Two‐way prediction of cotton yarn properties and fiber properties using multivariate multiple regression

This paper explains the feasibility of two‐way prediction by developing direct models relating fiber to yarn and reverse models relating yarn to fiber using multivariate methods simultaneously. These models evaluate the dependencies of cotton yarn properties on fiber properties and vice versa with minimum random errors and maximum accuracy. To this end, cotton fiber properties were measured from rovings carefully untwisted. An HVI system and an evenness tester of premier were used to measure the various properties. The samples of cotton yarns (108 samples) produced yarn counts ranging from 16 to 32 Ne with optimum twist factor. In this study, effective variables were selected by multivariate statistical test (m‐test). Then, multivariate analysis of variance (MANOVA) was used for evaluating the significance of obtained models. Next, the optimal separate equations were determined through multivariate multiple regression. After solving the linear equation system, a reverse model was achieved. By selecting fiber properties and machine factors as appropriate variables, the relative importance of these factors was also investigated. The results showed that the obtained equations were significant at the significance level α = 0.01.