Mapping gas permeability of sustainable packaging materials to link food barrier needs by clustering algorithms

Food packaging is critical for ensuring food safety, quality, and shelf life. However, growing environmental concerns with conventional plastics drive the search for sustainable alternatives. A major challenge is that many biobased and biodegradable materials show poor barrier properties, limiting their use for food. This study provides a proof-of-concept for classifying sustainable packaging materials by clustering oxygen transmission rate (OTR) and water vapor transmission rate (WVTR) data. A dataset from 49 studies (2000 to 2016) was analyzed using K-Means, Gaussian Mixture Model (GMM), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN emerged as best performing algorithm, achieving the highest Silhouette Score (0.910) and lowest Davies-Bouldin Index (0.374). Results validated that while many sustainable films exhibit high permeability, nanocomposites achieved improved barrier performance. This data-driven framework demonstrates clustering as a tool for systematic grouping of packaging materials, with future work requiring broader datasets, industrial benchmarks, and standardized reporting for practical application.