Natasha Sahgal

Potential for detection and discrimination between mycotoxigenic and non-toxigenic spoilage moulds using volatile production patterns: A review

There has been interest in the development of techniques for the rapid early detection of mycotoxigenic moulds in the food production chain. The development of sensor arrays that respond to the presence of different volatiles produced by such moulds has been examined as a potential method for the development of such detection systems. Commercial devices based on such sensor arrays, so-called ‘electronic noses’, have been examined extensively for the potential application of determining the presence of mycotoxigenic moulds in food raw materials. There is also interest in using the qualitative volatile production patterns to discriminate between non-mycotoxigenic and mycotoxigenic strains of specific mycotoxigenic species, e.g. Fusarium section Liseola, Penicillium verrucosum and Aspergillus section Nigri. This paper reviews the technology and available evidence that the non-destructive analysis of the headspace of samples of food raw materials or the discrimination between strains (mycotoxigenic and non-mycotoxigenic) can be determined using volatile fingerprints.

Electronic nose analysis of bronchoalveolar lavage fluid.

Eur J Clin Invest 2010; 41 (1): 52–58

Trichophyton species: use of volatile fingerprints for rapid identification and discrimination

Background  Fungal infection of the skin is a common clinical problem, and laboratory confirmation of the diagnosis is important to ensure appropriate treatment. The identification of the species of fungus is also important, because different fungal species have different modes of transmission, and this may be of importance both in preventing re‐infection and in avoidance of infection of others.

Analysis Of Volatile Fingerprints: A Rapid Screening Method For Antifungal Agents For Efficacy Against Dermatophytes

The potential of using an electronic nose (E. nose) for rapid screening dermatophytes to antifungal agents was studied. In vitro, the 50 and 90% effective concentration (EC) values of five antifungal agents for T. rubrum and T. mentagrophytes were obtained by mycelial growth assays. Then, the qualitative volatile production patterns of the growth responses of these fungi to these values were incorporated into solid medium were analysed after 96–120 hrs incubation at 25° C using headspace analyses. Overall, results, using PCA and CA demonstrated that it is possible to differentiate between various treatments within 96–120 hrs. This study showed that potential exists for using qualitative volatile patterns as a rapid screening method for antifungal agents for microorganism. This approach could also facilitate the monitoring of antimicrobial drug activities and infection control programmes and perhaps drug resistance build up in microbial species.

Fungal volatile fingerprints and machine learning: potential of discriminating and classifying dermatophyte species

Background Dermatophytes are responsible for one of the most common human fungal infectious diseases in the world – a leading cause of hair, nail and skin infections in humans known as tinea or ringworm. Conventional laboratory diagnoses comprising microscopy, in vitro culture and biochemical tests are time consuming (over three weeks), tedious and require skilled personnel. Molecular diagnostic approaches such as polymerase chain reaction (PCR), PCR-fingerprinting, restriction fragment length polymorphism (RFLP) and restriction enzyme techniques have shown improvements but are neither very cost effective nor feasible for routine clinical testing.