BEs of and based on thermal degradation data.

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.

抗坏血酸（维生素C）是一类热敏感化合物，广泛应用于制药、营养保健食品及食品工业。该物质在高温环境下发生降解，会损害产品质量与功效。准确预测极端热降解事件，对于保障产品稳定性、优化生产工艺以及符合监管标准至关重要。然而，传统降解模型往往无法捕捉到罕见却关键的降解行为，导致风险评估不够充分、工艺控制欠优化。

本研究构建了贝叶斯-逆威布尔（Bayesian-Inverse Weibull）建模框架，用于预测加速应力条件下抗坏血酸的极端热降解路径。逆威布尔分布（Inverse Weibull distribution）因在重尾数据建模中表现优异，将其与贝叶斯分层方法相结合，可整合先验知识、实验数据并实现不确定性量化。该框架可精准估算降解阈值、失效概率以及最优存储与工艺参数。

本研究借助实验热降解数据对所构建的模型进行验证，并展示了其在优化生产工艺以缓解降解风险中的应用。研究结果表明，该模型在预测罕见降解事件方面性能更优，可为提升产品稳定性、减少物料浪费以及确保合规性提供切实可行的决策依据。该方法为依赖抗坏血酸这类热敏感化合物的工业领域，提供了一款可靠的化学计量分析与工艺优化工具。