Effect of Pretreatment Strategies on White Skin Composting Assessed via Multivariate Analysis and Neural Network Modelling

This study examines the impact of various pretreatments on the composting of white grape skin (Vitis vinifera cv. Graševina) over a 30-day aerobic composting period. The pretreatments included the extraction of bioactive compounds from the grape skin and grinding. The research used an integrated approach combining physicochemical and microbiological analyses with advanced multivariate statistics and Artificial Neural Network (ANN) modeling. The combination of grinding and bioactive compound extraction was associated with improved composting performance under the investigated experimental conditions. All treatments exhibited reduced phytotoxicity, with germination indices exceeding commonly reported thresholds for phytotoxicity assessment. Spearman’s correlation and Principal Component Analysis (PCA) identified composting time and organic matter transformation as the primary drivers of variability, with the first three components explaining over 72% of the total variance. Furthermore, time-series Multi-Layer Perceptron (MLP) models successfully predicted the evolution of key maturity indicators, such as conductivity, organic matter content, and germination index, with high accuracy using composting time as the sole input. This integrated framework suggests that optimized conditions and predictive modeling can effectively transform winery by-products into stabilized compost, supporting sustainable waste management and circular economy practices in the wine industry.