Shelf-Life Prediction of Nano-Sol via pH Acceleration. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Yao, Yu-Cheng; Tseng, Sheng-Tsaing; Wong, David Shan Hill
• Is Part Of: Journal of quality technology. Volume 49:Issue 1(2017)
• Page Start: 46
• Page End: 63
• Publication Date: 2017-01-01
• Subjects: Expectation/Conditional Maximization (ECM) Algorithm -- Mixture of Two Normal Distributions -- Nano-Sol -- pH Acceleration Model -- Shelf-Life Prediction; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2017.11918184
• Languages: English
• ISSNs: 0022-4065

Abstract

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.