Work

Random forest regressor for predicting sensory texture of emotional designed packaging films

Year: 2025

Type: article

Abstract: A random forest (RF) regression model was developed to predict sensory texture preferences of packaging films, enhancing their emotional appeal to consumers. Five films, including matte and varnish-textured prints, were analyzed using a surface profilometer to measure roughness parameters ( R a, R y, R z, R q, and R -MAD) in compliance with ISO 4287 and ISO 24118–1. Sensory preferences were evaluated through tests involving 75 panelists, and correlations between roughness parameters and preferences were established. Power spectral density (PSD) analysis with Welch window preprocessing provided detailed surface texture insights. The RF model achieved a coefficient of determination of 0.977, outperforming partial least squares regression, and highlighted the importance of significant wavelength regions in predictive modeling. This study demonstrates a robust framework for integrating machine learning in packaging design to optimize sensory appeal.

Source: Results in Engineering

Authors: Yong Ju Lee, Min Jung Joo, Ha Kyoung Yu, Tai-Ju Lee, Hyoung Jin Kim

Institution: Kookmin University

Language: English

Topic: Surface Roughness and Optical Measurements

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,940

Funder: Ministry of Trade, Industry and Energy

Award: G1802886255