Sensory analysis and Principal Component Analysis: a sustainable approach for quality control of stretch denim fabrics

Abstract

This study proposes a simplified approach to optimise the portfolio of denim manufacturers by reducing the cost of unattractive or undifferentiated assortments for future consumers based on their ability to perceive and discriminate sensory comfort. A key factor in a successful textile value chain is end consumers, who can be considered naive evaluators when it comes to sensory analysis, as they tend to touch apparel fabrics to perceive the sensory comfort they feel when wearing them. In this context, 16 naive assessors were recruited to quantitatively characterise six bipolar sensory attributes as hand descriptors of an assortment of five washed stretch denim fabrics based on tactile properties. Statistical analysis of the extensive data was performed using the multivariate technique PCA. Two principal components that explained 70.46% to 76.67% of the total observed variance for the five washed denim fabrics provided an adequate summary of the sensory data reported, so relationships between sensory attributes as hand descriptors and ratings given by the 16 untrained assessors were examined. Statistical tests showed that all six bipolar attributes were important for sensory analysis and that inter-rater agreement was low. However, considering the sensory perception of the untrained evaluators (i.e., consumers), it was concluded that four of the five washed stretch denim fabrics could be an option for product portfolio diversification. Thus, from a strategic and sustainability perspective, PCA for sensory analysis may be helpful in the quality control of washed stretch denim