Using Design of Experiments to Formulate Clean-Label Products
Clean label is hot; so much so that I seem to be writing about it nonstop. Take for instance, yester…Clean label is hot; so much so that I seem to be writing about it nonstop. Take for instance, yesterday’s blog that discussed new research from Mintel that found 84 percent of American free-from consumers buy clean-label foods because they are seeking out more natural or less processed foods.
As consumers begin to look more closely at what goes into their food and beverages, the industry is reformulating and repositioning mainstream products and lines to have cleaner labels. Food product designers are paying attention to the trends and taking advantage of strategies and information to develop clean-label products that deliver the stability and sensory appeal consumers are used to, but doing so by swapping out less desirable ingredients for clean-label ones.
It’s not as easy as it sounds, but there are solutions to meet this challenge. One tool in a product developer’s toolbox is statistical design of experiments (DOE) methodology, which can be used as a tool to formulate and/or reformulate products for clean-label requirements.
This hot topic will be discussed during the Food Product Design track of the SupplySide West Education program, sponsored by BASF, Tuesday, Oct. 6 from 10-10:50 a.m. I invite you to join Ed Dudley, director of technical innovation, Griffith Laboratories, USA, who will lead the “Using Design of Experiments to Formulate Clean-Label Products" session.
Attendees will learn why identifying criteria for success up-front and quantifying those criteria is key to success, the importance of choosing the usage level ranges of ingredients is critical, and why a trial-and-error approach to formulating clean-label products will likely result in higher cost, longer timelines and poor results.
