Formulation Time Machine is an AI-powered simulation framework designed to predict the lifecycle of complex chemical formulations. By applying first-order kinetics and Arrhenius-based temperature acceleration, the engine forecasts when a product will fail commercial stability standards.

A data-driven fruit shelf life prediction system using computer vision and statistical modeling. The project analyzes aroma decay patterns and visual features to estimate spoilage timelines and optimize post-harvest storage and supply chain decisions.

Predictive microbiology for challenge-test style shelf-life simulation.

AI-powered food management system using FastAPI, React, and machine learning for image recognition and shelf-life prediction.

Analyze pharmaceutical stability study data: fit zero-order or first-order degradation kinetics and predict shelf life.

AI-powered shelf-life prediction system for climate-sensitive foods using image analysis, environmental metadata fusion, and deep learning.