TY - GEN
T1 - Terahertz Time-domain Spectroscopy (THz-TDS) for classification of blueberries according to their maturity
AU - Cruz, Jimy Oblitas
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - Non-destructive determination of blueberry compound using spectral detection method is still a challenge due to the spectral THZ variation caused by abundant biological variations, such as geographic origins and harvest seasons. In order to investigate the potential of Terahertz time-domain spectroscopy to classify fruit maturity states, terahertz spectra (0.5-10 THz) of 4 states of blueberry maturity were examined. The acquired data matrices were submitted to the application of MATLAB 2019b Classification Learner by using 24 classifier models. 84.3 is the highest accuracy, obtained by the Fine Gaussian SVM Algorithm Model with a 0.35 Kernel Scale and a Multiclass Method One vs One. The coefficients for this application of PCA are PC1 (79.9%) and PC2 (20.1%). It was concluded that the combined processing and classification of images obtained from Terahertz time-domain spectroscopy and using Machine learning algorithms can be used to classify the different maturity states of blueberries.
AB - Non-destructive determination of blueberry compound using spectral detection method is still a challenge due to the spectral THZ variation caused by abundant biological variations, such as geographic origins and harvest seasons. In order to investigate the potential of Terahertz time-domain spectroscopy to classify fruit maturity states, terahertz spectra (0.5-10 THz) of 4 states of blueberry maturity were examined. The acquired data matrices were submitted to the application of MATLAB 2019b Classification Learner by using 24 classifier models. 84.3 is the highest accuracy, obtained by the Fine Gaussian SVM Algorithm Model with a 0.35 Kernel Scale and a Multiclass Method One vs One. The coefficients for this application of PCA are PC1 (79.9%) and PC2 (20.1%). It was concluded that the combined processing and classification of images obtained from Terahertz time-domain spectroscopy and using Machine learning algorithms can be used to classify the different maturity states of blueberries.
KW - Blueberry
KW - Principal component Analysis
KW - Terahertz spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=85097846605&partnerID=8YFLogxK
U2 - 10.1109/EIRCON51178.2020.9254046
DO - 10.1109/EIRCON51178.2020.9254046
M3 - Conference contribution
AN - SCOPUS:85097846605
T3 - Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
BT - Proceedings of the 2020 IEEE Engineering International Research Conference, EIRCON 2020
T2 - 2020 IEEE Engineering International Research Conference, EIRCON 2020
Y2 - 21 October 2020 through 23 October 2020
ER -