[The Discrimination of Chinese Strong Aroma Type Liquors with Three-Dimensional Fluorescence Spectroscopy Combined with Principal Component Analysis and Support Vector Machine].

In this paper, a method for discrimination of different bands liquor with strong aroma type based on three-dimensional fluorescence spectrum technology was developed. Firstly, the three-dimensional fluorescence spectra of seven different brands liquor were measured by the FLS920 fluorescence spectrometer which produced by Edinburgh in England. The spectral show that different bands liquors have similar fluorescence characteristics and it's difficult to distinguish them only with Fluorescent characteristic parameters. Because of this, the first-order and second-order partial derivatives respect to fluorescence emission wavelength on each of the excitation wavelength were carried out in this paper. Daubechies7 (db7) orthonormal wavelet with compact support was used to compress the spectral data. The forth approximate coefficients were finally chosen as the new data matrix. Then the new data matrix was analyzed by principal component analysis (PCA) and the principal components were extracted to be used as the inputs of support vector machine (SVM). The K-fold cross validation was applied to optimize the parameters c and y and the prediction model was constructed in the end. Fourteen samples were selected randomly from each brand that in total of ninety-eight samples were selected as the training set, and the rest forty-two samples were collected as the prediction set. The effect of three different spectral data after processing on the model is compared, original data, the first-order and second-order partial derivatives on the spectral data. The results show that the three-dimensional fluorescence spectra with the pretreatment of second-order partial derivatives coupled with PCA and SVM can make a good performance on the brands identification of strong aroma type liquors, the accuracy of the established model and prediction accuracy were 98.98% and 100%, respectively. This method has the advantage of easy operation, high speed, low cost and provides a good help in the detection and identification of Chinese liquor.

[Soft Measurement of the Purity of the Synthetic Edible Pigment Powder Using Fluorescence Spectroscopy Combined with SVM].

The feature compression algorithm which was reformed from the original Moment method was used for the pre-processing of the fluorescence spectral data, then combined the data and the Weighted Least Squares Support Vector Machine(WLS-SVM) algorithm to establish a robust regression model, which is used for forecasting the purity of edible pigment powder. In this paper, brilliant blue and ponceau 4R served as an example to discuss the method of forecasting effect of edible pigment powder purity. The emission fluorescence spectra of two edible pigment at the optimal excitation wavelength were measured by FLS920 fluorescence spectrometer. The compression and transformation of the fluorescence spectral data was acquired by the feature compression algorithm reformed from the Original Moment method. On the one hand the feature compression algorithm shortened the operation time, on the other hand it improved the prediction accuracy of the model. Then, the concentration prediction model was established after inputting the fluorescence spectral data pre-processed into the Weighted Least Squares Support Vector Machine. The model gave anastomotic predicted spectral data with the actual experiments of the brilliant blue and ponceau 4R, and the average coefficient of determination in the half peak width was 0.700 and 0.930 respectively. There was a good linear relationship between the predicted and the nominal concentration of the brilliant blue and ponceau 4R, and the correlation coefficients were 0.997 and 0.992 respectively. It can be concluded that, the predicted concentration of the brilliant blue and ponceau 4R powder were got the results of 61.0% and 72.3% respectively.

[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.

[Commercial orange juice beverages detection by fluorescence spectroscopy combined with PCA-ED and PLSR methods].

In order to classify the orange juiice beverages effectively, the fluorescence character differences of two kinds of orange juice beverages including 100% orange juice and orange drink were analyzed and compared, principal component analysis combined with Euclidean distance was adopted to classify two kinds of orange juice beverages, and ideal classification results were obtained. Meanwhile, the orange juice content estimation model was established by using fluorescence spectroscopy combined with partial least squares regression method, and the correlation coefficient R, root mean square error of calibration RMSEC and root mean square error of prediction RMSEP were 0.997, 0.87% and 2.05%, respectively. The experimental results indicate that the calibration model offers comparatively accurate content estimation, which reflect the actual orange juice content in the commercial orange juice beverages. The exploration to classify orange juice beverages was carried out from two aspects of qualitative and quantitative analysis by employing fluorescence spectroscopy combined with chemometrics method, which can provide a new idea for the classification and adulteration detection of commercial orange juice beverages, and also can give certain reference basis for the quality control of orange juice raw material.