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United States Department of Agriculture

Agricultural Research Service

Research Project: FIBER QUALITY MEASUREMENTS, PROCESSING EFFICIENCY AND END USE QUALITY Title: Uv / Visible / Near-Infrared Reflectance Models for the Rapid and Non-Destructive Prediction and Classification of Cotton Color and Physical Indices

Authors
item Liu, Yongliang
item Gamble, Gary
item Thibodeaux, Devron

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 7, 2010
Publication Date: July 28, 2010
Citation: Liu, Y., Thibodeaux, D.P., Gamble, G.R. 2010. UV / Visible / Near-Infrared Reflectance Models for the Rapid and Non-Destructive Prediction and Classification of Cotton Color and Physical Indices. 2010 Transactions of the American Society of Agricultural and Biological Engineers. p. 1341-1348.

Interpretive Summary: Cotton is one of the most important agricultural commodities in the world, and the subsequent need for rapid and accurate determination of its quality indexes is a much discussed topic from policy makers to cotton fiber processors. Traditional methods, such as high volume instrumentation (HVI) and advanced fiber information system (AFIS), have been developed as viable tools to measure a number of cotton quality indexes. Although these methods can measure many different quality characteristics and are practiced throughout the cotton industry, the procedures are destructive, time consuming, and prone to day-to-day and location-to-location variations. The development of fast, non-destructive, accurate, and routine techniques is critical to enhance cotton fiber quality classing efficiency. Near infrared (NIR) spectroscopy, with an extension to the UV-visible region, could be an important alternative technique due to speed, ease of sampling, and low-cost. In this approach, cotton fibers were scanned in the region of 220-2200 nm and the corresponding reference values were analyzed by HVI measurement. Partial least squares (PLS) regression models were developed, and the calibrations were optimized by the coefficient of determination (R2), root mean square error of validation (RMSEV), and residual predictive deviation (RPD) in an independent test set. The results indicated that this technique could be used to predict such quality indexes as micronaire and +b for quality control and other properties, including Rd, mean length, and upper-half mean length, for screening programs. This outcome provides cotton fiber / fabric / textile engineers and researchers a new sight in applying both optical UV/visible / NIR and imaging spectroscopy for rapid and routine grading and classification of cotton qualities.

Technical Abstract: High volume instrumentation (HVI), utilized in the cotton industry to determine the qualities and classifications of cotton fibers, is time consuming, and prone to day-to-day and location-to-location variations. UV / visible / NIR spectroscopy, a rapid and easy sampling technique, was investigated as a potential method for the prediction of such key cotton color and physical attributes as reflectance (Rd), yellowness (+b), micronaire, strength, mean length, upper-half mean length, short fiber index, and uniformity index. Cotton fibers were scanned in the region of 220-2200 nm and HVI values were measured as the references. Partial least squares (PLS) regression models were individually developed and then compared for each property in three spectral ranges. The best performances for nearly all properties were obtained from the region covering the UV/visible absorptions, which was in consistent agreement with univariate correlations from HVI data alone. On the basis of residual predictive deviation (RPD) in the validation set, the suitability of UV/visible/NIR predictive models could be in the order of micronaire = +b > Rd = mean length = upper-half mean length > uniformity index = short fiber index = strength. Although a poor model was obtained for fiber strength, the overall results indicated that UV/visible/NIR spectroscopy is a promising method for implementation in cotton quality classification and screening programs.

Last Modified: 10/22/2014
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