Study on the Relation between Waxberry Color and its Nutrition Composition Based on BP Neural Network

Hong Xia Zhao; Zhi Xia Liu; Zhi Yang Luo; Guan Yun Xiao

doi:10.4028/www.scientific.net/AMR.304.268

Paper Titles

Evolvement Model of Eco-Industrial Cluster - Research Based on Complex Adaptive System
p.247

Agroforestry Systems in Northern Temperate Zone and Productive Perspectives
p.253

Study on Effects of Organic Carbon Emission of Land Use Change in Shenzhen City, China
p.259

Nanoscale Trap for Isolated Cold Atoms by the Nanoscale Solid-State System
p.263

Study on the Relation between Waxberry Color and its Nutrition Composition Based on BP Neural Network
p.268

Energy Entropy Used in Identification Based on Motor Imagery EEG
p.274

Decision Support System for Good Quality Flue-Cured Tobacco Based on Knowledge Model
p.279

Spatial and Temporal Analysis of MODIS Vegetable Index of Different Grassland Styles
p.284

Hardware Design of Soil Moisture Wireless Monitoring System
p.290

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 304Study on the Relation between Waxberry Color and...

Study on the Relation between Waxberry Color and its Nutrition Composition Based on BP Neural Network

Abstract:

The color of farm produce is a very important index of quality, its nutrition is correlative with itself color. At present, most of the analyses for pigment and nutrient composition still depend on chemical method; therefore the relation is studied between waxberry color and its nutrition composition based on BP neural network. The conversion relation is expressed by three-layer BP network, which hidden layer has 11 node numbers and its transfer function adopts tansig function; transfer function of output layer selects purelin function. The neural network and linear model of nutrition composition is compared respectively. The MSE value of linear model is 0.300892, and that training error of neural network is 0.0219585. From this result，we can find that the conversion relation between waxberry color and its nutrition composition is a complex non-linear relation, so neural network is adopted to complete this conversion.

You might also be interested in these eBooks

Multi-Functional Materials and Structures Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 304)

Pages:

268-273

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.304.268

Citation:

Cite this paper

Online since:

July 2011

Authors:

Hong Xia Zhao, Zhi Xia Liu, Zhi Yang Luo, Guan Yun Xiao

Keywords:

Acidity, Color, Neural Network (NN), Sugar Degree, Waxberry

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yan Heping, Zhang Jucheng and Xu Honggang: Study on extraction technology of red pigment from Myrica Rubra Sieb Et Zucc. Science and technology of food industry. Vol. 30 (2009), no. 6, pp.306-308.

Google Scholar

[2] Zhao Hongxia: Study on color model conversion for camera with neural network based on the combination between second general revolving combination design and genetic algorithm. Proceedings of SPIE, Vol. 6033 (2005), pp.603306-1~10.

DOI: 10.1117/12.668060

Google Scholar

[3] Rumelhart D. E., McClelland J. L., in: Parallel Distributed Processing: Explorations in the microstructure of cognition. vol. 1 (1986), Cambridge, MA: MIT Press.

DOI: 10.7551/mitpress/5236.001.0001

Google Scholar

[4] Lou Shuntian, Shi Yang, in: The system analysis and design based on MATLAB—neural network, Xi'an Electron Science Technology University Publishing Company (1999).

Google Scholar

[5] Wang Wencheng, in: The neural network and its application at automobile engineering. Beijing: Publishing Company of Beijing University of Technology (1998).

Google Scholar