TY - JOUR
T1 - Modeling inactivation kinetics for Enterococcus faecium on the surface of peanuts during convective dry roasting
AU - Casulli, Kaitlyn E.
AU - Igo, Matthew J.
AU - Schaffner, Donald W.
AU - Dolan, Kirk D.
N1 - Funding Information: This material is based upon work supported by the USDA NIFA [Award No. 2020-67034-31939, Hatch project 1010216, Hatch project NJ10235] and the Rutgers Food Microbiology Risk Reduction Project. The authors gratefully acknowledge Bühler, Inc, for use of their Cary pilot plant equipment and Mr. Michael James for preparation of inoculated peanuts. Publisher Copyright: © 2021 Elsevier Ltd
PY - 2021/12
Y1 - 2021/12
N2 - Dry roasting can reduce Salmonella contamination on peanuts. While previous studies evaluated impact of product temperature, process humidity, product moisture, and/or product water activity on Salmonella lethality, no published study has tested multiple primary and secondary models on data collected in a real-world processing environment. We tested multiple primary and secondary models to quantify Salmonella surrogate, Enterococcus faecium, inactivation on peanuts. Shelled runner-type peanuts inoculated with E. faecium were treated at various air temperatures (121, 149, and 177 °C) and air velocities (1.0 and 1.3 m/s) for treatment times from 1 to 63 min. Peanut surface temperature was measured during treatment. Water activity and moisture content were measured, and E. faecium were enumerated after treatment. Microbial inactivation was modeled as a function of time, product temperature, and product moisture. Parameters (Dref, zT, zaw, zMC, and/or n) were compared between model fits. The log-linear primary model combined with either the modified Bigelow-type secondary model accounting for aw or moisture content showed improved fit over the log-linear primary model combined with the traditional Bigelow-type secondary model. The Weibull primary model combined with the traditional Bigelow-type secondary model had the best fit. All parameter relative errors were less than 15%, and RMSE values ranged from 0.379 to 0.674 log CFU/g. Incorporating either aw or moisture content in the inactivation models did not make a practical difference within the range of conditions and model forms evaluated, and air velocity did not have a significant impact on inactivation. The models developed can aid processors in developing and validating pathogen reduction during peanut roasting.
AB - Dry roasting can reduce Salmonella contamination on peanuts. While previous studies evaluated impact of product temperature, process humidity, product moisture, and/or product water activity on Salmonella lethality, no published study has tested multiple primary and secondary models on data collected in a real-world processing environment. We tested multiple primary and secondary models to quantify Salmonella surrogate, Enterococcus faecium, inactivation on peanuts. Shelled runner-type peanuts inoculated with E. faecium were treated at various air temperatures (121, 149, and 177 °C) and air velocities (1.0 and 1.3 m/s) for treatment times from 1 to 63 min. Peanut surface temperature was measured during treatment. Water activity and moisture content were measured, and E. faecium were enumerated after treatment. Microbial inactivation was modeled as a function of time, product temperature, and product moisture. Parameters (Dref, zT, zaw, zMC, and/or n) were compared between model fits. The log-linear primary model combined with either the modified Bigelow-type secondary model accounting for aw or moisture content showed improved fit over the log-linear primary model combined with the traditional Bigelow-type secondary model. The Weibull primary model combined with the traditional Bigelow-type secondary model had the best fit. All parameter relative errors were less than 15%, and RMSE values ranged from 0.379 to 0.674 log CFU/g. Incorporating either aw or moisture content in the inactivation models did not make a practical difference within the range of conditions and model forms evaluated, and air velocity did not have a significant impact on inactivation. The models developed can aid processors in developing and validating pathogen reduction during peanut roasting.
KW - Dry roasting
KW - Enterococcus faecium
KW - Inactivation
KW - Modeling
KW - Parameter estimation
KW - Peanut
KW - Salmonella
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U2 - https://doi.org/10.1016/j.foodres.2021.110766
DO - https://doi.org/10.1016/j.foodres.2021.110766
M3 - Article
C2 - 34863505
VL - 150
JO - Food Research International
JF - Food Research International
SN - 0963-9969
M1 - 110766
ER -