Hernansanz-Luque, NataliaPérez-Calabuig, Ana M.Pradana-López, SandraCancilla, John C.Torrecilla Velasco, José Santiago2026-03-172026-03-17202610.1016/j.jfoodeng.2025.112675https://hdl.handle.net/20.500.14352/134053Food adulteration is a major concern in the food industry, particularly in widely consumed products such as coffee. This study presents a novel non-destructive approach for detecting melamine contamination in coffee capsules using infrared thermography (IRT) and convolutional neural networks (CNNs). Coffee samples (natural, blended, and decaffeinated) with different coffee-to-milk ratios (1:3, 1:1, and 3:1) were adulterated with melamine at 2.5, 5, and 7.5 ppm. A dataset of 24,296 thermographic images was analyzed using ResNet34, achieving a classification accuracy of 95.71 % in blind validation. Compared to conventional chemical methods, this approach is faster, cost-effective, and scalable, making it a valuable tool for real-time food safety screening. The proposed method offers a non-invasive and rapid alternative to conventional analytical techniques such as HighPerformance Liquid Chromatography (HPLC) and Mass Spectrometry (MS), making it highly suitable for realtime quality control in the food industryengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal articleopen access66.0Infrared thermographyConvolutional neural networksMelamine detectionCoffee quality controlIngeniería química3303 Ingeniería y Tecnología Químicas