Abstract：In order to quickly and accurately detect the content of titanium dioxide in the juice，a method combining chemometrics and Vis/NIR spectroscopy technique was used in the present study.First, the content of titanium dioxide in the juice sample was determined by using spectrophotometer and standard curve of titanium dioxide.Then, different amount of pure titanium dioxide was adulterated into the juice collected from the market to prepare eight different content samples.A total of 320 juice samples were studied.Two hundred samples (25 samples for each content) were randomly selected from the 320 samples to be the calibration set while the other 120 samples (15 samples for each content) were selected as the validation set.The spectra of juice were within near infrared(NIR)and mid-infrared (MIR).First six different preprocessing methods were compared, such as standard normal variate (SNV), moving average, derivative and multivariate scatter correction (MSC).The optimal partial least squares(PLS)was built after the performance comparison of different preprocessing methods .Another algorithm, principal component-artificial neural network (PC-ANN), was also used: first, the original spectral date was processed using principal component analysis, the best number of principal components (PCs) was selected, and the scores of these PCs would be taken as the input of the artificial neural network (ANN).The PC-ANN was trained with samples in the calibration collection and the samples in prediction set were predicted.After comparison, MSC was found to be the most appropriate spectral preprocessing method and the best number of PCs is 7.The correlation coefficients (R2) between the real values and predicted ones by discriminant analysis model were 0.900 8 (PLS) and 0.868 4 (PC-ANN) respectively.The root mean standard errors of prediction (RMSEP) by PLS and PC-ANN were 0.05 (PLS) and 0.04 (PC-ANN) respectively.The result indicated that the content of titanium dioxide in the juice powder to be quickly detected by nondestructive determination method was very feasible and laid a solid foundation for setting up the titanium dioxide content forecasting model of juice powder.
Key words：Partial least squares;Artificial neural network;Titanium dioxide;Principal component;Determination
段 敏，鲍一丹，何 勇* . 应用可见/近红外光谱技术快速检测果珍中二氧化钛的含量[J]. 光谱学与光谱分析, 2010, 30(01): 74-77.
DUAN Min, BAO Yi-dan, HE Yong*. Quickly Determination of Titanium Dioxide Content in Juice Based on Vis/NIR Spectroscopy Technique. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(01): 74-77.
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