Article ID Journal Published Year Pages File Type
1231772 Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2016 5 Pages PDF
Abstract

•NIR spectroscopy was proposed for sensing the sensory quality of Chinese rice wine.•A novel algorithm of Si-BP-AdaBoost was used for modeling.•Si-BP-AdaBoost showed superiority in modeling when compared with other algorithms.

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp = 0.9180 and RMSEP = 2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slide

Related Topics
Physical Sciences and Engineering Chemistry Analytical Chemistry
Authors
, , ,