Analysis of Listeria using exogenous volatile organic compound metabolites and their detection by static headspace-multi-capillary column-gas chromatography-ion mobility spectrometry (SHS-MCC-GC-IMS).

Listeria monocytogenes is a Gram-positive bacterium and an opportunistic food-borne pathogen which poses significant risk to the immune-compromised and pregnant due to the increased likelihood of acquiring infection and potential transmission of infection to the unborn child. Conventional methods of analysis suffer from either long turn-around times or lack the ability to discriminate between Listeria spp. reliably. This paper investigates an alternative method of detecting Listeria spp. using two novel enzyme substrates that liberate exogenous volatile organic compounds in the presence of α-mannosidase and D-alanyl aminopeptidase. The discriminating capabilities of this approach for identifying L. monocytogenes from other species of Listeria are investigated. The liberated volatile organic compounds (VOCs) are detected using an automated analytical technique based on static headspace-multi-capillary column-gas chromatography-ion mobility spectrometry (SHS-MCC-GC-IMS). The results obtained by SHS-MCC-GC-IMS are compared with those obtained by the more conventional analytical technique of headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The results found that it was possible to differentiate between L. monocytogenes and L. ivanovii, based on their VOC response from α-mannosidase activity.

Evaluation and application of static headspace-multicapillary column-gas chromatography-ion mobility spectrometry for complex sample analysis.

An evaluation of static headspace-multicapillary column-gas chromatography-ion mobility spectrometry (SHS-MCC-GC-IMS) has been undertaken to assess its applicability for the determination of 32 volatile compounds (VCs). The key experimental variables of sample incubation time and temperature have been evaluated alongside the MCC-GC variables of column polarity, syringe temperature, injection temperature, injection volume, column temperature and carrier gas flow rate coupled with the IMS variables of temperature and drift gas flow rate. This evaluation resulted in six sets of experimental variables being required to separate the 32 VCs. The optimum experimental variables for SHS-MCC-GC-IMS, the retention time and drift time operating parameters were determined; to normalise the operating parameters, the relative drift time and normalised reduced ion mobility for each VC were determined. In addition, a full theoretical explanation is provided on the formation of the monomer, dimer and trimer of a VC. The optimum operating condition for each VC calibration data was obtained alongside limit of detection (LOD) and limit of quantitation (LOQ) values. Typical detection limits ranged from 0.1ng bis(methylthio)methane, ethylbutanoate and (E)-2-nonenal to 472ng isovaleric acid with correlation coefficient (R(2)) data ranging from 0.9793 (for the dimer of octanal) through to 0.9990 (for isobutyric acid). Finally, the developed protocols were applied to the analysis of malodour in sock samples. Initial work involved spiking an inert matrix and sock samples with appropriate concentrations of eight VCs. The average recovery from the inert matrix was 101±18% (n=8), while recoveries from the sock samples were lower, that is, 54±30% (n=8) for sock type 1 and 78±24% (n=6) for sock type 2. Finally, SHS-MCC-GC-IMS was applied to sock malodour in a field trial based on 11 volunteers (mixed gender) over a 3-week period. By applying the SHS-MCC-GC-IMS database, four VCs were identified and quantified: ammonia, dimethyl disulphide, dimethyl trisulphide and butyric acid. A link was identified between the presence of high ammonia and dimethyl disulphide concentrations and a high malodour odour grading, that is, ≥ 6. Statistical analysis did not find any correlation between the occurrence of dimethyl disulphide and participant gender.