Fusion Technique for Honey Purity Estimation using Artificial Neural Network

Keywords

E-nose, FTIR, Fusion, Artifi-cial Neural Network

Abstract

This paper presents the estimation of pu-rity of honey using Artificial Neural Network (ANN). ANN method was used to automate the decision of estimation, replacing the manual human approximate method. A total of 21 honey purity sam-ples from various concentrations of pure honey, adulterated honey and pure sugar were collected. The data were collected using electronic nose (E-nose) and Fouri-er Transform Infrared (FTIR). Fusion method was applied in this work by com-bining the variable from both E-nose and FTIR to produce new sets of data. This data were used as input parameters for ANN learning. The results show that ANN was able to estimate the concentra-tion of honey purity in adulterated honey solution with less error using fusion data as compared to single modality data.

Copyright

© 2014, 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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TY  - CONF
AU  - Norazian Subari
AU  - Junita Mohamad Saleh
AU  - Ali Yeon Md Shakaff
PY  - 2014/01
DA  - 2014/01
TI  - Fusion Technique for Honey Purity Estimation using Artificial Neural Network
BT  - Proceedings of the 2013 International Conference on Advances in Intelligent Systems in Bioinformatics
PB  - Atlantis Press
SP  - 40
EP  - 46
SN  - 1951-6851
UR  - https://www.atlantis-press.com/article/11355
ID  - Subari2014/01
ER  -