## 4 Discussion/Conclusions  
This study presents a case of training a hand feel panel under industry-relevant conditions and facing limitations in time and personnel resources. A group of persons working in a textile field with different competencies, varying abilities, and availability, were given the task of assessing the hand feel of fabric samples, which are mostly very near to each other, and differ in only a few parameters. Agreeing on wording, meaning, and the corresponding hand action, the group was able to provide a scaled and ranked assessment of knitted and woven fabrics' hand feel, with results showing an acceptable correlation with instrumental as well as with referenced human assessment. Human and instrumental assessments widely agreed on the differentiation among the fabrics based on cellulosic fibers, showing the softness gradient from modal (micro) as the softest and cotton as the least soft in the investigated fabric types, whereas fabrics based on the 1.3 dtex modal and lycoll fibers came in the middle range, with a perceivable advantage of modal in comparison to lycoll. For knitted fabrics, the panel proved reliable in providing a scaled assessment as well as a ranking of hand feel. Analytical physical characteristics related to all-over hand feel appear not to be independent of each other. The reason may be in the three-dimensional nature or the similarity of these fabrics, being all cellulosic and having a comparable yarn and fabric construction. Surface smoothness is more difficult to assess than dynamic properties (bending, compression). The reason could be differences in the individually applied pressures as well as in the skin sensitivity and condition of each participant. Figure 7: Scaling of fabrics by the professional audience (consumers) and hand feel panel. Depending on the research task and fabric type, some parameters can be combined into an all-over softness evaluation. The Phabrometer RHV (Relative Hand Value) values correlate well with Kawabata mechanics, but the single descriptors derived from the interpretation and segmenting of the location/force curve do not. TSA noise signals show, in many cases, a good correlation with fiber softness as well as with fabric mechanics. A hand-feel algorithm for the investigated type of fabric could be developed by the provider and will be, besides alternative test settings for woven fabrics and fabrics with lower air permeability, the subject of further testing and validation. The hand feel assessment of the lightweight woven fabric resulted in more difficulty than on knits. The compact and rigid character of the fabric appears to lead to a different perception. The analytical properties are less connected to each other than in the case of the knits. The assessment of dynamic fabric properties also seems to be easier than surface-related ones. For the second, blindfolding (reference panel) proved to provide a better correlation with the instrumental friction measurements. The practical examples show that the panel was able to predict the consumer decision for a defined fabric type. In conclusion, the method offers fast training of an expert (or at least semiexpert) hand feel panel under daily business conditions to assess the main hand feel characteristics with minimal personnel resources, especially in cases where the fabrics have a known purpose (predefined use that facilitates evaluation and preference). Conceptualization, LD. ; methodology, M.A.-R.; software, I.D. and E.L.; validation, M.A-R. and E.L.; formal analysis, I.D., M.A.-R. and E.L.; investigation, I.D., M.A.-R. and E.L.; resources, M.A.-R.; data curation, I.D., M.A.-R. and E.L.; writing--original draft, I.D. and M.A-R; writing--review & editing, I.D., M.A.-R. and E.L.; visualization, I.D. and E.L.; supervision, I.D. ; project administration, I.D. ; funding acquisition, M.A.-R. All authors have read and agreed to the published version of the manuscript.