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Journal of Sensors
Volume 2016 (2016), Article ID 1035902, 11 pages
Research Article

Application of Temperature Modulation-SDP on MOS Gas Sensors: Capturing Soil Gaseous Profile for Discrimination of Soil under Different Nutrient Addition

1Electrical Engineering and Computer Science, Kanazawa University, Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan
2Agricultural Engineering, University of Jenderal Soedirman, Jalan Dr. Soeparno, Karangwangkal, Purwokerto, Central Java 53123, Indonesia

Received 24 January 2016; Accepted 28 February 2016

Academic Editor: Kourosh Kalantar-Zadeh

Copyright © 2016 Arief Sudarmaji and Akio Kitagawa. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A technique of temperature modulation-SDP (specified detection point) on MOS gas sensors was designed and tested on their sensing performance to such complex mixture, soil gaseous compound. And a self-made e-nose was built to capture and analyze the gaseous profile from sampling headspace of two soils (sandy loam and sand) with the addition of nutrient at different dose (without, normal, and high addition). It comprises (a) 6 MOS gas sensors which were driven wirelessly on a certain modulation through (b) a PSoC CY8C28445-24PVXI-based interface and (c) the Principal Component Analysis (PCA) and neural network (NN) as pattern recognition tools. The gaseous compounds are accumulated in a static headspace with thermostatting and stirring under controlled condition to optimize equilibration and gases concentration as well. The patterns are trained by backpropagation algorithm which employs a log-sigmoid function and updates the weights using search-then-converge schedule. PCA results indicate that the sensor array used is able to differentiate the soil type clearly and may provide a discrimination as a response to presence/level of the nutrients addition in soil. Additionally, the PCA enhances the classification performance of NN to discriminate among the predescribed nutrient additions.