A Statistical Approach for Determining the Technological Value of Cotton Using HVI Fiber Properties

Abstract

A statistical approach for determining the technological value of a variety of cotton is presented. The approach suggests that the market value of cotton should correspond to its technological value in a particular manufacturing system; that is, the value of a bale of cotton should be determined based on its expected performance in the textile mill and the yarn quality obtained from it. A model relating fiber to yarn properties is a basic requirement for implementing the approach. The procedures used in this approach include developing a multiple regression model relating HVI fiber properties to the desired quality parameter of yarn (skein break factor), determining the percent relative contribution of a fiber property with respect to skein break factor, selecting a reference set of HVI fiber properties, determining a difference factor of the difference in value between fiber properties of a particular variety and the reference set, and finally, developing a premium/discount formula. The main feature of the approach is its flexibility in accommodating different fiber properties and yarns of different counts produced on different spinning systems.The market value of cotton is determined largely by a subjective evaluation of a limited number of quality parameters. Traditionally, three parameters have been considered: grade (comprising trash, color, and preparation ), fiber length, and fiber fineness. The grade is determihed visually by comparing with standard cottons, fiber length is estimated visually, and fiber fineness is estimated instrumentally [ 5 ] .For many years, subjective cotton classing has been used without major conflict between cotton users and producers. This was attributed to three main reasons: the existence of the traditional ring spinning, which made both producers and users quite familiar with the desired quality of raw material; relative market stability; and the lack of rapid and accurate testing methods. I In recent years, the technology of ring spinning has undergone several changes, and new spinning techniques have been developed. A typical spindle speed of today's ring spinning machine is 16,000 rpm, compared to less than 7000 rpm fifty years ago. New spinning technologies such as open-end and air-jet are capable of producing yarn at rates much higher than that of ring spinning. In order to accommodate these mechanical capabilities, fibers must flow individually or in small bundles at very high speeds. They must also withstand the stresses resulting from opening and condensation actions involved in the new spinning technologies. The increased use of synthetic fibers that can be modified to suit a particular spinning technique makes determination of the value of cotton (based on its contributing quality) necessary for efficient blending and better processing performance.Traditional cotton 6ber testing methods are being replaced by high volume instrument (HVI) systems that are capable of providing accurate and rapid measurements of several cotton 6ber properties [ 5, 6 J . The data obtained...

Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties

“…Such an approach is used to investigate the interdependence of the different fiber properties and to estimate the relative contribution of each fiber property to the overall yarn properties. Several researchers [1][2][3][4][5] have established various regression equations using this method.…”

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties

“…Multiple regression analyses are the most common statistical methods. Several regression equations have been established [1][2][3][4][5]. Prediction of cotton yarn properties from fiber properties have also been reviewed in details [6].…”

A Generalized Model for Predicting Yarn Global Quality Index

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