Predicting extreme thermal degradation of ascorbic acid (Vitamin C) using Bayesian-Inverse Weibull models: Applications in stability analysis and process optimization

Ascorbic acid (Vitamin C) is a thermally sensitive compound extensively used in pharmaceuticals, nutraceuticals, and food industries, where its degradation under high-temperature conditions can compromise product quality and efficacy. Accurate prediction of extreme thermal degradation events is crucial for ensuring stability, optimizing manufacturing processes, and meeting regulatory standards. However, traditional degradation models often fail to capture rare but critical degradation behaviors, resulting in inadequate risk assessments and suboptimal process controls. In this study, we develop a Bayesian-Inverse Weibull modeling framework to predict extreme thermal degradation pathways of ascorbic acid under accelerated stress conditions. The Inverse Weibull distribution, known for its effectiveness in modeling heavy-tailed data, is integrated with a Bayesian hierarchical approach to incorporate prior knowledge, experimental data, and uncertainty quantification. This framework enables precise estimation of degradation thresholds, failure probabilities, and optimal storage and processing conditions. Using experimental thermal degradation data, we validate the model and demonstrate its application in optimizing manufacturing processes to mitigate degradation risks. The results highlight the model's superior capability in predicting rare degradation events, providing actionable insights for improving product stability, reducing waste, and ensuring regulatory compliance. This approach offers a robust tool for chemometric analysis and process optimization in industries reliant on thermally sensitive compounds like ascorbic acid.