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Direct analysis of volatile organic compounds in foods by headspace extraction atmospheric pressure chemical ionisation mass spectrometry
Author(s) -
PerezHurtado P.,
Palmer E.,
Owen T.,
Aldcroft C.,
Allen M.H.,
Jones J.,
Creaser C.S.,
Lindley M.R.,
Turner M.A.,
Reynolds J.C.
Publication year - 2017
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.7975
Subject(s) - chemistry , mass spectrometry , chromatography , analyte , analytical chemistry (journal) , sample preparation , chemical ionization , gas chromatography , extraction (chemistry) , atmospheric pressure chemical ionization , ionization , ion , organic chemistry
Rationale The rapid screening of volatile organic compounds (VOCs) by direct analysis has potential applications in the areas of food and flavour science. Currently, the technique of choice for VOC analysis is gas chromatography/mass spectrometry (GC/MS). However, the long chromatographic run times and elaborate sample preparation associated with this technique have led a movement towards direct analysis techniques, such as selected ion flow tube mass spectrometry (SIFT‐MS), proton transfer reaction mass spectrometry (PTR‐MS) and electronic noses. The work presented here describes the design and construction of a Venturi jet‐pump‐based modification for a compact mass spectrometer which enables the direct introduction of volatiles for qualitative and quantitative analysis. Methods Volatile organic compounds were extracted from the headspace of heated vials into the atmospheric pressure chemical ionization source of a quadrupole mass spectrometer using a Venturi pump. Samples were analysed directly with no prior sample preparation. Principal component analysis (PCA) was used to differentiate between different classes of samples. Results The interface is shown to be able to routinely detect problem analytes such as fatty acids and biogenic amines without the requirement of a derivatisation step, and is shown to be able to discriminate between four different varieties of cheese with good intra and inter‐day reproducibility using an unsupervised PCA model. Quantitative analysis is demonstrated using indole standards with limits of detection and quantification of 0.395 μg/mL and 1.316 μg/mL, respectively. Conclusions The described methodology can routinely detect highly reactive analytes such as volatile fatty acids and diamines without the need for a derivatisation step or lengthy chromatographic separations. The capability of the system was demonstrated by discriminating between different varieties of cheese and monitoring the spoilage of meats.