Xiaoyu Cui

Water can be a probe for sensing glucose in aqueous solutions by temperature dependent near infrared spectra

Near infrared (NIR) spectra are sensitive to the variation on water structure caused by perturbations, such as temperature and additives. In this work, water was applied as a probe to detect glucose in aqueous glucose solutions and human serum samples. Spectral changes of water were captured from the temperature dependent NIR spectra using multilevel simultaneous component analysis (MSCA). The first and second level model were established to describe the quantitative spectra-temperature relationship (QSTR) and the quantitative spectra-concentration relationship (QSCR), i.e., the calibration curve, respectively. The score of the first level model shows that the content of free OH in water molecules increases with temperature elevation. The correlation coefficients (R2) of the QSTR model between the score and temperature are higher than 0.99, and that of the calibration model (QSCR) between the spectral features of water clusters and the concentration of glucose are 0.99 and 0.84 for glucose solutions and serum samples, respectively. External validation of the calibration model was further performed with human serum samples. The standard error of the prediction is 0.45. In addition, the linearity of the QSCR models may reveal that glucose interacts with small water clusters and enhances the formation of the hydration shell. Therefore, using water as a probe may provide a new way for quantitative determination of the analytes in aqueous solutions by NIR spectroscopy.

Glucose induced variation of water structure from temperature dependent near infrared spectra

Water structure variation induced by carbohydrates is a fundamentally important problem in the fields of chemistry and biology for the understanding of life processes. The structural variation of water with concentration and temperature was investigated using temperature dependent near infrared (NIR) spectra of glucose solutions following the concept of aquaphotomics and the results obtained using terahertz (THz) spectroscopy. The spectra were processed using a continuous wavelet transform (CWT), from which the presence of different water species in solutions was confirmed. To further analyze the water structure, Gaussian fitting of NIR spectra was performed using a genetic algorithm optimization. Spectral components relating to six water species were obtained. Through the variation of these species with temperature, the breaking of the hydrogen bonds in water structures was observed. On the other hand, through the variation of these structures with glucose concentration, interactions between the solute and water molecules, as well as the enhancement of the ordered (tetrahedral) hydrogen bonded cluster induced by the interactions, were found. Comparing with the results obtained using THz spectroscopy, the hydration effect is revealed in high concentration solution. Therefore, the glucose in aqueous solution makes the water structure more ordered. This may be the reason for the bioprotective function of carbohydrates.

Mutual factor analysis for quantitative analysis by temperature dependent near infrared spectra

Temperature dependent near infrared (NIR) spectroscopy has been developed for analyzing multi-component mixtures and understanding the molecular interactions in solutions. In this work, a chemometric method named as mutual factor analysis (MFA) was proposed for the analysis of temperature dependent NIR spectra. The method extracts the common spectral feature contained in the spectra of different temperature or different concentration. The relative quantity of the extracted spectral feature is proportional to the temperature or concentration. From the spectra of water-glucose mixtures, both the spectral variations induced by temperature and concentration are obtained and the variations are correlated with the inducements, respectively, in a very good linearity. Serum samples were used for validation of the method. An acceptable calibration model with a good correlation coefficient (R2 = 0.8639) was obtained for glucose measurement. The relative deviations of the measured concentrations from the calibration model are in the range of -18.7-8.52%, which are in a reasonable level for clinical uses. More importantly, the calculations are based on the spectral information of water that has interactions with the analyte. This provides a new way for quantitative analyses of bio-systems.

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine. This study aimed at development of a method for rapid and reliable diagnosis using near-infrared (NIR) spectra of human serum samples with the help of chemometric modelling. The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed. Discrete wavelet transform (DWT) and variable selection were adopted to extract the useful information from the spectra. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLSDA) were used for discrimination of the samples. Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection. DWT-LDA produced 93.8% and 83.3% of the recognition rates for the validation samples of the two classes, and 100% recognition rates were obtained using DWT-PLSDA. The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection, and the differences can be used for discrimination of the sera from healthy and possible patients. NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.

Combination of heuristic optimal partner bands for variable selection in near-infrared spectral analysis: Variable selection for near infrared spectral analysis

Variable selection plays a critical role in the analysis of near‐infrared (NIR) spectra. A method for variable selection based on the principle of the successive projection algorithm (SPA) and optimal partner wavelength combination (OPWC) was proposed for NIR spectral analysis. The method determines a number of knot variables with sufficient independence by SPA, and candidate variable bands with a definite width are defined. The cooperative effect of the bands is then evaluated with the partial least squares regression model by using the method of OPWC. The performance of the proposed method was compared with those of SPA, OPWC, randomization test, competitive adaptive reweighted sampling, and Monte Carlo uninformative variable elimination by using NIR datasets for pharmaceutical tablets, corn, and soil. The results show that the proposed method can select informative variable bands with a cooperative effect and improves the model for quantitative analysis.

A novel bioemulsifier from Geobacillus stearothermophilus A-2 and its potential application in microbial enhanced oil recovery

Biosurfactants can improve the mobility of oils in porous media by changing the rock wettability and emulsifying oils, thus increasing the efficiency of crude oil recovery in the petroleum industry. Therefore, surfactant-producers play important roles in the microbial enhanced oil recovery (MEOR) process. In this study, a thermophilic, facultative anaerobic emulsifier-producing strain was isolated. The emulsifier produced and its potential applications in MEOR were investigated in the laboratory. The stain was identified as Geobacillus stearothermophilus, and was designated as A-2, which could use sodium acetate, which is relative abundant in reservoirs, as the carbon source. The produced bioemulsifier is a novel glycoprotein emulsifier, containing 71.4% sugar and 27.8% protein, wherein the monosaccharides were identified as mannose (33.5%), glucose (30.9%), galactose (29.7%), and glucuronic acid (5.9%); and the protein contained 17 types of amino acids. The bioemulsifier successfully emulsified various hydrocarbons at a wide range of salinity, temperature, and pH. Notably, the emulsion layer of diesel remained stable for 12 months at room temperature, with little change at the micron level in the particle size of oil droplets. Core flooding tests indicated that the fermentation broth of strain A-2 increased the oil recovery efficiency by 6.8% under lower oil saturation condition, showing potential applications in oil exploration in high-temperature oil reservoirs.

Modified linear model correction: A calibration transfer method without standard samples

Calibration transfer without standard samples is essential for practical applications of near infrared spectroscopy because, sometimes, it is difficult or even impossible to obtain the standard samples for measuring their spectra on the secondary instrument. In this work, a modified linear model correction method is proposed for improving the transfer accuracy and computational efficiency. The constraint of linear model correction was replaced by a robust convex equation to restrict the model similarity in the optimization. The near infrared dataset of pharmaceutical tablet measured with different instruments are used to test the performance of the method. The result shows that a modified linear model correction achieves a high efficiency of the transfer while the computational complexity can be considerably reduced. The method may provide a robust way in practical application.