Internet of Things (IoT): Operational automation in experimental and computational analyses of hop residues drying

Abstract

The drying of hop residues is a sustainable alternative for managing agro-industrial waste while preserving bioactive compounds. This study aimed to automate the drying process using Internet of Things (IoT) concepts, analyzing total phenolic compounds, α-acids, and β-acids, and evaluating variables with machine learning. Two systems were developed: one for acquiring temperature and moisture content data using sensors and a microcontroller, and another for weighing by capturing images of the balance. Data were transmitted and processed remotely. The drying operation was performed using a heat pump dryer and a tray dryer at 50 °C, 60 °C, and 70 °C until reaching 10 % product moisture content. Spectrophotometric analysis and ASBC methods showed average values of 37.14 mg GAE/g (phenolics), 7.07 % (α-acids), and 5.47 % (β-acids), with no significant differences between drying conditions. The IoT and machine learning approach proved efficient for remote monitoring and process automation, enabling real-time supervision and operational alerts.