Predictive Modeling of the Softness of Facial Tissue Products: A Spectral Analysis Approach

Abstract

Softness is a critical yet subjective characteristic of hygiene paper products such as facial tissues. In this study, softness values were obtained from the authors’ previous research using the Interval Scale Value (ISV) method, involving panelists’ round-robin pairwise comparisons. A machine-learning approach was developed to predict softness from one-dimensional power spectral density (1D-PSD) spectra of surface roughness profiles. Using seven commercial samples and an optimized multilayer perceptron model, a achieved high predictive performance (R² = 0.860) was achieved without additional measurements such as tensile modulus or surface friction. This work highlights the potential of combining spectral analysis and machine learning for objective softness evaluation.