Progress 10/01/23 to 09/30/24
Outputs
PROGRESS REPORT Objectives (from AD-416): 1. Develop on-bale and seed-cotton fiber quality measurements to provide real-time feedback to ginners and warehouses on fiber quality. 1A. Develop and implement methods to measure color and leaf grade on cotton bales as they are produced. 1B. Develop and implement methods to utilize the fiber maturity of seed cotton to improve the fiber quality of ginned lint. 2. Develop methods to detect and remove contaminants from ginned cotton fiber during commercial processing. 2A. Perform fate analyses on plastic contaminants during textile processing. 2B. Implement machine modifications to improve removal of plastic contaminants during processing. 2C. Develop a low-cost contamination detection and removal system. 2D. Use blending and processing parameter changes to improve the processing of cotton samples that have been contaminated with entomological sugars. 3. Develop methods to better measure fiber length distributions and nep content. 3A. Implement a capacitance measurement for producing a more accurate fibrogram from a cotton beard. 3B. Develop techniques to extract nep data from a fiber bundle. 4. Reduce the energy used in the post-ginning commercial processing of cotton. 4A. Study fiber-seed attachment force at a practical scale and identify cultivar-attachment force relationships. 4B. Identify fiber quality parameters that affect fiber frictional characteristics. 5. Identify links between fiber properties, textile construction, and micro-fiber generation during the lifecycle of commercial cotton products. 5A. Construct a device to monitor micro-fibers produced during dry abrasion of fabrics. 5B. Understand the roles of fiber quality, yarn construction and fabric construction in micro-fiber generation during abrasion. The U.S. cotton industry has a number of current problems, including plastic contamination of modules, bales and finished products, increasing competition from man-made fibers, and the need to improve the sustainability of the industry. Over the next five years, we will work to develop methods to remove contaminants from fiber, improve industry sustainability through increased efficiency in the movement of bales from field to market, reduce energy consumption during processing, address concerns about micro-fiber generation, and improve the understanding of length and nep content in cotton to better compete with man-made fibers. Approach (from AD-416): The U.S. cotton industry faces several problems, including contamination, competition from man-made fibers, and the need to improve sustainability. These problems will be addressed by developing methods to remove contaminants, improving the movement of bales from field to market, developing a better understanding of cotton fiber length and fiber entanglements (i.e., nep content), reducing processing energy costs, and understanding micro-fiber generation. The first objective will provide bale quality properties to ginners and warehouses by developing a robotic measurement platform to capture digital images as bales are produced. The images will be used to determine some fiber properties, and the data will allow gins to address quality issues in real-time, creating a more uniform and higher quality cotton that can better compete with man-made fibers. The data will enable warehouses to implement new strategies for the movement of bales from field to market, which will reduce the frequency of bale movements and reduce the energy used in staging bales. Contamination, a major issue impacting U.S. cotton, will be addressed by conducting processing trials that will provide information on the disposition of contaminants during textile processing. This data will be used to help design machinery modifications that aid in the removal of contaminants. Additionally, a low-cost system for the detection and removal of contamination as the fiber is cleaned will also be designed and built. Improved competition with man-made fiber will be achieved in the third objective through improved measurements of cotton properties. Improved fiber length measurement and high-speed measurement of neps, will aid mills in utilizing cotton, and the creation of new measurements will allow for the more predictable processing of cotton. Improving the sustainability of cotton is addressed in the fourth and fifth objectives. Reducing the energy used in the commercial processing of cotton can be achieved by developing practical methods for estimating the fiber-seed attachment force and fiber friction, which will be achieved by monitoring the energy used to gin cotton at a laboratory scale. Developing this knowledge will allow for seed attachment force to be considered when breeding improved cotton varieties. The fifth objective will identify links between fiber and textile properties and the amount of micro-fibers generated during the lifecycle of commercial textiles. Micro-fibers will be collected from dry fabric abrasion experiments, and methods will be developed to characterize and quantify the micro-fibers generated. Progress was made on most of the objectives of this project under National Program 306, Component 2, Non-Food Product Quality and New Uses. Critical vacancies, both new and ongoing, continue to impact progress on some of the objectives of this project; however, strategic collaboration with other ARS research units, university collaborators and stakeholders continue to aid project progress. Efforts to implement fiber quality testing technologies at commercial gins continue. In support of Sub-Objective 1A, the imaging system and algorithm for measuring bale leaf grade was further improved based on data collected in the first year of the trial. The improved imaging system was deployed at two additional commercial gin sites and a total of 30000 bale samples were examined. Work on this objective has yielded stronger collaborations with ginning industry stakeholders and ginning locations and provided insight into the impact of project plan objectives on stakeholders. This work was supported by reimbursable cooperative agreement 6054-44000-080-016-R with Cotton Incorporated. Work on Sub-objective 1B did not progress as originally planned. While analysis of Short-wave infrared (SWIR) data on seed cotton and cotton fibers from a laboratory trial was finalized and compared to Cottonscope instrument maturity data, implementation of a SWIR system to gin sites was not achieved given the difficulty of transferring the SWIR equipment to a commercial site. Contingencies were implemented including application of a Fourier transform-infrared (FT-IR) algorithm to measure fiber seed cotton and lint micronaire and distribution. Previous work with FT-IR algorithms also established its use for measuring fiber maturity. Adaptation of the algorithm at commercial gins using a portable IR system is under exploration. In support of Objective 2, plastic and sugar contamination of modules, bales and finished products continues to negatively impact the value and sustainable image of the cotton industry. Work on this objective was impacted by critical vacancies and conditions at the textile mill. Fate analysis of spike plastic contaminants was finalized and revealed significant removal with the original trial settings (Objective 2a). Additional trials will be performed examining the impact of various airflow settings on contaminant progression through the saw-type cleaners. The use of an air knife will be explored in the future through additional collaboration with USDA ginning laboratories (Objective 2c). Cotton fiber contamination with insect-generated sugars, often referred to as sticky cotton, can highly impact the value and reputation of US grown cotton. Economic studies have shown that cotton value in areas near previous reports of sticky cotton issues can be negatively impact value for years to come. While processing trials were not performed in part due to critical vacancies and textile plant conditioning issues, additional sticky cotton samples have been acquired and examined (Objective 2b). Testing laboratory conditions delayed work on capacitance-based length measurements related to Objective 3. Contingencies for fiber length measurements were thus explored. An ongoing collaboration with Texas Tech University, 6054-44000-080-015S, explored length distribution determinations based on the fibrogram information gathered using the High- Volume Instrument (HVI) (Objective 3a). The HVI instrument examines a beard of cotton fibers in a sample using an optical light. Reduction of the light intensity is turned into a graph called the fibrogram. While the HVI provides a few length measurements based on the fibrogram, prior research has indicated that much more information is contained in the fibrogram curve. Length distribution measurements are typically available only following examination with the Advanced Fiber Information System (AFIS) instrument. Having access to length distribution information following the rapid examination of the HVI would be of great benefit to the cotton industry. There is interest in identifying key length parameters that might be able to predict yarn quality. Several bales have been used to conduct spinning trials. To date, over 15 commercial bales have been subjected to spinning trials with the corresponding array of fiber and yarn quality data. The collaboration is also exploring the viability of using contemporary fiber analysis instruments for capturing hemp fiber parameters such as length, length uniformity, fineness, fineness distribution, color, strength, and trash content. Given the delays in implementing the capacitance length measurements in the modified HVI, work on capacitance- based nep determinations were impacted (Objective 3b). In support of nep measurements, a large number of fiber samples examined with the modified HVI with fibrogram data extraction were also examined using AFIS. Progress on seed attachment force measurements continues in collaboration with the Cotton Ginning Research Unit in Stoneville, Mississippi, under Objective 4a. A 10-saw breeder gin stand outfitted with a conveyer belt feeder system was used to examine a variety of seed cotton samples. Analysis of the energy consumption of the gin stand as recorded by a power logger showed negligible difference in the energy used by the system when various sample sizes were examined. Data noise contributions were minimized, and a wide number of seed cotton samples from a variety or cultivars and growing locations were examined with the system. Energy data is being analyzed to and compared to fiber friction data. Sliver samples were produced from a diverse set of fiber properties and cultivars, although attachment force information has not been determined at this time. The entire sample set has been tested for static fiber cohesion and a sub-set has been tested for dynamic fiber friction in support of Objective 4b. Work on identifying factors that influence microfiber generation in cotton fabrics continues in support of Objective 5b. The microfiber generation and capture system for microfiber generation has been improved to incorporate air capture of generated microfibers. Additional experimental trials with the abrasion tester revealed that most generated microfibers were recovered with the handheld vacuum system, however, the portion of microfibers recovered with the air capture system was not insignificant. Abrasion trials with a wide variety of cotton and natural viscose fabrics revealed higher generation of microfiber for fabrics with a lower twist level. Additional fabrics samples are currently being examined. Further validation of the Fiber Quality Analyzer was performed. Initial studies showed 91% detection of microfibers by the automated imaging system for cotton microfibers from a wide range of micronaire and maturity values. Similar detection levels were observed for viscose and ramie microfibers; however, preliminary results suggest lower detection levels for nylon and polyester fibers. ARS researchers at New Orleans, Louisiana are continuing our efforts to develop characterization and processing methods for industrial hemp fibers in support of agreement 6054-44000-080-013R with Oregon State University. Hemp fiber samples and industrial hemp stems are being provided by partnerships with other ARS researchers and university researchers. This work will provide the most comprehensive collection of hemp fiber processing and quality analyses available in public research, representing over 15,000 samples from over 100 cultivars grown in multiple locations and years. This work will serve as the basis for developing domestic industrial hemp industry quality standards for grading fiber quality. The efforts in hemp research also include the design and production of two instruments: a mechanical laboratory-scale device for fiber extraction, and a fiber-cleaning instrument for sample processing. These instruments also record the processability of each sample through power and current logging. These instruments are the first of their kind developed by an ARS group and have drawn high interest from researchers and private companies. The instruments prompted collaborations and project proposals with Louisiana State University, Rutgers University, and the Pennsylvania Industrial Hemp Engine Organization. Hemp characterization also includes 3D imaging and scanning systems that use artificial intelligence (AI) software to efficiently scan, record, catalog, and identify hemp phenotypic differences among and between lines. Parameters such as individual stem lengths, circumferences, node distances, colors, and total surface areas captured by the software determine phenotypic differences in line to assist partnered collaborators in selecting favorable fiber traits from field plantings in their breeding programs. Artificial Intelligence (AI)/Machine Learning (ML) AI methods have been employed in various aspects of this project. Establishing new methods for detecting and quantifying non-lint content (e.g., botanical trash, leaf grading or plastic contamination) in cotton fiber was identified as a research area that could potentially benefit from AI or ML. To examine the impact of AI-mediated image analysis Convolutional Neural Networks (CNN) have been employed to extract data from collected images. Comparison of the AI-mediated image analysis and traditional analysis methods are currently underway. AI methods have also been employed in the 3D imaging and scanning of hemp stems with the aim of cataloging, and potentially identifying hemp phenotypic differences between lines. ACCOMPLISHMENTS 01 Assessment of cotton fiber bundle micronaire and its distribution with Fourier Transform Infrared (FT-IR) spectroscopy. Micronaire is a popular quality parameter of cotton that provides a sense of the maturity of fineness of the fiber. Although conventional high-volume instrument (HVI) determinations of cotton fiber micronaire of regular cotton fibers are well recognized by the cotton industry, breeders, and researchers, current standard methods cannot make corresponding micronaire measurements for seed cotton of for cotton products such as yarn or fabrics. A group of ARS researchers in New Orleans, Louisiana, developed an innovative FT-IR spectroscopic approach for estimating cotton fiber micronaire using an algorithmic technique. Additionally, the method allows for micronaire distribution determinations, an accomplishment not possible with the HVI instrument. The results could provide cotton researchers with a sensitive and rapid tool for measuring fiber micronaire and for monitoring small differences within the fibers. 02 Generation of textile microfibers from dry abrasion quantified with an automated imaging system. The release and accumulation of microfibers from garments is raising concerns related to the environmental impact of textile apparel. Microfibers are small textile fragments that break away from the larger garment through wear and laundering. While synthetic microfibers have been shown to remain indefinitely in the environment, microfibers from cotton more readily degrade. An important component of highlighting the reduced environmental impact of cotton microfibers requires establishing reproducible methods for generating and quantifying these fragments. A team of ARS scientists in New Orleans, Louisiana, have used a modified abrasion tester and an automated imaging system for quantifying the count and average length of textile microfibers samples. A fiber collection system utilizing a vacuum pump and water trap provided consistent results during replicated trials. Optimal suspension of the microfiber samples in water was achieved by using a sonicating probe prior to the examinations. Ongoing trials with the system could provide researchers with insight on the role of fiber quality, yarn, and fabric construction on the propensity for microfiber generation.
Impacts
(N/A)
Publications
- Liu, Y., Kim, H.J. 2023. Attenuated total reflection FT-IR spectroscopy with soft independent modeling of class analogy/principal component analysis for classifying cotton fiber maturity phenotypes of cotton population composed of various genotypes. Applied Spectroscopy. https:// doi.org/10.1177/00037028231211942.
- Liu, Y., Tao, F., Yao, H., Kincaid, R. 2023. Feasibility study of assessing cotton fiber maturity from near infrared hyperspectral imaging technique. Journal of Cotton Research. p 6:21. https://doi.org/10.1186/ s42397-023-00158-7.
- Kim, H.J., Liu, Y., Zeng, L. 2024. Fourier transform infrared (FT-IR) spectroscopy and simple algorithm analysis for rapid and non-destructive assessment of cotton fiber maturity and crystallinity for plant mapping. Sensors. 24(9):2888. https://doi.org/10.3390/s24092888.
- Liu, Y. 2024. Cotton fiber strength measurement and its relation to structural markers from Fourier transform infrared spectroscopic characterization. Textiles. 4(1):126-137. https://doi.org/10.3390/ textiles4010009.
- Nam, S., Easson, M., Jordan, J.H., He, Z., Zhang, H., Santiago Cintron, M., Chang, S. Unveiling the hidden value of cotton gin waste: natural synthesis and hosting of silver nanoparticles. ACS Omega. 2023:8(34) :31281⿿31292. https://doi.org/10.1021/acsomega.3c03653.
- Armijo, C.B., Delhom, C.D., Whitelock, D.P., Tumuluru, J., Yeater, K.M., Rowe, C., Wanjura, J.D., Sui, R., Holt, G.A., Martin, V.B., Kothari, N. 2023. Evaluation of alternative-design cotton gin lint cleaning machines on fiber length uniformity index. AgriEngineering. 5(4):2123-2138. https:// doi.org/10.3390/agriengineering5040130.
- Hinchliffe, D.J., Thyssen, G.N., Condon, B.D., Zeng, L., Hron, R.J., Madison, C.A., Jenkins, J.N., Mccarty Jr, J.C., Delhom, C.D., Sui, R. 2023. Interrelationships between cotton fiber quality traits and tensile properties of hydroentangled nonwoven fabrics. Journal of Industrial Textiles. https://doi.org/10.1177/15280837231171312.
- Rodgers, J., Santiago Cintron, M. 2023. Molecular and Electronic Spectroscopy Methods. In: Beck, K.R.; Rodgers, J., editors. Analytical Methods for a Textile Laboratory. 4th edition. Research Triangle Park: American Association of Textile Chemists and Colorists. p.215-261.
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Progress 10/01/22 to 09/30/23
Outputs
PROGRESS REPORT Objectives (from AD-416): The U.S. cotton industry has a number of current problems, including plastic contamination of modules, bales and finished products, increasing competition from man-made fibers, and the need to improve the sustainability of the industry. Over the next five years, we will work to develop methods to remove contaminants from fiber, improve industry sustainability through increased efficiency in the movement of bales from field to market, reduce energy consumption during processing, address concerns about micro-fiber generation, and improve the understanding of length and nep content in cotton to better compete with man-made fibers. Objective 1: Develop on-bale and seed-cotton fiber quality measurements to provide real-time feedback to ginners and warehouses on fiber quality. Sub-Objective 1A: Develop and implement methods to measure color and leaf grade on cotton bales as they are produced. Sub-Objective 1B: Develop and implement methods to utilize the fiber maturity of seed cotton to improve the fiber quality of ginned lint. Objective 2: Develop methods to detect and remove contaminants from ginned cotton fiber during commercial processing. Sub-Objective 2A: Perform fate analyses on plastic contaminants during textile processing. Sub-Objective 2B: Implement machine modifications to improve removal of plastic contaminants during processing. Sub-Objective 2C: Develop a low-cost contamination detection and removal system. Sub-Objective 2D: Use blending and processing parameter changes to improve the processing of cotton samples that have been contaminated with entomological sugars. Objective 3: Develop methods to better measure fiber length distributions and nep content. Sub-Objective 3A: Implement a capacitance measurement for producing a more accurate fibrogram from a cotton beard. Sub-Objective 3B: Develop techniques to extract nep data from a fiber bundle. Objective 4: Reduce the energy used in the post-ginning commercial processing of cotton. Sub-Objective 4A: Study fiber-seed attachment force at a practical scale and identify cultivar-attachment force relationships. Sub-Objective 4B: Identify fiber quality parameters that affect fiber frictional characteristics. Objective 5: Identify links between fiber properties, textile construction, and micro-fiber generation during the lifecycle of commercial cotton products. Sub-Objective 5A: Construct a device to monitor micro-fibers produced during dry abrasion of fabrics. Sub-Objective 5B: Understand the roles of fiber quality, yarn construction and fabric construction in micro-fiber generation during abrasion. Approach (from AD-416): The U.S. cotton industry faces several problems, including contamination, competition from man-made fibers, and the need to improve sustainability. These problems will be addressed by developing methods to remove contaminants, improving the movement of bales from field to market, developing a better understanding of cotton fiber length and fiber entanglements (i.e., nep content), reducing processing energy costs, and understanding micro-fiber generation. The first objective will provide bale quality properties to ginners and warehouses by developing a robotic measurement platform to capture digital images as bales are produced. The images will be used to determine some fiber properties, and the data will allow gins to address quality issues in real-time, creating a more uniform and higher quality cotton that can better compete with man-made fibers. The data will enable warehouses to implement new strategies for the movement of bales from field to market, which will reduce the frequency of bale movements and reduce the energy used in staging bales. Contamination, a major issue impacting U.S. cotton, will be addressed by conducting processing trials that will provide information on the disposition of contaminants during textile processing. This data will be used to help design machinery modifications that aid in the removal of contaminants. Additionally, a low-cost system for the detection and removal of contamination as the fiber is cleaned will also be designed and built. Improved competition with man-made fiber will be achieved in the third objective through improved measurements of cotton properties. Improved fiber length measurement and high-speed measurement of neps, will aid mills in utilizing cotton, and the creation of new measurements will allow for the more predictable processing of cotton. Improving the sustainability of cotton is addressed in the fourth and fifth objectives. Reducing the energy used in the commercial processing of cotton can be achieved by developing practical methods for estimating the fiber-seed attachment force and fiber friction, which will be achieved by monitoring the energy used to gin cotton at a laboratory scale. Developing this knowledge will allow for seed attachment force to be considered when breeding improved cotton varieties. The fifth objective will identify links between fiber and textile properties and the amount of micro-fibers generated during the lifecycle of commercial textiles. Micro-fibers will be collected from dry fabric abrasion experiments, and methods will be developed to characterize and quantify the micro-fibers generated. Progress was made on all objectives of this project under National Program 306, Component 2, Non-Food Product Quality and New Uses. Delays in the work due to the impact of the COVID-19 pandemic and maximized telework, as well as critical vacancies, have impacted the overall progress but there has been substantial progress on all objectives of the research project. Progress in achieving some objectives of the project plan has been made through collaborations with ARS, university, and stakeholder projects to maximize efficiency. ARS researchers at New Orleans, Louisiana, have collaborated to address the measurement of energy during cotton processing and to develop ways to reduce plastic contamination in cotton. Partnering with collaborators allows for an industry-wide approach to solving problems and has led to enhanced collaborations and visibility of the research project. In support of Objective 1, we have conducted additional field testing of our cotton bale quality measurement system, previously developed in a new commercial gin. We significantly refined and simplified the design to lower cost and increase reliability. The new design collected bale measurements on over 7,000 commercial samples during the trial. We designed and carried out a new approach to the leaf grade algorithm to improve results and analysis time. A redesigned lighting system reduced glare and simplified the construction of the imaging system while increasing consistency between images. The 2022-23 ginning season was the most successful test of the system thus far. This work was supported by reimbursable cooperative agreement number 6054-44000-080-016-R with Cotton Incorporated. Work on Sub-objective 1B has not progressed as well as desired. The utilization of fiber maturity to improve the quality of ginned lint was hampered by challenges in measuring fiber maturity in seed cotton samples which contain significant non-lint content. However, we are exploring a contingency plan to utilize field production data such as planting date, growing degree days, and canopy temperature history is to provide the required data in an alternate form. Despite environmental conditioning issues in the textile pilot plant that have reduced the capacity to carry out some processing trials, we were able to conduct collaborative research trials at a research ginning facility on plastic contamination. This allowed the progress on Objective 2 to remain on schedule. We subjected different plastic contaminant sizes, thicknesses, and materials to processing through cylinder and saw-type cleaners. In this work, we identified various physical parameters of the plastic contaminants, such as stiffness, thickness, and cohesion, as influencing the ease of removal from cotton during processing. Stakeholders have encouraged this collaborative work and it is showing the potential to greatly reduce the level of plastic contamination that end up in finished textile products. Large-scale processing trials for cotton contaminated with excessive entomological sugars, so-called ⿿sticky cotton, were delayed due to the environmental conditioning issues in the textile pilot plant. We have collected additional samples of suspected sticky cotton and tested these additional materials using both the sticky cotton thermodetector and the mini-card in preparation for the future trials. The textile industry continues to request improvements to measurement of fiber length distributions and nep (fiber entanglements) content (Objective 3) due to the critical importance of these parameters. We continue to collect a large number of diverse fiber samples and carry out characterization with traditional methods and novel capacitance-based techniques. A new instrument, the OFDA 4000, was added to the testing protocol this year. New capacitance-based measurements are able to measure length distributions with higher resolution than the traditional electro-optical methods but are slower. The OFDA 4000 has reduced sample preparation time than traditional capacitance-based technique and shows promise, with modified software, to be an intermediate option. Achieving the goal of a capacitance-based technique which has the speed of the traditional electro-optical method remains viable. We have tested additional samples with a wide range of length distributions and nep contents using capacitance-based measurements as well as traditional techniques. Additional testing has been carried out through collaborations with researchers at Texas Tech University in Lubbock, Texas as part of Project Number 6054-44000-080-015S. Analysis to compare the results between new methods and the reference electro-optical approach continue and we are working on a digital signal processing (DSP) approach to identify discrete increases in capacitance which may correlate with the presence of neps. Progress on reducing the energy used during commercial processing of cotton (Objective 4) continues. Much of this work is progressing thanks to informal collaborations with research at the Cotton Ginning Research Unit in Stoneville, Mississippi. The datalogger for measurement of energy consumption which was originally designed within this project has been significantly improved upon by a collaborator at the Cotton Ginning Research Unit. Most significantly, the collaborator has developed an algorithm which automates the extraction of the useful data from processing trials. This system will be incorporated into the textile processing line, both full-scale and miniature, within the textile pilot plant and allow for a large amount of data to be collected as a co- product of all research processing trials. Static friction tests have been conducted on a set of samples representing diversity in cultivar and growing locations which allows for fiber quality parameter differences due to genetics, environment, and the interaction of genetics and environment (G x E) to be considered during the analysis of the relationship between fiber quality parameters and frictional characteristics. It is believed that this will further explain why different samples consume varying amount of energy during processing. We have developed a microfiber generation and collection system (Objective 5) which will allow the cotton industry to address consumer concerns about microfibers. The appropriate parameters for the abrasion tester have been identified which allow for consistent results during replicated trials. We have developed a sonication process to enhance the efficiency of microfiber measurements through the FQA-360 instrument by using ultrasonic vibrations to ensure well-distributed samples. A fiber collection system utilizing a vacuum pump and water trap has been developed and is providing consistent results during replicated trials and allowing for work to progress on relating the role of fiber quality, yarn, and fabric construction on influencing the propensity for microfibers to be generated during consumer use of textiles. We have published the National Cotton Variety Test data and Legacy on- farm variety trial (OVT) data on a public-facing dashboard within AgCROS (Agricultural Collaborative Research Outcomes System) as part of the Partnerships for Data Innovation (PDI) effort. Part of this work has led to the beta-testing of a new version of the cotton production management software, Cotman, which is being tested during the 2023 cotton season. The revised Cotman software is compatible with modern web-browsers and allows for storage of crop data within AgCROS. The Cotman program serves a Decision Support Tool (DST) to aid in production decisions. The revised DST is anticipated to provide the field data which will allow maturity of seed cotton to be transmitted to the gin prior to ginning to allow for the gin to maintain fiber quality during ginning (sub-objective 1B). We continue to establish a program to develop characterization methods for industrial hemp fibers. Hemp fiber samples and industrial hemp stems have been provided through partnerships with ARS researchers in Geneva, New York. This work is intended to provide the largest collection of hemp fiber quality available and currently over 2,000 samples representing over 100 cultivars are in the process of being characterized. This work will provide the basis for the development of a viable domestic industrial hemp industry. Artificial Intelligence (AI)/Machine Learning (ML) AI methods have been employed in the execution of this project. Image analysis techniques have been used to quantify non-lint content in images of cotton fiber. Convolutional neural networks (CNN) have been employed to extract data from complex images. The results from traditional image analysis as well as CNN have been employed to build a dataset which has been subjected to analysis by support vector machines (SVM) to develop an automated approach to image analysis and assignment of grades which are traditionally assigned by human operators using a variety of subjective parameters. Work was done using local computing hardware and has been optimized for deployment on single-board microprocessors. These AI methods have allowed for research projects to be achieved and has led to the expansion of the work with funding support from stakeholders and interest in eventual technology transfer for adoption by industry. ACCOMPLISHMENTS 01 Classification of cotton fiber maturity genotypes with Fourier Transform Infrared (FT-IR) spectroscopy. The development of the secondary cell wall of a cotton fiber through the deposition of cellulose is referred to as maturity. Cotton fiber maturity is an important trait for the textile industry as it impacts fiber strength, entanglements, and dyeing. In general, cotton fiber geneticists have identified a mutation that prevents fibers from maturing. Crossing the immature mutant with standard, wild-type, cottons results in offspring that either have the mutation causing immature fibers or do not. Researchers in New Orleans, Louisiana, developed an innovative data analysis approach for Fourier Transform Infrared (FT-IR) spectroscopic measurements of the fiber samples. With the help of a statistical technique called Soft Independent Modeling of Class Analogy of Principal Component Analysis (SIMCA/PCA) we could determine the difference between fibers from plants with and without the mutation. Conventional fiber measurements were unable to detect differences. The results could provide cotton researchers a sensitive and rapid tool for monitoring subtle differences within the fibers and further for understanding and quickly evaluate the impact of mutations that may affect fiber maturity. 02 Development of cotton leaf grade algorithm for use independent of cotton classification systems. Practically every bale of cotton is classified and graded by the USDA-Agricultural Marketing Service including the assignment of leaf grade, a measure of non-fiber content. Cotton classification takes place after samples are shipped from the gin to a Classing Office and results are delivered seven to ten days later. Leaf grade is determined using a non-public proprietary system. ARS researchers in New Orleans, Louisiana, have developed an independent method that can predict the assigned leaf grade correctly on 87% of the samples, as tested in a commercial gin. Because this method is fast, it will allow for correction of problems during the ginning process quickly. It may also reduce bale handling costs, labor and energy, by providing data for cotton warehouse organization more efficiently than the current delayed process.
Impacts
(N/A)
Publications
- Santiago-Cintron, M., Hinchliffe, D.J., Hron, R. 2023. Comparison of focal plane array FTIR pixel binning size for the nondestructive determination of cotton fiber maturity distributions. Fibers and Polymers. 24:1473-1482. https://doi.org/10.1007/s12221-023-00149-0.
- Delhom, C.D., Wanjura, J.D., Hequet, E.F. 2022. Cotton fiber elongation ⿿ a review. Journal of Textile Institute. Article 2157940. https://doi.org/ 10.1080/00405000.2022.2157940.
- Delhom, C.D., Wanjura, J.D., Pelletier, M.G., Holt, G.A., Hequet, E.F. 2023. Investigation into a practical approach and application of cotton fiber elongation. Journal of Cotton Research. 6. Article 2. https://doi. org/10.1186/s42397-023-00139-w.
- Kim, H.J., Delhom, C.D., Jones, D.C., Xu, B. 2023. Comparative analyses of a maturity distributional parameter evaluating immature fibre contents by reference microscopic analysis and conventional fibre measurement methods. Journal of Textile Institute. Article 2204460. https://doi.org/10.1080/ 00405000.2023.2204460.
- Hardin, R.G., Barnes, E.M., Delhom, C.D., Wanjura, J.D., Ward, J.K. 2022. Internet of things: cotton production and processing. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107294.
- Zeng, L., Wu, J., Delhom, C.D. 2022. Genetic improvement of lint yield by selections of within-boll yield components based on commonality analysis. Euphytica. 218. https://doi.org/10.1007/s10681-022-03071-3.
- Naoumkina, M.A., Florane, C.B., Kim, H.J., Santiago Cintron, M., Delhom, C. D. 2023. Overexpression of an actin Gh_D04G0865 gene in cotton reduced fineness of fiber. Crop Science. 63:740-749. https://doi.org/10.1002/csc2. 20888.
- Edwards, J.V., Prevost, N.T., Santiago Cintron, M. 2023. A comparison of hemostatic activities of zeolite-based formulary finishes on cotton dressings. Journal of Functional Biomaterials. 14(5):255. https://doi.org/ 10.3390/jfb14050255.
- Hron, R.J., Hinchliffe, D.J., Thyssen, G.N., Condon, B.D., Zeng, L., Santiago Cintron, M., Jenkins, J.N., Mccarty Jr, J.C., Sui, R. 2023. Interrelationships between cotton fiber quality traits and fluid handling and moisture management properties of nonwoven textiles. Textile Research Journal. https://doi.org/10.1177/00405175221132011.
- Kim, H.J., Liu, Y., Thyssen, G.N., Naoumkina, M.A., Frelichowski, J.E. 2023. Phenomics and transcriptomics analyses reveal deposition of suberin and lignin in the short fiber cell walls produced from a wild cotton species and two mutants. PLOS ONE. 18. Article e0282799. https://doi.org/ 10.1371/journal.pone.0282799.
- Delhom, C.D., Van Der Sluijs, M.J., Wanjura, J.D., Thomas, J.W. 2023. Evaluation of practices to unwrap round cotton modules. Journal of Cotton Science. 27:90-101. https://doi.org/10.56454/IPOU8527.
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Progress 10/01/21 to 09/30/22
Outputs
PROGRESS REPORT Objectives (from AD-416): The U.S. cotton industry has a number of current problems, including plastic contamination of modules, bales and finished products, increasing competition from man-made fibers, and the need to improve the sustainability of the industry. Over the next five years, we will work to develop methods to remove contaminants from fiber, improve industry sustainability through increased efficiency in the movement of bales from field to market, reduce energy consumption during processing, address concerns about micro-fiber generation, and improve the understanding of length and nep content in cotton to better compete with man-made fibers. Objective 1: Develop on-bale and seed-cotton fiber quality measurements to provide real-time feedback to ginners and warehouses on fiber quality. Sub-Objective 1A: Develop and implement methods to measure color and leaf grade on cotton bales as they are produced. Sub-Objective 1B: Develop and implement methods to utilize the fiber maturity of seed cotton to improve the fiber quality of ginned lint. Objective 2: Develop methods to detect and remove contaminants from ginned cotton fiber during commercial processing. Sub-Objective 2A: Perform fate analyses on plastic contaminants during textile processing. Sub-Objective 2B: Implement machine modifications to improve removal of plastic contaminants during processing. Sub-Objective 2C: Develop a low-cost contamination detection and removal system. Sub-Objective 2D: Use blending and processing parameter changes to improve the processing of cotton samples that have been contaminated with entomological sugars. Objective 3: Develop methods to better measure fiber length distributions and nep content. Sub-Objective 3A: Implement a capacitance measurement for producing a more accurate fibrogram from a cotton beard. Sub-Objective 3B: Develop techniques to extract nep data from a fiber bundle. Objective 4: Reduce the energy used in the post-ginning commercial processing of cotton. Sub-Objective 4A: Study fiber-seed attachment force at a practical scale and identify cultivar-attachment force relationships. Sub-Objective 4B: Identify fiber quality parameters that affect fiber frictional characteristics. Objective 5: Identify links between fiber properties, textile construction, and micro-fiber generation during the lifecycle of commercial cotton products. Sub-Objective 5A: Construct a device to monitor micro-fibers produced during dry abrasion of fabrics. Sub-Objective 5B: Understand the roles of fiber quality, yarn construction and fabric construction in micro-fiber generation during abrasion. Approach (from AD-416): The U.S. cotton industry faces several problems, including contamination, competition from man-made fibers, and the need to improve sustainability. These problems will be addressed by developing methods to remove contaminants, improving the movement of bales from field to market, developing a better understanding of cotton fiber length and fiber entanglements (i.e., nep content), reducing processing energy costs, and understanding micro-fiber generation. The first objective will provide bale quality properties to ginners and warehouses by developing a robotic measurement platform to capture digital images as bales are produced. The images will be used to determine some fiber properties, and the data will allow gins to address quality issues in real-time, creating a more uniform and higher quality cotton that can better compete with man-made fibers. The data will enable warehouses to implement new strategies for the movement of bales from field to market, which will reduce the frequency of bale movements and reduce the energy used in staging bales. Contamination, a major issue impacting U.S. cotton, will be addressed by conducting processing trials that will provide information on the disposition of contaminants during textile processing. This data will be used to help design machinery modifications that aid in the removal of contaminants. Additionally, a low-cost system for the detection and removal of contamination as the fiber is cleaned will also be designed and built. Improved competition with man-made fiber will be achieved in the third objective through improved measurements of cotton properties. Improved fiber length measurement and high-speed measurement of neps, will aid mills in utilizing cotton, and the creation of new measurements will allow for the more predictable processing of cotton. Improving the sustainability of cotton is addressed in the fourth and fifth objectives. Reducing the energy used in the commercial processing of cotton can be achieved by developing practical methods for estimating the fiber-seed attachment force and fiber friction, which will be achieved by monitoring the energy used to gin cotton at a laboratory scale. Developing this knowledge will allow for seed attachment force to be considered when breeding improved cotton varieties. The fifth objective will identify links between fiber and textile properties and the amount of micro-fibers generated during the lifecycle of commercial textiles. Micro-fibers will be collected from dry fabric abrasion experiments, and methods will be developed to characterize and quantify the micro-fibers generated. Progress was made on all objectives of this project under National Program 306, Component 2, Non-Food Product Quality and New Uses. Despite nearly half a year of maximized telework, there has been significant progress on the research project. Progress in achieving some objectives of the project plan has been made through collaborations with ARS, university, and stakeholder projects. ARS researchers have collaborated to address the measurement of energy during cotton processing and to develop ways to reduce plastic contamination in cotton. Partnering with collaborators allows for an industry-wide approach to solving problems. The cotton bale quality measurement system being developed (Objective 1) has undergone field testing in a commercial gin, and the design has been refined and simplified to lower cost and increase reliability. The revised design collected bale measurements on over 3,000 commercial samples during an abbreviated trial. Further improvements have been made to the leaf grade algorithm and lighting issues in the imaging system have been addressed to reduce glare and increase consistency between images. Additional trials are planned for the 2022-23 ginning season. One critical goal for the past year was to image more diverse bales and the 2021-22 season provided the most diverse set of samples collected so far in this project. Work on Sub-objective 1B, the utilization of fiber maturity to improve the quality of ginned lint, has not progressed as well as other objectives. Additional samples have been gathered; however, ginning trials have not been conducted. Data from previous work on the ginning of cottons with different maturity levels will be utilized to keep the progress on this sub-objective on schedule. ARS researchers at New Orleans, Louisiana, conducted processing trials in the textile pilot plant and at a research ginning facility to address plastic contamination in ginned lint (Objective 2). We tested multiple weights, sizes, and thicknesses of plastic to observe the aerodynamic behavior of the materials during processing. The results from these processing trials and previously conducted trials will guide the modifications of processing and machine parameters to enhance the passive removal of plastic contaminants during cotton processing. Trends were observed indicating that the plastic contaminant's mechanical properties influenced the contaminant's interaction with the airflow and machine components. We examined yarns which were made with contaminated lint. Although from a mass balance point, the majority, over 90%, of plastic contamination is removed, on a number basis the number of pieces of contamination increases during processing due to the conversion of larger pieces into numerous small pieces. We discovered that the small pieces are more fiber- like in behavior and therefore the difficulty of removal increases. The smaller particles did not impact open-end spinning systems' production efficiency as much as ring spinning systems. The quality of yarn, for both appearance and strength, was significantly degraded by the presence of plastic contaminants. Processing trials for cotton contaminated with excessive entomological sugars (sub-objective 2D) were delayed due to personnel shortages and equipment issues during the period of maximized telework. It is anticipated that we can conduct these trials in early FY23. The measurement of fiber length distributions and nep (fiber entanglements) content are of critical importance to the textile industry. To improve the measurement of these properties (Objective 3), we collected a large number of fiber samples which have been tested using traditional methods and novel capacitance-based techniques. The capacitance-based measurements are able to measure length distributions with higher resolution than the traditional electro-optical methods. Samples with a wide range of length distributions and nep contents have been tested using capacitance-based measurements. The length distribution results are being compared to traditional methods to assess the potential for more information to be gathered from the higher resolution capacitance measurements. We are analyzing the raw data from the capacitance signal for fluctuations which may be due to corresponding mass changes within the sample due to the presence of fiber entanglements known as neps. We are making progress on the reduction of energy used in the commercial processing of cotton (Objective 4) thanks to collaborations with ARS research unit in Stoneville, Mississippi. We developed a single board microprocessor data logger to record the energy consumption of a tabletop gin. The data logger design was improved upon by a collaborator from the Cotton Ginning Research Unit to increase the sampling rate. Data collected during benchtop ginning trials initially appeared too noisy to be of value; however, statistical analysis of the data revealed that useful information was captured, and software scripts are being developed to streamline the extraction of useful data from the energy trials. Results from these trials are being used to identify sources of error to allow for the efficient measuring of cotton-seed attachment force through tabletop ginning. Combining ginning energy with static and dynamic friction measurements that have been collected will provide a new tool for understanding the interaction of many fiber properties and their role in determining energy consumption during processing. Understanding microfibers and the role of textiles in generating microfibers has remained an area of interest for the textile industry (Objective 5). We designed a system to operate a Martindale tester inside of an enclosed area with the ability to collect microfibers that are generated during the abrasion of fabrics. We conducted studies to identify appropriate settings for abrasion pressure and the number of cycles. Fabrics of known fiber content and construction have been collected to create a set of materials to establish baseline conditions. We are beginning work on a method to characterize the microfibers being generated for their length, diameter, and other physical attributes. Significant progress has been made in developing a dashboard for accessing National Cotton Variety Test data as well as current and archived on-farm variety trial data provided by collaborators at Cotton Incorporated. This work was carried out as part of the ⿿Partnerships for Data Innovation effort and allows for data to be stored with AgCROS (Agricultural Collaborative Research Outcomes System). Additionally, we made progress in establishing a program to develop methods to characterize industrial hemp fibers. We obtained industrial hemp fiber samples through ARS, university, and industry partners. These fiber samples represent the wide range of characteristics common to industrial hemp, including length, fineness, and non-fibrous content. Work is underway to assess the ability of existing fiber test methods to characterize hemp and identify the areas most in need of research to allow for the development of a domestic industrial hemp production platform. ACCOMPLISHMENTS 01 Visualization of cotton leaf grade measurements. The cotton industry has adopted instrument rating of bales for non-lint content, otherwise known as leaf grade. Originally a visual observation, leaf grade is now determined instrumentally. ARS researchers in New Orleans, Louisiana, have developed an in-house data visualization and look-up table that maps the relationship of the total number of particles and percentage of the sample that is not lint with one of eight industry-defined leaf grades. This information is integral for developing new automated leaf grade measurement systems and will be available to researchers through a Partnerships for Data Innovation dashboard.
Impacts
(N/A)
Publications
- Kim, H.J., Delhom, C.D., Liu, Y., Jones, D.C., Xu, B. 2021. Characterizations of a distributional parameter that evaluates contents of immature fibers within and among cotton samples. Cellulose. 28:9023-9038. https://doi.org/10.1007/s10570-021-04135-8.
- Funk, P.A., Thomas, J.W., Yeater, K.M., Armijo, C.B., Whitelock, D.P., Wanjura, J.D., Delhom, C.D. 2022. Saw thickness impact on cotton gin energy consumption. Applied Engineering in Agriculture. 38(1):15-21. https:///doi.org/10.13031/aea.14535.
- He, Z., Liu, Y., Kim, H.J., Tewolde, H., Zhang, H. 2022. Fourier transform infrared spectral features of plant biomass components during cotton organ development and their biological implications. Journal of Cotton Research. 5:11. https://doi.org/10.1186/s42397-022-00117-8.
- Edwards, J.V., Prevost, N.T., Yager, D., Mackin, R.T., Santiago Cintron, M. , Chang, S., Condon, B.D., Dacorta, J. 2022. Ascorbic acid as an adjuvant to unbleached cotton promotes antimicrobial activity in spunlace nonwovens. International Journal of Molecular Sciences. https://doi.org/10.3390/ ijms23073598.
- He, Z., Liu, Y. 2021. Fourier transform infrared spectroscopic analysis in applied cotton fiber and cottonseed research: a review. Journal of Cotton Science. 25(2):167-183.
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Progress 10/01/20 to 09/30/21
Outputs
Progress Report Objectives (from AD-416): The U.S. cotton industry has a number of current problems, including plastic contamination of modules, bales and finished products, increasing competition from man-made fibers, and the need to improve the sustainability of the industry. Over the next five years, we will work to develop methods to remove contaminants from fiber, improve industry sustainability through increased efficiency in the movement of bales from field to market, reduce energy consumption during processing, address concerns about micro-fiber generation, and improve the understanding of length and nep content in cotton to better compete with man-made fibers. Objective 1: Develop on-bale and seed-cotton fiber quality measurements to provide real-time feedback to ginners and warehouses on fiber quality. Sub-Objective 1A: Develop and implement methods to measure color and leaf grade on cotton bales as they are produced. Sub-Objective 1B: Develop and implement methods to utilize the fiber maturity of seed cotton to improve the fiber quality of ginned lint. Objective 2: Develop methods to detect and remove contaminants from ginned cotton fiber during commercial processing. Sub-Objective 2A: Perform fate analyses on plastic contaminants during textile processing. Sub-Objective 2B: Implement machine modifications to improve removal of plastic contaminants during processing. Sub-Objective 2C: Develop a low-cost contamination detection and removal system. Sub-Objective 2D: Use blending and processing parameter changes to improve the processing of cotton samples that have been contaminated with entomological sugars. Objective 3: Develop methods to better measure fiber length distributions and nep content. Sub-Objective 3A: Implement a capacitance measurement for producing a more accurate fibrogram from a cotton beard. Sub-Objective 3B: Develop techniques to extract nep data from a fiber bundle. Objective 4: Reduce the energy used in the post-ginning commercial processing of cotton. Sub-Objective 4A: Study fiber-seed attachment force at a practical scale and identify cultivar-attachment force relationships. Sub-Objective 4B: Identify fiber quality parameters that affect fiber frictional characteristics. Objective 5: Identify links between fiber properties, textile construction, and micro-fiber generation during the lifecycle of commercial cotton products. Sub-Objective 5A: Construct a device to monitor micro-fibers produced during dry abrasion of fabrics. Sub-Objective 5B: Understand the roles of fiber quality, yarn construction and fabric construction in micro-fiber generation during abrasion. Approach (from AD-416): The U.S. cotton industry faces several problems, including contamination, competition from man-made fibers, and the need to improve sustainability. These problems will be addressed by developing methods to remove contaminants, improving the movement of bales from field to market, developing a better understanding of cotton fiber length and fiber entanglements (i.e., nep content), reducing processing energy costs, and understanding micro-fiber generation. The first objective will provide bale quality properties to ginners and warehouses by developing a robotic measurement platform to capture digital images as bales are produced. The images will be used to determine some fiber properties, and the data will allow gins to address quality issues in real-time, creating a more uniform and higher quality cotton that can better compete with man-made fibers. The data will enable warehouses to implement new strategies for the movement of bales from field to market, which will reduce the frequency of bale movements and reduce the energy used in staging bales. Contamination, a major issue impacting U.S. cotton, will be addressed by conducting processing trials that will provide information on the disposition of contaminants during textile processing. This data will be used to help design machinery modifications that aid in the removal of contaminants. Additionally, a low-cost system for the detection and removal of contamination as the fiber is cleaned will also be designed and built. Improved competition with man-made fiber will be achieved in the third objective through improved measurements of cotton properties. Improved fiber length measurement and high-speed measurement of neps, will aid mills in utilizing cotton, and the creation of new measurements will allow for the more predictable processing of cotton. Improving the sustainability of cotton is addressed in the fourth and fifth objectives. Reducing the energy used in the commercial processing of cotton can be achieved by developing practical methods for estimating the fiber-seed attachment force and fiber friction, which will be achieved by monitoring the energy used to gin cotton at a laboratory scale. Developing this knowledge will allow for seed attachment force to be considered when breeding improved cotton varieties. The fifth objective will identify links between fiber and textile properties and the amount of micro-fibers generated during the lifecycle of commercial textiles. Micro-fibers will be collected from dry fabric abrasion experiments, and methods will be developed to characterize and quantify the micro-fibers generated. Progress was made by ARS scientists at New Orleans, Louisiana on all objectives of this project under National Program 306, Component 2, Non- Food Product Quality and New Uses. There has been significant progress on the objectives of this research project, despite the impact of maximized telework. The cotton bale quality measurement system being developed by ARS scientists at New Orleans, Louisiana (Objective 1) has undergone additional modifications to reduce the number of moving components and simplify the electronics. The revised design will be more easily reproduced and adapted to different gin layouts. In addition, a second system has been built by ARS scientists at New Orleans, Louisiana. The systems will collect color and leaf grade information on bales from two commercial gins during the 2021-22 ginning season. The addition of a second commercial gin partner will allow data to be collected by ARS scientists at New Orleans, Louisiana from two distinct geographical area and gin facilities. As part of this objective, two approaches have been developed by ARS scientists at New Orleans, Louisiana to calculate leaf grade (related to the bales non-lint content) from bale optical images. The first method uses image analysis software to analyze each image for the number and size of non-lint particles, like the system used by the Agricultural Marketing Service (AMS) to classify cotton. The second method utilizes a neural network and machine learning approach, in which a set of images and AMS data are used to train the system. The two methods have been developed by ARS scientists at New Orleans, Louisiana using images collected from one cotton gin during the previous ginning season. The two approaches and a color measurement approach under development by ARS scientists at New Orleans, Louisiana will be tested against images collected from the two planned 2021-2022 gins. The additional location will provide a wider distribution of results, as the initial set did not contain as wide a distribution of leaf grades as desired. During the past year, processing trials were conducted by ARS scientists at New Orleans, Louisiana to address plastic contamination in ginned lint (Objective 2). Various plastics, such as commercial module wrap and common plastic contaminants (e.g., grocery store bags) have been obtained. Carding trials were carried out by ARS scientists at New Orleans, Louisiana in which plastic contaminants of known count and mass were added to clean ginned lint, and waste streams were monitored at each cleaning point in the opening line. The amount of contaminant used in the processing trials was based on data gathered from industry surveys. The waste was examined by ARS scientists at New Orleans, Louisiana for plastic contaminants by both weight and number to establish the effectiveness of current fiber cleaning system to remove the plastic pieces. Results will guide the modification of parameters to enhance the maximum amount of passive removal of plastic contaminants during cotton processing. To improve the measurement of fiber length distributions (Objective 3), fiber samples containing various length distributions and nep contents (fiber entanglements) were collected by ARS scientists at New Orleans, Louisiana. The Uster HVI instrument measures fiber length distributions by light adsorption. However, the method is unable to measure the shorter fibers of a sample accurately. As an alternative approach, measurement of capacitance is being studied by ARS scientists at New Orleans, Louisiana. For this, a few outdated High-Volume Instruments (HVIs) that have optical modules to measure length distributions have been sourced. These instruments will provide donor components that can be used to help design and test alternative measurement approaches. The modified instruments offer a testbed for standard length measurement components to be replaced with novel components equipped with customized sensors and software. ARS scientists at New Orleans, Louisiana have performed friction measurements on a large set of cotton samples. This work was completed by ARS scientists at New Orleans, Louisiana to study the relationship between fiber friction and the energy consumption of fiber processing (Objective 4). A low-cost, compact energy monitor has been built by ARS scientists at New Orleans, Louisiana. The energy monitor can be mounted on various textile processing machines to record the current and power used. The energy monitor can also be used by ARS scientists at New Orleans, Louisiana on breeder-scale tabletop cotton gins to measure power differences among samples, as well as logging the power consumption of either individual motors or assemblies of textile processing equipment. A feed control mechanism for tabletop cotton gins will enable measurement of the energy needed to gin the cotton sample while minimizing operator influence. The system will also be able to evaluate the effect of feed rates and seed roll densities. A literature search has been conducted by ARS scientists at New Orleans, Louisiana on feed control approaches, but the system is not designed or constructed yet. The final design and construction of the breeder-scale gin feed control is a priority for the next year. To allow the collection of cotton micro-fiber generation by dry fabric shedding (Objective 5), an enclosure and air-wash system has been designed by ARS scientists at New Orleans, Louisiana to use with a Martindale abrasion tester to generate and collect micro-fibers. The chamber was designed to examine different techniques for their ability to create micro-fibers. ARS scientists at New Orleans, Louisiana used an air- wash system that will collect and capture the micro-fibers, which can be compared with the (weight) lost from the fabric during abrasion. The material captured by the air-wash system can be examined by ARS scientists at New Orleans, Louisiana both quantitatively and qualitatively. An instrument typically used to measure the number and length characteristics of wood pulp by optical methods will be modified by ARS scientists at New Orleans, Louisiana to examine the microfibers. Trials have shown that the instrument shows potential for characterizing the material collected by the micro-fiber generation test protocol. Progress in achieving some objectives of the project plan has also been made through collaborations with related ARS, university, and stakeholder projects. ARS scientists at New Orleans, Louisiana have collaborated to reduce plastic contamination in cotton by partnering to develop ideas, concepts, and studies to address the issue at the field, gin, and textile mill level. This industry-wide approach is allowing for rapid progress in understanding how plastic contamination enters the cotton supply chain and evaluating approaches to both prevent and remove plastic contaminants. ARS scientists at New Orleans, Louisiana have also made significant progress in developing a comprehensive approach to archive cotton research data as part of the Partnerships for Data Innovation� effort. Data collection tools have been developed by ARS scientists at New Orleans, Louisiana to collect field data from variety and agronomic trials using a Survey123 application, which directly stores the data in AgCROS (Agricultural Collaborative Research Outcomes System). This eliminates the need to reenter data and allows direct data sharing among collaborators. In addition, dashboards have been developed by ARS scientists at New Orleans, Louisiana to display fiber quality, yield, and loan value information from archived variety trials, as well as the National Cotton Variety Tests. These dashboards allow viewers to develop custom filters and visually compare results across locations, varieties, and crop years in a more dynamic and informative way than the traditional static text files, while also bringing data from multiple sources together into a single interface. Record of Any Impact of Maximized Teleworking Requirement: The Covid-19 crisis and maximum telework has had some impact on the project and related collaborative activities by ARS scientists at New Orleans, Louisiana. The crisis has limited the numbers of employees and hours in the laboratories and testing mills, which has allowed some work to continue but not with the thoroughness or detail that was planned. Additionally, the lack of daily attention to the infrastructure has resulted in a few flooding events that have damaged equipment and there as been difficulty in maintaining the precise environmental conditions needed to process cotton (temperature and humidity). Also, some repair parts have been difficult to source and install during this time. ACCOMPLISHMENTS 01 New cotton research data collection and visualization tools. Traditionally, much of the data collected by ARS researchers at New Orleans, Louisiana, in the field is collected with pen and paper. This pen and paper approach is vulnerable to transcription errors during data reentry. A data collection system has been developed by ARS researchers in New Orleans, Louisiana; Stoneville, Mississippi; and Lubbock, Texas, that allows for the direct recording of field data on a phone, tablet, or computer. Data is initially stored on the local collection device but is then uploaded into the Agricultural Collaborative Research Outcomes System (AgCROS) database, where it can be stored, shared with others, or analyzed. In addition to the collection tools, standardized data dashboards have been created by ARS scientists at New Orleans, Louisiana, to allow the rapid visualization of fiber quality data, yield, and loan values. The system should reduce errors, allow data sharing, and facilitate research studies and be a benefit to many groups within the cotton research community, including breeders, ginners, and fiber processors. 02 Improved handling of seed cotton modules reduces plastic contamination. ARS scientists at New Orleans, Louisiana, believe the cotton industry has developed numerous approaches to transporting and unwrapping the plastic from cotton round modules; however, plastic contamination from the module wrap remains an industry problem. ARS researchers in New Orleans, Louisiana, Lubbock, Texas, and Stoneville, Mississippi, have completed a comprehensive review of systems that transport field cotton modules and remove plastic module wrap. Several systems were identified by ARS scientists at New Orleans, Louisiana, that lessen the potential for the plastic to contaminate the cotton processing stream. A time and motion study and safety factors were also evaluated by ARS scientists at New Orleans, Louisiana, to allow gins to select the method that best serves their operations. This information allows the U.S. cotton ginning industry to make informed decisions when upgrading or modifying module handling equipment.
Impacts
(N/A)
Publications
- Santiago Cintron, M., Von Hoven, T.M., Hinchliffe, D.J., Hron, R.J. 2021. Examination of cotton maturity and maturity distribution using an infrared focal plane array imaging system. American Association of Textile Chemists and Colorists Journal of Research. 8(1):14-24. https://doi.org/10.14504/ ajr.8.1.3
- Edwards, J.V., Prevost, N.T., Yager, D., Nam, S., Graves, E.E., Santiago Cintron, M., Condon, B.D., Dacorta, J. 2021. Antimicrobial and hemostatic activities of cotton-based dressings designed to address prolonged field care applications. Military Medicine. 186(1):116-121. https://doi.org/10. 1093/milmed/usaa271.
- Liu, Y., Kim, H.-J. 2020. Separation of underdeveloped from developed cotton fibers by attenuated total reflection Fourier transform infrared spectroscopy. Microchemical Journal. 158:105152. https://doi.org/10.1016/j. microc.2020.105152.
- Fortier, C.A., Delhom, C.D., Dowd, M.K. 2021. Source of metal ions on raw cotton fibers and their influence on dyeing. American Association of Textile Chemists and Colorists Journal of Research. 8(2):1-8. https://doi. org/10.14504/ajr.8.2.1.
- He, Z., Guo, M., Fortier, C., Cao, X., Schmidt-Rohr, K. 2021. Fourier transform infrared and solid state 13C nuclear magnetic resonance spectroscopic characterization of defatted cottonseed meal-based biochars. Modern Applied Science. 15(1):108-121. https://doi.org/10.5539/mas. v15n1p108.
- Delhom, C.D., Knowlton, J., Martin, V.B., Blake, C.D. 2020. The classification of cotton. Journal of Cotton Science. 24:189�196.
- Delhom, C.D., Hequet, E.F., Kelly, B., Abidi, N., Martin, V.B. 2020. Calibration of HVI cotton elongation measurements. Journal of Cotton Research. 3(31). Available: https://doi.org/10.1186/s42397-020-00073-1.
- Kim, H.J., Delhom, C.D., Fang, D.D., Zeng, L., Jenkins, J.N., Mccarty Jr, J.C., Jones, D.C. 2020. Application of the cottonscope for determining fiber maturity and fineness of an upland cotton MAGIC population. Crop Science. 60(5):2266�2279. https://doi.org/10.1002/csc2.20197.
- Van Der Sluijs, M.H., Delhom, C.D., Martin, V.B. 2020. Assessment of cotton fibre length measurement methods. Journal of Textile Institute. 1- 13. Available https://doi.org/10.1080/00405000.2020.1816684.
- Zeng, L., Bechere, E., Delhom, C.D. 2020. Potential of genetic improvement for neppiness traits in Upland cotton. Crop Science. 60:1876-1883. https:// doi.org/10.1002/csc2.20168.
- Fang, D.D., Zeng, L., Thyssen, G.N., Delhom, C.D., Bechere, E., Jones, D.C. , Li, P. 2021. Stability and transferability assessment of the cotton fiber strength QTL qFS-c7-1 on chromosome A07. The Crop Journal. 9(2):380- 386. https://doi.org/10.1016/j.cj.2020.06.016.
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