پديدآورندگان :
Alinoori Amir Hossein ah.alinoori@isrc.ac.ir Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan; , Amoozgar Fariborz Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan , Narimani Seyed Mahdi Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan , Hajialigol Saeed Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan , Sadeghian Kerdabadi Kamal Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan , Masjedi Esfahani Majid Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan , Sheikhbahaee Hamed Spectrometry and microelectronic devises department, Institute of Material and Energy, Iranian Space Research Center, Isfahan
كليدواژه :
Gaussian apodization factor analysis (GAFA) , Portable Columnless Gas Chromatography Ion Mobility Spectrometer (PCGC , IMS) , Singular value decomposition (SVD) , organic particles
چكيده فارسي :
Hyphenated chromatographic techniques have been used to separate and analyze complex samples to reveal the qualitative and quantitative data about eluted components along with their retention times[1]. Notwithstanding, overlapping chromatographic peaks may occur and can really complicate the interpretation and analysis of data[2]. Obtaining appropriate data analysis tools, which focus on the data to detect overlapped/embedded regions and to find the number of pure components that are hidden in these regions, is a problem in common analytical applications. In this paper Data analysis was enhanced with adapted Gaussian apodization factor analysis (GAFA) as a multivariate curve resolution algorithm. A unique Portable Columnless Gas Chromatography Ion Mobility Spectrometer (PCGC-IMS) was designed and fabricated this homemade customized instrument is an alternative to other time consuming technologies for monitoring of organic particles in real samples without sample preparation. GAFA has been developed as an enhanced algorithm to assess the purity of PCGC-IMS data. In GAFA method, submatrices are extracted by Gaussian apodization moving window through weighting the fixed-size moving window via Gaussian formula. Therefore, each submatrix mainly characterizes a spectrum and by performing factor analysis on this Gaussian weighted submatrix, the number of principal components for each evaluated spectrum is determined by Singular value decomposition (SVD)[3]. This precise and quick determination of a rank map is successfully used for extract pure components from PCGC-IMS.
