TY - GEN
T1 - Determinación del origen geográfico de dos variedades de café mediante espectroscopia NIR
AU - Oblitas-Cruz, Jimy
AU - Cieza-Rimarachin, Yuleyci
AU - Castro-Silupu, Wilson
N1 - Publisher Copyright:
© 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The objective was to implement a non-invasive classification system for green coffee beans by using near-infrared spectroscopy (NIR) and multivariate data analysis. For this, 4 types of coffee were analyzed, according to variety and geographical location. The samples were repeated 5 times. The observed NIR spectrum was absorbance in the range of 1100 and 2500 nm. In order to reduce the data, the analysis of main components was used by testing 24 classification models, from which the one that reached the highest level of precision was the Linear Support Vector Machine (SVM) algorithm, reaching 98.8%, achieving fairly satisfactory discrimination with values of PC1 (97.9%), PC2 (1.9%) and PC3 (0.1%), reaching a total cumulative variation of the contribution of the first 3 PCs of 99.9%. These values demonstrated that NIR spectroscopy is a valid alternative for classification by geographical origin and variety of green coffee beans.
AB - The objective was to implement a non-invasive classification system for green coffee beans by using near-infrared spectroscopy (NIR) and multivariate data analysis. For this, 4 types of coffee were analyzed, according to variety and geographical location. The samples were repeated 5 times. The observed NIR spectrum was absorbance in the range of 1100 and 2500 nm. In order to reduce the data, the analysis of main components was used by testing 24 classification models, from which the one that reached the highest level of precision was the Linear Support Vector Machine (SVM) algorithm, reaching 98.8%, achieving fairly satisfactory discrimination with values of PC1 (97.9%), PC2 (1.9%) and PC3 (0.1%), reaching a total cumulative variation of the contribution of the first 3 PCs of 99.9%. These values demonstrated that NIR spectroscopy is a valid alternative for classification by geographical origin and variety of green coffee beans.
KW - Geographical origin
KW - Green coffee beans
KW - NIR spectroscopy
UR - https://www.scopus.com/pages/publications/85122041424
U2 - 10.18687/LACCEI2021.1.1.111
DO - 10.18687/LACCEI2021.1.1.111
M3 - Contribución a la conferencia
AN - SCOPUS:85122041424
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Zapata Rivera, Luis Felipe
A2 - Aranzazu-Suescun, Catalina
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Prospective and Trends in Technology and Skills for Sustainable Social Development" and "Leveraging Emerging Technologies to Construct the Future", LACCEI 2021
Y2 - 19 July 2021 through 23 July 2021
ER -