Mathematical modeling to estimate furan formation in thermally processed foods: A preliminary analysis considering carrots as a model food

Ramírez C.; Sanchez E.; Pinto, M; Fardella M.; Valencia P.; Angulo, A; Almonacid, S; Simpson, R

Keywords: FuranThermal sterilizationMathematical modelingMicrostructure

Abstract

Mathematical modeling of furan formation can be a powerful tool for the design and optimization of thermal processes for furan minimization under real operation conditions. This research aimed to develop a simple mathematical model based on the dependence of furan kinetic formation on temperature to estimate the furan concentration using the temperature-time profiles measured at the surface and at the center in thermally processed food using carrots as a model food but also that can be extended to furan prediction in real system. The furan prediction model was developed by coupling kinetic and Arrhenius models and validated by the thermal profiles obtained from retorting jarred carrots at different temperatures. The results showed that the furan concentration fit first-order kinetics (R2-values>0.960), showing a dependence on temperature. The furan concentration in the retorted carrot varied between 8.54 ± 0.33 ng of furan/g of carrot and 19.46 ± 0.98 ng of furan/g of carrot depending on the process temperature. The furan prediction model compared with the experimentally quantified model presented an error between −6.95% and +11.21%. The simple furan prediction model developed in this study provides an adequate approximation of the furan concentration at the end of the thermal food processing but more importantly, the model could be used by the food processor to predict furan concentration in a real system, and as a tool to optimize the thermal sterilization process to minimize furan concentrations under real operation conditions.

Más información

Título de la Revista: JOURNAL OF FOOD ENGINEERING
Volumen: 332
Editorial: ELSEVIER SCI LTD
Fecha de publicación: 2022
URL: https://doi.org/10.1016/j.jfoodeng.2022.111136