Haijiang Tai

Yuting Yang

Shuangyin Liu

Daoliang Li

A Review of Measurement Methods of Dissolved Oxygen in Water

Keywords: Dissolved oxygen, Fluorescence quenching, Environmental monitoring

Abstract

Introduction

With the rapid development of industry and agriculture, a large number of industrial waste water and agricultural drain is discharged to rivers and lakes. At the same time, about 80% of sewage of city life is discharged directly without handled, towns and rural sewage is discharged in disorder state. As a result, water quality of many locals deteriorating, water pollution and the shortage of water resources increasing seriously.

※Yuting Yang, contributed equality with Haijiang Tai, and they are co-first author *Corresponding author, Tel: +86-10-62736764, Fax: +86-10-62737741, Email: dliangl@cau.edu.cn The need of real-time monitoring and effective treatment of wastewater is urgent, and dissolved oxygen content in water quality monitoring is an important indicator.

Dissolved Oxygen (DO) refers to oxygen dissolve in water molecules state, DO is vital for all higher aquatic life (B.A. Cox,2003;M.A. [START_REF] Best | Dissolved oxygen as a physico-chemical supporting element in the Water Framework Directive[END_REF]. A source of DO is atmospheric oxygen infiltrate water body when dissolved oxygen in water is not saturated; another source is released by water plant through photosynthesis. DO is changes as temperature, pressure, salinity changes, generally speaking, the higher of temperature and dissolved salts, the lower of DO content in water; the higher of air pressure, the higher of DO content in water.

DO is consumed by reducibility material, such as sulfide, nitrite and ferrous material.

Besides, breathing of microbes in the water and oxidative decomposition of aerobic microbes to organic matter can also consume DO. Consequently, DO is the capital of water, and the indication of water self-purification. DO is approximate to saturated value (9ppm) in natural water, DO content reduce when algae breeding exuberant.

DO content reduce as a result of water polluted by organic matter and reducing material. As to aquaculture, DO content is vital for all aquatic life, such as fish. When DO concentrations are under 4mg/L, fish will be dying by suffocation. As to human, DO content could not be less than 6mg/L. When the rate of Oxygen dissolves into water is lower than DO consumption rate, DO content will be reduce to 0 mg/L, anaerobic bacteria breed and water quality will be worse, so DO content can reflect the contaminated level of water, DO content is an important index of contaminated level of water and also a overall indicator of measuring water quality. Therefore, measurement of DO content has important significance to environmental monitoring, and the development of aquaculture industry.

Methods and principle of DO measurement

Iodometric method(GB7489-87)

Iodometric method (equivalent to international standard ISO 5813-1983) is the benchmark of DO measurement method, it is the first method of DO measurement by using chemical test method, and measurement accuracy is high (Irja Helm, 2009). Its principle is added manganese sulfate and alkaline potassium iodide in water samples, and produces manganic hydroxide. The nature of manganic hydroxide is unstable extremely at the time; it combined with DO quickly, and produces manganese manganic acid:

4 2 2 4 4 ) ( 4 8 4 SO Na OH MN NaOH MnSO     (1) 3 2 2 2 2 ) ( 2 MnO H O OH Mn   (2) O H MnMnO OH Mn MnO H 2 3 3 3 2 4 2 ) ( 2 2     (3)
Add in concentrated sulfuric acid and has combined dissolved oxygen (existence in the form of MnMnO3) react with potassium iodide added in solution, and separate iodine.

   (6)

Where V , is the amount used of

O S Na

, a is the volume of water samples used in titrating. DO could be calculated as following:

a M V L mol DO   ) / ( (7)
Iodometric method is applicable to clean water, such as source water and surface water. Iodometric method is a kind of traditional measurement method of DO, it has high measurement accuracy, and the measurement uncertainty is 0.19mg/L. But the method is a kind of pure chemical testing method, time consuming and program trivial, cannot satisfy the online measurement requirements. At the same time, the organic matter easy oxidizing, such as tannic acid, humic acid and xylogen, can lead to disturbance with measurement results.

Current determination method (Clark OXYSENS)

Current determination method measure DO content in water according to diffusion rate of molecular oxygen spread through the film This kind of film can only through gas and generally use the polytetrafluoroethylene film. Oxygen through the gas diffusion to electrolyte, reduction reactions occur immediately in the cathode (positive), oxidation Reaction occur immediately in the anode (negative), the current produced is proportional to oxygen concentration, by means of measuring the size of the current can get dissolved oxygen (DO) concentrations.

Oxygen is consumed at the cathode according to the reaction (Yongxia Zhang, 2003):

     OH e O H O 4 4 2 2 2 (8)
In response to the production of hydroxyl ions (  OH ) at the cathode, and to preserve the charge balance of the electrolyte (saturated KCl ) solution, chloride ions react with silver at the anode according to the reaction:

     e AgCl Cl Ag (9)
Therefore, the chloride ions in the electrolyte solution function as a carrier of the electric charges. Galvanic electrodes use silver or platinum as the cathode and lead, iron, or zinc as the anode: [START_REF] Campbell | Optical dissolved oxygen lifetime sensor based on sol-gel immobilization[END_REF] anode:

cathode :      OH e O H O 4 4 2 2 2
      e O H PbO OH Pb 4 2 2 4 2 2 (11)
The measurement speed of current determination method is fast than iodometric method, easy operation and less disturbance, and can realize automatic and continuous detection. However, its oxygenater membrane and electrode easy ageing, when water samples containing the algae, sulfide, carbonate, oil and other material, oxygenater membrane will be jam or damage, need to pay attention to protect and change in time. Besides, it determines the concentration of oxygen relies on the electrode occur oxidation reduction reactions under the action of oxygen. Currently, instruments obtainable in the market belong to the type of Clark electrode mostly, activated at set intervals and oxygenater membrane should be changed regularly.

Representative products is a series of portable dissolved oxygen meter of YSI company of United States, this device used widely in our country, such as YSI58 Dissolved oxygen meter, it could complete the test work of laboratory and field effectively, easy to carry and operate, measurement range is 0-20 mg/L, accuracy is ±0.03mg/L.

Fluorescence quenching

Fluorescence quenching refers to any process which decreases the fluorescence intensity of a molecule. A variety of processes can cause fluorescence quenching, such as excited state reactions, energy transfer, complex formation and molecular collision [START_REF] Lakowicz | Principles of Fluorescence Spectroscopy[END_REF]. This phenomenon may pose challenges for the implementation of fluorescence spectroscopy for water quality monitoring method due to the complex and variable water matrices that exists within water recycling systems and distribution networks. The potential influence on fluorescence measurements from temperature variations, pH, metal ions and oxidation processes need to be considered (R.K. [START_REF] Henderson | Fluorescence as a potential monitoring tool for recycled water systems: A review[END_REF].

From the beginning of the 80s, people have begun to explore the work of applied to oxygen probe of fluorescent indicator. Four alkyl amino ethylene has been adopted for chemical fluorescer, it fall into disuse for its attenuate gradually in 12 hours in application of response with oxygen. Pyrene, Pyrene butyrate and Fluorine anthraquinone are good indicator of oxygen.

Wolfbeis had reported a fluorescent sensor response to oxygen fast in 1984, took pyrene butyrate fixed in porous glass as indicator. The advantage of the sensor is response fast (low in 50 ms), and have a good stability. Philip had fixed three fluorescence indicators of coumarin 1, coumarin 103 and coumarin 153 in XAD-4, XAD-8 and silica gel respectively, make comparisons in sensitivity, launch of strength and stability, coumarin 103 fixed in XAD-4 is the best option as interface in fiber optic oxygen sensor. Fiber optic oxygen sensor takes it as indicator has the application scope widely. Later, organic compounds of transition metal (Ru, Os, Re, Rh and Ir) got attention for its special performance. In generally, selecting ruthenium complex as indicator. Fluorescence intensity of ruthenium complexes exist one-to-one correspondence with the partial pressure of oxygen. Excited state of ruthenium complexes has long life, no need oxygen; its chemical composition is very stable, and insoluble in water. During the process of metal ligand charge transfer (MLCT) from ground state to excited state of Ruthenium complexes of chromium, the nature of excited state is closely related with the ligand structure, usually with the ligand conjugated system increases, the fluorescence intensity and fluorescence life expectancy increases.

The most common method of optically detecting oxygen has been through the measurement of the fluorescent intensity of suitable fluorophores, the intensity decreasing with increasing oxygen concentration (Lee, S., Shin, 2001; Choi, M.M.F.,

1999).

The oxygen quenching process is described by the Stern-Volmer equations:

2 0 1 pO K I I SV   (12) 2 0 1 pO K SV     (13) 0  k K SV  (14)
Where I and  are, respectively, the fluorescence intensity and excited state lifetime of the fluorophores, the subscript 0 denotes the absence of oxygen, Much has been published on optical oxygen sensors that measure the intensity of phosphorescence. It has now been established that these sensors have many drawbacks. These include susceptibility to light source, detector drift, and changes in the optical path (A.K. [START_REF] Mcevoy | Dissolved oxygen sensor based on fluorescence quenching of oxygen: sensitive ruthenium complexes immobilized in sol-gel-derived porous silica coatings[END_REF]. These effects can be minimized by operating the sensor in the time domain instead of in the intensity domain. The lifetime  is an intrinsic property of the lumophore that, unlike intensity, is virtually independent of external perturbations. The fluorescence is quenched in the presence of oxygen, and this quenching is described in Eq. [START_REF] Xiong | Dissolved oxygen sensor based on fluorescence quenching of oxygen-sensitive ruthenium complex immobilized on silica-Ni-P composite coating[END_REF]. Using this relationship, the dissolved oxygen concentration can be measured.

Phase modulation fluorometry is the preferred method of conducting fluorescence lifetime measurements particularly with long lifetime fluorophores due to the simplicity and low cost of the equipment required in comparison to direct lifetime measurements. With a sinusoid ally modulated excitation signal, any fluorescence produced will also be modulated at the same frequency but will be time delayed, or phase shifted, relative to the excitation signal. The relationship between the lifetime,  , and the phase angle,  , for a single exponential decay is given by (A. Campbell, 2004):

   f 2 tan  (15)
Where, f is the modulation frequency. The optimum modulation frequency that maximizes sensitivity is generally found by adjusting the frequency so that the phase change between extremes of measurements (0 and 100% DO) is maximized. Experiments show the method overcome the disadvantage of iodometric method and current determination method, photochemical stability and reproducibility is very good, without delay, high precision, long life, Can be real-time monitoring of dissolved oxygen in water, The range is generally 0~20mg/L, the general accuracy is lower to 1%, response time is lower to 60s.

Other detection methods

Conductometry:Use conductive metal thallium or other compounds to react with dissolved oxygen to produce the thallium ion. Through the determination of water conductivity delta, can get the concentration of dissolved oxygen (DO).Experiments

show that increase by 0.035 S/cm of conductivity is equivalent to 1 mg/L dissolved oxygen (DO). This method is the most sensitive method for monitoring dissolved oxygen (DO).

Anodic stripping voltammetry: Using conductive metal thallium to react with dissolved oxygen to produce the thallous ion. Then use template leaching to determine the concentration of the Tl+ ions, thereby obtained the concentration of dissolved oxygen (DO). This method has the advantages of sampling quantity is little, high sensitivity and temperature effect is not seriously. In general, most of the commercial dissolved oxygen meter on the market is based on Clark oxygen electrode, fiber-optic oxygen sensor based on fluorescence quenching method is less.

The present situation of the research on DO in water at home and abroad

Conclusion

Currently the mainstream of international development is fiber-optic oxygen sensor To prevent contamination, corrosion and interference of water biological, anti-interference ability is the key of instrument. We should be research on chemical stability of sensing membrane, anti-corrosion apparatus and stability of the circuit.

View of the optical sensor based on fluorescence quenching method with high accuracy and strong anti-interference ability, and good repeatability and stability, can be used for water quality measurements in aquaculture and water pollution level measurement in agriculture, so research on this sensor has important practical applications and commercial value.

4

 4

  Volmer constant, k is the diffusion-dependent bimolecular quenching constant, and 2 pO is the oxygen partial pressure. According to the actual measurement of fluorescence intensity, 0

Fig. 1

 1 Fig.1 illustrates the principles of phase modulation fluorometry. Many optical oxygen sensors have been tested that utilize phase modulation or other lifetime measurements for oxygen and DO measurements.

Fig. 1

 1 Fig.1 Illustration of phase shift in fluorescence emission

  based on fluorescence quenching, According to fluorescence quenches characteristics, use the following method to realize dissolved oxygen detector: optical signal emitted by the source filter to a area of the fluorescent indicator, dissolved oxygen react with fluorescent indicator, causing change of the light intensity, wavelength, frequency, phase, polarization and other optical characteristics, then arrive in the detectors and signal processing devices, get information of the concentration of dissolved oxygen.

Acknowledgements. This work was supported by the programs "Development and

Applications of sensor network applied to monitor bloom of blue-green algae in Taihu Lake" (2010ZX03006-006), and Beijing Natural Science Foundation "Integrations methods of digitalization technologies in intensive fish farming" (4092024).


Jingying Tan
email: jingyingtan@163.com

Shi Li

Qing Wang

Experimental Study on Forced-air Precooling of Dutch Cucumbers

Keywords: forced-air precooling, air velocity, initial temprature, temperature gradient

Experiments about the forced-air precooling of cucumbers were carried out under different operation parameters. The results show that the relation between cooling rate and air velocity can be given by power function. The linear relationship between the initial temperature of cold air and cucumbers and the final temperature of cucumbers is gained. Temperature gradient exists in different radius position in the same cross section. But the temperature gradient is not significant for the beginning and the final during the procooling process.

Introduction

The precooling means that the tempreature is quickly decreased from the room temperature to the storage temperature. The precooling process is an important technological part for the food storage. The shelf life of the fruit-vegetable can be delayed significantly and the freshness and the nutritional ingredient can be kept better after precooling. There are several types for precooling, such as vacuum precooling, forced-air precooling, water precooling, water-air precooling. Several factors, such as precooling cost, precooling time, and the conditions of the cooling process, must be considered in the choice of the precooling types [START_REF] Jin | Study on Refrigeratory Compressor with Frequency Conversion and its Economical Efficiency[END_REF][START_REF] Tan | Study on the forced-air precooling process of torispherecal fruits and vegetables in cooling bed[END_REF]. The parameters of the precooling process can be predicted accurately according to the experimental data of precooling, experimental formula, and some charts. Ibrahim Dincer researched the precooling process of grape monocase by experiments [START_REF] Ibrahim | Air flow precooling of individual grapes[END_REF]. Vigneault researched the ventilation resistance of the precooling [START_REF] Vigneault | Pressure drop during forced-air ventilation of various shorticultural produce in containers with different opening configurations[END_REF]. Some researches did some experiments about the differential pressure precooling for the opening packing container [START_REF] Yang | Pressure Drop Characteristics in Forced-air Pre-cooling of Longan Fruits[END_REF][START_REF] Huang | Experimental study on pressure drop in pressure-difference precooling of cylinder fruits[END_REF][START_REF] Wang | Experimental study on parameters affecting grape pressure precooling[END_REF]. The experiements were done in the long and narrow duct.

The previous researches focused on the experiments and there were some differences comparing with the practical precooling production. The forced-air precooling, the best suitable types of the precooling for my country, is studied for the Dutch cucumbers in this paper. The effect of the precooling parameters during the precooling process is given.

Materials and Methods

Materials

The column Dutch cucumbers gained from the demonstration base of Country Farming Production in Tianjin were used in the experiment. The sizes of the cucumbers are uniform, diameter 25mm, and there are no plant diseases and insect pests and mechanical damnification.

experimental conditons

The experiments are conducted in the fruit-vegetable pre-refrigeratory and the conditions are follows:

(1) air velocity: 0.5m/s, 1.0m/s, 1.25m/s, 1.5m/s, 1.75m/s, and 2m/s.

(2) initial temperature for cold air: 2℃, 3℃, 4℃, 5℃, and 6℃.

(3) initial temperature for cucumber: 18℃, 19℃, 20℃, 21℃, 22℃, 23℃, 24℃, 25℃, 26℃, 27℃, and 28℃.

4) measuring position of the cucumber: center section, 1/2 radius, 3/4 radius, and surface.

data acquisition system

(1) temperature acquisition system The armoured thermocouple, WRNK-111, φ1×1000ms,leading wire 5m, is used to measure the temperature. The standard mercury filled thermometer is used to calibrate, ±0.1℃ for the measurement accuracy.

(2) humidity acquisition system EE21-FT6A23CA0170 system is used to defect the humidity real-time.

(3) air velocity acquisition system The sensor of EE65-VB5 ELEKTRONIK is chosen.

The data of the temperature, humidity, and the air velocity is collected by computer, 30s for the minimum collection interval. The interval of 1min is chosen for the data acquisition in the experiment.

The air velocity sensor, the temperature sensor, and the humidity sensor of SwemaAir300 are used for the experiment.

Results and Discussions

the effect of the air velocity on the falling temperature of the center

The falling temperature curves, the initial temperature of cold air 4℃ and cucumber 25℃, are shown in Fig. 1. It can be seen that the falling temperature curves are similar under air velocities. But the falling temperature effect is not proportional to the air velocity. The better precooling effect can not be gained by increasing air velocity, but the cost will quickly increase, especially more than 1.5m/s. So it can be concluded that the air velocity 1.5m/s is suitable.

The falling temperature equation for different air velocities is given by fitting the falling temperature curves.

0 00 Rt T T A e   (1)
The parameter T0, R0, and A0 are shown in Table 1. Equation 1 can be rewritten

0 0 0 Rt i TT e TT     (2)
Where T, center temperature at any time; T0, initial temperaure of center temperaure; Ti, cold air temperature; A0=Ti-T0, fitting value; R0, cooling rate in center.

The cooling rate will increase with the air velocity increasing and the relationship between them can be discribed by the power function according to table 1 Fig. 2. Fitting curves beteween the cooling rate and the air velocity in the center

the effect of the initial temperature of cold air during the precooling process

The results for the effect of the initial temperature of cold air during the precooling process is shown in Fig. 3, air velocity 1.5m/s and the initial temperature of cucumber 25℃. Temperature(℃)

Pre-cooling time(min) Fig. 3. the effect of the initial temperature of cold air during the precooling process in center It can be seen that the temperature decreases quickly when the lower initial temperature and the lower final temperature will be gained after precooling for two hours. The temperature curves between the initial temperature of cold air and the final temperature of cucumber are given in Fig. 4 and the linear relationship is given. The precooling curves of different initial temperature are given in Fig. 5, the initial temperature of the cold air 4℃ and air velocity 1.5m/s. Fig. 5. the relationship between the initial temperatures and the final temperature The linear relationship between the initial temperatures of airflow and final temperature of cucumbers is gained and it is similar to the previous results.

The temperature changes in different section for the whole precooling process

The temperature change curves for different sections, center, 1/2 radius, 1/4 radius, and surface, are given, air velocity 1.5m/s, initial temperature for cucumber 25℃, initial temperature for cold air 4℃. Pre-cooling time(min) Fig. 6. the temperature changes in different section during the precooling process It can be concluded that there is temperature gradient in different radius of the same cross section during the precooling process. The high cooling rate near the outside of the cucumber is gained due to the small resistance. The final temperature of the cucumber is close to the initial temperature of cold air 4℃ after the long precooling process.

Conclusions

It can be concluded from the forced-air precooling experiments for the cucumbers.

(1) Cooling rate increases with the increase in air velocity and the power function relationship between them is gained. air velocity 1.5m/s is best suitable for the precooling.

(2) There is the linear relationship between the initial temperature of the cold air and the cucumber and the final temperature of cucumber. The better cooling effect can be gained if the lower temperature of cold air is used, but the damage should be prevented when lower temperaure. The operation efficiency can be impoved when the fruitvegetables are picked not at noon but in dusk or in morning .

(3) There is temperature gradient in different radius of the same cross section. Sometimes the temperature gradient is significant during the precooling process due to the inside resistance of the cucumbers.

Fig. 1 .

 1 Fig. 1. the effect of the air velocity during the precooling process

Fig. 4 .

 4 Fig. 4. the relationship between the initial temperature of cold air and the final temperature of cucumber in center

Table 1 .

 1 and figure. 2. Equation 3 is given. Fitting results for different air velocities

	0.2207 Ru 	0.35352		(3)
	0
	A0	R0	T0	Correlation coerricent R2

Acknowledgements.

The authors are grateful for A Project Supported by Scientific Research Fund of Hunan Provincial Education Department (10B043), A Project Supported by Scientific Research Fund of department of science and technology Hunan Province P. R. China (2009GK3044) and A Project Supported by Scientific Research Fund of Hunan Institute of Science and Technology(2010Y38).


Yang Yang

Yu Zhao

Shuming Zhang

Yuanying Ni

Jicheng Zhan

Qualitative Analysis of Age and Brand of Unblended Brandy by Electronic Nose

Keywords: electronic nose, brandy, principal component analysis, cluster analysis

This paper reports the capability of electronic nose based on gas chromatograph (GC-Flash) in age identification and brand classification of brandy. Three different kinds of brandies by the static headspace sampling for the injection of the volatile compounds were analyzed. The data were disposed by multivariate data processing based on principal component analysis (PCA) and cluster analysis (CA). The results show that PCA could identify the age as PC1 represented the raw information of age well; PCA on small difference ages of the samples could classify the brands of the samples. The CA would cluster samples by age through its analysis on the three different kinds of brandies respectively; The CA on all samples indicated that significant differences existed among different brands with age under 20 years and clustering process performed firstly in each group by age information and then clustering progressively by age among all groups. GC-Flash electronic nose can be applied to identify the brand and age of different brandies.

Introduction

Grape brandy is a kind of alcoholic beverage, taking grapes as raw material and whose formation follows fermentation, distillation and storing in oak barrels [START_REF] Jiang Zhijun | Study on relation between components and ages of brandy[END_REF] . The unblended liquor after wine aging is namely original wine, which in the storing undergoes complicated physical, chemical and biological changes. The assessment by chemical methods and sensory evaluation is highly time-, labour-and capital-consumed; therefore the age of wine is regarded as a significant index to determine the quality and class of brandy [START_REF] Schaller | Electronic noses' and their application to food[END_REF] . With the development of detecting instruments, the rapid analysis technology (electronic nose, electronic tongue, electronic eyes and near-infrared spectroscopy, etc.), based on rapid analysis of the total sample information ("fingerprint") , is gradually applied to rapid identification of wine age, quality ,origin and brand. Electronic nose comprises an array of electronic sensors with partial specificity and an appropriate pattern-recognition system, capable of recognizing simple or complex odors [START_REF] Schaller | Electronic noses' and their application to food[END_REF] . Electronic nose technology is mainly applied in the food industry in quality control, process monitoring, freshness evaluation, shelf life prediction, authenticity assessment, etc. [START_REF] Schaller | Electronic noses' and their application to food[END_REF] .Researches in fermentation monitoring [START_REF] Pinheiro | Monitoring the aroma production during wine-must fermentation with an electronic nose[END_REF][START_REF] Pearce | Electronic nose for monitoring the flavor of beers[END_REF][START_REF] Kojima | Application of a new electronic nose with fingerprint mass spectrometry to brewing[END_REF][START_REF] Lozano | Electronic nose for wine ageing detection[END_REF][START_REF] Marti | Application of a headspace mass spectrometry system to the differentiation and classification of wines according to their origin, variety and ageing[END_REF] , identification of brand [START_REF] Garc | Electronic nose for wine discrimination[END_REF][START_REF] Pornpanomchai | Beer classification by electronic nose[END_REF][START_REF] Lozano | Identification of typical wine aromas by means of an electronic nose[END_REF] and origin [START_REF] Cynkar | Classification of Tempranillo wines according to geographic origin: Combination of mass spectrometry based electronic nose and chemometrics[END_REF][START_REF] Aleixandre | Analysis of grape variety and denomination of origin of several wines with an artificial nose[END_REF][START_REF] Berna | Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose[END_REF] , aroma evaluation [START_REF] Berna | Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose[END_REF][START_REF] Arroyo | Evaluation of wine aromatic compounds by a sensory human panel and an electronic nose[END_REF][START_REF] Ragazzo-Sanchez | Identification of different alcoholic beverages by electronic nose coupled to GC[END_REF][START_REF] Ragazzo-Sanchez | Off-flavors detection in alcoholic beverages by electronic nose coupled to GC[END_REF] and false distinguishing [START_REF] Guadarrama | Array of conducting polymer sensors for the characterisation of wines[END_REF] by electronic nose have been carried out at home and abroad with excellent performance. Electronic nose, as a rapid qualitative analysis instruments at first, after decades of development, has also made certain progress in quantitative analysis. The mass spectrometry-based electronic nose invented in the nineties [START_REF] Kojima | Application of a new electronic nose with fingerprint mass spectrometry to brewing[END_REF][START_REF] Berna | Comparison of metal oxide-based electronic nose and mass spectrometry-based electronic nose for the prediction of red wine spoilage[END_REF] , stepped forward a great deal in quantitative analysis.

The former researches on wine analysis by electronic nose have focused on the sensor-based or mass spectrometry-based electronic nose, but seldom on the ones analyzing brandy, and even the research on testing original brandy has not been reported yet. This study, using gas chromatography-based electronic nose, carried out studies on the wine age and brand identification of 3 domestic and overseas brands for the basis of wider application of electronic nose.

Experimental

Samples

The studies were performed on 29 brandies from three different brands (Group A, n = 12; Group B, n =13; Group C, n = 4). Group A, France Brandy, of which the ageing time is from 4 to 46 years old.

Group B and Group C are made in China, whose ageing time are 2 to 15 and 1 to 5, respectively. Those samples (brandies) came from the same variety of grape (Ugni Blanc) and the same traditional brewing process applied in Cognac region in France. They are all raw brandies without blending after maturing in oak barrels. The samples were taken directly from the winery and then stored at 4℃. All samples were kept at 20℃for 2 h before testing.

Electronic Nose

Experiments were performed with the HERACLES Flash Electronic nose (Alpha M.O.S Company, France) consisted of a Heracles host machine and a headspace auto-sampler unit. The HERACLES is a highly selective and sensitive specialty gas chromatograph, capable of performing very fast, low level hydrocarbon measurements in laboratory or field environments. Key features include versatility and speed of analysis, giving the productivity advantages over the more traditional gas chromatographs.

The HERACLES is a programmed temperature gas chromatograph using syringe or valve inlets to a flash evaporator. The sample is delivered to an adsorbent trap to concentrate the sample for delivery to twin capillary columns and flame ionization detectors (FID) simultaneously [START_REF] Yongmei | Electronic nose fingerprint of liquor of main flavor types[END_REF] . Trace level gas phase hydrocarbon samples down to a few parts per thousand million can currently be measured. Instrument control and the raw data process were performed by Alpha Soft (V11, Alpha M.O.S). A general scheme of the HERACLES Flash Electronic nose is presented in Fig. 1.

Fig. 1. Set-up applied in the measurement

In order to obtain maximum sensor response, operating parameters for the electronic nose were optimized for the brandy samples. The analysis parameters are as follows: ( 1 For the measurements, 1000 μL of the sample was placed in a 10mL glass vial provided with a pierceable disk in the cap. Before gas sample injection, sample was agitated by the auto-sample unit at 40 (±0.2) ℃, 500 rpm. All samples were analyzed in 7 times and the average of the chromatograms was used for subsequent statistical analysis.

Data Analysis

Two pattern recognition methods, principal component analysis (PCA) and Cluster analysis (CA) have been used for data analysis. Principal component analysis (PCA) is a chemometric linear, unsupervised and pattern recognition technique used for analyzing and reducing the dimensionality of numerical datasets in a multivariate problem. It can observe the classification results of sample principal components through the PCA map. PCA was performed in Alpha Soft (V11, Alpha M.O.S, France).

Cluster analysis (CA) is a kind of unsupervised pattern recognition. It is a method for grouping objects of similar kind into respective categories. CA of Samples was performed using SAS 9.2 software.

Results and Discussion

Detect and analyze Group A, B, C by electronic nose, eliminate abnormal data [START_REF] Hui | Comparison of total volatile basic nitrogen detection models in fishmeal based in electronic nose[END_REF] , obtain the peak figure (retention time and peak area) for each sample. After deducting the blank peak 24 peaks were obtained, including 12 peaks from gas spectrum column 1 and 12 peaks from column 2. Retention time and peak area of those 24 peaks are the "fingerprint" of static headspace volatile components which acquired by electronic nose, and also the raw data of principal component analysis and clustering analysis. For better understanding, the 24 retention time obtained by electronic nose can be defined as sensors while the corresponding peak area can be defined as the response value of sensors.

Principal Component Analysis Results

In this study, the PCA was firstly applied to group A. Using the raw data directly, three principal components were extracted: PC1-68.36%、PC2-9.90%、PC3-7.59%,The ellipses, which correspond to the 85.85% confidence intervals, are basically reflect the raw information [START_REF] Liu | Determination of egg freshness during shelf life with electronic nose[END_REF] . Fig. 2 demonstrates the score plot of the first two principal components of Group A. It is easy to distinguish brandy by their different ageing time and furthermore, with the extension of ageing time, the trends appear obviously in PC1: on the PC1 axis from right to left. Meanwhile, samples of close age which are distributed concentrated in the Fig. 2 and can be divided into 4 groups. Information from PC2 is inefficient in distinguishing samples of 30, 36 and 39 years and the supplement from PC2 turns to be necessary.

The PCA analysis results displays the similar scene in Fig. 3, four principal components were extracted: PC1-45.32%、PC2-18.34%、PC3-15.19%、PC4-6.29%,and 85.04% of the total variance of the data is displayed in the plot. The PCA analysis on Group A and B show that electronic nose is able to distinguish the ageing time of two brandies respectively clearly and the collected first principle components represent ageing time well. The PCA analysis on Group C represent an even better results, however, because of the limited volume of samples, the results are not listed in this paper. In the analysis process, we optimized sensors (eliminate the small peak with poor reproducibility and strongly linear-correlated sensor) and screened 12 sensors out of 24 sensors. Finally extracted two principal components:PC1 -70.32%, PC2-19.20% and the cumulative contribution rate is 89.52%. Fig. 4 shows the score and loading map of PCA analysis, in which we can see that electronic nose can distinguish the age and brand of different design groups. PC1 axis indicates information of wine ages and PC2 axis indicates information of brands. But PC2 is insufficient to differentiate the aged wine in Group B and the wine in Group A, which, is possibly relevant to the difference of wine ages among PC1 (68.36%) PC2 (9.90%) PC1 (45.32%) PC2 (18.340%) samples. At the same time, the loading map shows the information of ageing time and brand of sensors (retention time and peak area of electronic nose). In order to study the brand-recognition capacity of electronic nose for original brandies, we designed analysis process including samples of the three brands. Due to the large difference among the 3groups of wine, Group A (aged for 4 and 6 years) and Group B (aged for 2,3,4,5,6 years) and Group C(aged for 1,2,3,5 years) were chosen to conduct PCA .

The peak value at 4.17s、10.35s、14.17s、16.62s、21.79s of Column gas spectrum 1 and peak value at 4.53s、8.30s of Column gas spectrum 2 represent more brand information while the left 5 peak value represent ageing time information.

Clustering Analysis Result

In order to excavate more test results of the original brandy from the electronic nose, cluster analysis method was applied in Group A and equally in Group A, B and C respectively. Cluster analysis belongs to unsupervised pattern recognition, through which man can observe the effects of automatic classification and recognition of different ages and brands by electronic nose. Cluster analysis method is Group Average Method, and standardizes the data before the cluster analysis [START_REF] Aleixandre | Portable e-nose to classify different kinds of wine[END_REF] . (each data point minus its average point in the sensor column then divided by the standard deviation in its sensor column) Fig. 6 is the cluster result of Group A, the different age of the wine under the same brand, clustering of brandies of different brands was conducted according to the age of wine, firstly brandy of similar age can be divided into the same class, and then samples of different aging stage can be classified. The similar test result can be seen compared with the principal component analysis (Fig. 2) when the average distance of the classification is 5.0, three different types of the wine are less than 20 years, 20-30 year and 30-46 years. Fig. 6 Tree diagram from clustering analysis in Group Average method for Group A Cluster analysis results also showed in a regular pattern after cluster analyzing from the Group A, B and C. As we can see in the Fig. 7, three different brands and ages of the brandies were mixed together, clustering firstly process in the same brand of similar age. When the average distance is 3.89, the cluster can be divided into four groups: Group A, 4-14 years, Group B, 2-6 years, Group B, 7-15 year and Group C, 1-5 years, the brand element is now in the dominant place which illustrated that aroma components have significant differences when the age of the wine is under 20 years, electronic nose can distinguish the three brands. When the average distance is 5.43, the cluster can be assorted into three categories: Group A, B and C wine age less than 20 years, Group A, 22-24 years, Group A, 30-46 year, the component of the wine age placed the most important role during this time, the Average distance information reflected from electronic nose to different brand of the wine has been reduced. The principal component of wine sample A, B, C was analyzed. The principal component analysis represents the basic information of original data, and it also presents a good separation of brandy with different ages. The first principal component can reflect the wine age information. Principal component analysis also indicated that stage differences exist among the different brandy ages. Even though the sample age difference is big, distance between the brandy samples with small age difference is close; thereby several "ages' stage" was obtained.

Due to the large age difference among three samples, brandy samples with close age differences were chosen to study the identification function of electronic nose among different brands. Although the difference of age exists in different brands, principal component could still be useful in the identification among different brands. In addition, principal component analysis can reflect certain age information. The results indicated that when electronic nose distinguishes the brands of raw brandy, they can accept some wine age differences.

The clustering analysis was conducted on Group A, B and C. It was a process of clustering wine according they ages, which aggregated the brandy with close ages. Clustering analysis was performed on three sample groups together. Clustering process presented same brand wine with close ages firstly.

As for the samples with age less than 20 years, clustering according to different brands, brands differences were obvious. When the age gap was big (more than 20 years), brands identification was Average distance not good, but age difference became obvious. This result could be caused by the characteristics of brandy themselves. This phenomenon will make contribution to the use of electronic noses on brandy brands and ages identification. When a model was established, brandy ages should be taken into account. When electronic noses can identify wine age and wine brands correctly, the age difference between different brands could be allowed. However, model establishment should minimize the age differences, building models according to different brands wine with different ages respectively.

  ) Injector: injection time 1500 ms, injector temperature 200 ℃, injection volume 2000 μL; (2) Temperature program: initial temperature 40 ℃, final temperature 200 ℃, heating rate 2.0 ℃/s; (3) Detector temperature: 220 ℃; (4) Trap temperature: 250 ℃, purge time 15 s. (5) Acquisition time: 84 s.

Fig. 2 Fig. 3

 23 Fig. 2 Score plot of the first two principal components of Group A. The number 4, 6, 8,…,46 represents for the Brandies' ageing time

Fig. 5

 5 Fig.5is the score plot of PCA after decreasing the samples difference in wine age. The afterwards results illustrated a better effect in distinguishing , which means that ageing time difference will effect analysis results in brands distinguishing, overlarge difference in sample ages brings a great deal of difficulties to distinguishing brand information among different groups by electronic nose.

Fig. 4 Fig. 5

 45 Fig. 4 Score and loading plots of PCA for Group A, B and C. The figure represents for the retention time of peak and the number of column. (e.g. 4.35-2 represents the peak value at 4.53s of Column gas spectrum 2)

Fig. 7

 7 Fig.7 Tree diagram from clustering analysis in Group Average method for Group A, B and C


Liyong Liu

Daoliang Li

The measurement of the agricultural informatization contribution rate based on the analysis of the composite index and the C-D production function

Keywords: Agricultural Informationalization, Contribution rate, C-D production function

The agricultural informatization contribution rate is the proportion of the contribution of agricultural informatization to the algebraic summation of agricultural economy growth accounting for the agricultural economy growth. It is a comprehensive index which reflects the function of agricultural informatization in enhancing agricultural productivity and agricultural economy development. This paper combines the analysis of the composite index with C-D production function to measure the contribution rate of agricultural informatization. Firstly this paper is going to establish the agricultural information evaluation index system including the infrastructure, resources, application, personnel, and policy environment. Then it is going to calculate each index and the composite index of agricultural informatization by means of synthetic rating analysis. Based on the above, this paper is going to build up a C-D production function model to measure the agricultural informatization contribution rate by regression function.

Introduction

The agricultural informatization contribution rate is the proportion of the contribution of agricultural informatization to the algebraic summation of agricultural economy growth accounting for the agricultural economy growth. It is a comprehensive index which reflects the function of agricultural informatization in enhancing agricultural productivity and agricultural economy development. [START_REF] Liu | An empirical analysis on the agricultural informatization contribution based on the data envelopment analysis (DEA)[END_REF]. Its calculation method is shown as Formula (1): However agricultural informatization is a complex system which has too many influencing factors to be calculated directly. Therefore it is essential to establish a feasible way to measure the agricultural informatization indirectly in order to meet practical needs. Hence this paper is going to identify agricultural informatization indices, discuss the relation of agricultural informatization and agricultural economic growth, and finally set up the measure method for agricultural informatization.

ESTABLISHING THE AGRICULTURAL INFORMATIZATION EVALUATION INDEX SYSTEM

An index system scientific enough to truly reveal the object should be able to cover indices all-sidedly. Based on the comprehensiveness, scientificalness, objectivity, practicability, and systematicness principles of index, combining the practice of China's agricultural informatization and related statistical yearbooks, China's agricultural informatization evaluation index system should consist of five indices: the infrastructure (F), resources (R), application (A), personnel (H), and policy environment (E) and 19 sub-indices.

2.1

The infrastructure of agricultural informatization (F)

The infrastructure of agricultural informatization is the hardware basis and neural system of agricultural informatization; it is also the precondition for the functioning of agricultural information resources and technologies; and the infrastructure of agricultural informatization is composed of traditional network (postal network, television and radio network, and telephone and mobile network), and modern information network (internet services and computer equipment). The infrastructure of agricultural informatization enables people to transmit information beyond geographical and temporal constraints and facilitates the collaboration through resource sharing. In this paper the infrastructure of agricultural informatization includes five sub-indices, which are the population coverage of radio, the population coverage of television, telephone density, mobile phone penetration rate, and family computers with internet access (Zhao, 2007).

Resources of agricultural informatization (R)

Resources of agricultural informatization are the content carried by the infrastructure of agricultural informatization. It involves document, information, diagrams and data, and demonstrates connections of various subjects and inherent laws. This index includes two aspects of document information content and digital information resources covering four sub-indices of agricultural book types, publishing newspaper and magazines, libraries and websites.

The application of agricultural informatization (A)

Information technologies are essential to the development of informatization. Not only fundamentally change the form and content of human information activities, information technologies but also strong impact on people's lifestyle, behaviour pattern, and thinking and production mode (TKularahte, 1999). Moreover the application of information technologies into the agriculture serves the purpose of agricultural informatization which is accelerating the development of agriculture, rural area and farmers. Therefore in this paper three sub-indices, the population of netizens in rural area, number of key agricultural scientific and technological achievements, and the aggregation of tele-service in rural area [START_REF] Gao | A preliminary research on the measurement index system for agricultural informatization[END_REF].

2.4

Personnel in agricultural informatization (H)

Personnel in informatization have a decisive role in informatization and is a significant condition securing the success of informatization construction. This paper studies this issue by looking at personnel in scientific activities, communication and transportation, education and culture, radio and television, computer service and software development. As a result this paper selects the population taking up scientific activities out of ten thousand people, the population in information transmission by computer service and software industry, the population in transportation, logistics and telecommunication industry, and the population in education, cultural, art, television and radio sector, as four sub-indices to evaluation the personnel in agricultural informatization [START_REF] Yu | A study on the measurement index system of Chinese agricultural informatization development and the measurement method[END_REF].

The policy environment of agricultural informatization (E)

A favourable policy environment facilitates the informatization development as the informatization is a complex system which is influenced by the society, the state and the government (Li, 2009). Policies in papers refer to those laws, regulations, and policies made by legislatures, administrative authorities, and other related authorities in order to coordinate and accelerate the informatization, and those fund and supplies allocated by the government to support the informatization. Therefore this paper uses the proportion of S&T expenditure in GDP, local government fund for S&T, and the proportion of local government fund for S&T in the total expenditure of local government as three sub-indices to assess the government support to agricultural informatization.

testing indices of agricultural informatization

This paper measures the development of agricultural informatization by synthetic rating analysis. First of all, this paper uses Delphi method to determine the weight of each index and sub-index in the index system which is demonstrated by Table 1 All the data need to be processed with dimensionless method prior to calculation in order to unify units. As Table 3 shows, this paper sets the year 2001 as the benchmark and all the index data in this year as 1, then divides the absolute value of each index data in year t by the corresponding index data in the benchmark year, and obtains the dimensionless value of each index in year t. For simplicity, we set the weight of each index to equal. Since there are five sub-indices under the infrastructure of agricultural informatization, the weight of each index is Pi=0.2. Then we can use Formula (2) to calculate IIF which is the annual index value for infrastructure of agricultural informatization:

In Formula (2), Pi is the weight of each index, and the mean of each third-class index under the infrastructure of agricultural informatization is given 0.2. Wi is the dimensionless value of index i, and n is the amount of indices in the index system, which is five in this formula.

Measuring the overall index

Likewise, four index value, IIR, IIA, IIH, and IIE can be calculated for the other four indices which are resources, application, personnel and policy environment. Then we adopt Formula (3) which is listed below to find out the index II, the overall index of agricultural informatization. Its log-linear model is:

In Formula (4) and Formula (5), Y, K, L, and I represent the total yield of agricultural products, the quantity of capital input, the quantity of labour input, and the index of agricultural informatization respectively; α, β, andγ refer to the capital elasticity of the output, the elasticity of labour, and the elasticity of informatization respectively; A, which is set to be a constant, refers to the influence of other factors on agricultural economic growth except capital, labour and informatization.

Model regression

All the index data adopted in the model, including the overall index I, can be calculated through the index system established in this paper; the total yield of agricultural production (Y), the fixed asset investment (K), and the population working in agriculture (L) can be looked up in China Statistical Yearbook. Then a regression model can be established as shown in Table 4.

2001 Y 1 K 1 L 1 I 1 F 1 R 1 A 1 H 1 E 1 2002 Y 2 K 2 L 2 I 2 F 2 R 2 A 2 H 2 E 2 2003 Y 3 K 3 L 3 I 3 F 3 R 3 A 3 H 3 E
(L') (I') Standardized coefficient a j g B b h w m (T-statistic) t c f i R-squared r 2

F-statistic f

Durbin-Watson statistic d

According to the regression model above, we determine the fitting degree of selected samples and the entirety, and test the linearity degree of the model by F-statistic and the explanation degree of each variable to the economic growth by T-statistic. If the fitting degree, the linearity degree and the explanation degree is high, and variable K, L, I and the constant term is able to meet the test at a high probability, it means that the model can meet the test as a whole and have a good simulate effect on the reality. Thus the model of agricultural economic growth is shown as Formula ( 6):

Formula (6) can be converted into Formula (7):

Similarly, we can also set up regression models for the five first-class indices including the infrastructure of agricultural informatization (F), resources of agricultural informatization (R), the application of agricultural informatization (A), and personnel of agricultural informatization (H), and the policy environment of agricultural informatization (E), which this paper won't repeat.

conclusion

This paper finds out the standardized coefficient and the output elasticity of the three factors in agricultural economy which are capital, labour and information. From the perspective of standardized coefficient, in the regression result of the model of the overall index of agricultural informatization, the standardized coefficients of the fixed asset investment, population working in agriculture and information are a, j, and g respectively, which can assess the influence of the three factors on agricultural economy development.

From the point of view of the output elasticity, in the regression result of the model of the overall index of agricultural informatization, the output elasticity of K', L', and I' are: h, w, and m. According to the definition of output elasticity, the agricultural economy increases by m% for one further percentage point of growth of agricultural informatization; whereas one further percentage point of growing of capital and labour can increase the agricultural economy by h% and w%. Thus the contribution rate of agricultural informatization to the agricultural economic growth in any year can be calculated after the index of agricultural informatization is acquired.

Likewise, the contribution rate of each index in the model to agricultural economic can be measure`ed.

1 5 . The policy environment of agricultural informatization 5 . 1 5 . 3

 155153 The population of netizens in rural area (ten thousand) 3.2 Number of key agricultural scientific and technological achievements 3.3 The aggregation of tele-service in rural area (hundred million RMB) 4. Personnel in agricultural informatization 4.1 Population taking up scientific activities out of ten thousand people (per ten thousand people) 4.2 Population in information transmission by computer service and software industry (per ten thousand people) 4.3 Population in transportation, logistics and telecommunication industry (per ten thousand people) 4.4 Population in education, cultural, art, television and radio sector (per ten thousand people) The proportion of S&T expenditure in GDP (%) 5.2 Local government fund for S&T(hundred million RMB) The proportion of local government fund for S&T in the total expenditure of local government (%) For the sake of simplicity, this paper uses the measurement of the infrastructure of agricultural informatization (F) to demonstrate the measurement method for the indices and sub-indices and obtain the final index of agricultural informatization in order to avoid repetition. The corresponding data for sub-indices under the infrastructure of agricultural informatization from 2001 to 2010 is illustrated in

  Hereby, II is the overall index value of regional agricultural informatization; n is the amount of first-class indices in the evaluation system; mi is the amount of sub-indices under the index i; Wij is the standardized value of sub-index j under the first-class index i; and Pij is the weight of sub-index j accounting in the first-class index which it belongs to. Paul Dauglas break the hypothesis of fixed capital-output ratio and capital-saving ratio raised by Harrod-Domar model and establish C-D production function is established by to analyse the relation of input and output in a simple way.In general C-D production function divides the input into capital and labour[START_REF] Wang | An empirical analysis on the informatization's contribution to the economic growth in rural area in Shandong Province[END_REF]. But in order to calculate the contribution made by agricultural informatization to agricultural economic growth, this paper revises the C-D production function based on the new growth theory proposed by Paul M. Romer. Romer argues that it is not only capital and labour, but also S&T development that should be included in C-D production function as the third element[START_REF] Romer | The origins of endogenous growth[END_REF]. Therefore this paper argues that information is the foremost income of S&T development and consequently information can take the place of S&T development to be factored in the C-D production function as the third element in the input. Hence C-D production function can be revised to Formula (4):

Table 4 .

 4 The

Table 1 .

 1 . All the corresponding data for indices in Table 1 can be traced in China Statistical The evaluation index system of agricultural informatization development

	Yearbook, China's Major S&T Indicators Database, and China's Internet
	Development Survey. However this paper, as a method study, is not going to put any
	data into the model for calculation.

Table 2 .

 2 Table2. Sub-indices under the infrastructure of agricultural informatization(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)

	Year	Population coverage rate of radio	Population coverage rate of television	Telephone density	Mobile phone penetration rate	Family computers with internet access
	2001	F 11	F 21	F 31	F 41	F 51
	2002	F 12	F 22	F 32	F 42	F 52
	2003	F 13	F 23	F 33	F 43	F 53
	2004	F 14	F 24	F 34	F 44	F 54
	2005	F 15	F 25	F 35	F 45	F 55
	2006	F 16	F 26	F 36	F 46	F 56
	2007	F 17	F 27	F 37	F 47	F 57
	2008	F 18	F 28	F 38	F 48	F 58
	2009	F 19	F 29	F 39	F 49	F 59
	2010	F 10	F 20	K 30	K 40	K 50

Table 3 .

 3 The dimensionless processing of sub-indices under the infrastructure of agricultural

	informatization (2001-2010)
	Year	Population	Population	Telephone	Mobile	Family computers

  Table 4 is conducted based on the Enter method by SPSS 13.0. The regression result is shown in Table5. Based on the regression result, a regression model can be set up which can be sorted into a simulation equation consisting three variables, the fixed asset investment, working population in agriculture, and the overall index of agricultural informatization, another five variables regarding the five sub-indices, and the total yield of agricultural production.

	2007	Y 7	K 7	L 7	I 7	F 7	R 7	A 7	H 7	E 7
	2008	Y 8	K 8	L 8	I 8	F 8	R 8	A 8	H 8	E 8
	2009	Y 9	K 9	L 9	I 9	F 9	R 9	A 9	H 9	E 9
	2010	Y 10	K 10	L 10	I 10	F 10	R 10	A 10	H 10	E 10
	A linear regression analysis of Y', K', L', I', F', R', A', and H' corresponding
	to indices in
										3
	2004	Y 4	K 4	L 4	I 4	F 4	R 4	A 4	H 4	E 4
	2005	Y 5	K 5	L 5	I 5	F 5	R 5	A 5	H 5	E 5
	2006	Y 6	K 6	L 6	I 6	F 6	R 6	A 6	H 6	E 6

Table 5 .

 5 The regression result of the model (the overall index of agricultural informatization)

	Item	Constant term	(K')

Acknowledgement

This paper is proudly funded by Beijing Municipal Science & Technology Commission's Scholarship for PhD thesis in Soft Science. This article also draws on the Beijing New Rural Development Research Project (No. 1).


Xiaofei An

Minzan Li

Lihua Zheng

Estimation of Soil Total Nitrogen and Soil Moisture based on NIRS Technology

Keywords: Soil total nitrogen, Soil moisture, BP neural network, Support vector machine

Estimation model between soil moisture content and the near infrared reflectance was established by the linear regression method and the models between soil total nitrogen content and the near infrared reflectance were also established by the BP neural network method and Support Vector Machine (SVM) method. Forty-eight soil samples were collected from China Agricultural University Experimental Farm. After the soil samples were taken into the laboratory, NIR absorbance spectra were rapidly measured under the original conditions by the FT-NIR (Fourier Transform Near Infrared Spectrum) analyzer. At the same time the soil moisture (SM) and soil total nitrogen (TN) were measured by the laboratory analysis methods. The results of the study showed that a linear regression method achieved an excellent regress effect for soil moisture. The correlation coefficient of the calibration (R C ) was 0.88, and the correlation coefficient of the validation (R V ) was 0.85. The model was passed F test and t test. For soil total nitrogen, the model effect of BP neural network was better than that of SVM method, and the correlation coefficient of the calibration (R C ) coefficient and the validation (R V ) was 0.92 and 0.88, respectively. Both RMSE and PMSE were low. The results provided an important reference for the development of a portable detector.

Introduction

Precision agriculture requires a clear understanding of the spatial variability of soil characteristics and the nutrient status in real time. Spectroscopy, as a rapid, nondestructive, simple, and green measure and analysis technique, plays an important role in the determination of soil nutrients [START_REF] Li | Evaluating Soil Parameters with Visible Spectroscopy[END_REF]. Research based on real-time spectral analysis for determination of soil nutrients in precision agriculture can provide accurate scientific data and theoretical basis for the development of a low-cost and intelligent measure instrument. Many scholars have researched on the relation between the soil parameters and the spectral characteristics in the 20th century. Alabhas et al (1972) found that soil organic matter in the near infrared area had characteristics related with several functional groups in the organic compound [START_REF] Al-Abbas | Relating Organic Matter and Clay Content to the Multispectral Radiance of soils[END_REF]; [START_REF] Li | Spectroscopic Approach to Soil Parameters Sensing[END_REF] analyzed soil parameters, such as NO3-N, SOM, EC and pH based on NIRS, and then determined sensitive wavebands of the parameter [START_REF] Li | Spectroscopic Approach to Soil Parameters Sensing[END_REF]; Sun Jianying (2006) measured soil data of NIRS by a FT-NIR analyzer at the wavelength range of 830-2500 nm and several forecasting models for soil moisture, soil organic matter and soil total nitrogen were established [START_REF] Sun | Real-time Analysis of Soil Moisture, Soil Organic Matter, and Soil Total Nitrogen with NIR Spectra[END_REF];Zheng Lihua (2010) established the forecasting models for soil total nitrogen and soil organic matter by WT (Wavelet Transform) and SVM method [START_REF] Zheng | Forecasting Soil Parameters Based on NIR and SVM[END_REF]; Gao Hongzhi (2011) selected nine wavelengths to establish the forecasting models of soil total nitrogen and soil organic matter based on actual experience [START_REF] Gao | Near Infrared Spectral Analysis and Measuring System for Primary Nutrient of Soil[END_REF].

Remarkable progress has been made in the forecasting of soil moisture, soil organic matter and soil total nitrogen based on NIRS [START_REF] Yu | Near Infrared Spectrum Analysis of Soil Organic Matter and Nitrogen[END_REF][START_REF] Zheng | Evaluation of Soil Fertility with Spectrophotometer and Spectroradiometer. J.Infrared and Photoelectronic Ima gers and Detector Devices[END_REF][START_REF] Chen | Near Infrared Spectroscopy Real-time Measuring Soil Total Nitrogen and Phosphorus Content[END_REF][START_REF] Yuan | Total Nitrogen in Soil and the Total Phosphorus Content in the Near Infrared Spectrum of Real-time Detection Methods[END_REF][START_REF] Barthes | Determining the Distributions of Soil Carbon and Nitrogen in Particle Size Fractions using Near-infrared Reflectance Spectrum of Bulk Soil Samples[END_REF][START_REF] Song | Based on the OSC and PLS Soil Organic Matter of Near Infrared Spectrometry[END_REF][START_REF] Sun | Based on the North of the Near Infrared Spectrum Chao Soil Parameter Real-time Analysis[END_REF][START_REF] Zheng | Based on the Soil of Near Infrared Spectral Technology Parameters of the BP Neural Network Forecast[END_REF].However, above studies provided the dried and crushed soil samples in the experiment so that the results obtained could not be directly used to real-time evaluate soil parameters. Hence, it was important to reveal the feasibility and possibility of analyzing soil parameters with NIR spectroscopic techniques using raw soil samples and provide a reference for the development of a portable detector.

2

Materials and Methods

Experiments and collection

Forty-eight soil samples were collected from the experimental field of China Agricultural University located in the suburb of Beijing. Soil samples were collected from different experimental areas and depth for the different levels. A double-layer sealing plastic bag and kraft paper bag were adopted to avoid soil moisture loss. The NIRS of the forty-eight soil samples were measured in the laboratory by a MATRIX-I type of FT-NIR analyzer with a rotating sample pool produced by the Germany Bruker Optical Company. About 15 grams of soil sample could be directly placed into the rotating sample pool by a forty-eight mm diameter of quartz glass. The spectral measure range was 12798~3599 cm-1(781~2779nm), resolution 4 cm-1, wavenumber accuracy 0.1 cm-1, scan times 32. A spectral curve could be obtained after taking a small amount of soil sample into the rotating sample pool and setting the relevant parameters. Fig. 1 shows several spectral curves of soil samples in the range of 850~2450 nm. Soil moisture contents and soil total nitrogen contents were measured by physical method and chemical method respectively in the laboratory. Soil moisture contents were measured by the drying and weighing method with an electric heater, and soil total nitrogen contents were measured by the semi-micro kjeldahl determination with a FOSS2300 Kjeldahl nitrogen instrument.

Fig. 1. Spectral curves of soil samples

Modeling

Single linear regression analysis is a mathematical statistical method to deal with the correlation between variables [START_REF] Zheng | Based on the Soil of Near Infrared Spectral Technology Parameters of the BP Neural Network Forecast[END_REF]. A linear regression method can be used for the obvious correlation between soil moisture contents and the absorbance of the soil samples. But the correlation between the soil total nitrogen content and the absorbance was lower than 0.3 in full spectral range. In addition, several papers demonstrated that there was weak correlation and nonlinear relation between soil total nitrogen contents and the near infrared spectra [START_REF] Sun | Real-time Analysis of Soil Moisture, Soil Organic Matter, and Soil Total Nitrogen with NIR Spectra[END_REF][START_REF] Zheng | Forecasting Soil Parameters Based on NIR and SVM[END_REF][START_REF] Gao | Near Infrared Spectral Analysis and Measuring System for Primary Nutrient of Soil[END_REF][START_REF] Yu | Near Infrared Spectrum Analysis of Soil Organic Matter and Nitrogen[END_REF][START_REF] Zheng | Evaluation of Soil Fertility with Spectrophotometer and Spectroradiometer. J.Infrared and Photoelectronic Ima gers and Detector Devices[END_REF][START_REF] Chen | Near Infrared Spectroscopy Real-time Measuring Soil Total Nitrogen and Phosphorus Content[END_REF]. So that it was a better attempt to estimate the soil total nitrogen content by a nonlinear prediction model. In our study, soil total nitrogen contents were predicted by BP neural network method and support vector machine method respectively.

BP neural network is back-propagation network using nonlinear differentiable function to serve as weight value train [START_REF] Sun | Based on the North of the Near Infrared Spectrum Chao Soil Parameter Real-time Analysis[END_REF][START_REF] Zheng | Based on the Soil of Near Infrared Spectral Technology Parameters of the BP Neural Network Forecast[END_REF]. According to the theorem of BP neural network, single hidden layer BP network can be appropriated for any continuous function in a closed interval. A three layers BP network can complete the mapping from uncertain dimension to determined dimension. This network has strong nonlinear mapping ability, which is very suitable for nonlinear function approximation. In the training period, the error is inverted propagation.

Support vector machine was proposed in 1992 by Vapnik et al [START_REF] Li | Spectral Analysis Technology and Application[END_REF].One of the most important characters in SVM method is to generate hypotheses for the sparse matrix of dual representation, according to the general theory of reinforcement learning. This makes it become a very effective algorithm. Another important characteristic is that the corresponding optimization problem becomes convex problem and no local minimum, because of the introduction of kernel function. SVM has the advantages of simple structure and the solution of the small sample, nonlinear, high dimension and local minima, compared with the traditional BP neural network.

3

Results and discussion The soil spectral reflectance needed to be smoothed since the white noise interference could be weakened after smoothing processing in a certain degree. The first deviation could remove the baseline wander or flat background interference effects and improve the original spectral resolution and spectral profile transformation. The forecasting model of soil moisture content was established by the original absorbance spectral data, while the forecasting model of soil total nitrogen content was established by using the data processed by the first deviation of the spectral absorbance. The first deviation of the spectral absorbance was calculated using the Equation (1) [START_REF] Li | Spectral Analysis Technology and Application[END_REF][START_REF] Lu | Modem Near-Infrared Spectroscopy Analytical Technology[END_REF]

Data preprocessing

: ) 2 9 18 11 ( 6 1 3 2 1 x x x x y i i i i i h         (1)
Where:

Estimation of soil moisture

The forecasting model of soil moisture content was established by the linear regression method with the original absorbance spectral data. All the forty-eight soil samples were divided into two parts: calibration set (including 36 soil samples) and validation set (including 12 soil samples). Fig. 3 shows the comparisons between measured data and predicted data of soil moisture. The model formula is shown in Equation ( 2). The correlation coefficient of the calibration (R C ) was 0.88, and the correlation coefficient of the validation (Rv) was 0.85. The model was passed the F test and t test, which illustrated that the model was significant and the parameters were significant. It was proved that the wavelength of 1450 nm could be taken as an initiative light source selection for a portable detector.

1 .6 24 - 2 .7 43 i i x y   (2)
Where: i y is prediction of soil moisture content; i x is absorbance of soil at the wavelength of 1450 nm.

Fig.3.

Comparisons between measured data and predicted data of soil moisture

Estimation of soil total nitrogen by SVM

The correlation between soil moisture and absorbance at 1450 nm was observed, while the correlation coefficients between absorbance and soil total nitrogen were less than 0.3 in full spectral range. It showed difficult for use linear regression method to complete the prediction of soil total nitrogen. SVM and BP neural network were respectively chosen to establish the soil total nitrogen forecasting model.

There was no unified model to determine parameters of SVM. The parameters could be chosen by virtue of experience, experiment or a large range of search methods. After a lot of attempts, the parameters were determined: the kernel function was RBF, penalty parameter C was 11.314 and kernel parameter g was 2.0. Table 2 Fig. 4 shows the comparisons between measured value and predicted value of soil total nitrogen by SVM. The correlation coefficient of the calibration (R C ) was 0.89; the correlation coefficient of the validation (R V ) was 0.78. The result of calibration and validation demonstrated that the model to evaluate TN is feasible. The results showed could be taken as theoretic basis for the analysis of real-time TN. All the forty-eight soil samples were divided into two parts: calibration set (including 36 soil samples) and validation set (including 12 soil samples). The correlation coefficient of the calibration (R C ) was 0.92, and the correlation coefficient of the validation (Rv) was 0.88.

The forecasting effect of BP neural network was better than that of SVM method. It could provide a model reference for the development of a portable detector. Fig. 6 shows the comparisons between measured dataset and predicted dataset of soil total nitrogen by BP neural network.

Conclusions

The forecasting models of soil moisture content and soil total nitrogen were established by the linear regression method and nonlinear regression methods respectively. The following conclusions were obtained. 1. The soil moisture content could be predicted by a linear regression method, and the correlation coefficient of the calibration (R C ) and the validation (Rv) were 0.88 and 0.85, respectively. 2. The first deviation of absorbance data could effectively reduce the system error of the network model. The forecasting model of soil total nitrogen content established by the BP neural network was better than that of SVM method, and the correlation coefficient of the calibration (R C ) and the validation (R V ) were 0.92 and 0.88, respectively. 3. The models were robust. The accuracy of the model would not be affected when the number of the samples was many enough even no abnormal data were eliminated. All the seven wavelengths chosen in the model could be found the corresponding single wavelength LED in the market. Thus, it provided an excellent model reference for the development of a portable detector.

  soil moisture/% shows all the parameters. All the first deviation data was divided into two parts: training set and validation set. The data were used for calibration and validation by the SVM method after normalization.

Fig. 4 . 3 . 4

 434 Fig.4. Comparisons between measure value and predict value of TN by SVM

Fig. 5 .

 5 Fig.5. Bp NN training curve

Fig. 6 .

 6 Fig.6. Comparisons between measured dataset and predicted dataset of TN by BP neural network

Table 1 .

 1 Statistic characteristics of soil moisture contents and soil total nitrogen contents are shown in Fig.2. In the box graph, the first column is the soil moisture content distribution and the second column is the soil total nitrogen content distribution. It was observed from Table1that the standard deviation of the soil moisture content was larger than that of the soil total nitrogen content. The measured values of two soil parameters in the laboratory

	Absorbance	0.5 0.50 0.85 0.80 0.55 0.8 0.70 0.60 0.6 0.65 0.7 0.75 absorbance
		850 1000 1150 1300 1450 1600 1750 1900 2050 2200 2350 2500
		Wavelength/nm wavelength/nm

Fig.2. Statistic characteristics of soil samples

Table 2 .

 2 Parameters of SVM

	Parameters	Parameter values
	kernel function	RBF
	penalty parameter C	11.314
	kernel parameter g	2.0
	MSE	0.0124
	input variants	6
	output variant	1

Table 3 .

 3 Bp neural network parameters

	Parameters of BP NN	Parameter values
	input neurons	6
	output neurons	1
	neurons in hidden layer	27
	network structure	single hidden layer
	transfer function in input layer	tansig
	transfer function in output layer	purelin
	training function	trainlm
	error	0.00343

Acknowledgments.

This study was supported by NSFC program (61134011, 30871453 and 31071330) and Innovation Fund for Graduate Student of China Agricultural University (KYCX2011073).