2026-06-27T23:16:51Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1340532026-03-18T01:05:46Zcom_20.500.14352_14col_20.500.14352_15

Hernansanz-Luque, Natalia Pérez-Calabuig, Ana M. Pradana-López, Sandra Cancilla, John C. Torrecilla Velasco, José Santiago 2026-03-17T09:51:19Z 2026-03-17T09:51:19Z 2026 10.1016/j.jfoodeng.2025.112675 https://hdl.handle.net/20.500.14352/134053 Food 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 industry eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 International Real-time screening of melamine in coffee capsules using infrared thermography and deep learning journal article