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United States Department of Agriculture

Agricultural Research Service

Research Project: New and Improved Assessments of Cotton Quality

Location: Cotton Structure and Quality Research

2011 Annual Report

1a. Objectives (from AD-416)
Objective 1: Develop new industrially supported methods to assess cotton quality. Sub-objective 1a: Develop ways to characterize short fibers in cotton. Sub-objective 1b: Develop methods to measure seed coat fragments. Sub-objective 1c: Develop assessment methods for cotton properties that may contribute to cotton textile processability and product quality, but not conventionally assessed, such as micronaire and its components (such as maturity), three-dimensional color, and environmental impact on fiber properties. Objective 2: Develop new industrially supported methods to establish scientific foundations for standards and the next generation instruments for cotton classing. Sub-objective 2a: Develop new algorithms and methods to obtain fiber length distributions from a rapid fiber beard testing method. Sub-objective 2b: Characterize the distributions of key cotton properties as well as single cotton fiber measurement. Objective 3: Develop new industrially supported assessment techniques and methods for cotton producers, breeders, and others to evaluate fiber properties at various fiber development or processing stages based on small samples. Sub-objective 3a: Develop assessment techniques and methods to evaluate fiber properties at various fiber development stages based through sampling/measurement of the cotton product at-line and/or in the cotton field. Sub-objective 3b: Develop assessment techniques and methods to evaluate fiber properties and textile products during processing stages of small samples of fiber into textile goods.

1b. Approach (from AD-416)
This research is a comprehensive effort to develop improved or new testing methods that are not currently in the cotton classing system so that the textile manufacturers can more efficiently select and utilize cotton to reduce cost and improve product quality and so that our international customers can get quantified U.S. cotton quality. The value of adding new measurements will be studied by processing large numbers of cotton samples into textile yarns and fabrics. The first objective develops new methods to assess cotton quality. Statistical modeling will be used to characterize short fibers in cotton. An automated image analysis system will be developed to relate seed coat fragments to textile processability and product quality. Microscopy and molecular spectroscopy will be used to develop measurement methods and to characterize fiber micronaire and its components (maturity, fineness). Advanced color and spectroscopic instrumentation, combined with statistical modeling, will be used to measure color and trash components. A room whose environment (moisture level) can be changed and controlled will be used to determine the impacts of moisture on quality assessments and instrumentation. The second objective develops methods for fiber length distributions and single fiber measurements. New beard methods and statistical modeling will be used to obtain fiber length distributions. Automated constant-rate of transverse tensile testers and statistical modeling will be used to monitor key single fiber properties (strength, fineness, etc.) and to establish relationships between single fiber properties and conventional bulk properties. The third objective develops new quality assessment tools for cotton breeders and others to evaluate fiber properties at various fiber development or processing stages based on small samples. New molecular spectroscopy, imaging, and textile instrumentation will be used to assess fiber properties and quality at-line or in the field. Very small scale processing systems (50-100 grams) will be developed and used to assess fiber properties and processability from carding to knitting or weaving on miniature equipment.

3. Progress Report
Conventional and nonconventional fiber length parameters were calculated from a large set of cotton samples. Regression models were developed to improve the accuracy of predicting yarn properties. The advantages of those parameters, including parameters for characterizing short fibers, in predicting yarn properties were compared. A new version of Autorate for dark specks caused by seed coat fragments (SCF) and trash particles in cotton was evaluated and optimized. Testing protocol was determined. Various methods (hand-sort, Shirley, Image Analysis, AFIS and Seed Shearing) in predicting SCF potential were examined. A set of 40 samples was spun into yarns and weaved to fabric to compare Autorate dark speck data to the cotton test results. We determined the feasibility of using Near-Infrared (NIR) systems to measure fiber maturity. Optimum instrumental, sampling, and operational procedures and protocols were developed. The NIR systems yielded very good results for maturity. A spectroscopic method to identify common cotton trash components/non-lint content was demonstrated. NIR spectroscopy was employed to create a spectral library to classify pure botanical trash types, seed meat and field trash. Over 98% of the total trash types were correctly identified. We also compared the NIR spectroscopy and Ultra-Violet Visible (UV-Vis) spectroscopy and determined that NIR yielded a much higher correct identification compared to the UV-Vis. Substantial progress was made in obtaining fiber length distribution from the rapid fiber bundle testing method. A method was developed and refined to acquire optical signals from cotton fiber bundle measurements and to use the signals to construct the fiber staple diagrams and fibrograms. A model was developed to compute the entire length distribution of the original sample. The calculated length parameters from the rapid bundle test method showed good agreements with that from much slower single fiber testing methods. A large set of cottons from both domestic and international sources has been gathered and tested using standard bulk property test methods such as AFIS and HVI. A subset of these cottons is being characterized using single fiber measurement technologies. All of the relevant data derived from testing, plus descriptive information on the origin of the material, has been cataloged and stored as part of a comprehensive database. We determined the capabilities of a new instrument, the Cottonscope™, to rapidly and accurately measure cotton maturity and fineness simultaneously. The effectiveness and accuracy of the measurement were established. The impact of environmental conditions was identified. Very good agreements to fiber micronaire were also observed. Operational protocols for routine measurements were recommended. A processing system for the conversion of ~50g samples into textile products has been established, consisting of modified full-scale carding and drafting equipment and a lab spinning frame. Over 800 samples have been processed on the system. Over 150 samples have been processed on both the miniature-scale and large-scale systems for direct comparison.

4. Accomplishments
1. Portable color spectrophotometer measurements of cotton color in remote locations were developed. Cotton color is an important cotton quality and USDA classes every cotton bale with the Uster® High Volume Instrument (HVI), which costs roughly a quarter million dollars each. Some cotton bales, especially those transported overseas, may change significantly in yellowness from their initial classing values. The industry needed a portable device to measure cotton color on site (such as warehouse or mill). ARS scientists at the Cotton Structure and Quality Research Unit in New Orleans, LA, implemented a program to determine the feasibility of using portable color spectrophotometers to measure cotton fiber yellowness in remote locations. The remote location measurement of cotton fiber yellowness using a portable spectrophotometer was established, with excellent linear agreement, low spectrophotometer standard deviations, and a low number of outliers (less than 6% outliers). The portable instrument measurements were rapid, precise, and accurate, and the results can be transferred to external device for remote field operations. The technology was transferred to Cotton Incorporated, and it is being implemented into their Engineering Fiber Selection System and used by textile mills.

2. An improved miniature-scale textile processing system was established. Any new cotton variety or new ginning technology should be assessed by textile processing. However, a full-scale textile processing requires a lot of raw materials, uses more energy, and is time consuming. A miniature-scale textile processing system has been established at the ARS Cotton Structure and Quality Research Unit in New Orleans, LA. A full-scale carding machine and a drawing frame were modified to process samples as small as 50 grams. This allows researchers, breeders, and ginners quickly make decisions based on yarn properties. Over 800 samples have been processed on the miniature-scale processing system. Samples have been processed for comparison to large-scale processing and in-support of public cotton breeders and both government and academic researchers. Small-scale fabric formation has been optimized and used in direct support of public researchers and the domestic textile industry.

3. A new method was developed to obtain the cotton fiber length distribution rapidly. Cotton fiber length distribution can be obtained by a few relatively slow methods. On the other hand, the beard method is used in the current cotton classing system to rapidly test certain fiber length parameters, but it cannot obtain the fiber length distribution. A new method and algorithm were developed to construct fiber staple diagrams from beard tests to compute fiber length distributions. The computed probability density function (PDF) curves and length parameters showed a good match with that from the much slower single fiber test data. With this method, any length parameter can be calculated. Thus it provides a rapid and detailed evaluation of fiber length quality for cotton researchers, breeders, ginners and textile processors.

4. A set of basic guidelines and best practices for cotton testing laboratories to achieve and maintain environmental conditions were developed. Cotton fiber properties are affected by environmental conditions, particularly relative humidity. Therefore, testing labs are required to maintain under specific environmental conditions. It is important to maintain this condition in a cost and energy efficient way. ARS scientists at the Cotton Structure and Quality Research Unit in New Orleans, LA, carried out a survey of a large number of laboratories around the world, along with extensive testing and site visits, and performed a series of tests to determine the impact of environmental change on fiber test results. Based on those, a set of basic guidelines and best practices were developed to help cotton testing laboratories achieve and maintain required environmental conditions. The American Society for Testing and Materials standard for environmental conditions for textile testing is currently being revised to incorporate the results of this research.

5. A new mathematical model of cotton fiber length distributions was successfully established. As a natural product, cotton fiber length has an asymmetric bell-shaped distribution. No simple mathematical equations could accurately describe cotton distribution. In collaboration with Louisiana State University and the University of New Orleans, a new mathematical model of cotton fiber length distributions was successfully established by using the mixed Weibull function. This model was shown to fit the cotton fiber length distributions well, based on the data of fiber length distributions measured by using AFIS. Based on the work in modeling, algorithms were developed to use the Partial Least Squares (PLS) regression to obtain the original fiber length distribution through the rapid fiber beard test method such as HVI. Any and all fiber length parameters can be calculated from the fiber length distribution. The mathematical description of fiber length distribution facilitates the researches related to fiber length such as fiber breakage and yarn property modeling. The availability of the various length parameters helps breeders, ginners, and yarn spinners to better select and utilize cotton.

Review Publications
Mcavey, K.M., Guan, B., Fortier, C.A., Tarr, M.A., Cole, R.B. 2011. Laser-induced oxidation of cholesterol observed during MALDI-TOF mass spectrometry. Journal of American Society for Mass Spectrometry. 22:659-669. DOI: 10.1007/s13361-011-0074-3.

Rodgers III, J.E., Kang, S., Fortier, C.A., Cui, X., Delhom, C.D., Knowlton, J. 2011. Minimization of operational impacts on spectrophotometer color measurements for cotton. Journal of Cotton Science. 14:240-250.

Chun, D.T., Rodgers III, J.E. 2011. Two ways fungul spores can affect cotton color. Journal of Cotton Science. 15:52-60.

Rodgers III, J.E., Fortier, C.A., Montalvo Jr, J.G., Cui, X., Kang, S., Martin, V. 2010. Near infrared measurment of cotton fiber micronaire by portable near infrared instrumentation. Textile Research Journal. 80(15):1503-1515.

Rodgers III, J.E., Kang, S., Fortier, C.A., Cui, X., Davidonis, G.H., Clawson, E., Boquet, D., Pettigrew, W.T. 2010. Preliminary field measurement of cotton fiber micronaire by portable NIR. Spectroscopy Magazine. 25(9):38-44.

Fortier, C.A., Rodgers Iii, J.E., Santiago Cintron, M., Cui, X., Foulk, J.A. 2011. Identification of cotton and cotton trash components by fourier-transform near-infrared spectropscopy. Textile Research Journal. 81 (3)230-238.

Bel, P., Xu, B. 2011. White specks measured by autorate and the relationship to AFIS fiber data. Journal of Cotton Science. 11(4):59-65.

Delhom, C.D., Byler, R.K. 2011. Performance of a microwave bale moisture content meter. Journal of Agricultural Science and Technology. 5(2):181-187.

Cai, Y., Cui, X., Rodgers III, J.E., Thibodeaux, D.P., Martin, V., Watson, M., Pang, S. 2011. An investigation on different parameters used for characterizing short cotton fibers. Textile Research Journal. 81(3)239-246.

Condon, B.D., Gary, L., Sawhney, A.P., Reynolds, M.L., Slopek, R.P., Delhom, C.D., Hui, D. 2010. Properties of nonwoven fabrics made with UltraClean™ cotton. World Journal of Engineering. 7(2):180-184.

Belmasrour, R., Li, L., Cui, X., Cai, Y., Rodgers III, J.E. 2011. Obtaining Cotton Fiber Length Distributions from the Beard Test Method Part 2 – A New Approach through PLS Regression. Journal of Cotton Science. 15:73-79.

Sun, J., Yao, M., Xu, B., and Bel, P., 2011. Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier. Textile Research Journal. 81(9):902-913.

Hinchliffe, D.J., Meredith Jr, W.R., Delhom, C.D., Thibodeaux, D.P., Fang, D.D. 2011. Elevated growing degree days influence transition stage timing during cotton (Gossypium hirsutum L.) fiber development and result in increased fiber strength. Crop Science. 51:1683-1692. DOI: 10.2135/cropsci2010.10.0569.

Last Modified: 07/25/2017
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