Submitted to: Textile Research Journal
Publication Type: Peer reviewed journal
Publication Acceptance Date: 4/26/2007
Publication Date: 10/1/2007
Citation: Montalvo Jr, J.G., Von Hoven, T.M., Davidonis, G.H. 2007. Biased experimental fineness and maturity results. Textile Research Journal. 77(10):743-755. Interpretive Summary: The micronaire of cotton is a combined measure of fiber fineness and maturity. The two quantities are important because yarn made from fine fibers is stronger, and mature fibers absorb dye better. Breeders need accurate results to make important variety decisions. Biased fineness and maturity gives way to incorrect fiber perimeter and wall thickness information. In this study, experimental results are tested for biased fineness and maturity. The approach is based on the strong relationship between fineness, maturity and micronaire. The results from one sample set demonstrate no bias. Results from another sample set demonstrate biased data, which was fixed to produce bias-corrected results. This research may help to validate the fineness and maturity database on U.S. cottons, which could positively impact cotton consumption.
Technical Abstract: In Part I of this series, models were developed and computer simulations were performed to understand the variability in coefficients of determination (R2) between fineness and maturity, micronaire and fineness, and micronaire and maturity of cotton. Part II concentrated on derivation and testing of several diagnostic models to enhance the R2 and provide information about the analytical quality (accuracy) of the results. In Part III, modeling of biased fineness and maturity results was introduced. Error functions were derived based on micronaire values, specifically, Lord’s micronaire model. This paper demonstrates testing of a key diagnostic model on two different sample sets of 21 cottons. The results from one sample set – analyzed on the FMT -- fit the model. Results from the other sample set -- analyzed on both the AFIS A-2 and AFISPRO -- demonstrate a lack of fit to the diagnostic model. This lack of fit is due to bias in the AFIS fineness and maturity measurements compared to the more traditional Lord’s micronaire model. As a consequence of the bias, the dynamic range of the AFIS raw data for both fineness and maturity is very narrow. Results are confirmed by image analysis.