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oai:figshare.com:article/32640102 2026-10-01T16:34:13Z portal_1172 item_type_8 month_year_10_2026

The application of artificial intelligence in food fraud detection: a case study on the geographical origin discrimination of black tea Yicong Li (8497608) PUREID: 612730276 Artificial intelligence food fraud geographical origin detection Tea Machine learning This thesis applies artificial intelligence, primarily through machine learning and deep learning algorithms, alongside advanced analytical tools and innovative data fusion strategies for food fraud testing. Initially, a systematic review of reported tea fraud cases identifies prevalent types and highlights the limitations of current detection methods, with geographical origin substitution emerging as a critical concern. To address this, the research develops a novel two-tier system that transcends traditional chemometric analysis methodologies and improves food fraud detection capabilities. In tier one, non-targeted spectroscopic fingerprinting (using Fourier Transform Infrared spectroscopy and Near-Infrared spectroscopy), targeted elemental profiling (via X-Ray Fluorescence spectroscopy), and artificial intelligence algorithms classify black tea samples based on their geographical origins. The high-level data fusion methods developed integrate results from three spectroscopic tools, enhancing discrimination accuracy and demonstrating potential for real-time, on-site rapid testing throughout the global tea supply chain. In tier two, the developed non targeted liquid chromatography Time-of-Flight mass spectrometry based testing method discriminates black tea samples with 100% accuracy in both internal and external validations, paving the way for laboratory-based confirmation testing.<br><br><i>Thesis is embargoed until 31 December 2026.</i> 2026-10-01T16:34:13Z Text Thesis 10.17034/32640102.v1 https://figshare.com/articles/thesis/The_application_of_artificial_intelligence_in_food_fraud_detection_a_case_study_on_the_geographical_origin_discrimination_of_black_tea/32640102 All Rights Reserved Open Access after 2026-12-31