4.7 Article

Headspace Gas Chromatography Coupled to Mass Spectrometry and Ion Mobility Spectrometry: Classification of Virgin Olive Oils as a Study Case

Journal

FOODS
Volume 9, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/foods9091288

Keywords

gas chromatography; ion mobility spectrometry; mass spectrometry; olive oil classification; headspace; chemometric models

Funding

  1. Comunidad Autonoma de la Region de Murcia (CARM, Fundacion Seneca) [19888/GERM/15]
  2. Spanish MICINN [PGC2018-098363-B-I00]
  3. European Commission (FEDER/ERDF)
  4. Innolivar Project (Public Purchase of Innovation in its modality of Pre-Commercial Public Purchase)
  5. University of Cordoba (Spain)
  6. Ministry of Science, Innovation and Universities

Ask authors/readers for more resources

Due to its multiple advantages, ion mobility spectrometry (IMS) is being considered as a complementary technique to mass spectrometry (MS). The goal of this work is to investigate and compare the capacity of IMS and MS in the classification of olive oil according to its quality. For this purpose, two analytical methods based on headspace gas chromatography (HS-GC) coupled with MS or with IMS have been optimized and characterized for the determination of volatile organic compounds from olive oil samples. Both detectors were compared in terms of sensitivity and selectivity, demonstrating that complementary data were obtained and both detectors have proven to be complementary. MS and IMS showed similar selectivity (10 out of 38 compounds were detected by HS-GC-IMS, whereas twelve compounds were detected by HS-GC-MS). However, IMS presented slightly better sensitivity (Limits of quantification (LOQ) ranged between 0.08 and 0.8 mu g g(-1)for HS-GC-IMS, and between 0.2 and 2.1 mu g g(-1)for HS-GC-MS). Finally, the potential of both detectors coupled with HS-GC for classification of olive oil samples depending on its quality was investigated. In this case, similar results were obtained when using both HS-GC-MS and HS-GC-IMS equipment (85.71 % of samples of the external validation set were classified correctly (validation rate)) and, although both techniques were shown to be complementary, data fusion did not improve validation results (80.95% validation rate).

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available