Application of Temperature Modulation-SDP on MOS Gas Sensors: Capturing Soil Gaseous Profile for Discrimination of Soil under Different Nutrient Addition
Application of Temperature Modulation-SDP on MOS Gas Sensors: Capturing Soil Gaseous Profile for Discrimination of Soil under Different Nutrient Addition
复制标题
DOI：
10.1155/2016/1035902
复制
发表时间：
2016-01-01
影响因子：
1.9
通讯作者：
Kitagawa，Akio
中科院分区：
文献类型：
--
作者：
Sudarmaji，Arief;Kitagawa，Akio
设计了一种MOS气体传感器的温度调制技术-SDP（specified detection point），并测试了其对复杂混合物-土壤气态化合物的敏感性能。利用自制的电子鼻对添加不同剂量养分（不添加、正常添加和高添加）的两种土壤（桑迪壤土和砂土）的采样顶空气相剖面进行了采集和分析。它包括（a）6个MOS气体传感器，通过（B）基于PSoC CY 8 C28445 - 24 PVXI-的接口和（c）主成分分析（PCA）和神经网络（NN）作为模式识别工具，以特定调制方式无线驱动。在受控条件下，在恒温和搅拌的静态顶部空间中积累气态化合物，以优化平衡和气体浓度。模式通过反向传播算法进行训练，该算法采用对数S形函数并使用搜索然后收敛时间表更新权重。主成分分析结果表明，所使用的传感器阵列是能够区分土壤类型清楚，并可以提供一个歧视作为对存在/水平的土壤中的养分添加。此外，PCA增强了NN的分类性能，以区分预先描述的营养添加剂。
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.