Grouping of various stains to develop programs for a front-loading washer

Abstract

Stains from foodstuffs, cosmetics, and daily activities are frequently founded on our clothes. It is very annoying to remove stains, because extra treatment should be done before or after washing. A smart washing machine, which can operate adequately according to the types of stains on garments, will provide another convenience to users. In this study, the relative contributions of washing factors to the removal of stains depending on the type of stain and fiber, were investigated using domestic front-loading washers. The investigated washing factors were temperature, time, drum speed, and the ratio of motion time to pause time. Removal of 15 types of common stains on three types of cloths, such as cotton, polyester and cotton/polyester blend, was assessed. Stains were categorized by the origin: food, cosmetics, body, outdoor activities and factory work. Multiple regression equations containing interaction effects were obtained from the soiled cloths with these stains. Depending on the magnitudes of standardized regression coefficients for the effect of washing factors on the soil removal, the soiled cloths were divided into five groups: cloths soiled with oily materials, cotton cloths soiled with proteins, cloths soiled with insoluble particles, cloths soiled with highly unsaturated oils, and polyester cloths. Effective washing conditions for each group were suggested.