Thermal Conductivity of Food Products using: A Correlation Analysis Based on Artificial Neural Networks (ANNs)

Afis, Ajasa, Abiodun

doi:10.11648/j.abb.20140202.12

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…In general, the dependence of the food dielectric properties with frequency, temperature, and composition has been found to be nonlinear and the development of Artificial Neural Networks (ANNs) can be a feasible approach to model and predict it. ANNs have been applied for the estimation and prediction of several types of food properties, including thermal, physical, chemical, and rheological properties (Abiodun Afis, ; Mutlu et al, ; Rai, Majumdar, DasGupta, & De, ; Razmi‐Rad, Ghanbarzadeh, Mousavi, Emam‐Djomeh, & Khazaei, ; Saeidirad, Rohani, & Zarifneshat, ).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network for prediction of dielectric properties relevant to microwave processing of fruit juice

Kubo

Curet

Augusto

et al. 2018

J Food Process Engineering

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Dielectric properties are fundamental parameters to determine the heating behavior of food products using microwaves. Determining and modeling these properties as a function of process conditions and product composition is required to be implemented in Multiphysics modeling, in which heat transfer and electromagnetic aspects are solved, allowing the characterizing and optimization of microwave processing. This work evaluates the dielectric properties of model fruit juice solutions at concentrations ranging from 5 to 65°Brix. Measurements were carried out at frequencies between 200 and 3,000 MHz in the temperature range of 20–80 °C. The results show that the dielectric properties are non‐linearly influenced by temperature, frequency and sugar content. A comparison between the dielectric properties of model solutions and of real juices was performed, and similarities were found. Furthermore, based on Artificial Neural Networks, the modeling and prediction of the dielectric properties at different conditions were achieved. Practical applications In search of food processes that cause a minimum damage to the original organoleptic characteristics of the product, the use of microwaves is considered as a promising alternative to conventional thermal treatment. Microwave heating can offer rapid volumetric heating, shorter processing time, and lower surface temperature, resulting in better sensorial and nutritional qualities. Knowledge of dielectric properties is fundamental to study the response of the food material to the electromagnetic field and to determine the heating pattern. The need for dielectric properties data is even more evident during microwave process development and optimization using Multiphysics modeling. Therefore, the results presented in this work can be useful for further studies involving the design of microwave processing of fruit juices.

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