[Detection of the main quality indicators in red wine with infrared spectroscopy based on FastICA and neural network].
For the rapid detection of the ethanol, pH and rest sugar in red wine, infrared (IR) spectra of 44 wine samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of IR spectra, and their independent components and the mixing matrix were obtained. Then, the ICA-NNR calibration model with three-level artificial neural network (ANN) structure was built by using back-propagation (BP) algorithm. The models were used to estimate the contents of ethanol, pH and rest sugar in red wine samples for both in calibration set and predicted set. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation indexes. The results indicate that the r and RMSEP for the prediction of ethanol content, pH and rest sugar content are 0.953, 0.983 and 0.994, and 0.161, 0.017 and 0.181, respectively. The maximum relative deviations between the ICA-NNR method predicted value and referenced value of the 22 samples in predicted set are less than 4%. The results of this paper provide a foundation for the application and further development of IR on-line red wine analyzer.
Fang
Li-Min
LM
College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, China. fanglm1004@163.com
Lin
Min
M
D000465:Algorithms; D002138:Calibration; D002241:Carbohydrates / Q000032:analysis; D000431:Ethanol; D063427:Food Quality; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013055:Spectrophotometry, Infrared; D014920:Wine / Q000032:analysis*
D002241:Carbohydrates; D000431:Ethanol