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Abstract. Drying is a widely used technique to extend product shelf life, reduce post-harvest losses and allow for retention of essential nutrients. Currently, processing conditions/parameters applied to minimise energy consumption and production costs result in significant loss of product quality. Therefore, there is a need to optimise the drying process in a holistic manner that includes a balance between costs, energy demand and product quality. To that end, smart/intelligent drying has a high potential as an effective and sustainable solution to improve resource and process efficiency and ensure high quality products. Smart drying encompasses real-time monitoring of food products using non-invasive measurement techniques, hybrid modelling and integrated control systems..

To shift towards smart drying, the first step includes the collection and analysis of multidisciplinary data that improves understanding of the process-product relationship. Thus, an experimental investigation was conducted with organic carrots to understand the effect of different drying conditions and strategies - namely (i) air temperature controlled, (ii) product temperature controlled and (iii) stepwise air temperature controlled - on the product quality. Moisture content, total carotenoid retention, water activity, and rehydration ratio were measured as quality control parameters. The results from the investigation revealed that the product temperature controlled strategy led to a shorter drying time and higher or similar retention of carotenoid content within the carrot slices in comparison to the other strategies. Water activity and rehydration ratio showed no significant differences among the three strategies. The extensive data set collected within this investigation provided further knowledge to understand the co-relationship between process parameters, energy consumption and product quality. Thus acting as a foundational base for the development of a digital twin in order to develop smart drying systems.

Development of a digital twin is the next step in the shift of paradigm towards a smart drying process. The optical sensors (infrared, RGB, HSI) implemented within the above investigation provide insight for changes within the product. However, they are limited in their capacity as they fail to combine the information on the physical and chemical mechanisms. The development of a digital twin allows the agricultural product in question to be represented using a physics-based hybrid digital model that integrates all conditions while cross checking to the real time based sensor data. The current study will present the initial results, concerning the modelling of process and product quality, from the physics-based hybrid model digital twin developed to investigate efficient food and feed drying concerning process and product quality.