Submitted to: Journal of Engineered Fibers and Fabrics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 20, 2009
Publication Date: February 10, 2010
Citation: Foulk, J.A., Price, C., Senter, H., Gamble, G.R., Meredith Jr, W.R. 2010. Relationship of Fiber Properties to Vortex Yarn Quality via Partial Least Squares. Journal of Engineered Fibers and Fabrics. 4(4): 36-45. Interpretive Summary: Cottons in this study demonstrated fiber quality traits that allow them to operate at high speeds on the latest generation of textile equipment. Cotton quality is affected by cotton variety and growing conditions, which vary by year and harvesting location. Raw cotton fiber quality measurements were performed on several lots of cotton and subsequently spun into yarn at the CQRS laboratory by the vortex spinning method with several characteristics of the yarn measured for each lot. This manuscript explores the common fiber quality measurements obtained from the HVI™ and AFIS™. This manuscript explores fiber quality measurements and how they predict yarn quality and processing efficiencies.
Technical Abstract: The Cotton Quality Research Station (CQRS) of the USDA-ARS, recently completed a comprehensive study of the relationship of cotton fiber properties to the quality of spun yarn. The five year study, began in 2001, utilized commercial variety cotton grown, harvested and ginned in each of three major growing regions in the US (Georgia, Mississippi, and Texas). CQRS made extensive measurements of the raw cotton properties (both physical and chemical) of 154 lots of blended cotton. These lots were then spun into yarn in the CQRS laboratory by vortex spinning with several characteristics of the yarn and spinning efficiency measured for each lot. This study examines the use of a multivariate statistical method, partial least squares (PLS), to relate fiber properties to spun yarn quality for vortex spinning. Two different sets of predictors were used to forecast yarn quality response variables: one set being only HVI variables, and the second set consisting of both HVI and AFIS variables. The quality of predictions was not found to significantly change with the addition of AFIS variables.