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Fingerprinting black tea: when spectroscopy meets machine learning a novel workflow for geographical origin identification
Yicong Li
,
Natasha Logan
,
Brian Quinn
, Yunhe Hong
,
Nicholas Birse
, Hao Zhu
,
Simon Haughey
, Christopher T. Elliott
,
Di Wu
*
*
Corresponding author for this work
School of Biological Sciences
Institute for Global Food Security
Research output
:
Contribution to journal
›
Article
›
peer-review
50
Citations (Scopus)
309
Downloads (Pure)
Overview
Fingerprint
Student theses
(2)
Fingerprint
Dive into the research topics of 'Fingerprinting black tea: when spectroscopy meets machine learning a novel workflow for geographical origin identification'. Together they form a unique fingerprint.
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Biochemistry, Genetics and Molecular Biology
Learning
100%
Solution and Solubility
100%
Infrared Radiation
100%
Spectroscopy
100%
Fourier Transform
100%
Time
50%
Development
50%
Sample
50%
Accuracy
50%
Near Infrared Spectroscopy
50%
Fraud
50%
Computer Science
Identification
100%
Machine Learning
100%
Workflows
100%
Supply Chain
66%
Fourier Transform
66%
Support Vector Machine
33%
Accuracy
33%
User
33%
Machine Learning Algorithm
33%
Sustainability
33%
Models
33%
Chemistry
Spectroscopy
100%
Black
100%
Aqueous Solution
66%
Fourier Transform Infrared Spectroscopy
66%
Sample
33%
Food
33%
Time
33%
Transparency
33%
NIR Spectroscopy
33%
Food Science
Tea
100%
Food Product
20%
Non Alcoholic Beverage
20%