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Terahertz Imaging and Machine Learning in the Classification of Coffee Beans

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Patricia Uceda
Hideaki Yoshida
Pedro Castillo
Published in
Proceedings of the 6th Brazilian Technology Symposium (BTSym’20)
Publisher
Springer International Publishing

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Abstract

The chapter delves into the application of terahertz imaging and machine learning for classifying coffee beans, a topic of increasing importance due to the growing global coffee production. It provides a comprehensive background on the significance of identifying coffee varieties and the potential of spectroscopic methods. The study focuses on the use of THz spectroscopy and machine learning algorithms, such as neural networks, support vector machines, and random forests, to discriminate between coffee bean varieties. The authors highlight the advantages of these methods over traditional techniques, showcasing their high accuracy and potential for non-destructive analysis. The chapter also discusses the preprocessing steps, model training, and performance metrics, offering insights into the practical application of these technologies in the coffee industry.
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Title
Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
Authors
Patricia Uceda
Hideaki Yoshida
Pedro Castillo
Copyright Year
2021
Book
Proceedings of the 6th Brazilian Technology Symposium (BTSym’20)

Print ISBN: 978-3-030-75679-6

Electronic ISBN: 978-3-030-75680-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75680-2_94
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