Project Number: 6655-41440-005-00-D
Project Type: Appropriated
Start Date: Jun 16, 2010
End Date: Jan 12, 2012
1. Assess the impact of newly developed varieties, agronomic practices, and ginning practices upon fiber and yarn quality. 2. Develop methods for the determination of cotton fiber frictional properties and their relationship to fiber convolutions, fiber maturity, metal content, wax and pectin content, and sugar content, with the aim of improving the understanding of factors affecting processing efficiency (roughly defined as the ratio of theoretical processing time vs. real processing time), utilizing state-of-the-art processing equipment. 3. Develop a new generation of instrumental methods and improve existing methods for the assessment of cotton fiber and yarn quality characteristics. 4. Develop fiber quality measurements and standards for flax fibers in order to efficiently assess quality characteristics of resultant short staple blends with cotton and in non-woven applications for composite materials.
Cottons produced under a variety of commercial/experimental conditions will be studied using modern manufacturing procedures. Genetic, agronomic and ginning variables will be correlated with textile processing performance in cooperation with collaborators (40% of effort). Chemical and microbiological properties of cotton will be studied for their effects on processing performance, yarn-fabric quality, worker safety and environment contamination (25%). Measurements of color, leaf, trash and contaminants will be improved for better prediction of processing performance and yarn fabric quality (20%). Reference test methods for strength and other HVI measurements will be developed-implemented (15%). Research will focus on improved predictive relationships between processing performance and yarn fabric quality over and above those obtained by using traditional measures of length, strength, color, fineness and trash by identifying developing rapid measurements of other important fiber properties. These measurements will be adapted for use in classification and marketing.