Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: 4/15/2006
Publication Date: 6/15/2006
Citation: Montalvo Jr, J.G., Davidonis, G.H., Von Hoven, T.M. 2006. Relations between micronaire and afis maturity and fineness data. Proceedings of the Beltwide Cotton Conference. CD-ROM. P. 1881-1886. Interpretive Summary: Fineness and maturity are important fiber properties. This is because yarns made with fiber of fine perimeters are stronger and mature fibers with the thicker cellulose wall absorb dye better. The micronaire of cotton is a measure of both properties and there are strong associations between the parameters. There is a need to validate Advanced Fiber Information System (AFIS) fineness and maturity results on U.S. cottons. One way to probe a data set for error in AFIS values is to extract diagnostic information about one of the associations between the properties. For this work, a data set was selected with different varieties of U.S. cottons and a specific association between micronaire and AFIS values was used to demonstrate that the AFIS data is, in fact, biased. The practical implication of this research is that inaccurate fiber quality information is being given to the producer and consumer. Advances in this area of work may lead to more accurate classing data, which could positively impact U.S. cotton consumption
Technical Abstract: A diagnostic model of the specific relationship between micronaire and maturity times perimeter wass tested on experimental data from 13 varieties of U.S. cottons. Micronaire was analyzed on a high volume instrument and maturity and perimeter by the Advanced Fiber Information System (AFIS). The diagnostic model provides a tool for analysis of data related to the definition of maturity and Lord's micronaire equation. Perimeter's role in the model is to normalize the data and stabilize model output. Model output includes the linear regression coefficient of determination, slope and intercept. The results were significantly different compared to model predictions. Simulation data with added error was used to help interpret the findings. The study showed that the AFIS fineness and maturity values are biased and recalibation is recommended.