Fungal contamination patterns and surface material susceptibility in manufacturing environments.

Abstract

Fungal contamination poses significant quality and health risks in manufacturing environments, yet patterns and environmental drivers in textile sectors such as footwear manufacturing remain poorly understood. This study examined surface bioburden and fungal diversity across materials in Indonesian footwear manufacturing facilities by combining culture-based enumeration with environmental monitoring. We collected 84 surface samples, 43 contaminated samples were sequenced, revealing 21 fungal genera. Material type had minimal effect on contamination (Kruskal-Wallis H = 9.342, P = 0.0531, ε² = 0.0008), though wood surfaces consistently showed higher loads. Humidity was the most consistent environmental predictor, while CO₂ correlated positively with wood contamination (β = 0.01, P = 0.004) but negatively with leather (β = -0.01, P = 0.01), suggesting surface-dependent operational influences. Aspergillus and Penicillium were the dominant genera, with no substrate-specific clustering. Zero-inflated negative binomial models more accurately captured contamination patterns on fabric and plastic, while standard models were more suitable for consistently contaminated materials. These results highlight humidity as a key predictor of surface contamination and suggest that environmental monitoring could inform contamination surveillance strategies in footwear manufacturing.