TY - GEN
T1 - Espectroscopía de terahercios en el dominio del tiempo para la clasificación de queso madurado
AU - Oblitas-Cruz, Jimy
AU - Miano, Alberto Claudio
AU - Terrones, Gilmer
N1 - Publisher Copyright:
© 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Terahertz time-domain spectroscopy is a useful technique to determine some physical characteristics of materials, which is based on the selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify maturity states of Gruyere-type cheese, terahertz spectra (0.5-10 THz) of 4 samples of cheese made in the livestock area of Cajamarca - Peru were examined during 60 days. The acquired data matrices were analyzed with the application of MATLAB 2019b where absorbance curves were obtained and maturity states were classified by testing 24 classifier models, achieving differences of around 90%, obtained by the Gaussian SVM Algorithm Model with a 0.35 Kernel Scale and a multiclass method one vs one. It was concluded that the combined processing and classification of images obtained from Terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to classify the different maturity states of cheeses.
AB - Terahertz time-domain spectroscopy is a useful technique to determine some physical characteristics of materials, which is based on the selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify maturity states of Gruyere-type cheese, terahertz spectra (0.5-10 THz) of 4 samples of cheese made in the livestock area of Cajamarca - Peru were examined during 60 days. The acquired data matrices were analyzed with the application of MATLAB 2019b where absorbance curves were obtained and maturity states were classified by testing 24 classifier models, achieving differences of around 90%, obtained by the Gaussian SVM Algorithm Model with a 0.35 Kernel Scale and a multiclass method one vs one. It was concluded that the combined processing and classification of images obtained from Terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to classify the different maturity states of cheeses.
KW - Cheese ripening
KW - Gruyere cheese
KW - Principal component analysis
KW - Terahertz spectroscopy
UR - https://www.scopus.com/pages/publications/85122024016
U2 - 10.18687/LACCEI2021.1.1.59
DO - 10.18687/LACCEI2021.1.1.59
M3 - Contribución a la conferencia
AN - SCOPUS:85122024016
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
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 -