Wine Oracle: A Clandestine Journey into Quality Prediction with Neural Alchemy

In the modern context, it is crucial to prioritize wine quality assurance in order to satisfy consumer demands and sustain a competitive advantage in the marketplace. The objective of this research is mainly focused on: (1) predicting the quality of wine through the utilization of sophisticated fused deep learning and machine learning methods namely Multichannel Convolutional Neural Network (MCNN). (2) Integrate machine learning models such as Multilayer Perceptron (MLP) to increase the model’s prediction. These two methods exhibit a commendable degree of precision in forecasting the quality of wine. This study highlights the importance of utilizing advanced computational methods to improve quality control procedures, thus facilitating the wine sector’s adjustment to changing consumer preferences and quality benchmarks.