New Algorithm Improves the Reliability of Standard Deviation Measurements

Achievement date: 
2013
Outcome/accomplishment: 

Researchers at the NSF-funded Engineering Research Center (ERC) for Structured Organic Particulate Systems (CSOPS) at Rutgers, The State University of New Jersey, have developed a method to better predict the reliability of relative standard deviation (RSD) measurements.

Impact/benefits: 

RSD measurements, also known as the coefficient of variability, are used ubiquitously for decision making in pharmaceutical manufacturing processes. RSD is indicative of product quality and the uniformity of mechanical and chemical properties. However, RSD measurements can have large margins of error and the extent of uncertainty is often difficult to calculate. This project has resulted in a simple and convenient tool for calculating RSD reliability.

Explanation/Background: 

RSD is a measure of precision, or how close a set of numbers are to each other. In the world of particles and pharmaceutical engineering, this property translates into a measure of product consistency. RSD calculations are used to select ingredients during product development, to fine tune manufacturing processes, and to determine if the final product meets the requirements for release. However, because of the relatively small sample sizes and the randomness associated with particle formation, calculating margins of error and confidence intervals for RSD measurements can be difficult and cumbersome. The algorithm developed by CSOPS researchers aims to solve this problem and is immediately applicable in their own research.