Xuezhen Hong
email: xzhong@zju.edu.cn

Jun Wang
email: jwang@zju.edu.cn

Discrimination and Prediction of Pork Freshness by E-nose

Keywords: Electronic nose, Pork freshness, Prediction, Back Propagation Neural Network, Multiple Linear Regression

An electronic nose (e-nose) was used to establish a freshness evaluation model for pork. A pre-experiment was performed to acquire optimum parameters (10 g sample mass with 5 min headspace-generation time in 500 mL vial) for later e-nose detection of pork. Responding signals of the e-nose were extracted and analyzed. Linear Discriminant Analysis (LDA) results showed that the e-nose could classify pork with different storage time (ST) well. Back Propagation Neural Network (BPNN) was performed to predict the ST, and the results showed that 97.14% of the predicting set (with 95.71% of the training set) was classified correctly; and Multiple Linear Regression (MLR) was used to predict the sensory scores, with the results showing that the correlation coefficients (R 2 = 0.9848) between the e-nose signals and the sensory scores was high. These results prove that e-nose has the potential of assessing pork freshness.

Introduction

Due to its high nutritional value and tasty taste, the consumption of pork has been increasing dramatically during the last decades. However, pork is highly susceptible to spoilage and contamination by micro-organisms. The main ingredients of pork are water, protein, fat and a small amount of carbohydrates. During the storage, the ingredients will be decomposed by enzymes and bacteria, producing odor: the protein will be decomposed into ammonia, hydrogen sulfide, ethyl mercaptan, etc.; the fat will be decomposed into aldehydes and aldehyde acids odor; the carbohydrates will be decomposed into alcohols, ketones, aldehydes, and carboxylic acid gases [START_REF] Kong | Meat Science and Technology (肉品科学与技术)[END_REF]. The odor gets more and more intense with the decrease of pork freshness. Consumption of spoilage pork could cause serious health hazards [START_REF] Gram | Food Spoilage-Interactions Between Food Spoilage Bacteria[END_REF]. Thus, it is necessary that a rapid and accurate detection system be developed for microbiologically spoiled or contaminated pork [START_REF] Archer | The Validation of Rapid Methods In Food Microbiology[END_REF][START_REF] Ellis | Rapid and Quantitative Detection of The Microbial Spoilage of Beef by Fourier Transform Infrared Spectroscopy and Machine Learning[END_REF].

At present, there are mainly three traditional methods in the meat industry to detect pork freshness: sensory evaluation based on the texture, color, organization status, viscidity and odor of the pork [START_REF] Shirsat | Texture, Colour and Sensory Evaluation of A Conventionally and Ohmically Cooked Meat Emulsion Batter[END_REF]; detection of the total volatile basic nitrogen (TVBN) [START_REF] Malle | Total Volatile Basic Nitrogen Rates In Salt-Water Fish Muscle[END_REF]; and aerobic plate counts of the pork samples using standard protocols (FDA-US Food and Drugs Administration, 1998) [START_REF]FDA-US Food & Drugs Administration: Bacteriological Analytical Manual[END_REF]. The first method provides immediate quality information but suffers from some disadvantages, for example, the subjective nature of the assessment. Furthermore, errors may arise from fatigue of panelists and low threshold concentrations of stale odor compounds may not be perceived [START_REF] Limbo | Freshness Decay and Shelf Life Predictive Modelling of European Sea Bass (Dicentrarchus Labrax) Applying Chemical Methods and Electronic Nose[END_REF]; The latter two methods are objective, but destructive, complicated and time consuming. Moreover, they are just used to analyze one or two specialized components instead of giving the whole information of pork quality. Consequently, these traditional methods are unsuitable for fast and on-line application in pork industry.

Electronic nose (e-nose), also known as artificial olfactory, is a simulation of biological functions to identify some simple or complex odor [START_REF] Gardner | A Brief History of Electronic Nose[END_REF][START_REF] Yu | Research Developments of Electronic Nose and Its Application in Processing of Agriculture Products[END_REF]. A typical e-nose system contains a selective chemical sensor array, a signal processing subsystem and a pattern recognition subsystem. The sensors in the sensor array are sensitive to different substances. For example, some sensors can discern ammonia and some can discern aldehydes. Thus the whole sensor array can discern complex odor. Instead of detecting one or two components of the substances, the e-nose extracts the whole information for identification. In the last decade, a few researchers have been studying the potential of using e-nose as a non-destructive method for food detection.

Garcí a et al. [START_REF] Garcí | Electronic Nose For Wine Discrimination[END_REF] used a metal oxide semiconductor thin-film sensors based electronic nose to characterize and classify four types of red wines of the same variety of grapes which came from the same cellar. Two pattern recognition methods: Principal Component Analysis (PCA) and Probabilistic Neuronal Network (PNN) were performed, and the results showed that electronic nose was able to identify the wine well; Torri, Sinelli, and Limbo [START_REF] Torri | Shelf Life Evaluation of Fresh-Cut Pineapple by Using An Electronic Nose[END_REF] used a commercial electronic nose to monitor the freshness of minimally processed fruit (packaged pineapple slices) during storage.

The samples were stored at three different temperatures (4-5, 7-8, and 15-16 o C) for 6-10 days. After a continuous monitoring of the headspace around the fruit, the result showed that the fruit freshness was maintained for about 5 days at 4 o C, 2 days at 7.6 o C and 1 day at 16 o C.

In the previous research, most sensor arrays used in e-nose are Metal-Oxide Semiconductors (MOS). However, seldom information about the quality assurance of the e-nose performance has been given regarding sensor drift and humidity (MOS sensors used in e-nose are water sensitive), so it is impossible to rule out the fact that their research results may be significantly affected by either a day to day sensor drift of the sensor system, or the fact that the temporal changes observed in the sensor reading is due to the fact that the sensors perceive increasingly proportions of humidity due to increase in water vapor during storage. Moreover, few papers have mentioned the study of optimum experimental parameters, and the recognition models used are just focused on discrimination rather than prediction. In most cases, only e-nose was used, with no other experiments carried out. So even if we could predict the storage time of the food, we still can't precisely identify its freshness degree, since we don't have other indexes for cross-reference.

In this research, two experiments were conducted: e-nose detection and sensory evaluation. The main objective of this research is to evaluate the capacity of an electronic nose to classify pork samples stored for different time, as well as to predict their storage time and sensory scores. As for the e-nose detection, the sensor drift and humidity problem were taken care of by controlling the environment parameters (temperature and humidity) and by choosing calibrated data as the initial data. A pre-experiment was conducted to study the effects of headspace-generation time and pork sample mass on the response of e-nose performance. The optimum experimental parameters of headspace-generation time and sample mass were determined after employing Multivariate Analysis of Variance (MANOVA) and One-Way Analysis of Variance (ANOVA), and the later e-nose experiment was taken under the optimum experimental parameters. Linear Discriminant Analysis (LDA) and Back Propagation Neural Network (BPNN) were employed to observe if the e-nose could classify the pork samples stored for 0-6 d well, as well as to predict the storage time; and Multiple Linear Regression (MLR) was employed to build the prediction model between e-nose data and sensory scores.

2

Materials and Method

Sample Preparation

Fresh lean pork samples were purchased twice: the first purchase was for the pre-experiment, and the second purchase was for the discrimination of pork samples stored for 0-6 d. For both of the time, all the samples were obtained from the same parts of pigs and the same supplier in the local farmers' market (30.26 N, 120.19 E, Zhejiang province, China) 4 h after been killed, and were minced immediately on the spot. All the samples were packaged immediately using polystyrene base trays and were covered with commercial food grade polymer wraps before being transported to the lab. The samples were stored at 10 o C in the fridge before detection, except the ones detected on the first day (marked as day 0). The experiment was performed with a portable electronic nose (PEN2, Airsense Analytics, GmBH, Schwerin, Germany) (Fig. 1), which is consisted of an auto-sampling apparatus that is exposed to the volatiles, a sensor array, and pattern recognition software that is ran on a computer. The sensor array is composed of ten MOS. A description of the ten MOS is given in Table 1. The operating process is based on Win Muster V1.6 software. There are two kinds of data obtained from the e-nose, one is R (the resistance value of the sensors when the sample gas flow through them), the other is G/G 0 , where G and G 0 are the conductivities of the sensor when exposed to the sample gas and the zero gas, respectively. The G/G 0 value is more reliability cause it could avoid sensor drift in some degree, so in this study, the G/G 0 value is chosen as the initial data.

Electronic Nose System

Experimental Procedure

Sensory Evaluation. A sensory evaluation of pork is a method for description of the quality in an objective way. A descriptive method in accordance with GB/T 5009.44─2003 [START_REF]Method for analysis of hygienic standard of meat and meat products[END_REF] was carried out at the Department of Biosystem Engineering and Food Science by a selected and trained sensory panel consisting of 8 assessors. All assessors were familiar with pork characters and descriptive analysis procedure.

Scoring standard is given in Table 2. 3). After acquiring the optimum experimental parameters, the pork samples stored for 0-6 d (15 replicates each day) were detected by e-nose under such parameters.

Each pork sample was placed in a 500 mL airtight glass vial that was sealed with plastic wrap. The glass vial was closed for a certain time (headspace-generation time)

to collect the volatiles from the pork sample. During the measurement process, the headspace gaseous compounds were pumped into the sensor array at a constant flow rate of 50 mL min -1 through Teflon tubing connected to a needle in the plastic wrap, making the ratio of conductance of each sensor change. The measurement phase lasted for 65 s, which was long enough for the sensors to reach stable signal values.

The signal data from the sensors were collected by the computer once per second during the measurements. When the measurement process was complete, the acquired data were stored for later mathematic analysis. After each measurement, zero gas (air filtered by active carbon) was pumped into the sample gas path from the other port of the instrument for 50s (flush time). In case of sensor pollution which could cause sensor drift, after all the measurements were done every day, nitrogen gas was pumped into the sample gas path to clear the sensor array. All the measurements were carried out at a temperature of 20 o C ± 1 o C and 50% to 60% relative humidity (controlled by air-conditioning).

Statistical Analysis Methods

LDA is a widely used statistic method. Similar to PCA, it is also a linear combination of the original variable to construct a discriminant function [START_REF] Buratti | Characterization and Classification of Italian Barbera Wines by Using An Electronic Nose and An Amperometric Electronic Tongue[END_REF]. Compared with PCA, the LDA method can notice the distribution of points in the same category and the distance between them. It maximizes the variance between categories and minimizes the variance within categories to improve the resolution of classes [START_REF] Neely | Assessment of Cooked Alpaca and Llama Meats From the Statistical Analysis of Data Collected Using An Electronic Nose[END_REF].

The graphical view of LDA analysis is similar to a PCA display.

BPNN has been a widely-used method for e-nose [START_REF] Magana | Volatiles As An Indicator of Fungal Activity and Differentiation Between Species, and The Potential Use of Electronic Nose Technology For Early Detection of Grain Spoilage[END_REF][START_REF] Zhang | Characterization of Chinese Vinegars by Electronic Nose[END_REF]. BPNN can be described as a non-linear projection between the input vectors and output vectors. A typical BPNN model includes the input layer, the hidden layer (one layer or more) and the output layer. The dimensions of the input and output layer are usually decided by the dimensions of the input and output vectors, respectively, while the dimensions of the hidden layer is usually decided by try and error methodology. While determining the suitable network topology, the network processed the inputs and compared the resulting outputs against the desired outputs. Errors were then propagated back through the system, causing the system to adjust the weights that control the network [START_REF] Pang | Discrimination of Storage Age for Wheat by E-nose[END_REF]. This process continued over and over until the error matched the training goal error.

MLR analysis is a common method used in quantitative analysis. Equations relating the dependent variable behavior to the descriptors are developed with the following form: contribution, with numbers enclosed in parentheses and set on the right margin.

Y i = β 0 + β i X i , (i = 1, 2, 3, …, N). (1)
Where Y i is an independent variable; β 0 is the intercept and β i are the regression coefficients of the independent variables X i ; and N is the number of independent variables.

ANOVA is a method of portioning variability into identifiable sources of variation and the associated degree of freedom in an experiment. The frequency test (F-test) is utilized in statistics to analyze the significant effects of the parameters, which form the quality characteristics [START_REF] Muthukrishnan | Optimization of Machining Parameters of Al/SiC-MMC with ANOVA and ANN Analysis[END_REF]. MANOVA is a generalized form of univariate ANOVA. It is used when there are two or more dependent variables. It helps to answer : 1. do changes in the independent variable(s) have significant effects on the dependent variables; 2. what are the interactions among the dependent variables and

among the independent variables [21].

The data processing method LDA was performed in WinMuster, which is combined in the e-nose software; (M)ANOVA and MLR were performed in SAS software, and BPNN was proceeded using the network toolbox in MATLAB R2008a.

3

Results and Discussions

Discussions of Sensory Evaluation Results

Sensory evaluation result is shown in Fig. 2. The sensory scores of color, elasticity, viscosity and odor attributes decreased as the storage time increased. It is also noticeable that both of the curves had declined quickly since the third day, while in the first 3 days, the total score curve only declined slightly, and the odor score nearly kept the same. This manifested that the pork sample didn't corrupt until the third day.

So in the first 3 days, it remained fresh, with its appearance or odor change little.

Fig. 2. Sensory result Discussions of Pre-experiment Results

Responding Curves of E-nose. Fig. 3 shows two typical responding curves of the ten sensors during measurement of a pork sample in day 0 (Fig. 3a) and day 1 (Fig. 3b), respectively. Each curve represents a sensor's ratio of conductance (G/G 0 ), which increased in the first few seconds and finally stabilized at about the 60 th s. Compare Fig. 3a and Fig. 3b, the ratio of conductance values of all the sensors increased with the storage time of pork. It is also noticeable that the sensor MOS 2, which is sensitive to nitrogen oxides (one of the main odor in pork putrefaction), increased most observably with storage time, with its G/G 0 value differing significantly by the storage time. This indicates that it is potential to monitor pork freshness by e-nose. The MANOVA results are summarized in Table 4, where the magnitudes of the F-values indicate the relative importance of the factors to some extent. As is shown in Table 4, M, T, and ST all have very significant effect on the response of the e-nose respectively (Sig. < 0.001). The interaction of M × T, and M × T × ST also have a significant effect on the response of the e-nose (Sig. < 0.05). It can also be observed that the ST has the highest F-value, which means the e-nose signals are differed between samples with different storage time. This proves the feasibility of using the e-nose to distinguish pork freshness. The mass factor has the second highest F-value, next is the headspace-generation time, suggesting that it is very important to determine the sample mass and the headspace-generation time.

One-Way Analysis of Variance Result. ANOVA (the factor is storage time) was applied for group 10-5, 10-15, 10-25, 25-5, 25-15, 25-25 and got each group a F-value, respectively. The results of six groups are summarized in Table 5, which

shows that all the groups have very significant effects on the response of e-nose, and the combination 10-5 has the highest F-value. This means that when the mass is 10 g and the headspace-generation time is 5 min, the e-nose has the most obvious difference in its responding values towards samples with different storage time.

Discrimination Power (DP).

DP is another index used to observe the magnitude of difference among samples [START_REF] Backhoff | The Level of Difficulty and Discrimination Power of The Basic Knowledge and Skills Examination (EXHCOBA)[END_REF]. All combinations with different mass and headspace-generation time (as was described before) were applied the DP analysis and got a value respectively (listed in Table 6). The number format is storage timemass-headspace-generation time. For example, 0-10-5 means stored 0 day and 10 g sample mass with 5 min headspace-generation time. As is shown in Table 6, the combination 10-5 has the highest DP value, which means the e-nose response of this combination has the most obvious difference between day 0 and day 1. This result is the same with the ANOVA result. explains 23.72% of the total variance. The total contribution rate is 88.42%, which means these two reflect 88.42% of the original information.

As is shown in Fig. 4, the samples stored for 1-3 days are very close to each other.

This may be explained as follows: in the first 0-3 days, fresh pork stored at 10 o C in the fridge still remained fresh, and the change in their volatile gases was subtle so the e-nose could not notice the difference well. Thus, the data that e-nose extracted was similar to each other and concentrated. However, freeze & unfreeze could cause cell damage and affect the quality of pork, so the samples stored 0 day, which were taken for experiment directly without being stored in the fridge, are discriminated from those stored in fridge for 1-3 days. It is also noticeable that the samples stored 6 days are obviously discriminated. In general, except a little overlapped among the samples stored 1-3 days, all the samples can be clearly divided into seven regions according to their storage time. shown in Table 7 and Table 8.

The total identification rate of the simulated results for the training set was 95.71%, and the total identification rate for the predicting set was 97.14%. It should be noticed that neither the predicting set nor the training set could correctly discriminate the samples stored between 2 and 3days.

where S is odor score, while X 1 、X 2 、X 3 、X 4 5 、X 6 、X 7 、X 8 、X 9 and X 10 represent the 60 th s signal data of the ten sensors MOS1 -MOS10, respectively. The R 2 of this model is 0.9848.

Fig. 1 .

 1 Fig. 1. Schematic diagram of the electronic nose (e-nose) measurements

  mass and headspace-generation time of the samples[START_REF] Olsson | Detection and Quantification of Ochratoxin A and Deoxynivalenol In Barley Grains by GC-MS and Electronic Nose[END_REF], so a set of pre-experiments were performed to determine the optimum experimental parameters. Three main factors were considered: mass of the pork samples (M: 10 and 25 g), storage time (ST: 0 and 1day) and headspace-generation time (HGT: 5, 15 and 25 min). The samples stored for 0 and 1 day were divided into six groups respectively, marked as 10-5, 10-15, 10-25, 25-5, 25-15, 25-25. The number format was massheadspace-generation time, for example, the 10-5 group means 10 g sample mass with 5 min headspace-generation time. The multifactor pre-experiment was conducted with seven replicates of each group (Table

Fig. 3 .

 3 Fig. 3. Responding curves of sensors for fresh pork samples: (a): stored for 0 day; (b): stored for 1 day

Table 6 .

 6 Results of DP test of six combinations 1The pork samples stored for 0-6 days (15 replications of each day) were detected by e-nose under the optimum experimental parameters, and the signals of e-nose were analyzed by multiple data analysis methods. Linear Discriminant Analysis (LDA). For all 105 pork samples (7 storage time × 15 duplicates), the response signal values of the e-nose at the 60 th s were extracted and analyzed by LDA. The results are shown in Fig. 4, which is a two-dimensional spatial plot defined by two discriminant functions. The first discriminant function (LD 1) explains 64.70% of the total variance, and the second discriminant function (LD 2)

Fig. 4 .

 4 Fig. 4. LDA analysis of pork stored for 0-6 days

Fig. 5

 5 Fig. 5 shows the prediction ability of the e-nose, where each triangle represents the predicted values versus the real value of each measurement. The figures illustrate a

Table 1 .

 1 Sensors used and their object substances in PEN2

	Array number Sensor	Substances for Sensing
	MOS 1	W1C	Aromatics
	MOS 2	W5S	Nitrogen oxides
	MOS 3	W3C	Ammonia, aromatic molecules
	MOS 4	W6S	Hydrogen
	MOS 5	W5C	Methane, propane, and aliphatic non-polar molecules
	MOS 6	W1S	Methane
	MOS 7	W1W	Sulfur-containing organics
	MOS 8	W2S	Broad alcohols
	MOS 9	W2W	Aromatics, sulfur-and chlorine-containing organics
	MOS 10	W3S	Methane and aliphatics

Table 2 .

 2 Sensory evaluation standards

			Scores
	Parameters	5	3	1
	Color	Muscle: shiny, red, uniform Fat:white	Muscle: a little dark red Fat:lack of luster	Dark color with sign of depravity
	Viscosity Slightly dry or moist	Sticky or dry moist new cut section	High viscosity
	Elasticity	Instantly and completely recover from acupressure	Slowly and incompletely recover from acupressure	Low elastic
	Flavor	Normal	Ammonia or sour odor	Strong odor

E-nose Sampling Procedure. The concentration of the volatile gas is affected by the

Table 3 .

 3 Pre-experiment method

	Storage time/ d Mass/ g	Headspace-generation time/ min	Number of replications

Table 4 .

 4 MANOVA (factors are storage time, mass, and headspace-generation time)

	Different Source Sum of Squares Degree of Freedom Mean Squares F	Sig.
	M	17.880	1	17.880	172.231 0.000
	T	8.043	2	4.021	38.736	0.000
	ST	22.875	1	22.875	220.338 0.000
	M × T	0.890	2	0.445	4.288	0.017
	M × ST	0.145	1	0.145	1.393	0.241
	T × ST	0.217	2	0.108	1.044	0.357
	M × T × ST	0.678	2	0.339	3.267	0.043
	MANOVA was performed to see how these factors affected the response of e-nose.

Table 5 .

 5 F-values of six combinations

	Combinations	F-value	Sig.
	10-5	209.883	0.000
	10-15	44.131	0.000
	10-25	66.676	0.000
	25-5	89.451	0.000
	25-15	159.226	0.000
	25-25	55.049	0.000
	Therefore, in this research, the optimum parameters are 10 g pork sample with 5 min
	headspace-generation time.

Table 7 .

 7 BP results of the original data in the training set

	ST NS			Recognition results			Identification rate	Identification
			0	1	2	3	4	5	6	of each day	rate of all days
	0	10	10							100%
	1	10		10						100%
	2	10			8	2				80%
	3	10			1	9				90%	95.71%
	4	10					10			100%
	5	10						10		100%
	6	10							10 100%

Table 8 .

 8 BP results of the original data in the predicting set The MLR algorithm establishes the model that describes the relationship between sensor signals and odor scores. All variables used in the models are significant at the 0.01 level. The sample data (105 pork samples from 7 storage time) were separated randomly into two groups: one group for the calibration set was used to develop the calibration models (70 pork samples, 10 samples each day) and the other group containing the remaining samples was used for the prediction set (35 pork samples, 5 samples each day). Prediction of odor scores by MLR model The predictive model for the odor score is given below. Y i = 6.695285 + 1.314430X1 -0.552935X 2 + 2.401326X 3 -4.553837X 4 + 3.023900X 5 -2.250302X 6 -6.150372X 7 -1.827269X 8 -1.609080X 9 + 8.197955X 10 ,

	ST NS			Recognition results			Identification rate	Identification
			0	1	2	3	4	5	6	of each day	rate of all days
	0	5	5							100%
	1	5		5						100%
	2	5			4	1				80%
	3	5				5				100%	97.14%
	4	5					5			100%
	5	5						5		100%
	6	5							5	100%

Acknowledgments

. The authors acknowledge the financial support of the Chinese National Foundation of Nature and Science through Project 31071548, the Research Fund for the Doctoral Program of Chinese National Higher Education through Project 20100101110133, the Fundamental Research Funds for the Central Universities and the Seed Fund Project on Cross Research For Young Teachers of Zhejiang University.


Jiannan Jia

Haiyan Ji

Plant Leaf Water Detection Instrument Based on Near Infrared Spectroscopy

Keywords: reflection measurement, water detection, leaves, portable instrument

In the near infrared spectral region, a reflection plant water detection instrument was designed by using microcontroller STC12C5A60S2. The instrument consisted of signal acquisition system, microcontroller system, software system and calibration model. The signal acquisition system was composed of three LED of different wavelength and a light-to-frequency converter. Three LED of different wavelength were lighted by turns for avoiding interaction. Light-to-frequency converter was used as the receiving tube, thus simplifying the circuit structure greatly. This paper described the instrument's hardware design, software design, modeling of Forsythia leaf water content and forecasting. Predicted results were consistent with the true values of water, and the correlation coefficient between them was about 0.820.

Advantages of this instrument were small, simple structure, low power consumption and so on. The experimental results showed that the instrument could detect plant water content rapidly on fieldwork.

Introduction

Water is a major component of crop. Water deficit directly affect the plant's physiological and biochemical processes and morphology, thus affecting plant growth, yield and quality [START_REF] Zhang | Advances in Automated Monitoring and Diagnosis of Crop Water Status (in Chinese)[END_REF] . Getting plant water status rapidly is very important for agriculture, horticulture, forest water management and potential fire assessment [START_REF] Tian | Relationship between Canopy Reflectance and Plant Water Status of Wheat (in Chinese)[END_REF] .

Water absorption spectrum has five absorption bands, their central wavelength are located in near 760nm, 970nm, 1145nm, 1450nm and 1940nm. Based on this, the spectral information of this region has been widely used to analyze the water status in plants [START_REF] Hunt | Detection of Changes in Leaf Water Content Using Near-and Middle-Infrared Reflectance[END_REF][START_REF] Wang | Study on the Water Status of the Wheat Leaves Diagnosed by the Spectral Reflectance (in Chinese)[END_REF][START_REF] Liu | Estimating Winter Wheat Plant Water Content Using Red Edge Parameters (in Chinese)[END_REF][START_REF] Rollin | Processing of High Spectral Resolution Reflectance Data for the Retrieval of Canopy Water Information[END_REF] .

Several researchers have described relationships between leaf water content and infrared (700-2500nm) reflectance. Tucker [START_REF] Tucker | Remote Sensing of Leaf Water Content in the Near Infrared[END_REF] considered simulated leaf reflectance along with atmospheric transmission properties, and determined the spectral region between 1550 nm and 1750 nm was best suited for remotely measuring leaf water content. Carter et al [START_REF] Carter | Effect of Competition and Leaf Age on Visible and Infrared Reflectance in Pine Foliage[END_REF] found that visible wavelengths (551nm) correlated best with the total water potential of loblolly pine needles. Hunt et al [START_REF] Hunt | Measurement of Leaf Relative Water Content by Infrared Reflectance[END_REF] and Hunt and Rock [START_REF] Hunt | Detection of Changes in Leaf Water Content Using Near and Middle Infrared Reflectance[END_REF] described a liquid water content index that was used to estimate leaf relative water contents. Penuelas et al found that the water index WI (WI=R 900 /R 970 ) or minimum of first derivative in the near infrared band can clearly indicate the change of water status [START_REF] Penuelas | The Reflectance at the 950-970nm Region as an Indicator of Plant Water Status[END_REF] . The subsequent study of Penuelas and Inoue also showed that the ratio WI/NDVI (WI=R 900 /R 970 , NDVI=(R 900 -R 680 )/(R 900 +R 680 )) not only could be used to predict the moisture content of leaves but also could be used to predict the water content of plant or canopy, and significantly improved the prediction accuracy [START_REF] Penuelas | Reflectance Indices Indicative of Changes in Water and Pigment Contents of Peanut and Wheat Leaves[END_REF][START_REF] Penuelas | Estimation of Plant Water Concentration by the Reflectance Water Index WI (R900/R970)[END_REF] .

By using derivative spectrum, Yan Shen established the water content model of monocots and dicots, it provided new ideas of remote sensing classification for identifying monocots and dicots [START_REF] Shen | Establishment of Leaf Water Content Models Based on Derivative Spectrum Variables (in Chinese)[END_REF] . By using principal component regression, Hanping Mao established the moisture content model of grape leaves based on spectral reflectance characteristics, and found that grape leaves dry-basis moisture content was significantly correlated with derivative spectra of 703nm [START_REF] Mao | Model Establishment for Grape Leaves Dry-Basis Moisture Content Based on Spectral Signature (in Chinese)[END_REF] . By using spectral stepwise regression analysis and constructed spectral index method, Yong Yang analyzed the relationship between citrus leaf spectral reflectance and moisture content, and established the moisture content model of citrus leaves based on spectral reflectance [START_REF] Yang | Model for Citrus Leaves Water Content Based on Spectral Signature (in Chinese)[END_REF] . These findings show that diagnosing plant water status by using spectral reflectance is feasible.

Near infrared water detection instrument generally has two measurement methods [START_REF] Li | Modern Near Infrared Spectral Analysis Technology of Farm Products[END_REF][START_REF] Yuan | A New Infrared Spectrometer with Charge Coupled Device Detector (in Chinese)[END_REF][START_REF] Zhou | Instrument Technology and Applications of Modern Fourier Transform Near Infrared Spectrometer[END_REF] : transmission method and reflection method. Reflection method, namely, determine the water content of samples by detecting the reflected light intensity from the sample surface. Near infrared water detection instrument designed by Xiaoying Lin [START_REF] Lin | Develop on Near Infrared Moisture Instrument[END_REF] , measured water by reflection method and used three beams of 1700nm, 1940nm and 2100nm. The instrument consisted of electronic circuit and complex optical probe, therefore its size was relatively large. The instrument in this paper is a battery-powered handheld device, so its Light source used LED. The system selected 970nm as measuring wavelength. In the band from 900nm to 680nm, the absorption of water is less and the curve of absorption tends to flat. Therefore, the system selected 900nm and 680nm as reference wavelength.

Instrument Design and Modeling

System Components

According to the logic function, the hardware circuit of this instrument was divided into several sub-modules. Modules could be divided into: microprocessor, light source, detector, LCD display unit, keyboard control unit and storage unit. System diagram of hardware is shown in Fig. 1. Where PWC is plant water content, k and b are coefficients.

Fig. 1 . 2 . 5 )PWC=k ×WI + b ( 6 )

 1256 Fig.1. System diagram of hardware

Acknowledgments

. This work was supported by 863 National High-Tech Research and Development Plan (No. 2007AA10Z211).

Results and Discussion

37 Forsythia leaves were collected for the experiment. First, by using the instrument, water index WI and NDVI were measured. And then, water standard content of these leaves was measured through drying method. The true values of water content (water standard content/fresh leaf weight×100%) were calculated. Two prediction models between water index and the real values of water content were built by using least square method according to the formula ( 6) and (7). By comparing two prediction models, we could find that the prediction model built by formula (6) was better.

Scatter plot of WI and true values is shown in Fig. 3. Figure 3 shows, plant water content PWC=88. 3957×WI-11.3608. This formula could be downloaded to the instrument. After measuring the WI, the instrument can obtain water predictive values and their correlation coefficient is 0.820. Among the 37 samples, the maximum of 37 relative errors is 4.66 percent, predictive value of this sample is 67.36 percent, and actual value is 70.65 percent.

There were not filters in the sample room. Therefore, the collected light intensity signal was not a single wavelength, but light intensity count values in a bandwidth.

Because the angle between LED and light-to-frequency converter could not be corrected accurately, so the size of leaves had some influence on the measurement result. These would affect the measurement result. In the experiment, because the range of water content was limited, so result would have a big error if it was used to forecast some water content outside this range. In order to expand the measuring range, segmentation modeling in different ranges of water content could be built.

When Measuring leaves of different plants, different forecasting models need to be built.

Conclusions

In the near infrared spectral region, a water detection instrument based on the sensitive spectrum of water absorption was designed. Using near infrared light 650nm, 880nm and 940nm, microcontroller STC12C5A60S2 and a new type of light-to-frequency converter TSL230, initial design of the reflection plant water detection instrument and debugging were completed. Modeling and forecasting in water content of the experimental samples were finished, and got some satisfactory results. To different plant species, different forecasting models can be built to achieve the rapid measurement of leaf water. In the hardware design and software code writing process, features such as low power consumption and anti-interference were fully took into account. The instrument has good stability and good reproducibility, simple operation, small size, low cost, and it is easy to implement the commercialization. But, in order to using the instrument in the real life, filters need to be used, the angle between LED and light-to-frequency converter need to be corrected accurately.


Xiao Longjia

Xin Wang

Optimization Strategy on Cash Transportation Routing of Banks

Keywords: Cost control, Vehicle routing, Optimization model, Genetic algorithm

This paper studies the cost control strategy on cash transportation of China's banking industry under the context of the global financial crisis. It analyses the actual work law of the bank cash transportation profoundly and the impact factors at the transportation costs, and constructs the single-material vehicle routing optimization model with rigid time constraints. It uses the genetic algorithm to design a solution for the model. It establishes a practical example based on the actual data from the savings network of CCB and traffic information of Hohhot urban road network, and gets a satisfactory solution, which verifies the effectiveness of the optimization model.

Introduction

In March 2007, the U.S. subprime crisis outbreak formally, then evolved and deepened, which triggered the most serious financial crisis since 30 years of last century of American history and caused a big shock in global financial markets. It led to an international financial crisis. Lots of financial institutions had been closed down one after another, follow that the world's major stock markets fell sharply, investors suffered heavy losses, world economic growth in severe decline. According to incomplete statistics, the global stock market capitalization had suffered the loss of more than 27 trillion U.S. dollars since the beginning of 2008 to October 2008 [START_REF]triggers the global financial crisis: Causes and strong potential[END_REF] . Impact of the financial crisis is extensive and far-reaching, which also led to a range of effects on our economy. In response to the growing spread of financial crisis, in second half of 2008, our government adopted a series of decisive measures, the companies also made a variety of means to reduce costs. Meanwhile, the low-carbon economy with purpose that reduces the carbon emissions is developing rapidly worldwide. How to reduce emissions and pollution is which the scholars from various countries study actively. Number of savings outlets in the front line of banking services, which are most grassroots organizations in the entire banking system, also is the extremely important foundation institution. Because of their large number and wide distribution, the daily transportation costs to transport all savings of the savings outlets, which is a huge expenditure over many years. In the context of the global financial crisis, how to control the cost of transportation effectively has an important practical significance to the healthy development of China's banking industry.

The decision of armored car transportation route based on the structure of urban road network

Control of transportation costs has to use the means of optimization of transportation scheduling, which is based on the characteristics of transportation tasks, road network data, the condition of the vehicles that can be used, assigns the transport vehicles and determines driving routes, transports the monetary savings needed to be shipped to from the savings outlets to return the specified location within the limited time, which makes comprehensive evaluation index on transport costs and transport time best. The core issue is the selection of the shortest path.

Problem Description

The grassroots organizations of banking institutions are savings outlets, which are service window for the general customer, the contents of daily work fixes relatively. They need get the cashes daily from armored car from 8:00 to 8:40, and bags the day cashes then transport it back to treasury by the armored car when settlement is completed at 5:20-6:10. Also, if the day savings in a single savings outlet exceed the prescribed limits, you need to return the excess amount of units to treasury by the armored car. Therefore, the number of used armored car of banking Management is greater than or equal twice daily, the distance is long and the arrival time of the armored car has a strict limit. Considering the long term, lower cash-carrying transportation costs to improve transport efficiency have a great significance to costs control on the banking operation. According to the actual work requiring, in the rigid time constraints, based on the key considerations of economic rationality of transport, it need to consider vehicle loading rate, the number of dispatched trucks, a reasonable line and other issues. Therefore, this research can be attributed to the optimization decision problem based on urban road network structure, that with single varieties of goods, fully loaded, through all outlets, time constrained, and to minimize the total transportation cost from the starting point of delivery to the receiving point, that a "vehicle routing with time windows problem" (Vehicle Routing Problem with Time Windows, VRPTW). In order to accurately establish the mathematical model of the problem, following assumptions are made particularly:  Urban transport network is the complete network and no direction.

 The transport sector banks have sufficient and different load vehicles which can be scheduled using, and does not consider the problem of vehicle maintenance.

 The starting and finishing point of armored car is the same point, that is the starting point and the end of transportation.

 The savings outlets only be accessed each time in a activities of a particular cash-carrying, each armored car services only one route.

 A collection of savings outlets and monetary transportation route is known, and the time transformation weights associated with the path and transport costs transformation weights are known.

 All savings outlets have deadline constraints, but the sooner or later two deadline fixed, the extra conditions are determined by the status of deposits.

Model

The idea of modeling

To build a single variety of materials VRPTW model, it must focus on analyzing the factors and weight that affect the transport costs with strong time request, and try to simplify the model structure so as to solve easy. At the same time it adds the last time constraints to the model and quantifies the time period. Following, it focuses on the function construction with last time deadlines.

The VRPTW vehicle routing problems with time windows comply with the armored vehicle transport characteristics in the urban road network. The crux of the matter is to transfer the time function cost to the cost function so that it comply with the goal of economic unity, if it violates the time constraints, the cost function will tend to infinity. If the armored cars reach the savings outlets too early, its security will not be guaranteed. If it reaches too late, it will affect the efficiency and safety of whole system. Therefore, it sets

  ' ' 0 i i i i i i i i D T D GT M T D T D         或 (1)

Model Objective function

0 0 1 1 min k k n n ij ij ijv v i j v v Z d X C            (2)
Constraints:

1 k i iv v i R Y Q    , vV  (3) 1 1 n iv v Y    , vV  , 1, 2, , ik   (4) 0 k ijv jv i XY    , vV  , 0,1, 2, , jk   (5) 0 k ijv iv j XY    , vV  , 0,1, 2, , ik   (6) 1 ijv X  , j i iu ijv T T S S       j ijv i iu ijv T X T S S     , 0,1, 2, , ik   , 0,1, 2, , j k   , ij  (7) ' i i i D T D      ' ' 0 i i i i i i i i D T D GT M T D T D         或 (8)
Where: i, j-the savings outlets, i = 0 point is the starting point of armored car, but also the end point, the treasury.

V-All vehicles armored car collection; K -savings outlets collection;

1 0 ijv v from i j X v from i j    
when car drive to when car donot drive to

1 sup 0 sup iv ply of i is served Y ply of i is served    
the cash point by car v the cash point not by car v

Objective function ( 2) makes the costs of armored vehicle transportation (the sum of total costs and fixed costs of vehicles) minimum. Constraint equation ( 3) is the load capacity constraint to ensure that the money supply responded by the vehicles v don't exceed the maximum capacity; Constraint (4) ensure that the cash to be sent in each savings outlets is delivered only by a single vehicle; Constraint (5) said that if the money to be delivered of savings outlets j is distributed by the vehicle v, the vehicle v must travel from the savings outlets i to j; Constraint (6) said that if the money to be delivered of savings outlets i is distributed by the vehicle v, then after the vehicle v has deposited the cashes of savings outlets I, it must go to another savings outlet; Constraint (7) is the service time expression, which reflects the arrival time relationship between the savings outlets j and the previous savings outlet serviced by the same truck, ji TT  ; Equation ( 8)is the last time constraints, which ensures the timeliness of armored vehicles transportation. In order to facilitate solving the model calculation, it need to convert the directly constraints into the penalty function, which is well versed in the objective function, then there is:

  ' 0 0 1 1 1 min max ,0 max , , 0
k k n n k ij ij ijv v i iv v i i i i i j v v i Z d X C M R Y Q M T D D T                            (9)
3 Model algorithm (1) chromosome structure design: Chromosome structural design is to determine the length and way of the chromosome encoding. Considering the specific circumstances of the vehicle path decision problem, it should use natural number encoding, which represents the starting point of armored car with "0" and a collection of savings outlets with "1,2, ..., k". For example, chromosomal "03450120670", represents that the vehicle assignment and route options is that the three cars start from the starting point of departure, to deliver the currency for seven savings outlets. "0" is the starting point; the middle of the "0" does not have to be expressed in the specific procedure code. As follows:  The first armored car path: the starting point → dot3 →dot5 →dot 4 → the starting point;  The second armored car path: the starting point outlets → dot2 →dot1 → the starting point;  The third armored car path: the starting point for network → dot6 → dot7 → the starting point.  Chromosome length = the total number of savings outlets + 1.

(2) Determination on the genetic control parameters:Genetic algorithm parameter settings has a large effect on the algorithm, so which needs to be selected carefully. The usually parameters include: population size N, crossover probability c P , mutation probability m P and the set of the end computing conditions.Groups of population size N is the number of solutions which realizes search in each generation, which usually is selected within 10 to 200; crossover probability c P is the proportion that the offspring number by cross-algebra takes up individuals number of the total population, to ensure the expansion of the search space, it general values 0.5 ~ 1; mutation probability m P is the ratio that the mutated gene number take up the total number of genes, which set the range of 0 ~ 0.05. When the genetic algorithm satisfies certain conditions, terminate the evolution cycle, and stop rules often are: the maximum number of failures (the iterations number when it can not get a better solution), the maternal chromosomes have reached the pre-set standards, achieved the goals set in advance and the setting maximum number of evolution generations MAXGEN and so on. (3) To generate initial population:Genetic algorithm is a population-based search method, it must prepares for genetic operators with the N initial population composed of individuals, and each individual is representation of a solution. Therefore, the initial group is the groups at the beginning of "evolutionary" computation, is the initial solution set, it usually generated initial population with the random method so as to reach all the states. To improve the convergence speed of the algorithm, it can generate the initial groups according to the following steps:  Step1: Random sorting for the savings outlets.  Step2: It calculates from left to right, if the capacity of first car is greater than the savings amount of first k saving outlets (per unit volume), and less than the savings amount of first k +1 saving outlets, which are the savings outlets substring 1: "1 2 ⋯ k" responsible by the first vehicles;  Step3: Deleting the first k savings outlets in sort, with the same method determining the savings outlets substring 2: "k +1 k +2 ⋯ l" served by the second vehicle. Repeating that until all the armored car and savings vehicles are assigned completely;  Step4: Connecting all the substrings after "0" is inserted between the two substrings, then it can get an initial chromosome after head and tail is added "0".  Step5: Giving the savings outlets new order random, it can be another chromosome with the same steps. Calculation operation repeatedly until the number of chromosomes is equal to population size N.  In order to avoid a large number of infeasible solutions generated in the initial population, it check on the chromosome with constraints (3) and ( 8), the chromosome corresponds to a feasible solution of the problem if satisfying the condition, which is added to the population; otherwise, chromosome corresponds to the feasible solution, which should be discarded. (4) Fitness evaluation:Genetic algorithms need evaluate the fitness of chromosomes whether good or not through the size of chromosomes fitness function in the process of solution. Fitness is calculated by the (3.1), the fitness function is obtained by the transformation of the objective function, the variable Aim at the problem on transportation route decision of the armored car, the chromosome whose fitness is large corresponds to the transportation routes options whose transportation cost is small, which have a larger probability of genetic to the next generation.

value ijv X 、 iv Y 、 i T in
(5) The genetic operations: selection, crossover and mutation 1) The selection operator: In this paper designs the selection operator through the method with retaining the best chromosome combined with roulette.

2) The crossover operator: Crossover operator uses the parent genetic to generate a new chromosome, whose cross-process carry out in two steps: First, it pairs the chromosomes in father groups randomly; the second is that the paired parent chromosome gets genes cross-recombination and exchange the genetic information, which generates a progeny chromosome. The cross-operator happens in a certain probability, which is called crossover probability c P . The convergence of genetic algorithm is mainly decided by the convergence of crossover operator [ 2] .

For the path problem of the cash-carrying transportation network, it is disorder between the chromosome sub-paths and order within the sub-path, if it uses the past some crossover operator, which will not be able to maintain the excellent characteristics of the parent, and may even receive a large number of infeasible solutions. Therefore, it preserve good gene combinations from their parents, this paper structures the maximum special retained crossover.

3) The mutation operator:Mutation operation is to avoid the permanent loss of some genetic information caused by the cross-selection process, which can avoid the shortcomings of only getting local solutions. Exchange mutation operator is a local optimization technique, in essence, which is the exchange to the sections of feasible solution to improve the driving line of armored cars.

Because there are many 0 in the chromosomes of transport path decision-making program, normal crossover and mutation operators are not suitable for transport routes decision-making problem, so it need to construct a special bit-based exchange mutation operator. Such as chromosomes"01203450670", to identify the gene "5" and gene "6" to exchange, it gets a new chromosomal "01203460570" by the exchange of location [START_REF] Zhang Yi | Research on the method and decision theory of disaster relief supplies and logistics[END_REF] .Variation as follows: , it goes into the next step directly;

 Step3: After exchanging the location of the selected two non-zero genes, it constitutes a new chromosome. (6) Genetic evaluation:The crossover and mutation operators do not produce better chromosomes necessarily. For that, it need to take the genetic evaluation in order to ensure that the convergence rate. It calculates the each fitness chromosome after crossover and mutation and compares with the fitness before the cross then chooses the largest fitness populations to enter the next generation.

Algorithm Flow

the practical examples

According to the actual data information of the savings outlets in Inner Mongolia Branch of China Construction Bank, the operating conditions on armored car department of Management units and traffic network data of Hohhot, it constructs the optimization model of the transport routing of armored car based on the urban road network. The actual operational procedures: the CCB armored cars (normalized and owned by the operation and management section of Construction Bank) usually stops in the Construction Bank vault, it sends several armored cars within the time limit to perform the tasks of all saving outlets in Hohhot with the daily job requirements, and then shipped back the deposit to the vault of each saving outlets security. In this case, the CCB vault is the starting point (point i = 0) of armored car and destination point after the transport task is completed. In the practice work, the CCB savings outlets requires that the armored cars arrive as early as 8:00-8:40 am and 4:40-5:30 pm, if not it will affect the normal operation of the savings outlets, which will take a security risk to the transportation. The CCB Branch Office of Inner Mongolia in Hohhot has 52 savings outlets, due to the limited space of this article, from which it takes the data of 10 savings outlets and only considers the activities of cash transportation in afternoon to establish an example. If it needs to solve a greater number of network problems the actual work, just modify the input data of program. 10 savings outlets require to transport the 70 units of volume (special bag that loads models of bank), after the clearing the amount of money in the afternoon. The maximum capacity of a single armored car is 30 units of volume and the cost of vehicle operation is 100. It requires assigning the armored car to start from the treasury and selects transportation routes, which makes the transportation cost to minimize under the conditions of time requirements of the savings network. Because the title design is known , they take the Treasury to be the starting point, at between 5:20-6:10 pm daily, arriving 10 savings outlets to perform models transportation tasks; the volume capacity of each armored car is 30 units, a total of cash that should be transported of 10 savings Outlets is 70 units volume, so it first 4.5 below. Using of algorithm design and the above process, based on the AMD Athlon (tm) 64 X2 Dual Core Processor 3600 + processor, 1.91Ghz, 1.50GB RAM, Window xp computer system, through the preparation of C + + program, using VC + + operators it gets the results shown in Figure 4.3.

Fig. 3. The running results of example program

Operation Results: The best chromosome 0-6-7-8-2-0-9-3-1-5-0-4-10-0, the optimal distance 53.4491km. The specific arrangements for transport path are shown in Table 4.5. In the conditions that the transport equipment is adequate and meet the time requirements, in order to reduce the total mileage to save the cost of the emergency distribution, in principle, the different needs of the same point should be responsible by one car (or team) to transport as far as possible from. In addition, the numerical example of solutions is the global satisfaction solution, which distributes in strict time limit. And the penalty cost is zero. It solved the decision problem of cash distribution path.

Conclusion

It outbreaks the global financial crisis in 2008, which cause a great extent to the countries in the world, national economists and government put together a large number of human and financial resources to research the strategy. China's economy also suffered a certain degree of influence. In the backdrop of the world financial crisis, it is particularly important to study the control strategy on the transport costs of China's banking shall. This paper focuses on constructing the optimization model of single-material vehicles routing with rigid time constraints and establish a practical example based on that the actual network data of Construction Bank savings outlets and Hohhot urban traffic network data, which obtains a satisfactory result and verified the validity of the optimization model. It plays an important role at the bank's real cash-carrying transport work of grass-roots institutions. Meanwhile, the decision research on cashcarrying transport path in the case of complexity traffic is the contents needed to continue to study in the next step.

D

  to the first time deadlines and i D to the latest time deadlines for cash-carrying of savings outlets, M is a large enough positive number, according to the actual time of point i i T get the construction function with the last time deadlines constraint as follows:

ijd-T

  The distance from point i to point j; ij  -the economical weights per unit distance (transportation costs); ij  -the timeliness weights per unit distance (time consumption factor); v C -the fixed cost of vehicle v be used (average daily cost of hiring staff, routine maintenance costs, all taxes, etc.); v Q -The maximum load capacity of vehicle v; i T -the actual time of reaching point i , 0 is the time from the starting point; ijv S -the time taking from point i to j; iu S -the cash-installation time at the point i; M -large enough positive number; i R -the money supply of savings outlet i needed to transport (the multiple of defined volume) earliest time when armored car arrived savings outlets; i D -the latest time when armored car arrived savings outlets;

f

  the objective function is obtained by selecting calculation of each chromosome codes. represents the fitness function of chromosome h, min Z represents the transportation costs of best chromosome in the same group, h Z represents the transportation costs of the chromosome h.



  Step1: to produce a random number r between [0,1];  Step2: If m rP  ,it selects a chromosome randomly, in which selects two non-zero genes arbitrarily; if m rP 

Fig. 1

 1 Fig.1 the genetic algorithm solving process of path decision-making model in cash truck transportation network Note: section of information from: zhang yi .Research on the method and decision theory of disaster relief supplies and logistics [D]. Doctorate thesis, Chang'an University, 2007 The flow of solving the path optimization decision model with genetic algorithm is shown in Fig.1.

Table 4 .Fig. 1 .Fig. 2 .

 412 Fig. 1. the distribution map of savings network of Construction Bank in Hohhot area and its shortest path

  more or equal to 3 armored car to perform the transportation task of models to 10 savings outlets; according to the general models of armored car, which gets the actual city driving Hundred kilometers to 16L/100km and the 93 # gasoline the average price of the 3,4month in 2009 of Hohhot is 5.3RMB / L, which obtain the cost of actual fuel consumption per kilometer that 0.832RMB/km. The chromosome length is 10 +1 = 11, it takes the population size N = 150 and according to the nature, type and experience of this example takes the crossover probability 99999. Algorithm termination condition is set to meet: genetic evolution generations reach 100 generations, that is 100 s G  . According to the known data, the parameters listed in

Table 4 .

 4 5 the number of deposits needed to transport, time of loading cash,

3.1 Model Algorithm Genetic

  Algorithm is the mathematical model that imitation of genetic selection and survival of the fittest evolutionary, which was first proposed in 1975 by the professor J.H.Holland in University of Michigan. At the same time, he published the representative monographs <Adaptation in Natural and Artificial Systems>, the genetic algorithm proposed by Professor JH. Holland was often called simple genetic algorithm (SGA).

	and model features, this paper selects the genetic algorithm to solve VRPTW model,
	solution procedure is as follows:
	Genetic algorithms include: the creation of the initial population, evaluation of each
	chromosome, selection, crossover, mutation in the population and post-assessment
	procedures, and include encoding and decoding, initialization groups, fitness function,
	reproduction, crossover and mutation factors. Considering the nature of the research

Table 2 the cost matrix of pots in inter-network transformed from the distance

 2

	Saving outlets	1	2	3	4	5	6	7	8	9	10
	0	3.650	1.780	1.414	2.973	1.821	2.891	2.164	1.038	0.519	1.763
	1	0	3.149	3.018	6.598	4.932	5.418	6.829	4.205	4.263	5.278
	2	3.149	0	1.331	4.983	2.896	3.651	4.216	2.407	2.533	3.742
	3	3.018	1.331	0	4.270	1.387	2.390	2.904	1.133	1.812	3.034
	4	6.598	4.983	4.270	0	5.290	5.778	4.528	4.029	2.880	1.219
	5	4.932	2.896	1.387	5.290	0	1.342	1.774	0.418	1.543	3.226
	6	5.418	3.651	2.390	5.778	1.342	0	1.269	1.778	2.892	4.554
	7	6.829	4.216	2.904	4.528	1.774	1.269	0	1.780	1.635	3.306
	8	4.205	2.407	1.133	4.029	0.418	1.778	1.780	0	1.106	2.824
	9	4.263	2.533	1.812	2.880	1.543	2.892	1.635	1.106	0	1.672
	10	5.278	3.742	3.034	1.219	3.226	4.554	3.306	2.824	1.672	0
	According to the r the armored car's average speed in the city, this paper takes 60km /
	h (1km/min), which can sets different values in practical applications at different
	cities according to traffic conditions. Here comes the time consumption matrix
	between the saving outlets.

Table 4 .3 the time-consuming matrix of pots in inter-network transformed from the distance (min)

 4

	Saving outlets	1	2	3	4	5	6	7	8	9	10

Table 4 .4 the deposits needed to transport in every savings pots (unit volume)

 4

		1	2	3	4	5	6	7	8	9	0	1
	savings	6	8	7	8	6	7	8	6	7		7

Table 4 .

 4 1, Table 4.2, Table 4.3 and Table 4.4, Table


Wenhao Ou

Wei Su

Chen Wu

Zhongzheng Zhu

Yanmin Li

Shi Shen

Drought monitoring based on the vegetation temperature condition index by IDL language processing method

Keywords: Words:VTCI, Drought Monitoring, Batch Processing, IDL

Abstract:Landsat TM5 images are used to calculate and retrieve normalized difference vegetation index (NDVI) and land surface temperature (LST). Combining with two index mentioned, vegetation temperature condition index (VTCI) can be retrieved for drought monitoring indicator applied in Junchuan farm of Heilongjiang Province in Northeast China.

With well performance in matrix operation of IDL language, retrieving VTCI in a short time, fast batch calculation and mapping work as well, to a great extent, saving time and laborites, also providing real-time data for the government's macroeconomic regulatory policy.

Introduction

Drought, which happens periodically with complex procedure, has been a kind of natural catastrophe that mankind has to deal with of all time. It takes place in all kinds of climate zones and is listed as one of the main natural catastrophes around the world. Of all kinds of natural catastrophes, drought has the strongest impact on agriculture. According to conservative statistics, drought takes 61% percent of the disaster-affected area in China, while flood takes 24%, hail takes 9%, and frost takes 6% [START_REF] Pengxin | Drought monitoring mode based on vegetation index and land surface temperature[END_REF][START_REF] Xiaoping | Progress of drought monitoring and the research methods of remote sensing[END_REF]. Traditional method to monitor a drought is by using meteorological data to decide the level and range and it is only restrict to monitoring on a certain spot. However, in order to monitor a large area of drought condition, remote sensing should be used as the main method [START_REF] Lambin | The surface temperature-vegetation index for land cover and land cover change analysis[END_REF][START_REF] Liu | Monitoring regional drought using the vegetation condition index [J][END_REF].

With the development of remote sensing techniques, available data have been increasing all the time. To monitor drought, vegetation index (NDVI) and land surface temperature (LST) are often used to retrieve. NDVI is to express the condition of vegetation by using the spectral character of red light, near infrared band or their simple combination or linear combination, based on the strong absorbing property that chlorophyll has on lights [START_REF] Shupeng | Reaserch of remote sensing information[END_REF]. There are many indexes to show the drought condition, for example, Anomaly Vegetation Index (AVI), Vegetation Condition Index (VCI), Land Surface Temperature (LST), Vegetation Temperature Condition Index (VTC) etc. VTCI is based on the triangle composed of scatter diagram (feature space) of Normalized Difference Vegetation Index (NDVI) and LST and is used to study the drought level and its variance of a certain area during a certain period of a year. It takes the variance of NDVI into consideration and at the same time, the variance of LST under the same NDVI, that is, the harm that high temperature can have on plants [START_REF] Pengxin | The application of drought monitoring with vegetation temperature condition index[END_REF]. This method, which is put forward by Pengxing Wang, is now used to monitor the drought in Shanxi Province (China), Texas and Oklahoma (USA). Results show that it is an effective way and can be a real-time procession [START_REF] Wan | Using MODIS land surface temperatureand normalized difference vegetation index products for monitoring drought in the southern Great Plains,USA[J][END_REF].

In order to provide data faster and better, VTCI by IDL language processing method, combined with remote sensing image, should be used. IDL is a simple matrix-oriented and cross-platform language and it has the ability to process a large amount of data in high speed. Therefore, IDL language is an ideal tool to process remote sensing image [START_REF] Quan | Remote sensing systems development methods based on the secondary development with ENVI/IDL[END_REF]. This paper is mainly discussing drought monitoring based on the vegetation temperature condition index by IDL language processing method.

In 2007, Heilongjiang Province suffered from the most serious drought in 50 years and then the importance of drought monitor emerged. Take the Junchuan farm (115/027 in TM5) for example. It is a temperate continental climate place located at 131 °02′ to 131 °30′east longitude, 47°20′ to 47°40′ northern latitude in Luobei and Suibin. We analyse the drought condition on the farm to capture the level of the drought. This paper use VTCI, combined with LandSat TM5 remote sensing data to monitor the drought, providing the support for some decisions on the farm.

In the process of pre-processing original TM remote sensing image, radiative correction and geometrical correction are usually carried out. Radiative correction is the first setp, followed by atmospheric correction.

Then geometrical correction is carried out to pick some control point on the image by polynomial method.

Gauss-Kruger projection is used to do the geometrical correction. All the steps above is processed by ENVI 4.7. 4  refers to reflectivity of band 4, and 3  refers to that of band 3.

2)The calculation of land surface bright temperature

2 1 ln( 1) K BT K L    (Formula 2)
L  refers to radiance of thermal infrared band. 3)The calculation of land surface temperature Land surface temperature is calculated by Landsat TM5, band 6, mainly the surface thermal infrared radiation [START_REF] Zhiwu | Remote sensing technology and SEBAL model in the application of arid evaporation estimation[END_REF].

1/4 BT LST   (Formula 3)
1.009 0.047ln NDVI

 

, BT refers to the spectrum radioactive brightness. 4)The calculation of VTCI

Export VTCI image with ENVI/IDL

According to requirements above, we designed a reasonable scheme, which can be processed spontaneously. In the program above, ";" is a sign of comment. We can see that IDL is a language of simple grammar.

It processes the matrix as a whole without pixels by pixels and it saves a lot of time when processing large matrixes. By using the method above, it took 18.658 seconds to process this image of Junchuan farm on September 25th, which saves much time than hand operation, and there's no need to wait in front of the computer for the next step. Figure 3 is the resulting VTCI image. From it we can see that the area is in middling drought or severe drought, which is exactly the same with the real condition.

Conclusion

This paper uses spontaneous and time-saving method to retrieve VTCI based on IDL language as an example to put forward a way to process large amount of remote sensing data also spontaneously and automatically to monitor drought condition. By using IDL, we can realize that:

1) Modularization of the process and the ability to be transplanted through different operating systems.

2) All-day automatic work, which can output real-time VTCI result, and make it easier for government to know the drought and take emergency measurement.

3) Once successfully set, there's no need to interfere or guard it with labor, which is time and labor saving.

Computers can make automatic procession and save result as the settings, file names and path, which can avoid mistakes.

Acknowledgments

This

Figure 1

 1 Figure 1 Landsat TM5 image of study area (Red: Band7, Green: Band4, Blue: Band3)

  b are undetermined coefficient and need to be determined by linear fitting on borderline of scatter diagram.

Figure 2 2 ) 3 ) 4 )Figure 3

 22343 Figure 2 Computing in the process of IDL

  research is funded by the Natural Science Foundation of China entitled Estimation of tree height using LIDAR data and QuickBird imagery (40801128). All of the authors appreciate the co-operation and support of many experts from the Research Institute of Forest Resource Information Techniques of Chinese Academy of Forestry, and we all would like to thank Professor Liu Qinfu of the College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), for his support with human resources.

* *Corresponding author, E-Mail: suwei@cau.edu.cn.


Jin-Heng Zhang

Y U Xin

L V Yongliang

Ya O Zhen

L I Dapeng

Han Chao

A Kind of Rice Nitrogen Status Rapid Diagnostic Tool

Keywords: Rice Nitrogen Content, Vegetation Index, Reflectance, M eter

Using data from three deformation positions (680 nm, 730 nm, and 765 nm) of spectral reflectance and derivative spectra curves from red to near infrared spectral bands, red edge reflectance spect ra index was developed. Nitrogen contents of rice canopy leaves were found to be significantly correlated with the red edge reflectance spectra index values at 0.01 probability level for different rice growth stages and genotypes studied. Four models established -for four rice growth stages were used to predict the nitrogen content of canopy leaves. Significant correlations were found between measured nitrogen contents and predicted nitrogen contents with high coefficient of determination (R2 = 0.97) at 0.01 probability level. Based on the four field exp eriments, we developed a new rice nitrogen status rapid diagnostic meter. The working principle of the meter was introduced, and the measuring accuracy of the meter was analyzed. Results showed that the precision of nitrogen status rapid diagnostic meter for predicting nitrogen content was more than 80% at tiller stage and more than 90% at booting stage at 0.01 probability level. The nitrogen status rapid diagnostic meter appears to be a promising tool for rapid, on-farm analysis of rice nitrogen status.

Introduction

Synthetic fertilizers especially nitrogen fertilizer have acquired a bad reputation in recent decades for cau sing serious ecological damage when farmers use them excessively. Nitrogen status of various crops is an very important factor which can reflect the nitrogen apply level. In agricultural research, organic chemical constituents have been successfully measured by using multiple linear regression to relate the constituents of interest to the reflectance spectra of dried ground samples. Appro ximately 42 minor absorption features in the visible and near-infrared portions of the spectrum have been successfully correlated with foliar chemical contents, including those of nitrogen, protein, and lignin as demonstated [START_REF] Curran | Remote sensing of foliar chemistry[END_REF]. Nitrogen status of various crops has been related to reflectance at certain wavelength, and various reflectance rat ios and indices have been used to detect nitrogen deficiencies in plants as demonstrated [START_REF] Goel | Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn[END_REF][START_REF] Serrano | Remote sensing of biomass and yield of winter wheat under different nitrogen supplies[END_REF][START_REF] Lukina | Effect of row spacing, growth stage, and nitrogen rate on spectral irradiance in winter wheat[END_REF]. Many relationships between the spectral response of crops and growth factors have been elucidated, based on reflectance obtained fro m handheld and aircraft-mounted sensors providing images in visible and other parts of the electromagnetic spectrum. More recently these procedures have been extended to the laboratory analysis of dried ground foliage as demonstrated [START_REF] Card | Prediction of leaf chemistry by the use of visible and near infrared reflectance spectroscopy[END_REF] and fresh forest foliage as demonstrated [START_REF] Zhang | Rice nitrogen nutrition diagnosis using continuum-removed reflectance[END_REF]. Spectral determination has been proved to be an automatic, quick, and nondestructive method for assessment of nutrients.

Spectroscopy can provide informat ion about a substance by relat ing its interaction with electro magnetic rad iation as a function of wavelength to its chemical co mposition and physical properties. Spectral bands in the visible and near-infrared regions of the spectrum have been used to develop a number of indices for estimat ing nitrogen content. Vegetation index is a simp le, effective, and experiential me asurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. Most of the indices based on reflectance spectrum including single band spectral reflectance and reflectance band ratios or d ifference have been used as indicators of nitrogen content. Some studies have been conducted to measure nitrogen content of crops using field spectrometry. The nitrogen status of various crops has been related to reflectance at specific wavelength, and various reflectance ratios and indices have been used as indicators of nit rogen deficiencies in p lants as demonstrated [2-4][7-13]. Spectral determinations can provide an automatic, quick, and nondestructive method for estimating crop nutrient status.

As a fundamental research tool for the application of current and future hyperspectral and even mu lti-spectral sensing systems, a field spectroradio meter was used to collect reflectance data. But, until now, there is no report on predicting the nit rogen content of rice using handheld spectral meter. Therefore, the purpose of this study was to verify the feasibility and effectiveness of a newly developed RERI Nitrogen Meter to diagnose rice nitrogen nutrient content.

Materials and Methods

Experi mental Description

Four field experiments were conducted in 2002, 2009, and 2010.The first experiment was conducted in a ploughed field of 3.02 h m2 near JiaShan County of Zhejiang Province (Latitude 30.84°, Longitude 120.92°).

The field was d ivided into three N supply areas: an area of 0.47 h m2 d id no t receive any N fert ilizer, an area of 0.23 h m2 received half of the reco mmended rate, and the remainder of the field received the reco mmended rate.

The recommended N applicat ion rates were 135 kg urea h m-2, 202.5 kg urea h m-2, and 120 kg urea h m-2 for tiller, booting, and heading stage, respectively. Three genotypes of rice (Ory za sativa L) were used: late rice 101, late rice C-67, and late rice 004.

The second field experiment was laid out in a split -plot design with three replications at Yuhang Town, Hangzhou City, Zhejiang Province (Latitude 30.43°, Longitude 120.3°). The main plots were arranged in a randomized co mplete block and consisted of six basal N rates: 0, 150, 225, 300, 375, and 450 kg N h m-2. Subplots were seeded with two genotypes of late rice, Bing 9363 and Bing 9652, wh ich had the same growth cycles and different foliar co lors (dark and bright, respectively). The main plot size was 5.0 m × 3.6 m with four repeats.

The third field experiment was extended over 0.7 h m2 at Chengyang Qingdao of Shandong Province (Latitude 36.07°, Longitude 120.33°). It was designed in a rando mized co mplete b lock with three rep lications, using three varieties of rice, Shengdao 13, Lindao 11, and Yangguang 200, with similar gro wth stages. Four basal n itrogen (N) rates were used: 0 kg urea h m-2, 270 kg urea h m-2, 585 kg u rea h m-2, and 750 kg urea h m-2.

The fourth field experiment was laid out in a split-p lot design with three replications at Yutai town, Jin ing city (Latitude 35°, Longitude 116.65°), Shandong province. The main p lots were arranged in a randomized complete b lock and consisted of 4 basal N rates: 0, 156, 310, and 403 kg N h m-2. Subplots were seeded with three genotypes of rice (Zhendao88, Shengdao16, and Shengdao15), which had similar growth cycles. The main plot size was 5.0 m × 8 m with three repeats.

For the 1st, 2nd and 3rd field experiments, 50%, 40%, and 10% N fert ilizer was applied before transplan ting, at tillering, and at heading, respectively. But for the 4th field experiments, N fert ilizer was applied according to the following rat io: 20% before transplanting, 55% at tillering, and 20% at heading.

Canopy Reflectance S pectrum Measurements and Pl ant Sampling

In the first and second field experiments, rice can opy reflectance at 350-2500 n m in 1-nm-wide wavebands were measured using the portable spectroradiometer Field Spec FR held 70 cm above the canopy top. In the third and fourth field experiments, the reflectance spectrum was measured at 350-1100 n m in 2.4-nm-wide waveband by the portable spectroradiometer AvaSpec-2048FT-SPU held 100 cm above the canopy top using the controllable field spectral measurement device (Ch ina patent of invention 200810180572.7)(see figure 1). All canopy reflectance spectra were measured under cloudless conditions and as close to solar noon as possible.

After measuring the reflectance spectra, several rice clu mps were obtained and their leaves were used as subsamples. Total N was determined for the subsamples based on the Kjeldahl method. Total N was e xp ressed in g N kg-1 dry weight.

Results and Analysis

The feasibility and effecti veness of vegetati on i ndex and predicti on models

The RERI standard bands were selected according to Figure 2. The fo llo wing equation was developed to estimate the canopy leaf N content [14] 680 730

730 765 R R R R RERI    (1)
Where, Ri indicates the rice canopy reflectance at the band i (n m).

According to the reference [14], the RERI increased with increasing N levels (the 1st, 2nd and 3rd experiments). Correlation coefficients between RERI and the N content were significant at 0.01 probability level for eight rice genotypes in the 1st, 2nd and 3rd experiments.

The relationship between RERI and the canopy N content can best be described by linear regression models of the form y = ax + b. Data fro m the third experiment were used in canopy leaf N content prediction models (Table 1) [14].

The models described in Table 1 were tested using two validation data sets from the first and second experiments. The canopy reflectance spectra of the validation samples were used to calculate RERI, and the alg orith m equations were used to predict canopy leaf N content. We observed go od agreement between the predicted and measured values (Table 2). A high positive correlation was observed between the predicted and actual N co n- the present study could be used to accurately estimate the N fert ilizer level and canopy lea ves' N contents. Regression analysis indicated that RERI was a better indicator of the canopy leaves' N contents. The power of predictors and prediction models was tested using two validation datasets, and revealed good agreement between the predicted and measured canopy leaves' N contents. We concluded that satisfactory results were obtained using the new vegetation index RERI to predict the rice N nutrient status.

2.2.Introduction of RERI Nitrogen Meter

According to the research described above, the meter (figure3, figure4) only records three wavelengths of rice canopy spectral reflectance. Recording speed for testing and spectral reflectance can reach millisecond timescale. The meter acco mplishes data rectificat ion, processing, and recording functions automatically and intelligently. The meter performs simp ly and fast, without requiring manual intervention wh ich can avoid the influence of hu man factors . The micro processing system will record canopy spectral reflectance at wavelengths of 680 n m, 730 n m, and 765 n m. RERI values are calculated using the following formulas: Nitrogen content of rice canopy is automatically calculated and displayed by the onboard software (figure 6).

Eli mination of coarse errors and calculati on of ni trogen content

In order to ensure the accuracy of RERI Nitrogen Meter, when using RERI Nitrogen Meter, we should s elect as many repetition points as possible (at least 30). Because of the milliseconds response time, very short time will be spent on measuring process.

A software with data processing function was developed to select and eliminate errors caused by operation processes while predicting the nitrogen content (N%) (see figure 6). When the number of data points is less than or equal to 30, Ro manvschi rule (T d istribution) is used, otherwise Grubbs rule is used to remove coarse errors fro m the measured data. In this software, measured data files can be imp orted by selecting the menu "import". Then, the file name and the amount of data are shown automatically. After selecting rice gro wth stages, object models will be called by the software to calculate the nitrogen content automatically. The results are saved as log files.

Verification of the precision of di agnosis

The precision of diagnosis of RERI Nit rogen Meter was verified by using the data from the 4th experiment.

In addition, the meter's feasibility and practicability were also confirmed.

Co mparing the RERI values calculated fro m canopy spectral reflectance with those measured by the RERI Nitrogen Meter showed that calculated values were significantly correlated to the measured values (correlation coefficients more than 0.8). Significant correlat ion coefficients were more than 0.7 between the RERI Nitrogen

Meter readings and the level of nitrogen fertilizer. Both measured RERI using RERI Nitrogen Meter and calculated RERI using spectral reflectance increased with increasing nitrogen fert ilizer levels (figure 7) . As expected, when the growth stages changed from t iller to heading stage, RERI Nitrogen Meter readings increased(figure 8).

After importing RERI Nit rogen Meter readings fro m the 4th experiment the onboard software (figure 6), the regression models (table 1) were called and the nitrogen contents of rice canopy leaves were shown automat ically. Correlation analysis between the N content predicted by the RERI Nitrogen Meter and the N content measured using the common laboratory Du mas method showed that the precision of total N content diagnosis was mo re than 80% at tiller stage and 90% at booting stage at the 0.01 probability level(table3).

Therefore, this study indicated that the RERI Nitrogen Meter can not only qualitatively classify the level of nitrogen, but can also be used for quantitative diagnosis of rice nit rogen content of canopy leaves. The precision of diagnosis reaches 80-90% and even higher.

Conclusions

A RERI Nitrogen Meter developed based on red edge reflectance index (RERI)was investigated for practical application in agricultural production management. Based on the investigation, it can be co ncluded that because of the millisecond response time and the precision of diagnosis more than 80%, the meter can be used as a rapid nitrogen content diagnostic tool, and is suitable for not only handheld but also lo w altitude platfo rms such as helicopters and hot air balloons. Pract ical applicat ions indicated that RERI Nitrogen Meter is suitable for real time diagnosis of nitrogen content from canopy level, and should be an important part of the precision agriculture technology to imp rove variable fert ilization technology.

Figure 1 .

 1 Figure 1. Controllable field spectral measurement device. (China patent of invention 200810180572.7)

Figure 2 .

 2 Figure 2. S pecial waveban ds of the first derivative and reflectance spectrum curves of rice canopy

(

  This meter uses sunshine as light source and optical filters as optical co mponents to measure the reflectance of crops at 680 n m, 730 n m, and 765 n m. Then, the RERI values are calcu lated. Low power consumption TI single-chip with six filtering systems is used as core component. Three filtering systems are installed under the transmitting diffuser which is on the top of the instrument (toward sun). Sun light reaches the sensors after going through transmitting d iffuser and optical filter. Then, the sun optical signals at 680 n m, 730 n m, and 765 n m are obtained. The next set of three filtering systems is installed on the bottom of the instrument (toward crop canopy). The filtering systems installed on the bottom of the instru ment collect canopy reflected light and send them to the sensors after going through optical filter and objective lens. Subsequently, the crop canopy reflected ligh t signals are obtained. Every channel signal is transformed into digital signals through Analog -to-Digital Converter. RERI values are calculated with dig ital signals. Meanwhile the results are shown on the LCD. A ll data files are stored in the memory and they can be downloaded on to a computer by RS 232 or USB serial port (figure 5).

Figure 3 .

 3 Figure 3. RERI sensor meter.

Figure 4 .

 4 Figure 4. RERI sensor used in field experiment. (In the 4th field experiment, handheld RERI sensor was used to measure rice canopy RERI values. The handheld RERI sensor was held 100 cm above the canopy under cloudless or partly cloudy conditions.)

Figure 5 .

 5 Figure 5. Working principle diagram.

Figure 6 .

 6 Figure 6. Software for elimination of coarse errors and calculation of nitrogen content. (Rice genotype is Zhendao88. Tiller stage. The line is the average line. S mall circles are the measured data using RERI sensor. Big circles are the valid data after removing coarse errors.)

  V1N3 V2N0 V2N1 V2N2 V2N3 V3N0 V3N1 V3N2 V3N3 Calculated RERI values using spectral data The RERI sensor measured values (2010.8.3)

Acknowledg ments Project supported by the Hi-Tech. Research and Develop ment Program of Ch ina ( 863 Program) (2007AA10Z205) and the National Natural Science Foundation of China (40601062)