Additive manufacturing or 3D printing can be applied in the food sector to create food products with personalized properties such as shape, texture, and composition. In this article, we introduce a computer aided engineering (CAE) methodology to design 3D printed food products with tunable mechanical properties. The focus was on the Young modulus as a proxy of texture. Finite element modelling was used to establish the relationship between the Young modulus of 3D printed cookies with a honeycomb structure and their structure parameters. Wall thickness, cell size, and overall porosity were found to influence the Young modulus of the cookies and were, therefore, identified as tunable design parameters. Next, in experimental tests, it was observed that geometry deformations arose during and after 3D printing, affecting cookie structure and texture. The 3D printed cookie porosity was found to be lower than the designed one, strongly influencing the Young modulus. After identifying the changes in porosity through X-ray micro-computed tomography, a good match was observed between computational and experimental Young’s modulus values. These results showed that changes in the geometry have to be quantified and considered to obtain a reliable prediction of the Young modulus of the 3D printed cookies.

In the present work, effect of potato variety, pretreatments (5% brine, 0.5% potassium Meta bi-sulphite (KMS) and blanching at 70°C for 3 minutes) and airflow mode on drying kinetics and effective moisture diffusivity of potato slices was investigated in a cabinet solar dryer with a heat storage system. The experiment was conducted in a full factorial design arranged in a completely randomized block design and the drying kinetics was modelled using commonly used well known equations. From the result; potato variety, air flow mode (natural vs. forced convection) and pre-treatments significantly affected the drying kinetics of the slice in which the brine-treated sample took a shorter drying time followed by blanched and KMS-treated slices. However, the blanched sample exhibited a higher effective moisture diffusivity and drying rate than untreated and KMS-treated samples. Brine treated sample has the lowest effective moisture diffusivity and drying rate. Tolcha variety dried faster than the Jalo variety followed by the Gera variety. The goodness of fit to the tested models were also evaluated based on R2, chi-square and RMSE values in which a model with the highest R2 ,lowest X2 and RMSE values were selected as a best fitted model. As result, Boltzmann model with R2 (0.9985) was the best fitted model in Blanched sample, whereas Midihilli and Kucuk model was best fitted with brine-treated and control samples, with R2 values ranging from 0.9995 in the control sample to 0.9999 in the brine-treated sample.