The objective of this study was to predict the moisture content, soluble solids content, and titratable acidity content in bell peppers during storage, based on hyperspectral imaging (HSI) in the 1000–1500 nm wavelength range. The mean spectra of 148 mature bell peppers were extracted from the hyperspectral images, and multivariate calibration models were built using partial least squares regression to predict MC, SSC, and TA content in bell peppers with different preprocessing techniques. The selected optimum wavelengths were used to create distribution maps for MC, SSC, and TA content of bell peppers. The results revealed that HSI coupled with multivariate analysis can be used successfully to predict the MC, SSC, and TA content in bell peppers.

Date Published: 15 May 2018
PDF
Proc. SPIE 10665, Sensing for Agriculture and Food Quality and Safety X, 1066506 (15 May 2018); doi: 10.1117/12.2307709

Published in SPIE Proceedings Vol. 10665:
Sensing for Agriculture and Food Quality and Safety X
Moon S. Kim; Kuanglin Chao; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)