Qiuxiang Jiang
email: jiangqiuxiang914@163.com

Yongqi Cao

Ke Zhao

Zhimei Zhou

Risk Assessment of Water Resources Shortage in Sanjiang Plain

Keywords: water resources, shortage risk, analytic hierarchy process, Sanjiang Plain

In view of the problems existing in the development and utilization of water resources in Sanjiang Plain, this paper made a study on the risk assessment of water resources shortage in Sanjiang Plain. From the perspective of water resources shortage risk, analytic hierarchy process was applied to establish assessment index system and evaluation criteria, and then make a comprehensive assessment and regional difference analysis of water resources shortage risk in Sanjiang Plain. Research results showed that Shuangyashan City belonged to high degree of water resources shortage risk, while Jixi, Hegang, Jiamusi, Qitaihe, Muling and Yilan belonged to extremely high water resources shortage risk, which provided a theoretical principle for sustainable and efficient utilization of regional water resources.

Introduction

Water resources are the natural resources for human survival, but also an important resource for the economic development of a country or a region. However, China is a drought and water shortage nation in the world. Fewer amounts, uneven space distribution and serious pollution of water resources make water resources shortage in China more serious. The shortage of water resources in a certain extent has seriously restricted the sustainable development of social economy in China [START_REF] Xiaohong | AHP assessment model application in water shortage [J][END_REF]. Therefore, the identification of water resources shortage risk factors, evaluation of its degree to reduce economic losses caused by the shortage, and the optimal allocation of water resources have important practical significance for achieving water resources sustainable utilization.

Water shortage risk assessment is a comprehensive evaluation of multiple factors. Currently, the evaluation methods include analytic hierarchy process (AHP), fuzzy mathematics method, projection pursuit clustering, etc. The calculation process and the advantages and disadvantages of various methods were studied and compared by many former scholars [START_REF] Yongping | The theory and practice of water resource shortage risk management[END_REF][START_REF] Jiuyi | Framework for Water Scarcity Assessment and Solution at Regional Scales: A Case Study in Beijing-Tianjin-Tangshan Region[END_REF]. Due to the impact of many uncertainties, any single method is difficult to evaluate comprehensively. If AHP was combined with fuzzy mathematical method, it could make the abstract evaluation process and algorithm specific, and the evaluation results would be distinct. Therefore, based on the use of AHP to identify the source of water resources shortage risk, membership function was used to evaluate the risk degree of each index, and realize the risk assessment of regional water resources shortage.

2

Research area 3 Water shortage risk assessment

Evaluation index system construction

In order to comprehensively and systematically analyze water resources shortage, risk index and main risk sources of each city in Sanjiang Plain area, and contrast risk differences in different areas, a series of evaluation index were needed to construct a unified and complete evaluation index system. Each evaluation index should reflect the water resources shortage and shortage risk from different angles. It should be able to connect with each other, complement each other, and will be independent and representative. Combined with the characteristics of resource utilization and socioeconomic development in the study area, the risk assessment index system of water resources shortage in Sanjiang Plain was constructed. The index system is made up of target layer, state layer and index layer, in which the target layer includes one target factor (I), the state layer includes five state factors (A to E), and the index layer includes 18 indicators (A1 to E3) shown in Table 1. In the risk assessment index system of water resources shortage, the state layer indicates the risk of regional water resources shortage from different angles: water resources quantity reflects the quantity and background of water resources in evaluated area; social demand mainly reflects the intensity of water demand and social economic scale born by regional water resources; water resource reserve status mainly reflects the ability of relieving regional water shortage; water supply indicators mainly reflect the regional water supply capacity; ecological environment indicators mainly reflect the status of regional ecological balance [START_REF] Juxing | Risk Assessment of Water Shortage of Han River Drainage in Hubei Province [J][END_REF].

Calculation of index weight based on AHP

There are five steps in the weight calculation of analytic hierarchy process: systemizing problem, valuing index, constructing judgment matrix, determining index weight, and testing judgment matrix consistency [START_REF] Yongqi | Application of AHP and Fuzzy Evaluation to Water-Saving Potential in Irrigation District[END_REF]. The judgment matrix is an n×n comparison matrix composed by quantification of the comparison of n sub index. In this paper, the relative importance of quantitative value is obtained by using 1-5 scaling method [START_REF] Shouyu | Engineering fuzzy set theory and its application[END_REF], when strong factors were compared with weak factors, the numerical 1-5 were used to characterize the degree of importance, respectively, indicating equal, slightly important, obviously important, strongly important, extremely important. When weak factors were compared with strong factors, the reciprocal of the numerical 1-5 were used to represent. The judgment matrix of this study includes the target -state level (A-E), the state-index layer (A1-A3、B1-B4、 C1-C3、D1-D4、E1-E2), which has 6 judgment matrixes.

The index weight was calculated by using the characteristic root of the judgment matrix, and the maximum eigenvalues and eigenvectors of the matrix were calculated by using the MATLAB software. After single ordering the 6 judgment matrix, normalized weight value was obtained by the multiply of each index weight with the corresponding state level factor. The CR value in consistency test of judgment matrix is calculated by the following formula [START_REF] Jihong | Evaluation of the influence on the economy of water source areas in Shanxi province of middle route of south-to-north water diversion project based on the analytic hierarchy process[END_REF]:

       ) 1 /( ) ( / max n n CI RI CI CR  (1)
Where CI is the consistency index of judgment matrix; λ max is the maximum eigenvalue of judgment matrix; n is the dimensionality of judgment matrix; RI is the random consistency index related to the dimensionality of judgment matrix, when n was 3, 4, 5, 6, 7, 8, 9, 10, RI = 0.52, 0.90, 1.12, 1.26, 1.36, 1.41, 1.46, 1.49. When CR is less than 0.1, it is considered that the judgment matrix is in good agreement, and the weight is rational; elsewise it will need to adjust the judgment matrix until it is satisfied.

In this paper, the CR values from the consistency test of each index layer were all less than 0.1, which indicated that the judgment matrix satisfied the consistency, and the weight distribution was reasonable. The calculation results were shown in Table 2.

Membership evaluation construction

After the establishment of the risk evaluation index system of water resources shortage, we need to connect each index value with water resource shortage risk index, and the common method solving this problem is fuzzy mathematics evaluation method. By dividing the numerical interval, the index value was converted into the risk degree, and the relationship between the index value and risk index was established. By transforming the measured values of each and every index in evaluation area into the risk degree, risk index was obtained by weighted sum of the risk of all the indexes in the evaluation area [START_REF] Benqing | Fuzzy comprehensive assessment of water shortage risk [J][END_REF]. In this paper, the membership of risk is 1~8, the higher the number, the higher the membership of risk. Based on water resources shortage risk level, the membership degree is divided into 5 levels (V 1 ~V5 ) as shown in Table 3.

4 Evaluation results and analysis

In accordance with the above evaluation process, the water resources shortage risk assessment of Jixi City, Hegang City, Shuangyashan City, Jiamusi City, Qitaihe City, Muling City, Yilan County and the whole Sanjiang Plain was conducted. The risk membership of index in each city was shown in Table 4, and the comprehensive risk index and risk level were shown in Table 5. The evaluation index in target layer of water resources shortage risk are between 1~8. According to the cluster analysis of risk value, the water resources shortage risk was divided into 4 levels: extremely high risk (>4.0), high risk (4.0~3.75), medium risk (3.75~3.5), low risk (<3.5). The results showed that the whole area of Sanjiang Plain had extremely high risk. Water resources shortage risk mainly came from three aspects: small amount of water resources, high social demand and shortage of water reserves. Low risk of water supply and water environment eased the water shortage risk level to a certain extent. The water shortage risk rank of seven administrative regions in Sanjiang Plain from high to low was as follows: Qitaihe City, Hegang City, Yilan County, Muling City, Jiamusi City, Jixi City, Shuangyashan City.

Jixi City, Hegang City, Qitaihe City, Jiamusi City, Muling City and Yilan County belonged to extremely high water resources shortage risk, and the main reasons were small amount of water resources, high social demand and shortage of water reserves. Shuangyashan City belonged to high water shortage risk and the risk index was close to the extremely high risk, and the main reason was that the region had less rainfall, less reservoir storage and high industrial water consumption. Therefore, the shortage of water resources in Sanjiang Plain is very serious.

Conclusions

In order to make risk assessment of water resources shortage in Sanjiang Plain, the study made full use of the uncertainty information contained in the object of study, used numbers to process and express, and quantified decision makers' subjective identification and judgment to simplify complex problem and to analyze the details by hierarchy method. Combining quantitative and qualitative analysis with quantitative data, simply calculating, judging and comparing various factors in each level were carried out, and the weight value of each factor was obtained. By calculating the ordering weights of individual factors relative to the overall goal of the whole system, the overall risk condition of these factors was analyzed, namely, regional water resources shortage risk level. The model constructed in this paper based on AHP, with the advantages of simple structure, simple calculation, clear concept, easy operation, simple and effective modeling method could be applied to regional natural resources shortage risk grade evaluation.

After the evaluation of the analytic hierarchy process, the results showed that Jixi City, Hegang City, Qitaihe City, Jiamusi City, Muling City, Yilan County belonged to the extremely highly water shortage risk, the main reasons were small amount of water resources, high social demand and shortage of water reserves. Shuangyashan City belonged to high water shortage risk and the risk index was close to the extremely high risk. According to the evaluation results,for the region with both extremely high and high shortage risk, the demand of water resources will increase along with population growth and economy development. Therefore it is necessary for us to integrate regional characteristic, make full use of the rich water resources of Heilongjiang River and Xingkai Lake, strengthen water resources management, improve water use efficiency, and then as soon as possible to realize the transformation of economic structure from water-intensive economic structure to water-saving economic structure. At the same time, the government needs to increase investment in water conservancy projects, pay attention to the deep development of water resources, and then guarantee the sustainable water supply ability to socioeconomic development in Sanjiang Plain.

  Sanjiang Plain lies in the northeast region of Heilongjiang Province, China, including the triangle confluence region of the Heilongjiang, Songhua and Wusuli Rivers, and Woken River and Muling River Basin and Xingkai Lake Plain. Geographical position of Sanjiang Plain is 43°49′55″~48°27′20″ north latitude, 129°11′20″~135°5′10″ east longitude, north to the Heilongjiang River, south to the Xingkai Lake, east to the Wusuli River, west to the southeast part of Xiaoxinganling mountains. Its east-west width is 430km, and north-south long is 520km, with a total area of 1.09×10 5 km 2 and 24% of Heilongjiang Province total area. The administrative area of Sanjiang Plain includes Hegang City, Jiamusi City, Shuangyashan City, Qitaihe City, Jixi City and Muling City and Yilan County. The Sanjiang Plain region is essential for Heilongjiang Province to ensure grain produce capacity and food security and is also a significant marketable grain base China. Low water resources pollution emissions compliance rate, unreasonable development and utilization, low industrial water reuse rate and low water resources use rate[START_REF] Qiuxiang | Study on carrying capacity evaluation and dynamic simulation of sustainable utilization of water and land resources in Sanjiang Plain[END_REF], coupled with increasingly severe extreme weather and water waste caused the serious shortage of water resources. Therefore, it is of great significance to construct the risk assessment model of water resources shortage, and to analyze and evaluate the situation of water shortage in Sanjiang Plain.

Table 1 .

 1 Water resources shortage risk evaluation index

	Target layer	State layer	Index layer	Index measurement method
			A1 Per capita water resources	Total water resources / total population
		A Water resources	A2 Per square water resources	Total water resources / land area
			A3 Surface water coefficient	Surface water resources / precipitation
			B1 Population density	Total population / land area
		B Social needs	B2 GDP per water consumptions B3 Per square water demand modulus	Gross Regional Product / total water demand Water demand / land area
			B4 Per capita water demand	Total water demand / total population
	I
	Water		C1 Water retention coefficient	Total water resources / precipitation
	Shortage risk index	C Water source reserve	C2 Reservoir storage ratio C3 Groundwater resource coefficient	Reservoir water storage / total water resources Total groundwater / total water resources
			D1 Per capita water supply	Total water supply / total population
			D2 Water supply rate	Water supply / total water resources quantity
		D Water supply
			D3 Groundwater supply ratio	Total groundwater supply / total water supply
			D4 Surface water supply ratio	Total surface water supply / total water supply
		E Ecological	E1 Forest coverage	Forest area / total land area
		environment	E2 Ecological water	Ecological water consumption / annual
			consumption rate	average water resources

Table 2 .

 2 Calculation results and consistency test of the weight of the index system

	State factor weight	Conformance test (CR)	w1	Index factor weight w2 w3	w4	Conformance test(CR)
	A 0.4082		0.1750	0.1750	0.0582	-	0
	B 0.2562		0.0202	0.0349	0.0639	0.1372	0.0258
	C 0.1048	0.0498	0.0419	0.0419	0.210	-	0
	D 0.1860		0.0700	0.0700	0.0230	0.0230	0
	E 0.0448		0.0075	0.0373	-	-	0

Table 3 .

 3 Grade standard of risk membership degree of water resources shortage index system

	Status factor	Index factor	Unit	V1(1)	Risk membership V2(2~3) V3(4~5) V4(6~7) V5(8)
	A	A1 Per capita water resources	m 3	>2000 2000~1600 1600~1200 1200~800 <800
	Water	A2 water resources per square	m 3	>4.5	4.5~3.5	3.5~2.5	2.5~1.5 <1.5
	resources	A3 Surface water coefficient		>0.4	0.4~0.3	0.3~0.2	0.2~0.1 <0.1
		B1 Population density	capita/k m 2	<25	25~50	50~100 100~300 >300
	B Social needs	B2 GDP per water consumptions B3 Per square water modulus	yuan/m 3 >120 m 3 <2	120~100 2~5	100~60 5~10	6 10~20	<20 >20
		B4 Per capita water demand	m 3	<300	300~400 400~500 500~600 >600
	C	C1 Water retention coefficient	-	>0.6	0.6~0.45 0.45~0.3 0.3~0.1 <0.1
	Water source	C2 Reservoir storage ratio	-	>0.8	0.8~0.5	0.5~0.3	0.3~0.1 <0.1
	reserve	C3 Groundwater resource coefficient	-	>0.4	0.4~0.35 0.35~0.3 0.3~0.25 <0.25
		D1 Per capita water supply	m 3	>800	800~600 600~400 400~200 <200
	D	D2 Water supply rate	%	>0.6	0.6~0.4	0.4~0.2	0.2~0.1 <0.1
	Water supply	D3 Groundwater supply ratio	%	>0.05 0.05~0.01	<0.01	-	-
		D4 Surface water supply ratio	%	>0.95 0.95~0.9	-	-	-
	E	E1 Forest coverage	%	>50	50~40	40~20	20~10	<10
	Ecological environment	E2 Ecological water consumption rate	%	<1	1~2	2~3	3~5	>5

Table 4 .

 4 Index risk membership of evaluation area

	City	A1 A2 A3 B1 B2 B3 B4 C1 C2 C3 D1 D2 D3 D4 E1 E2
	Jixi	1	8	1	5	7	1	8	2	8	3	1	1	1	1	4	1
	Hegang	1	8	1	4	8	1	8	1	8	8	1	1	1	1	2	1
	Shuangyashan	1	8	1	4	7	1	8	1	8	1	1	1	1	1	4	1
	Jiamusi	1	8	3	4	8	1	8	3	8	3	1	1	1	1	6	1
	Qitaihe	6	8	4	6	7	1	8	5	8	3	6	1	1	1	3	1
	Muling	1	8	4	3	8	1	8	4	8	3	7	1	1	1	1	1
	Yilan	1	8	3	5	8	1	8	5	8	6	1	1	1	1	4	1
	Sanjiang Plain	1	8	8	5	7	1	8	8	8	4	1	1	1	1	4	1

Table 5 .

 5 Risk index and risk rank of evaluation area

	City	A	B	C	D	E	Total risk index	Risk level
	Jixi	1.6332 1.5068 1.0490	0.1860	0.0673	4.4423	Extremely high risk
	Hegang	1.6332 1.5215 2.0571	0.1860	0.0523	5.4501	Extremely high risk
	Shuangyashan 1.6332 1.4866 0.5871	0.1860	0.0673	3.9602	High risk
	Jiamusi	1.7496 1.5215 1.0909	0.1860	0.0823	4.6303	Extremely high risk
	Qitaihe	2.6828 1.5270 1.1747	0.5360	0.0598	5.9803	Extremely high risk
	Muling	1.8078 1.5013 1.1328	0.6060	0.0448	5.0927	Extremely high risk
	Yilan	1.7496 1.5417 1.8047	0.1860	0.0673	5.3493	Extremely high risk
	Sanjiang plain 2.0406 1.5068 1.5104	0.186	0.0673	5.3111	Extremely high risk

Acknowledgment

The study was supported by National Natural Science Foundation of China (51209038), Public Science and Technology Research Funds Projects of the Ministry of Water Resources (201301096), Foundation of Heilongjiang Educational Committee (12531009), Heilongjiang Postdoctoral Fund (LBH-Z13049), Doctoral Foundation of Northeast Agricultural University (2012RCB58) and "Young Talents" Project of Northeast Agricultural University (14QC47).


Jizhang Wang

Yuli Peng

Pingping Li
email: clipingping@ujs.edu.cn

Propagation Characteristics of Radio Wave in Plastic Greenhouse

Keywords: radio wave, Propagation characteristics, Received Signal Strength Index(RSSI), plastic greenhouse

In order to realize the deployment of wireless sensor network in large areas of plastic greenhouses. The propagation characteristics of radio wave in plastic greenhouse were studied. The Received Signal Strength Index(RSSI)in greenhouse and between greenhouses were studied, and the logarithmic path loss model for RSSI was established. The results show that the radio wave attenuation parameters A and n between greenhouses were 30.785 and 2.89. The attenuation index was larger than the index of free space. In the plastic greenhouse planted with tomatoes, the radio wave attenuation parameters A and n in the ground were 34.99 and 3.64, and in the top of canopy were 35.14 and 2.85, its show that the radio wave transmission has been significantly affected by the crop in the plastic greenhouse.

Introduction

As a new method for information acquiring and processing, wireless sensor network technology was applied in agricultural environmental monitoring and controlling [START_REF] Ning | Wang Maohao.Wireless sensors in agriculture and food industry-Recent development and future perspective[END_REF][START_REF] Wei | Status quo and progress of data transmission and communication technology in field information acquisition[END_REF]. In WSNs, it's characterized by realization the transmission of signals by radio waves, while radio waves will be affected by the surrounding environment in the transmission process [START_REF] Yick | Wireless sensor network[END_REF]. So radio propagation is affected by the barrier of crops in the agriculture environment. Therefore, propagation characteristics of wireless channel in agricultural environment has attracted considerable attention, and the research in the propagation characteristics of wireless channel in the typical agricultural environment such as wheat [START_REF] Liu | Performance assessment of short-range radio propagation in crop fields[END_REF][START_REF] Siyu | Impact of antenna height on propagation characteristics of 2.4 GHz wireless channel in wheat fields[END_REF][START_REF] Zhen | Path-loss prediction for radio frequency signal of wireless sensor network in field based on artificial neural network[END_REF] 、 apple [START_REF] Andrade-Sanchez | Performance assessment of wireless sensor networks in agricultural settings[END_REF][START_REF] Xiuming | Propagation characteristics of 2.4 GHz wireless channel at different heights in apple orchard[END_REF] 、 citrus [START_REF] Tao | Test of wireless sensor network radio frequency signal propagation based on different node deployments in citrus orchards[END_REF] 、 durian [START_REF] Phaebua | Statistical characteristic measurements of propagation in durian orchard for sensor network at 5.8 GHz , Communications and Information Technologies[END_REF] and plum [START_REF] Vougioukas | Influence of foliage on radio path losses (PLs) for wireless sensor network (WSN) planning in orchards[END_REF] was conducted.

In recent years, as an important content of modern agricultural development, the protected agriculture has developed rapidly, and China has become the largest area country of the protected agriculture. At present, the protected agriculture is developing to the direction of standardization, automation, intelligence and network [START_REF] Pingping | Research Progress of Intelligent Management for Greenhouse Environment Information[END_REF]. The related research has been conducted in the aspect of wireless measurement and control of the greenhouse environment [START_REF] Zhongfu | GPRS and WEB based data acquisition system for greenhouse environment[END_REF][START_REF] Song | Design of wireless sensor network-based greenhouse environment monitoring and automatic control system[END_REF][START_REF] Park | A study on greenhouse automatic control system based on wireless sensor network[END_REF][START_REF] Park | Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention[END_REF][START_REF] Li | Design of greenhouse environment monitoring and controlling system based on Bluetooth technology[END_REF][START_REF] Li | A monitoring system for vegetable greenhouses based on a wireless sensor network[END_REF][START_REF] Ping | Intelligent measurement and control system of facility agriculture based on ZigBee and 3G[J[END_REF]. The plastic greenhouse is mainly used for insulation of autumn winter and early spring to realize cultivation in the early spring and lateautumn, which is widely distributed in the south area of china. In the plastic greenhouse, the transmission of radio waves are affected by planting environment and coverings, for the complex environment , so the plastic greenhouse is the typical application environment with obstacles . In this paper, the plastic greenhouse as the research object, research of propagation characteristics of radio waves between and in plastic greenhouse were study in order to provide basis for the layout of wireless sensor network in the large-scale plastic greenhouse groups.

Experiments and Methods

The experiment test systems

The experiment test systems were shown in Fig. 1, which mainly consisted of receive and send node、 USB debugging simulator、PC(Personal Computer)、data packet analysis software Packet Sniffer、 tripod and tape, etc.

Fig. 1. Experiment test systems

The function of receive and send node was to receive and send radio signals, and the sensor node CC2430 was used as receive and send node. During the testing, the receive node was attached to PC through USB debugging simulator, fixed to the starting point, while the send node moved in sequence according to test points in the direction of test. Radio signals were extracted and analyzed through protocol analyzer software Packet Sniffer. The tripod provided the supporting point in order to keep the receive and send node at the same height, and the tape was used to measure the distance between the receive node and send node.

Experimental method

The test place was located in the vegetable planting center of Garden Village ,Danyang city Jiangsu province, and linked plastic greenhouse with the same specification were chosen as the experimental subjects, of which the length and width were respectively 70m and 7.6m,and the distance between the two sheds was 1.7m while the distance between the two separated by the road was 12m;The test of propagation characteristics of radio waves on the crop canopy were respectively conducted between and in the plastic greenhouse.

Experiments between the plastic greenhouse

In order to test the effects of propagation characteristics of radio waves. In plastic greenhouse, this paper designed the experimental scheme as shown in Fig. 2. The plastic greenhouse were numbered, and the left ones were the odd-numbered while the right were the even-numbered. When testing, the receive node was fixed at the plastic greenhouse NO.1, and the antenna height was 1m on the ground, and the node was attached to PC computer through USB debugging simulator, and it received signal strength and the number of packet dropout through software, The send node was fixed at the tripod, and the antenna height was also 1m ,which moved respectively in the odd-numbered and even-numbered plastic greenhouse, experiments were divided into 2 groups as shown in the following figure , and 3 test points were set in each plastic greenhouse. And the distance between receive and send node was obtained through calculation after testing.

Fig. 2. Experiment scheme

Experiments in the plastic greenhouse

Tomatoes planting in the plastic greenhouse were chosen as the experimental subjects, which was in the fruiting period, and the average canopy height was 80cm while the average row spacing was 60cm. In order to analyze the optimum deployment location for nodes, two groups of tests were conducted located in the ground and canopy and the height from the antenna height was 90cm when the nodes were located in the canopy. During testing, the receive node was fixed at the starting point, while the sender moved in sequence in the middle of the row until there were no signals.

Test parameters

(1) Received Signal Strength Index (RSSI) RSSI (Received Signal Strength Index) was used to judge the link quality, and in this experiments, the protocol analyzer software Packet Sniffer was used for extraction of signals.

(2) Path loss Path loss is the difference power between effective send radiations and received, which presents attenuation degree of signals, a positive value in dB. The study results [START_REF] Liu | Performance assessment of short-range radio propagation in crop fields[END_REF] show that path loss conform to logarithm path loss model, which can be expressed as:

d n A P r log 10   (1)

Results and Discussion

Propagation characteristics of radio waves between the plastic greenhouses 3.1.1 Effect on intensity of received signals

The RSSI changing with the different distance between receive and send node when radio waves transmitted between the plastic greenhouses was shown as Fig. 3. In the figure, effective propagation path of the two groups of tests was the same, and signal strength of the receive node tended to digression with the increasing distance between the receive and send node, and the strength of received signals decreased sharply when the distance between the receive and send node is smaller while the strength of received signals decreased gently when the distance between the receive and send node increased.

Fig. 3. Received signal strength varies with distance

The change of the test 1 and test 2 was the same, and signal strength of both test has the jumping phenomenon, and the data of test 2 was fluctuated obviously. For electromagnetic wave transmitting between the plastic greenhouse is a process of the combination of transmission and reflection, the penetrating power of electromagnetic wave was depended on electric characteristic of the substance, and the insulator will allow electromagnetic wave through with smaller loss, while the good conductor will block the most of radio waves, which accelerated the attenuation of electromagnetic wave. Although the surface of the plastic greenhouse is a layer of plastic with good insulation, the difference of humidity of each plastic greenhouse would change electromagnetic characteristics of the plastic, which leads to the difference of transmission loss in each greenhouse. On the another hand, the reflection of electromagnetic wave can lead to interference phenomenon in which field strength was promoted or down, which will aggravate the jumping phenomenon of the signal strength of electromagnetic wave in short distance. So the combined effect of transmission and reflection leads to fluctuation of signal strength of individual test point in test 1 and test 2.

Result analysis

A regression analysis was conducted of the value of tested RSSI between the plastic greenhouse in the path loss model by using the least square method , the value of the parameters A and n was obtained as shown in Table 1.The table showed that the relative coefficients 2 R of the test 1 and 2 were 0.943 and 0,916, which means it was reasonable to describe and predict the propagation characteristics between the plastic greenhouse using log-path loss model. The value of parameters of the two groups of tests were basically the same, and the difference of A and were respectively 1.25 and 0.054, which means the propagation characteristics of radio waves was the same in both cases.

Propagation characteristics of radio waves in the plastic greenhouse

Effect on the strength of received signals

The RSSI changing with the distance between the receive and send node in the plastic greenhouse was shown as in Fig. 4. The results showed that the strength of received signals both in the ground and canopy were decreased with the increasing distance between the receive and send node. The strength of received signals decreased sharply when the distance between the receive and send node was shorter, and the decay rate in the ground was sharper than in the canopy. When the strength of received signals declined to about -83dBm, the transmission distance was 15m in the ground and 39m in the canopy.

With the increasing distance of the receive and send node, the decay rate of the received signals in the ground and canopy tended to be gentle, and the transmission distance was 15m in the ground and 39m in the canopy .The transmission distance in the canopy was obviously longer than that in the ground, and the distance that could be tested was only 30m in the ground.

Result analysis

A regression analysis was conducted of the value of tested RSSI of tomatoes by using the least square method, and the value of the parameters A and n was obtained as shown in Table2.

The results showed that the relative coefficients 2 R in the ground and canopy were respectively 0.9699 and 0.9091, which means it was reasonable to describe and predict the propagation characteristics of tomatoes using log-path loss model. Attenuation index n in the ground was greater than that in the canopy, and the difference between the two location was 0.79, which meant decay rate of radio waves of sensor nodes located in the ground were better than that in the canopy. For the lower location of the tape when the sensor nodes were in the ground, the transmission of the radio waves were affected by leaves of tomatoes greatly, which made attenuation index increased. That also meant the location of nodes in crops decided the attenuation index of radio waves and was related to the transmission distance of sensor nodes.

Conclusions

This paper analyzed propagation characteristics of 2.4GHz radio waves between and in the plastic greenhouse, and conclusions were as follows:

(1)Propagation characteristics of radio waves between the plastic greenhouse conformed to log-path loss model, and the relative coefficients were respectively 0.943 and 0.916. The average value of decay parameters between the plastic greenhouse of A and n were 30.785 and 2.89, and decay index n was greater than that of free-space, which was caused by transmission loss of radio waves.

(2)Propagation characteristics of radio waves in the plastic greenhouse for cultivation of tomatoes conformed to log-path loss model, and the relative coefficients in the ground and canopy were respectively 0.9699 and 0.9091. Decay rate of radio waves was slower when sensor nodes located in the canopy. The sensor nodes should be deployed in the tomato canopy or above of it when the wireless sensor network was deployed in tomatoes.

rP

  is the average intensity of received signals, A is a parameter of regression analysis model, which presents the effect of application environment on the attenuation of the intensity of radio signals, d is transmission distance of signals; n is attenuation index, which presents decay rate of signal intensity.

Fig. 4 .

 4 Fig. 4. Received signal strength varies with distance

Table 1 . Regression parameters of log-path loss model

 1

	Test	A	n	R2
	Test 1	30.16	2.863	0.943
	Test 2	31.41	2.917	0.916

Table 2 . Regression parameters of log-path loss model

 2

	Location	A	n	R2
	Ground	34.99	3.64	0.9699
	Canopy	35.14	2.85	0.9091

Acknowledgment

Funds for this research was provided by the prospective research Foundation of Jiangsu province (BY2013065-07), the fund of Jiangsu University (14JDG188) the Priority Academic Program Development of Jiangsu Higher Education Institutions.


Zhe Liu
email: liuz@cau.edu.cn

Zhenhong Zhang
email: dzhangxd@cau.edu.cne

Shaolong Fu

, Xiaodongzhang

Dehai Zhu
email: zhudehai@cau.edu.cn

Shaoming Li
email: flshaoming@sina.com

Method and System of Maize hybridized Combination Based on Inbred SSR and Field Test

Keywords: :maize, breeding, heterosis, DNA fingerprint

Molecular breeding is considered an important way to improve the breeding efficiency. But due to the deletion of data、method and instrument, molecular design breeding is basically at the concept stage, without operational technology process and breeding practice. On the basis of breeding data from Beijing Kings Nower Seed S&T CO, LTD, this paper explored a set of methods for maize design breeding based on molecular detection and phenotypic testing information. Firstly, the parents of the combinations were obtained from the inbred lines with high homozygosity through SSR detecting and good comprehensive traits through field testing; secondly, the heterosis rate of the parents was got by calculating both the genetic differences and phenotypic differences of the parents according to the SSR detection results and field testing results respectively to express the special combining ability of their own; finally, in this paper it constructed the hybridization group model by using the comprehensive characters、special combining ability、orthogonal anti value, calculated the comprehensive characters、advantages、disadvantages of the hybridizations, and screened hybrid combinations to the next round field breeding,it also developed a software system of hybrid combination to support the technology route. Applying the software, 37 hybrid combinations resistance to Ralstonia solanacearum were got based on 179 inbred lines with molecular and phenotypic data. Thus, the method and software preliminary provides technical support for our country to carry out and perfect the molecular design breeding.

Introduction

Breeding output is a small probability event, only through large-scale investment can we convert the small probability event into an inevitable event, making the originally accident breeding output become predictable and designable. Due to the lack of proper methods and tools support, domestic breeding team cannot manage and control the large-scale breeding materials and data, and it is difficult to accumulate the breeding rules of selections and combination, which leads to it popular that combination with experience and there are a lot of uncertainty with the maize breeding output in China [START_REF] Jingrui | Science and Technological Innovation of Maize Breeding in China[J][END_REF] . Molecular breeding is considered an important way to improve the breeding efficiency. But due to the deletion of data、method and instrument, molecular design breeding is basically at the concept stage, without operational technology process and breeding practice. Based on this, this paper explored a set of design breeding method which included from the integrated management of maize varieties' molecular marker data and phenotypic data of field testing to maize inbred lines comprehensive evaluation, auxiliary screening. Besides, the design breeding method was also based on the heterosis analysis of inbred lines with molecular markers and phenotypic differences and the simulation distribution of maize hybrids and performance prediction.The paper also developed a software system of hybrid combination to support the technology route, providing methods and tools support to improve China's maize breeding scale and information management level.

2 Materials and Methods

1 Research technical route

In this paper, it calculated the parent value, yield and disease resistance characteristics, superiority and defect index for each variety to evaluate the screening inbred lines and calculate the phenotypic distance after making the inbred lines' phenotypic testing data be standardized; Similarly,it calculated the effect locis, heterozygosity index for each variety to evaluate the screening inbred lines and calculated the genetic distance based on the inbred lines' SSR molecular marker data; combined with phenotypic distance and genetic distance to establish the indices of selected parents' heterosis rate; combined the seed performance indices of selected parents such as flowering and height difference coordination to establish the indices of double cross and reciprocal value; finally, composited the comprehensive value of the parents, the hybrid advantage, reciprocal value, the yield and resistance characteristics to establish hybridization group model and predict the comprehensive value, superiority, defect degree, the yield and resistance characteristics of the hybrid combinations, then screened the hybrid combination meeting the demands. (The technical route was shown in figure1). SSR molecular markers are mainly used for identification of new germplasm and inbred lines heterotic group in maize breeding [START_REF] Xiangtuo | Molecular Markers and Maize Breeding[J[END_REF][START_REF] Changsheng | Genetic diversity analysis of maize verities based on SSR markers[J][END_REF][START_REF] Song | QTL mapping of kernel oil concentration with high-oil maize by SSR markers[END_REF] . Molecular marker is a direct reflection of genetic polymorphism on DNA level, the earliest molecular markers developed were RFLP and then RAPD、 AFLP、 ISSR、 SSR, which were based on PCR, SNP were developed in recent years based on single base mutation. Simple sequence repeat (SSR) markers have been widely used because of its advantages, such as simple, fast, high repeatability, high polymorphism and codominant markers.Li Xue et al. research showed that the differences between SSR and SNP two kind of markers in data integrity, ability to distinguish among species and site stability was samall.A in another study it showed that both RAPD and SSR two kind of markers were suitable for the study of genetic diversity on maize germplasm, but SSR was more desirable.

Detection technology of SSR molecules is an acceptable cost of gene sequencing technology [START_REF] Pegic | Comparative analysis of genetic similarity among maize inbred lines detected by RFLPs, RAPDs, SSRs, and AFLPs[END_REF][START_REF] Enoki | SSR analysis of genetic diversity among maize inbred lines adapted to cold regins of Japan[END_REF][START_REF] Xia | Genetic diversity among CIMMYT maize inbred lines investigated with SSR markers[END_REF][START_REF] Kumar | Genetic diversity of maize genotypes on the basis of morpho-physiological and simple sequence repeat(SSR) markers[J][END_REF] for domestic seed companies; therefore, Kings Nower Seed S&T CO., LTD. began to establish detection department of molecular based on SSR from 2008, which provided data support to this study. In this paper, the SSR molecular marker data and the phenotypic test data were applied to the estimation of genetic differences between the hybrid parents, and inspected the comprehensive development value of hybrid combination from these aspects such as genetic differences, combining ability, major defects, the advantage performances, the results were applied to hybrid combinations.

Standardization and evaluation of inbred lines field identification data

The standardization of inbred lines field identification data refers to the transform process of field data to relative value using the pilot average as the

Code of inbred lines

Primer

1 Primer2 Primer3 Primer4 Primer5 A01 - - a - - B01 A b a a a
reference frame. The inbred lines usually use the absolute value or relative value of data to describe in different environment. The absolute value of data generally are obtained through direct observation to the indices, the dimension used is the independent reference system which has nothing to do with the specific observations. If the recorded data do not mark the data unit, the absolute value of data will be meaningless. The relative value of data take the data recorded in the general order or associated with the overall statistics as reference. Since rich in information and easy to undersatand, the relative value is the main evaluating method in the inbred lines evaluation. Liu Zhe [START_REF] Liu Zhe | Optimal method of transforming observables into relative values for multi-environment trials in maize[END_REF] proposed 5 kinds of relative value transform methods of variety test data such as the 3s and 2s conversion value taking group mean as the reference value , standard of value, the class NDVI value taking the sample mean as the reference value and so on, and used the "standard deviation ratio" to analysis the transform effect of the 5 methods, the analysis results indicated that the 3s and 2s conversion value taking the group mean as reference were more close to the genetic characteristics of the varieties. This paper adopted the method to transform the inbred lines identification data of which the type was numerical to relative value.

When the inbred lines identification data were normalized, it calculated the production, seeding, earning, economic value, disease resistance, insect resistance, lodging resistance, growth period and plant panicle characteristics and the comprehensive value, and used them to calculate the phenotypic distance of inbred lines selected.

The comprehensive value of inbred lines is a comprehensive study to the inbred lines field observation value, which is calculated by the following formula:

* k k k v w HD w    (1)
In the formula(1), k v is the standard value of the kth field observation index value, k w is the weight of the kth field observation index value.

To evaluate the advantages and disadvantages of the inbred lines and hybrid combinations, this paper designed the defect degree and excellence degree two indices on the basis of the existing observed phenotypic traits.

Defect degree and excellent degree are the statistics of inbred lines (or hybrid combinations)whose field observation indices perform "very good", "good", "bad", "poor". The statistics are the reached degree of the field identification index, followed by "very good", "good, "bad, "poor" to calculate the weighted average income in a ratio 3:2:2:3 of attached weights. The "very good" standard means that the value of the hybrid combination in a certain index is more than the average value of all combinations in the index pulsing 2times the standard deviation, then we regard the combination performing "very good" in the index. While if the hybrid combination in a certain index is below the average value of all combinations in the index subtracting 2times the standard deviation, it will be regarded performing "poor" in the index. Among them, the standard deviation multiple of which "very good", "good", "bad", "poor" can be set by software. Then the inbred lines were screened by using the difference, defect, inbred lines output resistance characteristics and comprehensive value.

Treatment and evaluation of inbred SSR data

The effective sites, hybrid bands and heterozygous ratio for each inbred line can be calculated based on the inbred lines SSR molecular marker data. The number of the effective sites refers to the n loci of the selected SSR data, of which having the value is effective site; Generally the measured SSR data of inbred lines are homozygous, which means each site should be the same value, but actually it may not be homozygous; Heterozygous brand refers to how many different values in the n sites, and it will be the number of the heterozygous brand; Heterozygous ratio refers to the number of the heterozygous brand/n. These three indicators are used to judge the pure degree of the inbred lines, screen the inbred lines which have higher pure degree, more effective sites into hybrid combination, and then calculate the genetic distance of the inbred lines selected.

Calculation of parent heterosis rate

The additive gene effect is the main factor of genetic effects of quantitative characters in maize, the molecular marker data and phenotypic value of the field test data are approximate value of the gene additive effect, both of them contain some information of additive effects, at the same time the information may be complement to each. If combine the information of these two parts to predict the comprehensive heterosis of the single cross hybrid F1 generation, it is expected to get higher choosing efficiency. Specifically to say, to show the phenotypic differences of hybrid parents we use phenotypic data to calculate the phenotypic distances, to show the genotype differences we use the DNA fingerprint data to calculate the molecular marker genetic distances, both of them do not completely overlap, which describe the genetic differences of parents from different angles. This paper used a parent heterosis(MD) to generalize the genetic differences of the parents, which was written as linear equations of the phenotypic genetic distance and SSR molecular marker genetic distance:

GD r PD r MD * 2 * 1   (2)
Among them, 1 2 1 r r   , PD refers to the phenotypic difference of the parents, GD refers to the genetic difference of the parents,R1 and R2 refers to the weight of phenotypic distance and genetic distance. 2.5.1 Algorithm of molecular marker genetic distance Genetic distance is the index that measures the size of some characteristics of the comprehensive genetic differences among varieties. More than one traits are required in the breeding target, in order to be able to more fully reflect the genetic differences between the parental varieties, multiple traits need to be comprehensively considered, thus will extend the concept of genetic distance. Calculate the multidimensional geometric distance consisted of multiple traits according to the method of multivariate statistics analysis, called genetic distance. There are mainly in two ways to calculate the DNA fingerprint genetic distance:

m 1 GD m n    (3)
M denotes the number of same bands between the two varieties, n donates the number of different bands between the two varieties.

2

1 ij i j N GD N N    (4) ij N
denotes the number of same bands between the two varieties(lines), i N and j N respectively denotes the band number of their own in i and j varieties(lines). The differences between two kinds of molecular markers in genetic distance calculation are as flowing: 1)When there are invalid detection sites at least in one of the both detection sides, the calculated results of formula (4) are more conservative than formula (3), which means the method 2 tends to judge the two sides involved in the calculation have a genetic difference. For example, for the DNA fingerprint data shown in Table 2, the invalid sites do not participate in calculation, the GD value is 0 according to formula (3), meaning that the genetic basis of inbred line"A01" and "B01" are very close, almost the same; the GD value is 0. 67 according to formula (4), apparently it is more conservative using formula (4) to calculate.

2)Formula ( 4) is suitable for the parents' fingerprint data that are not using the same primer combinations to detect. If the fingerprint data are obtained using different primer combinations to detect, you cannot determine the different bands of varieties(lines) to be detect(i.e n in formula (3)).

Considering the two points above, this paper used formula (4) to calculate the molecular marker genetic distance.

Calculation of the phenotypic distance

It makes principal component analysis to quantitative traits in the conventional phenotypic genetic distance calculation, after which uses the Euclidean distance to calculate the comprehensive index. The essence of the principal component analysis is based on the multivariate statistical method, extracting a few comprehensive indices as the distance analysis dimensions, and uses the index contribution rate to distribute weights of the comprehensive indices. Its purpose is to reduce the dimension so as to simplify the problem, and the weight distribution is just the result of the calculation. The disadvantages are:

1) It will spend a lot of time to calculate multiple inbred lines, the more the data of inbred lines, the bigger the calculation scale, which is not suitable for real-time calculation in software.

2) It only considers the genetic differences of quantitative traits, ignoring the genetic differences of varieties (lines) in quality traits.

3) There is no clear meaning of the comprehensive index extracted by principle component analysis.

In order to calculate the phenotypic genetic distance of both quantitative traits and quality traits, and be easy to soft program, combining with the data standardization processing described in this article, we adopted the following calculation:

  2 2 * * ik jk k ij k V V w PD n w     (5)
Among them, ij PD is the phenotypic distance of inbred lines i and j, ik V and jk V respectively denote the standard value of inbred lines i and j in index k, k w is the weight of kth index, n is the effective index number. In this formula, the quality traits related to breeding after numerical processing are also used to express the phenotypic differences among inbred lines. Because the index weight can be configured flexibly, the data do not need to go through the orthogonal transformation and calculate the principal component values, thus the cost of the system decreases, and the calculation goes fast.

Calculation of reciprocal value

Reciprocal value mainly considers the special traits' combining degree of the parents, the parents in these characters are not the closer the better but with certain differences can play a greater role for hybrid seed production or maximizing the heterosis. These special traits are such as plant height-ear position difference of the parents, Anthesis-silking interval (ASI). This paper calculated the orthogonal value and reciprocal value respectively, choosing the larger of them as the exchange value of crossbreds. Orthogonal value(reciprocal value) is calculated as flowing:

1 2 *( ) *( ) ij i j i j V HD r t t r h h      (6)
Among them, HD is the comprehensive value of the inbred lines, ij V is the orthogonal of the parents both i and j, ( , )

ij ji SV Max V V  (7)

Hybrid group model and evaluation and screening of the combination

Hybrid combination value (HV) is a comprehensive index which is used to describe the development value of the hybrid combination, this paper used the dear in heterosis rate(MD), the average of the parents' comprehensive value( HD ) and the reciprocal value(SV) to denote the value of hybrid combination. The average of the parents' comprehensive value( HD ) is calculated by (the male comprehensive value + the female comprehensive value)/2,which is HD. So the hybrid combination value is calculated as following, where r is the weight.

* HV MD HD r SV

   (8)
3 Realization of the system and case analysis

The screening results of the SSR fingerprint information

Screening can be carried out only after put the SSR fingerprint into database and complete the eigenvalue calculation: the example in this paper screened according to the heterozygous ratio >=0.1 or the number of the effective site<=18 or the number of mixed band>=2. According to the screening conditions 94 inbred lines were removed (The screening condition interface is shown in(Fig. 2): Fig. 2. The SSR fingerprint screening conditions of the inbred lines

The screening results of the parental phenotypic traits

The screening of the parental phenotypic traits consists of three steps: 1) According to the indices of defect degree and excellent degree in the "very good", "good", "bad", "poor"4 categories of computing standard, it calculated the category each inbred lines belonged to for each of the phenotypic traits, and then calculated the defect degree and excellent degree of each inbred lines, and sorted them, then screened the inbred lines before 50% to enter the next step.

2) This paper aimed to prepare the hybrid combination resistance to Ralstonia solanacearum, this step according to the performance of each inbred line' resistance to Ralstonia solanacearum, we screened the inbred lines which were "very good " to enter the next step.

3) According to the formula 1 which calculated the comprehensive evaluation index, we got the comprehensive value of each inbred line and sorted them, then screened the top 50% to enter the final hybrid group. According to the SSR molecular marker data and phenotypic testing data, 78 inbred lines were got for combination after a quality of indices screening.Then these 78 inbred lines would be combined each other.

The results of hybridization group and combination screening

78 inbred lines were got according to the inbred lines SSR data and phenotypic data screening.For each hybrid combination in this paper, it calculated the heterosis rates using formula2, and calculated the orthogonal value or reciprocal value using formula 6, and calculated the comprehensive value using formula 8, then sorted the hybrid combinations by the comprehensive value(the sorting interface was shown in Figure 3 below).

Fig .3.The interface of hybrid combination order

Due to the field identification data of the inbred lines only contained 2009 one year's data in the case, the number of test points was a little less and its reliability was less higher than DNA fingerprint data, so we gave the molecular marker genetic distance a bigger weight. On the reciprocal cross(row-column, column-row)value of the parameter setting, this paper thought that the elevation difference was more important than spin-powder interval, thus gave the row-column flowering, column-row height difference respectively the weight of 0. 05 and 0. 1. According to the hybrid combinations screening conditions, finally we got 37 hybrid combinations. (partial results were shown in Table 3) 3 is corresponding to Fig. 3, which is the result calculated in the software system. "Name combination" refers to the hybrid combination who are their parents, such as "A11&A12" is the parents of the first hybrid combination, because the maize varieties need to keep secret, it just uses A11、B11 to substitute in the paper."order" refers to the hybrid combinations' rank."combination value" describes the development value of the hybrid combination, which is calculated by formula 8 in the paper. "very good, good, bad, poor" refer to the defect degree and excellent degree of the combinations. "comprehensive value" refers to the comprehensive advantages or distanvages of the combinations. "heterosis" refers to the additive gene effect, the bigger the better. "average molecular marker genetic distance of the parents" refers to the parent heterosis(MD). "special combining ability" refers to the productivity and other characters of the combinations. "Molecular marker genetic distance" measures the size of some characteristics of the comprehensive genetic differences among varieties, calculating by formula 4. "phenotypic distance" refers to the calculation of the quantitative traits. "row-column value, column-row value" refers to reciprocal value. "row-column flowering, column-row flowering, row-column height difference, column-row height difference" refer to the seed performance indices, aiming to establish the indices of double cross and reciprocal value.)

Discussions

In this paper, a kind of hybrid combination technology and method on maize was proposed, in which the main idea was:

Based on the SSR molecular detection results and field test results of current or previous years, first, the paper screened the inbred lines with high homozygote through SSR detection results, then it screened the inbred lines with good comprehensive traits as the parents of the combination; then it calculated the genetic differences of both of the parents each other according to the SSR detection results, similarly, it calculated the phenotypic differences of both of the parents each other according to field testing results, thus we could calculate the heterosis rate of the parents to express their special combining ability; in the last according to the comprehensive characters, special combining ability, orthogonal anti value, the paper constructed the hybridization group model, and calculated the comprehensive characters, advantages, disadvantages of the hybridizations which the parents combined with each other and screened hybrid combinations to the next round field breeding.

With the combination technology methods applied, this paper simulated combined 37 hybridizations resistance to Ralstonia solanacearum, but the distribution group only based on one year's performance data of the parents. Whether the combination had good anti-bacterial blight resistance and comprehensive performance, it also needed hybrid combinations to prove with multiple years and points testing.

In this paper, the software system was developed suitable to the combination technology method, which supported the management of a quality of SSR molecular marker data, field testing data together with the calculation of massive indices, formulas and models, but the efficiency still needed to be improved.

It was also a molecular design breeding practice carried by the Beijing Kings Nower Seed S&T CO., LTD.There were some key technological differences here compared with traditional group technological process: First, it did not consider the selection process of inbred lines, but directly determined the parents' selection, simulated distribution group through the molecular and phenotypic characters of the inbred lines. Second, in this paper it did not use the genetic background of inbred lines, genealogical relationship and other information, especially when calculating hybrid heterosis rate it was only based on genetic distance and phenotypic distance without analyzing group relationship of each inbred line.Thus, it was likely to emerge the phenomenon that the parents had a high heterosis rate but belonged to the same group, which did not match the actual combining ability. Third, the phenotypic information of the inbred lines were obtained through multiple years and points field identification, but at present the domestic breeding institutions have not regarded the inbred lines multi-point identification as a conventional breeding work, mainly inferred the possible performance of the parents through measuring the combination process. Fourth, there was a good effect on classification using inbred lines SSR data [START_REF] Senior | Utility of SSRs for Determining Genetic Similarities an Relationships in Maize Using an Agarose Gel System[END_REF][START_REF] Reif | Use of SSRs for establishing heterotic groups in subtropical maize[END_REF][START_REF] Wende | Genetic interrelationships among medium to late maturing tropical maize inbred lines using selected SSR markers[END_REF] , but because this method did not do division group to inbred lines, so it was difficult to increase measurement-matching through selecting test species , which may have an effect on the simulated group. It was the focus of the method in next step.

Conclusions

In this paper, it made some new attempts which were as follows: Firstly, in the method, it first attempted to calculate using SSR fingerprint data together with phenotypic data, in this way more information about the inbred lines were considered to get good maize hybrids.

Secondly, it attempted to get the phenotypic information of the inbred lines through multiple years and points field identification, but at present the domestic breeding institutions mainly inferred the possible performance of the parents through measuring the combination process.

Thirdly, the paper explored a set of design breeding method which included from the integrated management of maize varieties' molecular marker data and phenotypic data of field testing to maize inbred lines comprehensive evaluation, auxiliary screening. Besides, the design breeding method was also based on the heterosis analysis of inbred lines with molecular markers and phenotypic differences and the simulation distribution of maize hybrids and performance prediction. It also developed a software system of hybrid combination to support the technology route, providing methods and tools support to improve China's maize breeding scale and information management level. Applying the software, 37 hybrid combinations resistance to Ralstonia solanacearum were got with 179 inbred lines' molecular and phenotypic data. The method and software preliminary provides technological support for our country to carry out and perfect molecular design breeding.

Fig . 1 . 2 . 2

 122 Fig .1. The technology roadmap of maize hybrid breeding group based on inbred lines SSR and field test data

it

  is the ith parent's standard value at the anthesis stage, j t is the jth parent's standard value at the silking stage, i h is the plant height of the ith parent, j h is the plant height of the jth parent, j h is the ear position height of the jth parent, 1 r and 2 r respectively denote the important weight of spinning-powder interval and plant height-ear position difference. The calculation of reciprocal value is as flowing:

Table

Table 1 .

 1 The field identification standard data of the 482 inbred lines(the recoreded indices Of the inbred lines)

	Pilot name	Cell number	Sowing date	Emergence date	Total number of emergence
	Gongzhuling	CZ10001	2010/5/7	2010/5/21	43
	Gongzhuling	CZ10002	2010/5/7	2010/5/21	37

), of which 179 copies of materials were analyzed by SSR molecular marker detection(Table2).

Table 2 .

 2 The primers of SSR molecular detection to the 179 inbred lines

Table 3 .

 3 The results of hybrid combination In this table, the results are ranked assending by combination value. Table

	Name combination	Order	Combination value	Very good	good	bad	poor	value	Comprehensive	heterosis	parents	distance of the	marker genetic	Average molecular	ability	special combining	genetic distance	Molecular marker	Phenotypic distance	Row-column value	Column-row value	flowering	Row-column	flowering	Column-row	difference	Row-column height	difference	Column-row height
	A11&A12 1 0. 94 2 13 7 0 0. 077 0. 58		0. 52		0. 20 0. 75 0. 24 0. 12 0. 03 -0. 15 -0. 20 0. 03 -0. 61
	B11&B12 3 0. 92 4 14 6 0 0. 073 0. 56		0. 46		0. 23 0. 65 0. 38 0. 13 0. 09 -0. 46 -0. 15 0. 27 0. 41
	C11&C12 7 0. 91 1 14 6 0 0. 051 0. 61		0. 56		0. 12 0. 75 0. 33 0. 14 0. 06 -0. 51 -0. 27 0. 15 -0. 85
	(

Acknowledgment

This work is supported by the Chinese Universities Scientific Fund (Method and Software of hybridized Combination for Maize, 2013XJ022), and the National Science-technology Support Plan Projects (Research and Demonstration of North China Corn Commercialized Breeding Technique, 2014BAD01B01) and Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture.


Yande Liu

Yuxiang Zhang

Haiyang Wang

Bingbing He

PhD Liu Yande

Quantitative Detection of Pesticides Based on SERS and Gold Colloid

Keywords: SERS, gold colloid, pesticide residue

The detection of pesticide residued in fruit is an important concern for consumers.

Surface enhanced Raman spectroscopy (SERS) coupled with gold colloid was applied to analyze two kinds of pesticides (phosmet, chlorpyrifos) which were mainly used on the navel orange. The concentration of the phosmet samples of range from 3 to 33 mg/L and chlorpyrifos samples of range from 4 to 34 mg/L. Using Partial least squares (PLS) regression and the different preprocessing method for the spectral data analyses, and different pretreatment methods such as the Savitzky-Golay were compared. The optimal model of phosmet pesticide and chlorpyrifos pesticide were set up. The prediction correlation coefficient (R) and the root mean square error of prediction (RMSEP) of phosmet pesticide were 0.924 and 4.293 mg/L; The R and RMSEP of chlorpyrifos pesticide were 0.715 and 6.646 mg/L. It indicated that SERS technology is a effective method in the field of pesticide residue detection in fruit.

Introduction

SERS is an ultrasensitive testing technique, and It has achieved notable achievements in the last years in some areas such as food safety inspection [1][2], environmental monitoring [3], materials science, surface science, biomolecular Sensing [4] and analytical chemistry. With the photons as probes, SERS has the advantages of high sensitivity, good selectivity, non-destructiveness, and not require vacuum conditions. Now, SERS has become a widely used method of analysis. Organophosphosphate(OP) pesticides has been used for decades to protect the growth of crops, and made a great contribution to ensure the agricultural production and famers' income [5]. However, with the increasing scale of pesticide use, agricultural products with the pesticide residues have a negative impact on human health.

The common methods used to detect the pesticides residues in agricultural products are mature, such as Gas Chromatography (GC) [6,7], High Performance Liquid Chromatography (HPLC) [8][9][10], and etc. Although these methods have good accuracy, their sample pretreatment process is complex, time-consuming and testing cost is high. SERS has the advantages of faster, convenience, high sensitivity and nondestructive analytical method for determination of fruit pesticides. And the Raman analyses could be used in liquids [11][12][13]. For the past few years, SERS, which is a promising technology for the residues detection, has attracted much interest by researchers from the entire world. The theory of SERS is putting probed molecules onto the roughened surface of transition metals, enhancing the Raman signals more than one million times because of the chemical enhancement and electromagnetic field enhancement [14]. The strength of SERS depends on the interaction between analyte and Nanostructures substrate surface, and the most typical substrate is gold (Au), silver (Ag) and copper (Cu) [15]. Guerrini et al [16](2011) obtained the SERS spectra of dimethoate (DMT) and omethoate (OMT) by aggregated Ag hydrosols, and The result showed that the detection limit of DMT was 10 -5 mol/L. Dhakal et al [17](2014) collected the Raman spectroscopy of organophosphorus (chlorpyrifos) pesticide. Detection limit for the chlorpyrifos residue in apple surface of the developed system was 6.69 mg/kg . The purpose of this study was to discuss the feasibility of choosing Gold Nanostructures as SERS substrates for qualitative and quantitative analysis of fruit residues. Ganan navel orange was selected as the object of research. Choose phosmet and chlorpyrifos as the experimental subject. Spectral data collected were qualitative and quantitative analyzed. Nowadays, there are many reports on the application of using Gold Nanostructures as SERS Substrates in the field of pesticide residues, and the goal was to detect and characterize pesticide residues using Gold Nanostructures as SERS substrates.

Experiments

Materials

Two OP pesticides (phosmet and chlorpyrifos) were purchased from AccuStandard, Inc. HAuCl 4 • 4H 2 O was purchased from Sinopharm Chemical Reagent Co., Ltd. NaCl, acetonitrile, methanol and sodium citrate were analytically pure, and purchased from Nanchang huake Instrument Co. Ltd.

Sample Preparation

Pure pesticide solutions: 5000 mg/L of phosmet and chlorpyrifos solutions were prepared by methanol and H 2 O (1:1, v/v).

Oranges were cleaned repeatedly with ultrapure, and dry naturally. Clean the orange skin and Cut it into square pieces (2cm×2cm), and a certain amount of standard pesticide solution of phosmet and chlorpyrifos were added on it. After waitting pesticides air-dry, the skin was grated and added to an acetonitrile solution. We could get the solution of pesticide after filter it. The 31 different concentrations of phosmet range from 3 to 33 mg/L at 1 mg/L interval. The 31 different concentrations of chlorpyrifos range from to 34 mg/L at 0.5 mg/L interval.

SERS Measurement

The SERS substrates of Gold Nanostructures were prepared by sodium citrate reduction method. The pesticide samples, colloidal gold and sodium chloride were mixed based on a certain volume ratio (10:3:3), shocked evenly on the shock tester. Mixed solution was put on the quartz plate, and collected SERS of it.

Data Analysis

The data analysis used Unscrambler v8.0 software. Pesticide original spectral data were processed by partial least squares (PLS) and the different pretreatment methods. The prediction model was established between the Raman spectrum and pesticide content using different modeling algorithms (PLS, PCR), and the effect of models was analyzed and evaluated using the following indicators:

         n i i n i i i y y y y R 1 2 1 2 - - ˆ   1 - - 1 2 n y y RMSEC n i i i    ˆ   1 - - 1 2 n y y RMSEP n i i i    ˆ
In these equations, n is the number of samples, y i is the reference pesticide concentration (mg/L), i y ˆis the predicted pesticide concentration (mg/L), y is the average of the reference pesticide concentration (mg/L).

The R, RMSEC and RMSEP were used to evaluate the model. In the same concentration range, if the R value is higher and the RMSEP value is lower, the better predictability the model will get.

Results and discussion

Characteristic Raman shift of phosmet, chlorpyrifos

The chemical structures of phosmet and chlorpyrifos are quite similar (Fig. 1). Raman spectra of the phosmet powder were obtained, as shown in Fig. 2. The very strong peak at 650cm -1 is the P =S stretching vibration; the strong peak at around 501cm -1 , 605cm -1 , 1189cm -1 , 1774cm -1 is attributed to CH 3 torsional vibration, C=O in-plane deformation, P-O-CH 3 out-of plane deformation and C=O stretch, respectively; the weak peak at around 1013 is attributed to P-O-C deformation [18,19].

Raman spectra of the chlorpyrifos powder were obtained, as shown in Fig. 3. The very strong peak at 631 and 678 cm -1 is the P=S stretching vibration [20]. The peak at 1569 cm -1 is the C=C stretching. The peaks at 160 and 1240 cm -1 is the P-O vibration and ring breathing.

SERS spectra of phosmet and chlorpyrifos

Raman spectrum has fingerprint characteristics, namely the spectrum of each substance has a specific peak. Its characteristic peak should be identified before the quantitative analysis about this two pesticides. With the increase of phosmet content in solution, the corresponding peak intensity was rise accordingly as shown in Fig. 4. In addition to differences in signal intensity, the peak shape and peak value remained the same. According to the peak position, the main characteristic peaks of phosmet was at 501cm -1 , 605cm -1 , 798cm -1 , 1013cm -1 , 1189cm -1 , 1774cm -1 . Compared with the Raman spectra of phosmet solid, the SERS spectra of phosmet solution were not obvious peak appeared at 650cm -1 , and the intensity of peak vibration at 1013cm -1 , 1189cm -1 , 1774cm -1 also significantly decreased in phosmet solution.

Fig.4 Contrast of phosmet in different concentrations

Using gold colloid as SERS substrates, enhanced spectrum of chlorpyrifos in orange skin was collected, the average SERS spectrum as shown in Fig. 5, and the spectral range was 400cm -1 ~ 1800cm -1 . Compared with SERS spectra of chlorpyrifos and the Raman spectra of chlorpyrifos solid, as shown in Fig. 6, there were enhanced effect in multiple bands using gold colloid. The intensity of peak vibration at 970cm -1 , 1013cm -1 , 1164cm -1 , 1443cm -1 as shown in Fig. 6, and the spectrum peaks belonging as shown in table 1. With the increase of chlorpyrifos content in solution, the corresponding peak intensity was rise accordingly. Some peaks displacement of SERS spectra of solution changed in chlorpyrifos pesticide residues solutions, but the drift range was less than 10 cm -1 and not affect the accuracy of the model.

SERS spectral pretreatment of phosmet and chlorpyrifos

The SERS spectra of phosmet samples ranged from 3 mg/L to 33 mg/L was collected by confocal Raman spectroscopy using gold colloid as SERS substrate. The original spectra data were pretreated by Savitzky-golay smoothing, baseline processing, first derivative, second derivative and multiple scatter correction etc. The quantitative analysis mode of phosmet was built by PLS, in which 24 samples for calibration set and 7 samples for prediction set. Compared six different pretreatment methods, the models were evaluated by Rp and RMSEP as shown in table 2.

The best prediction model was achieved with Rp of 0.924 and RMSEP of 4.293 mg/L, with the Savitzky-golay smoothing and 2nd derivative data preprocessing.

Building model of phosmet

The original spectral data were processed by different modeling algorithms combined with the savitzky gold smoothing and second derivatives data preprocessing. The analysis model of Raman spectroscopy and phosmet content was established using PLS and principal component regression(PCR), the prediction effect of the model was evaluated by R P and RMSEP, as shown in table 3. The value of factor lower resulted in that the information of model was not complete, and the predictive ablity of model was lower; on the other hand, the value of factor higher resulted in that the model was too complex, and fitting phenomenon appeared in the training. As shown in Fig. 7, when the factor is 6, the RMSEP of PLS model is minimum. The PLS model predicted values of chlorpyrifos concentrations were showed in Fig. 8.

SERS spectral pretreatment of chlorpyrifos

The SERS spectra of chlorpyrifos samples ranged from 4 mg/L to 34 mg/L was collected by confocal Raman spectroscopy using gold colloid as SERS substrate. The quantitative analysis mode of chlorpyrifos was established by PLS, in which 23 samples for calibration set and 8 samples for prediction set. Table 3 shows the results of the models with different preprocessing methods obtained by PLS regression. The optimal model of chlorpyrifos pesticide was Rp of 0.715 and RMSEP of 6.646 mg/L, with the baseline and MSC data preprocessing.

Building model of chlorpyrifos

The original spectral data were processed by different modeling algorithms combined with baseline and MSC data preprocessing. The analysis model of Raman spectroscopy and chlorpyrifos content was established using PLS and PCR, the prediction effect of the model was evaluated by R P and RMSEP, as shown in table 4. As shown in Fig. 9, when the factor is 13, the RMSEP of PLS model is minmum. The PLS model predicted values of chlorpyrifos concentrations were showed in Fig. 10.

Conclusions

Two kinds of pesticides (phosmet, chlorpyrifos) can be quantitatively measured and distinguished using SERS coupled with gold colloid. Pesticide residues on the surface of orange could be measured and the model has a good effect in the qualitative analysis of their SERS spectral data. It indicated that determination of pesticide residues using SERS coupled with gold colloid is feasible, compared the SERS method and other chemical detection methods such as GC and HPLC. But two kinds of pesticides are not enough for determination of pesticide residues, so we need to rich the kinds of pesticides.

Fig. 3

 3 Fig. 1 Chemical structures of phosmet and chlorpyrifos

Fig. 6

 6 Fig.5 Average Raman spectra of chlorpyrifos sample

FactorsFig. 7 Fig. 8

 78 Fig.7 Determination of principal factor number for detection of phosmet content

Fig. 9 Fig. 10

 910 Fig.9 Determination of principal factor number for detection of chlorpyrifos content

Table 1 Band assignments of major peak in Raman spectra acquired from chlorpyrifos

 1

	Band (cm -1 )	Assignment
	160	P-O vibration
	341	N-cyclopropyl bending vebration
	411	C-Cl stretch
	631	P=S
	678	P=S
	1102	P-O-C stretch
	1240	Ring mode
	1277	Ring mode
	1453	C-H deformation
	1569	Ring stretching mode

Table 2 Comparison results for phosmet solution

 2

			Calibration Set		Prediction Set
	Preprocessing method
		R C	RMSEC(mg/L)	R P	RMSEP(mg/L)
	Savitzky-golay smoothing	0.897	3.726	0.835	4.705
	Base line	0.898	3.706	0.827	4.738
	1st derivatives	0.968	2.120	0.873	4.754
	2nd derivatives	0.966	2.173	0.883	4.662
	MSC	0.890	3.853	0.665	6.017
	S. G smoothing+ 2nd	0.988	1.254	0.924	4.293

Table 3 Modeling comparison results for phosmet pesticide residues

 3

				Prediction set
	modeling alorithm	PCs
			R P	RMSEP(mg/L)
	PCR	8	0.893	4.527
	PLS	6	0.924	4.293

Table 3 Comparison results for chlorpyrifos solution

 3

			Calibration set	Prediction set
	Pretreatment method	PC	RMSEC	RMSEP
		R C	R P
			(mg/L)	(mg/L)

Table 4 Modeling comparison results for chlorpyrifos pesticide residues

 4

	Modeling algorithms	PC	R C	RMSEC (mg/L)	R P	RMSEP (mg/L)
	PLS	8	0.989	1.273	0.715	6.646
	PCR	13	0.941	2.981	0.780	6.006

Acknowledgments: This project is supported by the National Natural Science Foundation of China (31160250, 61178036), the National High Technology Research and Development Program of China (863 program) (no.2012aa101906), East China Jiaotong University Legislation Research Fund (14JD01), and Key Laboratory of Carrying Tools and Equipment in Jiangxi.