Fa Liu

Jianping Hu

Yingsa Huang

Xiuping Shao

Wenqin Ding

Design and simulation analysis of Transplanter's planting mechanism

Keywords: Transplanter, planting mechanism, ADAMS, Groove cam, Plant-arm's kinematics locus

A new-style transplanter's planting mechanism was designed, which was composed of Planetary Gear, Planetary Carrier, Connecting rod, Groove cam and Planting arm. Built the kinematics model and determined the main parameters which influenced the Plant-arm's locus by analyzing of the Kinematic model. Created the 3D Model in PRO/E and imported it into the Kinematics simulation software ADAMS, analyzing Groove-cam's offset angle,

Connecting-rod's length and its impact on the Plant-arm's kinematics locus.

The effect laws of the structural parameters on the Plant-arm's locus were obtained through analyzing the Plant-arm's locus, which got by changing the groove cam's horizontal offset angle ranging from 0° to 20°, the sum of Connecting-rod's length ranging from 130mm to 150mm, and the subtract of Connecting-rod's length ranging from 15mm to 25mm. The analysis results are of theoretical significance to the dimension synthesis and optimization design.

Introduction

Raise seedling and transplant can increase crop production in every unit area, and make upgrowth ahead of time, which can withstand gale, harmful rain, low temperature, and other nature disaster. Besides, it also saving seed. Seeds are usually grown under the film by farmer in many place, because it is useful to improve the soil temperature, keep moisture and restrain weeds.

The nacelle-type and dibble-type transplanting mechanism are good mechanism for transplanting film, but they are not convenient and safe to directly drop seedling and they are easy to leave out seedling when the machine are operated and their work efficient are low when seedling are transplanted over the larger areas. Now a simple and credible type of transplanting mechanism was designed, which was easy and convient to be operated and adjusted. the efficiency of the machine was much more greatly improved. There was less seedling to leave out in work progress. The three dimension model of the transplanting mechanism was establish in pro/e software. The relationship between the planting arm's locus and structural parameters was analyzed by the mechanical simulation software ADAMS in this paper.

2 Kinematic analysis of the planting mechanism A transplanter's planting mechanism was shown in Figure 1, which was composed of planetary gear, planetary carrier, link, groove cam and planting arm. The numbers of sun-wheel's teeth was twice times that of the planetary gear's teeth. The cam groove was divided into two parts and a angle-off between the two part. Planting arm, fixed on the connecting rod 2, moved with the connecting rod 2.

2.2.Transplanter's planting mechanism kinematics equations

The initial position of planting bodies was shown in Figure 1

O O x l t l t α ω α ω = + ⎧ ⎪ ⎨ = + ⎪ ⎩ ( ) y
The Coordinate equation of the connecting rod's endpoint A is:

2 2 2 0 2 0

cos( ) sin( )

A O A O x x l t y y l t β ω β ω = + + ⎧ ⎪ ⎨ = + + ⎪ ⎩
The Coordinate equation of planting arm's endpoint B is:

3 3 cos sin B A B A x x l y y l γ γ = + ⎧ ⎨ = + ⎩ The Coordinate equation of planting arm's endpoint D is: B cos( ) 2 sin( ) 2 D D B x x y y π ρ θ γ π ρ θ γ ⎧ = + + - ⎪ ⎪ ⎨ ⎪ = - + - ⎪ ⎩
The rate equation of the planetary gear's center O 2 is:

2 2 1 0 1 0 sin( ) cos O O x y v l t v l t ω α ω ω α ω = + ⎧ ⎪ ⎨ = + ⎪ ⎩ ( )
The endpoint A of the connecting rod 1 rate equation is:

sin( ) cos( )

A O A O x x y x v v l t v v l t ω β ω ω β ω = + + ⎧ ⎪ ⎨ = + + ⎪ ⎩
The rate equation of the planting arm's endpoint B is:

2 3 2 3 sin cos B A B A x x y x v v l v v l ω γ ω γ = + ⎧ ⎪ ⎨ = + ⎪ ⎩
The rate equation of the planting arm's endpoint D is:

2 2 sin( ) 2 cos( ) 2 D B D D x x y x v v v v π ω ρ θ γ π ω ρ θ γ ⎧ = - + - ⎪ ⎪ ⎨ ⎪ = + + - ⎪ ⎩ 2 ω γ = i α 0 --

3.Virtual Prototype Model of the Planting mechanism

The planting mechanism's three-dimensional model was established and assembled in the pro/e and then transmited it into the mechanical simulation software Adams. The Planting mechanism's Virtual Prototype Model was established in the mechanical simulation software Adams as in Fig. 2. The stability of catching seedling were influenced by the value of c. As is shown in Table 2, Fig. 3 shows a set of curves of a width of about 97mm, a height ranging from 276mm to 335mm and a declination angle ranging from 0° to 7.9°. Fig. 4 shows a set of curves of a width ranging from 25mm to 183mm, a height ranging from 292mm to 336mm and a declination angle ranging from 1.9° to 16°. Fig. 5 shows a set of curves of a width of about 94mm, a height ranging from 256mm to 360mm and a declination angle ranging from 3° to 5.6°.

Conclusion

(1) The width of planting arm's locus depends on the subtract of Connecting-rod length. With the subtract of Connecting-rod length increasing, the width of planting arm's locus became wider and wider.

(2) The height of planting arm's locus depends on the sum of Connecting-rod length, the subtract of Connecting-rod length and the groove cam's horizontal offset angle.

With the sum of Connecting-rod length, the subtract of Connecting-rod length and the groove cam's horizontal offset angle increasing, the height of planting arm's locus became larger and larger.

(3) The deflection angle of planting arm's locus depends on the groove cam's horizontal offset angle and the subtract of Connecting-rod length. With the groove cam's horizontal offset angle and the subtract of Connecting-rod length increasing, the deflection angle of planting arm's locus width of planting arm's locus became wider and wider.

2. 1 Fig. 1

 11 Fig.1 planting mechanism structural chart

1 l --the length of the planet carrier 2 l --the length of the connecting rod 1 3 l

 123 the initial angle between the planetary carrier and the horizontal β 0 --the initial angle between connecting rod 1 and the horizontal γ --the angle between connecting rod 2 and the horizontal θ --CBD ∠ --the length of the connecting rod 3 ω--the angular velocity of the planet carrier

Fig. 2 .Kinematics simulation 4 . 1 . 4 and Fig. 5 .

 24145 Fig.2 The Planting mechanism's Virtual Prototype Model

  1.

Fig 3 ,

 3 Fig 3, Fig4 and Fig 5 is the planting arm's locus in different structural parameters respectively.

Fig. 3 Fig. 4 Fig. 5 4 . 2

 34542 Fig.3 Planting arm's locus in different groove cam's horizontal offset angle

Table . 2

 . Simulation results analysis

	Structural	Results		Indicator value
	parameters	Fig.	Width(a)	Length(b)	Declination(δ)
	A1B3C3		93	276	0°
	A2B3C3		97	281	0.6°
	A3B3C3	Fig.3	97	292	3°
	A4B3C3		96	315	4.4°
	A5B3C3		98	335	7.9°
	A3B1C3		25	315	4.5°
	A3B2C3		49	300	1.9°
	A3B3C3	Fig.4	97	292	3°
	A3B4C3		138	326	9°
	A3B5C3		183	336	12°
	A3B3C1		94	256	4.5°
	A3B3C2		93	286	5.8°
	A3B3C3	Fig.5	97	292	3°
	A3B3C4		93	330	4.4°
	A3B3C5		94	360	5.6°

Acknowledgements

This work was financially supported by the three agricultural machinery project of Jiangsu Province for 2008.


Chang Qing

Yang Jing

Liu Lin

Su U Yuan

Li I Jinbin

Zhu Youyong

Li Chengyun
email: li.chengyun@gmail.com

Qing Chang

Chengyun Li

Chlorimuronethyl Resistance Selectable Marker Unsuited for the Transformation of Rice Blast Fungus (Magnaporthe Grisea)

Keywords: Magnaporthe grisea, chlorimuronethyl resistance gene, selectable marker, fungi transformation

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Fungi play important roles in many human, plant, and animal activities, including biotechnological processes, phytopathological and biomedical research. They are also excellent models for molecular and genetic studies (Casas-Flores et al., 2004). Molecular studies of fungal biology have been greatly advanced by Agrobacterium tumefaciens-mediated transfromation (ATMT) techniques.

Transformation via non-homologous integration of plasmid DNA carrying a selectable marker has been widely used for fungal transformation [START_REF] Chang | Filamentous fungi (Magnaporthe grisea and Fusarium oxysporum)[END_REF]. For comprehensive and in-depth study of the interaction between pathogen and host, a series of vectors bearing more available selectable markers (e.g. more antibiotic resistant genes) must be constantly developed to meet ATMT.

Magnaporthe grisea has been extensively utilized as a model of fungal pathogen for understanding the molecular basis of host plant-fungus interaction, due to its genetic and molecular tractability [START_REF] Dean | Signal pathways and appressorium morphogenesis[END_REF][START_REF] Talbot | Having a blast: exploring the pathogenicity of Magnaporthe grisea[END_REF], as well as the economical importance of the disease it caused. ATMT have been widely used to investigate the infection process and the genes involved in the complex interaction between M. grisea and rice. To date, many selectable markers have been used for fungal transformation, such as hygromycin, bialaphos, zeocin and chlorimuronethyl resistance genes. Based on current reports, chlorimuronethyl has not yet been reported to be used for M. grisea transformation, but other selective markers have been used for the purpose. Chlorimuronethyl belonged to the sulfonylureas series herbicide. The chlorimuronethyl resistance gene has been engineered to be modified and eliminate sites for the most common restriction enzymes, and chlorimuronethyl selectable markers have been used to construct a series of vectors for fungal transformation [START_REF] Sweigard | A series of vectors for fungal transformation[END_REF].

Sulfonylureas, especially chlorimuronethyl, which was the active ingredient of the herbicide Classic reg , inhibits acetolactate synthase [START_REF] Sweigard | A series of vectors for fungal transformation[END_REF]. The sulfonylurea resistant allele of M. grisea ILV1 has been subcloned as a 2.8 kb fragment and modified by the elimination of eight enzyme sites [START_REF] Sweigard | A series of vectors for fungal transformation[END_REF]. The chlorimuronethyl resistance gene that was cloned from allele of the Magnaporthe grisea ILV1 encoded acetolactate synthase involved isoleucine and valine synthesis [START_REF] Sweigard | A series of vectors for fungal transformation[END_REF]. ILV1 was a homologue of MGG_07224 (threonine dehydratase) from Magnaporthe grisea 70-15 [START_REF] Hoffmann | A gene network for navigating the literature[END_REF]. Chlorimuronethyl selectable markers was used for

Neurospora crassa [START_REF] Li | A mitogen-activated protein kinase pathway essential for mating and contributing to vegetative growth in Neurospora crassa[END_REF], Cercospora nicotianae [START_REF] Chen | Functional characterization of three genes encoding putative oxidoreductases required for cercosporin toxin biosynthesis in the fungus Cercospora nicotianae[END_REF] and other fungal transformations. Although M. grisea transformation has many available selectable markers, in some special cases, other selectable markers such as the marker bearing the chlorimuronethyl resistance gene has been developed to cater for special requirements. This begs the question whether this selectable marker could be successfully used for M. grisea transformation. In the present study, the chlorimuronethyl resistance gene has been studied as a potentially available marker for M. grisea transformation.

Materials and methods

Fungal strain and cultural conditions

Thirty field isolates and two transformants of M. grisea were used in this study. Field field isolates were collected from different regions of Yunnan Province and the two transformants were obtained through Y98-16 transformation. pBIMgNIP04 was constructed plasmid bearing the chlorimuronethyl resistance gene. Fungal cultures were grown on oatmeal agar (OMA; 40 g of oatmeal for 1 L) at 25°C under continuous fluorescent light to promote conidiation [START_REF] Lee | Calcium/calmodulin-dependent signaling for appressorium formation in the plant pathogenic fungus Magnaporthe grisea[END_REF]. Conidia were harvested from 7-to 10-day old cultures using sterilized water.

Transformation

The A. tumefaciens strain GV3101, containing pBIMgNIP04, was grown at 28℃ for 48 h in a minimal medium (MM; [START_REF] Hooykaas | Regulation of the transfer of Ti-plasmids of Agrobacterium tumefaciens[END_REF] supplemented with kanamycin (50 g/ml). Bacterial cells in a 2 ml aliquot of this culture were harvested and washed with an induction medium (IM; [START_REF] Bundock | Trans-kingdom T-DNA transfer from Agrobacterium tumefaciens to Saccharomyces cerevisiae[END_REF]. The cells were resuspended with 5 ml of IM containing 200 M acetosyringone (AS). The cells were grown for an additional 4-6 h before mixing with an equal volume of conidial suspension of M. grisea field strain Y98-16. This mix (200 l) was plated on filter paper on a co-cultivation medium, adding 200 M Following co-cultivation at 25C for 72 h, the filter paper was cut into strips using scissors, and they were reversely plated on PDA medium plates containing chlorimuronethyl

DNA extraction and PCR determination

Based on chlorimuronethyl resistance gene nucleotide sequence, PCR primer pairs were designed by software Primer Designer. The primer sequences followed as 5'-GCAAGGAGTGGTTCGACCAGATCAA-3' and 5'-GTCAGAGCATCACCGACATCGTCAG-3', and the PCR product size was 562 bp. DNA was extracted from fungal mycelia grown in potato dextrose broth for 4 days at 28℃ at 120 rpm. The DNA extraction method followed [START_REF] Chadha | Genetic diversity of India isolates of rice blast pathogen (Magnaporthe grisea) using molecular markers[END_REF]. PCR reaction was performed on an Eppendorf PCR machine. Each tube contained a 25 µl reaction mixture, including Taq polymerase (TAKARA Biotechnology (Dalian) Co. Ltd). Thermal cycling conditions consisted of 2.5 min at 95℃

following by 35 cycles of 30 s at 94℃ and 30 s at 62℃, 1 min at 72℃, and one final cycle of 10 min at 72℃.

Results

PCR results showed that all tested strains, including the two transformants, were expected to appear at ~500 bp band (Fig. 1). To confirm whether the PCR products were identical, the PCR product from wild type strain Y98-16 and one transformant MgNIP04-1 bearing the chlorimuronethyl resistance gene were cloned into pGEM-T vector and sequenced.

The sequencing results were aligned with 70-15 genome sequences and partial coding sequence of chlorimuronethyl resistance gene using BioEdit (Fig. 2). Based on alignment results, a section of DNA sequences, which located in supercontig 6.18, ranging from gene and PCR product from Y98-16. For all aligned sequences, almost all of bases were identical.

Chlorimuronethyl resistance gene sequence was blasted in NCBI, the result showed that the gene appeared higher identity with M. grisea acetolactate synthase gene and partial coding sequence of MGG06868 (E-value: 0.0), while appeared lower identity with Herpetosiphon aurantiacus ATCC 23779 (E-value: 4e-05), but it is absent in other organisms presented database, that it is suitable marker for transformation of other fungi and organisms.

To understand the genomic environment of the gene, sequences located upstream and downstream of the gene was carried out. There was no any transponson around the gene.

G+C% content of M. grisea homologue to chlorimuronethyl gene, -1818 bp of 5' and 3' terminal of the homologue was analyzed using Seqool, respectively. The result showed that GC% content of the homologue was 52.96%, -1818bp of 5' terminal was 55.23%, and -1818bp of 3' terminal was 47.47%.

Based on the concentration of chlorimuronethyl introduced by Sweigard (1998), which was 100 g/ml, it was necessary to verify whether the wild type strains were resistant to chlorimuronethyl under different concentrations. The transformant Y98-16 and CY2 were screened on PDA medium plates containing chlorimuronethyl (100, 200 and 300 g/ml, respectively) as the selection agent. Results showed that all test strains, including Y98-16 and CY2, showed resistance to chlorimuronethyl in the tested concentrations (100, 200 and 300 g/ml)(Fig 3).

Discussion

Fungal genetic transformation has greatly accelerated the analysis of gene function.

Fungal transformation methods include protoplast, biolistic and agrobacterium tumefaciens-mediated transformations (ATMT). And Agrobacterium tumefaciens-mediated transformation (ATMT) is used for functional mutagenesis of the fungus [START_REF] Jeon | Genome-wide functional analysis of pathogenicity genes in the rice blast fungus[END_REF]. No matter which methods are used, there must be antibiotic resistance genes as selectable markers for successfully selecting incorporated genes for a desired trait during transformation. For transformants to be successfully screened, the plasmid containing an antibiotic resistance gene must be constructed. There are four types of selectable marker genes, including antibiotic resistant (e.g. neomycin and kanamycin), herbicide tolerant (e.g. bialaphos and chlorimuronethyl), metabolic/auxotrophic and screenable marker genes (www.nuffieldfoundation.org/bioethics/publication/modifiedcrops/rep0007969.html). Of these selectable markers, herbicide tolerant marker was originally used in screening transgenic plants, but now this marker was also used for fungal transformation.

Since green evolution, quantities of herbicides have been used to control grasses. Some grasses had developed tolerance to these chemical components during long-term competition with chemicals. While chlorimuronethyl as for a kind of herbicide was applied in fields in early 1980s and it widely applied in rice fields, soybean fields, maize fields, wheat crop fields, rape fields, lawn and other weeds in non-cultivated land for a long time. In addition, increasing transgenic organisms carrying herbicide-resistance genes such as chlorimuronethyl and bialaphos were released into the fields, and possibility of gene flow from these transgenic organisms to other organisms, especially under herbicide stress is present. If a specific DNA sequence of a strain had higher or lower G+C content than its genome mean G+C content, or up-and down-stream sequence, which indicated that this specific DNA sequence was obtained from exogenous bacterium or plasmid of other species [START_REF] Li | Methods for the identification of horizontal gene transfer (HGT) events and progress in related fields[END_REF]. The GC content of M. grisea homologue to chlorimuronethyl gene was analyzed, the result showed that GC content of the homologue and its 5' terminal were higher than M. grisea genome (GC% of content was 51.57%), and its 3'terminal was lower than 51.57%. which offered a speculation that M. grisea homologue to chlorimuronethyl gene possibly was obtained from exogenous bacterium or plasmid of other species. It was necessary to verify the speculation through experimental method. So, the selectable marker carrying chlorimuronethyl gene was unsuitable for M. grisea genetic transformation.

If transgenic plants or fungi carrying Chlorimuronethyl were released into fields or markets, it would inevitably threaten human and/or animal health. Therefore, it is necessary to develop suitable and safe selectable markers in the future. Crop developers have been seeking more useful markers for selecting transgenic plants, animal or fungi and these methods have been adopted in the selection process [START_REF] Dale | Gene transfer with subsequent removal of the selection gene from the host genome[END_REF][START_REF] Ebinuma | Selection of marker-free transgenic plants using the isopentenyl transferase gene[END_REF]. In conclusion, some markers carrying antibiotic genes such as chlorimuronethyl, were neither suitable for M. grisea transformation nor other fungi or plant transformation from the long-term perspective of global food and environmental safety.

(

  purchased from Japan, CAS: 9082-32-4, No.036-16671) as the selection agent for fungal transformants. Concentrations of chlorimuronethyl were 100, 200 and 300 g/ml. In order to eliminate the remaining A. tumefaciens cells, the cefotaxime (200 g/ml) and spectinomycin (200 g/ml) were added in the PDA medium. After 3-day old selective cultivation, individual transformants were transferred into OMA medium containing chlorimuronethyl (300 g/ml) and incubated until conidiation. Conidia of the individual transformants were picked and transferred to OMA.

ACKNOWLEDGEMENTS

We thank Dr M.A. Fullen for helpful suggestions and manuscript modification. This work is partially supported by the National Basic Research Program (2006BC100202) and the National Natural Foundation (30860161).


Hongbin Zhang
email: hbzhang@caas.net.cn

Guixia Yang

Qing Huang

Gang Li

Baorui Chen

Xiaoping Xin

Analysis on dynamic Characteristics of landscape patterns in Hailer and around areas

Keywords: Landscape patterns, Hulunbuir, Spatial-Temporal Dynamic

This paper analyzed the spatial-temporal dynamic changes of landscape patterns in Hailer and around areas. Firstly, landscape patterns types of research area were divided into water, sand, farmland, city and grassland based on remote sensing images of 1986, 1991, 1996 and 2001 and field investigation. Then the grassland was classified into higher coverage grassland, high coverage grassland, medium coverage grassland and low coverage grassland by Normalized Difference Vegetation Index. Finally, the spatial-temporal dynamic changes of above-mentioned eight kinds of landscape patterns were analyzed using landscape ecology principle. The results indicated that human activities intensified significant from 1986 to 2001in research area. The area of grassland landscape decreased quickly, and the fragmentation extent intensified. The dominant landscape in research area changed from higher-high coverage grassland to medium-low coverage grassland. The expansion of sand landscape is obvious in periphery of road, city and farmland. The grassland vegetation degenerated seriously. Fragmentation of city landscape lightened, and city landscape patches tended to decrease and centralized. Economy development pattern of research area is in a stage that is transforming from extensive pattern to intensive urbanization pattern.

Introduction

Landscape spatial patterns are strongly connected with dynamic procession [START_REF] Jianguo | Landscape Ecology -Pattern, Progress, Scale and level[END_REF][START_REF] Nagendra | People within parks-forest villages, land -cover change and landscape fragmentation in the Tadoba Andhari Tiger Reserve[END_REF]. Biologic factors, abiotic factors and human factors drive landscape patterns spatial-temporal evolution together and restrict development direction of ecological process. Analysis on landscape patterns spatial-temporal evolution can open out driving mechanism and development trend of ecological process [START_REF] Su | Influences of continuous grazing and livestock exclusion on soil properties in degraded sandy grassland, Inner Mongolia, northern China[END_REF] [START_REF] Yili | Grassland degradation in the source region of the yellow river[END_REF]. In the study of grassland degeneration, analysis on the landscape patterns spatial-temporal evolution makes for understanding relations between landscape pattern and grassland degeneration process and provides scientific foundations on grassland ecological system management and recovery(Wang [START_REF] Hui | Landscape structure of desertification grassland in source region of yellow river[END_REF][START_REF] Mulan | Analysis on dynamic change of landscape structure of Hunshandake sands[END_REF][START_REF] Mulan | Analysis on dynamic change of landscape structure of Hunshandake sands[END_REF]. So many experts and scholars devote themselves to studying landscape patterns spatial-temporal evolution in grassland deterioration process. But most of them focus on highly degraded grasslands. Their natural habitats are not good and they are very easy to be disturbed by outside environment (Li [START_REF] Yuechen | Study on land use/cover change in Xilinhot city[END_REF] [START_REF] Weiguo | Advances and factors affecting the estimating of grass production using remote sensing[END_REF]). So almost nobody studies meadow steppe grassland and their ecology recovery functions are very good. Hulunbuir grassland is one of the grasslands which are conserved most completed (Pan [START_REF] Xueqing | Hulunbuir Grassland in China[END_REF]. It has unique location features and typical ecological system features and advanced pasture animal husbandry production and management methods. At the same time, it is an important husbandry manufacturing base in northern China fescue grassland [START_REF] Lo | The rangeland degradation in North China and its preventive strategy[END_REF][START_REF] Xinshi | Chinese Grass Situation[END_REF]. But since the 1980s, under pressures of economy development and accretion of population, area of farmland and towns has been increasing rapidly in Hulunbuir grassland and degeneration tendency is still very serious. Vegetation productivity has fallen significantly. Sand expands promptly. Landscape spatial pattern changes violently. Especially Halaer area and surrounding area are typical representing regions in which human actions influence most strongly [START_REF] Liu Dongxie | Evaluation of human carrying capacity in the Hulunbeier grassland-a case study in Chenbaerhu Qi in Inner Mongolia, China[END_REF][START_REF] Komatsu | Evaluation of agricultural sustainability based on human carrying capacity in dryland -a case study in rural villages in Inner Mongolia ,China, Agriculture[END_REF][START_REF] Huiying | The Hulunbei er prairie sand present situation draws back and the preventing and controlling counter measure[END_REF]Zhang Deping et al., 2007;[START_REF] Yuling | Change of grass growth in the Hulun Buir steppe in response to global warming[END_REF][START_REF] Haogang | Present situation, evolution trend and causes of sandy desertification in Hulunbuir steppe[END_REF]. So association study on intra-regional landscape spatial pattern has important academic value and useful efforts in Hulunbuir grassland ecological system management and recovery (Ren [START_REF] Jizhou | Study method of practaculture science[END_REF].

2

Material and Method Because the above data belongs to primary production, we must use Erdas Image software to adjust images and registration error is controlled in 0.5 pixel. At the same time, we transferred projection to Albers Equal Area projection(the first and second normal latitude and central meridian are:25、47、105). Based on secular field study in survey region and combined with supervised classification methods, we used Erdas Image software to interpret the images and divided to water, sand, farmland, city, higher coverage grassland, high coverage grassland, medium coverage grassland and low coverage grassland. And the grassland was classified by Normalized Difference Vegetation Index (Tian [START_REF] Qingjiu | Min Xiangjun: Advances in study on vegetation indices[END_REF].NDVI vegetation indexes of higher coverage grassland, high coverage grassland, medium coverage grassland and low coverage grassland are separately (0,0.25]、(0.25,0.5]、(0.5,0.75]、(075,1]. At last, ArcGIS was used to make 1986、1991、1996、2001 landscape pattern distribution maps and the results as shown in the figure 1234.

Land utilization conversion ratio calculation

Use land utilization conversion ratio formula From landscape area changes in table 2, area of every type landscape has wider fluctuation. Compared with 1986, landscapes of city, sand, farmland, low coverage grassland and medium coverage grassland expanded quickly and the expanded areas separately are 29.46、 75.77 、 51.8 、 725.42、 1354.78 km2.At the same time, landscapes of water, high coverage grassland tended to reduce and the reduced areas separately are 24.57、1876.85、335.81km2. In the mass, the whole area reduced 132.46km2. From landscape fragmentation features, compared with 1986, in 2001 patches quantity increased 54.99%, the largest patch area reduced 58.24% and patches density increased 55%. The landscape fragmentation degree increased significantly. But landscape species richness and diversity decreased significantly. Especially grassland landscape fluctuated intensely and weakness species increased in grassland community. A mount of poisonous plants started to emerge. The leading landscapes changed from higher and high coverage grasslands to medium and low coverage grasslands.

% 100 T U U U K

Landscape type area percent conversion

According to landscape type areas in different period in survey region in table 2, took advantage of land utilization percent conversion formula to calculate annual land utilization percent conversion (table 3). From annual land utilization percent conversion in table 3, from 1986 to 2001, in the fifteen years areas of city, sand, farmland, low coverage grassland and medium coverage grassland expanded quickly, especially low and medium coverage grasslands. On the other hand, areas of water, higher and high coverage grasslands reduced significantly. On the whole, the grassland degeneration trend was very obvious. Especially from 1996 to 2001, medium and low coverage grasslands expanded dramatically and annual expanded areas account to 14 to 245 times. On the other hand, areas of high and higher coverage grasslands deduced obviously but sand expanded quickly. Grassland vegetation condition deteriorated sharply.

Landscape type Spatial Transformation

According to 1986 and 2001 landscape type distribution maps in survey region, ArcGIS software was used to extract land utilization transformation matrix (table 4). Every grassland landscape type transformed dramatically. There was 673.5 km2 higher coverage grassland changed to low coverage grassland accounting for 92.77% of 2001's low coverage grassland. And there were 1118.59km2 higher coverage grassland and 160.19 km2 high coverage grassland transformed to medium coverage grassland separately accounting for 81.04% and 11.60% of 2001's medium coverage grassland. There was 151.98 km2 high coverage grassland transformed to higher coverage grassland accounting for 52.89% of 2001's higher coverage grassland. On the whole, grassland vegetation degenerated seriously.

Single landscape type Analysis

The city landscape area was expanding in the fifteen years and expanded 4.67% every year. The occupied area was mainly grassland and farmland. Especially after Hulunbuir city began to take out reform of economic system from 1988, city construction made great development to make city area expand quickly. From 1986 to 1991 annual city area expanded 10.95%. Besides, city landscape patches quantity and density tended to decline, but the largest patch index tended to increase obviously. Overall city landscape fragment degree declined. This condition matched very well with the actual situation that degree of urbanization had enhanced in the past 15 years in survey region.

In the past 15 years, annual farmland landscape increased 0.78% and occupied area mainly was different coverage grasslands landscape. Because strict returning cultivated land measures were taken out after broad scale land clearing from 1980 to 1986 to make farmland landscape area decreased in 1991 compared with 1986. But with the price of food crop rising, under driving of economic interest, land clearing began to ride again in survey region. Patches quantity, density and the largest patch index of farmland landscape tended to increase after 1991 to indicate that farmland landscape tended to expand on the whole, especially the southeastern area in 2001.

Compared with 1986, the other years' water landscape tended to decrease except 1991. In a mass, water landscape area decreased 1.97% in the past 15 years. The decreased water landscape mainly changed to different coverage grassland landscapes. Besides, patches quantity, density and the largest patch index of water landscape were inclined to decrease to indicate natural habitats of grassland vegetation were inclined to deterioration.

Sand landscape area increased 7.74% annually and the occupied land was mainly higher coverage grassland accounting for 88.60% of the whole occupied area to indicate that desertification was most serious in the regions with better vegetation condition which was significantly related to human actions. In the 15 years, patches quantity, density of sand landscape increased obviously. Patches quantity and density increased 4 times in 2001 compared with 1986 especially patches quantity in sides of river channel and surrounding of city areas increased obviously. The largest patch index decreased to indicate sand landscape fragment degree strengthened. That is because people ignored to protect unsandy grassland vegetation when they supervises serious sandy region.

The grassland vegetation degenerated seriously in 2001. The first 7 months accumulated rainfalls separately are 88.2、185.7、282.9、134mm in 1986、1991、 1996、2001 in survey region and 1996's rainfall is the best. But according to monitoring result of NDVI vegetation index, 1991's vegetation condition is the best and 2001's is the worst and 1986's and 1996's are almost the same. That mainly is because ever since a long time ago, the population and grazing capacity have increased too quickly and grassland utilization intensity continued to increase to make community structure occur to serious retrogressive succession and grassland lose their own ecology self-recovery function. So although the first 7 months' rainfall in 2001 substantially exceeded 1986's, a short term better outside condition can not help vegetation to recovery quickly.

4

Conclusions and discussion

Meadow steppe self ecology recovery function is limited

Grassland ecology system has definite patent ecology recovery function. Because of better natural conditions, meadow steppe has the strongest recovery function in all types of grasslands. But there are also conditions in exertion of any patent ecology recovery function. Extravagant utilization can make community structure and soil environment occur retrogressive succession and destroy material base where exertion ecology recovery patent function develop the role. Even the recovery function will be lost and grassland makes for continued degeneration. Especially Hulunbuir grassland which has a long collecting grass and barn feeding history, this utilization patterns destroyed completeness of seed resources base and conflicted exertion of self ecology recovery function.

Jamming is the key factor in grassland vegetation degeneration

In the past 15 years, natural factors influencing grassland vegetation had not degenerated dramatically. On the contrary, compared with 1986, accumulated rainfalls of the other three years' first 7 months were far above 1986's meanly. But in the past 15 years, in survey region, population, stock capacity and utilization intensity had increased remarkably; jamming such as land clearing, road repairing, coal digging, oil extraction, crude drugs digging had become quick degeneration accelerator of vegetation condition. All of the jamming led grassland area continued to reduce and vegetation to degenerate especially sand landscape in surroundings of roads, cities and farmlands expanded remarkably. So jamming of human actions was the key factor to lead grassland ecology system deterioration.

The urbanization level increased and economy development pattern changed from extensive pattern to intensive urbanization pattern

In the past 15 years, city landscape area increased remarkably. Fragmentation of city landscape lightened, and city landscape patches tended to decrease and centralized than ever especially Hailer region. Although city landscape area increased to occupy a mass of surrounding quality grassland, economy development pattern changed from extensive pattern to intensive pattern to enhance city server function and admit to absorb more surplus manpower in other pasturing area. Especially infrastructure in recreation industry was improved and employment opportunities were increased and economy development pressure faced by grassland landscape was relieved on the whole. It is the only way leading to realizing ecology environment improvement and economy continued development.

Fig.1The distributionFig. 3

 3 Fig.1The distribution map of the research area landscapes, 1986 Fig.2 The distribution map of the research area landscapes, 1991

  year dynamic fluctuation velocity between types of landscape in survey region.In above formula, K is a sort of land utilization type dynamical degree in T timeslot; b U and a U are separately a sort of land utilization type quantity in childhood studies and final stage. T is the length of study period and the unit is year to express annual percentage change of a sort land utilization type .

  (Class Area):The sum of the areas of all patches of the corresponding patch type. NP(Number of Patches):The number of patches of the corresponding patch type.

  ; Wu R et al., 2002; Huang Qing et al., 2007; He Chunyang et al., 2001; Zhang

  ; Cao Chengyou et al., 2006; Tao Weiguo et al., 2007; Chen quangong et al., 2007; Chen Quangong et al., 1998; Wang Qian et al., 2007; Li Jianpinget al., 2006; Wu Yunna et al., 2000; Liu Xuelu et al., 2000; Zhang

2.2 Remote sensing data analysis

	potential grass yield and optimal herbaceous regions in Inner Mongolia grassland. We
	selected central region of Hulunbuir grassland as survey region including Hailer, Old
	Barag Banner, parts of Evenk Autonomous Banner and the whole area is 3160.82km2.
	In survey region, hydrothermal condition is very good and it belongs to temperate
	continental climate. Hailer River and Yimin River mixes here. There are abundant
	water resource and 110 days frost free period. Mean annual temperature is -2℃.The
	soil is mainly chernozem. The area of farmland is very large. Main land types include
	city, farmland, grassland, sand and water. It has convinent transportation and rich
	economies. It also is population accumulation area in Hulunbuir grassland. Especially
	Hailer is political, economical and cultural centre and main resources collection and
	distribution point of Hulunbuir. It also is the point of human action maximum
	intensity region in Hulunbuir grassland.
	The selected Data is three terms 1/4 view LandSat-5 TM data and one term 1/4 view
	LandSat-7 ETM data from earth station of Chinese Academy of Sciences.
		Tab. 1 The parameters of remote sensing data
		Number	Orbit	Imaging	Satellite
				Time		Average
						Cloud
		1	123/25	1986.8.6	LandSat5	<10%
		2	123/25	1991.8.4	LandSat5	<10%
		3	123/25	1996.7.16	LandSat5	<10%
		4	123/25	2001.7.22	LandSat7	<10%
	2.1	Survey Region Overview
	Hulunbuir grassland is located in the west of Daxinanling and from east to west
	distributed regularity. It spans forest steppe, meadow steppe and steppe. It is one of

  From landscape type spatial transformation situation in table 4, from 1986 to 2001, in the fifteen years, landscape spatial transformation was obviously and tended to reduce. There were 32.4 km2 grassland landscape transformed to city accounting for 45.3% of 2001's city landscape, 84.95 km2 grassland landscape transformed to sand accounting for 60.23% of 2001's sand landscape, 151.27 km2 grassland landscape transformed to farmland accounting for 30.74% of 2001's farmland landscape. Besides there are 45.45% water landscape transformed to different coverage grasslands and 2.49% transformed to sand landscape.

	Vegetation
	Cover
	Moderate
	Vegetation	0.93	5.17	0.73	1.30	9.28	6.19	0.45	0.00	24.05
	Cover
	High
	Vegetation	30.40 78.75	140.38 9.22	673.5	1118.59	114.22	0.46	2165.53
	Cover
	Higher
	Vegetation	1.06	1.00	10.16	4.55	8.06	160.19	151.98	5.83	342.81
	Cover
	2001 year	71.53 141.04 492.08 58.42	726.00	1380.36	287.35	6.51	3163.29
	Tab. 4 Transformation matrix of landscape types in research area, 1986-2001	Unit: km 2
	landscape
	type landscape	City	Sand	Farm land	Water	Low Vegetation Cover	Moderate Vegetation Cover	High Vegetation Cover	Higher Vegetation Cover	1986 year
	type
	City	36.98 0.47	0.07	0.24	2.40	1.78	0.10	0.00	42.04
	Sand	0.66	52.17	0.06	0.19	9.01	3.15	0.02	0.00	65.27
	Farmland	1.35	1.39	340.44 0.03	19.81	74.63	2.70	0.00	440.34
	Water	0.14	2.07	0.24	42.82	3.87	15.75	17.87	0.23	83.00
	Low	0.01	0.03	0.00	0.07	0.07	0.07	0.00	0.00	0.26

ACKNOWLEDGEMENTS

This work is supported by Special Fund Project for Basic Science Research Business Fee, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences and The National Science & Technology Program (Grant No. 2006BAC08B0404 、 2007BAC03A10) and Project 863 of China: (Grant no.2007AA10Z230) and National Natural Science Foundation of China (Grant No: 30770327) and Commonweal Industry Scientific Research Special Funds Project (GYHY200906029-2). Acknowledgments This work is supported by The National Science & Technology Program (Grant No. 2006BAC08B0404 、 2007BAC03A10) and Project 863 of China: (Grant no.2007AA10Z230) and National Natural Science Foundation of China (Grant No: 30770327) and Meteorological science and technology of China, No.GYHY200906029-2 and Special Fund Project for Basic Science Research Business Fee and.

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Ping Han
email: hanp@nercita.org.cn

Jihua Wang

Zhihong Ma

Anxiang Lu

Miao Gao

Ligang Pan

Application of fuzzy clustering analysis in classification of soil in Qinghai and Heilongjiang of China

Keywords: fuzzy clustering analysis, soil classification, elements

Soil classification systems organize soil variability into useful groupings that can be identified by field investigation and documented in soil survey, and form the basis for the exchange and extension of soil science research and soil resources management. Fuzzy clustering analysis may be used whenever a composite classification of soil incorporates multiple parameters. In this paper, seventy-seven topsoil samples were collected from Qinghai and Heilongjiang of China, and the element contents of topsoil were detected by wavelength dispersive X-ray fluorescence spectroscopy. In fuzzy clustering analysis, all data were standardized, and then a fuzzy similarity matrix was established and the fuzzy relation was stabilized.

The results showed that topsoil samples of Qinghai and Heilongjiang were completely grouped into two clusters according to their districts, when given a suitable threshold λ= 0.7580. This work supplied the quantification classification method of alpine soil (Qinghai) and unsaturation siallitic soil (Heilongjiang).

Introduction

Soils are the complicated natural bodies and soil system acts as a component for various ecological functions.

Soil classification is the process of grouping soil individuals into more or less homogeneous groups with respect to defined objectives [START_REF] Cline | Basic principles of soils classification[END_REF], thereby highlighting the essential differences in soil properties and functions between classes [START_REF] Rossiter | Classification of urban and industrial soils in the word reference base for soil resources[END_REF]. Soil classification systems organize soil variability into useful groupings that can be identified by field investigation and documented in soil survey activities to promote effective resource management and technology transfer [START_REF] Effland | The genesis, classification, and mapping of soils in urban areas[END_REF]. Clustering is useful and plays a key role in searching for structures in data. Each of these structures is called a cluster or class [START_REF] Sarbu | Study of traffic-emitted lead pollution of soil and plants using different fuzzy clustering algorithms[END_REF]. Cluster analysis is developed in taxonomy and its principal aim is to partition multivariate observations into a number of meaningful multivariate homogeneous groups [START_REF] Templ | Cluster analysis applied to regional geochemical data: problems and possibilities[END_REF]. Fuzzy clustering analysis method is based on the fuzzy set theory [START_REF] Zadeh | Fuzzy sets[END_REF] and is one of the most important methods of unsupervised learning and has significant advantages over traditional clustering [START_REF] Liu | Improved fuzzy clustering method based on entropy coefficient and its application[END_REF]. Feng Lixiao et al(1992) [START_REF] Feng | Application of fuzzy clustering method on the classification of yellow brown soil and yellow cinnamon soil in southern Shaanxi[END_REF] selected active acid, substitution acid, hydrolysis acid, active aluminum, cation exchange capacity (CEC) and degree of base saturation as the parameters of soil fuzzy clustering analysis to distinguish yellow brown soil and yellow cinnamon soil. Wu Kening et al (1994) [START_REF] Wu | Fuzzy cluster analysis of soils in transition regions of northern Subtropics in China[END_REF] studied fuzzy clustering analysis method in soils of transition regions of northern Subtropics in China. The results implicated that fuzzy clustering analysis was in accordance with pedogenesis classification and diagnostic classification. For the development of precision agriculture, fuzzy cluster classification performed to delineate management zones [START_REF] Moral | Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques[END_REF][START_REF] Li | Determination of potential management zones from soil electrical conductivity, yield and crop data[END_REF]. This article explores the method of soil classification which applied fuzzy clustering analysis. we selected the element contents, especially metal element contents as the soil parameters to do fuzzy clustering analysis and discussed the relationship between element concentration and soil pedogenesis. This would extend soil science and supply classification method for soils which have different soil form processes.

Materials and Methods

Samples

Seventy-seven topsoil samples (0-20cm) were collected from Qinghai and Heilongjiang provinces of China. In these samples, thirty-nine topsoil samples belonged to Qinghai, which were located between longitudes 93.652°E and 95.768°E and latitudes 36.387°N and 36.793°N with an area of about 0.084 km 2 , and, thirty-eight samples belonged to Heilongjiang, which were located between longitudes 131.570°E and 133.304°E and latitudes 46.399°N and 47.605°N with an area of about 0.031 km 2 . At each sampling site, 5 sub-samples were taken from the 4 vertexes and the center of a square block (10m×10m) and mixed thoroughly to select 0.5 kg soil as the representative sample of site. All samples came from farmland.

Soil samples preparation and measurements

The samples were air-dried, ground, passed 250mm nylon sieve. Four grams of soil powder were pressed into pellet at 10 tones using a manual hydraulic press (pellet diameter = 32 mm). The concentrations of Cu, Pb, Zn, Cr, Ni, Fe, Mn, Rb, Sr, V, MgO, CaO, Na 2 O, F, S, Cl, Ce, Ba, Co, Ga, Zr, La, Al 2 O 3 , TiO 2 and As in soil pellets were analyzed by wavelength dispersive X-ray fluorescence spectroscopy (TW2404, PHILIPS Company).

Fuzzy clustering analysis

All data except content value of Mn, x ij were standardized by formula (a). i and j represented soil sample ID (1, 2, ……, 77) and soil parameters ID (1, 2, ……, 24). Note: In formula (a), ' i j

x was standardization value.

According to the standardization data, the relation ' ik r between i th and k th objects of classification was calculated by formula (b). Then, the similar matrix ' R was established and described as equation (c).

The fuzzy similar matrix R was established via the r ik which was set by the relation ' ik r on the interval [0, 1] by ' ( )

i j j ij j x x x a s - = 77 2 1 ( ) 77 1 i j j x x s j - = - ∑ 77 1 1 77 j ij x x = ∑ (h) (i)
ordering r ik =0.5+0.5× ' ik r and was described as equation (d). The fuzzy similar matrix R was not a stabilized one. That is, it met the reflexivity [START_REF] Zadeh | Similarity relations and fuzzy orderings[END_REF] and symmetry [START_REF] Zimmerman | Fuzzy set theory and its applications[END_REF], but not transitivity [START_REF] Zimmerman | Fuzzy set theory and its applications[END_REF][START_REF] Kung | Fuzzy clustering analysis in environmental impact assessment-a complement tool to environmental quality index[END_REF]. The fuzzy similar matrix R will have to be changed into the fuzzy equivalent matrix via self-squared method when clustering [START_REF] Wang | A fuzzy comprehensive clustering method[END_REF]. We transformed R into R* using formula (e): r ij =∨(r ik ∧r jk )=(R×R) ij (e). If R* = R k =R 2K (f), then the R* had become a stabilized fuzzy relation. In this case the R* was stable at R 4 = R 8 . And R* was described as equation (g).

The R* value embodied the similarity and classification probability of all soil samples. If the value was 0, then the two soil samples did not have similarity as different classifications. If the value was 1, then the two soil samples had very similarity as the same classification. If the value was on the interval [0, 1], then the value indicated the probility that the two soil samples could be classified into the same clusters. λ was set as probility threshold. If λ was greater than r* ij , then r* ij was on the order of 1. If λ was less than r* ij , then r* ij was on the order of 0. We assigned a value from 1 to 0 to λ, reduced the value gradually, and according to the same λ value, divided some soil samples into same class as r* ij which was 1 or 0. The algorithms repeated the above steps at different λ value until no soil samples were divided into the same class.

Data analysis

All data were analyzed by Office Excel 2003, SPSS 18.0, and MATLAB 7.0.

Results and discussion

T-test of the data

Table 1 showed the statistical results of the 25 metal concentrations in the topsoil of Qinghai and Heilongjiang.

All data were analyzed by independent samples t-test analysis in SPSS 18.0 software. T-values of metal contents as soil parameters showed significant differences or very significant differences except Mn between Qinghai and Heilongjiang.

⎛ ⎞ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ = : ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ 1,1 1,
⎛ ⎞ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ : = ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ ⎠ 2 2 1 1 ' () n i j kj j ik n n i j i j j j x x r b x x = = = ∑ ∑ ∑

Fuzzy clustering

The fuzzy clustering graphic was made by MATLAB7.0 soft (Fig. 1). The fuzzy clustering graphic showed that the topsoil samples of Qinghai and Heilongjiang were completely grouped into two clusters according to their districts when given probility threshold λ= 0.7580. According to the soil order of China, the soil of Qinghai and Heilongjiang belonged to alpine soil and un-saturation siallitic soil respectively [START_REF] Wei | Study on soil environmental background values of China[END_REF]. So alpine soil and unsaturation silallitic soil could be divided into two classes based on their metal content. The topsoil samples of Qinghai and Heilongjiang were regarded as the same class when given probility threshold λ= 0.6492. It implied that topsoil of Qinghai and Heilongjiang had 64.9% similarity based on their 24 species of metal concentration. When given probility threshold λ= 0.7580 and λ= 0.8532, the topsoil soil samples of Heilongjiang and the topsoil soil samples of Qinghai were divided into the same class respectively. The results showed that similarity of the 39 topsoil samples of Qinghai was greater than the 38 topsoil samples of Heilongjiang, that is, with respect to total homogeneity based on 24 species of metal concentration, Qinghai was better than Heilongjiang. Maybe this result was related to the topsoil sample sites.

All topsoil samples of Qinghai were collected from the Golmud region, as well as, the topsoil samples of Heilongjiang were collected from the Tongjiang region and the Shuangyashan region. On the other hand, this difference could come from soil pedogenesis of the two districts.

Conclusions

The metal element content of soil can be used for qualitative classification. For this purpose, the 77 soil samples from Qinghai and Heilongjiang of China have been analyzed to determine their Cu, Pb, Zn, Cr, Ni, Fe, Mn, Rb, Sr, V, MgO, CaO, Na 2 O, F, S, Cl, Ce, Ba, Co, Ga, Zr, La, Al 2 O 3 , TiO 2 and As contents. T-test analysis was conducted on all soil metal element concentration. By way of T-test results, all metal element contents except Mn were used as parameters in the fuzzy clustering analysis. Fuzzy classification algorithms of the soil samples based on the 24 element concentrations allowed for an objective interpretation of their similarities and differences. The results show that this fuzzy clustering method can be applied in qualitative classification of the soil of Qinghai and Heilongjiang. This method helps develop the qualitative classification of soil, extend soil science, and is propitious to farmland management.

Fig. 1

 1 Fig.1 The fuzzy clustering graphic

Table 1

 1 Data analysis results of elements in topsoil of Qinghai and Heilongjiang a The concentration units of Fe, MgO, CaO, Na 2 O, Al 2 O 3 , TiO 2 were percentage, others were mg/kg;

	Parameters b	Sampling sites c	N	Mean	Std. Deviation Std. Error Mean	t
	Cu	HLJ QH	38 39	22.31 17.80	3.95 5.09	0.64 0.81	4.332	**
	Pb	HLJ	38	22.09	3.52	0.57	7.318	**
		QH	39	13.59	6.31	1.01
	Zn	HLJ QH	38 39	61.81 54.82	15.77 9.97	2.56 1.60	2.316 *
	Cr	HLJ QH	38 39	63.61 52.17	9.42 8.51	1.53 1.36	5.594 **
	Ni	HLJ QH	38 39	47.19 43.77	7.61 3.85	1.24 0.62	2.476 *
	Fe	HLJ QH	38 39	3.32 3.04	0.78 0.19	0.13 0.03	2.163	*
	Rb	HLJ QH	38 39	102.10 60.77	10.13 13.84	1.64 2.22	14.980 **
	Sr	HLJ QH	38 39	177.58 216.10	34.55 36.23	5.61 5.80	-4.772 **
	V	HLJ QH	38 39	86.11 62.05	10.97 9.84	1.78 1.58	10.135 **
	MgO	HLJ QH	38 39	1.21 3.05	0.43 0.50	0.07 0.08	-17.268 **
	CaO	HLJ QH	38 39	1.89 9.44	1.30 0.86	0.21 0.14	-30.037 **
	Na 2 O	HLJ QH	38 39	1.27 1.65	0.20 0.15	0.03 0.02	-9.243	**
	F	HLJ QH	38 39	435.39 742.31	134.00 98.18	21.74 15.72	-11.441 **
	S	HLJ QH	38 39	402.11 811.10	141.35 482.37	22.93 77.24	-5.076 **
	Mn	HLJ QH	38 39	580.23 612.82	276.38 41.74	44.84 6.68	-0.719
	Cl	HLJ QH	38 39	65.49 923.28	19.64 1082.80	3.19 173.39	-4.946 **
	Ce	HLJ QH	38 39	82.10 52.89	16.99 14.20	2.76 2.27	8.194 **
	Ba	HLJ QH	38 39	631.13 445.21	36.12 19.56	5.86 3.13	27.985	**
	Co	HLJ QH	38 39	12.23 9.97	2.63 1.86	0.43 0.30	4.332 **
	Ga	HLJ	38	17.95	2.75	0.45	6.751	**

b c HLJ and QH represented Heilongjiang and Qinghai respectively.

Acknowledgments

The financial support of National High Technology Research and Development Program 863 (2010AA10Z403) , (2007AA10Z202), and Beijing Municipal Science and Technology Commission Program (Z09090501040901). Thanks are due to Heilongjiang Academy of Agricultural Science and Golmud Bureau of Agriculture and Animal Husbandry of Qinghai Province for collecting topsoil samples.


Zhanli Liu

Congcong Yan

Xiangyou Wang

Xiangbo Han

Design for Real-time Monitoring System of High Oxygen Modified Atmosphere Box of Vegetable and Fruit for Preservation

Keywords: High oxygen, Modified atmosphere box, Real time monitor, Design

According to the disadvantages of traditional box for preservation, including low oxygen and high carbon, a new control system of high oxygen is designed. This paper presents the design and implementation method in this system. The system combines traditional PID control with fuzzy control which can adjust parameters of the whole system automatically. The box can control the dynamic content of oxygen and carbon dioxide, and monitor nitrogen flow, temperature and humidity real-timely so that preservation time can be prolonged. It also can collect and keep the data of the dynamic content of oxygen and carbon dioxide which suits for fruit and vegetable for preservation. The former environment can be reappeared when need. The system works steadily and has strong functions.

Introduction

Day, the British scholar for the first time made clearly the application of high-oxygen (>70% O 2 ) in modified atmosphere packaging of fresh-cut vegetables and fruits in 1996. Domestic and foreign research of high-oxygen (21%-100% O 2 ) on the effect of postharvest gradually increased, and the treatments of high oxygen are expected to play an important role in fruit and vegetable storage [START_REF] Zheng | Superatmospheric oxygen and postharvest physiology of fresh fruits and vegetables[END_REF] . Research has shown that highoxygen treatment of fruits and vegetables can reduce the respiration and ethylene production, slow their browning, and improve the preservation effects [START_REF] Li | Effects of high oxygen treatments on respiration intensity and quality of 'DongZao' jujube during shelf-life[END_REF][START_REF] Escalona | Changes in respiration of fresh-cut butterhead lettuce under controlled atmospheres using low and superatmospheric oxygen conditions with different carbon dioxide levels[END_REF][START_REF] Conesa | Respiration rates of fresh-cut bell pepper under superatmospheric and low oxygen with or without high carbon dioxide[END_REF] . The application of high-oxygen or even pure oxygen modified atmosphere technology on fruit and vegetable preservation has aroused close attention. So it is necessary to make a study of a reliable and stable high oxygen modified atmosphere control system for further development of high-oxygen fresh-keeping equipment. However, the impact mechanism of the high oxygen on the postharvest physiology and quality is not indepth, so it severely limits the development of high oxygen storage [START_REF] Liu | Progress in effects of high oxygen on postharvest physiology and quality of fresh fruits and vegetables[END_REF] . Thus, most of *Corresponding author. Email: wxy@sdut.edu.cn high oxygen experiments lack of accurate monitoring of oxygen, carbon dioxide, nitrogen flow rate, temperature and humidity, and can not control stably the environment of high oxygen. In this paper, the self-made high oxygen experimental apparatus were studied, and a data acquisition system and related test equipment were developed providing a scientific basis for high oxygen modified atmosphere theory.

Experimental device of high oxygen modified atmosphere box

Central processing section formed by a microprocessor is the heart of the whole control system, which completes the functions of data acquisition and processing, keyboard and display processing and system control. Temperature and humidity transmitter is mainly to measure gas concentration of the tested environment, and output the standard current signal corresponding to the surrounding gas concentration after the signal processed by the internal data processing. Gas flow meter part is mainly to measure the volume of the input nitrogen. It can select 4-20mA output, level output, or pulse output module according to needs. Analog multiplexer switch is mainly to turn the switch channel between separate ways analog and A/D converter, allowing one way analog signal input to the A/D converter in a specific period of time, and it can achieve the purpose of time-conversion to reduce the number of A/D converter and reduce costs. Current transfer voltage circuit is used to change the transmitter and flow meter standard output current signal into the corresponding voltage signal and conduct the follow-up signal processing. The role of sample retainer is used to maintain analog signal voltage of the A/D converter input constant during the conversion period, then ensure a higher conversion precision, and greatly increase the collection frequency of data collection. Some data memory stores the measured data immediately and preserves the measured environmental data for longterm. And data can be transferred out to external memory by a data interface, facilitating accurate analysis. Optical isolation part completes the isolation and amplification from weak signal to strong signal. The conversion of manual control and auto-control can be set by Panel.

In the case of automatic failure or manual intervention (for example, using the manual control can quickly reach steady state before reaching the set high oxygen concentration), some manual control part can control all of the conditioning systems of the atmosphere storage environment. Program memory is used to store system procedures. Solenoid valve is used to control nitrogen access.

Working principle of high oxygen modified atmosphere box

The concentration of oxygen and carbon dioxide, temperature and humidity of box are measured by the corresponding transmitter. Transmitter output signal is transmitted to the microprocessor after a series of treatments, and compared with the set initial values, when any kind of gas concentration is below the set value, appropriate instructions will be issued by the microprocessor, then the electric value corresponding is opened with the gas to let that kind gas in. By controlling the opening of the electric valve to control the air flow rate, when the concentration closes to the set value, the microprocessor controls the corresponding procedure to reduce the opening to prevent the excessive ventilation and avoid its concentration exceeding the set value. When the value of any gas concentration is higher than the set alarm value, the system alarms, and the corresponding unit of the micro-processor will give appropriate instructions, so that the corresponding electric valve of the gas will be closed, and the nitrogen valve will be opened, by inletting into constant flow of nitrogen the concentration of this gas will be reduced. When the concentration of this gas closes to the set value, the micro-processing time relay control valve will close for 10 seconds, leaving some buffer time to avoid excessive nitrogen filled.

Then process it according to the measured actual situation. The gases coming out from nitrogen generator, oxygen, and carbon dioxide bottles pass into the inlet pressure regulator box, and then pass into the test box. The gas cylinder export pressure of the nitrogen generator, oxygen and carbon dioxide bottles can be set much higher, and then adjust the inlet air pressure to the required. This can avoid failing to reach the export settings when the cylinder pressure is insufficient, and the pressure can be precisely adjusted by the intake air pressure tank.

Data acquisition system

Oxygen sensor is most important one of all the sensors of the system, and it has a very big impact on the system, so its selection is of great importance. It must meet the fast, reliable and accurate measurement of the oxygen concentration. Now commonly used oxygen sensor is electrochemical sensors. According to the principle, CO 2 sensor can be divided into electrochemical CO 2 sensor and infrared CO 2 sensor, the former is cheaper, but the accuracy is lower, and life is shorter, and preheat time is longer than the latter. Compared to carbon dioxide transmitter, carbon dioxide sensor is low precision, poor linearity, less function, and also needs to set up a dedicated signal processing devices for filtering and V/I conversion for processing, so the costs are increased. In view of the advantages of the transmitter, we use the transmitter (with imported high-grade infrared sensor) to replace the specialized sensors.

Experimental debugging

Agaricus bisporus were used as the materials to test the stability of the high oxygen experimental box. The concentrations of oxygen and carbon dioxide were set respectively to 75% and 25%, and stabilized at a predetermined value (fig. 1 and fig. 2). From the results, the high oxygen modified box can real-time control the dynamic contents of oxygen and carbon dioxide.

Fig. 1 Fig. 2

 12 Fig. 1 Change trends of oxygen in box

Acknowledgments. This study was supported by the National Natural Science Foundation of China (No. 30871757).


Xiuping Shao
email: shaoxiuping858@gmail.com

Jianping Hu

Yingsa Huang

Fa Liu

DEM Simulation and Analysis of Seeds Supply by the Vibrating Seed Box of Magnetic Cylinder Seeder

Keywords: Seed Box, Discrete Element Method, Seed Supply, Vibration, Frequency

Regarding the vibrating seeds box of magnetic cylinder seeder as the research subject and making simulation and analysis for the motion law of seeds which supplied by seed box base on DEM (Discrete Element Method). Researching the mechanism of the seed supply and analyzing the condition of seed supply by the vibration of seed box, when the frequency change from 10Hz to 50Hz and horizontal and vertical amplitude of the seed box are 0.5mm. The results shows that the vibrating frequency has greater influence on the seed supply; seeds can't be supplied when the vibrating frequency less than10Hz; seed can be supplied best and seeds thickness nearly the cylinder wall is highest when the vibrating frequency equal to 40Hz.

Introduction

Magnetic cylinder seeder can realize the seeding of vegetables, flowers and other small seeds [START_REF] Li | Discrete Element Method Simulation of Seeds Motion in Vibrated Bed of Precision Vacuum Seeder[END_REF]. Its important working assembly is the vibrating seed box which relied on vibrator to supply seeds continuously and stably to the cylinder wall, so the vibration parameters directly affect the property of seeder. There are complex interrelationship collision and friction in the motion of the seeds, which kinematics and dynamics relationship is quite complex, that the traditional continuum mechanics can't analyze the actual movement of seeds [2] [3]. This paper using discrete element method, studying the seed supply situation under the condition of seed box vibration, which provided theoretical basis for the further research of magnetic cylinder seeder.

Structure and Working Principle of Magnetic Cylinder Seeder

The structure of magnetic cylinder seeder is shown in fig. 1, including cylinder, drive shaft, permanent magnet, seed box, vibration exciter etc. When the magnetic head turns to the region of seed-filling, the magnetic head adsorb one seed and continue rotates with cylinder, when it turn to the downside of cylinder roller, the seed falls down then one time metering completely.

Contact Force Model and Parameter Definition

Contact force model is the core of DEM, the simplify model is always used for calculation during DEM simulation due to much more complex structure particles. The linear viscoelastic contact model has been widely used in the research of the agricultural machinery [4~6], so adopting the linear viscoelastic contact model as the contact force model of seed-seed and seed-seeder elements.

The normal force of liner viscoelastic model [START_REF] Cundall | A Discrete Numerical Model for Granular Assemblies[END_REF]:

n n n n n c k F θ δ - - = . ( 1
)
n F : The normal force between two contact particles; Tangential contact force is:

s s s s s s c t K t t F t F θ θ - Δ - Δ - = ) ( ) ( . ( 2)
) (t F s : The tangential force between two contact particles at time t; All the parameter needed in the simulation are got from experiment, the details are shown as table 1.

Simulation Results

According to the actual experiment, when the seed box amplitude was 0.5mm, metering requirement can be satisfied. During simulation experiment, 0.5mm was selected for the horizontal and vertical amplitude. And simulated the seed supply situation and the movement law of the seeds under the vibration frequency was 10~50Hz.

During the Simulation, cylinder diameter was 100mm, the rotation speed was 20r/min, the size of the seed box was 170 x 60 x 50 mm (length x width x height). 1500 seeds were generated with a normal distribution, which average diameter equal to 3.5mm and the variance was 0.05. Once the filling completed, the seed box started to make a simple harmonic oscillation with the vertical and horizontal direction. There can be clearly shows that influence of frequency for motion of seed supply according to the simulation result. The duration time of numerical simulation was 3 seconds.

The Motion Law of Seeds

During the vibration process, seeds always kept the moving condition with friction and collision. When the vibration frequency was low, the seeds moved slowly to the cylinder wall direction, and no longer moved forward when seeds moved to the certain condition. Meanwhile the seeds can only peristalsis with the seed box and unable to be supplied for the seeder, as shown in Fig. 2; When the vibration frequency was high, the impetus force that took seeds moved towards to the roller was bigger than the friction force, so seeds moved to the roller direction at a certain speed, and formed a accumulative in the roller wall edge, until seeds movement were steady. At this moment, the seeds would be supplied to seeder stably, as shown in Fig. 3. In order to understood the motion of seeds supply situation deeply, this paper simulated the vibration process of seed box when frequency was 50Hz. Fig. 4 shown the instantaneous velocity vector chart of the seeds movement when the vibration frequency was 50Hz. It can be seen clearly from the figure that the seeds were influenced by both l horizontal and vertical direction amplitude, and moved towards to the cylinder and upward directions. It can be seen from fig. 4(a) that after the seed box vibrated 0.3s, the majority seeds moved towards the roller direction by a certain speed; it can be seen from Fig. 4(b) that after the seed box vibrated 0.5s, partial seeds contacted and collided with the roller wall, and had a tendency to rebound, enabled these seeds collided once more with the seeds which were moving towards the cylinder direction, there formed a disordered area that the spacing from the roller was 50mm; fig. 4(c) showed that, after seed box vibrated 1.5s, the seeds movement basically tends to be stable, with merely creeping motion along with the vibration of the seed box, now seeds in seed box achieved a steady situation for seeds supply.

Frequency Effect on Seed Supply Situation

Seeds moved towards to the and finally form a certain altitude along the edge of cylinder wall. In this paper, the accumulation height on the cylinder wall edge H was used as the indicator quality of seed supply, as shown in Fig. 5. The vibration frequency of the seed box was respectively set at 10Hz, 20Hz, 30Hz, 40Hz, 50Hz, simulated the movement process of the seeds along the cylinder wall edge. The simulation result was shown in Fig. 6. It can be seen from the figure that the vibration frequency influenced the seeds movement obviously. When the vibration frequency of the seed box was set at 10Hz, the seeds accumulation height was nearly 0, therefore the seed supply were defeated; with the increase of the vibration frequency the accumulation height along the roller wall edge also increased; when the vibration frequency of the seed box was 40 Hz, seeds accumulation height was largest, at this condition seed supply got the best state. As shown in fig. 8, seeds entered the small selection along the edge of cylinder wall after seed box vibrating for a period of time; after some time the quantity of seeds in the selection reached a plateau ,and the time turned short with the vibration frequency increased; after the quantity of seeds reach a plateau, the quantity of seeds has a greatly difference between different frequencies; the larger of the frequency, the more the seeds in the selection, and the better of the condition of seed supply.

Conclusion

Made the vibration seed box of precision magnetic cylinder seeder as research object in this paper, the seed motion law and supply with different frequencies base on DEM were numerical analysis, such conclusions were derived:

1. Using DEM can easily and accurately simulate seeds motion law in the vibration seed box.

2. At the beginning of the vibration, seeds moved toward to cylinder with a certain speed; when seeds contacted with the cylinder, part of them rebounded and collided with the behind seeds which moved to the cylinder wall, then raised a disturbance area within 50mm leave from the cylinder wall; seeds campaign achieved a smooth situation again and only did upward or downward reciprocating movement, then the seed provided in the seed box became to stabilized.

3. Vibration frequency has great influence on seed accumulate height nearly the cylinder roller edge. Seed accumulate height nearly the cylinder roller edge go up with the vibration frequency increased. The seed box can't supply seed when vibration frequency was lower than 10Hz and the best supply seed with the vibration frequency equal to 40Hz.

4. The seeds reached the edge of the cylinder roller wall after a period of vibration; the time required for achieving stability of the seed near the cylinder wall was proportional to the vibration frequency, moreover the more the seeds due to higher frequency, the better for the seed supply situation.

1 .Fig. 1 .

 11 Fig. 1. The structure of precision magnetic cylinder seeder Powder coated seeds are placed in half-open vibrating seed box, the open side nearly close to the cylinder wall to prevent seeds falling down; Seeder wheel drives by shaft and makes one way rotating, permanent magnet mounts on the rotating shaft, the magnetic head mounts on the channel of the cylinder and rotates with the cylinder.When the magnetic head turns to the region of seed-filling, the magnetic head adsorb one seed and continue rotates with cylinder, when it turn to the downside of cylinder roller, the seed falls down then one time metering completely.

1 k

 1 , : The contact stiffness coefficients of two contact particles; 2 k n δ : The normal overlap of two contact particles; n C : The normal viscous damping coefficients,

1 C

 1 , : The normal viscous damping coefficient for two contact particles; 2 C n θ : The normal contact relative velocity.

  t Δ : The time step; s K : The tangential contact stiffness coefficient.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Seed supply diagram (f=10Hz) Fig. 3. Seed supply diagram (f=40Hz)

Fig. 4 .

 4 Fig. 4. Instantaneous velocity vector of seeds movement(f=50HZ)

Fig. 5 .

 5 Fig. 5. Seeds accumulation height diagram

Fig. 6 .

 6 Fig. 6. Seeds accumulation heights under different frequenciesFor particular described the condition of seed supply by the vibration of seed box, set a small selection that length×high×weight was 10mm×15mm×30mm along the edge of cylinder wall, as shown in fig.7.Analyzed the quantity of seeds of this selection in different moment when the vibration frequency of seed box was 10Hz,20Hz,30Hz,40Hz.

Fig. 7 .

 7 Fig. 7. Small selection along the edge of cylinder wall

Fig. 8 .

 8 Fig. 8. Seeds total number in small selection under different times

Table 1 . Parameter used in DEM analysis

 1

	Seed type	Coating rapeseed (iron 20%)
	Particle diameter/mm	1.75
	Poisson's ratio of particle	0.3
	Shear modulus of particle/Pa	4.65x10 8
	Particle density/Kg﹒m 3	2.1x10 3
	Coefficient of restitution in seed-seed	0.2
	Static friction coefficient in seed-seed	0.4
	Rolling friction coefficient in seed-seed	0.06
	Coefficient of restitution in seed-seeder elements	0.4
	Static friction coefficient in seed-seeder elements	0.11
	Rolling friction coefficient in seed-seeder elements	0.02

Acknowledgements

This work was financially supported by eleventh five-year-plan national scientific & technological supporting project(2006BAD11A10)and agricultural mechanization in three agriculture projects,Jiangsu province (NJ2009-41).


Zhu Zhou

Xiaoyu Li
email: lixiaoyu@mail.hzau.edu.cn

Peiwu Li
email: peiwuli@public.wh.hb.cn

Yun Gao
email: gaoyun@webmail.hzau.edu.cn

Jie Liu

Wei Wang
email: wangwei@mail.hzau.edu.cn

Discriminate of Moldy Chestnut based on Near Infrared Spectroscopy and Feature Extraction by Fourier Transform

Keywords: Near infrared spectroscopy, Fourier Transform, feature extraction, BP neural network, chestnut

As near infrared spectra has the characters of multi-variables and strong correlations, to solve the problem, Fourier transform (FT) was used to extract feature variables of shelled chestnuts spectra. FT coefficients and the status of 178 chestnuts were selected as inputs and outputs of the back-propagation neural network (BPNN) classifier to build a recognition model. For comparison, principal component analysis (PCA) was utilized to compress the variables, which then was introduced as input of the neural network model. The results demonstrate that FT is a powerful feature extraction method and is better than PCA as a feature extraction method when employed together with BPNN. When the preprocessing method of standard normal variate transformation(SNV) was carried out and the first 15-point FT coefficients were used as the input, an optimal network structure of 15-6-1 was obtained, where discriminating rates of qualified chestnut, surface moldy chestnut and internal moldy chestnut in prediction set are 100%、100% and 92.31%, respectively.

Introduction

Chestnut is one of the most popular nuts in the world and China is the biggest chestnut producer. It is reported that the annual yield of chestnuts in China is ca. 9.25×105 metric tons (in 2007) which accounts for 75.61% of the total world yield [START_REF] Yao | Equilibrium, kinetic and thermodynamic studies on the biosorption of Cu(II) onto chestnut shell[END_REF]. But chestnuts, which are rich in carbohydrates and low in fat, are susceptible to getting moldy after harvest. In China, manual sorting or brine floatation is the primary method to pick out moldy and spoiled chestnuts, which proves low sorting efficient and high misjudgment rate [START_REF] Zhou | Discrimination of moldy Chinese chestnut based on artificial neural network and near infrared spectra[END_REF]. Therefore, finding a fast, effective and applicable method to sort moldy chestnut is urgently required.

Near infrared (NIR) spectroscopy can record the response of the molecular bonds (e.g. C-H, N-H and O-H) of chemical constituents to near infrared radiation and thereby build a characteristic spectrum that performs as a fingerprint of the sample [START_REF] Cozzolino | Effect of temperature variation on the visible and near infrared spectra of wine and the consequences on the partial least square calibrations developed to measure chemical composition[END_REF][START_REF] Xie | Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics[END_REF]. Being nondestructive, simply applicable and fast, it requires minimal sample processing prior to analysis [START_REF] Chen | Comparison of combination and first overtone spectral regions for near-infrared calibration models for glucose and other biomolecules in aqueous aolutions[END_REF]. NIR spectroscopy has become a rapid and well-established technique for the quantitative and qualitative analysis of agricultural products. However, NIR spectra typically consists of broad, weak, non-specific, and extensively overlapped bands, and may have hundreds or thousands of wavelength variables [START_REF] Xie | Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics[END_REF][START_REF] Blanco | NIR calibration in non-linear systems: different PLS approaches and artificial neural networks[END_REF]. The use of all variables for classification purposes is not an adequate strategy because it produces the so-called "curse of dimensionality" [START_REF] Acevedo | Classification of wines produced in specific regions by UV-Visible spectroscopy combined with support vector machines[END_REF]. Moreover, some of these variables may include useless or irrelevant information for calibration model like noise and background, which can worsen the predictive ability of the whole model [START_REF] Han | An ensemble of Monte Carlo uninformative variable elimination for wavelength selection Anal[END_REF]. Therefore, the data dimensionality needs to be reduced.

Principal component analysis (PCA) [START_REF] Li | Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy[END_REF][START_REF] Liu | Classification of brands of instant noodles using Vis/NIR spectroscopy and chemometrics[END_REF][START_REF] Cynkar | Classification of Tempranillo wines according to geographic origin: Combination of mass spectrometry based electronic nose and chemometrics[END_REF][START_REF] Fernández-Ibáñez | Development and validation of near infrared microscopy spectral libraries of ingredients in animal feed as a first step to adopting traceability and authenticity as guarantors of food safety[END_REF] and Fourier transform (FT) [START_REF] Wu | Feature reduction by Fourier transform in pattern recognition of NIR data[END_REF][START_REF] Pasti | Application of Fourier transform to multivariate calibration of near-infrared data[END_REF][START_REF] Xiao | Near-infrared spectral detection of soil moisture based on feature extraction of FFT[END_REF] can be applied to reduce the dimensionality of the NIR data. PCA is data set dependent, whereas FT is independent of the data set. It means that with PCA a whole data set is simultaneously treated, while with FT each spectrum is treated individually. If changes occur in one spectrum, this does not affect the FT of the other spectra, but it does affect in PCA [START_REF] Wu | Feature reduction by Fourier transform in pattern recognition of NIR data[END_REF][START_REF] Pasti | Application of Fourier transform to multivariate calibration of near-infrared data[END_REF].

In our previous work [START_REF] Zhou | Discrimination of moldy Chinese chestnut based on artificial neural network and near infrared spectra[END_REF], NIR spectroscopy and PCA were used to discriminate moldy chestnut. This work aims to study the use of FT to reduce data dimensionality for discriminate classifier and to compare the results with that of PCA method.

Materials and methods

Sample preparation

Chestnuts used in the experiment were from Macheng, Hubei Province in China. The weight scope of chestnuts was between 8.50g ~ 20.41g. After purchased, they were stored according to Chinese commercial profession standard SB/T10192-1993.

Samples were divided into two categories: qualified chestnut and moldy chestnut which include surface moldy chestnut and internal moldy chestnut, the judgment of which is made in accordance with GH / T 1029-2002 requirements. To determine internal moldy chestnut, they should be hulled after spectral measurements. All the samples were laid at room temperature (25 ℃, 60% relatively humidity) for 24 h to equilibrate to experiment environmental before spectra collection. Finally, 69 qualified chestnuts, 66 surface moldy chestnuts and 43 internal moldy chestnuts were analyzed.

Spectral measurement

NIR diffuse reflectance spectra of chestnut samples were collected by a FT NIR spectrometer (Vector 33, Bruker Optics, German). The system consists of a gold-plated integrating sphere, a sample rotator, a 12 mm quartz glass and a PbS detector.

The FT NIR spectrometer was completely software-controlled by the OPUS software Version 5.0 which was provided by Bruker Optics. The spectra of chestnut samples were acquired between 12000 cm -1 to 4000 cm -1 at 8 cm -1 spectral resolution, taking the average of 64 scans and were analyzed at room temperature. Three replicates of each sample were taken and their mean values were calculated by using OPUS. Reference spectrum for air and dark spectrum were measured and stored prior to sample spectra measurement.

2.3

Fourier transform

Fourier transform is usually used in signal processing. An NIR spectrum is a signal measured in the wavelength domain. FT enables transitions between the wavelength and frequency domain [START_REF] Wu | Feature reduction by Fourier transform in pattern recognition of NIR data[END_REF].

If f(1)、f(2),..., f (N) represents the recorded spectral values at N equally spaced wavelengths, denoted by 1, 2, 3, λ, then, the discrete Fourier transform of the signal is defined as:

[ ]

1 1 0 0 ( )= ( ) exp( j2 / )= ( ) cos( j2 / ) sin( j2 / ) N N k k F w f k wk N f k wk N wk N π π - - = = ⋅ - ⋅ - ∑ ∑ π (1)
With j= -1 and the =1, 2,…, N. w

After Fourier transform, the magnitude of ( ) F w is defined as:

1 2 2 2 ( ) ( ) ( ) F w R w I w = + (2)
where 、 ( ) R w ( ) I w are the real and imaginary parts of ( ) F w , respectively.

In order to save computing time, the fast Fourier transform (FFT) can be used when the number of variables equals to 2 n , where n is a positive integer.

Back-propagation neural network

The back-propagation neural network (BPNN), owing to its excellent ability of non-linear mapping, generalization, self-organization and self-learning, it has been proved to be of widespread utility in pattern recognition [START_REF] Sadeghi | A BP-neural network predictor model for plastic injection molding process[END_REF][START_REF] Panda | Flank wear prediction in drilling using back propagation neural network and radial basis function network[END_REF][START_REF] Archer | Application of the back propagation neural network algorithm with monotonicity constraints for two-group classification problems[END_REF][START_REF] Lin | A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks[END_REF][START_REF] Bayar | Modeling leaching behavior of solidified wastes using back-propagation neural networks Ecotoxicol[END_REF]. BPNN The experiments were carried out on a range of 11895 cm -1 to 4000 cm -1 with a total of 2048 data points so that FFT could be used to the spectrums.

Fourier feature extraction of Chestnuts NIR spectrum

When BP neural network applies to pattern recognition, and if the input has too many characteristic quantities, it will reduce the network training speed and efficiency, and lead to non-convergence in severe case. McClure [START_REF] Burns | Handbook of Near-Infrared Analysis[END_REF] pointed out that if one transforms the NIR data, most of the information is in the range of the first 50 Fourier coefficients and the remainder can be discarded because it is mainly noise, so fewer Fourier coefficients can instead of the original spectral data, make the spectral dimension reduction.

In this paper, we applied FT to transforming the processed NIR data from the wavelength domain into the frequency domain, and we used the first 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 points of Fourier coefficients in the Fourier spectra respectively as the input of the BP neural network classifier. Fourier coefficients after different preprocessing were respectively used as the input of BPNN to build the model, with the figures 0 and 1 expressed the qualified chestnuts and moldy chestnuts separately. Deviation value was set to ±0.1, if the difference of true value and the predicted value was between ±0.1, it meant the correct identification, otherwise meant the mistake. Trial-and-error method were used to determine the number of node in hidden layer, when the highest correct discriminating rate reached, the best network structure were obtained.

The BPNN predicting model

Fig. 2 shows the optimal results of BP neural network classifier with different processing methods and numbers of Fourier coefficients. Although the correct classification rates vary to processing and numbers of Fourier coefficients, the overall correct classification rates are higher than 80%. Since SNV and VN could avoid the effects caused by chestnut sizes and spectral scattering, the correct classification rates are higher than the other processing methods. The lowest discriminating rate of internal moldy chestnut obtained by FD method was only 76.92%.

Comparison with PCA

In our previous work [START_REF] Zhou | Discrimination of moldy Chinese chestnut based on artificial neural network and near infrared spectra[END_REF], six processing methods including smooth(Savitzky-Golay method, gap size = 9 data points), vector normalization(VN), min-max normalization(MMN), standard normal variate transformation(SNV), multiplication scattering correction(MSC)and first derivative(Savitzky-Golay method, gap size = 17 data points,FD), were processed to the original spectrum, then PCA method was used to reduce the dimension,and BPNN was utilized to establish the model. Table 2 below listed the recognition rate and parameters of BP neural network.

From a comparison of the results of table 1 and table 2, it was obvious that both PCA and FT achieve acceptable results, but FT has higher correct classification rates.

By using PCA method, we can get the optimal predicting model when the NIR data was pretreated by the vector normalization (VN). The correct discriminating rates of qualified chestnut and internal moldy chestnut were 94.74%、94.74% and 92.31%, respectively. Obviously, the results were lower than the way that they were extracted by FT. However, the network structure of the BP model with principal component analysis is 7-4-1, which should be simpler than the 15-6-1 with Fourier feature extraction. Thus, for a further study, Fourier coefficients should be optimized by genetic algorithms (GA).

Conclusions

The application of BP neural network with NIR data, after different processing methods and transformation to FT coefficients, was studied. It was found that the preprocessing methods and the numbers of FT coefficients affect the CCR of the BPNN classifier. When preprocessing method of standard normal variate transformation was utilized and the first 15 point of FT coefficients were used as the input, an optimal network structure of 15-6-1 was obtained, where discriminating rates of qualified chestnut, surface moldy chestnut and internal moldy chestnut in prediction set were 100%、 100% and 92.31%. It is better than the BPNN model which used vector normalization (VN) processing and PCA methods. As the Fourier feature extraction is not dependent on the spectral data set, only treats each spectrum individually, therefore, we recommend applying FT as a dimensionality reduction method in pattern recognition of NIR data.

Figure. 1

 1 Figure.[START_REF] Yao | Equilibrium, kinetic and thermodynamic studies on the biosorption of Cu(II) onto chestnut shell[END_REF] shows average spectrums of the qualified chestnut, surface moldy and the internal moldy chestnuts between the wave number range from 12 000 cm -1 to 4 000 cm -1 . As it can be seen from the figure, the spectral shape of three chestnut samples has little difference, and the spectrum of the qualified and internal moldy chestnuts overlap in the range of 12000~9000 cm -1 , which increases the difficulty of identifying the internal moldy chestnut. For reducing noise, offset and bias, 6 kinds of preprocessing techniques including smooth(Savitzky-Golay method, gap size = 9 data points), vector normalization (VN), max-min normalization (MMN), standard normal variate transform (SNV), first derivative(Savitzky-Golay method, gap size = 17 data points,FD) and no process(NP) were applied to the original spectrum respectively.

Fig. 1 .

 1 Fig. 1. Chestnuts mean spectra from raw data

19 qualified chestnuts、 18

 18 surface moldy chestnuts and 13 internal moldy chestnuts from each variety were selected randomly as the prediction set. The remaining 128 samples (50 qualified chestnuts、48 surface moldy chestnuts and 30 internal moldy chestnuts involved) were used as the training set to build the training model which was validated by the samples in the prediction set.

Fig. 2 .

 2 Fig. 2. The classification results of BPNN as a function of the number of the selected Fourier coefficients

Table 1 .

 1 Parameters and CCRs of different preprocessing techniques under FT method

		Number of	Hidden		Prediction set /%		Training set /%
	Method	Fourier	layer			moldy			moldy
		coefficients	nodes	qualified	surface	internal	qualified	surface	internal
	NP	15	6	100	88.89	84.62	98	100	100
	smooth	15	9	100	100	84.62	100	100	100
	MMN	25	29	100	100	92.31	100	100	100
	VN	30	17	100	100	92.31	100	100	100
	FD	15	12	100	94.44	76.92	100	100	100
	SNV	15	6	100	100	92.31	98	100	100

Table 2 .

 2 Parameters and CCRs of different preprocessing techniques under PCA method

			Hidden		Prediction set /%		Training set /%
	Method	Number	layer		moldy		moldy
		of PCs	nodes	qualified	surface	internal	qualified	surface	internal
	smooth	4	14	89.47	94.44	53.85	96	100	96.67
	SNV	5	4	89.47	94.44	76.92	98	97.92	93.33
	MSC	5	10	78.95	94.44	84.62	98	100	100
	MMN	5	10	68.42	100	76.92	98	100	100
	VN	7	4	94.74	94.44	92.31	98	100	100
	FD	10	4	84.21	94.44	84.62	98	100	100

Acknowledgement

The financial support provided by Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20090146110018) was appreciated.