Development of a target component extraction method from GC-MS data with an in-house program for metabolite profiling

作者：Choe Sanggil; Woo Sang Hee; Kim Dong Woo; Park Yonghoon; Choi Hwakyung; Hwang Bang Yeon; Lee Dongho; Kim Suncheun^*

来源：Analytical Biochemistry, 2012, 426(2): 94-102.

DOI：10.1016/j.ab.2012.04.010

• Metabolite profiling
• GC-MS
• AMDIS
• Chemstation
• Hierarchical cluster analysis
• Principal component analysis

After gas chromatography-mass spectrometry (GC-MS) analysis, data processing, including retention time correction, spectral deconvolution, peak alignment, and normalization prior to statistical analysis, is an important step in metabolomics. Several commercial or free software packages have been introduced for data processing, but most of them are vendor dependent. To design a simple method for Agilent GC/MS data processing, we developed an in-house program, %26quot;CompExtractor%26quot;, using Microsoft Visual Basic. We tailored the macro modules of an Agilent Chemstation and implanted them in the program. To verify the performance of CompExtractor processing, 30 samples from the three species of the genus Papaver were analyzed with Agilent 5973 MSD GC-MS. The results of CompExtractor processing were compared with those of AMDIS-SpectConnect processing by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The two methods showed good classification according to their species in HCA. The PC1 + PC2 scores were 54.32-63.62% for AMDIS-SpectConnect and 56.65-85.92% for CompExtractor in PCA. Although the CompExtractor processing method is an Agilent GC-MS-specific application and the target compounds must be selected first, it can extract the target compounds more precisely in the raw data file with batch mode and simultaneously assemble the matrix text file.

• 出版日期2012-7-15