Characterization and classification of medicinal plants according to their antioxidant profile estimated by thin layer chromatography assisted by chemometric expertise

This study focuses on the characterization and classification of 42 medicinal plants extracts according to their antioxidant activity. Principal component analysis (PCA), cluster analysis (CA) and the combination of PCA with linear discriminant analysis (PCA-LDA) were used as multivariate exploratory techniques for chromatographic data analysis. For the separation of the compounds a mobile phase containing ethyl acetate: toluene: formic acid: water (30:1.5:4:3 v/v/v/v) and different HPTLC plates (Silica gel 60 and HPTLC Silica gel 60?F₂₅₄) were used. The chromatographic plates were evaluated using the images obtained after spraying the plates with 2-aminoethyldiphenylborate solution (NTS, 0.2% in ethanol) and also after their reaction with 2, 2-diphenyl-1-picrylhydrazyl solution (DPPH^?) (0.2% in ethanol). The score projection on the plane defined by first two components (PC1 and PC2) revealed two large groups of the investigated samples depicted according to their antioxidant capacity. A better classification of samples according to their antioxidant capacity was obtained using the CA and PCA-LDA methodology in all cases. The excellent results obtained in this study concerning the classification of medicinal plants according to their antioxidant capacity using the PCA-LDA methodology applied to TLC chromatograms might lead to a new paradigm in the field of medicinal plant holistic evaluation.     

Comparison of depth profiles in SIMS by a fuzzy method

Automated quantitative comparison of depth-profiles recorded by SIMS based on a fuzzy difference measure has been used to characterize Sb and B implantation profiles in a marker experiment to study the diffusion of As in silicon. The variations of the concentration (intensity) measurements are described by a fuzzy set that is specified by smoothing the data with a polynomial digital filter. For each depth an individual spread as the size of variation is defined. Applications of the method enabled the influence of As-concentration and of annealing conditions on the implantation profiles of Sb and B to be quantified.     

稳健灰色模型在色谱保留值研究中的应用

本文提出了具有稳健性的ＧＭ（１，１）灰色模型，并用此模型对８种多环芳烃化合物容量因子与流动相组成间的关系作了研究，建立了其间的稳数学模型，结果表明，稳健ＧＭ（１，１）模型比常规ＧＭ（１，１）模型具有更好的抗干扰性能和受异常点影响小的优点，是一个值得推广的好方法。既丰富了灰色系统理论，又拓宽了灰色理论在分析化学中的应用范围。     

分析化学计量学中的新方法─—小波分析

小波分析在分析信号处理中具有诸多显著的优点。本文介绍了小波分析的一般描述，综述了化学计量学在的小波新方法，并展望了小波分析在分析化学计量学中的应用前景。     

Computer-aided library search of combined LC-Retention and diode-array-UV-spectral data

An automated library search system for computer-aided identification of organic compounds using combined UV-spectral and LC-retention data, as acquired by HPLC with linear diodearray-UV-detection, is presented.The keystone of the system is a set of similarity indices which have been developed on the basis of mathematical-statistical models of the reproducibility of the spectral and retention data. The similarity indices have the form of a significance probability, a quantity originating from the general theory of hypothesis testing. The output of the LC-UV retrieval system is a compound identity or a list of identities (if any) for which the relevant similarity index has a value above a preset threshold. The data base used in this investigation contained 186 sets of UV-spectral and LC-retention data of 74 organophosphorus pesticides measured from standard solutions under various experimental conditions. A test set consisted of 186 alternative data sets of the same compounds as contained in the data base, however measured under different experimental conditions. The performance of the LC-UV reproducibility-based retrieval (LUVRR) system was evaluated using recall/reliability plots. The results appeared to be quite promising: for 95% of the unknowns, the target reference (correct positive) was on top of the hit list with a similarity index value being significantly higher than values found for false positives.The software is written in Pascal. The present (experimental) version of the system runs on a Data General Eclipse MV/4000 minicomputer.     

Evaluation and comparison of mid-infrared,Raman and near-infrared spectroscopies for characterization and determination of the compositions in fully biodegradable poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) blends combined with chemometrics

In this work, mid-infrared (MIR), Raman and near-infrared (NIR) spectroscopies were evaluated and compared for characterization and determination of the compositions in poly(lactic acid)/poly(propylene carbonate)/poly(butylene adipate-co-terephthalate) (PLA/PPC/PBAT) blends via chemometrics. Qualitative analysis of MIR, Raman, and NIR spectra of the three compositions was performed. Partial least squares (PLS) models were developed based on each spectroscopy for quantitative determination of the concentrations. The data suggested that MIR and Raman have an advantage over NIR in terms of qualitative recognition of the three compositions. The data also showed that Raman and NIR succeeded in determining the concentrations, while the concentration determined via MIR was inaccurate. Hence, Raman is the optimal analytical tool for qualitative characterization and quantitative determination of the compositions in fully biodegradable PLA/PPC/PBAT blends. The characteristic bands in the Raman spectra clearly identify PLA, PPC, and PBAT to be 392 cm^?1 (δ CCO), 948 cm^?1 (ν C?O?C) and 1600 cm^?1 (ν C ? C in benzene ring), respectively. The optimal calibration models based on Raman for PLA, PPC, and PBAT exhibited root mean square error of prediction (RMSEP) values of 3.140%, 3.576%, and 2.538%, respectively.     

基于近红外光谱分析技术的栽培香菇产地快速鉴别

建立了一种基于近红外光谱分析技术的香菇产地鉴别方法。利用近红外光谱仪扫描不同主产地的香菇干样,获得样品的近红外漫反射光谱。利用偏最小二乘判别分析(PLSDA)分别建立了吉林、湖北、福建3个省份栽培香菇的产地判别模型,同时使用光谱预处理和波长筛选技术对判别模型进行优化,最后使用预测样品对模型进行验证。结果表明,使用原始光谱建立的模型能够初步实现对产地的判别,使用光谱预处理技术扣除光谱中的背景信息,同时利用波长筛选技术选择特定波长对模型进行优化后,可进一步提高预测正确率。该方法为香菇产地真实性溯源提供了一种新方法,对香菇产业发展具有重要的实际意义。     

高效液相色谱全轮廓指纹图谱结合化学计量学区分不同地区的紫苏叶

利用高效液相色谱全轮廓指纹图谱结合化学计量学方法对不同栽培地区的紫苏叶样品(共84个)进行区分。全轮廓色谱数据经自适应迭代加权最小二乘法(airPLS)和相关优化翘曲法(COW)校正后,基线和保留时间漂移现象均得到明显改善。经预处理后的色谱数据采用主成分分析(PCA)进行解析,结果表明不同来源的样品能按其特性各自聚为一类;而分段间隔压缩变量后的色谱数据经主成分分析处理可得到与全轮廓色谱数据为输入变量时相一致的结果。此外,偏最小二乘判别分析(PLS-DA)对于紫苏叶样品分类的识别能力和预报能力分别为92.8%和89.6%。     

计算(机)化学学科进展(2011-2013)

计算(机)化学已成为化学学科的重要组成部分,在理论计算、分子模拟、数据挖掘以及复杂体系分析中发挥了重要作用。本文总结了近年来化学信息学的研究进展,包括化学信息学方法、软件及数据库技术以及化学信息学在结构、性质、相互作用、反应机理,蛋白质及功能材料的性能研究,复杂体系化学数据分析中的应用。共引用参考文献78篇。