Predicting extreme thermal degradation of ascorbic acid (Vitamin C) using Bayesian-Inverse Weibull models:...

Area of Science:

• Chemical Engineering
• Materials Science
• Statistical Modeling

Background:

• Ascorbic acid (Vitamin C) is vital in pharmaceuticals, nutraceuticals, and food but degrades under heat.
• Predicting extreme thermal degradation is critical for product quality, stability, and regulatory compliance.
• Traditional models struggle with rare degradation events, leading to inaccurate risk assessments.

Purpose of the Study:

• To develop a robust Bayesian-Inverse Weibull framework for predicting extreme thermal degradation pathways of ascorbic acid.
• To enhance the accuracy of risk assessments and optimize manufacturing processes for thermally sensitive compounds.
• To provide actionable insights for improving product stability and ensuring regulatory adherence.

Main Methods:

• Development of a Bayesian-Inverse Weibull modeling framework.
• Integration of Inverse Weibull distribution with Bayesian hierarchical modeling.
• Incorporation of prior knowledge, experimental data, and uncertainty quantification.
• Validation using experimental thermal degradation data of ascorbic acid.

Main Results:

• The model accurately predicts extreme thermal degradation pathways and thresholds for ascorbic acid.
• Demonstrated superior capability in capturing rare degradation events compared to traditional models.
• Provided precise estimation of failure probabilities and optimal storage/processing conditions.

Conclusions:

• The Bayesian-Inverse Weibull framework offers a powerful tool for chemometric analysis and process optimization.
• The model enhances product stability, reduces waste, and ensures regulatory compliance for industries using thermally sensitive compounds.
• This approach enables more reliable risk management and process control in manufacturing.