Data-driven prediction of methane potential of food waste collected from diverse waste sources.

Understanding the drivers of methane (CH4) yield variability in food waste (FW) is essential for predicting anaerobic digestion performance. This study evaluated the influence of feedstock origin and quality attributes on CH4 yield and developed multiple linear regression models to estimate bioenergy potential. FW samples were collected quarterly over seven sampling periods from grocery, restaurants, schools, and landfill-bound residential streams. Samples were characterized for solids, pH, alkalinity, organics, and structural composition, and were tested using standardized biochemical CH4 potential assays with inter-laboratory validation. Methane yield differed significantly among sources (p < 0.001): grocery FW produced the highest yield (802 mL CH4/g VS), restaurant, and school FW ranged from 500-650 mL CH4/g VS, and residential waste produced the lowest yields. Inter-laboratory reproducibility was acceptable, with relative standard deviations of 11-16.2 % and relative ranges of 15.6-22.9 %. Permutational multivariate analysis of variance showed source, time, and their interaction explained 75 % of variation, with source accounting for the largest contribution (30 %), indicating feedstock origin influenced CH4 yield more than collection month. Source, solids, chemical oxygen demand, ORP, alkalinity, and protein content showed the strongest influence on CH4 production. Model performance improved from the simple model (R2 = 0.57; RMSE = 134 mL CH4/g VS) to the practical model (R2 = 0.63; RMSE = 121 mL CH4/g VS), while the full model achieved the highest accuracy (R2 = 0.73; RMSE = 107 mL CH4/g VS). These findings support renewable energy prediction using feedstock attributes to enhance FW pretreatment and management across diverse waste sources.