A comprehensive review on recent developments of radio frequency treatment for pasteurizing agricultural products.

Recent pathogen incidents have forced food industry to seek for alternative processes in postharvest pasteurization of agricultural commodities. Radio frequency (RF) heating has been used as one alternative treatment to replace chemical fumigation and other conventional thermal methods since it is relatively easy to apply and leaves no chemical residues. RF technology transfers electromagnetic energy into large bulk volume of the products to provide a fast and volumetric heating. There are two types of RF technology commonly applied in lab and industry to generate the heat energy: free running oscillator and 50-Ω systems. Several reviews have been published to introduce the application of RF heating in food processing. However, few reviews have a comprehensive summary of RF treatment for pasteurizing agricultural products. The objective of this review was to introduce the developments in the RF pasteurization of agricultural commodities and to present future directions of the RF heating applications. While the recent developments in the RF pasteurization were presented, thermal death kinetics of targeted pathogens as influenced by water activity, pathogen species and heating rates, non-thermal effects of RF heating, combining RF heating with other technologies for pasteurization, RF heating uniformity improvements using computer simulation and development of practical RF pasteurization processes were also focused. This review is expected to provide a comprehensive understanding of RF pasteurization for agricultural products and promote the industrial-scale applications of RF technology with possible process protocol optimization purposes.

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

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.

Thermal inactivation of Aspergillus flavus in peanut kernels as influenced by temperature, water activity and heating rate.

Infection of Aspergillus flavus, which can produce aflatoxin, is a major problem for peanut safe storage. Thermal inactivation kinetics of Aspergillus flavus is essential to design an effective heat treatment process. In this study, thermal inactivation kinetics of Aspergillus flavus in peanut kernel flour at four water activity (aw) levels (0.720, 0.783, 0.846, and 0.921) with three temperatures for each aw was studied using a thermal-death-time heating block system and fitted with first-order kinetic and Weibull models. The influence of heating rates on thermotolerance of Aspergillus flavus was also investigated. The results showed that the Weibull distribution model had better coefficient of determination from 0.954 to 0.996, as compared to that (from 0.866 to 0.980) of the first-order kinetic model. An upward concavity was found with the inactivation curve, indicating a tailing effect. Model parameters (D, δ, and p) were estimated with the modified Bigelow equations to predict survival curves of Aspergillus flavus at any temperature and aw. The reduced heat resistance of Aspergillus flavus at high heating rates above 1 °C/min suggests that developing fast thermal processes is preferred for pasteurizing peanuts in food industry. A case study was presented for applying the cumulated lethal time model to design the industrial heating process based on the thermal kinetics of Aspergillus flavus.