|SAYEED, ABU - Texas Tech University|
|KELLY, BRENDAN - Texas Tech University|
|HEQUET, ERIC - Texas Tech University|
Submitted to: American Society of Agronomy Meetings
Publication Type: Abstract Only
Publication Acceptance Date: 11/10/2020
Publication Date: 11/10/2020
Citation: Sayeed, A., Kelly, B., Wanjura, J.D., Hequet, E. 2020. Utilization of the fiber length variation captured by the HVI fibrogram to improve the prediction of yarn quality. American Society of Agronomy Meetings. Presentation. San Antonio, TX, November 10-13, 2019.
Interpretive Summary: Cotton grown and ginned in the United States is graded by the High Volume Instrument (HVI) system before marketing. This system measures the length of fibers using a light-attenuation based method and reports two fiber length parameters based on that distribution measurement. The two length parameters reported, upper half mean length (UHML) and uniformity index (UI), provide partial information on the distribution of fiber length in the bale. This research demonstrates a new approach using modern statistical procedures to fully characterize the fiber length distribution measured by the HVI. Information from this new approach is better able to predict yarn quality than the original data from HVI and data from an advanced fiber information system (AFIS). While additional work is needed to calibrate HVI instruments to allow use of this new method, the results could significantly enhance the value of US cotton to mills by providing more useful fiber data.
Technical Abstract: Within-sample variation in cotton fiber length impacts yarn quality and processing speed. Excessive within-sample variation can slow processing and contribute to imperfections in the yarn structure. 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 follows the fibrogram principle for fiber length measurements, where a fiber beard is scanned over a red light, and a detector is used to measure the variation of light attenuated by the beard. HVI uses this signal to generate a curve called the fibrogram and reports two length parameters, UHML and UI. In this experiment, we demonstrate that the whole fibrogram holds more information than is currently reported, and this information improves the prediction of yarn quality. Length variation captured by the whole fibrogram were characterized from two sets of samples representing a range of sample types. To predict the yarn quality such as yarn tenacity, CVm%, thick and thin places, three Partial Least Square Regression models were designed. In the first model only the non-length HVI parameters such as micronaire, strength, elongation, Reflectance (Rd) and +b are used. In the second model UHML and UI are added to the first model. Fiber length variation captured by the whole fibrogram and AFIS length distribution by number are added to the first model separately to design third and fourth models respectively. Our results suggest that the model with fiber length variation captured by the fibrogram explains better yarn quality than the model with typical HVI length parameters. The results also show that the model with the HVI fibrogram is at least as good at predicting yarn quality as the model with the AFIS length distribution by number.