A Comprehensive Framework for Nutrition Analysis and Ingredient Substitution using Machine Learning

Nutrition is a core concern for public health, since it is the basic tool to prevent diseases and ensure well-being in general. However, classical methods of nutritional evaluation and selection of substitute ingredients often fail and do not support personalized nutrition plans. This paper addresses this problem by developing a framework based on machine learning techniques. It uses synthetic data and CNNs to analyze the nutritional content and recommend alternative ingredients that are healthier. The major challenge here is to ensure that the replacements maintain nutritional equivalence without sacrificing functionality or flavor. The focus of this research is to develop an automated system that determines the nutritional content of food items and provides intelligent substitution according to dietary goals such as reducing fat or increasing protein content. The methodology involved preprocessing of a synthetic dataset of food items and training of a CNN model on the task of nutritional classification with implementation of a rule-based substitution mechanism to propose substitutes. Expected outcomes include high accuracy of nutritional predictions along with meaningful substitutions that can enhance the quality of dietary. It has been found that there is an accuracy of 90.00%, balanced precision, recall, and F1 scores. Future extensions will include the following: more real-world dataset integration, reinforcement learning for substitution, and scalable deployment.