Implementation of multi capillary column ion mobility spectrometry (MCC-IMS) for medical and biological applications

Hariharan, Chandrasekhara

doi:10.17877/de290r-7002

Cited by 1 publication

(2 citation statements)

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“…Nonetheless, the MCC separation power is limited, which might lead to chemical cross sensitivities in the IMS due to co-eluting compounds. Moreover, because of the low mobility resolving power of the IMS [17], two or more analytes with similar drift times might not be separated. Furthermore, the systems need continuous flow of dry and clean drift gas provided by a gas cylinder or laboratory gas supply.…”

Section: Introductionmentioning

confidence: 99%

“…Since IMS has the potential to be further developed towards point-of-care applications, we developed a prototype of a compact, closed gas-loop GC-IMS [18]. Compared to the IMS used in the BioScout MCC-IMS, which has a mobility resolving power of about R = 15 (drift time/full width at half maximum (FWHM)) [17], our IMS combines much higher mobility resolving power of R = 90 with highest sensitivity [19]. Due to its high sensitivity, only 0.2 ml of breath are required.…”

Section: Introductionmentioning

confidence: 99%

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Measurement of exhaled volatile organic compounds from patients with chronic obstructive pulmonary disease (COPD) using closed gas loop GC-IMS and GC-APCI-MS

et al. 2016

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Due to its high sensitivity, compact size and low cost ion mobility spectrometry (IMS) has the potential to become a point-of-care breath analyzer. Therefore, we developed a prototype of a compact, closed gas loop IMS with gas chromatographic (GC) pre-separation and high resolving power of R = 90. In this study, we evaluated the performance of this GC-IMS under clinical conditions in a COPD study to find correlations between VOCs (10 ppbv to 1 ppmv) and COPD. Furthermore, in order to investigate possible correlations between ultra-low concentrated breath VOCs (0.1 pptv to 1 ppbv) and COPD, a modified mass spectrometer (MS) with atmospheric pressure chemical ionization (APCI) and GC pre-separation (GC-APCI-MS) was used. The GC-IMS has been used in 58 subjects (21 smokers with moderate COPD, 12 ex-smokers with COPD, 16 healthy smokers and 9 non-smokers). GC-APCI-MS data were available for 94 subjects (21 smokers with moderate COPD, 25 ex-smokers with COPD, 25 healthy smokers and 23 non-smokers). For 44 subjects, a comparison between GC-IMS and GC-APCI-MS data could be performed. Due to service intervals, subject availability and corrupt data, patient numbers were different for GC-APCI-MS and GC-IMS measurements. Using GC-IMS, three VOCs have been found showing a significant difference between healthy controls and patients with COPD. In the GC-APCI-MS data, we only observed one distinctive VOC, which has been identified as 2-pentanone. This proof-of-principle study shows the potential of our high-resolution GC-IMS in the clinical environment. Due to different linear dynamic response ranges, the data of GC-IMS and GC-APCI-MS were only comparable to a limited extent.

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Section: Introductionmentioning

confidence: 99%