Shelf-Life Prediction of Nano-Sol via pH Acceleration

Abstract

This article addresses the shelf-life prediction of a nano-sol product. From a preliminary analysis of the dataset in a real example, we found pH is an appropriate accelerating variable for the purpose of predicting the shelf-life of a nano-sol product. In addition, we found that the histogram-valued frequency plots of the particle-size distribution of a nano-sol can be reasonably decomposed into a mixture of two normal distributions. Therefore, a pH accelerated degradation model is proposed to characterize the time evolution of the particle size distributions. By using an expectation/conditional maximization algorithm, we analytically obtain the shelf-life prediction of nano-sol (under the use condition) and its corresponding 95% confidence interval. The main contribution of this study is that the shelf-life prediction of the nano-sol products can be statistically achieved via the pH acceleration.

• Expectation/Conditional Maximization (ECM) Algorithm
• Mixture of Two Normal Distributions
• Nano-Sol
• pH Acceleration Model
• Shelf-Life Prediction

Additional information

Notes on contributors

Yu-Cheng Yao

Dr. Yao has a PhD from Institute of Statistics, Tsing-Hua University. His email address is [email protected].

Sheng-Tsaing Tseng

Dr. Tseng is a Professor in the Institute of Statistics. He is a senior Member of ASQ. His email address is [email protected].

David Shan Hill Wong

Dr. Wong is a Professor in the Department of Chemical Engineering. His email address is [email protected].