A method was established using capillary electrophoresis with indirect UV detection for analysis of monosaccharides liberated from exopolysaccharides by acidic hydrolysis. Tangential flow filtration was used to isolate high molecular weight polysaccharides from seawater. The capillary electrophoresis method included the use of a background electrolyte consisting of 2,6-dimethoxyphenol and cetyltrimethylammonium bromide. Several neutral sugars commonly existing in marine polysaccharides were separated under optimized conditions. The relative standard deviations were between 1.3% and 2.3% for relative migration time and 1.3-2.5% for peak height. Detection limits (at S/N 3) were in the range of 27.2-47.8 mu M. The proposed approach was applied to the analysis of hydrolyzed colloidal polysaccharides in seawater collected from the Baltic Sea. Nanomolar levels of liberated monosaccharides in seawater samples can be detected by preconcentration up to 30,000 times. (C) 2010 Elsevier B.V. All rights reserved.

A sparse coefficients wavelength selection and regression (SCWR) method is proposed in the present study. SCWR can rapidly and simultaneously operate regression and select wavelengths on NIR datasets with multiple response variables without any random procedure and cross-validation in the model. The method expresses a normal spectral calibration as a form of least absolute shrinkage and selection operator (LASSO), then the problem is reformulated into the alternative direction multiplier method (ADMM) form. Sparse coefficients wavelength selection (SCWS) method is developed by planting a positive-negative counteract strategy into SCWR, it can select a specified number of wavelengths. A specified number SCWR (NSCWR) is also suggested in order to perform regression using a specified number of wavelengths. SCWR methods have been tested on three NIR datasets (potato, corn, and soil), and these methods have better performance and use fewer feature wavelengths than existing simultaneous regression and wavelength selection methods on predicting almost all attributes in these datasets. Results indicate that SCWR-based methods could select wavelengths with more useful information. For the determination of hyperparameters in SCWR, manual adjustment of hyperparameters is available on sparsity control because the regression performance of SCWR is robustness and insensitive when hyperparameters are in proper ranges.

Abstract This paper presents a novel spectrum analysis tool named synergy adaptive moving window modeling based on immune clone algorithm (SA-MWM-ICA) considering the tedious and inconvenient labor involved in the selection of pre-processing methods and spectral variables by prior experience. In this work, immune clone algorithm is first introduced into the spectrum analysis field as a new optimization strategy, covering the shortage of the relative traditional methods. Based on the working principle of the human immune system, the performance of the quantitative model is regarded as antigen, and a special vector corresponding to the above mentioned antigen is regarded as antibody. The antibody contains a pre-processing method optimization region which is created by 11 decimal digits, and a spectrum variable optimization region which is formed by some moving windows with changeable width and position. A set of original antibodies are created by modeling with this algorithm. After calculating the affinity of these antibodies, those with high affinity will be selected to clone. The regulation for cloning is that the higher the affinity, the more copies will be. In the next step, another import operation named hyper-mutation is applied to the antibodies after cloning. Moreover, the regulation for hyper-mutation is that the lower the affinity, the more possibility will be. Several antibodies with high affinity will be created on the basis of these steps. Groups of simulated dataset, gasoline near-infrared spectra dataset, and soil near-infrared spectra dataset are employed to verify and illustrate the performance of SA-MWM-ICA. Analysis results show that the performance of the quantitative models adopted by SA-MWM-ICA are better especially for structures with relatively complex spectra than traditional models such as partial least squares (PLS), moving window PLS (MWPLS), genetic algorithm PLS (GAPLS), and pretreatment method classification and adjustable parameter changeable size moving window PLS (CA-CSMWPLS). The selected pre-processing methods and spectrum variables are easily explained. The proposed method will converge in few generations and can be used not only for near-infrared spectroscopy analysis but also for other similar spectral analysis, such as infrared spectroscopy.