Within the context of forecasting U.S. inflation, this study explores the predictive power of food commodities futures prices, focusing on enhancing the precision of both short- and long-term forecasts. We develop single commodity models for twelve different food commodities and also construct two aggregated models: a simple component model and a Principal Component Analysis (PCA)-based model, both utilizing price indices of the selected commodities. Monthly futures data from 1996 to 2023 for the nearest maturity dates are segmented into in-sample fitting and out-of-sample forecasting, covering forecast horizons of 3, 6, 9 and 12 months. Our findings indicate that aggregated models, particularly the PCA-based method, exhibit superior forecasting performance. Furthermore, to verify model robustness, we conduct parallel forecasts using spot prices and perform subsample analyses. Collectively, these results underscore the predictive power of commodity futures for forecasting food inflation.