Pathogen exposure to multiple hurdles could result in variation in the number of survivors, which needs to be carefully considered using appropriate regression models for dealing with survivor dispersion. The aim of this study was to evaluate the impact of the hurdles on the random component of the measured variation and on its unexplained part (over or under-dispersion) representing the departure from randomness, i.e. non-randomness, in survivors of a multi-strain mixture of L. monocytogenes. The pathogen inactivation curves were fitted to the Weibull model within the Conway-Maxwell-Poisson process. In all the 20 hurdle combinations, the surviving cells, whether they showed an upward curvature or linear kinetics, displayed the randomness revealed by the degree of dispersion of the inactivation parameters (-b and p). In 15 combinations, a significant dispersion coefficient (c0), which reflected the non–random component of variation was evident, denoting either over-dispersion (c0 >0 in 13 combinations) or under-dispersion (c0 < 0 in 2 combinations). The observed dependence of the under- and over-dispersion conditions on the inactivation rate was confirmed by a Monte Carlo simulation based on the inactivation parameter -b. Including both randomness and non-randomness provides a more accurate estimation of survivors, which certainly impacts on intervention practices.
Impact of multiple hurdles on Listeria monocytogenes dispersion of survivors
Mara Lucia StecchiniUltimo
Writing – Original Draft Preparation
2022-01-01
Abstract
Pathogen exposure to multiple hurdles could result in variation in the number of survivors, which needs to be carefully considered using appropriate regression models for dealing with survivor dispersion. The aim of this study was to evaluate the impact of the hurdles on the random component of the measured variation and on its unexplained part (over or under-dispersion) representing the departure from randomness, i.e. non-randomness, in survivors of a multi-strain mixture of L. monocytogenes. The pathogen inactivation curves were fitted to the Weibull model within the Conway-Maxwell-Poisson process. In all the 20 hurdle combinations, the surviving cells, whether they showed an upward curvature or linear kinetics, displayed the randomness revealed by the degree of dispersion of the inactivation parameters (-b and p). In 15 combinations, a significant dispersion coefficient (c0), which reflected the non–random component of variation was evident, denoting either over-dispersion (c0 >0 in 13 combinations) or under-dispersion (c0 < 0 in 2 combinations). The observed dependence of the under- and over-dispersion conditions on the inactivation rate was confirmed by a Monte Carlo simulation based on the inactivation parameter -b. Including both randomness and non-randomness provides a more accurate estimation of survivors, which certainly impacts on intervention practices.File | Dimensione | Formato | |
---|---|---|---|
1-s2.0-S0740002022001125-main-2.pdf
non disponibili
Licenza:
Non pubblico
Dimensione
5.94 MB
Formato
Adobe PDF
|
5.94 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.