title: [Dynamic predictive modeling of extraction process for red ginseng using near-infrared spectroscopy].
abstract: It is the objective of this study to develop dynamic predictive model for the extraction process of red Ginseng using NIR spectroscopy. NIR spectroscopy was collected online and PLSR models were developed for total quantity of ginsenosides. The performance of NIR prediction model achieved R, RMSEC, RMSEP of 0.996 09, 0.018 9, 0.016 8, respectively. A first order dynamic mass transfer model was combined with NIR prediction of the quality indicator to predict the trajectory of the extraction process based upon the initial 3 or 4 data points. The results showed good agreement with actual measurements indicating reasonable accuracy of the predictive model. It could potentially be used for advanced predictive control of the extraction process.
authors.forename: Jie-Qiang
authors.initials: JQ
authors.lastname: Zhu
authors.forename: Wan-Fang
authors.initials: WF
authors.lastname: Pan
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhong
authors.forename: Xiao-Hui
authors.initials: XH
authors.lastname: Fan
authors.forename: Li-Yuan
authors.initials: LY
authors.lastname: Kang
authors.forename: Zheng
authors.initials: Z
authors.lastname: Li
mesh_terms: D005591:Chemical Fractionation / Q000379:methods*; D036145:Ginsenosides / Q000737:chemistry / Q000302:isolation & purification*; D008962:Models, Theoretical; D005894:Panax / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared
chemical_list: D036145:Ginsenosides