A comprehensive approach for human hand evaluation of split or large set of fabrics

• Main Authors: Malengier, B. (Author), Musa, A.B.H (Author), Van Langenhove, L. (Author), Vasile, S. (Author)
• Format: Article
• Language: English
• Published: SAGE Publications Ltd 2019
• Subjects: comfort; Fabric; fabric hand; Fabric hand; fabric handle; Fabric handles; Fabric smoothness; fabric touch; Instrumental methods; Large dataset; Methods; Number of samples; Panels; Precision; Softness; Splitting; Statistical Analysis; Statistical tests; subjective assessment; Subjective assessments; Testing; Testing sessions
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 00405175 (ISSN)
• ISSN: 00405175 (ISSN)
• DOI: 10.1177/0040517519832834

Assessment of fabric handle relies on the feel of humans. The precision of the results greatly depends on the size of the fabric sets. The precision decreases with increasing number of samples as a consequence of assessors' fatigue and loss of concentration. Given the importance of handle assessment and in the absence of guidelines that assist assessment of large sample sets, this study proposes a comprehensive approach for testing large sets of fabrics by dividing them into several testing sessions, each of 10 samples at most. In the proposed way, tests can also be split over different panels, even at different locations, provided the panel accuracy is verified beforehand. The method to select the panel members, link the results obtained in different sessions and normalize the data are discussed in this paper. The proposed method was tested on 13 fabrics. Three fabric sensorial attributes (i.e. smoothness, softness and warmth) were assessed in two sessions by a panel consisting of 28 blindfolded members or assessors. Good agreement was found between the panel members for fabric smoothness and softness but the warmth of the fabrics was judged differently as shown by high disagreements between panel members. No significant origin-, gender- or age-based differences on the judgements were found. The findings of this test study are in agreement with previous studies where well-established assessment methods (i.e. instrumental methods or human panels on a smaller dataset) were applied and suggest that the proposed method can be successfully applied to assess large sets of fabrics. © The Author(s) 2019.