonotrile, suitable for liquid chromatography, gas chromatography, and residue analysis used in the extraction and cleanup steps, were purchased from EMD Biosciences Inc(Mississauga, ON). Reagent grade MgSO 4 and sodium chloride were also purchased from EMD Biosciences Inc.

Apparatus

The gel permeation chromatography (GPC) equipped with gas chromatography mass spectrometry system (GC/MS) was used. The GPC consists of two LC-10ADvp pumps, a SIL-10ADvp auto-sampler, a Shodex CLNpak EV-200AC column (2mm i.d.×150 mm) and CTO-10ASvp column oven, a SPD-10Avp UV detector, two FCV-12AH flow channel selectionvalves (RV.A, RV.B) and a SCL 10Avp system controller. GC/MS machine is a Shimadzu GC/MS-QP2010 instrumentation equipped with a PTV-2010 large-volume injection device. GC/MS data analysis was triggered by a contact closure start signal from the HPLC controller. Data acquisition was performed using a C-R8A plus data processor. All these parts are the products of Shimadzu (Kyoto, Japan), except the Shodex CLNpak EV-200AC column (Shoko Co., Tokyo, Japan).

Acetone/ cyclohexane mixing solvent (3/7, v/v) was used as the mobile phase of GPC [START_REF] Schafer | Performance of the Chemcatcher passive sampler when used to monitor 10 polar and semi-polar pesticides in 16 Central European streams, and comparison with two other sampling methods[END_REF], and the flow rate was set at 0.1 mL/min. The mobile phase was degassed using DGU-14A degasser (Shimadzu), and the GPC column was kept at 40℃ in the column oven.

Sample preparation

All samples were purchased at local markets in Beijing. Samples were extensively collected to achieve good sample homogeneity. The phoxim compounds were applied using an air blast sprayer and application rates guaranteed to contain 50% phoxim emulsifiable with the dilution multiple of 1:500. Individual fruits were cored and sliced into 5 segments (pericarp(P), sarcocarp1(S1), S2,S3,S4 and kernel) using a corer/slicer retailed and peeling machine for domestic use. The first and alternate slices of each apple were taken, chopped manually using a knife, and placed in a plastic bag for storage at -25℃ until extraction and analysis. The remaining segments of individual apples were retained in separate bags for preparation of composite samples. Composites were constructed from the apples prepared from each group by randomly selecting 8 apples for each treat, without regard to the style and weight of each apple. Composite samples were prepared by thoroughly mixing the chopped apple pieces from the bags containing the retained portions of the individual apples. Composite samples were frozen until extraction and analysis. To prepare each sample, 10 g of a previously homogenized food materialwas transferred into a suitable glass vessel. Then, 10mL acetonitrile was added to each sample using an adjustable-volume solvent dispenser. The glass vessels were capped before vortex mixing for 1 min at maximum speed. Once the initial sample mixing was completed, 1 g NaCl and 4 g anhydrous MgSO 4 were added and mixed immediately on a Vortex mixer for 1 min. It is important to note that this step must be taken immediately after the initial mixing step to prevent the formation of MgSO 4 conglomerates. To separate phases, samples were centrifuged for 10 min at 1570×g. Using an adjustable repeating pipette, 1.0mL aliquot of upper acetonitrile layer was transferred into a 1.5mL flip-top microcentrifuge vial containing 150 mg anhydrous MgSO 4 and 50 mg PSA sorbent. The vial was tightly capped and shaken on a vortex mixer for 1 min before extraction. Then the mixed extraction solution was centrifuged for 5 min to separate solids from solution. The solution was then transferred into an autosampler for GPC-GC/MS analysis.

Determination of phoxim in apple by GPC-GC/MS

Analysis was performed by GPC-GC/MS. The analytical GC/MS equipped with a PTV-2010 large-volume injection device was carried out using deactivated silica tubing (5m×0.53mm i.d.) , a Rtx-5MS pre-column (5m×0.25mm) and a Rtx-5MS column (30m×0.25mm×0.25μm film thickness; coated with 5% phenyl and 95% methylpolysiloxane; Restek Corporation, Bellefonte, Palo Alto, USA).

The temperature of the PTV injector was set at 120℃ for the initial 5 min of sampling time, and then increased to 250℃ at 100℃/min. The oven temperature was maintained at 80℃ for 5 min, subsequently increased to 280℃ at a rate of 8℃/min, and then held constant for 10 min [START_REF] Schafer | Performance of the Chemcatcher passive sampler when used to monitor 10 polar and semi-polar pesticides in 16 Central European streams, and comparison with two other sampling methods[END_REF]. The quadrupole mass spectrometer was operated in the electron impact ion (EI) mode. Ion source temperature and interface temperature were set at 200℃ and 250℃, respectively. The mass spectrometer was operated in an ionizing energy of 70 eV. Injection volumes were 1 μL for all analyses. Helium(99.999%)was used as the carrier gas [START_REF] Schafer | Effects of Pesticides Monitored with Three Sampling Methods in 24 Sites on Macroinvertebrates and Microorganisms[END_REF].

Quality Assurance/Quality Control

Apples from market with no phoxim insecticide application were obtained, prepared the same as the rinsed and peeled apples, and were used to prepare blank apple matrix for quality assurance testing.

With each set of samples 10-15 samples per set) extracted and analyzed, two aliquots of blank apple matrix were prepared for separate extraction followed by extraction, cleanup, and analysis as for all other samples. Background levels of some analysis were periodically detected in the blank matrix samples and were used for background subtraction in the determination of recovery from spiked matrix only; residue concentrations in samples were not blank corrected. No traces of the phoxim were observed in any of the reagent blanks analyzed.

Results and discussion

Determine of the parameters for surface sampling

In order to determine the exact thickness of pericarp and sampling style, the materail was cut by 1.5mm-2mm each piece from the pericarp to kernel, and pesticide residues in fruit peel, kernel and pulp of different depth were determined. Taking the Fuji apple as example, according to dilution ratio of 1:1000 dilution of Phoxim EC, more than fifteen days after spraying pesticide on fruit, pesticide residues in samples was detected according to the method of NY/T 761.1-2004 "vegetables and fruits organophosphorus, organochlorine, pyrethroid and carbamate pesticide multi-residue methods-Part 1 of fruits and vegetables organophosphorus pesticide multi-residue method" and GB/T5009.199-2003 "vegetables of organophosphorus and carbamate pesticide residues in rapid detection." for lab and rapid detection. Each sub-sample was taken from 15 samples of different shape and weight. The results are presented in Figure 1.

A B

36.72395 Experimental study of the pesticide residues in pome fruit, which is attached to the fruit surface and the distribution of pesticides within the fruit was carried out. From experiments performed and analysis of pesticide residues in different layer from pericarp to kernel, it is showed that, pesticide was detected maily in layer P and S1, the pesticide in pericarp and the first layer with 2mm pulp account for 98.49% of the whole sample. Through different parts of the fruit residue test results revealed that the major part of remaining in the apple peel, fruit pulp and other parts of the residual is minimal.

According to the apple skin to the stone from the stratified analysis, pesticide residues, mainly in the skin and pulp of the following 2.5mm, this part of the sample selected sampling to detect the levels of residues of pesticides, because this part of the high levels of pesticide residues, can increase rapidly screening test accuracy.

According to the experimental results, the standard design of the skin sampling methods, reduction in access to sub-samples the following 2.5mm thick skin and flesh as the test object.

Study of the Weight Coefficient of pesticide residues in pericarp

Selected based on the standard higher levels of pesticide residues in samples of skin as a target for different types of fruits, pesticide residues on its surface and overall there is a certain amount of pesticide residue conversion relationship [START_REF] Bernard | Sampling household surfaces for pesticide residues: comparison between a press sampler and solvent-moistened wipes[END_REF]. The research was supported by the skin of pesticide content in the trend over time to determine the appropriate sampling time interval after application, and in accordance with the standard parameters of the specific sampling techniques to obtain test samples, the research team through the following experiments, made pome fruit surface pesticide residue content and the overall coefficient of pesticide content in the skin pesticide residues.

Using Apple, pear, and papaya(n=15), sampling the surface homogenized pericarp(with 2.5mm pulp) as detection material, homogenized with a whole apple parallel processing [START_REF] Rosenheck | Determination of a standardized sampling technique for pesticide transferable turf residues[END_REF], determination of pesticide content, access to pesticide skin factor, diluted with water according to the proportion of 1:1000 dilution phoxim EC, using the method of spraying pesticide on apples [START_REF] Teng | Investigation of the AP-42 sampling method[END_REF]. According to NY/T761.1-2004 "vegetables and fruits organophosphorus, organochlorine, pyrethroid and carbamate pesticide multi-residue methods-Part1 of fruits and vegetables organophosphorus pesticide multi-residue method" approach with gas chromatography of whole fruit and pericarp of the accurate detection of pesticide residues pesticide residues in order to obtain skin factor [START_REF] Li | Controlled field evaluation of water flow rate effects on sampling polar organic compounds using polar organic chemical integrative samplers[END_REF].

As the result in Fig2-4, the three kinds of fruit showed various levels of pesticide in pericarp and the whole fruit, though the content in pericarp was much higher than the total homogenate. So, the ratio of the pesticide in pericarp and the whole fruit (Cp/Ct) was got from the three kinds of fruits with different shape and quality as shown in Table1.

Formula for calculating the amount of pesticide residues in the epidermis

Although the ratio of the pesticide in pericarp and the total (Cp/Ct) ranged from 6.241 to 9.262 in apple, 4.254 to 5.069 in pears, and 4.39 to 5.037 in papayas in different groups of weight, the ratio of Cp/Ct and quality of specific group is essentially unchanged for a certain kind of fruit (Table 2). According to these results, a conversion formula of the content of pesticide residues in whole fruit and pericarp was set based on surface sampling method.

The weight of a pesticide(a) refers to the epidermis and fruit pesticide content ratio of the overall pesticide content(1). shown in Table 3. Take apples, pears and papaya samples of different quality and shape of the three groups of 15 individuals, according to this standard involve the rapid detection of pesticide residues in fruit skin sampling methods in the technical parameters, take the skin (with 2.5mm pulp) as sample, while the overall homogenized fruit sample, according to GB/T 5009.199-2003 "vegetables of organophosphorus and carbamate pesticide residues in the rapid detection," Determination of pesticide content. It can be seen from the results, obtained in accordance with the samples of fruit skin test results of pesticide residues, pesticide residues on the enzyme that the inhibition rate was significantly greater than the overall sample of inhibition of fruit (see Table 3).

Table3. Comparision of the results of rapid detectors using different sampling method

Sample

Inhibition rate (%) The result of rapid detectors using whole apple sample and surface sample demonstrated that, a surface sampling method for analysis of pesticide residues in pome fruit could increased sensitivity and reduced the average impact of whole fruit homogenates. Combination of the phoxim residues of apples, pears and papayas determined by GC-MS in different layers of the fruit and the formula for calculating the amount of pesticide residues in the epidermis amount of pesticide residues in the epidermis, an efficient surface sampling method was studied and established.

Discussion

According to the reseach of pesticide residues in pome fruit by layer-by-layer scan from pericarp to kernel, the previous solution of the significant differences in the contents of residues between the pericarp and entire apple was demonstrated, further more these research revealed the pesticide residues, mainly remained in the pericarp and pulp of the following 2.5mm thick, this part of the sample could be selected as sample to detect the levels of residues of pesticides, because this part of the high levels of pesticide residues, can increase rapidly screening test accuracy [START_REF] Rabiet | Assessing pesticide concentrations and fluxes in the stream of a small vineyard catchment--effect of sampling frequency[END_REF]. From the experimental results obtained, the standard design of the skin sampling methods, reduction in access to sub-samples the following 2.5mm thick skin and flesh as the test object.

Scientific and standardized sampling method can reduce the skin samples and pulp composition on the average of the impact test results, reduce false positives and improve accuracy [START_REF] Berger | Pesticide residues in products of plant origin in the European Union. Sampling strategy and results from the co-ordinated EU monitoring programmes in 1996 and 1997[END_REF]. But different types of fruit, types of pesticides and interval time after spraying should be further studied to verify the coefficient and formula. Effect of surface waxes on the persistence of pesticide in Fuji apples, pears, and papayas would effect the diffusion of pesticide to pulp.

Coefficient A refers to the epidermal skin pesticide pesticide content of the fruit content of the overall contribution of major factors affecting A include the following factors: (1) differences include different types of fruit and fruit surface area for adsorption of pesticides in different penetrate the skin of the fruit impact of pesticides [START_REF] Gaultier | Regional assessment of herbicide sorption and degradation in two sampling years[END_REF]. (2) determination of pesticide residues in skin samples when differences in sampling methods must be standardized to ensure that the skin sample, and with the standard method for sampling the epidermis consistent with the provisions. (3) the time factor after the recommended interval of sampling.

Therefore, this surface sampling method and the corresponding formula can be applied to various methods of detection of pesticide residues, in particular, the portable rapid detection machines. As the rapid development of materials science, biology and chemistry technologies, the surface sampling method would be widely used in sensor and other equipments.

Figure1.

  Figure1. Compartmentalization and sampling methord of apple (A) and the content of Phoxim in each depth (B) P: pericarp of apples with 0.5mm pulp, S1: The first layer with 2mm pulp, S2: the second layer with 2mm sarcocarp , S3: the third layer with 2mm sarcocarp, S4: the sarcocarp near kernel, K: kernel or core of apples.

Figure 2 :

 2 Figure 2: GC diagram of phoxim in Fuji apple A phoxim 5.25 mg/kg; B sample of pericarp;C sample of the whole fruit; D and E as CK

Figure 3 :

 3 Figure 3: GC diagram of phoxim in pear A phoxim 5.25 mg/kg; B sample of pericarp;C sample of the whole fruit; D and E as CK

Figure 4 :

 4 Figure 4: GC diagram of phoxim in papaya. A phoxim 5.25 mg/kg; B sample of pericarp;C sample of the whole fruit; D and E as CK

2 . 4

 24 Weight coefficient of pesticide residues in pericarp,Ct : total pesticide content, C p : pesticide content in the epidermis Coefficient A refers to the contribution of the content of epidermal pesticide to the whole fruit pesticide coefficient, a: Weight coefficient of pesticide residues in pericarp, m: the quality of a single sample According to the data of Weight coefficient of pesticide residues in pericarp of common pome fruits in Table1, the surface pesticide coefficient could be calculated based on formula[START_REF] Fenske | Lessons learned for the assessment of children's pesticide exposure: critical sampling and analytical issues for future studies[END_REF]. As shown in Table2, the surface pesticide coefficient A of Fuji apple, pear and papaya were 0.041, 0.031 and 0.004 respectively.So fruit samples with pesticide residues in total mass fraction of w, in units of milligrams per kilogram (mg / kg), according to the measured pesticide residues in skin samples according to the formula w -the total pesticide residues in fruits, in milligrams per kilogram (mg / kg); w p -the amount of pesticide residues in fruit pericarp, in milligrams per kilogram (mg / kg); m -single sample sample quality of the sample, in grams (g); A -surface pesticide coefficient Table Surface pesticide coefficient in common pome fruit Application of the surface sampling method on Rapid detector of Pesticides Select the most commonly used Rapid detector of Pesticides or detector have a fast detector function ,to detect pesticides in food safety rapid detector to verify the sampling method, the results

Acknowledgements

We thank the Special Fund for Agro-scientific Research in the Public Interest, the Ministry of Agriculture of the People's Republic of China (Project 201003008 and 201203046) and National Key Technology R&D Program (Project 2009BADB9B07), for financial support.