A Supervised Machine Learning Classification Framework for Beverage Quality Prediction

Abstract

Since the production of food and beverages is energy-intensive, the quality of food and beverage is important for the consumers as well as the food and beverage industry, the economic, political and social condition are posing challenge to Food and beverage small and medium and large industries assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. With the start-up business success help of a machine learning model and several features of beverages, this thesis would focus on important features that affect the quality of beverage production and have a model to predict a beverage quality. This review would also compare and discuss each technique and provide suggestions based on the current technology. This review would deliberate technology integration and the involvement of deep learning to enable several types of current technologies and the results demonstrate the model's ability to accurately predict beverage quality based on chemical composition. Furthermore, the developed model allows for the identification of critical chemical parameters influencing beverage quality. Manufacturers can use this information to make targeted adjustments in the formulation and production process, leading to enhanced product quality and consistency.