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ARS Home » Southeast Area » New Orleans, Louisiana » Southern Regional Research Center » Cotton Structure and Quality Research » Research » Publications at this Location » Publication #263200
Title: Improving the accuracy of predicting yarn properties by selecting proper fiber length parameters

Author
item Cui, Xiaoliang
item Delhom, Christopher - Chris
item Rodgers Iii, James
item CAI, YIYUN - Louisiana State University
item Thibodeaux, Devron
item MARTIN, VIKKI - Cotton, Inc
item WATSON, MIKE - Cotton, Inc

Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 1/20/2011
Publication Date: 4/25/2011
Citation: Cui, X., Delhom, C.D., Rodgers III, J.E., Cai, Y., Thibodeaux, D.P., Martin, V., Watson, M. 2011. Improving the accuracy of predicting yarn properties by selecting proper fiber length parameters. National Cotton Council Beltwide Cotton Conference. p. 1282-1287.

Interpretive Summary: Fiber length is considered a very important factor in yarn production and yarn quality. Many cotton fiber length parameters have been developed and used. These different length parameters represent different fiber length characteristics. We conducted a research to compare the effectiveness of different length parameters in predicting yarn properties, thus to improve the accuracy of the predictions. Statistical models that include a comprehensive set of fiber length parameters were presented. The effectiveness of these parameters and their combinations in predicting yarn properties were studied. The importance of parameters related to short fiber in predicting yarn properties were also focused.

Technical Abstract: Fiber length is a very important factor in yarn production. A study was carried out to investigate the effects of various cotton fiber length parameters (conventional and non-conventional) and their combinations on yarn properties. Linear regression models with different number of fiber length parameters and their combinations were developed to predict different properties of yarns (ring, OE). Our results indicate that, for the models with only length parameters, four-parameter models are more proper models considering their R2s and Mallow’s Cp values. If other fiber properties (strength, Mic, etc.) are included, the R2 can be further increased. Short fiber parameters and length CV are important for improving prediction accuracy of yarn properties. Lower Half Mean Length (LHML) predicts yarn properties similar to short fiber content (SFC). Best prediction models for different yarns (ring, OE, etc.) and different yarn properties (strength, irregularity, etc.) include different length parameters. Not all yarn properties can be predicted well by linear regression models with length parameters. For yarn properties such as neps, ends-down, elongation, and CV (coefficient of variation) of strength, all models had low R2s.