A fuzzy logic-based model for the multistage high-pressure inactivation of Lactococcus lactis ssp. cremoris MG 1363.

ABSTRACT

The high-pressure inactivation (200 to 600 MPa) of Lactococcus lactis ssp. cremoris MG 1363 suspended in milk buffer was investigated with both experimental and theoretical methods. The inactivation kinetics were characterised by the determination of the viable cell counts, cell counts of undamaged cells, LmrP activity, membrane integrity, and metabolic activity. Pressures between 200 and 600 MPa were applied, and pressure holding times were varied between 0 and 120 min. Experiments were carried out in milk buffer at pH values ranging between 4.0 and 6.5, and the effect of the addition of molar concentrations of NaCl and sucrose was furthermore determined. The inactivation curves of L. lactis, as characterised by viable cell counts, exhibited typical sigmoid asymmetric shapes. Generally, inactivation of the membrane transport system LmrP was the most sensitive indicator of pressure-induced sublethal injury. Furthermore, the metabolic activity was inactivated concomitant with or prior to the loss of viability. Membrane integrity was lost concomitant with or later than cell death. For example, treatments at 200 MPa for 60 min in milk buffer did not inactivate L. lactis, but fully inactivated LmrP activity and reduced the metabolic activity by 50%. The membrane integrity was unaffected. Thus, the assay systems chosen are suitable to dissect the multistep high-pressure inactivation of L. lactis ssp. cremoris MG 1363. A fuzzy logic model accounting for the specific knowledge on the multistep pressure inactivation and allowing the prediction of the quantities of sublethally damaged cells was formulated. Furthermore, the fuzzy model could be used to accurately predict pressure inactivation of L. lactis using conditions not taken into account in model generation. It consists of 160 rules accounting for several dependent and independent variables. The rules were generated automatically with fuzzy clustering methods and rule-oriented statistical analysis. The set is open for the integration of further knowledge-based rules. A very good overall agreement between measured and predicted values was obtained. Single, deviating results have been identified and can be explained to be measurement errors or model intrinsic deficiencies.