Hybrid Rule-based and Machine Learning Framework for Comprehensive Quality Evaluation of Freeze-Dried Jamun (Syzygium Cumini) Powder

The intelligent hybrid quality evaluation system of freeze dried Jamun (Syzygium cumini) powder. This system combines a rule based approach and machine learning. The system will ensure accuracy, efficiency, and sustainability. The system analyzes raw material condition, freeze-drying process, packaging and storage critical parameters to assess nutritional quality, physical and chemical properties for shelf life evaluation. A Decision Tree classifier based on 5,000 samples giving 98.70% accuracy is more reliable than conventional methods of classification. A web-based application is developed that consists of various visualizations such as confusion matrices, feature importance graphs and training trends that will help the researcher or industry professional in taking better decisions. Food quality will improve, waste generation will go down and processing will optimize. The modular framework developed in this study is comprehensive and easily versatile for other fruit powders and food quality applications.