Fuente: PubMed "essential OR oil extract" J Sci Food Agric. 2026 Mar 9. doi: 10.1002/jsfa.70549. Online ahead of print.ABSTRACTBACKGROUND: Artificial neural network (ANN) models have become essential for precise predictions and improving engineering systems. This study investigated the effects of air velocity (2.0, 3.0, and 4.0 m s-1), grain flow rate (5.5, 7.0, and 8.5 kg min-1), and infrared temperature (40, 50, and 60 °C) on drying kinetics, thermal performance, and quality properties in corn using a graphene-based far-infrared dryer. An ANN was used to predict optimal drying conditions to balance heating characteristics for corn quality attributes.RESULTS: The results showed that a 4.0 m s-1 air velocity, 60 °C infrared temperature, and 8.5 kg min-1 flow rate reduced drying time from 8.5 to 3.5 h, lipase activity from 18.92% to 10.78%, and acidity content from 1.88 to 1.22 g NaOH kg-1. The highest drying conditions achieved the lowest energy usage, resulting in a maximum thermal efficiency of 82.28% at minimum drying time. However, increasing the infrared temperature to 40-60 °C while maintaining the same 5.5 kg min-1 grain flow rate and 4.0 m s-1 air velocity resulted in an improved antioxidant activity, from 10.22 to 12.11 g catechin gallate equivalents kg-1 dry weight. The study used precise ANN modeling to highlight the complex interactions between drying parameters and thermal performance, which recorded a strong predictive performance of 99% accuracy. Principal component analysis showed that acidity and energy consumption have commonalities.CONCLUSION: This study highlights the potential of cutting-edge computational tools to enhance energy efficiency through graphene-based heating without compromising product quality. © 2026 Society of Chemical Industry.PMID:41802985 | DOI:10.1002/jsfa.70549