Data · dataset · 2026
The application of artificial intelligence in food fraud detection: a case study on the geographical origin discrimination of black tea
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32640102.v1
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.
Description
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.
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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>
Links
Where it is published
- DOI doi.org/10.17034/32640102.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Artificial intelligence · Artificial intelligence · Artificial intelligence · Computer Science & AI · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Machine learning · Machine learning · Machine learning
- Inferred from text
- Mass spectrometry 75%
Provenance · 3 source records, 18 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32640102 | 13 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32640102 | 13 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32640102 | 13 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].anzsrc:group:4602 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial intelligence'] |
| concepts[field].anzsrc:group:4602 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['Artificial intelligence'] |
| concepts[field].anzsrc:group:4602 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Artificial intelligence'] |
| concepts[field].anzsrc:group:4611 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['Machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['Machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[modality].local:modality:mass-spectrometry | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |