Xiangou Zhu

Uninformative variable elimination for improvement of successive projections algorithm on spectral multivariable selection with different calibration algorithms for the rapid and non-destructive determination of protein content in dried laver

The potential of using partial least square based uninformative variable elimination algorithm (UVEPLS) on successive projections algorithm (SPA) for spectral multivariable selection was evaluated. A case study was done on the visible and shortwave-near infrared (Vis-SNIR) spectroscopy for the rapid and non-destructive determination of protein content in dried laver. Three calibration algorithms, namely multiple linear regression (MLR), partial least square regression (PLS) and least-square support vector machine (LS-SVM), were used for the model establishment based on the selected variables of SPA, UVEPLS and UVEPLS-SPA, respectively. A total of 175 samples were prepared for the calibration (n = 117) and prediction (n = 58) sets. The performances of different pretreatments were compared. Both linear calibration algorithms of MLR and PLS and non-linear calibration algorithms of LS-SVM with linear kernel and RBF kernel obtained similar results based on certain variable selection strategies of SPA, UVEPLS and UVEPLS-SPA. The average improvement percentage of RPD values of four calibration algorithms was 38.66% by calculating SPA on UVEPLS processed variables. Therefore there was much improvement of using UVEPLS on SPA spectral multivariable selection with both linear and nonlinear calibration algorithms in this case. Moreover, the RPD values of both linear and non-linear models based on the thirteen selected variables of UVEPLS-SPA show that coarse quantitative predictions of the protein determination in dried laver is possible based on Vis-SNIR spectra. We hope that the results obtained in this study will help both further chemometric (multivariate selection and calibration analysis) investigations and investigations in the sphere of applied vibrational (Near infrared, Mid-infrared and Raman) spectroscopy of sophisticated multicomponent systems.

Rapid Determination of Enantiomeric Excess of Tert-Butoxycarbonyl (BOC-Protected) Amino Acids Based on Infrared Spectra Technique with Optimal Wavelet Packet Transform Decomposition Frequency Band

A method was employed to determine enantiomeric excess (ee) value of chiral tert-butoxycarbonyl (Boc-protected) amino acids in a rapid way by using an infrared spectroscopy technique combined with wavelet packet transform (WPT) and least squares support vector machines (LS-SVM). Infrared spectral data were decomposed by using WPT algorithm. Simulated annealing (SA) algorithm was then used to search the optimal decomposed frequency band that had the greatest contribution to the quantitative analysis of ee values. As a result, the band (7, 34) with 34 variables and the band (5, 1) with 116 variables were determined as the optimal ones for the determination of Boc-protected proline and alanine, respectively. The selected variables in the optimal band were used as the inputs of LS-SVM models. The spectral variables selected by the WPT-SA method had lower predicting errors than full range spectra and the spectral variables selected by some traditional variable selection methods. Reasonable good results with root mean-square error of prediction (RMSEP) of 7.51 and 3.80 were obtained for the determination of ee values of two Boc-protected amino acids, showing that it is possible to rapidly determine ee values of amino acids by using IR spectroscopy rapidly.

Rapid determination of enantiomeric excess of protected amino acids by catalytic amounts of chiral reagent

A protocol for the determination of enantiomeric excess (ee) of protected chiral amino acids using the frequency shifts in nuclear magnetic resonance (NMR) spectra induced by catalytic amounts of chiral auxiliary is described. Principal component analysis (PCA) method shows the difference in NMR shifting which shows no obvious pattern, Least Square-Support Vector Machine (LS-SVM) technique with high nonlinear processing ability is employed to analyze unknown ee values.

Determination of enantiomeric compositions of ibuprofen by infrared spectrometry with catalytic amount of simple chiral recognization reagent

Catalytic amount of cheap chiral recognization reagent (quinine) combined with chemometric and infrared spectroscopy (IR) is used for quickly and accurately determining the enantiomeric compositions of ibuprofen. First of all, full spectrum was used as input variable of partial least regression (PLS) to establish calibration model, good prediction rate of average error of 4.65% is obtained. Then, wavelet transform (WT) algorithm with strong compression ability is employed to get more concise model. The low-frequency coefficients are extracted by the simplest Harr wavelet function, and the as input variables to establish the calibration, and good prediction rate of average error of 3.60% is obtained. All the results show that the combination of partial least squares and infrared spectroscopy can be used for quickly and accurately predict the enantiomeric excess (ee) value of ibuprofen.

Notice of Retraction Combination of ^1H NMR and Chemometrics for Discrimination of Rapeseed Oils

1H NMR spectroscopy was utilized to distinguish the brands of rapeseed oils. As there are more than four hundreds of NMR variables, uninformative variables should be eliminated to improve models discrimination ability and save the calculation time. A hybrid variable selection which is combined with uninformation variable elimination (UVE) and successive projections algorithm (SPA), was used to achieve this objective. 77 effective variables were selected from the full-spectrum variables. They were inputted into least-square support vector machine (LS-SVM) to establish the discrimination model. A good result of 92.5% correct answer rate was obtained. It is improved compared to the result of the full spectrum- SPA-LS-SVM model. It is proved that it is necessary to do UVE before SPA. As a conclusion, the performance of UVE-SPALS-SVM model shows that 1H NMR spectroscopy is a feasible way to distinguish rapeseed oils fast and accurately.

Notice of Retraction Growing area determination of Tremella fuciformis using visible and near-infrared spectroscopy

Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA) was applied to select the effective variables from the full-spectrum (FS) which have 675 spectra variables. Finally eleven variables were selected. Effective variables based LS-SVM model obtain 100% determination correct rate. It was proved that SPA was an effective algorithm for spectra variable selection. As a conclusion, Vis-NIR spectroscopy could be used to determine the growing areas of Tremella fuciformis fast and accurately.

A Hybrid Method for Visual Near-Infrared Spectral Calibration for Discrimination of Panax Species

A novel method which was combination of uninformative variable elimination by partial least squares (UVE-PLS) and simulated annealing (SA) was proposed to extra relevant information among different varieties of panax. A total of 78 (26 for each variety) samples were characterized on the basis of Visual and infrared spectroscopy (VIS-NIR), 63 (21 for each variety) samples were selected randomly for the calibration set, whereas, the remained 15 samples (5 for each variety) for validation set. UVE-PLS was executed to obtain the stability of each input variables. The best cutoff values of stability was determined as 34 using SA. Finally, 289 variables were mined, and inputted into partial least squares regression (PLSR) to build recognition model. The optimal model with the correlation coefficient of 0.9878, root mean square error of prediction (RMSEP) of 0.1297 was obtained. The performance was better than common PLS model which was established using the whole spectral wavelengths. The overall results indicated that the proposed method of UVE-PLS-SA was a powerful way to select diagnostic information for discrimination of different varieties of panax

Application of an Integration of Uninformative Variable Elimination and Least-Squares Support Vector Machine for Discriminating Soy Milk Powder Based on Visual Near-Infrared Spectral Calibration: Disc

A novel method which is combination of uninformative variable elimination by partial least squares (UVE) and least-square support vector machine (LS-SVM) was proposed to discriminate soy milk powder. A total of 240 (60 for each variety) samples were characterized on the basis of visual and infrared spectroscopy (VIS-NIR), 160 (40 for each variety) samples were selected randomly for the calibration set, whereas, the remaining 80 samples (20 for each variety) for prediction set. UVE was executed to obtain the stability of each input variables. 327 wavelengths were selected by UVE, and inputted into LS-SVM to build recognition model. The classification rate reached 100%, and the performance was better than common LV-SVM model which was established using the whole spectral wavelengths. The overall results indicated that the proposed method of UVE-LS-SVM based VIS-NIR technology was a powerful way for discrimination of different varieties of soy milk powder.