Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.
This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.
Zhao
Weixiang
W
Department of Mechanical and Aeronautical Engineering, One Shields Avenue, University of California, Davis, CA 95616, USA.
Sankaran
Shankar
S
Ibáñez
Ana M
AM
Dandekar
Abhaya M
AM
Davis
Cristina E
CE
D000465:Algorithms; D002849:Chromatography, Gas / Q000145:classification / Q000379:methods*; D005638:Fruit / Q000737:chemistry; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis