Open AccessEvaluation of the moisture content of tapioca starch using near-infrared spectroscopy
Author(s) -
Kittisak Phetpan,
Panmanas Sirisomboon
Publication year - 2015
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545815500145
Subject(s) - starch , water content , moisture , near infrared spectroscopy , absorption of water , partial least squares regression , chemistry , food science , materials science , analytical chemistry (journal) , chromatography , mathematics , composite material , statistics , physics , geotechnical engineering , quantum mechanics , engineering
The purpose of this study was to develop a calibration model to evaluate the moisture content of tapioca starch using the near-infrared (NIR) spectral data in conjunction with partial least square (PLS) regression. The prediction ability was assessed using a separate prediction data set. Three groups of tapioca starch samples were used in this study: tapioca starch cake, dried tapioca starch and combined tapioca starch. The optimum model obtained from the baseline-offset spectra of dried tapioca starch samples at the outlet of the factory drying process provided a coefficient of determination (R2), standard error of prediction (SEP), bias and residual prediction deviation (RPD) of 0.974, 0.16%, -0.092% and 7.4, respectively. The NIR spectroscopy protocol developed in this study could be a rapid method for evaluation of the moisture content of the tapioca starch in factory laboratories. It indicated the possibility of real-time online monitoring and control of the tapioca starch cake feeder in the drying process. In addition, it was determined that there was a stronger influence of the NIR absorption of both water and starch on the prediction of moisture content of the model