An assessment of alternative cotton fibre quality attributes and their relationship with yarn strength

Abstract

Abstract. Knowing the yarn-strength performance potential of cotton fibre is advantageous to spinners during mill preparation, and to researchers developing new genotypes and management strategies to produce better fibre. Standard High Volume Instrument (HVI) fibre quality attributes include micronaire (a combined measure of fibre linear density and maturity) and bundle tensile properties. While these attributes relate well to yarn strength, alternative fibre quality attributes may better explain the variation in yarn strength. Two field experiments over two seasons were conducted to assess the fibre and yarn performance of some Australian cotton genotypes. The aim was to assess and compare alternative measures for micronaire, and to compare bundle and single-fibre tensile measurements, and assess the relative yarn-strength predictive performance of these attributes. Specific fibre measurement comparisons were for linear density (double-compression Fineness Maturity Tester (FMT) and gravimetric), maturity ratio (FMT, polarised light, calculated, and cross-sectional), and tensile properties (HVI bundle and Favimat Robot single fibre). Multiple linear regression models for yarn strength that included yarn manufacturing variables and standard HVI fibre quality parameters performed well (standard error of prediction (SEP) 2.40 cN tex -1 ). Multiple linear regression models performed better when alternatives to micronaire were used, e.g. using gravimetric linear density (SEP, 2.15 cN tex -1 ) or laser photometric determined ribbon width (SEP 1.71 cN tex -1 ). Yarn strength models were also better when single fibre tensile properties were substituted for bundle tensile properties (SEP 1.07 cN tex -1 ). The substitution of alternative fineness variables for micronaire or single-fibre strength for bundle strength in a simple fibre quality index also improved the prediction of yarn strength.

Evaluating Intraplant Cotton Fiber Variability

“…In this dataset, many fiber traits had significant genotype by year interactions. While environmental impacts on fiber quality have been well documented, results from this study as well as many others (Campbell et al, 2012, Smith et al, 2010, Long et al, 2013 suggest that fiber-quality parameters such as length, strength, and elongation are highly heritable traits.…”

“…These interactions lead to a high degree of variability in fiber quality across different species and cultivars, within the same cultivars, within fields, across and within rows, within bales, within a plant, and even within a single boll. Keeping pace with the requirements set by spinning and weaving technologies, the need to control cotton fiber variability along with maximizing yield is urgent (Clouvel et al, 1998; Davidonis et al, 1999; Wilkins and Jernstedt., 1999; Davidonis et al, 2004; Bednarz et al, 2006; Krifa, 2012).…”

Comprehensive analysis of cellulose content, crystallinity, and lateral packing in Gossypium hirsutum and Gossypium barbadense cotton fibers using sum frequency generation, infrared and Raman spectroscopy, and X-ray diffraction

“…The fiber from G. barbadense is now used in premium textile fabrics because it has several superior properties compared to G. hirsutum fiber: greater length and strength, but smaller diameter and gravimetric fineness (mass per unit length) (Wakelyn and French 2007). All of these factors contribute to the ability to spin stronger yarns from G. barbadense fiber (Long et al 2013). For G. hirsutum cv Deltapine90 and G. barbadense cv Phytogen 800 used in these experiments, the fiber of G. barbadense had the expected superior fiber qualities: greater length (1.27 vs. 1.15 in.…”

Comparative Relationship of Fiber Strength and Yarn Tenacity in Four Cotton Cultivars

“…Cotton fiber, as a raw and starting material, could impact its intermediate yarn production and quality. Since yarn manufacturing requires specific equipment and uses a large amount of cotton fibers, substantial research has been done to optimize the methods of predicting yarn properties, such as tenacity (strength), uniformity, hairiness, and elongation, from available properties of raw fibers (Cai et al, 2013;Faulkner et al, 2012;Frydrych, 1992;Long et al, 2013;Pan et al, 2001;Ramey et al, 1977;Thibodeaux et al, 2008;Üreyen & Kadoglu, 2006). In general, two approaches (theoretical vs. statistical) were explored in these studies.…”