Evaluation of identification accuracy using AIQS for GC-MS for measuring heavily contaminated samples. (December 2021)

• Record Type: Journal Article
• Authors: Omagari, Ryo; Nakayama, Takashi; Miyawaki, Takashi; Yagishita, Mayuko; Hashimoto, Shunji; Kadokami, Kiwao; Nakajima, Daisuke
• Is Part Of: Chemosphere. Volume 285(2021)
• Publication Date: 2021-12
• Subjects: Heavily contaminated sample -- Automated identification and quantification system (AIQS) -- Identified accuracy -- GC-QTofMS -- Mass error -- Comprehensive screening analysis; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2021.131401
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Automated identification and quantification systems with gas chromatography-mass spectrometry (GC-MS) (i.e., AIQS-GC) are used as a simple and comprehensive method for screening chemicals existing in the environment and are expected to be useful for emergency surveys in the event of a disaster. However, reports on the potential of AIQS-GC in heavily contaminated samples (HCSs) are limited. In this study, the identification performance of AIQS-GC was confirmed by comparing the exact mass of the targets identified by AIQS-GC with the measured accurate mass using GC-quadrupole-time-of-flight MS (GC-QTofMS) and by employing firefighting wastewater as HCS. In HCS, the mass spectrum interference was determined to cause false positives. The GC-QTofMS method revealed the presence of false positives and the false rate of AIQS-GC in HCS. Herein, AIQS-GC showed high identification accuracy in a normal sample such as river water. Conversely, in HCS, AIQS-GC may lead to incorrect evaluations. The combination of AIQS-GC and support method using GC-QTofMS, which can avoid the false positive is extremely useful for the rapid and easy analysis of HCS. Highlights: AIQS-GC would lead to incorrect evaluation in a heavily contaminated samples (HCS)-like disaster sample. Identification performance of AIQS-GC was confirmed by comparing accurate mass with exact mass using GC-QTofMS. It was confirmed that AIQS-GC caused the incorrect evaluation in HCS. The support method by GC-QTofMS can avoid the incorrect evaluation.