Publicación:
Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
Terahertz Imaging and Machine Learning in the Classification of Coffee Beans
| dc.contributor.author | Uceda P. | es_PE |
| dc.contributor.author | Yoshida H. | es_PE |
| dc.contributor.author | Castillo P. | es_PE |
| dc.date.accessioned | 2024-05-30T23:13:38Z | |
| dc.date.available | 2024-05-30T23:13:38Z | |
| dc.date.issued | 2021 | |
| dc.description | Acknowledgments. P. Uceda and H. Yoshida acknowledge the financial support from Project Concytec – The World Bank “Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE through Fondecyt [contract no 006–2018]. | |
| dc.description.abstract | The geographical origin of coffee beans represents an effect on the attributes and quality of the product due to the different soil and weather conditions for a specific location. Therefore, the development of methods for rapid classification and authentication of coffee beans based on their geographical origin is essential. This research was done with the purpose of determining the capacity of coffee (Coffea arabica) varieties classification with the use of Terahertz (THz) imaging and machine learning. THz images of coffee beans samples from 3 different geographical origins were acquired with a time-domain spectrometer and then used to measure the classification performance of methods such as neural networks, random forests, and support vector machines. The results obtained reached an accuracy up to 91.2%, which showed that the use of THz imaging and machine learning is an effective method for the non-destructive analysis of coffee variables and classification based on geographical origin. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. | |
| dc.description.sponsorship | Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec | |
| dc.identifier.doi | https://doi.org/10.1007/978-3-030-75680-2_94 | |
| dc.identifier.scopus | 2-s2.0-85111350964 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12390/3062 | |
| dc.language.iso | eng | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.relation.ispartof | Smart Innovation, Systems and Technologies | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | THz spectroscopy | |
| dc.subject | Chemometrics | es_PE |
| dc.subject | Coffee post-harvest | es_PE |
| dc.subject | Supervised learning | es_PE |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#2.02.022 | |
| dc.title | Terahertz Imaging and Machine Learning in the Classification of Coffee Beans | |
| dc.type | info:eu-repo/semantics/conferenceObject | |
| dspace.entity.type | Publication |