Extrapolation of a predictive model for growth of a low inoculum size of Salmonella Typhimurium DT104 on chicken skin to higher inoculum sizes

Abstract

Validation of model predictions for independent variables not included during model development can save time and money by identifying conditions for which new models are not needed. A single strain of Salmonella Typhimurium DT104 was used to develop a general regression neural network (GRNN) model for growth of a low inoculum size (0.9 log) on chicken skin with native microflora as a function of time (0 to 8 h) and temperature (20 to 45°C). The ability of the GRNN model to predict growth of higher inoculum sizes (2, 3, or 4.1 log) was evaluated. When the proportion of residuals in an acceptable prediction zone (pAPZ) from-1 log (fail-safe) to 0.5 log (fail-dangerous) was ≥0.7, the GRNN model was classified as providing acceptable predictions of the test data. The pAPZ for dependent data was 0.93 and for independent data for interpolation was 0.88. The pAPZs for extrapolation to higher inoculum sizes of 2, 3, or 4.1 log were 0.92, 0.73, and 0.77, respectively. However, residual plots indicated local prediction problems with pAPZs of < 0.7 for an inoculum size of 3 log at 30, 35, and 40°C and for an inoculum size of 4.1 log at 35 and 40°C where predictions were fail-dangerous, indicating faster growth at higher inoculum sizes. The model provided valid predictions of Salmonella Typhimurium DT104 growth on chicken skin from inoculum sizes of 0.9 and 2 log at all temperatures investigated and from inoculum sizes of 3 and 4.1 log at some but not all temperatures investigated. Thus, the model can be improved by including inoculum size as an independent variable.