Non-linear calibration models for near infrared spectroscopy.

Different calibration techniques are available for spectroscopic applications that show nonlinear behavior. This comprehensive comparative study presents a comparison of different nonlinear calibration techniques: kernel PLS (KPLS), support vector machines (SVM), least-squares SVM (LS-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS-SVM is also attractive due to its good predictive performance for both linear and nonlinear calibrations.

Data fusion in multivariate calibration transfer.

We report the use of stacked partial least-squares regression and stacked dual-domain regression analysis with four commonly used techniques for calibration transfer to improve predictive performance from transferred multivariate calibration models. The predictive performance from three conventional calibration transfer methods, piecewise direct standardization (PDS), orthogonal signal correction (OSC) and model updating (MUP), requiring standards measured on both instruments, was significantly improved from data fusion either by stacking of wavelet scales or by stacking of spectral intervals, as demonstrated by transfer of calibrations developed on near-infrared spectra of synthetic gasoline. Stacking did not produce as significant an improvement for calibration transfer using a finite impulse response (FIR) filter, but application of SPLS regression to FIR-transferred spectra improves predictive performance of the transferred model.

Wavelet orthogonal signal correction-based discriminant analysis.

We report the use of wavelet orthogonal signal correction (WOSC) for multivariate classification. This new classification tool combines a wavelet prism decomposition of a spectral response and orthogonal signal correction to significantly improve the classification performance, reducing both classification errors and model complexity. Two spectroscopic data sets are examined in this paper. We show that a discriminant analysis based on WOSC effectively removes irrelevant classification information from spectral responses. WOSC-based discriminant analysis performs favorably as compared to a wavelength-domain filtering approach, such as that used in orthogonal partial least-squares discriminant analysis (OPLS-DA).

[Research of extracting and purifying taxol from the branches of Taxus media].

OBJECTIVE: The study was to explore extracting and purifying technology of taxol from the branches of Taxus media. METHOD: In extracting phase, the solvent extraction, ultrasonic extraction and the supercritical CO2 fluid extraction were studied respectively; In purifying phase, the extracts were disposed by silica gel column chromatography and preparation lamella chromatography, then crystaled by N-Hexane. The content of taxol was detected by HPLC. RESULT: The results showed that the recovery of extracting toxal with the method of ultrasonic was the highest and the selectivity of supercritical CO2 fluid extraction was the best; The sample after being extracted should be purified two stages by silica gel column chromatography with dichloromethane-chloroform-methane (53:44:3) as elution and the lamella chromatography was chloroform-ethyl acetate-methane (88:7:5) as elution, finally we reached the fawn crystal. CONCLUSION: In that production, the content of taxol reached 87. 3% and the recovery 89.7%, which indicat that the taxol is well enrichment.

Supercritical CO2 extraction of emodin and physcion from Polygonum cuspidatum and subsequent isolation by semipreparative chromatography.

Emodin and physcion are abundant anthraquinone compounds found in the traditional Chinese medicinal herb Polygonum cuspidatum Sied. et Zucc. In this paper, emodin and physcion were successfully extracted with supercritical CO2 plus ethanol modifier after the extraction conditions were optimized with uniform design-sequential optimization. Results showed that the ethanol modifier concentration was the main factor for the effective extraction of the emodin. The optimal extraction condition was obtained: 20 MPa, 30 degrees C, and 95% ethanol, at which the yields of emodin and physcion were 0.616 and 0.178 g/100 g, respectively. The yield obtained by supercritical fluid extraction (SFE) was a little lower than that obtained by sonication extraction (SE). The crude extract obtained by SFE was further isolated and purified by semipreparative chromatography with the mobile phase composed of methanol-water (90:1, v/v). Emodin and physcion were obtained with purity 98.6 and 99.1%, respectively, when determined by HPLC, and identification was performed by retention time and UV spectra of the standards. The result suggested that SFE is an alternative and promising method for extraction of the two compounds from P. cuspidatum owing to its environment-friendly properties and fewer coextracts.