Peijin Tong

Analysis of polycyclic aromatic hydrocarbons in water with gold nanoparticles decorated hydrophobic porous polymer as surface-enhanced Raman spectroscopy substrate.

A method for surface-enhanced Raman spectroscopy (SERS) sensing of polycyclic aromatic hydrocarbons (PAHs) is reported. Gold nanoparticles (AuNPs) decorated hydrophobic porous glycidyl methacrylate-ethylene dimethacrylate (GMA-EDMA) polymer is developed as the SERS substrate. GMA-EDMA material with porosity and permeability shows rapid and efficient adsorption of PAHs through presumed hydrophobic interaction, which brings the analytes close to the substrate. Meanwhile, the three dimensional porous morphology might benefit AuNPs distribution for high SERS enhancement. Studies on the effects of AuNPs surface coverage on the substrate and time of PAHs-substrate interaction are presented. The qualitative analysis and quantitative tendency of this method for PAHs detection are investigated with anthracene, phenanthrene and pyrene as probe molecules, showing that the characteristic fingerprint vibrational peaks of each PAH can be readily identified, and the limit of detections are 0.93×10(-7), 4.5×10(-7) and 1.1×10(-7) M respectively. Moreover, the substrate exhibits high reproducibility with the relative standard deviation about 16% in spot and spot SERS intensity. Using this method for rapid screening of PAHs mixture in some water samples are performed well, which might be useful for environmental pollutions monitoring.

Development of on-line spectroscopic determination approach of dispersive liquid–liquid microextraction based on an effective device

A novel, rapid, simple, and low-cost on-line determination approach of dispersive liquid-liquid microextraction (DLLME) with low-density solvents was developed with the support of a specially designed effective homemade device. The proposed method surmounted the drawbacks of conventional DLLME of the need of high-density solvents as extractants, and the requirement of centrifugation operation to obtain phase separation, and the difficulties to realize on-line determination. The amount of sample utilized can conveniently change according to practical needs by varying the volume of the extraction tube of the device to perform a more effective DLLME. A case study was carried out to assess this method utilizing the dye rhodamine B as the model analyte. The experiment parameters influencing the extraction were systematically investigated. Under optimum conditions, the linearity was obtained in the range of 0.015-1.000 μg/mL with the correlation coefficient (r(2)) of 0.9980. The limit of detection and quantification were 6.1 and 20.4 μg/L, respectively. Good repeatability was achieved with the relative standard deviations (RSD) for five replicate measurements of different concentration samples less than 4.06%, and the presented method was successfully employed to quantify rhodamine B in three real samples.

Simultaneous detection of trace metal ions in water by solid phase extraction spectroscopy combined with multivariate calibration.

Solid Phase Extraction Spectroscopy (SPES) developed in this paper is a technique to measure spectrum directly on the solid phase material where the analytes are concentrated in SPE process. Membrane enrichment and UV-Visible spectroscopy were utilized to fulfill SPES, and multivariate calibration method of partial least squares (PLS) was used to simultaneously detect the concentrations of trace cobalt (II) and zinc (II) in water samples. The proposed method is simple, sensitive and selective. The complexes of analyte ions were collected on the cellulose acetate membranes via membrane filtration after the complexation reaction with 1-2-pyridylazo 2-naphthol (PAN). The spectra of the membranes which contained the complexes of metal ions and PAN were measured directly without eluting. The analytical conditions including pH, reaction time, sample volume, the amount of PAN, and flow rates were optimized. Nonionic surfactant Brij-30 was absorbed on the membranes prior to SPES to modify the membranes for improving the enrichment and spectrum measurement. The interference from other ions to the determination was investigated. Under the optimal condition, the absorbance was linearly related to the concentration at the range of 0.1-3.0 μg/L and 0.1-2.0 μg/L, with the correlation coefficients (R(2)) of 0.9977 and 0.9951 for Co (II) and Zn (II), respectively. The limits of detection were 0.066 μg/L for cobalt (II) and 0.104 μg/L for zinc (II). PLS regression with leave-one-out cross-validation was utilized to build models to detect cobalt (II) and zinc (II) in drinking water samples simultaneously. The correlation coefficient between ion concentration and spectrum of calibration set and independent prediction set were 1.0000 and 0.9974 for cobalt (II) and 1.0000 and 0.9956 for zinc (II). For cobalt (II) and zinc (II), the errors of the prediction set were in the range 0.0406-0.1353 μg/L and 0.0025-0.1884 μg/L.

Determination of trace analytes based on diffuse reflectance spectroscopic techniques: development of a multichannel membrane filtration-enrichment device to improve repeatability

The determination of trace analytes based on membrane filtration-enrichment and diffuse reflectance spectroscopic techniques has gained increasing interest in the past decade due to its simplicity, rapidity and high sensitivity. However, poor repeatability primarily attributed to the differences of characteristics between membrane filters limits the development of this technique. In the current study, a simple and effective multichannel device is specially designed for the membrane filtration-enrichment process. The device is able to enrich six samples simultaneously on different positions of a membrane filter and allows the spectroscopic measurement of six samples with only one membrane filter. The proposed approach avoided the effects caused by the nonuniform membrane filters on the performance of the enrichment process. Accuracy and repeatability have been improved significantly for the subsequent on-line spectroscopic detection. A case study was carried out to assess this method utilizing the carcinogenic dye rhodamine B (RhB) as a model analyte. Under the optimal conditions, linearity of the calibration curve based on the Kubelka–Munk function was achieved in the concentration range of 2–30 μg L−1 with the correlation coefficient (R2) of 0.9924. Good repeatability was achieved with three average relative standard deviation (RSD) values of 3.6%, 3.8% and 3.8% corresponding to the solutions of 30, 10 and 5 μg L−1 RhB, respectively. The presented method was successfully employed to quantify RhB in soft drink and river water samples.

A novel alternating least-squares method based on fixed region scanning evolving factor analysis (FRSEFA) and its application in process monitoring

A new method called fixed region scanning evolving factor analysis (FRSEFA) is proposed based on fixed-size moving window evolving factor analysis (FSMWEFA). In FRSEFA, a base set matrix Xbs that is a submatrix of the entire spectra data matrix X, corresponding to a special region in the time direction, is selected. Then, the base set Xbs combines a moving window xi containing only one time point to construct a submatrix Xt,i. A series of eigenvalues are obtained to accurately determine the starting time or ending point because the moving window contains only one time point, so that the problem of time shift in FSMWEFA can be solved. The combination of FRSEFA and alternating least squares (ALS) is utilized to determine the precise time of the transformation of components during the reaction and to obtain the profiles of concentration and spectrum of the components of interest in the mixture. Two real experimental data were studied by the developed algorithm. The results showed that the precise time of transformation could be obtained and spectrum and concentration profiles were also estimated, providing more useful information for reaction processes.

Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometrics methods

Near infrared spectroscopy (NIRS), coupled with principal component analysis and wavelength selection techniques, has been used to develop a robust and reliable reduced-spectrum classification model for determining the geographical origins of Nanfeng mandarins. The application of the changeable size moving window principal component analysis (CSMWPCA) provided a notably improved classification model, with correct classification rates of 92.00%, 100.00%, 90.00%, 100.00%, 100.00%, 100.00% and 100.00% for Fujian, Guangxi, Hunan, Baishe, Baofeng, Qiawan, Sanxi samples, respectively, as well as, a total classification rate of 97.52% in the wavelength range from 1007 to 1296 nm. To test and apply the proposed method, the procedure was applied to the analysis of 59 samples in an independent test set. Good identification results (correct rate of 96.61%) were also received. The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model (290 variables) into account. The results of the study showed the great potential of NIRS as a fast, nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classification of Nanfeng mandarins.

Ensemble Regression Coefficient Analysis for Application to Near-Infrared Spectroscopy

A new variable selection method called ensemble regression coefficient analysis is reported on the basis of model population analysis. In order to construct ensemble regression coefficients, many subsets of variables are randomly selected to calibrate corresponding partial least square models. Based on ensemble theory, the mean of regression coefficients of the models is set as the ensemble regression coefficient. Subsequently, the absolute value of the ensemble regression coefficient can be applied as an informative vector for variable selection. The performance of ensemble regression coefficient analysis was assessed by four near infrared datasets: two simulated datasets, one wheat dataset, and one tobacco dataset. The results showed that this approach can select important variables to obtain fewer errors compared with regression coefficient analysis and Monte Carlo uninformative variable elimination.