A head space gas chromatographic approach for the monitoring of the microbial cell activity and the cell viability evaluation

Abstract

A gas chromatographic method, based on the analysis of volatile metabolites in the head space of cultures or food systems has been developed and is widely used for the modelling and prediction of microbial behaviour in food systems and fermentation processes. In this paper, different applications based on two experimental approaches are described: (1) the detection of contamination levels of specific organisms or groups on the basis of previously prepared calibration curves; (2) the monitoring of the activity of microorganisms actually present in fermentation medium or in food model systems, by means of periodical gas chromatographic analysis of the vapours of the head space. The sensitivity and linearity within a value range from 10 1 to 10 8 g CFU/ml are the principal advantages offered by the head space gas chromatographic approach for detecting the contamination level of a system, based on previously prepared calibration curves. Moreover the gas chromatographic analysis of the head space presents the advantages of being inexpensive, relatively rapid and giving linear responses within wide intervals of cell loads. Preliminary research demonstrated that good selectivity could be obtained for Salmonella by inoculating samples in duplicate in a medium containing dulcitol as the only carbon source and with and without the presence of a polyvalent H antiserum as already used for radiometric Salmonella detection. The monitoring of the metabolic activity of microorganisms, in real or model systems, by means of an automatic GC analysis of the vapour in equilibrium in the head space, provided data suitable for analysis with equations, previously developed for data expressed as log CFU. The possibility of fitting CO 2 data by means of equations, in which parameters with biological significance are calculated, is an important instrument for modelling the growth of microorganisms as a function of the environmental variables.