Inverse problems in food engineering: A review

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With the increase in the computational power, numerical simulations play an increasing role in designing, development, and optimization of various food processing operations. Inverse and ill-posed problems have been studied extensively in many branches of science and engineering, including mechanical, chemical, aerospace, biology, physics, and chemistry. The inverse techniques have been currently witnessing a growing trend in the food processing field for a decade. Inverse problems are usually performed when direct measurements of heat and mass transfer properties and boundary conditions, are not feasible. They are very sensitive to measurement errors; require optimization methods to tackle the inverse problem. It is necessary to consider the coupling heat and mass transfer for the solution of inverse problem since food process involves simultaneous heat and mass transfer within the food products. To date, inverse methods have been applied in few food processing operations, including drying, baking, freezing, and thermal processing. However, studies related to these areas on the estimation of unknown quantities for various fruits and vegetables, and food products are limited. This review covers the statistical concepts which include confidence interval, confidence region, model validation and minimization techniques discussed in detail. The optimal experimental design, D-optimality and Fisher Information Matrix, and sensitivity analysis are all extensively presented. The optimization techniques and their algorithms used in the area of food processing are explained. Finally, it mainly focuses on the inverse estimation of unknown quantities, namely, heat and mass transfer parameters in different food processing operations.