Proceedings of the 2016 6th International Conference on Applied Science, Engineering and Technology

Research and Application of Quality Detection Method for Corn Oil Based on Neural Network Algorithm

Authors
Wei-Feng Gao, Ming-fen Wu
Corresponding Author
Wei-Feng Gao
Available Online May 2016.
DOI
10.2991/icaset-16.2016.34How to use a DOI?
Keywords
Neural network algorithm, Corn oil, Infrared spectrum, Quality detection
Abstract

The nutritious value of corn oil is gradually recognized by the public, while with the expansion of its production, how to detect its quality has become a tough problem. The research performs data analysis on the infrared spectrum of corn oil to inspect the quality of corn oil by adopting neural network algorithm. Experiments have proved that the method can test the quality of corn oil effectively and compared with other ways, it is qualified with the advantages of high efficiency and accuracy, so it is worthy to be promoted and used.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 6th International Conference on Applied Science, Engineering and Technology
Series
Advances in Engineering Research
Publication Date
May 2016
ISBN
978-94-6252-186-5
ISSN
2352-5401
DOI
10.2991/icaset-16.2016.34How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Wei-Feng Gao
AU  - Ming-fen Wu
PY  - 2016/05
DA  - 2016/05
TI  - Research and Application of Quality Detection Method for Corn Oil Based on Neural Network Algorithm
BT  - Proceedings of the 2016 6th International Conference on Applied Science, Engineering and Technology
PB  - Atlantis Press
SP  - 169
EP  - 174
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaset-16.2016.34
DO  - 10.2991/icaset-16.2016.34
ID  - Gao2016/05
ER  -