Discriminant analysis of Chinese patent medicines based on near-infrared spectroscopy and principal component discriminant transformation.

Principal component discriminant transformation was applied for discrimination of different Chinese patent medicines based on near-infrared (NIR) spectroscopy. In the method, an optimal set of orthogonal discriminant vectors, which highlight the differences between the NIR spectra of different classes, is designed by maximizing Fisher's discriminant function. Therefore, a model for discriminating a class and the others can be obtained with the tiny differences between the NIR spectra of different classes. Furthermore, because NIR spectra contain a large amount of redundant information, principal component analysis (PCA) is employed to reduce the dimension. On the other hand, continuous wavelet transform (CWT) is taken as the pretreatment method to remove the variant background. For identifying the method, different medicines and the same medicine from different manufactures were studied. The results show that all the models can provide 100% discrimination.

Quantitative analysis of 17 amino acids in tobacco leaves using an amino acid analyzer and chemometric resolution.

A method was developed for quantifying 17 amino acids in tobacco leaves by using an A300 amino acid analyzer and chemometric resolution. In the method, amino acids were eluted by the buffer solution on an ion-exchange column. After reacting with ninhydrin, the derivatives of amino acids were detected by ultraviolet detection. Most amino acids are separated by the elution program. However, five peaks of the derivatives are still overlapping. A non-negative immune algorithm was employed to extract the profiles of the derivatives from the overlapping signals, and then peak areas were adopted for quantitative analysis of the amino acids. The method was validated by the determination of amino acids in tobacco leaves. The relative standard deviations (n = 5) are all less than 2.54% and the recoveries of the spiked samples are in a range of 94.62-108.21%. The feasibility of the method was proved by analyzing the 17 amino acids in 30 tobacco leaf samples.

Fast determination of ginsenosides in ginseng by high-performance liquid chromatography with chemometric resolution.

Ginseng is a well-known traditional Chinese medicinal herb, and ginsenosides are its major active components. A method for the fast determination of ginsenosides in ginseng samples by high-performance liquid chromatography was developed and used for the quantitative analysis of four ginsenosides in three different ginseng samples. In this method, instead of time-consuming gradient elution, isocratic elution was used to speed up the analysis. Under strong isocratic elution, all the ginsenosides are eluted in 2.3 min. Although the measured signal is composed of overlapped peaks with the interferences and background, the signal of ginsenosides can be extracted by chemometric resolution. A non-negative immune algorithm was employed to obtain the chromatographic information of the target components from the data. Compared with conventional chemometric approaches, the method can perform the extraction for one-dimensional overlapping signals. The method was validated by the determination of four ginsenosides in three different ginseng samples. The recoveries of the spiked samples were in the range of 94.08-107.3%.

A strategy for enhancing the quantitative determination ability of the diffuse reflectance near-infrared spectroscopy.

Near-infrared diffuse reflectance spectroscopy (NIRDRS) has been proved to be a convenient and fast quantitative method for complex samples. The high detection limit or the low sensitivity of the method, however, is a big problem obstructing its application in the analysis of low concentration samples. A strategy for quantitative determination of low concentration samples was developed by using NIRDRS. The method takes an adsorbent as a substrate for gathering the analytes from a solution, and uses the multivariate calibration technique for quantitative calculation. So, the detection limit can be improved and the interferences can be eliminated when complex samples are analyzed. Taking benzoic and sorbic acids as the analyzing targets and the alumina as the adsorbent, partial least squares (PLS) model is built from the NIRDRS of the adsorbates. The results show that the concentrations that can be quantitatively detected are as low as 0.011 and 0.013 mg mL(-1) for benzoic and sorbic acids, respectively, and the co-adsorbates do not interfere each other.

SHEF: a vHTS geometrical filter using coefficients of spherical harmonic molecular surfaces.

SHEF (spherical harmonic coefficient filter), a geometrical matching procedure constituting a preliminary step in the virtual high throughput screening of large databases of small drug-like molecules, is demonstrated. This filter uses a description of both the binding site of the target and the ligand surfaces using spherical harmonic polynomial expansions. Using this representation, which is based on limited sets of spherical harmonic coefficients, considerably reduces the complexity of surface complementarity calculation. As a first test, 188 known protein-ligand complexes were used, and the results of docking the abstracted ligands into the bare proteins using SHEF were compared to the original X-ray structures. The ability of SHEF to retrieve known ligands "hidden" in a virtual library of 1,000 randomly selected drug-like compounds is also demonstrated.

A fast surface-matching procedure for protein-ligand docking.

A very simple, fast, and efficient scheme is proposed for performing preliminary protein-ligand docking as the first step of intensive high-throughput virtual screening. The procedure acts as a surface-complementarity filter that first calculates the 2D-contour maps of both the protein cavity and of the ligands using a spherical harmonics description of the associated molecular surfaces. Next, the obtained 2D-fingerprint images are compared to detect their complementarity. This scheme was tested on three typical cases of protein cavities, namely, a well-closed pocket, a small open pocket, and a large open one. For that purpose, for each case, a sample of 101 ligand conformers was generated (the X-ray one and 100 different conformers generated using simulated annealing), and these conformational samples were ranked according to the complementarity with the protein cavity surface. Compared to traditional docking procedures such as FRED (considered as typical of a very fast rigid body docking algorithms) and GOLD (considered as typical of the more accurate flexible docking algorithms), our procedure was much faster and more successful in detecting the right X-ray conformation. We did, however, identify a certain weakness in the case of the very large pocket where results were not as expected. In general, our method could be used for incorporating indirectly flexibility in protein-ligand docking calculations as such a scheme can easily handle several conformational states of both the protein and the ligand.