## 4 Discussion
The drying time (t) also shows a strong positive correlation with mass-related parameters, which aligns with drying principles where prolonged exposure to heat and air movement leads to more moisture removal. The energy efficiency parameter (EE) and other energy-related factors exhibit a negative correlation with moisture-related parameters. As drying removes more moisture, energy efficiency tends to decrease because it becomes harder to remove the remaining water. In the research of [PERSON] et al., and [PERSON] et al., the strong correlation between water loss and drying time supports these findings [50; 51]. The negative correlation between energy efficiency and moisture parameters aligns with findings from [PERSON] [52], which state that energy efficiency decreases as drying progresses due to the reduced moisture diffusion rate in the later drying stages. Research by [PERSON] et al. [53] highlights that IR energy input shows variable effects on drying kinetics depending on material properties and initial moisture content. The relatively weak correlation of E(IR) with mass retention in this dataset aligns with this observation. The biplot (Figure 8, axes F1 and F2: 85.20%) shows the relationships between variables and models (different drying conditions) in a two-dimensional space defined by the first two principal components (PCs) [54]. The first two PCs explain 85.20% of the total variance in the data, indicating that the biplot provides a good representation of the overall data structure. F1 captures the variation related to drying progress. Variables such as WLm, m and m\({}_{\rm d}\) have strong positive loadings on F1. F2 captures the variation related to energy input and efficiency. EE, E(IR), and E have strong positive loadings on F2. Model D is associated with high weight loss and intermediate values of E(IR), E and EE, model C is associated with higher E(IR), E and EE but lower drying, model B is associated with higher mH20 and model A is associated with higher WL\({}_{\rm I}\). The analysis highlights a critical trade-off between energy consumption and drying efficiency, with Model D offering the most promising optimization pathway. Key implications include adjusting energy inputs (e.g., reducing energy in model C), improving moisture removal because of Maillard reactions (model B), and leveraging model A's initial drying rate in multi-stage processes. The high level of correlation between measured and integrated values is consistent with the research of [PERSON] et al. [9]. It has been demonstrated that combined drying methods are significantly more efficient and productive compared to individual methods. The rationale for selecting specific infrared (IR) power levels and distances of IR lamps above the drying material in combined convective and IR drying methods lies in optimizing drying efficiency, energy use, and product quality. Higher IR power levels are often chosen to accelerate drying rates by providing sufficient radiant energy for rapid moisture removal, while lower power levels may be used to minimize energy consumption and prevent overheating of the material. The distance between the IR lamp and the drying material is critical for controlling heat intensity; shorter distances enhance energy transfer efficiency but may risk uneven heating or surface degradation, whereas longer distances reduce heat intensity and allow for gentler drying, especially for heat-sensitive products. These parameters are tailored based on factors such as slice thickness, material type, desired drying time, and others. This study has shown that optimal combinations of IR power and lamp distance can significantly reduce drying time, improve energy efficiency, and preserve product quality, making these variables essential for process optimization. The study's scope was limited to bread slices under specific conditions of power and distance. Additional variables such as airflow rate or ambient temperature could further influence outcomes.