Abstract
Salmonella is a common chicken-borne pathogen that causes human infections. Data below the detection limit, referred to as left-censored data, are frequently encountered in the detection of pathogens. The approach of handling the censored data was regarded to affect the estimation accuracy of microbial concentration. In this study, a set of Salmonella contamination data was collected from chilled chicken samples using the most probable number (MPN) method, which consisted of 90.42% (217/240) non-detect values. Two simulated datasets with fixed censoring degrees of 73.60% and 90.00% were generated based on the real-sampling Salmonella dataset for comparison. Three methodologies were applied for handling left-censored data: (i) substitution with different alternatives, (ii) the distribution-based maximum likelihood estimation (MLE) method, and (iii) the multiple imputation (MI) method. For each dataset, the negative binomial (NB) distribution-based MLE and zero-modified NB distribution-based MLE were preferable for highly censored data and resulted in the least root mean square error (RMSE). Replacing the censored data with half the limit of quantification was the next best method. The mean concentration of Salmonella monitoring data estimated by the NB-MLE and zero-modified NB-MLE methods was 0.68 MPN/g. This study provided an available statistical method for handling bacterial highly left-censored data.
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