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Computational modelling of survival of Aspergillus flavus in peanut kernels during hot air-assisted radio frequency pasteurization.

Authors
  • Zhang, Shuang1
  • Lan, Ruange1
  • Zhang, Lihui1
  • Wang, Shaojin2
  • 1 Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi, 712100, China. , (China)
  • 2 Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi, 712100, China; Department of Biological Systems Engineering, Washington State University, 213 L.J. Smith Hall, Pullman, WA, 99164-6120, USA. Electronic address: [email protected] , (China)
Type
Published Article
Journal
Food Microbiology
Publisher
Elsevier
Publication Date
May 01, 2021
Volume
95
Pages
103682–103682
Identifiers
DOI: 10.1016/j.fm.2020.103682
PMID: 33397605
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In recent years, radio frequency (RF) heating is getting popular as an alternative pasteurization method for agricultural commodities and low moisture foods. Computer simulation is an effective way to help understand RF interactions with food components and predict temperature distributions among food samples after RF treatments. In this study, a computer model based on Joule heating and thermal inactivation kinetic of A. flavus was established to predict both temperature distribution and microbial reduction among peanut kernels after RF processing. For the process validation, three 2-g peanut samples inoculated with 40 μL A. flavus were placed at three representative locations among 2.17 kg peanut kernels and subjected to various processing conditions in a 27.12 MHz, 6 kW RF heating unit together with hot air system. Results showed that the average difference of the sample temperature and microbial reduction between simulation and experiment was small with RMSE values of 0.009 °C and 0.012 °C, and 0.31 log CFU/g and 0.42 log CFU/g for peanut moisture contents of 7.56% and 12.02% w. b., respectively. Nonuniform RF heating resulted in the least lethality of A. flavus at the cold spot. The validated computer model was further used to estimate microbial reduction distributions at other target temperatures based on predicted temperature profiles. This computer model may help design the RF pasteurization protocols for peanut kernels without extensive experiments in food industry. Copyright © 2020 Elsevier Ltd. All rights reserved.

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