Food shelf life: estimation and optimal design

Abstract

Shelf life is a specified percentile of the time-until-spoilage distribution of a food product. This paper investigates statistical properties of various estimators of shelf life and develops a genetic algorithm for finding near-optimal staggered designs for estimation of shelf life. MLEs and their associated confidence intervals for shelf life have smaller bias, better performance, and better coverage than the corresponding ad hoc regression-based estimates. However, performance of MLEs for common sample sizes must be evaluated by simulation. The genetic algorithm, coded as an SAS macro, searched the design space well and generated near-optimal designs as measured by improvement to a simulation-based performance measure.

Keywords:

• genetic algorithm
• adaptive design
• staggered design
• Weibull
• censoring

Additional information

Notes on contributors

Ross A. Larsen

Present address. Department of Educational Psychology, TexasA&M University, College Station, TX, USA