Optimizing Microwave-Convectional Drying of Probiotic-Infused Apple Snacks: Impact on Quality Attributes and Predictive Modeling with Equations and Artificial Neural Network.

Fruit drying in the modern food industry requires easily operable, energy-saving, inexpensive, and efficient drying technologies. Furthermore, these technologies are critical for snacks that meet the various dietary sensitivities and requirements of consumers and provide a benefit to overall well-being. In this context, the aim was addressed to produce snacks of apple infused with probiotic microorganism Lactobacillus rhamnosus GG. Fresh osmotically dehydrated apple cubes with ultrasound were dried by convectional and microwave-convectional technologies in line with an experimental plan with varying power and temperature levels. The impacts of the treatments on the color and texture quality attributes of the dried apple cubes and the survival of probiotic bacteria were investigated. The Pearson and principal correlation analysis between color and texture parameters showed that hardness (HA) and color changes were proportional to each other. Alongside the microwave-assisted hot air drying, notable alterations were detected particularly in the products' gumminess, chewiness, HA properties, and redness and yellowness values. In regard to statistical analysis of six mathematical equations used to model the kinetic data, the Midilli and others model offered the best fit for the drying operations. Feed-forward neural network approach was employed to describe the association between drying process inputs and outputs, and it consistently showed its predictive capacity with a high R