## 1 Introduction
Additionally, most hybrid dryers are still at the laboratory scale, making scaling up more complex than traditional methods, which calls for the development of guidelines for transitioning from lab to pilot-scale operations, along with life cycle assessments to evaluate their environmental and economic viability while further exploring the dynamics of transport phenomena and product quality during the hybrid drying process [12; 13]. Recent advancements in sensor technology have significantly enhanced the precision of drying process control and monitoring. The integration of sensors into drying systems enables real-time data collection on critical parameters such as temperature, humidity, and moisture content [14; 15]. These data facilitate the optimization of drying conditions, ensuring consistent product quality and energy efficiency [16; 17]. The MEMS (BME280) sensor is a tool in the food drying industry, where precise control of environmental conditions is vital for product quality. Proper calibration ensures that the data collected are reliable, enhancing the efficiency and effectiveness of drying processes [18; 19]. By integrating this sensor into a drying system, manufacturers can improve their product quality and save energy. The BME280 is a compact, low-power, environmental sensor designed for accurate measurement of temperature, humidity, and pressure. It is widely used in applications such as weather monitoring, smart home systems, and agriculture because of its ability to provide precise data on environmental conditions. Its calibration guarantees reliable performance, with temperature, humidity, and pressure readings adjusted against known references to ensure accuracy [20; 21]. This study further develops the hybrid dryer design principles established in earlier foundational works by presenting a framework suited for continuous industrial ovens. Recent advances in MEMS sensor technology [22; 23], such as the BME-280's capability to measure absolute air moisture content with a systematic error reduced to 0.09 mg/kg after calibration, now allow precise tracking of moisture loss and temperature gradients during bread drying. This alleviates the temporal resolution limitations of prior studies, which relied on post-drying quality assessments. For instance, [PERSON] et al. (2023) demonstrated that integrating MEMS sensors into convective dehydators enables PID-controlled temperature stability (\(\pm\)0.5 \({}^{\circ}\)C) and real-time energy efficiency optimization [24]. When applied to CIRD, such sensors provide continuous data on crust-to-crumb moisture migration, directly informing process adjustments to prevent over-drying or structural collapse--key issues in baked goods. By bridging the divide between agricultural hybrid drying models and baked goods' unique material properties, this sensor-enhanced CIRD framework advances the UN's 2030 food waste targets through scalable, data-driven drying optimization [25]. This study investigates the effects of combining convective and infrared drying on bread slices, employing advanced sensor technology to monitor and optimize the drying process. By employing advanced sensor technology, the study aims to monitor critical parameters such as temperature, moisture content, and drying rate in real time (without altering or interfering with the sample undergoing the drying process). This approach seeks to optimize the drying process for improved efficiency, uniformity, and food product quality while minimizing energy consumption and potential quality degradation.