Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer

Abstract

In this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption, XGBoost demonstrates superior performance with an R2 of 0.9957 during training and 0.9971 during testing, alongside minimal MSE of 0.0034 during training and 0.0008 during the testing phase indicating high predictive accuracy and low error rates. Conversely, LGBM shows lower R2 values (0.9061 training, 0.8809 testing) and higher MSE of 0.0747 during training and 0.0337 during testing, reflecting poorer performance. Similarly, for shrinkage prediction, XGBoost outperforms LGBM, evidenced by higher R2 (0.9887 training, 0.9975 testing) and lower MSE (0.2527 training, 0.4878 testing). The comparative statistics showed that XGBoost regularly outperformed LightGBM. The game theory-based Shapley functions revealed that temperature and product types were the most influential features of the energy consumption model. These findings illustrate the practical applicability of the XGBoost and LightGBM models in food drying operations towards optimizing drying conditions, improving product quality, and reducing energy consumption.

Keywords

• Banana slice
• Solar drying
• Machine learning
• XGBoost
• LightGBM
• Energy consumption