Open AccessHyperspectral imaging technology for monitoring of moisture contents of dried persimmons during drying process
Author(s) -
JeongSeok Cho,
JiYoung Choi,
Kwang-Deog Moon
Publication year - 2020
Publication title -
food science and biotechnology/food science and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.595
H-Index - 38
eISSN - 2092-6456
pISSN - 1226-7708
DOI - 10.1007/s10068-020-00791-x
Subject(s) - hyperspectral imaging , water content , partial least squares regression , moisture , absorption (acoustics) , near infrared spectroscopy , chemistry , analytical chemistry (journal) , remote sensing , materials science , environmental chemistry , mathematics , optics , composite material , statistics , geotechnical engineering , organic chemistry , physics , engineering , geology
The moisture content of persimmons during drying was monitored by hyperspectral imaging technology. All persimmons were dried using a hot-air dryer at 40 °C and divided into seven groups according to drying time: semi-dried persimmons (Cont), 1 day (DP-1), 2 days (DP-2), 3 days (DP-3), 4 days (DP-4), 5 days (DP-5), and 6 days (DP-6). Shortwave infrared hyperspectral spectra and moisture content of all persimmons were analyzed to develop a prediction model using partial least squares regression. There were obvious absorption bands: two at approximately 971 nm and 1452 nm were due to water absorption related to O-H stretching of the second and first overtones, respectively. The R-squared value of the optimal calibration model was 0.9673, and the accuracy of the moisture content measurement was 95%. These results indicate that hyperspectral imaging technology can be used to predict and monitor the moisture content of dried persimmons during drying.