Cloud-Integrated Smart Food Labeling Systems for Allergen Detection with Logistic Regression

Accurate allergen detection methods are essential for protecting consumers from food allergies, which may have serious health impacts. In this research, it provides a new method for allergy identification that makes use of cloud-integrated smart labeling systems and logistic regression (LR) approaches. The proposed approach improves customer safety and faith in food items by combining cloud computing with smart labeling technology to provide real-time allergy detection capabilities. The main objective is to provide a thorough framework that explains how to use smart label data to train LR models for allergy prediction. It shows that the system method reliably and effectively identifies allergenic components in different foods by conducting extensive experiments and validation allows for flexible deployment and access in any setting. It highlights the promise of LR in conjunction with cloud-integrated smart labeling systems to improve food safety and handle issues connected to allergies in the food industry.