Jinsong He
email: hejinsong@mail.tsinghua.edu.cn

Taihua Mu
email: mutaihua@126.com

Application of an Artificial Neural Network for Predicting the Texture of Whey Protein Gel Induced by High Hydrostatic Pressure

Keywords: Hydrostatic high pressure, Whey protein isolate, Gelation, Artificial neural network

The effects of high hydrostatic pressure (HP), protein concentration, and sugar concentration on the gelation of a whey protein isolate (WPI) were investigated. Differing concentrations of WPI solution in the presence or absence of lactose (0-20%, w/v) were pressurized at 200-1000 MPa and incubated at 30°C for 10 min. The hardness and breaking stress of the HPinduced gels increased with increasing concentration of WPI (12-20%) and pressure. Lactose decreased the hardness and breaking stress of the gel. Furthermore, these results were used to establish an artificial neural network (ANN). A multiple layer feed-forward ANN was also established to predict the physical properties of the gel based on the inputs of pressure, protein concentration, and sugar concentration. A useful prediction was possible, as measured by a low mean square error (MSE < 0.05) and a regression coefficient (R 2 > 0.99) between true and predicted data in all cases.

Introduction

Whey is a by-product of cheese manufacturing and contains about 13% protein by dry weight. Whey proteins possess outstanding physicochemical properties in gelation and binding, making them widely used as functional ingredients in many formulations of bakery, dairy, and sausage products [START_REF] Kinsella | Proteins in whey: chemical, physical, and functional properties[END_REF]. The major constituents of milk whey protein are β-lactoglobulin (β-Lg), α-lactalbumin (α-La), bovine serum albumin (BSA), and immunoglobulins [START_REF] Eigel | Nomenclature of proteins of cow's milk: fifth revision[END_REF]. Five kinds of industrial whey proteins are currently produced, four of which have a whey protein concentration (WPC) classified into four grades according to the protein content (35%, 50%, 65%, and 70-90%). A whey protein isolate (WPI) contains >90% protein on a dry weight basis [START_REF] Mulvihill | Production, functional properties and utilization of milk protein products[END_REF].

One of the functional properties of whey proteins is gelation, which is induced by heat treatment [START_REF] Mulvihill | Whey proteins and their thermal denaturation-a review[END_REF][START_REF] Mulvihill | Gelation characteristics of whey proteins and βlactoglobulin[END_REF][START_REF] Errington | Factors determining fracture stress and strain of finestranded whey protein gels[END_REF], although other factors such as salt addition [START_REF] Barbut | Calcium-induced gelation of preheated whey protein isolate[END_REF], acidification [START_REF] Mleko | pH induced aggregation and weak gel formation of whey protein polymers[END_REF][START_REF] Britten | Acid-induced gelation of whey protein polymers: effects of pH and calcium concentration during polymerization[END_REF], and enzyme treatment can cause gelation with or without heating [START_REF] Britten | Acid-induced gelation of whey protein polymers: effects of pH and calcium concentration during polymerization[END_REF][START_REF] Faergemand | Transglutaminase: effect on rheological properties, microstructure and permeability of set style acid skim milk gel[END_REF][START_REF] Faergemand | Cross-linking of milk proteins with transglutaminase at the oil-water interface[END_REF][START_REF] Wilcox | Immobilization and utilization of the recombinant fusion proteins trypsin-streptavidin and streptavidintransglutaminase for modification of whey protein isolate functionality[END_REF][START_REF] Wilcox | Modification of the rheological properties of whey protein isolate through the use of an immobilized microbial transglutaminase[END_REF]. Hydrostatic pressure has been shown to induce gelation in whey proteins under appropriate conditions [START_REF] Van Camp | A comparative rheological study of heat and high pressure induced whey protein gels[END_REF][START_REF] Van Camp | High pressure-induced gel formation of a whey protein and haemoglobin protein concentrate[END_REF][START_REF] Van Camp | High-pressure-induced gel formation of haemoglobin and whey proteins at elevated temperatures[END_REF][START_REF] Dumay | Characteristics of pressure-induced gels of β-lactoglobulin at various times after pressure release[END_REF][START_REF] Kanno | Gel formation from industrial milk whey proteins under hydrostatic pressure: effect of hydrostatic pressure and protein concentration[END_REF][START_REF] Kanno | Gel formation of individual milk whey proteins under hydrostatic pressure[END_REF][START_REF] Keenan | Mechanism of pressure-induced gelation of milk[END_REF]. The rheological properties of whey protein, i.e., storage modulus G' and loss modulus G" [START_REF] Van Camp | A comparative rheological study of heat and high pressure induced whey protein gels[END_REF], increase with increasing protein concentration. The gel strength also increases with increasing pressure holding time [START_REF] Van Camp | A comparative rheological study of heat and high pressure induced whey protein gels[END_REF][START_REF] Van Camp | High-pressure-induced gel formation of haemoglobin and whey proteins at elevated temperatures[END_REF][START_REF] Kanno | Gel formation from industrial milk whey proteins under hydrostatic pressure: effect of hydrostatic pressure and protein concentration[END_REF]. In addition, the contribution of intermolecular S-S bonds to the aggregation and gelation of whey proteins has been demonstrated by Tanaka et al. [START_REF] Tanaka | Modification of the single unpaired sulfhydryl group of beta-lactoglobulin under high pressure and the role of intermolecular S-S exchange in the pressure denaturation[END_REF] and Kanno et al. [START_REF] Kanno | Gel formation from industrial milk whey proteins under hydrostatic pressure: effect of hydrostatic pressure and protein concentration[END_REF].

In addition, artificial neural networks (ANNs) are among the most commonly used nonlinear techniques. An ANN is a mathematical algorithm whose structure and function is inspired by the organization and function of the human brain. The important property of a neural network is its ability to learn to improve its performance. ANN can handle nonlinear data and tend to produce lower prediction errors. ANN provides several advantages over conventional digital computations because of its faster data processing, learning ability and fault tolerance.

Controlling the texture of gel is important for the utilization of the gel in the food processing industry. In this context, new approaches are required to modulate and predict the physicochemical properties of the gel. In this present work, we studied the effect of pressure, protein concentration, and sugar concentration on the rheological properties of the pressure-induced gels formed from a WPI. The results should provide the basic information to modulate the texture of the gels by using the pressure, protein concentration, and sugar concentration as working parameters. Furthermore, a multilayer feed-forward neural network trained with an error back-propagation algorithm was employed to provide approaches to predict the physicochemical properties of the gel using these working parameters.

2

Materials and Methods

Materials

WPI powder from bovine milk was donated by the Central Research Institute of the Snow Brand Dairy Industry Co. (Saitama, Japan). This product contained 6.1% moisture, 89.8% protein, 1.8% ash, 1.3% lactose, and 0.5% lipids, and the fraction of individual whey protein was 74% β-Lg, 18% α-La, 6% BSA, and 2% immunoglobulins, according to the manufacturer. All the chemicals used were of analytical grade and were obtained from Wako Pure Chemical Industries (Osaka, Japan).

Preparation of the WPI Solution

A WPI solution (10-20%, w/v) was prepared in a 50 mM sodium phosphate buffer (pH 6.8), the lactose being added to make a final concentration of 0-20% (w/v).

Each WPI solution was poured into a Teflon tube (4 mL volume, 15 mm internal diameter and 22 mm depth) fitted with a screw cap and then subjected to 800 MPa pressure with a HR15-B2 hand-operated oil-pressure generator (Hikari Koatsu, Hiroshima, Japan). The temperature was maintained at 30°C. In each experiment, the indicated pressure was achieved within 1.5 min, held for 10 min, and then released to atmospheric pressure within 0.5 min. The pressurized sample was analyzed within 1 h of releasing the pressure.

Rheological Measurements

The hardness and breaking stress of each gel sample was measured at room temperature with a Fudoh rheometer operated under RT-2005DD software (Rheotech, Tokyo, Japan) as described elsewhere [START_REF] Kanno | Gel formation from industrial milk whey proteins under hydrostatic pressure: effect of hydrostatic pressure and protein concentration[END_REF].

Modeling of the Neural Network

The multilayer feed-forward neural network has proven to be an excellent universal approximator of non-linear functions. In this work, a feed-forward neural network trained with an error back-propagation algorithm was employed using MATLAB (Version 2007a, Mathworks, Natick, MA). The Neural Network Toolbox was used to model the hardness and breaking stress of the gel. The input parameters chosen in this study were pressure, WPI concentration, and sugar concentration. Supervised learning was used to train this network. The predicted and desired output were compared with one another while the errors were calculated between the predicted and actual output. An error back-propagation algorithm was used to adjust the network weights. It used a Levenberg-Marquardt approach, in which the weights were changed in proportion to the value of the error gradient. The training iterations were stopped when the validation error reached a set minimum.

Statistical Analysis

Statistical analysis was performed using the package DeltaGraph, Version 5 for the Mac (SPSS Inc., USA). In order to visualize the results of multiple experiments, the average and the standard deviation were calculated.

Results and Discussion

Effects of Pressure and Protein Concentration

The WPI solution at a concentration of 10-16% formed a gel at 600 MPa, while the 20% WPI solution formed a gel at 400 MPa. The gels formed from the 20% WPI solution at 400 MPa and from the 16% solution at 600 MPa were soft and translucent in appearance.

The gels formed from 10% WPI solution under 600 MPa, and 12% WPI solution under 400 MPa were too soft to measure texture properties using the rheometer.

Figure 1 shows the hardness and breaking stress of those gels that were sufficiently firm formed from 12 to 20% WPI at different pressures. The hardness and breaking stress of the WPI gels increased with WPI concentration, increasing from 12 to 18% at a constant pressure and, similarly, those of each solution at a constant concentration rose as hydrostatic pressure was increased from 600 to 1000 MPa. In addition, no water was expelled from the gel matrix that had been induced from these WPI samples under pressure during the measurement of hardness and breaking stress.

The gel-forming ability of a protein depends on a critical balance between the attractive and repulsive forces of the protein molecules [START_REF] Ziegler | The gelation of proteins[END_REF]. Hydrogen bonds, electrostatic bonds, hydrophobic interaction, and intermolecular disulfide linkages may play a role in gel formation in protein solutions [START_REF] Ziegler | The gelation of proteins[END_REF][START_REF] Clark | Structural and mechanical properties of biopolymer gels[END_REF]. Under high pressure, β-Lg unfolds, resulting in the exposure of its free SH group [START_REF] Tanaka | Modification of the single unpaired sulfhydryl group of beta-lactoglobulin under high pressure and the role of intermolecular S-S exchange in the pressure denaturation[END_REF][START_REF] Botelho | Pressure denaturation of beta-lactoglobulin: Different stabilities of isoforms A and B, and an investigation of the Tanford transition[END_REF]. Unfolded β-Lg can interact, through SH/SS interchange reactions, with proteins containing disulfide bonds, e.g. α-La, BSA, and β-Lg [START_REF] Kanno | Gel formation of individual milk whey proteins under hydrostatic pressure[END_REF][START_REF] He | Effects of sugars on the cross-linking formation and phase separation of high pressure induced gel of whey protein from bovine milk[END_REF]. Regarding pressure-induced gelation of a WPI solution, the formation of intermolecular S-S bonds between proteins has been confirmed [START_REF] Kanno | Gel formation from industrial milk whey proteins under hydrostatic pressure: effect of hydrostatic pressure and protein concentration[END_REF][START_REF] He | Effects of sugars on the cross-linking formation and phase separation of high pressure induced gel of whey protein from bovine milk[END_REF], and it is likely that the gel is mainly formed by the cross-linking of intermolecular S-S bonds [START_REF] He | Effects of sugars on the cross-linking formation and phase separation of high pressure induced gel of whey protein from bovine milk[END_REF].

Effects of Sugar Concentration

To study the effect of the sugar concentration (2% to 20%) (w/v) on gels prepared from WPI in a phosphate buffer by pressurizing at 800 MPa and 30°C for 10 min, the lactose content, the main sugar contained in bovine milk, was varied. Figure 2 shows the changes in rheological properties as a function of the lactose content. The hardness and breaking stress of the gel decreased with increasing concentration of lactose. This indicated that the sugars weakened the intermolecular interactions of the protein, seemingly preventing the cross-linking between proteins. Sugar is preferentially excluded from the protein domain in an aqueous mixture of protein and sugar. This exclusion effect of the sugar can affect the surface tension of the water and minimize the protein-solvent interface [START_REF] Dumay | Characteristics of pressure-induced gels of β-lactoglobulin at various times after pressure release[END_REF][START_REF] Timasheff | The control of protein stability and association by weak interactions with water: how do solvents affect these processes?[END_REF][START_REF] Lee | The stabilization of proteins by sucrose[END_REF]. As a result, the sugar protected the protein from unfolding and subsequent aggregation under pressure [START_REF] Dumay | High-pressure unfolding and aggregation of β-lactoglobulin and the baroprotective effects of sucrose[END_REF]. Sugars can also stabilize a protein against pressure-induced denaturation [START_REF] Dumay | Characteristics of pressure-induced gels of β-lactoglobulin at various times after pressure release[END_REF][START_REF] Dumay | High-pressure unfolding and aggregation of β-lactoglobulin and the baroprotective effects of sucrose[END_REF]. The presence of 5% sucrose in 2.5% β-Lg decreased the protein unfolding, slightly increasing the rate of reversibility of aggregates by pressurization at 450 MPa [START_REF] Dumay | High-pressure unfolding and aggregation of β-lactoglobulin and the baroprotective effects of sucrose[END_REF]. The gelation of protein is influenced in a complex manner by the processes of protein denaturation and aggregation [START_REF] Ziegler | The gelation of proteins[END_REF][START_REF] Van Camp | Influence of pH and sodium chloride on the high pressure-induced gel formation of a whey protein concentrate[END_REF]. Sugars decrease the degree of intermolecular S-S bonding of proteins [START_REF] He | Effects of sugars on the cross-linking formation and phase separation of high pressure induced gel of whey protein from bovine milk[END_REF], and restrain phase separation during the gelation of WPI under high pressure. Fig. 2. Effect of lactose concentration on the hardness and breaking stress of a pressure-induced gel from WPI. WPI (20%, w/v) and the indicated concentration of lactose were dissolved in a 50 mM sodium phosphate buffer at pH 6.8, and the mixture was pressurized at 800 MPa and 30°C for 10 min. Bars show the standard deviation for three measurements with different gel samples.

Evaluation of Model Predictability

The ratio of the explained variation to the total variation, R 2 , reflects the degree of fit for the mathematical model. The closer the value is to 1, the better the model fits the actual data

            n i m di n i di i y y y y R 1 2 1 2 2 1 (1)
where n is the number of points, y i is the predicted value obtained from the neural network model, y di is the actual value, and y m is the average of the actual values.

MSE is another important index to show the degree of fit of the model. It is calculated using the following equation.

 MSE  1 n ( y i  y di ) 2 i1 n  (2)
In this work, the range of three independent variables (pressure, WPI concentration, and sugar concentration) for building the neural network was set. The input matrix and the properties of gel are shown in Figs. 1 and2. To understand the generalization capacity of the network, 19 input values were divided into three sets: 13 values for the training set and three values each for the validation and testing set. Figure 3 shows the plot of the predicted values and actual values of the hardness and breaking stress of the gels as well as R 2 and MSE. The trained network gave a R 2 of 0.998 and a MSE of 0.047. The R 2 values of the training, the validation and the testing sets were 0.998, 0.999 and 0.999, while the MSE values of the three sets were 0.047, 0.042 and 0.045, respectively. The network could predict the properties of the gel within a range of ±7.8% of the actual value. The R 2 , MSE and prediction range indicated a good agreement between the predicted value of the neural network model and the actual value, which also confirmed good generalization of the network.

Conclusion

The hardness and breaking stress of pressure-induced gels from WPI increased with increasing WPI concentration (12-18%) and hydrostatic pressure, and decreased with increasing lactose concentration (0-20%). The ANN provided a model to search for the non-linear nature between induced conditions and rheological properties in an efficient manner. The trained network gave an R2 value of 0.999 and an MSE value of 0.047, which implied a good agreement between the predicted values and the actual values for the hardness and breaking stress of the gels, and confirmed good generalization of the network.

Fig. 1 .

 1 Fig. 1. Effect of pressure and WPI concentration on the hardness, and breaking stress of pressure-induced gels from WPI. WPI (12~20%, w/v) was pressurized at 600, 800, or 1000 MPa and 30°C for 10 min. Bars show the standard deviation for three measurements with different gel samples.

Fig. 3 .

 3 Fig. 3. Correlation between the predicted values of the neural network and the actual values for the hardness and breaking stress of a pressure-induced gel from WPI. The input matrix and the properties of gel as the output matrix are shown in Figs. 1 and 2.

Acknowledgments. The project was supported by the Zhejiang Key Scientific and Technological Innovation Team Project (2009R50001), the Department of Education of Zhejiang Province (Y201018520), and the Fundamental Research Funds for the Central Universities.


Zhifeng Xiao

Fan Zhang

Nanxing Wu

Xiangdong Liu
email: xdliu@cau.edu.cn

CFD Modeling and Simulation of Superheated Steam Fluidized Bed Drying Process

Keywords: Mathematical model, Eulerian-Eulerian, Heat and mass transfer, Rapeseed

An unsteady mathematical model of superheated steam fluidized bed drying process is established based on the transport process principles and computational fluid dynamics (CFD) method. The vapor-solid two-phase turbulent flow in the drying chamber is described with the Eulerian-Eulerian multiphase model. The model is solved by computer numerical simulation. The drying experiments of wet rapeseeds are conducted in a normal atmosphere. The experimental results agreeing well with the simulation results show that the mathematical model of drying process is effective.

Introduction

Using superheated steam as the drying medium and fluidizing medium, superheated steam fluidized bed drying technology combines the advantages of superheated steam drying and fluidized bed drying to improve drying quality, reduce drying time and energy consumption by high heat and mass transfer efficiency. [START_REF] Kudra | Advanced Drying Technologies[END_REF][START_REF] Xiao | Numerical Simulation and Experimental Study on Fluidized Bed Drying Process with Superheated Steam[END_REF] In recent years, some superheated steam drying models have been developed to simulate drying wet materials but are limited by their simplifications or assumptions. Taechapairoj et al. [START_REF] Taechapairoj | Modelling of parboiled rice in superheated steam fluidized bed[END_REF] developed a drying model for granular solid, which steam flow was assumed as plug flow, and interaction between particles and steam was ignored. One-dimensional single-particle models were established and integrated with a two-phase hydrodynamic model incorporating the effects of initial condensation and superficial gas flow [START_REF] Tatemoto | Drying characteristics of porous material immersed in a bed of glass beads fluidized by superheated steam under reduced pressure[END_REF] , but the sample particle was fixed and immersed in inert beads and hygroscopic porous particles that were fluidized by superheated steam under reduced pressure. The initial steam condensation rate, amount of condensed water adsorbed by particles, and water evaporation rate were considered in a model used to describe the changes in temperature and moisture content of the product with time. [START_REF] Soponronnarit | A superheated steam fluidized bed dryer for parboiled rice: Testing of a pilot-scale and mathematical model development[END_REF][START_REF] Poomjai | Mathematical model of pork slice drying using superheated steam[END_REF][START_REF] Pakowski | On prediction of the drying rate in superheated steam drying process[END_REF] A coupling model was used to simulate drying of stationary porous material by superheated steam, but the initial condensation was not considered in the whole drying process. [START_REF] Erriguible | Simulation of superheated steam drying from coupling model[END_REF] The volume shrinkage of material during drying was negligible in some models. [START_REF] Tatemoto | Drying characteristics of porous material immersed in a bed of glass beads fluidized by superheated steam under reduced pressure[END_REF][START_REF] Wu | Simulation of the hydrodynamics and drying in a spouted bed dryer[END_REF] Superheated steam drying is a complex process accompanied by complicated heat and mass transfer, vapor-solid interaction, and particle-particle interaction in vapor-solid two-phase turbulent flow. The above simplified models are not suitable to describe the realistic drying process. Hydrodynamic behavior of solid particles is affected by interfacial force and solid stress, which are caused by vapor-solid interaction and particle-particle interaction, respectively. The interfacial force and solid stress can be described by the Gidaspow drag model [START_REF] Wu | Simulation of the hydrodynamics and drying in a spouted bed dryer[END_REF][START_REF] Gidaspow | Hydrodynamics of circulating fluidized beds, kinetic theory approach[END_REF] and the well-known granular kinetic theory [START_REF] Du | Computational fluid dynamics(CFD) modeling of spouted bed: Assessment of drag coefficient correlations[END_REF][START_REF] Neri | Riser hydrodynamics: simulation using kinetic theory[END_REF] . Nienwland et al. [START_REF] Nienwland | Bubble formation at a single orifice in gas fluidized beds[END_REF] studied the bubble formation in fluidized bed and found that the bubble formation, size and shape are affected by particle size, density, and minimum fluidized velocity. It is helpful to establish an mathematical model for superheated steam fluidized bed drying process.

In this paper, the wet rapeseed drying experiments are conducted using superheated steam fluidized drying technology. The CFD model is established to describe the rapeseed drying process under normal pressure.

Experiments

Experimental Setup

Fig. 1. Schematic diagram of the drying system 1-electric heater; 2-gas distribution orifice plate; 3-drying chamber; 4-feed inlet; 5-safety valve; 6-measurement and control system; 7-discharge outlet; 8-cyclone separator; 9-condenser; 10-blower.

Fig. 1 presents a schematic diagram of the experimental setup for the superheated steam fluidized drying. It is a circulating system consisting of a blower, electric heater, drying chamber, condenser, measurement and control system, and other devices. The cylindrical drying chamber, whose inside diameter and height are 120 and 250 mm respectively, has a conical section at the lower and upper enda. An orifice plate is placed between the cylindrical drying chamber and the lower conical section; the percentage of open area is 9.8% and the diameter of the holes is 1.5 mm. Before drying, some water is dripping into the drying chamber continuously and is heated to superheated steam by the electric heater.

Material

A small material is needed for the small drying chamber. Rapeseed has a small diameter and large specific surface area, is easily fluidized, and is simplified as a sphere in the drying model because it has an approximately spherical shape. Rapeseed was selected as the experimental material in the drying process.

Experimental Procedure

Some physical parameters of rapeseed, such as moisture content, temperature, particle density, size, and bulk porosity, are required for simulation. These parameters were measured by a moisture analyzer (MA150C-000230V1, Sartorius, Goettingen, Germany), infrared thermometer (Raynger ST6L, Raytek, Santa Cruz, CA), electronic balance (TE124S, Sartorius), oven (DHG-9140A, Jinghong, China), pycnometer, and vernier caliper. Bulk porosity can be obtained by particle density and bulk density. The normal pressure drying experiments were operated respectivly with superheated steam and hot air under the same drying conditions. The drying condition parameters (such as steam temperature, rotary velocity of blower) were set according to the experimental program, and the rapeseed was fed into the drying chamber quickly from the feed inlet. The feed inlet was closed and the rapeseeds were fluidized and dried. Sampling were performed at certain times (0, 6, 45, 90, 135, 180, 225, 270, 315, 360, 405 s) from the discharge outlet.

Modeling

Physical Model

The simulated object is the vapor-solid two-phase flow process and drying process in the drying chamber. To facilitate simulation, an axisymmetric cylindrical drying chamber was selected. Fig. 2 presents a schematic of the cylindrical drying chamber under a cylindrical coordinate system, in which the bottom center of the drying chamber is the origin of the coordinates. The bottom of the drying chamber is an orifice plate. Rapeseed was selected as the solid material to be dried in the small drying chamber due to its approximately spherical shape and small diameter.

Mathematical Model

The vapor-solid two-phase flow was treated as two-dimensional axisymmetric flow. The solid material was taken as isometric spherical particles, and the particle size was taken as constant during drying.

Control volume of two-phase flow system

The vapor-solid two-phase flow system in fluidized bed is generally viewed as a two-phase mixture, which vapor phase and solid phase occupy common space, interpenetrate and have respective properties such as dimension, velocity and temperature. [START_REF] Zhou | Theory and Numerical Modeling of Turbulent Gas-Particle Flows and Combustion[END_REF] A control volume (dV) with a surface area (dA), shown in Figure 3, was chosen from the two-phase mixture in the drying chamber.

The volume fraction equation below was obeyed by at all times:

1   s v a a (1)

Continuity equation

The mass conservation equations are expressed as follows: Vapor phase:

sv vz v v vr v v v v m u a z ru a r r a t           ) ( ) ( 1 ) (    (2)
Solid phase:

vs sz s s sr s s s s m u a z ru a r r a t           ) ( ) ( 1 ) (    (3)
The moisture mass fraction of granule X', can be predicted by Equation (4):

vs s w s s sz s s sr s s s s m X D a X u a z X ru a r r X a t                   ) ( ) ( ) ( 1 ) ( ,     (4)

Momentum conservation equations

Steam phase:

    v v v v v v v v v v v vs sv sv u u u P a g R m u t                  (5) Solid phase:     s s s s s s s s s s s s sv vs vs u u u P P a g R m u t                     (6) vs R  and v s R 
are drag forces between the two phases, which are defined as follows.

Energy conservation equations

The energy conservation equations of two-phase flow are formulated as follows: Vapor phase:

    : v v v v v v v v v v sv sv evp P H u H u Q m H tt                 (7) Solid phase:     : s s s s s s s s s s vs vs evp P H u H u Q m H tt                  (8)

Heat and mass transfer model

The drying process can be divided into three distinct periods: the condensing and heating period, constant drying rate period, and falling drying rate period. Condensing and heating period (T s0 ≤ T s < T b ): The condensation convective heat transfer coefficient is determined according to the method of McAdams [15] :

1/ 4 23 [ ( )] 6 1.13 () evp pv v b l l s c s l s v b H C T T g h d d T T             (9)
The mass transfer rate between steam and particles:

()

sv vs evp pv v b Q m H C T T   (10)
Where ()

sv c b v Q h T T  (11)
Constant drying rate period (T s = T b , X ≥ X cr ): The convective heat transfer rate:

2 6 v s s s a Nu h d   (12)
Where

1 1 3 2 2.0 0.74 ss Nu
Re Pr  (13) The mass transfer rate:

vs sv evp Q m H  (14)
Where ()

vs v s Q h T T  (15)
Falling drying rate period (T b < T s ≤ T v0 , X eq ≤ X < X cr ):

The mass transfer rate in particles:

             (16)
The heat transfer rate:

()

s vs v s s s ps sv evp dT Q h T T C m H dt      (17)

Numerical solution

The finite volume method was introduced to numerically solve the drying model using FLUENT. Furthermore, the heat and mass transfer model, drag force model, and physical parametric model were programmed using C and then incorporated into FLUENT through a user-defined function (UDF) to solve the drying model.

RESULTS AND DISCUSSION

Drying Condition Parameters and Equipment Parameters

The inlet velocity of superheated steam was selected as 2.5 m•s -1 during the drying experiments. Some parameters used in the experiments are shown in Table 1.

Drying Dynamic and simulation results

Figure 4 shows the simulation and experimental results for moisture content of rapeseed. Figure 5 shows the drying rate curves from simulation and experiment. The whole drying process can be divided into three periods: the condensing and heating period, constant drying rate period, and falling drying rate period. The simulated drying curve is in agreement with the experimental results in three periods. A certain difference was found between the simulated drying curve and the experimental curve at the beginning. The simulated moisture content was greater than the experimental result in this period because the simulated curve ascends faster than latter. This discrepancy may be due to the fact that only steam condensation was taken into account and water evaporation from the material was ignored in simulation, though water evaporation from the material occurred during steam condensation. During the constant drying rate period, the simulated moisture content decreased faster; that is, the simulated drying rate was greater than that during the experiment.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Schematic of the drying chamber

Fig. 4 .

 4 Fig. 4. Moisture content of rapeseed from simulation and experiment

Table 1 .

 1 Drying condition parameters and equipment parameters

	Parameters	Unit	Value
	Steam pressure	Pa	1.01×10 5
	Environmental temperature	K	300.2
	Inlet steam temperature	K	423
	Initial moisture content of rapesed (db)	kg -1 kg -1	0.433
	Initial particle density of rapeseed	kgm -3	1184
	Bone dry particle density of rapaseed	kgm -3	817
	Mean particle size of rapeseed	mm	1.76
	Depth of static rapseed bed	mm	80
	Porosity of papeseed in the static bed	-	0.43
	Size of drying chamber	mm	250×ф120
	Rotary velocity of fan	r min -1	1840
	Rated power of electric heated	kW	9

Author: ZhiFeng Xiao, PhD, mainly engaged in Drying Theory and Computer Simulation.

Conclusions

The simulated drying curve obtained from the drying model was in good agreement with the experimental results. A negative drying rate occurred in the first period caused by initial steam condensation and lasted for about 6 s, and the moisture content of material increased up to a maximum. In the constant drying rate period, the simulated constant drying rate was greater than that of the experiment, which resulted in a shorter constant drying rate period. The simulated drying rate curve declined faster and a higher material temperature was obtained from simulation in the falling drying rate period.


Hongqian Chen

Zhenhua Tu

Zhaoshen Qing

Xiaobin Qiu

Chaoying Meng

Feasibility Study of Veterinary Drug Residues in Honey by NIR Detection

Keywords:

In order to vertify the feasibility of the near-infrared to detect the veterinary drug in honey, studying the impact of different background correction methods for linear modeling and nonlinear modeling. Use PLSR to establish the linear model between near-infrared spectroscopy and to be measured, and LSSVR to establish the nonlinear model between near-infrared spectroscopy and to be measured. At the same time, we used Wavelet transform, Multiplicative Scatter Correction, Standard Normalized Variate, First derivative transform, Second derivative transform, Orthogonal Signal Correction and other background correction methods to improve the feasibility of the near-infrared detection.

Introduction

Honey is the nectar which is collected by bees from many plants, or is the sweet substance which is fully brewed in a special substance. In the process of feeding bees, beekeeper fed the bees with a large number of antibiotics such as tetracycline, chloromycetin and so on, in order to prevent the illness of the bees. This drug can be stored in honey's cells, tissues, organs, and with the food chain will threat human health. Especially in 2008, the exposure of the melamine incident highlights the problems in the field of food safety in China.

According to Hygienic Standard for Honey(GB/T5009.95): tetracycline antibiotics which are the major residues of veterinary medicine have been explicitly included in the detection range, and its limit of physicochemical index residue must be less than 0.05mg/kg. At present, as the requirements of European Union and The United States of America on pesticide and veterinary medicine residue, the quality of honeys which China exports should be improved. In January 2002, The European Union banned imports of Chinese products which originated in animals, because chloramphenicol residues of shrimp exceeded the standard. It also caused a chain reaction of Japan, Canada and Hong Kong. The "Green barriers" policy which EU adopted in the honey trade has not only seriously affected the honey export of China, but also influenced the whole industry [START_REF] Daviesa | A preliminary study on the characterisation of honey by near infrared spectroscopy[END_REF] . In China, the phenomenon that residues of tetracycline exceed the standard becomes more and more serious. In July 2004, Industrial and commercial bureau of Sichuan Province investigated the honey on the market: only 4 batches of honey qualified in the 30 batches, and the unqualified rate is 87%. The most prominent problem is the tetracycline antibiotic residues exceeding, and 10 batches of honey didn't qualify while the unqualified rate is 33%. In order to safeguard the interests of consumers, to ensure the quality of honey for import and export and find an effective detection method of tetracycline antibiotic residues exceeding is imminent. Efficient and practical detection technology contributes to the safety appraisal of honey, and it can ensure that China's export of honey meets all the international standards.

At present, there are several methods to detect the veterinary drug residue of honey, such as traditional chemical method, liquid chromatography, ultraviolet spectrophotometer, electrochemical analysis and so on. However, these methods are defective, as some of them are devastating while others are time-consuming and expensive. So, it needs a quick, lightweight, accurate and effective detection method in the research of tetracycline compositions in honey.

Some of previous studies have shown that Near-Infrared Spectroscopy can precisely detect the composition of honey. However, the content of tetracycline ingredients in honey is quite low, which leads that the information of near infrared region was disturbed by the background noise, so it is difficult to extract and utilize it. Meanwhile, Near infrared spectrum detection technology has been studied and applied in the detection of honey quality, identification of adulteration, plant source and place of origin. The near infrared spectrum detection technology has been used in the quality detection of honey products, and the adulteration detection, and the detection of plant source, and origin detection, but the detection of veterinary drug residues in honey has not been reported. In this study, the feasibility of detecting veterinary drug residues in honey by Near-Infrared Spectroscopy was investigated, in which variety of spectral preprocessing and non-linear quantitative model will be used.

2

Materials and methods

Materials

This study collected the main origin of honey, include Sichuan, Qingzang, Beijing and so on. The honey's variety include uninoral honey and multifloral honey, like Robinia pseudoacacia L, Friobotryajaponica (Thunb.) Lind, Zizyphus jujuba NM ver. iaerirus(Bunge.)Relrl, Astragalus sivius L, Vites negundo van heterophylla (Franch.)Rehd, Tilia amurensis Rupr, Dimocarpuslongan Lour, Brassico campestris L, Litchi chine-is Soon, Eurya, Citrus reticulata Blanco, Sophoraviciifclia Hanue. There are 153 honey samples, and stored at 4℃ freezer.

Instruments and parameters

In this study, spectral acquisition used ISF/28N Fourier near-infrared spectrometer instrument which is product in BRUKER co. This instrument's range is 3600~12500 cm -1 , a minimum resolution is 1cm -1 . When collecting the spectrum of liquid, we can use transmission pool (optical path is 2mm or 20mm), quartz liquid transmission and reflection optical fiber (fixed optical path is 2mm) Annex.

The acquisition of near-infrared spectroscopy

The experimental spectra are collected in a temperature controlled laboratory (In this study, the temperature is 26°C). The instrument must be warm up for 30 minutes or more before test. At the same time, if honey is crystallized, the crystal honey samples must be heated to nine hours in 40 ℃ water bath and then place samples in 26°C environment temperature.

Detection of tetracycline content in honey

We use high performance liquid chromatography -ultraviolet detect method to detect the content of tetracycline in honey. Honey samples dissolved in 0.1mol/L Na2EDTA-Mcllvaine solution, after centrifuged, the supernatant use Oasis HLB Solid phase extraction column and the carboxylic acid anion exchange column to purify, and use Elution to determine the volume. Finally, we use high performance liquid chromatography with UV detection at 350nm, and use external standard method (peak area) -the standard curve for quantification, and the concentration of tetracycline is 0.005mg/kg [6] . In 153 samples, we select 101 samples to detect, there are 41 samples have chemical value, and 60 samples don't have the chemical value.

3

Results and Analysis

PLSR model of tetracycline content in honey

There are 6 abnormal samples in the 41 samples, so we get 35 samples to calculate. After removing the abnormal samples, in order to get more stability of the model, the calibration set and predict set are in the ratio of 2:1, 3:1 ,7:3, 4:1, 3:2, according to K-S law to divide. And then, all set establish PLSR model respectively, all indicators shows in Table 1. The calibration set and the prediction set be calculate with Auto-scaling method, and then we use PLSR method for multivariate statistical analysis of the experimental data. Nonlinear iterative partial least squares (NIPALS) algorithm is used to get partial least square factors. The optimum factor number of the calibration model (# LV) is determined by two factors, LOOCV and prediction residual sum of squares (PRESS). The result of honey tetracycline Fourier transmission and reflection spectra PLSR model showed in Table 2. The predict effect of PLSR modeling to establish honey tetracycline Fourier transmission and reflection spectra is not good, because the tetracycline content in honey is too low. The second division is much better, r is 0.6170, RSD is 52.12%. Figure 1 shows the correlation of the predictive value and the true value of the model built in the 2nd division of the tetracycline content in honey. From the figure, poor model prediction accuracy is difficult to directly meet the forecast requirements.

Map of the signal optimization

Multiplicative scatter correction, standard variable transformation, the Fourier transform and wavelet transform are separately processed for the spectral matrix (X matrix), without taking into account the specific characteristics of the test the amount of information to be analyzed. Therefore, they are difficult to remove extraneous interference while without loss and to be measured on the spectral information. For this case, Wold, orthogonal signal correction (Orthogonal Signal Correction, OSC) method used to avoid the removal of important information on the forecast to be measured. Wold use Orthogonal Signal Correction method to resolve this problem. The OSC algorithm make the measurement information is also taken into account, and it remove the spectral information which is independent with variable matrix Y. The OSC algorithm has so many improved algorithms, one is a direct orthogonal signal correction (Direct Orthogonal the Signal Correction. DOSC), which is Westerhuis proposed, a direct orthogonal signal correction (DOSC) algorithm provides exact solution to orthogonal signal correction proposed by Wold. Just by a simple least squares method, DOSC can figure out those with the Y orthogonal, in the X matrix space and to the greatest extent describe the X matrix variation (variation) of the orthogonal factor.

We use DOSC to preprocess the near-infrared Fourier transmission and reflection spectra of honey tetracycline, and DOSC removed irrelevant information, which makes the difference among the spectra of each sample more obvious. Removing the irrelevant information with DOSC, we use PLSR model to establish the near-infrared spectrum and honey tetracycline.

Selecting parameters of DOSC and establishment the PLSR model, the number of factor selected from 1,2, and tolerance factor of DOSC from the 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, and PLSR best the main factor number is selected from 1-20. When DOSC tolerance factor is set too high, the result of model appeared over-fitting; contrary, DOSC tolerance factor is set too low, the unwanted information filter not complete, and the precision of the model are also affected. When removing a factor of DOSC, and the tolerance factor is set to 0.35, the accuracy of the model is the highest, r increased from 0.617 to 0.6781, RSD p (%) decreased from 52.12% to 45.65%.

Nonlinear calibration model of Tetracycline content in honey

The tetracycline content in honey is very low (10 -9 ~10 -7 ), its non-linear effects and interference may also be larger. Therefore, we use LSSVM to establish the nonlinear model of tetracycline in honey, in order to explore the feasibility of quantitative detection. RBF kernel is used in this article, and LSSVR which used RBF kernel just need confirm two parameters, namely the regularization parameter γ and kernel bandwidth parameters. The parameters γ and σ 2 used two-step grid-search and remove one across-validation to determine.

Firstly, we follow the exponential growth to primaries parameter γ and σ 2 , and in the first choice, the range of parameter γ is from 1 to 109, and the range of parameter σ 2 is from 0.01 to 100. After first choice, the value of parameter γ is 1 and the value of parameter σ 2 is 81.92. Secondly, we use uniform growth to feature parameter γ and parameter σ 2 , in the first choice the initial range of the parameter γ is 0.1-10, and the initial range of σ 2 is 0.01~100. The growth step of parameter γ is 0.1, and the growth step of parameter σ 2 is 0.1. After second choice, the value of parameter γ is 1.2 and the value of parameter σ 2 is 9.8. After selecting the best parameter γ and the bandwidth parameters of kernel function, we establish the LSSVR model of Tetracycline content in honey, and the result of the model shown in Table 3. From the modeling results, the prediction of using LSSVR model is better than the PLSR model, RSD p (%) decreased from 52.12% to 48.95%.

Conclusion

DOSC remove the spectral information with Y orthogonal, and it make the linear relationship between spectra and to be measured is more obvious, which can improve the predictive ability of PLS Model and also simplify model. The value of RSDp from 52.12% drop to 45.65% after DOSC treatment, so DOSC is a very effective preprocessing method in PLSR regression model.

We use LSSVR to build nonlinear model of tetracycline in honey, because our data base has less number of samples. The predict precision of LSSVR is better than PLSR, the value of RSD p (%) decrease from 52.12% to 48.95%. However, due to complex background, peak overlap, and the strong absorb effect of water, it is hard to accurately extract the effective information of tetracycline in honey with these methods, and to achieve rapid detect requirements.

Fig. 1 .

 1 Fig. 1. Correlation between the measured and predicted values of honey tetracycline content for the second time: FT transflectance spectra (4000-12500cm -1 ).

Table 1 .

 1 Statistic data of honey including calibration and prediction for 5 times.

	Honey quality indicators	Divide number	Sample set	Number of samples	Range	Average	Standard deviation
		1	Calibration set Prediction set	27 8	10.2-66.8 11.3-47.6	22.1333 24.9125	14.1161 13.2533
		2	Calibration set Prediction set	24 11	10.2-66.8 11.3-47.6	21.7958 24.8909	14.9162 11.2625
	Tetracycline(μ g/kg)	3	Calibration set Prediction set	21 14	10.2-66.8 11.3-47.6	21.8571 24.1357	15.4482 11.2371
		4	Calibration set Prediction set	25 10	10.2-66.8 11.3-47.6	22.1080 24.42	14.6854 11.7571
		5	Calibration set Prediction set	28 7	10.2-66.8 11.3-47.6	21.8143 26.5857	13.9547 13.3714

Table 2 .

 2 The PLSR models results of honey tetracycline content for 5 times.

	Honey quality	Divide			Calibration set			Prediction set
	indicators	number	n a #LV b	r	SEC	RSD c (%) n c	SEP	RSD p (%)
		1	27	4	0.6287 10.9774	49.6%	8	14.6035	58.62%
	Tetracycline( μg/kg)	2 3 4	24 21 25	4 4 4	0.6170 11.7386 53.86% 11 12.9852 0.5946 12.4208 56.83% 14 11.7928 0.6165 11.5629 52.3% 10 13.6492	52.12% 48.86% 55.89%
		5	28	4	0.6271 10.8693	49.83%	7	15.2219	57.26%

Note: a: n is number of samples in the calibration set, b: # LV is the best number of factors in calibration set, c: n is the number samples in prediction set

Table 3 .

 3 The PLSR and LSSVR models constructed with different spectral preprocessing methods. The number of main factors; n: DOSC, Number of factors; tol: DOS, The number of tolerance factor; J: Wavelet decomposition level; γ: LSSVR, The regularization parameter, σ 2 RBF, Bandwidth of kernel function parameters

	Honey quality indicators	Model	Parameter	r	SEP	RSD p (%)
		PLSR	LV=4	0.6170 12.9852 52.12%
		DOSC-PLSR	n=1,tol=0.35, LV=1	0.6781 11.3634 45.65%
		MSC-PLSR	LV=2	0.5736 12.4985 50.21%
		SNV-PLSR	LV=2	0.5734 12.4955	50.2%
	Tetracycline(μ g/kg)	WT-PLSR	Bior3.3,J=1, LV=4	0.6169 12.9852 52.17%
		First derivative -PLSR	2-9,LV=2	0.5327 12.3201 49.50%
		Second derivative -PLSR	2-9,LV=2	0.5744 12.3930 49.79%
		LS-SVR	γ =1.2,σ 2 =9.8 0.6618 12.1838 48.95%
	LV: PLSR,

 This work is supported by National Natural Science Foundation of China(30901127) and Rapid detection of food in small and medium-sized enterprises of public service platform construction project(2011GH552096), National torch plan that environment of industrialization of construction project,


Chuanjia Hu
email: chuanjiahu@163.com

Yujia Dai

Jiahe Wang

Mengyan Song

Xiugen Jiang
email: jiangxiugeng@tsinghua.org.cn

Min Ding

Study on Anti-collapse Behavior of Solar Greenhouses Covering Rigid Plate under Snowstorm

Keywords: solar greenhouses, rigid plate covering, stressed skin action, anticollapse behavior, snow load

To analyze the effect of stressed skin action of rigid plate covering on anti-collapse behavior of solar greenhouses under snow load, the numerical simulation on the overall collapse process of single skeleton structure and 6skeleton overall spatial structure with rigid plate covering were conducted on ANSYS. The collapse modes of solar greenhouses and snow load-displacement curves were obtained. The effects of different parameters on the anti-collapse behavior of solar greenhouses under snow load were also analyzed. The results showed that the stressed skin action of the covering could provide lateral support for the skeleton and increase integral rigidity of the structure and the bearing capacity to resist snowstorm. The lateral support of 8mm thick PC sun board equals that of 4 purlins, 10mm thick PC sun board equals 6 purlins, and 12mm thick PC sun board equals 8 purlins. It is suggested that skeleton interval is about 1m.

Introduction

The solar greenhouse is an agricultural building as well as a production facility. Its security under various loads is always the priority [START_REF] Chaodong | Pei Xianwen :Comparing the Front Roof Permeated Sunlight Performances and the Arch Mechanical Performances of Four Curvilinear Roofs of Solar Greenhouse[END_REF][START_REF] Xuzhen | Single-side Slope Sunlight Greenhouse Skeleton Finite Element Optimization[END_REF][START_REF] Chengzhi | Spatial Finite Element Analysis of Heteromorphous Greenhouse[END_REF][START_REF] Ruigang | Research on Design of Glass Greenhouse[END_REF] . Much research on solar greenhouses has been focused on the lighting, insulation, heat transfer etc., whereas little was on mechanical behavior and design methods [START_REF] Engel | Using Simulation to Optimize Solar Greenhouse Design[END_REF][START_REF] Tiwari | Design and Optimization of a Winter Greenhouse for the Len-Type Climate[END_REF][START_REF] Tiwari | Analytical Study of an Active Winter Greenhouse[END_REF] . The lack of scientific guidance to the construction of solar greenhouses leads to many accidents, especially during extreme snowstorms in recent years [START_REF] Liu | Structure Optimization Design for Sunlight Greenhouse Skeleton Truss-work[END_REF][START_REF] Yikui | Analysis of Influence Factor on Safety and Durability of Steel Skeleton of Solar Greenhouse[END_REF] . The collapse not only causes the loss of greenhouse facilities but also results in a pause in agricultural production. Therefore, it is essential to investigate the mechanical behavior and the collapse mechanism of solar greenhouses under various loads.

Stressed skin action refers to the strengthening effect of building's surface covering on structure's integral rigidity with its own rigidity and strength [START_REF] Xu | Skin-effect Theory and Research Profiles[END_REF] . It is always treated as only a structural safety reserve in the construction. This can sometimes achieve simply safety results. But sometimes the result is opposite. The translucent covering can be used as lateral support for the greenhouse skeleton when it is rigid plate such as PC sun board or plate glass etc. Then the contribution to integral rigidity from rigid plate covering can be used. This can ensure structure security with less or no extra lateral support systems. The economy objective can thus be achieved. However, research on stressed skin action has only been emphasized in light steel frames and rigid-framed structures at present. There was little research on collapse mechanism and design theory about solar greenhouse considering stressed skin action of rigid plate. In order to make designing model of solar greenhouse better match with the actual working state, and to ensure its security and economy under extreme snowstorms, it becomes necessary to develop this research.

The numerical simulation on overall collapse process of solar greenhouses with rigid plate covering under snow load is carried out on ANSYS. The effects of parameters including component material, structure size and construction techniques on anti-collapse behavior of solar greenhouses under snowstorm are discussed.

2

Finite Element Model

Solar Greenhouse Dimension

The sectional view and dimensions of selected solar greenhouse (Liaoshen I type) are shown in Fig. 1 and table 1. Single circular steel tube is used as the skeleton [START_REF] Engel | Using Simulation to Optimize Solar Greenhouse Design[END_REF] . The back wall is reinforced concrete structure [START_REF]Greenhouse Load Design Specifications[END_REF] . In practice of Liaoshen I type, skeleton interval is generally 1m and the section size of circular steel tube is generally 30 mm × 2 mm, both of which are regarded as reference dimension. Rigid plate covering of solar greenhouse generally consists of PC sun board or plate glass etc. [START_REF]Greenhouse Load Design Specifications[END_REF] . The thickness is generally from 6mm to 12 mm. 8 mm thick PC sun board is used as the reference. The 6-skeleton overall spatial structure with rigid plate covering and no purlins is used as the reference model (hereinafter referred to overall spatial structure) to investigate the effect of rigid plate on anti-collapse behavior of solar greenhouse structure.

Material Model

Structural steel of solar greenhouses is Q235 steel and the multi-linear kinematic hardening elastic-plastic model is used as its stress-strain relation to take material plasticity changes [START_REF] Bin | Song Jianxia: Finite Element Analysis of Load Bearing Capacity of Steel Skeletons Solar Greenhouses Under Vertical Loads[END_REF] . Mechanical properties of the steel, the PC sun board, and the plate glass are shown in Table 2. VonMises yield criterion is used as the criterion of all materials.

Element Type and Meshing

The ANSYS element BEAM188 is chosen for solar greenhouse skeleton. To PC sun board (hereinafter referred to as stressed skin), element SHELL181 is suitable for its consideration of in-plane shear deformation. Every single skeleton is divided into 40 units, and the stressed skin is divided into hexahedral elements using rules of free meshing [START_REF] Xinmin | ANSYS Numerical Analysis of Engineering Structures[END_REF] .

Constraint Condition and Loading mode

Only translational degrees of freedom of stressed skin elements are coupled with those of beam elements. Constraint condition takes the case both top and bottom hinged as the reference condition. The loading mode is to impose on the skeleton vertical down line load q to simulate snow load. The snow load can be conversed from the line load by multiplying q with skeleton length, and then dividing stressed skin area between two adjacent single skeletons. In this paper, the ultimate load refers to ultimate snow load.

Calculation Model and Analysis Method

Finite element models of solar greenhouse structure are created according to the method above, and shown in Fig. 2. The displacement of solar greenhouse structure under actual loads is always very large due to the softness of components. Large displacement affects bearing capacity [12, 16] . Therefore, the NL GEOM command is opened to activate the large deformation effect in the analyzing process. Arc-length method based on Newton's law of Laplace is adopted. It is convenient to use static analysis of progressive loading to obtain the structure's ultimate bearing capacity by means of reasonable adjustments to overall load and loading sub-steps [START_REF] Yonghua | Nonlinear Finite Element Analysis of the Bearing Capacity of the Film in Plastic Greenhouse[END_REF] . The descent stage of snow load-displacement curves can be received via arc-length method. And the vertex of the curve is the theoretical ultimate bearing capacity of the structure [START_REF] Yonghua | Nonlinear Finite Element Analysis of the Bearing Capacity of Arch Structure in Plastic Greenhouse on Snow Load Working Condition[END_REF] .

Results Comparison

Effect of Different Structural Calculation Model

Solar greenhouse structure is usually simplified to two-hinged arch structure or two-hinge truss arch structure to calculate the strength and deformation in plane ignoring the interaction and contribution of covering material to the greenhouse structural capacity. As shown in Fig. 3, in the elastic stage, vertical deformation of the structure increases with the snow load increasing. After the ultimate load, the curve of overall spatial structure begins to decline, while the curve of single skeleton structure becomes horizontal. The ultimate load of overall spatial structure is 582 Pa and the maximum displacement is 448 mm. The ultimate load of single skeleton structure is only 142 Pa and the maximum displacement is 227 mm. The ultimate load of the former is 4.10 times of that of the latter, and the maximum displacement of the former is 1.97 times of that of the latter. The reason is that the single skeleton structure has no lateral restraint support systems. Its collapse mode is out-of-plane buckling, and the steel does not fully play its role. Whereas with the lateral support provided by the stressed skin, the collapse mode of overall spatial structure is in-plane buckling, and the steel can fully play its role.

Fig. 4 and Fig. 5 present the comparison results of deformation before and after the ultimate load to show the buckling modes of different structural calculation models.

Effect of Different Number of Purlins

According to Part 2.1, rigid plate covering can effectively improve the bearing capacity to resist snowstorms and it can partly replace purlins to prevent out-of-plane buckling of skeletons. Here, the cross section of purlins is circular steel, and its section size is 10mm × 1mm.

Effect of Different Skeleton Interval

As shown in Fig. 8, changing skeleton interval is equivalent to change the degree of lateral support of stress skin on the structure. The greater the skeleton interval is, the smaller the ultimate load is, and the greater the maximum displacement is. The shape of curves of structures with different skeleton interval is similar, and collapse modes are all in-plane buckling. The ultimate load of structures with 1m, 1.5m and 2m skeleton interval is respectively 582 Pa, 492 Pa and 358 Pa, reduced by 15.5% and 27.2% in order. The recommended skeleton interval is 1 m considering the supporting role of stressed skin.

Effect of Different PC Sun Board Thickness

As shown in Fig. 9, the greater the PC sun board's thickness is, the greater the lateral support is, the greater ultimate load is, and the smaller the maximum displacement is. And the shape of curves is similar. The ultimate load of structures with 6 mm, 8 mm, 10 mm and 12 mm thick PC sun board, is respectively 448 Pa, 582 Pa, 672 Pa and 745 Pa, increased by 29.9%, 15.5% and 13.2% in order. Compared with the result of structures with different number of purlins, the ultimate load to resist snowstorms of the structure with 8 mm thick PC sun board is comparable to that of the structure with 4 purlins, and 10mm thickness comparable to 6 purlins, 12mm thickness comparable to 8 purlins.

Effect of Different Covering Materials

As shown in Fig. 10, the ultimate load of the structure with PC sun board is 582 Pa; while the ultimate load of the structure with plate glass is 448 Pa. The former is increased by 29.9% than the latter. The reason is that the in-plane shear stiffness of PC sun board is greater than that of plate glass. And in both cases the shape of curves is similar. It indicates that the collapse modes are both in-plane buckling.

Effect of Different Constraint Condition

The skeleton rotation capacity at both ends is directly determined by constraint condition at each end, and the ultimate bearing capacity of overall spatial structure is also affected by constraint condition. As shown in Fig. 11, the ultimate load of both top and bottom clamped case is 716Pa, while both top and bottom hinged case is 582 Pa. The ultimate loads of top hinged and bottom clamped, and top clamped and bottom hinged are respectively 627Pa and 645 Pa .

Conclusions

Under snow load, the collapse mode of overall spatial structure is in-plane buckling whenever the lateral support is provided by rigid plate covering or purlins. The skeleton interval determines the lateral support of rigid plate covering, thus affecting the ultimate bearing capacity to resist snowstorms. The increment of rigid plate covering's thickness improves the anti-snowstorm capacity as well. PC sun board is more effective comparing with other covering materials. Besides, increasing the skeleton steel tube section size can improve the ultimate capacity, but it will increase the amount of steel and construction cost simultaneously. Nevertheless, increasing constraint stiffness at both ends of the skeleton can increase the anti-snowstorm capacity, but causes less construction cost. Finally, rigid plate covering, such as PC sun board as translucent covering, can reduce the need of purlins. It is equivalent to purlins in some way.

However, the effect of stressed skin action in this paper is only investigated under snow load. For other loading cases, it needs further exploration. To simplify the calculation, the effect of gable at both ends of solar greenhouse is not taken into consideration. It is necessary to construct more realistic models for analysis to obtain more accurate results in future study.

Fig. 1

 1 Fig.1 Sectional view of the solar greenhouse

  Fig.2 Calculation models of solar greenhouse

Fig. 3

 3 Fig.3 Snow load-displacement curves of different structural calculation model

Fig. 5

 5 Fig.4 Deformation of single skeleton structure

Fig. 6

 6 Fig.6 snow load-displacement curves of structures with different purlinsAs shown in Fig.6, without covering materials, the more the purlins are, the greater ultimate load is, and the smaller the maximum displacement is. Curves of structures with 4, 6 and 8 purlins are similar in shape. It indicates that they have the same collapse modes and all of them are in-plane buckling. The ultimate load of the structures with 4, 6 and 8 purlins is respectively 537 Pa, 627 Pa and 761 Pa, increased

Fig. 8

 8 Fig.8 snow load-displacement curves of structures with different skeleton interval

Fig. 9

 9 Fig.9 Snow load-displacement curves of structures with different thickness of PC sun board

Fig. 10

 10 Fig.10 Snow load-displacement curves of structures with different covering material

Fig. 11

 11 Fig.11 Snow load-displacement curves of structures with different constraint condition

Table . 1 The solar greenhouse dimension [8]

 .

	Parameter	Value	Parameter	Value
	Span	8.00 m	Projected length of front slope	6.40 m
	Ridge height	3.26 m	Projected length of back slope	1.60 m
	Elevation of front roof	27°	Height of back wall	2.00 m
	Elevation of back roof	40°	Reference interval of skeleton	1.00 m

Table . 2

 . Mechanical properties of materials

					[5, 12, 14, 15]
	Materials	Thick-	Density	Poisson	Elastic	Yield
		ness	ρ	ratio	modulus	stress
		(mm)	(kg• m 3 )	μ	E (GPa)	(MPa)
	Q235	2	7850	0.3	206	235
	Plate glass	5	2400	0.25	70	58.8
	PC sun board	8	1200	0.3	2.4	63

Acknowledgments

Support for this research by Chinese Universities Scientific Fund No. 2011JS126, the Specialized Research Fund for the Doctoral Program of Higher Education of China No. 20110008120017, and the National Natural Science Funds No. 50979108.


Laiqi Xu

male Xinhua Wei
email: wei_xh@126.com

Xinyun Zhou

Dazhi Yu

Jinmin Zhang

Study on Identification Method of Foreign Fibers of Seed Cotton in Hyper-spectral Images Based on Minimum Noise Fraction

Keywords: seed cotton, foreign fibers, MNF, hyper-spectral data, dimensionality reduction, feature extraction, template matching

In order to improve the recognition accuracy of seed cotton foreign fibers, a study on identification method in hyper-spectral images based on Minimum Noise Fraction (MNF) was proposed ,which was applied to feature extraction to reduce the dimension of hyper-spectral images. This method reduced the numbers of hyper-spectral data, lessened the images noise to the minimum , but also decreased the computational requirements for subsequent processing. The white foreign fibers and cotton which were in small discrimination were selected in this paper as the research object. The hyper-spectral images were displayed in software ENVI with 256 bands in the wavelenghth range of 871.60nm-1766.32nm. Afterwards, the images would be processed with the iteration threshold segmentation method, inflation and corrosion. Meanwhile, the correlation of template images and destination images were calculated to find the spectral peaks so that to make template matching to eliminate the images of the cotton seeds. Results of experiments show that the above methods is suitable for identifying foreign fibers of seed cotton which achieved 84.09% rate of recognition.

Introduction

The provision of GB1103-1999 is that foreign fibers refer to the non-cotton fibers and colored fibers which have seriously affected the quality of cotton, such as chemical fiber, hair, silk, linen, dyed rope, plastic film, plastic rope (rope, cloth) and so on [START_REF] Heyi | Analysis of application for the MQZY seed cotton foreign fibers[END_REF] . The harm caused by the foreign fibers in the textile business is huge, its performance is to reduce the production efficiency of cotton mixed with foreign fibers first. Meanwhile, it is easy to be labeled as numerous small fiber blemish, not only is difficult to remove in textile processing, but will innocently influence the quality of cloth dyeing processes that pulled off shorter and finer fiber. The formation of a large number of small fibrous blemish pinning can easily result in the efficiency weaving [START_REF] Miaofu | Features and application of online foreign fiber detecting and clearing device[END_REF] . Therefore, in order to realize the automatic sorting of foreign fibers in cotton, a variety of techniques are tried. For example, Liu Huanjun used spectral reflectance to classify of soils [START_REF] Huanjun | et a1.Soil taxonomy on the basis of reflectance spectral characteristics[END_REF] , where machine vision technology is the primary means of on-line detection of foreign fibers in cotton.

Using the spectral analysis technology distinction sample type is a fast and accurate method. For example, Chenyang created that the characteristic spectra of 3 kinds of gases and the weights was classified by similarity analysis [START_REF] Yang | A classification method for nonlinear fluorescent spectra based on edges matching[J][END_REF] . These methods all directly realized the variety distinction using the test specimen spectrum characteristic peak. However, machine-based visual of the cotton foreign fibers used in indirect detection is the spectral characteristics of the material. Thus, Li Bidan in Tsinghai University examined the colorless plastic, the jute, the white hair silk by using the infrared absorption characteristic of foreign fiber [START_REF] Bidan | Design of a sophisticated foreign fiber separator[END_REF] . Jia Dongyao and Ding Tianhuai produced the differences of foreign fibers of composition, physical and chemical parameters [START_REF] Dongyao | The use of fiber infrared absorption characteristics of lint impurities detection method[END_REF] . Church J.S. et al. used the near-infrared wave Bands within 1000nm-l700nm to realize in the wool polymer impurity examination [START_REF] Church | The detection of polymeric contaminants in loose scoured wool[END_REF] . Hyper-spectral image technologies for the detection of foreign fibers in cotton are not much visible in the literature at home and abroad. By analyzing the foreign fibers in the near-infrared hyper-spectral transformation algorithm to choose the best band of foreign fibers in cotton, has not been found in Chinese literature.

This paper takes advantage of the high-resolution digital camera system to get the high spectral images between seed cotton and 5 kinds of white foreign fibers in the wavelenghth range of 871.60nm-1766.32nm. Hyper-spectral imaging overcomes the limitation of the traditional single-band, as well as the band range limitations which have a relatively narrow band interval. And more number of characteristics of the band are able to get the spectral space features continuous and fine spectral characteristics. Because of the large amount of letter of hyper-spectral, multiband and high redundancy of the feature makes its information processing difficult, and data dimensionality reduction problem has been the application of hyper-spectral information processing problems [START_REF] Qingxi | Hyper-spectral remote sensing principles techniques and applications[END_REF][START_REF] Peijun | Advances in remote sensing science and[END_REF] . Yang Wenzhu et al.in China Agricultural University proposed a method for selecting optimal detection bands, based on the extreme distribution of reflectance differences between cotton fiber and foreign fibers [START_REF] Wenzhu | Selection of optimal band for detecting foreign fibers in lint cotton using spectroscopic analysis[END_REF] . On one hand, data dimension reduction can make the images away from the noise and improve the data quality .On the other hand, removing the worthless band image can reduce the amount of computation and the band number of purposes, as well as the image processing efficiency. To effectively use of hyper-spectral data, dimension reduction is an integral part. The MNF transformation would solve the problem which mentioned.

Research data

In this study, seed cotton and five typical white fibers including white confetti, white hair, polypropylene yarn, and white chemical silk, white plastic were selected.

This paper takes advantage of the high-resolution digital camera system to get the high spectral images at the range of 871.60nm-1766.32nm. Each of the band is 4.3nm

The Minimum Noise Fraction (MNF) transformation

MNF (Minimum Noise Fraction) is one of the common methods of feature extraction in hyper-spectral data [START_REF] Pingping | Dimensionality reduction method of Hyperion Eo-1 data[END_REF] . The MNF transformation is essentially two cascaded principal components(PC) transformations [START_REF] Green | A transformation for ordering multispectral data in terms of image quality with implications for noise removal[END_REF] . First, the high-pass filter is used in processing the whole image data to get the noise covariance matrix C N and digitalization.

T NN D U C U  (1)
In the formula, D N is the diagonal matrix of the C N characteristic value which is according to the size descending sequence arrangement. U is the orthogonal matrix which is constituted by the C N characteristic vector, N is the wave band number. The formula (1) can be further transformed into

T N I P C P  (2)
Where I is the unit matrix, P is the transformation matrix,which P=UD N -1/2 . The original image X can be transformed to the new space Y. The data after transforming contained in the noise is with unit variance and without correlation between bands. Standard PC transformation is carried in images. Transformation formula is

ˆT D C P C P  (3)
C D is the covariance matrix of the original image, Ĉ is the transformed matrix. The above equation can be further transformed into a diagonal matrix:

T D J CJ  (4)
According to the size descending sequence arrangement constitution, D is the diagonal matrix of Ĉ characteristic value , J is the orthogonal matrix which constitutes by the D characteristic vector. Through the above steps, MNF is got to the transformation matrix:

Iteration threshold segmentation

For the online visual examination system, it is the key aspects with algorithm rapidity and accuracy. Therefore, choosing a quick division method is necessary under the guarantee division precision. It is more effective for border-based segmentation method in dealing with some relatively high contrast edges images. However, the content of foreign fibers in cotton is extremely small, and became finer after opening. With the low contrast between the target and background, segmentation method based on boundary does not apply to processing the cotton fibers images. Due to the gray of the cotton fiber image varies from the type of foreign fibers, the fixed threshold method can not achieve precise segmentation. In this case, dynamic threshold method is needed. Iterative threshold segmentation method is statistically significant for the best segmentation threshold, which is more suitable for cotton fiber.

The principle of the iterative method is that the gray distribution between foreground and background in the images do not overlap with each other. With this premise, the threshold segmentation is realized in two types of objects based on the idea of approximation. In General, the gray images within the change between 0-255 gray-scale changes is 256 levels. Some steps are given below:

Step1: Find out the maximum and minimum gray value of images to record as Z max and Z min respectively. Thus , make the initial threshold T 0 = (Z max + Z min ) / 2;

Step2: According to the threshold T K , to do image segmentation for the foreground and background to get the average gray values Z o and Z B ;

Step3: Get the new threshold for

T K + 1 = (Z o + Z B ) / 2; Step4: If T K = T K + 1
, the income is for threshold; Otherwise, turn to the Step2 to do the iterative calculation.

Template matching, corrosion and dilation

Template matching is an important part of digital image processing. After the image processing, cotton seed in-depth can have the remarkable images characteristic, in this case, a method that eliminate cotton seed image is needed. Establishing a template of the average size model and carrying on the match with the image is named template match. In this article, the average value of cotton seed template was first found out, and then pattern recognition method was used. Meanwhile, two dimensional Fourier were transformed between the template image and matching image, between which the correlation was calculated .The highest position of the spectrum that match template objects.

After the MNF processing, iterative processing, corrosion and dilation, the foreign fibers were divided with the division effect achieves 84.09%. Now enumerates two pictures to take the demonstration, the demonstration is as follows:

Results of iteration threshold segmentation

In order to deal with further better in the image processing, appropriate image segmentation is needed. First of all, the iterative method is imported into the MATLAB to figure an iterative process. Combined with histograms, the threshold values used for the calculation of the iterative method are proposed. The method is based on 256 gray-scales of foreign fiber images, and the initial value of the last iteration is selected.

Results of Template Matching

To calculate correlation of template image and destination image, the template matching should be selected . They are listed as follows.

Step1: Convert into a template (396,398) binary image, and then do a matching image to convert a (396,398) binary image.

Step2: To do two-dimensional Fourier transformation of the template image and match the image.

Step3: Calculate correlation of template image and destination image. The method is to match the image rotated 180 degrees, then convolution calculation techniques based on fast Fourier transform evaluated. If the convolution rotates 180 degrees, the convolution and related calculations are equivalent.

Step4: Find one of the three highest spectral peaks from the generated spectrum image spectral peaks. The three highest positions of the spectral peaks are matched with the template object.

Data statistical results

With the proposed method, segmentation and dimensionality treatment has been used in the hyper-spectral images of the cotton foreign fiber, in order to show the method, there are 176 sets of data tests, and the experimental results are expressed as follows.

Table 2 Results of testing sets of samples 5 Conclusions

Through theoretical analysis and experimental analysis of this article, we find that Green and other people's classic MNF transformation methods have the good effects in dimensionality reduction, separation of the of noise and feature extraction for similar surface features gathered data. Currently result fully illustrates the advantages of MNF transformation method. However, for the time complexity of the algorithm of hyper-spectral images, it is necessary for us to do further simplifying and further mining algorithm.

Name

Fig. 1

 1 Fig.1 Foreign fiber samples in seed cotton

Fig. 2

 2 Fig.2 Images processed before and after by MNF

a) Image 1 b) Image 2 Fig. 3 Results and analysis 4 . 1

 12341 Fig.3 Final segmentation images

Fig. 4 Fig. 5 the

 45 Fig.4 Eigenvalue of the MNF and 10 top features images of the cumulative variance It is known that the transformed image of cumulative variance reaches top 10 bands of 87.92%. And most amounts of images are contained in top 10 bands.Typically, after the MNF transformation, the band that contains the data Eigenvalues is greater than 1. When the Eigenvalue is close to 1, it indicates that the images have only noise. As we can see in figure4, the fifth band characteristics are 1, which contains part of the noise. The first 4 after MNF processing components is shown in Figure5.

Fig. 6

 6 Fig.6 Gray images 1,2,3 and its histograms According to the principles of iterative methods, iterative calculation should be done. Finally, the thresholds are as shown in table 1. Table1 The results of optimal threshold Image number Image 1 Image 2 Image 3 Optimal threshold 154 139 112

Fig. 9

 9 Fig.9The result of segmentation process

1 Received date:2012-7-20 Revised date:2012-8-1 Foundation items : Supported by a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(Jiangsu financial education (2011) No. 8)), the key laboratory of agricultural equipment intelligent high technology research in Jiangsu (BM2009703), and the Program for New Century Excellent Talents in University(NCET-09-0731


Xiaorong Yang

Qingtian Zeng

Nengfu Xie

Lihua Jiang

Study on Agricultural Information Push Technology Based on User Interest Model

Keywords: User interest model, Information push, Personalized service

Traditional agricultural information systems lack availability because of ignoring user preferences. This paper proposes a user interest modeling method which combines auto-modeling with artificial modeling. The value of user interest measure can be obtained by counting the frequency of information accesses. The statistics method based on the heat of information accesses is adopted to perform cluster agricultural information push. The result of experiment proved the user interest modeling method can express a user's interest in agricultural information better. The intelligently and personalization of the system are improved.

Introduction

Traditional agricultural information systems focus on the integration and query optimization of information resources. The systems lack availability because of ignoring user preferences. Personalized services are the inevitable tendency of Internet. The personalized system can push satisfactory information resources to users actively by analyzing their personality and habits. Pablo Castells from Spanish Madrid university applied semantic-based personalized techniques to develop an extensible retrieval system which could automatically estimate the value of user personalization. Alexander Pretschner from Germany and Susan Gauch from America cooperatively studied an ontology-based personalized retrieval system. The system could reorder and filter search results by matching search results and user character so that the search performance of the system was improved. Automatic Department of Tsinghua University presented a maximum distance-based Ranking algorithm to match information and developed the Bookmark system. The system adopted expanding the Bookmark function of the browser to record a user's access behavior and obtained a user's demand by analyzing his commentary. Professor Qingtian Zeng from Shandong Science and Technology University studied the obtaining technique of users' explicit and implicit knowledge needs. And he applied the technique to develop the E-learning system and multi-user interactive Question-Answering system.

Usual user interest model can be based on neural network, evaluation matrix, vector space model or ontology. However the neural network-based model cannot be readily intelligible and is now rarely applied. The poor adaptability of evaluation matrix-based model results in the difficulty in renewing a user's interest. The ontology-based model adopts multi-level domain knowledge to denote users' interest.

At present building ontology is only manual or semiautomatical so that it takes too much time. The vector space-based model often results in deviation. So this paper integrates auto-modeling and artificial modeling to build user interest model. The value of user interest measure can be obtained by counting the frequency of information accesses. The statistics method based on the heat of information accesses is adopted to perform cluster agricultural information push.

User Interest Model

User interest model is a kind of user description which is algorithms-oriented and has a specific data structure. It can store and manage a use's background information and history behavior of accesses. It can record a user's interest points and describe his relatively inflexible information needs at a certain time. This paper integrates auto-modeling and artificial modeling to build user interest model. On one hand In this paper, a combination of automatic modeling and artificial modeling construct user interest model, on the one hand, to record the user's behavior in the data access and mining user access logs to complete the modeling of the user, while allowing users to manually customize their own user model, which can be as effective as possible, complete access to users' needs and to meet agriculture personalized information retrieval service requirements(Barrett,R. et .al,2007).

Automatic Modeling

Automatic modeling by mining the log information can analyze the behavior of different users, and dynamically learn and improve in order to obtain the explicit needs and implicit needs of the user. For example, whenever the user to enter the agricultural science and technology information sharing system, user interest in a variety of behaviors will be recorded, including his clicking on the information classification of agricultural information resources, entering your search keywords, and browsing information. The order of these information classification accessed in one operation will be recorded such as A→D→E→C→D→B is a resource access sequence after a user login system which means that the user has accessed the system

Artificial Modeling

On the basis of the automatic modeling, by personalized mapping the fields of data sheet in an agriculture database, each user can customize his favorite search criteria of agricultural information resources so as to meet the requirements of personalization.

Besides, a user can improve the user model by filling in his personal information such as gender, age, occupation, education, personal interests and so on. Thus the system can cluster users so as to improve the user model.

Push Technology of Agricultural Information Resources based on User Demand-driven

User Interest Statistics based on the Number of Access

As a traditional user interest statistical method, the principle of user interest statistics based on the number of access is that the more a user is interested in some information resource, the more frequently he access the resource. The resource tree is But this method simply focuses on the number and frequency of a user's access to resources without the attenuation of the user's interest with time. On this basis, this paper adds time decay for the user interest. Time-attenuation coefficient α can be gotten according to the following formula: α=10 -passedTime/secsInMonth [START_REF] Barrett | C.How to Personalize the Web.Proceedings of the Conference on Human Factors in Computing Systems[END_REF] PassedTime represents elapsed time. And secsInMonth is a constant which is 30 days a month, that is 2592000 seconds.

User's actual interest can be derived according to the following formula(Yong Li,2002):

I'=I*α (3)
User interest calculated according to the above algorithm corresponds to the resources tree which is displayed after a user logins system. Database accessed more frequently will be enhanced its sort. A user's interest in agricultural information resources can be counted step by step and information push can be achieved implicitly.

Agricultural Information Resources Push Based on the Heat of Access

For groups of users, sometimes a certain category of information resources is generally concerned. For example "the price of wheat today " is closely related to life.

During individual users are searching, this kind of information is easily ignored due to different interest areas of users or different concern in current time. But the information is actually helpful for the user. By mapping multiple users' interest for an information resource, the weight of the information resources can be calculated:

I(r)=∑I(i)* α,i=1,2,3…n (4)
According to the weight, the sort of information resources produces the hottest resources sequences. When he retrieves in the agricultural science and technology information database, a user can get not only matching results but also pushed information which is the most accessed by other users. Pushed information may be useful for him. Thus clustering information resources push is achieved.

In view of a larger amount of user group's access, a certain threshold should be set to ensure the effectiveness and timeliness of agricultural information resources recommended. When the access weight of certain type of information resources within a certain time limit T can not reach the specified experience value e, the information resources will be removed from the recommended resource queue to ensure the efficiency and timeliness of information resources recommended queue.

The Experimental Results

According to the above techniques and methods, the intelligent retrieval platform of agricultural science and technology information based on user interest model was designed and developed. The platform adopts the user interest model to obtain users' need preferences and pushes the interested information to them. Not only the information meeting the search keywords is retrieved, but also the information which contains the synonyms and related information of the search keywords is also retrieved. Retrieval results are displayed to the user in accordance with his interest priority. By sorting resources dynamically based on user interest, customizing personalized fields and recording accessed information, personalized service can be achieved. As the log of user behavior, user history records are very important for building user model, speculating user behavior and mining user interest. Because user behavior is difficult to speculate and users ' history data are more complicated, a user can not get effectively feedback when he checks his history data. This platform uses a uniform approach-"unified recording, classify browsing" to integrated into users' access records to the user model. The platform collects a user interest in the classification of resources based on each user's behavior. Whenever a user accesses a resource, the system will record his behavior. When he login the system next time, the system will automatically display the frequently accessed database resources to the top row.Besides, he can define the fields in the database as own favorite language or vocabulary in order to make retrieval conditions more in line with his habits. He can also operate his own accessed records to determine whether they are useful. By analyzing the accessed information resources, the system can obtain user interest for a certain type of information resource and improve user interest model. The example showed that the proposed user interest model is better able to push useful resources for users.

Conclusion

This paper used an agricultural information push method based on user interest model and presents the combination of automatic modeling and artificial modeling to build user interest model. Further the study proposed the user interest statistics techniques based on the number of access and agricultural information push based on the heat of access. According to the above techniques and methods, the intelligent retrieval platform of agricultural science and technology information based on user interest model was designed and developed to verify the effectiveness of the proposed method.

  resources in the class A, D, E, C, D, B. There are many ways which can automatically create a user model. This paper uses a word frequency mining algorithms accessed high-frequency access in the recent time(Zeze Wu et .al, 2009)to establish the user interest model. The algorithm filters out the retrieval keywords which were most frequently used recently, and represents the user model with a weighted directed graph or a weighted vector model. The user interest model is shown in Figure 1. In order to distinguish different resources junction point between a user's interest points set and interest vector set, a set of rectangular nodes with weights is used to represent a user's interest point set and a directed graph of circular nodes with weights is used to represent a user's interest vector set.

Figure 1

 1 Figure 1 User Interest Model Map

1 )

 1 displayed to users at agriculture database granularity. When a user accesses the resource R of a database D in a time TS, Interest (D) of the database D increases. Combined with the user's access frequency Freq (d), the interest of the user on the database D can be gotten according to the following method(Xiaomin Ying,2003): I=Interest(D)*Freq(D) (Interest (D) represents a user's degree of interest for database D. And Freq (D) represents the frequency of a user access to the database D.

Acknowledgements

The work is supported by the special fund project for Basic Science Research Business Fee "The demonstration of Tibet agricultural information personalized service system", AII (No. 2012-J-08).