TY - GEN
T1 - A non-destruction measurement system based on hyperspectral imaging for sugar content in Banana (Musa sp.)
AU - Akmalia, Dina
AU - Saputro, Adhi Harmoko
AU - Handayani, Windri
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/29
Y1 - 2017/11/29
N2 - Sugar content is one of the important parameters to determine the quality of banana. In this paper, a non-destruction measurement system for sugar content is introduced using hyperspectral camera system over the VIS/NIR (400-1,000 nm) spectral range. Hyperspectral image (HSI) calibration was performed to compute reflectance value of banana surface in full wavelength range while spectral and spatial analysis was conducted using a partial least squares regression (PLSR) to create a model that computing relationship between the HSI spectra and the sugar content. The ground truth value of sugar content was measured using digital refractometer on the extracted banana sample. The proposed system was evaluated using 90 Ambon bananas (Musa acuminata Colla) which consist of 30 raw, 30 mature and 30 overripe banana. The PLSR model provided the root mean square error of 0.79 % and the correlation coefficient R2 of 0.988 in the full wavelength band. Finally, the proposed non-destruction prediction system could be implemented as an instrument for sugar content measurement of banana fruit.
AB - Sugar content is one of the important parameters to determine the quality of banana. In this paper, a non-destruction measurement system for sugar content is introduced using hyperspectral camera system over the VIS/NIR (400-1,000 nm) spectral range. Hyperspectral image (HSI) calibration was performed to compute reflectance value of banana surface in full wavelength range while spectral and spatial analysis was conducted using a partial least squares regression (PLSR) to create a model that computing relationship between the HSI spectra and the sugar content. The ground truth value of sugar content was measured using digital refractometer on the extracted banana sample. The proposed system was evaluated using 90 Ambon bananas (Musa acuminata Colla) which consist of 30 raw, 30 mature and 30 overripe banana. The PLSR model provided the root mean square error of 0.79 % and the correlation coefficient R2 of 0.988 in the full wavelength band. Finally, the proposed non-destruction prediction system could be implemented as an instrument for sugar content measurement of banana fruit.
KW - Hyperspectral
KW - Non-destruction system
KW - Partial least squares
KW - Sugar content
UR - http://www.scopus.com/inward/record.url?scp=85043458986&partnerID=8YFLogxK
U2 - 10.1109/ISSIMM.2017.8124269
DO - 10.1109/ISSIMM.2017.8124269
M3 - Conference contribution
AN - SCOPUS:85043458986
T3 - Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology: Innovation for the Advancement and Competitiveness of the Nation, ISSIMM 2017
SP - 91
EP - 94
BT - Proceedings - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Seminar on Sensor, Instrumentation, Measurement and Metrology, ISSIMM 2017
Y2 - 25 August 2017 through 26 August 2017
ER -