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dc.contributor.authorChuquizuta Trigoso, Tony
dc.contributor.authorOblitas Cruz, Jimy
dc.contributor.authorArteaga Miñano, Hubert
dc.contributor.authorYarleque, Manuel
dc.contributor.authorCastro Silupu, Wilson
dc.date.accessioned2022-08-09T20:28:25Z
dc.date.available2022-08-09T20:28:25Z
dc.date.issued2021-09-21
dc.identifier.citationChuqizuta, T., ...[et al.]. (2021). Dielectric spectral profiles for andean tubers classification: a machine learning techniques application. 2021 International Conference on Electromagnetics in Advanced Applications, ICEAA. http://dx.doi.org/10.1109/ICEAA52647.2021.9539623es_PE
dc.identifier.urihttps://hdl.handle.net/11537/31119
dc.descriptionEl texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.es_PE
dc.description.abstractCurrently, the agri-food industry prioritizes the development of non-destructive methods, such as dielectric spectroscopy, for quality control. The obtained dielectric spectral properties can be coupled to multivariate statistical methods as "machine learning" when identification of attributes is wanted. However, these techniques have not been applied to andean tubers classification. Therefore, the objective of the present investigation is to evaluate the possibility of discriminating four andean tubers using dielectric spectra properties and machine learning techniques (Support Vector Machine - SVM, K-Nearest Neighbors-KNN, and Linear Discriminat - LD). For this purpose, samples of Tropaeolum tuberosum (Killu isañu), Solanum tuberosa (yellow) and two varieties of Oxalis tuberosa (Puka kamusa and Lari oqa) were acquired, 30 units per tuber. The dielectric spectral profile was extracted twice for each tubers sample, in the range from 2 to 8 GHz. Then, the dielectric constant (e') were calculated, and its dimensionality was reduced using principal component analysis. Finally, models for classification were built by employing KNN, SVM and LD techniques. The results showed that three components can explain the variance at 99.6 %. Likewise, the accuracy in the discrimination values varied between 79.17 - 83.04, being SVM the best discrimination technique. Consequently, it is concluded that the technique of dielectric spectroscopy and machine learning presents potential for andean tuber discrimination.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherIEEEes_PE
dc.rightsinfo:eu-repo/semantics/closedAccesses_PE
dc.sourceUniversidad Privada del Nortees_PE
dc.sourceRepositorio Institucional - UPNes_PE
dc.subjectEspectroscopiaes_PE
dc.subjectProductos agrícolases_PE
dc.subjectTubérculoses_PE
dc.subjectControl de calidades_PE
dc.subjectIndustriaes_PE
dc.subjectSpectroscopyes_PE
dc.subjectQuality controles_PE
dc.subjectAndean tuberses_PE
dc.titleDielectric spectral profiles for andean tubers classification: a machine learning techniques applicationes_PE
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.publisher.countryUSes_PE
dc.identifier.journal2021 International Conference on Electromagnetics in Advanced Applications, ICEAAes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.11.04es_PE
dc.description.sedeCajamarcaes_PE
dc.identifier.doihttp://dx.doi.org/10.1109/ICEAA52647.2021.9539623


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