[Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural Network].

Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors. The excitation-emission fluorescence matrices (EEMs) of 120 samples with various years were measured by FLS920 fluorescence spectrometer. The trilinear decomposition of the data array was performed and the loading scores of and the excitation-emission profiles of four components were also obtained. The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed. 10 samples were collected from 10, 20 and 30 years of liquors respectively, and 30 samples were selected as the test sets. The remaining 90 samples were used as the training sets to build the training model. The year prediction of unknown samples was also carried out, and the prediction accuracy was 90%, 100% and 100%, respectively. Meanwhile, the discrimination analysis method and the multi-way partial least squares discriminant analysis were compared, namely PARAFAC-BP and NPLS-DA. The results indicated that parallel factor combined with the neural network (PARAFAC-BP) has higher prediction accuracy. The proposed method can effectively extract the spectral characteristics, and also reduce the dimension of the input variables of neural network. A good year discrimination result was finally achieved.

Determination of components of mixed color solutions by fluorescence spectroscopy combined with calibration algorithms.

The three-dimensional spectra of mixed solutions of allure red, sunset yellow and brilliant blue were obtained. Then the three synthetic food colors were determined by parallel factor analysis (PARAFAC) and alternating trilinear decomposition (ATLD) algorithms, respectively. The component number of model is three by core-consistency diagnostic. The average recoveries of allure red, sunset yellow and brilliant blue obtained by PARAFAC were 98.75% +/- 8.9%, 97.22% +/- 2.9% and 99.00% +/- 2.9% and those by ATLD algorithm were 99.78% +/- 5.9%, 92.52% +/- 5.5% and 97.23% +/- 5.8%, respectively. Results show that both of the algorithms can be used in direct and rapid determination of multi-components of mixtures. From further comparison, the PARAFAC is more stable and advantageous.