The feasibility of rapid recognition of an Hg-contaminated plant as a soil pollution indicator was investigated using near-infrared spectroscopy (NIRS) and chemometrics. The stem and leave of a native plant, Miscanthus floridulus (Labill.) Warb. (MFLW), were collected from Hg-contaminated areas ( 1 = 125) as well as from regular areas ( 2 = 116). The samples were dried and crushed and the powders were sieved through an 80-mesh sieve. Reference analysis of Hg levels was performed using inductively coupled plasma-atomic emission spectrometry (ICP-AES). The actual Hg contents of contaminated and normal samples were 16.2-30.5 and 0.0-0.1 mg/Kg, respectively. The NIRS measurements of impacted sample powders were collected in the mode of reflectance. The DUPLEX algorithm was utilized to split the NIRS data into representative training and test sets. Different spectral preprocessing methods were performed to remove the unwanted and noncomposition-correlated spectral variations. Classification models were developed using partial least squares discrimination analysis (PLSDA) based on the raw, smoothed, second-order derivative (D2), and standard normal variate (SNV) data, respectively. The prediction accuracy obtained by PLSDA with each data preprocessing option was 100%, indicating pattern recognition of Hg-contaminated MFLW samples using NIRS data was in perfect consistence with the ICP-AES results. NIRS combined with chemometrics will provide a tool to screen the Hg-contaminated MFLW, which can be potentially used as an indicator of soil pollution.

Two novel chiral Brønsted acids 3b and 3c were prepared without involving the complexity of Suzuki coupling. Catalyst 3c bearing two additional hydroxyl groups at 3 and 3' positions of axially chiral 1,1-binaphthalene-2,2'-diol phosphoric acid was applied in a model Mannich reaction to afford β-amino ester in high yield (92%) and enantiomeric excess (91%) at low reacting temperature of −40 ℃. In addition, those β-amino ester derivatives with high yields and excellent enatioselectivities were obtained in the presence of catalyst 3c under the above condition.

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A series of novel Schiff base derivatives containing 4(3

Multivariate calibration (MVC) and near-infrared (NIR) spectroscopy have demonstrated potential for rapid analysis of melamine in various dairy products. However, the practical application of ordinary MVC can be largely restricted because the prediction of a new sample from an uncalibrated batch would be subject to a significant bias due to matrix effect. In this study, the feasibility of using NIR spectroscopy and the standard addition (SA) net analyte signal (NAS) method (SANAS) for rapid quantification of melamine in different brands/types of milk powders was investigated. In SANAS, the NAS vector of melamine in an unknown sample as well as in a series of samples added with melamine standards was calculated and then the Euclidean norms of series standards were used to build a straightforward univariate regression model. The analysis results of 10 different brands/types of milk powders with melamine levels 0~0.12% (w/w) indicate that SANAS obtained accurate results with the root mean squared error of prediction (RMSEP) values ranging from 0.0012 to 0.0029. An additional advantage of NAS is to visualize and control the possible unwanted variations during standard addition. The proposed method will provide a practically useful tool for rapid and nondestructive quantification of melamine in different brands/types of milk powders.