Junxiong Zhang
email: zhang_junxiong@163.com

Fen He

Wei Li

Motion Blurring Direction Identification Based on Second-order Difference Spectrum

Keywords: Image processing, Direction identification, Second-order difference, Motion blur, Fourier spectrum

An identification method for uniform linear motion blurring direction based on second-order difference spectrum was proposed. The power spectrum of the blurred image was calculated after the Laplace second-order difference, and then the power spectrum was processed by the homomorphic filtering and the circular low-pass filtering. The blurring direction was attained by linear fitting of the frequency points with highest amplitude selected from the spectrum image. The experiments were carried out by using the blurred images which were simulated by the Lenna standard images with the blur extent being 20 pixels, and the mean square error of detection were 1.32° without additional noise and 2.27° with Gauss noise that the variance was 0.01. When using the real blurred images, the accuracy of the detection was -0.54°. This proposed method is proved to be available for motion blur of random plane direction and adaptable for noise.

Introduction

Motion blur occurs when there is relative motion between the camera and the object in the process of images collection. And it always exists in dynamic, real time image inspecting systems. In order to restore the blurred images, we often need to know Point Spread Function (PSF), which becomes the key of identification of motion blurring direction and extent.

According to the Fourier transform features of uniform linear motion PSF, the zero value dark stripes with equal interval existed in the power spectrum of the blurred images and the direction of dark stripes is perpendicular to the direction of motion blurring. In 1976, Cannon used this characteristic to estimate the uniform linear motion PSF of blurred images, while this method was sensitive to noise, it was usable when images' signal-to-noise ratio is high [START_REF] Cannon | Blind deconvolution of spatially invariant image blurs with phase[END_REF]. Chang and Savakis proposed bispectrum and residual spectrum matching methods, which overcome Cannon method's disadvantages [START_REF] Chang | Blur identification using bispectrum[END_REF], [START_REF] Savakis | Blur identification by residual spectral matching[END_REF]. But the bi-spectrum method need large numbers of data observed which have three-order stability, and the residual spectrum matching method need original image spectrum, noise variance, PSF styles, and other transcendental knowledge. Yitzhaky got some achievements in PSF estimation of motion blurring and vibrational blurring images, adopted 2×2 differential operator to identify motion blurring direction, and calculated the blurred extent according to differential image autocorrelation function [START_REF] Yitzhaky | Evaluation of the blur parameters from motion blurred images[END_REF], [START_REF] Yitzhaky | Identification of blur parameters from motion blurred images[END_REF], [START_REF] Yitzhaky | Direct method for restoration of motion-blurred images[END_REF], [START_REF] Yitzhaky | Comparison of direct blind deconvolution methods for motion-blurred image[END_REF]. But this method's effect was better when the motion blurring direction was in the range of 0°to 45°. Chen thought that any planar motion blurring PSF was generalized tri-diagonal matrix, and firstly carried out the motion blurring direction identification [START_REF] Chen | Identif ication of the motion blurred direction of motion blurred images[END_REF], [START_REF] Chen | Identification of Motion-blur direction from motion blurred image via directional derivation using C spline interpolation and weighted average[END_REF], [START_REF] Chen | Motion blur direction identification in motion blur image by Laplacian[END_REF]. Some other researches identified blurred direction through Radon transform of image Fourier spectrum [START_REF] Zhao | A New blind restoration of motion blurred images based on super-resolution method[END_REF], [START_REF] Lin | Parameters estimation of motion blurred images based on Radon transform[END_REF].

In this paper, a novel motion blurring direction identification method was proposed based on blurred image second-order difference Fourier spectrum, and motion blurred images collected in experiments and standard images were validated and the accuracy was detected.

Motion blurred image frequency character

The process of image motion blurred degeneration is the convolution of original image ( , ) f x y and PSF adding noise to get degeneration image ( , ) h x y ( , ) n x y ( , ) g x y , and it is shown by formula (1), ( , ) ( , ) ( , ) ( , ) g x y h x y f x y n x y = * +

The both sides of formula (1) are Fourier transformed, and according to convolution theorem, the generation model's frequency expression is ( , ) ( , ) ( , ) ( , )

G u v H u v F u v N u v = + (2)
Where , ( , ) G u v ( , ) H u v , ( , ) F u v and were the Fourier transform of ( , ) N u v ( , ) g x y , , ( , ) h x y ( , ) f x y and respectively. In particular, for uniform linear motion in horizontal direction, and without noise influence, ( , ) n x y 1 L -( L is blurred extent) dark stripes perpendicular to the motion blurring direction occurred in power spectrum of blurred images, and the motion blurring direction was obtained according to the dark stripes' direction.

Figure 1(a) is the static fruit image, and (b) is the Fourier power spectrum of (a). (c) is the image that (a) was simulated with blurred extent being 11 pixels along the horizontal direction, and (d) is the Fourier power spectrum of (c). (e) is a practical collected blurred image, and (f) is the Fourier power spectrum of (e). When neglecting the noise, the dark stripes perpendicular to motion blurring direction obviously existed in Fourier power spectrum of blurred image. But in practical image collecting, noise is inevitable, dark stripes in vertical direction can be observed constrainedly in Figure 1(d). The reason is that in original image Figure 1(a) the system noise after motion blurred was processed equably, and its effect was reduced. But for practical collected image, in Figure 1(e), the system noise was not neglected, in Figure 1(f), the dark stripes were not obvious, then, it was hard to estimate the motion blurring direction according to the dark stripes' direction.

Motion blurred direction identification

Observed from Figure 1(d) and (f), the linear motion blurring weakened the image high frequency signal, but in motion blurring direction and its vertical direction, the image high frequency detail part changing extent were different. Though the dark stripes in frequency spectrum image were hard to be observed, the middle vertical bright stripes were obvious oppositely, which showed that the high frequency information were strengthened along the motion blurring direction, according to this, then the motion blurring direction can be identified.

For the bright part in power spectrum image was circular or fusiform, which was hard to be identified directly, and the accuracy can not be assured. If through high pass filtering and restraining the low frequency part, the high frequency part in bright stripes can be strengthened, so that the bright stripes direction was easy to be identified.

When using high pass filtering to process the image, the 8 neighborhood Laplace operator was convolution of image in spatial domain.

( , ) ( , 8

L g x y g x y Lap = * (3)
Where Lap8 is a non-direction second-order difference operator, and the secondorder difference image of the blurred image could be obtained after the convolution operate.

The second-order difference image was Fourier transformed to get , which range was large. And to compare easily, it was need to be homomorphic filtered.

( , )

L G u v ( ) 10 log ( , ) fL L G G x = y (4)
Lap8 difference operation was used for blurred image in Figure 1(c), and the result was shown in Figure 2(a). After Fourier transform and homomorphic filtering, the zero frequency was moved in image middle and the result was shown in Figure 2(b). In order to detect the direction of middle bright stripes in Figure 2(b), the brightest points should be picked up, and then using the least squares method for linear fitting. The number of brightest points should be appropriate. When the number is too large, the influence is strong and the operation quantity is great. The few number influences the reliability. The brightest points can be chosen by threshold segmentation, but it is hard to determine the right threshold. In this paper, the bright points can be obtained by threshold segmentation, sorting, and filtering.

Firstly, estimate a threshold to segmentation (choosing double average grey value of power spectrum image as threshold), segment the Figure 2(b) using threshold 85, and the result was Figure 2(c). Then account the number of brightest points. Ensuring the number needed for linear detecting, and reducing the data quantity after sorting, the brightest points number is 50 to 300, if not in this range, the threshold should be adjusted and then segment the image. Lastly, sort and filter the brightest points after being arranged from high to low according to the grey value, and choose the front brightest 51 points (according to the experiment, when choosing 40 to 100 points, the results was better) for linear fitting y ax b = + using the least squares method.

According to the symmetry of Fourier transform, the linear intercept was 0. The motion blur direction can be calculated by linear slope , Where the clockwise angle was positive, and anticlockwise was negative. Figure 2(c) was the front 51 brightest points filtered. Figure 2(d) was the linear fitting result, and the motion blur direction was 0.63°.

The method before-mentioned was available for the blurred image with high signal-to-noise ratio, but for low signal-to-noise ratio, it was need to be improved. Figure 3(a) was the result that the real blurred image collected was processed by Lap8 difference calculation, Fourier transform and homomorphic filtering. In blurred image, noise can be processed by space domain filtering, but at the same time, it also will weaken the edge information. For the influence of noise, the high frequency part of Figure 3(a) has large intensity. The brightest points obtained by threshold segmentation distributed in high frequency part, and if using the brightest points to linear fitting, the estimating result was wrong.

The high frequency part influenced by noise distributed in peripheral of spectrum image, so a circular low-pass filtering was used to weaken the influence of high frequency part. In spectrum image, make the center of a circle as the image center, remove the area outside the circle with diameter being d which decided by the intensity of high frequency chosen, and threshold segment the area inside the circle. Figure 3(b) was the segmentation result that d was 140 pixels and the threshold was 85. The brightest pixels were selected and fitted by least squares method, and lastly the direction was received. In Figure 3(c), the direction of motion blurring was -0.54°.

Concluding the above steps, identifying image motion blurring direction was shown in Figure 4.

Identification accuracy test

The standard image Lenna was chosen to test the algorithm correctness and validity. Firstly, Lenna was blurred with motion blur extent being 20 pixels, the blur direction being 90° to -80°. Then the algorithm in Figure 4 (Do not need circular low-pass filtering) was utilized to identify the motion blur direction. Figure 5 was the partial result when the motion blur direction was -30°, and the whole identification results were in Table 1, which showed the mean square error of detection was 1.32°. In order to validate the application of the algorithm for low signal-to-noise ratio motion blurred image, the standard image Lenna was blurred with motion blur extent being 20 pixels , the blur direction being 90° to -80°, and adding Gauss noise with variance being 0.001. Then the algorithm in Figure 4 (need circular low-pass filtering) was utilized to identify the motion blurring direction, and the result was shown by Table 2.

Table 2 showed the mean square error of detection was 2.27°, which was lower than 1.32° without additional noise. When adding noise and using low-pass filtering, the number of brightness can not reach 50 to 100, which will influence the fitting accuracy. Seen from Table 1and Table 2, when motion blurring direction was parallel to original image edge, the blurred image still had strong boundary information, and its identification accuracy was high. On the contrary, if the blurred direction was vertical to original image edge, the boundary information was weak, the accuracy was low.

Conclusions

Uniform linear motion blurring can strengthen the Fourier power spectrum of image perpendicular to the direction of motion. According to this, the blurred image can be removed a great deal of low and high frequency information after second-order difference high-pass filtering and the circular low-pass filtering. Then the motion blurring direction was attained by linear fitting of the frequency points with highest amplitude selected from the spectrum image. This proposed method is proved to be available for motion blur of random plane direction and adaptable for noise.

Fig. 1 .

 1 Fig. 1. Fruit image and its Fourier power spectrum

Fig. 2 .

 2 Fig. 2. Detection of blurred image simulatedIn Figure2(a), the two edges of fruits superimposed in horizontal direction. Compared with Figure1(d) and Figure2(b), the whole high frequency part was strengthened, which was caused for the second-order difference being sensitivity to noise. But the middle vertical bright stripes in Figure2(b) were more obvious than Figure1(d), and the motion blur direction can be judged by detecting the bright stripe direction.In order to detect the direction of middle bright stripes in Figure2(b), the brightest points should be picked up, and then using the least squares method for linear fitting. The number of brightest points should be appropriate. When the number is too large, the influence is strong and the operation quantity is great. The few number influences the reliability. The brightest points can be chosen by threshold segmentation, but it is hard to determine the right threshold. In this paper, the bright points can be obtained by threshold segmentation, sorting, and filtering.Firstly, estimate a threshold to segmentation (choosing double average grey value of power spectrum image as threshold), segment the Figure2(b) using threshold 85, and the result was Figure2(c). Then account the number of brightest points. Ensuring the number needed for linear detecting, and reducing the data quantity after sorting,

Fig. 3 .

 3 Fig. 3. Detection of real blurred image

AttainingFig. 4 .

 4 Fig. 4. Flow chart of motion blurring direction detection

Fig. 5 .

 5 Fig.5. The detection of L=20pixel,α=30°

Table 1 . Motion blur direction identification

 1

	Theoretic direction(°)	90	80	70	60	50	40	30	20	10
	Result(°)	90.36	81.29	70.82	58.20	51.24	41.28	31.61	21.69	11.78
	Error(°)	0.36	1.29	0.82	-1.80	1.24	1.28	1.61	1.69	1.78
	(Table 1 continued)
	Theoretic direction(°)	0	-10	-20	-30	-40	-50	-60	-70	-80
	Result(°)	-0.13	-10.88	-20.91	-30.59	-40.11	-50.86	-61.19	-71.93	-82.07
	Error(°)	-0.13	-0.88	-0.91	-0.59	-0.11	-0.86	-1.19	-1.93	-2.07

Table 2 . Motion blur direction identification with additional noise

 2

	Theoretic direction(°)	90	80	70	60	50	40	30	20	10
	Result(°)	90.63	80.57	69.03	60.68	51.36	40.18	32.36	24.25	14.23
	Error(°)	0.63	0.57	-0.97	0.68	1.36	0.18	2.36	4.25	4.23

Acknowledgments

. The project supported by the foundation (No.20050019005 and No.200800191014) of Specialized Research Fund for the Doctoral Program of Higher Education (P. R. China).


Hao Zhang
email: zhanghaohnnd@126.com

Li Zhang

Yanna Ren

Juan Zhang

Xin Xu

Xinming Ma
email: xinmingma@126.com

Zhongmin Lu

Design and Implementation of Crop Recommendation Fertilization Decision System Based on WEBGIS at Village Scale

Keywords: Nutrient balance model, Fertilization decision-making, Crop, WEBGIS, Village scale

To study crop recommendation fertilization of the rural farmers as the main in our country, the paper took towns and villages of Hua County as the study area, took recommendation fertilization of wheat, maize and peanut as the study object, designed model components of crop balance fertilization by using Object-Oriented technique, and developed the decision-making system about crop recommendation fertilization based on ArcGIS Server at village scale. The decision-making system realized farmland nutrient management and fertilization recommendations decision-making according to soil output capacity, agricultural production level and crop target yield. It was successfully applied in crop production in Hua County. The research results show that the system has the characteristic of better expansibility than before, and it is significantly simple and practical to reduce crop production cost and increase agricultural production efficiency, which provides technical support for crop fertilization decision-making and is significant to improve agricultural ecological environment and increase the comprehensive production capacity of farmland.

Introduction

Soil testing and balance fertilizer is one of the important technologies of precision agriculture, and also currently the development direction of scientific fertilization in agriculture production [START_REF] Wang | Farmers Need Real Information[END_REF][START_REF] Zhang | Design and Realization of GIS-Based County Testing Soil for Wheat Formulated Fertilization System[END_REF][START_REF] Zhang | Design Of Decision-making System about Wheat Survey and Directions for Soil Based on GIS in County[END_REF]. Precision fertilization is the optimized combination of information technology (RS, GIS and GPS), biotechnology, chemical technology and mechanical technology, and it can be divided into basic fertilizing and aftermanuring according to crop growth period, arable farming and broadcast application according 2 Hao Zhang1, Li Zhang1, Yanna Ren1, Juan Zhang1, Xin Xu1,Xinming Ma1,2* , Zhongmin Lu3 to fertilization methods, real-time and non real-time precision fertilization according to the timeliness.

Currently, using of modern information technology to build decision-making system of precision fertilizer for changing the traditional fertilization custom is the research hotspot of precision fertilization. Many scholars has researched and developed GIS-based precision fertilizer management systems and decision-making systems mostly taking farmland as object [START_REF] Sheng | An Information System of Soil Nutrition Management and Crop Fertilizer Recommendation Based on GIS[END_REF][START_REF] He | Research of Farmland Resource Management and Application Model Based on Web GIS[END_REF], but ignored the status of farmers as the main farming in the village of China. The developed system has limitations of regional application, and it led to complex management, high cost, high pollution and difficult popularization. So, it is significant to realize a simple, expansible and practical system for lowering system complexity, reducing crop production cost and strengthening system pervasiveness, in order to increase agricultural production efficiency, improve agricultural ecological environment and enhance agricultural comprehensive production capacity.

With the development of modern information technology, ArcGIS Server is lowcost, complete and efficient. The paper took crop fertilization decision-making at administrative village scale as research object, collected soil information and map village-level vector by ArcView, then constructed crop recommendation fertilization decision-making system based on ArcGIS Server and implemented village-level farmland nutrient management and on-line fertilizer recommendation. It facilitated on-line soil information query and fertilization decision-making, and provided technical support for the scientific fertilization.

2

Data Source and Research Method

Data Source

Research data included meteorological data, soil data and crop data. Meteorological data was made up of daily maximum temperature, daily minimum temperature, daily average temperature, daily rainfall and daily sun actual exposure hours, provided by the Weather Bureau of Henan Province. Soil data was consist of soil type, soil nutrients and soil texture, collected from the Henan TuRang DiLi [START_REF] Wei | Henan TuRang Dili[END_REF] or provided by the county soil and fertilization station. Crop data was consist of crop species, crop varieties, planting region, planting area, yield per hectare, the annual production and the multiple cropping index, collected from the Statistical Yearbook of Henan Province.

Research Method

Taking into account the decentralized status of crop fertilization management in village and the traditional hand fertilizing feature with farmers as the main farming, the paper took administrative villages in Henan Hua County as the study region, food Design and Implementation of Crop Recommendation Fertilization Decision System Based on WEBGIS at Village Scale 3 crop (wheat and maize) and cash crop (peanut) decision-making fertilization as the study object, designed the components of nutrient balance model and fertilization decision-making model by using componentware technology [START_REF] Zhao | Development Platform for Agricultural Intelligent System Based on Techno-Componentware Model[END_REF][START_REF] Xi | Design and Implementation of Crop Growth Simulation System Based on Techno-Componentware Model[END_REF][START_REF] Zhang | Research of Crop Production Potential System Based on OMT and Software Component Technique[END_REF], and implimented the system of on-line soil nutrient management and fertilization decision-making recommendation by using .NET Framework, ArcGIS Server and network database.

Nutrient Balance Model and Fertilization Decision-Making Model

Generally, crop fertilization models included ASI nutrient grading model [START_REF] Yang | Study on the Correlation between ASI and Routine Method to Determine Soil Available P, K, Zn, Cu, Mn and the Conventional Chemical Methods[END_REF][START_REF] Xiong | Study on the Correlation between ASI and Routine Method to Determine Soil Available P and K and Ammonium Nitrogen[END_REF], nutrient balance model [START_REF] Wang | A Systematic Study on Nutrient Status and Its Application in Balanced Fertilization[END_REF] and subtraction method of soil fertility [START_REF] Zhou | Application of Dissimilar Subtraction Method of Soil Fertility in Recommend Fertilizer Practice for Maize Planting in Yellow Clay Soil[END_REF]. In view of the status of farmer as the main farming in China, decentralized farmland division, diverse soil type and different soil fertility, so, it was very difficult to extract all plots' nutrients accurately. The paper extracted soil nutrient information of the representative point at all directions in the villages, and gave fertilization scheme by using nutrient balance model and fertilization decision-making model. The given fertilization scheme included crop target output, fertilizer recommendations and fertilization technologies recommendations. Fertilizer recommendations included fertilizer selection and main nutrient content conversion of organic fertilizer. Fertilization technologies recommendations included when and how to fertilize. In practice, in order to apply fertilizer expediently, the paper only considered N, P and K [START_REF] Xie | Spatial Continuous Variation of Environment and Precision Agriculture[END_REF]. Formula 1 shows the balance fertilization model:

( * ) / / M U T S P C  (1)
In formula (1): M is fertilization weight per hectare. U is crop absorbable nutrient weight from soil pre 100kg, which is queried from table of crop absorbable nutrient. T is crop target output, which is divided into 3 grades according to the soil output capability. Table 1 shows the target output of wheat, maize and peanut in Hua county. S is soil nutrient supply, which equals to tested soil nutrient value multiplied by 0.15 and effective coefficient of soil nutrient. Table 2 shows the effective coefficient of soil nutrient about wheat, maize and peanut in Hua county. P is crop fertilizer absorptivity in planting season, which is queried from fertilizer classification table, shown by Table 3. C is fertilizer nutrient content, which is also queried from fertilizer classification table. System adopted three-tier structure: Brower/ Server/DBMS(application layer, logic components layer and data layer). Fig. 1 shows the system architecture.

System Function Structure

System function included data management, information statistic and summary, balance fertilization model management, fertilization recommendation and print, special topic analysis and release, user roles management and systems help. Fig. 2 shows the system function structure. Fertilization Recommendation and Print. According to monitoring point nutrient information and crop output target, system gave and printed the unit fertilization weight and recommendation technology.

Data

Special Topic Analysis and Release. It included the topic analysis and release of microelement(Cu, Ca, Mn), macroelement(N, P, K) and organic matter.

User Rights Management. It distributed authority of user role and added, deleted, updated, and queried user information.

Database Design

System database structure was made up of spatial database, attribute database, model library and knowledge library, shown in Fig. 3(a). Spatial database was built by using ArcView in scale of 1:20,000, and was consist of town point and line vectorgraph, administrative village point and line vectorgraph and soil monitoring point vectorgraph, shown in Fig. 3(b).

Hao Zhang1, Li Zhang1, Yanna Ren1, Juan Zhang1, Xin Xu1,Xinming Ma1,2* , Zhongmin Lu3 Fig. 2. System function structure Attribute database was built by using E-R model, and was consist of administrative division data, farmland data, soil monitoring point data and crop cultivation and harvest data. Administrative division was made up of towns and administrative villages. Farmland data included the latitude and longitude, area, soil type, soil productivity level, their villages and their farmers. Soil monitoring point data was made up of soil texture, total nitrogen, alkali-hydro nitrogen, P 2 O 5, K, Organic matter, pH, and so on. Crop cultivation and harvest data included crop type, crop variety, crop planting acreage, crop yield and multiple index. Fig. 3(c) shows attribute database.

Model library was made up of the decision-making field ID, model ID, model type, model name, model application conditions, model premise items, units, data type, data length, decimal digits and credibility. Knowledge library was made up of expert fertilization scheme, fertilizer type selection, main nutrient content conversion of organic fertilizer and fertilization technique recommendation. System may be installed in Win2000/2003/XP on PC or touch screen system. First, farmer may select and query the fertility information of soil monitoring point at 4 orientations of each administrative village, shown in Fig. 4. Second, system buffered and analyzed the selected point, and the nearest soil monitoring point close to the selected point from the buffer was chosen. Third, crop target output was selected according to the level of local production. Finally, the system gave formula fertilization weight decision and fertilization technology decision according to soil monitoring point information, crop target output and local production level, shown in Fig. 5.

Conclusion

System is developed by following OOAD standard and farmland nutrient balance components and fertilization decision-making model components are customized by using componentware technology. The system has the characteristics of modularization, encapsulation, reusability and inheritance. It can be installed in Win 2000/2003, and is easy and convenient for farmers to query farmland nutrient information and on-line fertilization recommendation at village scale through network. The system has been applied in Hua county agriculture production and it is popular with farmers. The results show that it lowers the complexity of system management by using componentware technology, and reduces crop production cost and strengthens system pervasiveness by using WEBGIS and crop nutrient balance model and fertilization decision-making model. According to the actual situation of soil fertility distribution, soil type and fertility are different in the same direction. So, it is deficient that one selected soil monitoring point represents all small-scale plots' fertility, even selecting one sampling point in hundreds of acres of the same direction. So, further issue is the selection of soil fertility points at a smaller scale and the analysis of soil nutrient variability.

Acknowledgments. This work is supported by "Eleventh Five-Year" national scientific and technological support and major project plan: "High-yield Crop Science and Technology Engineering"(2006BAD02A07-4), specific industry research sponsored by ministry of agriculture(20083028) and "863" plan(2006AA10Z271). Sincerely thanks are also due to the Hua County Agricultural Technology Popularization Center for providing the data for the work.

Fig. 1 .

 1 Fig. 1. System architecture Data layer provides data service for logic components layer. The logic components layer is based on data layer and provides logic service for system application layer. It is consist of the balance fertilization model component and precise fertilization technology model component, designed and implemented by using C # and relatively independent from each other. Application layer is based on logic components layer and made up of the module of model management and the module of farmland and crop management. Model management function includes importing and exporting models, rule revise and parameter adjustment. Farmland and crop management function includes farmland information management, cultivated land fertility evaluation, crop high yield region and grain core region construction. Based on the

Fig. 3 . 7 4

 37 Fig. 3. System database design (a)System database structure (b)Spatial database (c)Attributte database

Fig. 4 .

 4 Fig. 4. The fertility information interface of crop monitoring points

Fig. 5 .

 5 Fig. 5. Decision-making fertilization interface

Table 1 .

 1 Crop target output(kg• hm -2 )

	Crop type	High target	Middle target	Low target
		output	output	output
	Wheat	9 000~9 750	8 250~9 000	6 750~8 250
	Maize	9 750~11 250	8 250~9 750	6 750~8 250
	Peanut	7 500~9 000	6 000~7 500	4 500~6 000

Table 2 .

 2 Effective coefficient of soil nutrient

	Crop type	N	P	K
	Wheat	0.30~0.60	0.40~0.60	0.30~0.80
	Maize	0.20~0.60	0.50~0.60	0.40~0.80
	Peanut	0.20~0.55	0.50~0.60	0.30~0.70

Table 3 .

 3 Crop fertilizer absorptivity in planting season

		Crop type	N	P	K
		Wheat	0.30~0.45	0.15~0.25	0.15~0.40
		Maize	0.20~0.40	0.10~0.25	0.05~0.40
		Peanut	0.40~0.45	0.15~0.25	0.45~0.60
	3	System Design
	3.1	System Architecture

  Management. It included spatial and attribute data. Spatial data was made up of all types of point, line and plane vectorgraph. Attribute data was made up of meteorological data, soil data and crop data.

Information Statistic and Summary. It included vectorgraph and attribute query, statistic and summary for all kinds of information. Crop Fertilization Model Management. Crop fertilization models included crop nutrient balance model and fertilization decision-making model. The management function was made up of model import and export, rule revise and parameter adjustment.

* Corresponding author.


Xiaohui Yang

Yuxiang Huang

Shuqin Li

Sheng Huang

Research on Fertilizer Efficiency of Continuous Cropping Greenhouse Cucumber Based on DEA Model

Keywords: Greenhouse cucumber, Fertilization, Efficiency, DEA method

Fertilizer efficiency directly affects the production of continuous cropping greenhouse cucumber. So analyzing the fertilizer efficiency is of great significance in providing decision-making support for fertilization of greenhouse cucumber. By establishing the DEA models of continuous cropping greenhouse cucumber and taking existing data samples as study objects, the problems of fertilizer efficiency in traditional fertilization, target yield fertilization and mathematical model fertilization were studied. And the technical efficiency, pure technical efficiency, scale efficiency and redundancy rates of the three types of fertilization methods have been obtained by analyzing the relationship of three inputs (N, P 2 O 5 , K 2 O) and one output (Yield).

Through analyzing fertilizer efficiency of the three types of fertilization methods, it could get the sort by the size of mathematical model fertilization, target yield fertilization and traditional fertilization. The results showed that the method of mathematical model fertilization is more reasonable in fertilizing and the DEA method is efficient and practical in analyzing the fertilizer efficiency.

Introduction

Due to the limited understanding, the dependence on experience and the lack of the guidance of scientific fertilization theory, problems, such as excessive fertilization and low fertilize efficiency, are universally existent in production practice of greenhouse cucumber in China. Meanwhile, owing to the higher economic benefits of planting greenhouse cucumber, the pursuit of high yield and utilization ratio of installations, the continuous cropping of greenhouse cucumber is fairly common.

But continuous cropping causes many problems, such as soil nutrient imbalance and deterioration of soil properties. Fertilizing reasonably and balancing the soil nutrient are the fundamental ways to improve the fertilizer utilization ratio, reduce pollution, and solve the problem of continuous cropping in sunlight greenhouse. So the guidance of scientific fertilization theory should be needed to avoid fertilizer over-using and fertilizer improper-applying. Data Envelopment Analysis (DEA) is an effective optimization method used for measuring the relative efficiency of Decision Making Unit (DMU) (Charnes, A., et al., 1978). C 2 R model and BCC model are the two most commonly used DEA models. Suppose there are n DMUs, and each DMU has m inputs and k outputs, then the technical efficiency(TE), pure technical efficiency(PTE), scale efficiency(SE), redundancy rates(RR) of each DMU can be calculated by using the C 2 R model and BCC model (Quanling [START_REF] Wei | Data Envelopment Analysis[END_REF][START_REF] Shoukang Qin | Principle and Application of Comprehensive Evaluation[END_REF].

In this paper, the fertilize efficiency analysis models of continuous cropping greenhouse cucumber were established. Based on the samples from [START_REF] Wang | Effect of Balanced Fertilization on Yield and Quality of Sunlight Greenhouse Cucumber and Soil Characteristics under Continuous Cropping[END_REF] (Liying [START_REF] Wang | Effect of Balanced Fertilization on Yield and Quality of Sunlight Greenhouse Cucumber and Soil Characteristics under Continuous Cropping[END_REF] and DEA method (Charnes, A., et al., 1978), the fertilization efficiency of input and output of three fertilization methods (mathematical model fertilization, target yield fertilization and traditional fertilization)were studied from the vews of TE, PTE, SE and RR. Where TE and PTE reflect the relation between fertilizer rate and the yield of greenhouse cucumber under the assumption of that the scale benefit is constant and mutative, respectively. SE Fig. 1 The frame of analysis model for the greenhouse cucumber input-output efficiency is the ratio of TE to PTE. And RR is the ratio of input slacks to the actual input. And the efficiency of three fertilization methods were compared and analyzed. This paper aims at finding out methods to improve the fertilization efficiency of greenhouse cucumber and provide information for fertilization decision-making and practice.

Materials and methods

Data resources

In this paper, six 3-years continuous cropping plots were used for example to analyze the fertilize efficiency of greenhouse cucumber with the help of DEA Solver3.0 software. And the data of three inputs (N, P 2 O 5 , K 2 O ) and one output (Yield) from [START_REF] Wang | Effect of Balanced Fertilization on Yield and Quality of Sunlight Greenhouse Cucumber and Soil Characteristics under Continuous Cropping[END_REF] were took as samples. The calculation formulas for the fertilizer rate of mathematical model fertilization and target yield fertilization are available from [START_REF] Chen | Fertilizer Application Technique on Vegetable Nourishment[END_REF] (Lunshou Chen, Jingling Lu, 2002) and [START_REF] Zhang | Study on the Optimum Fertilization Technology of Cucumber[END_REF] (Yancai [START_REF] Zhang | Study on the Optimum Fertilization Technology of Cucumber[END_REF].

Hypotheses

To simplify, the fertilize efficiency analysis models of continuous cropping greenhouse cucumber were established under two hypotheses as follow: (1) N, P 2 O 5 , K 2 O are the major factors that affect the yield levels of greenhouse cucumber. (2) For different planting years and seasons, soil nutrient of different plots is the same.

Procedures

The C 2 R model and BCC mode were used to establish the fertilize efficiency analysis models of continuous cropping greenhouse cucumber. For each plot, its TE, PTE, SE and RR were evaluated and analyzed. Each field (DMU) has three inputs

Results and discussion

By calculating the TE, PTE, SE and RR of greenhouse cucumber of three types of fertilization, the results were reported in table 1.In addition, the fertilization efficiency of three types of fertilization was described in the figure 2, figure 3 and figure 4.

The fertilization efficiency without considering the diversity of fertilize methods

According to the table 1 and regarding the technical efficiency, there are 4 DEA effective units, accounting for 22.22 percent of the total samples. And the technical efficiency of 9 units exceeds the average of the samples' TE which is 0.6826, about half of all. To the pure technical efficiency, there are 7 DEA effective units, accounting for 38.89 percent of the total samples. And the pure technical efficiency of 14 units outperforms the average of the samples' PTE which is 0.8579, about 77.78 percent of all. And to the scale efficiency, there are 4 DEA effective units, accounting for 22.22 percent of the total samples. And the scale efficiency of 9 units exceeds the average of the samples' SE which is 0.7734, about 50 percent of all. respectively, which can led to the preliminary inference that the unreasonable degree of the inputs was in the increasing order of P 2 O 5 , N, K 2 O.

The fertilization efficiency with the consideration of the diversity of fertilize methods

In the table 2, the fertilization efficiency of three types of fertilization was analyzed. According to the figure2, figure3 and figure4, the average of TE, PTE, SE of mathematical model fertilization are obviously highest (0.8108, 0.9611, 0.8379, respectively) among that of target yield fertilization and traditional fertilization. And the fertilization efficiency of target yield fertilization is the second highest with that of traditional fertilization following behind. Furthermore, the average of redundancy rates of the three fertilize methods were also showed in table 2. Obviously, the average of redundancy rates of N, P 2 O 5 , K 2 O were the lowest (18.92%, 5.26%, 23.32%, respectively) while using mathematical model fertilization. And the average of redundancy rates of target yield fertilization comes next while that of traditional fertilization turns out to be the maximum.

Conclusions

The fertilizer efficiency of continuous cropping greenhouse cucumber was researched by using DEA. Based on two assumptions (N, P 2 O 5 , K 2 O are the major factors that affect the yield levels of greenhouse cucumber. For different planting years and seasons, soil nutrient of different plots is the same) and the analysis of technical efficiency, pure technical efficiency, scale efficiency and redundancy rates of three types of fertilization methods, it can come to conclusion that the fertilizer efficiency of mathematical model fertilization is the highest, and the second is target yield fertilization(the exception was that the average of SE lower than that of traditional Fertilization) while the traditional fertilization is the lowest.

  (N, P 2 O 5 , K 2 O) and one output (yield). If θ =1 (θ is the score of fertilizer efficiency), it indicates that the field (DMU) is a DEA effective unit, and its TE or PTE reach the highest level. If θ <1, it declares that the field (DMU) is not a DEA effective unit and its input of fertilizer is unreasonable. For a non DEA efficient (C 2 R or BCC) DMU, its input and output slacks and RR can be calculated by using the projection principle and related formulas. Comparing the value of θ of the same field which was under the same continuous cropping years and three types of fertilization, it can find out that which type of fertilization is more reasonable or the unreasonable reasons of the irrational one. By comparing the RR of the same field, it can be analyzed how much input of N, P 2 O 5 , K 2 O can be saved and which input should be adjusted. Besides, the fertilize efficiency of the same field under different types of fertilization and different continuous cropping years can be compared. The analysis steps were showed in the Figure 1.

Fig. 2 Fig. 4

 24 Fig.2 The pure technology efficiency of the continuous 3 years cropping greenhouse cucumber

Table 1

 1 The analysis results of fertilization efficiency of the continuous 3 years cropping greenhouse cucumber

	Redundancy Rates (%)

Table 2

 2 The comparison of the fertilization efficiency of three fertilize methods

		The Average	The Average	The Average	The Average of Input Redundancy Rates (%)
		of TE	of PTE	of SE	N	P 2 O 5	K 2 O
	Traditional Fertilization	0.5455	0.6790	0.7465	45.45	50.91	55.85
	Target yield Fertilization	0.6915	0.9338	0.7357	31.80	23.51	30.85
	Mathematical Model Fertilization	0.8108	0.9611	0.8379	18.92	5.26	23.32


Jinheng Zhang

Junqiang Wang

Yongliang Lv

Jianting Liu

Dapeng Li

Zhenxuan Yao

Xi Jiang

Ying Liu

Study on Vegetable Field Evaluation Index System for Non-point Source Pollution of Dagu River Basin

Keywords: Gray correlation analysis, Nitrogen, Phosphorus, COD, Precipitation, Soil physical properties, Soil type, Index system

This research combined data from soil, terrain and meteorological surveys, with quantitative data from soil and water field studies to determine the leaching processes of nitrogen and phosphorus contaminants from field vegetation into the Dagu River. Using the Gray correlation analysis method, precipitation and irrigation were established as the major driving force behind leaching losses from cultivated land. The type of fertilizers used, and the chemical and physical properties (porosity and bulk density) of soils in the region were set as parent sequences. COD values of nitrogen and phosphorus concentration were calculated from soil and water samples. Also, the depth of core samples was factored into the analysis. The result showed that the supply of soil nitrogen and phosphorus, which were mainly derived from fertilizers, were not generally high levels in Qingdao. According to fuzzy association degree, this investgation confirmed main influencing factors which affected COD, total nitrogen, total phosphorus of soil and groundwater, ammonia nitrogen and nitrate nitrogen of groundwater respectively. And according to these factors the initial vegetable field evaluation index system for non-point source pollution of Dagu River basin was constructed.

Introduction

Excessive nitrogen and phosphorus values used in fertilization will significantly decreases the economic benefits of enhanced crop production, as well as increases the risk of nitrogen and phosphorus contaminants in water reserves [START_REF] Xing | Situation of nitrogen pollution in water bodies in SuZhou region[END_REF][START_REF] Xiong | Non-point N pollution of lakes, rivers and wells in the Taihu Lake region[END_REF][START_REF] Xu | Approaches for reduction of nitrate contamination on vegetable by appropriately applying fertilizers[END_REF]. Approximately, 60% of water pollution is caused by non-point source NPS) pollution in America [START_REF] Corwin | Application of the Modeling of non-point Sources Pollutants in the vadose zone[END_REF]. In the Northern Australia, NPS pollution flowing into water reserves is also the major source of nitrogen contamination [START_REF] Griffin | Introducing NPS Water Pollution[END_REF]. In Denmark, 94% of the nitrogen load and 52% of the phosphorus load in 270 rivers is a result of non-point source processes [START_REF] Kronvang | Diffuse nutrient losses in Denmark[END_REF]. The same effects can be seen in the Netherlands, where 60% or the total nitrogen contaminants and 40-50% of the total phosphorus contaminants are caused by NPS pollution [START_REF] Boersp | Nutrient emissions from agriculture in the Netherlands: causes and remedies[END_REF].

In recent years, as agricultural development mirrors population growth in China, NPS pollution has become the main source of water pollution. Currently, China contains 1600 million hm 2 of cultivated land, which amounts to 35♂ of the agricultural land on the planet [START_REF] Feng | The effects of continuous cropping of vegetables on the biochemical properties of soil[END_REF]. Statistics measuring the amount of fertilizers, livestock and poultry manure and the cultivated land area from 2000 to 2006 in Qingdao were investigated.

Subsequently, an index was established to evaluate the nitrogen and phosphorous content in livestock manure as NPS pollutants [START_REF] Zhang | Agricultural non-point source pollution investigation and assessment in Qingdao[END_REF]. A Grey Correlation study to evaluate water pollution from vegetable fields in the Dagu River Basin of Qingdao [START_REF] Zhang | Evaluation of surface water pollution in Qingdao vegetable area based on gray correlation method[END_REF].

Over the years, the Dagu River has become increasingly more important to the national economy and to the lives of local residents. However, as the region surrounding the Dagu River Basin is developed, pollution of the river has become increasing obvious. It is the fertilizers and pesticides that have aided the agricultural development that threaten the water reserves. The numerous and excessive applications of fertilizers and pesticides cause soil erosion and serious water pollution from increasing nitrogen and phosphorus concentrations. This study integrates data from soil, terrain and meteorological surveys of Dagu River basin, with quantitative analysis studies of thousands of soil samples to construct an indexing system of NPS pollutants entering the Dagu River. The Dagu River is located in the western Shandong Peninsula, between 120 ° 03 '~ 120 ° 25'E and 36 ° 10 '~ 37 ° 12'N and covers an area of 4631.3 km 2 in Qingdao. The river runs through five districts in Qingdao, Laixi City, Pingdu, Jiaozhou, Chengyang, Jimo (see Figure 1). The annual precipitation in the region is about 685.3 mm and an annual runoff is about 6.311×10 m 3 [START_REF] Zhou | Application of the fuzzy mathematics in evaluation Dagu River water quality[END_REF].

Materials and Methods

Sample Collection

For this study, soil and water samples were collect in December of 2009 from cultivated land throughout the region (Figure 1). The samples of vegetable field soil were divided into two categories, surface soil 0-20 cm and submerged soils (80-100 cm). Additionally, surface water and ground water samples were collect from the same areas. GPS was used to map the latitude and longitude of sample areas. All samples were analyzed in the laboratory, using quantitative methods.

Sample Analysis

To determine the COD of water samples, they were heated in a strongly acid solution, containing potassium dichromate. A Euro Tech ET3150B multiple digester and an ET1151M COD Monitor were used for characterization. K 2 S 2 O 8 digestion molybdenum blue colorimetry was used alongside the COD digester to analyze the total concentration of phosphorus in samples. Nitrite has a visible absorption at 220nm, so a 752SP UV was used to measure its concentration in aqueous samples. Nessler's reagent was added to pretreated samples, and, then, a spectrometer was used to measure their absorbance at 420 nm. Also, oxidation-ultraviolet spectrophotometry was used to determine the total nitrogen content of samples, after the addition of potassium sulpersulthephate at 120~124 . @ Soil samples were first treated with alkaline potassium persulfate. Then, UV spectrophotometry was used to measure each solutions absorbance at 220nm, 275nm and 700nm to measure their total nitrogen and phosphorus content. Potassium dichromate was added to soil samples before COD analysis.

Results and Analysis

The main source and loss of non-point source pollution

Fertilizer, irrigation, and atmospheric deposition are the main sources of nitrogen and phosphorus in cultivated land within the basin. Among these, fertilizers are the most abundant source of the elements.

There was a large quantity of NO 3 --N in samples, as it is in not readily absorbed by soil colloid [START_REF] Zhang | Advances of cropland Nitrogen Leaching[END_REF]. Rainfall and irrigation contributed to the loss of accumulated NO 3 --N, via surface run-off (procedure C in figure 2) and/or through leaching (procedure A and B in figure 2) [START_REF] Liu | The yield response of vegetable to phosphate fertilizer and soil Phosphorus accumulation in a Chinese Cabbage-capsicum Rotation[END_REF], [START_REF] Shi | Change of soil nutrients under greenhouses under long-term fertilization condition[END_REF].

The substance basis of fertilizer leaching and driving force of vegetable field soil nitrogen and phosphorus

A classification system was developed from the analysis of more than 7000 soil samples, which were classified on the bases of total nutrient content. Results were classified into six categories based on nitrogen supply level from vegetables. The data is shown in table 1. From the above statistics, we found that nitrogen and phosphorus supply level in Qingdao vegetable land were not very high. Additionally, fertilizer was the main source of nitrogen and phosphorus. Subsequently, it was reasonable to study fertilizer as a basic source of fertilizer leaching. Meteorological precipitation data combined with crop irrigation were overwhelmingly contributed to the leaching loss of nitrogen and phosphorus from soil.

Determine leaching loss factors of nitrogen and phosphorus according to grey relational analysis

Types of fertilizer and soils, the physical properties of different soils (porosity and bulk density) precipitation data, soil nitrogen and phosphorus content, and soil COD were indexes in the analysis of leaching nitrogen and phosphorous. Gray correlation was used to analyze the effect of these indexes on the contaminant concentrations in the local water reserve. An index system based on well-correlated factors related to nitrogen and phosphorous loss from agricultural soil was established. The factors are listed below, in decreasing order: ammonia content, nitrate content, total nitrogen of groundwater, soil type, soil porosity (from shallow to deep), soil bulk density (from shallow to deep), precipitation, soil COD and soil total nitrogen content. Fertilizer plays the most significant role in affecting the quality of groundwater, followed by soil physical properties and soil chemical properties. The order of factors affecting total phosphorus content of groundwater was soil type, soil phosphorus content, soil COD, phosphate fertilizer of per hectare, soil porosity (from shallow to deep), soil bulk density (from shallow to deep) and precipitation, respectively. Phosphorus leaching from soil was the main source of phosphorus in groundwater.

Single factor assessment index of non-point source pollution

Fertilizer grade of nitrogen and phosphorus leaching loss.

Compound fertilizer, with similar nitrogen and phosphorous contents commonly used fertilizers, was applied to survey region (total nitrogen content 15% according to N, total phosphorus content 15% according to P 2 O 5 ). The According to probability density calculations (figure 2), the second center should be considered as the average nitrogen concentration in fertilizer per hectare in every season. The standard deviation was divided into five intervals: 0~80, 80~220, 220~360, 360~500 and >500 kg•hm -2 •season -1 (see table 4). An advantage of this division was that similar values can be drawn in same level, which reduces the risk that similar dispersed within the data. )

Table3. Sequence of gray correlationTable3a

Groundwater

Grade Description <80 F1 Fertilizer less than the average fertilizer level. The substance basis of leaching loss was not enough, and the leaching loss won't be happened.

80~220 F2

Fertilizer was near the average amount. On the occasion of heavy rain or over-irrigation, light leaching loss will be happened.

220~360 F3

The light leaching loss will be happened. The degree will increase according to rate of fertilizer and water. 360~500 F4 Middle leaching loss level. But heavy leaching loss can be happened. The rate of fertilizer is more than average rate. >500 F5 (Super) heavy leaching loss level.

The precipitation grade of nitrogen and phosphorus leaching loss.

When long-term soil leaching occurs, the optimum conditions were as follows: the effects of the sum of precipitation and irrigation are more than the effects of the sum of run-off, evaporation and good soil infiltration.

Conditions of short-term soil leaching mirrored large amount of precipitation or irrigation [START_REF] Johnes | Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters, the export coefficient modeling approach[END_REF]. In the study area, precipitation is heaviest from June to August, about 500mm, which accounts for more than 50% of the annual precipitation. In the summer irrigation was lessened, due to the increase precipitation.

In this analysis, 200mm precipitation was used as the lower limit and was divided into 5 levels in 100 mm intervals. Short-term soil loss reached measurable level when single precipitation/irrigation events exceeded 50mm (see the table 5).

2.4.3

The soil type grade of nitrogen and phosphorus leaching loss.

Soil type played an important role in leaching loss. Under similar conditions, clay soil showed minimum nitrogen and phosphorus leaching loss, whereas, the largest nitrogen and phosphorus leaching loss was from sand soil. Medium loss was observed in loam soil [START_REF] Chen | Dry calcareous soil characteristics and nitrogen volatilization loss of ammonia Channels[END_REF], [START_REF] Chen | Law of phosphorus loss and its affecting factors in red soil slopeland[END_REF]. The soils of Dagu River, which can be used to cultivate vegetables, are brown earth, aquic brown earth, brown paddy soil, cinnamon soil, eluvial cinnamon soil, developed cinnamon soil, lime concretion black soil, and fluvo-Aquic soil. Nitrogen and phosphorus leaching loss was divided into three categories, according to an analysis of the physical property of the soils, including total soil porosity and soil bulk density (see table 6).

Table 6 The soil type grade of nitrogen and phosphorus leaching loss

The soil body configuration grade of nitrogen and phosphorus leaching loss.

According to results of table 5, when sandy soil is present in the upper layer, leaching will occur more readily.

On the contrary, when clay soil is present, leaching will be respectively lower. Consequently, we classified the nitrogen and phosphorus leaching loss according to the soil composition. Areas of sand-layered and thin-layered soil were classified as enhanced nitrogen and phosphorus leaching loss types. Clay layered, intercalated clay layered, Mengyu type, and Mengyin type soils were as classified as reduced nitrogen and phosphorus leaching loss types.

Conclusions

Fertilizer was as the main source of nitrogen and phosphorus NPS pollution. Precipitation was the most significant driving force of leaching loss of nitrogen and phosphorous in regional soils. The application of fertilizer is the most significant contributor to nitrogen content in groundwater, followed by the physical and chemical properties of regional soils. The results showed that the most influential factors determining the total nitrogen content of groundwater was the soil type, chemical properties of the soil, phosphorus content, the physical properties of the soil, and precipitation, respectively. The amount of nitrogen per hectare and the precipitation in a season were divided into five levels respectively. Nitrogen and phosphorus leaching loss per soil type was classified as follows, enhanced grade, reduced grade and uncertain grade. Soil body configurations of nitrogen and phosphorus leaching loss were classified as enhanced and reduced grades.

Fig. 1 .

 1 Fig.1. Soil samples distribute

result from formula ( 1 , 2 )

 12 gave a mean value of fertilizer in survey region of 150.7 kg/hm2

  in the hill, valley and the front slope of mountain. The land is thick, and there is clayey layer generally. It played reduced role on nitrogen and phosphorus leaching loss. aquic brown earth High degree of maturation, viscous soil. It played reduced role on nitrogen and phosphorus leaching loss brown paddy soil Thin layer, rough texture, high impurity content. Lower capability of moisture and fertilizer conservation. An enhanced nitrogen and phosphorus leaching loss type. cinnamon soil The process of sticky soil obviously, soil deep. Higher capability of moisture and fertilizer conservation. It played reduced role on nitrogen and phosphorus leaching loss. Eluvial cinnamon soil Distribution in the slope and valley. Thick layer. Higher capability of moisture and fertilizer conservation. It played reduced role on nitrogen and phosphorus leaching loss. developed cinnamon soil Thin layer. Gravel. An enhanced nitrogen and phosphorus leaching loss type. lime concretion black soil, Thick layer. Sticky. It played reduced role on nitrogen and phosphorus leaching loss. fluvo-Aquic soil / Different physical and chemical properties and different degree of reposado. The soil type grade of nitrogen and phosphorus leaching loss was unclear.

Fig. 3 .

 3 Fig. 3. The Soil Body Configuration of nitrogen and phosphorus leaching loss

Table 1 .

 1 Classification total nitrogen in soil (Data from Qingdao soil, edited by the Soil and Fertilizer Workstation of

	Qingdao City)
				field
	supply level	Upland area	Vegetable land
		acre	%	acre	%
	high	-	-	--	-
	extremely high 5938	0.07	10174	3.16
	general	1060672	12.14	211928	65.78
	lower	4830122	55.31	100042	31.06
	low	2676373	30.65	-	-
	extremely low 160226	1.83	-	-
	We analyzed more than 1000 soil samples and classified the soluble phosphorus content. Results showed
	that very high soluble phosphorus supply level of vegetable land was only 7.83%, but general and lower
	supply level were 76.99% (Table 2).

Table 2 .

 2 Classification soluble phosphorus in soil (Datas from <Qingdao soil >edited by the Soil and Fertilizer

	Workstation of Qingdao City)
				field
	Supply level	Upland area	Vegetable land
		acre	%	acre	%
	high	-	-	-	-
	higher	-	-	25207	7.83
	general	277110	3.17	147990	45.93
	lower	1797677	20.58	129624	40.24
	low	3419031	39.15	19323	6
	extremely low	3239513	37.1	-	-

Table 4

 4 Fertilizer grade of nitrogen and phosphorus leaching

	Compound
	fertilizer
	(kg.hm -2 .season	-1

Table 5

 5 Driving factor grade of nitrogen and phosphorus fertilizer leaching

	Precipitation (mm. season -1 )	grade	description
	<200	W1	Shortage of precipitation. The general leaching loss won't be happened.
	200~300	W2	Precipitation met the requirements of leaching loss. Light leaching loss may be happened.
	300~400	W3	Precipitation met the requirements of leaching loss. Light leaching loss may be happened.
	400~500	W4	Precipitation was adequate, and moderate leaching loss may be happened.
	>500	W5	Precipitation was extremely adequate, and heavy leaching loss may be happened.

Acknowledgments. Project supported by the Science and Technology Projects of Qingdao (08-2-1-36-nsh and 09-1-1-53-nsh).


Shiping Zhu

Jing Liang
email: liangjing_lj@163.com

Ling Yan

Study on Rapid Identification methods of Transgenic Rapeseed Oil Based on Near Infrared Spectroscopy

Keywords: Near infrared spectroscopy, DPLS, Transgenic food, Rapeseed oil, Rapid identification

A rapid identification method of transgenic rapeseed oil based on near infrared spectroscopy was proposed. As transgenic food attracted more and more attention and in order to meet the requirement about labeling transgenic food, an accurate, fast, easy, efficient and low cost detection technology is needed. In this study, 117 rapeseed oil samples (including 64 non-transgenic rapeseed oil and 53 transgenic rapeseed oil samples) were used as test material. Principal component analysis (PCA) and discriminant partial least squares (DPLS) were applied to classify transgenic and non-transgenic rapeseed oil. Firstly, the paper studied the plot of principal component scores for the NIR spectra of transgenic and non-transgenic rapeseed oil. The result was that Non-transgenic and transgenic samples can be broadly distinguished in principal component space. Secondly, 35 samples were used to build DPLS identification model, and the other 82 samples as prediction set samples were identified by the model. The overall correct identification rate acquired was 96.34%, in which the correct identification rate of non-transgenic rapeseed oil was 95.56% and the one of transgenic rapeseed oil was 97.30%. The results showed that the attempt to discriminate transgenic rapeseed oil using NIR is feasible and excellent classification can be obtained by combining DPLS method.

Introduction

Transgene means that implant the gene which extracting from another organism into an organism via transgenic biotechnology. Transgene involves animal and plant, whereas transgenic plant is discussed more at present. The food adopted transgenic plant as raw material is called Transgenic food [START_REF] Jeong | Molecular analysis and quantitative detection of a transgenic rice line expressing a bifunctional fusion TPSP[END_REF] or Genetically Engineered food [START_REF] Batista | Facts and fiction of genetically engineered food[END_REF].

With the rapidly development of biotechnology, transgenic plant have been cultivated in abundance and transgenic food, such as soybean oil, rapeseed oil and maize oil, have entered the daily life of people. According to report, 90 percent of the edible oil sold on markets is transgenic oil at present and the consumption of oil per capita every year is up to 13kg in some metropolises. Consequently the security of transgenic food has increasingly captured the highly attention from governments, non-governmental organizations, academia and even ordinary people [START_REF] Yourong | The safety of Gentic engineering and genetically food[END_REF].

Since 1900, European Union issued over 10 laws about safety administration of GMO (short for genetically modified organism), and required labeling GMO as well as its production. Beginning in 1998 EU prescribed the mandatory label for GM food. In China, for protecting lawful rights of consumer, State Council issued Biosafety Administration Regulations on Agricultural GMO in 2001 and Ministry of Agriculture issued Labeling Administration Regulations on Agricultural GMO in 2002 [START_REF] Jinhua | Detection of transgenic ingredient in food[END_REF].

With the increasing demand of labeling transgenic food, the techniques for detection and identification of transgenic food are improving constantly and the methods are various. At present, the qualitative and quantitative detection for transgenic elements in most of food are available.

The detection methods of transgenic food include two broad categories, viz. the qualitative detection for exogenous gene and foreign protein. The detection methods for exogenous gene include Polymerase Chain Reaction PCR, semi nested PCR, nested PCR, Multip-lex PCR, DNA blotting etc. The quantitative detection methods for exogenous gene include quantitative PCR, PCR-ELISA method, gene chip technique. The detection methods for foreign protein include Western blotting, enzyme-linked immunosorbent assay (ELISA), Latera Flow, Lateral Flow Strip etc. [START_REF] Farid | Detection of genetically modified organisms in food[END_REF][START_REF] Zhou Shaoyun | Progress in Study of Genetically modified food detection[END_REF] The various methods above have different traits. But with the intensified research for transgenic produce at home and abroad as well as the increasing detection demand for transgenic produce, a strong demand for detection technique which is more accurate, rapid, easy to use, efficient and of low cost, has generated.

In the field of quality testing of agricultural products, near infrared spectroscopy analytical technology, as a new method, can make rapid and accurate analysis or detection for the element or quality of chemical products, medicament, food, agricultural products etc. Compared with traditional chemical analysis methods, it has advantages such as rapid (analytic time <1min), accurate, no chemical pretreatment for samples, nondestructive and so on.

At present, introducing NIR analytical technology into rapid detection of transgenic food is the cutting edge topic on detection method study of transgenic food. [6.10.11] S. A. Roussel et al in Grain Quality Lab of Lovwa Sate University distinguished Roundup Ready soybeans from conventional soybeans using NIR. 95% accurate classification for 20 samples of conventional soybeans and 84% accurate classification for 19 samples of Roundup Ready soybeans were obtained [START_REF] Roussel | Detection of Roundup Ready™ Soybeans by Near-Infrared Spectroscopy[END_REF]. Ronald J. F. J. Oomen et al studied the discrimination of transgenic potato using NIR principal component space [START_REF] Ronald | Modulation of the cellulose content of tuber cell walls by antisense expression of different potato (Solanum tuberosum L.) CesA clones[END_REF].

Interiorly, Rui Yukun et al identified transgenic maize and their parents using back propagation (BP) algorithm by NIR [START_REF] Rui Rukun | Application of near-infraraed diffuse reflectance spectroscopy to the detection and identification of transgenic corn[END_REF]. Xie Lijuan, Ying Yibin et al applied NIR spectrometer to discriminate transgenic tomatoes and their parents [START_REF] Xie | Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique[END_REF][START_REF] Xie | Discrimination of transgenic tomatoes based on visible/near-infrared spectra[END_REF][START_REF] Xie | Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics[END_REF]. In 2008, Xie Lijuan, Ying Yibin et al used Vis/NIR spectrum diffuse reflection method to perform rapid and nondestructive qualitative analysis for transgenic tomato leaves and non -transgenic ones. The accurate classification was 89.7% [START_REF] Xie Lijuan | Application of Vis/NIR Diffuse Reflectance Spectroscopy to the Detection and Identification of Transgenic Tomato Leaf[END_REF].

In spite of Rui Yukun et al had measured the erucic acid and Glucosinolate content of transgenic rapeseed and their parents by NIR spectrometer [START_REF] Rukun | Detection of Erucic Acid and Glucosinolate in Intact Rapeseed by Near-Infrared Diffuse Renectance Spectroscopy[END_REF], they hadn't identified transgenic rapeseed and their parents. Besides, at home and board there isn't any report on the research that whether rapeseed oil edible is transgenic. Therefore, it has theoretical significance and application value that developing rapid discrimination method for transgenic rapeseed oil based on NIR.

The objective of this study is to apply NIR spectroscopic techniques for detection of transgenic rapeseed oil. The specific goals were to (1) investigate the feasibility of near infrared spectroscopy in detecting transgenic rapeseed oil, (2) combine the PCA for classifying NIR rapeseed oil spectra into two groups, non-transgenic ones and transgenic ones and (3) establish the DPLS model to discriminate the transgenic samples. This research can provide discrimination of transgenic rapeseed oil as well as other transgenic farm produce with new method and technical support.

Materials

Samples

117 rapeseed oil samples were collected from seven brands such as Jin Long Yue, Li Yu, Hong Qingting, Xiao Mifeng, Lu Hua, including 64 non-transgenic rapeseed oil samples and 53 transgenic ones. The volume of every sample was 100ml. 35 samples (19 non-transgenic rapeseed oil samples and 16 transgenic ones) were used for calibration set, and the rest 82 ones for validation set. All the samples chose for calibration and validation sets were randomly.

Spectral measurement

The NIR spectra of every rapeseed oil sample were collected by MATRIX-F portable Fourier transform infrared spectrometer of BRUKER in the spectral regions of 11997~3999cm -1 . Spectrometer parameters setting, spectral data collecting were performed via software OPUS. The number of scanning was 32, the resolution was 4cm -1 and the spectral points were 4148.

Methods

PCA

Principal component analysis (PCA) is a multivariate modelling and analysis technique commonly used in chemometric studies. It can reduce the number of dimensions without much loss of information based on their similarities and differences to emphasise their similarities and differences. It can define the ''principal components" (PCs) which describe independent variation structures in the data. These PCs are orthogonal, so that the data set presented on these axes are uncorrelated with each other. Visualizing the PCs by various plotting systems can view interrelationships between different variables. [START_REF] Shin A | Chemometric approach to fatty acid profiles in Runner-type peanut cultivars by principal component analysis (PCA)[END_REF]

DPLS

The PLS algorithm has been widely used as a chemometric tool for near-infrared spectral analysis. Ii is a regression method based on characteristic variable. Its advantage is that it decomposes not only matrix X, but also matrix Y. While decomposing matrix Y it uses the information of matrix X; while decomposing matrix X it uses the information of matrix Y. Through that the regressive results become better. The DPLS method applies partial least squares (PLS) regression to binary classification problems, in which the dependent variabley codifies the class of each sample. In this study, the integer 1 codifies the sample as belonging to non-transgenic rapeseed oil and the integer 2 codifies the sample as belonging to transgenic ones. [START_REF] Ruy | DPLS and PPLS: two PLS algorithms for large data sets[END_REF][START_REF] Botella | Classification from microarray data using probabilistic discriminant partial least squares with reject option[END_REF]

Results and discussion

PCA

PCA can indicate relationships among groups of variables in a data set and show relationships that might exist between objects. All the spectra of 64 non-transgenic rapeseed oil and 53 transgenic ones are used for PCA. The spectra were original, without any optimizing or pre-treatment. According to MATLAB software, the score plots of principal components can be obtained.

Fig 2 showed the three-dimensional score plot of NIR principal components ( PC2、PC3、PC4) for 117 rapeseed oil samples. Fig 3 showed two-dimensional score plot of principal components PC2 and PC4. In this figure, non-transgenic rapeseed oil were represented by "△", and transgenic ones by "〇". From this figure, it can be found that non-transgenic rapeseed oil and transgenic ones can be discriminated roughly in their score plot of principal component, but the dividing line was not obvious.

DPLS

The software OPUS was used to establish DPLS (Discriminant partial least squares) model on original NIR spectra of calibration set. Then the model was used to predict validation set. The prediction result was that R 2 =0.7487 and RMSEP value of 0.249.

Fig 4 showed the predictions of rapeseed oil varieties in the calibration set using the DPLS model with the original NIR spectra. Non-transgenic rapeseed oil were represented by "△", and transgenic ones by "〇". Non-transgenic rapeseed oil with predicted values ranged form 0.5 to 1.5 and transgenic samples from 1.5 to 2.5 were all considered to be broadly correctly classified by the model. Fig 5 showed the true value of 82 prediction categories vectors .The prediction categories vectors within the range from 0.5 to 1.5 were identified as "1" and the ones from 1.5 to 2.5 were identified as "2". The discriminate functions acquired were showed in fig 6. From fig 6, it can be found that there were 3 inaccurate decision samples. Two non-transgenic samples that labeled as "1" were discriminated as "2" and one transgenic sample that labeled as "2" was discriminated as "1". So the general discrimination accurate rate was (82-3)/82=96.34%; the discrimination accurate rate of non-transgenic samples was (45-2)/45=95.56%; the one of transgenic samples was (37-1)/37= 97.30%.

Conclusion

The study showed that the attempt to discriminate transgenic rapeseed oil using NIR is feasible. The DPLS model obtained the prediction result was that R 2 =0.7487, RMSEP value of 0.249 and the discrimination accurate rate of 96.34% which is broadly satisfactory result using DPLS model on the original spectra. The results showed that excellent classification can be obtained by combining NIR and DPLS method. Introducing NIR into rapidly discrimination of transgenic food can provide new method and technical support for the detection of transgenic food. However it is still in the infant and exploration stage. There are some limitations consisting in the methods that used to establish models. Thus the research about optimizing model should be concerned in the filed of rapid detection for transgenic food using NIR.

  Fig 1 showed the NIR spectra of 117 rapeseed oil samples.

Fig . 1 .

 1 Fig . 1. NIR spectra of 117 rapeseed oil samples

Fig. 2 .Fig. 3 .

 23 Fig.2. Three-dimensional Score plot of three principal components (PC2、PC3、PC4)

Fig. 4 .Fig. 5 .Fig. 6 .

 456 Fig.4. Plot of actual vs calculated values of transgenic and non-transgenic rapeseed oil

Acknowledgments

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of Chongqing (No. CSTC2008BB1091).


Ling Xiang

Hua Ping

Liu Zhao

Zhihong Ma

Ligang Pan
email: lgpan@nercita.org.cn

Rapid Determination of Ascorbic Acid in Fresh Vegetables and Fruits with Electrochemically Treated Screen-Printed Carbon Electrodes

Keywords: Ascorbic Acid, Electrochemical Pretreatment, Screen-Printed Electrodes, Vegetables, Fruits

This study demonstrates a new electrochemical method for rapid determination of ascorbic acid in real samples with electrochemically pretreated screen-printed carbon electrodes. Cyclic voltammetry results indicated that the electrochemically activated electrodes possessed an excellent electrocatalytic activity toward ascorbic acid oxidation and can be successfully used for its selective and sensitive determination in fresh vegetables and fruits. The proposed method was further validated by means of specificity assay with ascorbate oxidase and a comparison with an accurate method such as high-performance liquid chromatography using ultraviolet detection. The electrodes presented the advantages over other existing methods such as mass production and without artificial modification. This study essentially offers a facile and reliable method for rapid determination of ascorbic acid in food analysis with high selectivity and sensitivity as well as low cost.

Introduction

Increasing evidences have revealed that ascorbic acid (AA) possesses lots of different functions in the physiological and pathological processes, such as an important antioxidant and free radical scavenger in neuroprotection, an essential cofactor for biosynthesis of neuropeptides, and an effective neuromodulator of dopamine-and glutamate-mediated neurotransmission [START_REF] Rice | Ascorbate Regulation and Its Neuroprotective Role in the Brain[END_REF]. As fresh vegetables and fruits are the most important suppliers of AA in normal diets, the determination of AA can offer a marker for food analysis and quality evaluation.

There are several reported methods for the determination of AA in foodstuffs, including chromatography [START_REF] Han | Distribution of Ascorbic Acid in Potato Tubers and in Homeprocessed and Commercial Potato Foods[END_REF], spectrophotometry [START_REF] Dürüst | Ascorbic Acid and Element Contents of Food of Trabzon (Turkey)[END_REF], and titration [START_REF] Verdini | Voltammetric Iodometric Titration of Ascorbic Acid with Dead-Stop End Point Detection in Fresh Vegetables and Fruit Samples[END_REF], etc.. However, these methods usually require complex steps, which may increase the chance of AA oxidation during the pretreatment of fresh samples. On the other hand, some methods may lack good specificity and require high-cost equipments. Thus, the development of rapid, facile, and reliable methods for AA determination is particularly important in food industry. Recently, electrochemical methods have shown unique advantages in rapid determination of AA [START_REF] Terry | The Application of Biosensors to Fresh Produce and the Wider Food Industry[END_REF]. AA can be electrochemically oxidized through a two electron and one-proton pathway followed by an irreversible hydrolysis process to produce an electroinactive product at neutral pH [START_REF] Zhang | Continuous On-Line Monitoring of Extracellular Ascorbate Depletion in the Rat Striatum Induced by Global Ischemia with Carbon Nanotube-Modified Glassy Carbon Electrode Integrated into a Thin-Layer Radial Flow Cell[END_REF]. Such an electrochemical property of AA can be used to realize direct electrochemical determination of AA with simple steps. But the high over-potential of AA oxidation at the conventional electrodes leads to poor specificity in real samples which also contain other electroactive substances, such as thiols and phenols [START_REF] Malinauskas | Electrochemical Response of Ascorbic Acid at Conducting and Electrogenerated Polymer Modified Electrodes for Electroanalytical Applications: a Review[END_REF]. Focused on the solution of this problem, most research used chemically modified electrodes [START_REF] Nassef | Amperometric Sensing of Ascorbic Acid Using a Disposable Sscreen-Printed Electrode Modified with Electrografted o-Aminophenol Film[END_REF] or the additional use of ascorbate oxidase (AAox) to differentiate the current response for AA from the total response obtained [START_REF] Mello | Biosensors as a Tool for the Antioxidant Status Evaluation[END_REF]. O'Connell et al. proposed a sensor modified with electropolymerized aniline (PANI) to selectively catalyze the oxidation of AA at low potential (+100 mV) so as to minimize the effects of most common interfering agents in juices and pharmaceuticals [START_REF] O'connell | Development of an amperometric ascorbic acid (Vitamin C) sensor based on electropolymerised aniline for pharmaceutical and food analysis[END_REF]. A carbon paste electrode modified with SiO 2 /SnO 2 /phosphate/Meldola's blue was used to study the electrocatalytic oxidation of AA in commercial fruit juices [START_REF] Castilho | Meldola's Blue Immobilized on a SiO 2 /SnO 2 /Phosphate Xerogel, a New Sensor for Determination of Ascorbic Acid in Medicine and Commercial Fruit Juice[END_REF]. Civit et al. successfully demonstrated amperometric determination of AA in several kinds of fruits and vegetables using a disposable screen-printed electrode modified with electrografted o-aminophenol film [START_REF] Nassef | Amperometric Determination of Ascorbic Acid in Real Samples Using a Disposable Screen-Printed Electrode Modified with Electrografted o-Aminophenol Film[END_REF]. With the appearance of carbon nanotubes (CNTs), recent electrochemical studies have revealed that CNTs can facilitate the oxidation of AA at a low potential, and demonstrated possible application of CNTs for selective determination of AA [START_REF] Zhang | Carbon Nanotube-Modified Carbon Fiber Microelectrodes for In Vivo Voltammetric Measurement of Ascorbic Acid in Rat Brain[END_REF]. Crevillén et al. developed carbon nanotube-modofied disposable detectors in microchip capillary electrophoresis for AA determination in pharmaceutical quality control [START_REF] Crevillén1 | Carbon Nanotube Disposable Detectors in Microchip Capillary Electrophoresis for Water-Soluble Vitamin Determination: Analytical Possibilities in Pharmaceutical Quality Control[END_REF]. However, Most of the above mentioned mehods showed a certain degree of complexity in the phase of electrode modification.

This study exploits a new electrochemical sensor for determination of AA in fresh vegetables and fruits with an electrochemical pretreated disposable screen printed carbon electrode. The integrated screen printed carbon electrode, which consists of a carbon counter electrode, a silver pseudo-reference electrode and a carbon working electrode, is amenable to rapid fabrication and mass production. In addition, this method demonstrated here just requires simple activation of electrodes to enhance the selectivity of AA determination without artificial modification, and could find some interesting applications in food and pharmaceutical industry.

2

Experimental Methods

Reagents and Solutions

All chemicals were of analytical grade or higher and used as received. Aqueous solutions were prepared with MilliQ water (Millipore, Inc., 18 MΩ cm -1 ). AA and AAox (Cucurbita species, EC 1.10.3.3) were all purchased from Sigma and used as supplied. A stock solution of AA (1.0 mM) was prepared in 0.1 M phosphate buffer at pH 7.0 just before use. Oxalic acid and other reagents were all purchased from Beijing Chemical Co., Ltd. (Beijing, China). AAox was used to prove the absence of collateral reactions by means of specificity assays. The enzyme (250 U) was dissolved in 1.0 mL 0.1 M phosphate buffer (pH 7.0), and divided into portions and stored at -40 °C until use. One portion was taken out and diluted with 0.1 M phosphate buffer (pH 7.0) to get a fresh AAox solution with the concentration of 25.0 U/mL for use.

Electrode Preparation

The planar screen printed carbon electrodes (SPCEs) are designed by our lab and fabricated by Beijing Meiborui Co., Ltd. (Beijing, China) as previously described. 15 The electrode 4.0cm×1.2cm×0.05cm (length×width×height) consists of three main parts which are a carbon counter electrode, a silver pseudo-reference electrode and a carbon working electrode. The carbon working surface is 4 mm in diameter. The Ag/AgCl reference electrode was formed by applying 0.3 M FeCl 3 solution on the silver electrode for 10 min [START_REF] Cui | Effect of Pre-Treatment on the Surface and Electrochemical Properties of Screen-Printed Carbon Paste Electrodes[END_REF]. The fabricated SPCEs were first sequentially sonicated in ethanol, 3.0 M HNO 3 , 1.0 M KOH, and distilled water each for 3 min., and then typical electrochemical pretreatment of SPCEs was carried out by applying +2.5 V for 60 s in 1.0 M NaOH (vs. Ag/AgCl reference electrode on the same SPCE).

Sample Preparation

Fresh orange and cabbage samples were obtained from a local supermarket. The operative processes were the usual ones: green cabbage leaves were washed in running water, drained, and finally weighed (about 50 g/sample). Oranges were peeled and cut into suitable, representative portions of the proper weights (about 50 g/sample).

Each sample was prepared by weighing a sufficient amount of vegetable or fruit to provide ca. 10 mg of AA/sample. 125 mL 0.1% (w/w) oxalic acid was immediately added to each sample, and the sample was homogenized with a Braun knife homogenizer for a few seconds. The homogenate was immediately submitted to 5 000 rpm in a refrigerated centrifuge for 5 min. An aliquot of the clear supernatant solution was filtered through a 0.45 μm pore-size filter (Millipore, Bedford, MA) before being injected into the system of high-performance liquid chromatography (HPLC).

Electrochemical Measurements

Electrochemical measurements were carried out on a computer-controlled electrochemical analyzer (CHI660C, CHI, TX), and were performed by placing a drop of the corresponding solution on the working area. A 0.10 M phosphate buffer solution (pH 7.0) was used as supporting electrolyte for the electrochemical studies.

For real sample analysis, 1.0 mL of the clear supernatant solution containing AA after centrifugation was first mixed with 1.0 mL of the working buffer solution (0.1 M phosphate buffer at pH 7.0), and then a 50 μL drop of the corresponding solution was placed on the working area before measurement.

HPLC Analysis

Determination of AA by HPLC was performed in a Waters 2690 HPLC system (Milford, Massachusetts), with a column (5 μm; C18; 150 × 3.9 mm). Detection was done by ultraviolet (UV) absorption at 254 nm (Waters 2487 Tunable Absorbance Detector) [START_REF] Robards | Methods for Assessing the Authenticity of Orange Juice: a Review[END_REF]. At a flow rate of 0.6 mL/min (Waters 2690 Pump), the retention time for AA was 2.75 min. Identification of the AA peak was carried out using a highly purified standard solution prepared as 20 ppm in 0.1% (w/w) oxalic acid.

The HPLC mobile phase was a 97%:3% V/V mixture of oxalic acid (0.1% (w/w), adjusted to pH 2.5 before mixing) and methanol, which was filtered through a 0.45 μm pore-size filter (Millipore, Bedford, MA).

Specificity Assays

Aliquots of 5.0 μL of the enzyme solution (25.0 U/mL) were added into the 1.0 mL mixture, which was composed of clear supernatant solution obtained in the sample preparation and 0.1 M phosphate buffer at pH 7.0 (v/v 1:1), in order to achieve utter oxidation of the AA present in a period not longer than 15 min.

Results and Discussion

AA Oxidation at the Fresh and Pretreated SPCEs

As shown in Fig. 1, at a fresh SPCE without activation, the oxidation of AA began irreversibly at 0 V, showing poor electrochemical kinetics with high over-potential and low electron transfer rate. McCreery et al. have systematically studied electrochemical reactions at carbon electrodes and demonstrated that the oxidation of AA is an inner-sphere reaction with electron-transfer kinetics sensitive to the electrode surface [START_REF] Mccreery | Electroananlytical Chemistry[END_REF]. Thus the electrode activity shows a dramatic effect on electrochemical oxidation property of AA, which is generally determined by one or more of the following factors: (1) surface microstructure, (2) surface cleanliness, (3) hydrophilicity/hydrophobicity, (4) electronic structure, and (5) surface functional groups.

It is well known that carbon electrodes are often necessarily preconditioned by electrochemical pretreatment to enhance the electron transfer properties at the electrode surface [START_REF] Guo | Surface Chemistry and Electron-Transfer Kinetics of Hydrogen-Modified Glassy Carbon Electrodes[END_REF]. No matter electrochemical pretreatment are carried out in an acid, base or neutral solution, the main purpose of pretreatment on SPCEs is to remove organic ink constitutes or contaminates and to increase surface roughness or the density of oxygenated groups [START_REF] Prasad | The Role of Oxygen Functionalities and Edge Plane Sites on Screen-Printed Carbon Electrodes for Simultaneous Determination of Dopamine, Uric Acid and Ascorbic Acid[END_REF]. To find appropriate electrochemical pretreatment for optimum electrochemical performances of SPCEs designed by our group, different electrolytes and conditions were studied using potassium ferricyanide as an electrochemical probe. By gradually changing the concentration of NaOH, applied potential as well as pretreatment time, an electrochemical activation procedure, applying +2.5 V for 60 s in 1.0 M NaOH aqueous solution, was finally selected as the most reproducible and advantageous. As shown in Fig. 2(A), the response of a fresh SPCE without electrochemical activation was poor with a large peak separation of oxidation and reduction ( Ep ⊿ ﹥1000 mV), indicating a slow electron transfer rate and a irreversible electrochemical process. As to the electrode after pretreated in 1.0 M NaOH at +2.5 V for 60 s, Ep was reduced to ca. 160 mV, ⊿ showing a rapid electron transfer rate and a quasi-reversible electrochemical response. The result also showed that the current response of Fe(CN) 63-/ 4-at pretreated SPCE obviously increased, compared with the fresh SPCE, due to the improvement of electron transfer rate and electrode surface roughness caused by the electrochemical activation procedure.

Unlike the fresh SPCE just after rinse in different solutions, AA oxidation started at -0.4 V after electrode pretreatment shown in Fig. 2(B), which was obviously more negative compared with that at the fresh electrode. This result clearly indicated that the effective electrochemical activation leaded to a faster electron-transfer kinetics for the AA oxidation, which benefited for selective determination of AA at lower potential on the pretreated electrode. The comparison of the voltammograms obtained for AA oxidation at the SPCEs before and after pretreatment also showed the enhancement of current response of AA and background charging current, due to the increase of electrode surface caused by electrochemical pretreatment. I / μA E / V vs. Ag/AgCl In addition, the electrochemical activated SPCEs showed a good linearity for AA measurement, as depicted in Fig. 3. The currents recorded clearly increased with increasing AA concentration in solution (Fig. 3(A)), and were linear with AA concentration within a range from 0.01 to 1.00 mM (I/μA= 28.02C/mM -0.39, γ=0.9990) (Fig. 3(B)). The detection limit, based on a signal-to-noise ratio of 3, was calculated to be 5.0 μM. The activated electrode was also stable for repetitive use, and the sensitivity remained essentially identical after the daily measurements of AA standard (0.10 mM) for 4-6 h on at least consecutive 10 days. On the other hand, the electrode showed good reproducibility for the determination of AA, which was illustrated by the small relative standard deviation (RSD) obtained with the same electrode used for the measurements of AA for several times. For example, the RSD recorded for the AA standard (0.10 mM) was calculated to be 2.3% (n=10). The good performance of activated SPCEs mentioned above is indeed advantageous to be applied in determination of AA in real samples.

Analysis of AA in Cabbage and Orange

As detailedly described in experimental section, the AA content in cabbage and orange samples was analyzed by use of electrochemically pretreated SPCEs. As displayed in Fig. 4, the current response obtained for the addition of cabbage (Fig. 4(A)) and orange sample (Fig. 4(B)) showed well-defined shape of voltammograms, which were remarkably the same as the one obtained in AA standard solution before the real sample analysis. According to the oxidation current of real sample, AA content can be calculated in terms of the plot of sensor calibration in Fig. 3 Considering the importance of the specificity of the AA determination in the real sample analysis demonstrated in this study, we further confirmed such a critical factor after the determination of AA content in both cabbage and orange by using AAOx to specifically catalyze the oxidation of AA [START_REF] Akyilmaz | A New Enzyme Electrode Based on Ascorbate Oxidase Immobilized in Gelatin for Specific Determination of L-Ascorbic Acid[END_REF]. The dotted lines in Fig. 4 showed the current response of a mixture of cabbage or orange with AAOx. As shown in Fig. 4(A) and Fig. 4(B), the presence of AAOx into the sample resulted in a clear decrease in the current response almost to the baseline level, confirming that it was feasible to selectively determine AA virtually interference-free from other electroactive species coexisting in the cabbage and orange.

In this study, the results obtained via an electrochemical voltammetric method have also been compared with those obtained using classical HPLC-UV analysis, in order to validate its reliability for rapid determination of AA in fresh vegetables and fruits. As displayed in Table 1, the comparison of the results obtained by the proposed electrochemical method to those of the reference method demonstrated an excellent correlation. These demonstrations suggest that the analytical system described in this study could be useful for rapid, facile, and reliable determination of AA in food analysis and other fields with low cost.

Conclusions

By taking advantage of facile electrochemical pretreatment and disposable screenprinted carbon electrodes, we have successfully developed a new electrochemical method for voltammetric measurement of AA with electrochemically activated electrodes. The integrated disposable screen printed carbon electrode, which consists of a carbon counter electrode, a silver pseudo-reference electrode and a carbon working electrode, is amenable to rapid fabrication and mass production, which simplifies the determination equipment and reduces the cost. In addition, this method demonstrated here just requires simple activation of electrodes to enhance the selectivity of AA determination, which avoids any artificial modification and reduces the determination time. Specificity assay and method comparison have demonstrated the electrodes possess a high selectivity and could thus be used for selective and reliable measurement of AA at lower applied potential. The method has also found to be sensitive, reproducible, and thus essentially finds some interesting applications in rapid food analysis and other fields.

Fig. 1 .

 1 Fig. 1. CVs obtained at a fresh SPCE in 0.1 M phosphate buffer (pH 7.0) in the absence (dashed line) and presence (solid line) of 0.50 mM AA before electrochemical pretreatment. Scan rate, 100 mV s -1 .

Fig. 2 .

 2 Fig. 2. (A) Typical cyclic voltammograms (CVs) at a fresh SPCE in 0.1 M phosphate buffer (pH 7.0) containing 5 mM K 3 Fe(CN) 6 before (dashed line) and after (solid line) electrochemical pretreatment. Scan rate, 100 mV s -1 . (B) CVs obtained at a fresh SPCE in 0.1 M phosphate buffer (pH 7.0) in the absence (dashed line) and presence (solid line) of 0.50 mM AA after electrochemical pretreatment. Scan rate, 100 mV s -1 .

Fig. 3 .

 3 Fig. 3. (A) Typical CVs obtained at activated SPCE in 0.1 M phosphate buffer (pH 7.0) containing AA with different concentrations of 0.00, 0.01, 0.02, 0.05, 0.10, 0.20, 0.40, 0.60, 0.80 and 1.00 mM. Scan rate, 100 mV s -1 . (B) Plot of current response vs the concentration of AA.

Fig. 4 .

 4 Fig. 4. CVs obtained at activated SPCEs in 0.1 M phosphate buffer (pH 7.0) in the absence (dashed lines) and presence (solid lines) of diluted cabbage sample (A) and orange sample (B). The dotted lines in (A) and (B) refer to the responses of the mixture of AAox and each diluted sample after 15min, respectively. Scan rate, 100 mV s -1 .

Table 1 .

 1 Method

			comparison
	sample	cabbage (mg/kg) voltammetry HPLC	orange (mg/kg) voltammetry HPLC
	1	96.3	97.6	420	431
	2	98.9	100	435	425
	3	99.7	97.8	428	435
	4	101	98.9	439	427
	mean	99.0±2.0	98.6±1.1	431±8	430±4

Acknowledgments

. This research was financially supported by the National High Technology Research and Development Program (2010AA10Z403, 2007AA10Z202), and Beijing Municipal Science and Technology Commission Program (Z09090501040901).