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Multi‐Channel Surface Acoustic Wave Sensors Based on Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) for Organic Vapors
Author(s) -
Hsu HuiPing,
Shih JengShong
Publication year - 2006
Publication title -
journal of the chinese chemical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 45
eISSN - 2192-6549
pISSN - 0009-4536
DOI - 10.1002/jccs.200600107
Subject(s) - principal component analysis , chemistry , surface acoustic wave , analytical chemistry (journal) , coating , electronic nose , acoustics , chromatography , nanotechnology , materials science , organic chemistry , artificial intelligence , physics , computer science
A multi‐channel surface acoustic wave (SAW) detection system which is employed to detect various organic molecules in a static system was prepared using 315 MHz one‐port quartz resonators and a home‐made computer interface for signal acquisition and data process. The oscillating frequency of the quartz crystal decreases on adsorption of organic molecules on the coating materials. The principal component analysis (PCA) method with SAS software was applied to select the appropriate coating materials onto the SAW crystals for organic vapors, e.g. hexane, 1‐hexene, 1‐hexyne, 1‐propanol, propionaldehyde, propionic acid, and 1‐propylamine. A dataset for a multi‐channel sensor with 19 SAW crystals for 7 analyses was collected after comparing the correlation between the 19 coating materials and the first six principal component (PC) factor. Furthermore, linear discriminate analysis (LDA) with SPSS software and a profile discrimination map were also applied and discussed for the discrimination of these organic vapors. These organic molecules could be clearly distinguished by the six‐channel SAW static sensor. The effect of concentration for various organic vapors was investigated and discussed.