Characterizing the total within-sample variation in cotton fiber length using the HVI fibrogram

Authors: SAYEED, ABU - Texas Tech University; SCHUMANN, MITCHELL - Texas A&M University; SMITH, WAYNE - Texas A&M University; Wanjura, John; KELLY, BRENDAN - Texas Tech University; HEQUET, ERIC - Texas Tech University

Submitted to: Textile Research Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/27/2020
Publication Date: 6/28/2020
Citation: Sayeed, A., Schumann, M., Smith, W., Wanjura, J.D., Kelly, B., Hequet, E. 2020. Characterizing the total within-sample variation in cotton fiber length using the HVI fibrogram. Textile Research Journal. https://doi.org/10.1177/0040517520935212.
DOI: https://doi.org/10.1177/0040517520935212

Interpretive Summary: Cotton fiber within a bale exhibits natural variation in regard to all measured fiber quality parameters (length, strength, micronaire, color, and trash content). This variability influences the quality of the yarn that is manufactured from the raw fiber. Current systems used to measure fiber quality report length characteristics using two single-number parameters derived from measurements of the full distribution of fibers, namely upper-half mean length and uniformity index. These two parameters do not provide adequate information on fiber length in order to predict yarn quality. This manuscript reports a new technique that uses all of the information captured during measurement of the fiber length distribution. It is subsequently demonstrated that yarn quality parameters can be predicted with much greater accuracy using the results of this new technique. The findings of this paper will help to improve the value and utilization of USA grown cotton that is graded using the systems discussed herein.

Technical Abstract: Within-sample variation in cotton fiber length is an important parameter to consider when explaining variation in yarn quality. However, the most widely used cotton fiber length parameters, Upper Half Mean Length (UHML) and Uniformity Index (UI), provided by the High Volume Instrument (HVI), do not characterize the total within-sample variation in fiber length. HVI fiber length measurements are based on the fibrogram principle where a fiber beard is scanned over a beam of red light from its base to the tip and a detector is used to measure the amount of light attenuated across the fiber beard which generates a curve called the fibrogram. Our results, based on samples from 19,628 commercial bales, reveal that the typical HVI length measurements are not characterizing unique types of length variation in the fiber beard. Further, fibrogram measurements taken from a subset of 538 of these commercial samples were used to identify independent types of fiber length variation in the fibrogram that are not currently being characterized by UHML and UI. The results obtained suggest that the HVI fibrogram does capture additional within-sample variation in fiber length that is not being currently reported. Two additional sets of samples were then used to evaluate the importance of this currently unused information about length variation. Partial Least Square Regression (PLSR) models were used to determine the importance of this new information for explaining variation in yarn quality. The four PLSR models were designed where the first model contains only non-length HVI parameters. The second model contains the current HVI length parameters along with all the non-length HVI parameters. The model three and model four contain fiber length variation captured by the fibrogram or the AFIS (length by number), respectively, along with non-length HVI parameters. The results presented here suggest that the additional variation captured by the fibrogram provides better yarn quality prediction (Model 3) than current HVI length parameters (Model 2) and are comparable to the results obtained using the AFIS length distribution by number (Model 4). The PLSR models were then validated using a leave-one-out cross-validation and they show that the models built with information extracted from the fibrogram are better at predicting yarn quality than models with the most commonly used HVI length parameters. These results suggest that the information from the fibrogram is at least as good as the AFIS length distribution by number when characterizing variation in yarn quality. The additional information provided by the whole fibrogram could provide a new tool to breeders for selecting their breeding lines and spinners for purchasing cotton bales.