Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics

Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using ¹H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.     

Cross fitted partial least squares (CF‐PLS): an alternative algorithm for a more reliable PLS

This paper presents a modified version of the NIPALS algorithm for PLS regression with one single response variable. This version, denoted a CF‐PLS, provides significant advantages over the standard PLS. First of all, it strongly reduces the over‐fit of the regression. Secondly, R² for the null hypothesis follows a Beta distribution only function of the number of observations, which allows the use of a probabilistic framework to test the validity of a component. Thirdly, the models generated with CF‐PLS have comparable if not better prediction ability than the models fitted with NIPALS. Finally, the scores and loadings of the CF‐PLS are directly related to the R², which makes the model and its interpretation more reliable. Copyright © 2011 John Wiley & Sons, Ltd.     

Genetic algorithm‐based wavelength selection method for spectral calibration

In this paper, we propose a genetic algorithm‐based wavelength selection (GAWLS) method for visible and near‐infrared (Vis/NIR) spectral calibration. The objective of GAWLS is to construct robust and predictive regression models by selecting informative wavelength regions. To demonstrate the ability of the proposed method, regression models for soil properties and sugar content of apples are constructed by using GAWLS and other variable selection methods. Copyright © 2010 John Wiley & Sons, Ltd.     

复方氯丙那林质量检控的近红外光谱快速分析

基于近红外光纤漫反射技术,利用偏最小二乘法分别建立了复方氯丙那林胶囊的三种药效成分盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的快速同时测定方法。所建立的盐酸氯丙那林、盐酸溴己新和盐酸去氯羟嗪的定量分析多元校正模型的相关系数分别为99.7%、99.4%和99.0%,校正集的均方根残差分别为0.028、0.145和0.250,预测均方根误差分别为0.055、0.120和0.210。由于该方法是在不经任何预处理的情况下的光纤快速同时分析,因此可用于复方氯丙那林的过程质量控制。     

Computational performance and cross‐validation error precision of five PLS algorithms using designed and real data sets

An evaluation of computational performance and precision regarding the cross‐validation error of five partial least squares (PLS) algorithms (NIPALS, modified NIPALS, Kernel, SIMPLS and bidiagonal PLS), available and widely used in the literature, is presented. When dealing with large data sets, computational time is an important issue, mainly in cross‐validation and variable selection. In the present paper, the PLS algorithms are compared in terms of the run time and the relative error in the precision obtained when performing leave‐one‐out cross‐validation using simulated and real data sets. The simulated data sets were investigated through factorial and Latin square experimental designs. The evaluations were based on the number of rows, the number of columns and the number of latent variables. With respect to their performance, the results for both simulated and real data sets have shown that the differences in run time are statistically different. PLS bidiagonal is the fastest algorithm, followed by Kernel and SIMPLS. Regarding cross‐validation error, all algorithms showed similar results. However, in some situations as, for example, when many latent variables were in question, discrepancies were observed, especially with respect to SIMPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

Complexation of Hg2+ with α‐Lipoic and Dihydrolipoic Acids: Study by Differential Pulse Voltammetry on Rotating Au‐Disk Electrode and ESI‐MS

The complexation of the natural antioxidants α‐lipoic acid (ALA) and its reduced form dihydrolipoic acid (DHLA) with Hg²⁺ was investigated by a recently proposed differential pulse voltammetric (DPV) method using the rotating Au‐disk electrode. Complexation processes are proposed from the multivariate curve resolution by alternating least squares (MCR‐ALS) analysis of DPV titration data. Main complexes were both 1 : 1 Hg : ALA and Hg : DHLA, although the formation of 1 : 2 complexes can be also deduced. ALA and DHLA show different Hg²⁺‐binding patterns at different pH. Voltammetric findings are completed with the data obtained by electrospray ionization mass‐spectrometry (ESI‐MS), especially in negative mode.     

基于傅里叶变换红外光声光谱的包膜控释肥料聚合物膜疏水性的表征

以不饱和有机硅改性的丙烯酸酯为基材,在不同条件下制备了30种水基聚合物包膜控释肥料模型膜,测定了模型膜的溶胀度及模型膜的傅里叶变换红外光声光谱.分别以红外光声光谱和溶胀度为自变量和因变量进行简单相关分析,同时也构建了偏最小二乘模型,并利用该模型对模型膜的疏水性进行预测.结果表明,聚合物模型膜具有明显不同的疏水性,其红外光声光谱具有相似的吸收特征,但不同吸收带的相对强度发生明显改变;常用的简单相关分析无法实现模型膜疏水性的预测,表明模型膜的疏水性与谱区更多的特征峰相关;基于偏最小二乘法的多元校正分析具有很好的预测能力,模型的校正系数(R2)为0.9864,校正标准误差为0.70%,验证标准误差为1.92%.此模型可用于模型膜疏水性的预测.本方法样品用量少,测定快速,操作简便且可实现原位测定,为控释肥料包膜材料的研制提供新的手段.     

A first‐order system least‐squares finite element method for the Poisson‐Boltzmann equation

The Poisson‐Boltzmann equation is an important tool in modeling solvent in biomolecular systems. In this article, we focus on numerical approximations to the electrostatic potential expressed in the regularized linear Poisson‐Boltzmann equation. We expose the flux directly through a first‐order system form of the equation. Using this formulation, we propose a system that yields a tractable least‐squares finite element formulation and establish theory to support this approach. The least‐squares finite element approximation naturally provides an a posteriori error estimator and we present numerical evidence in support of the method. The computational results highlight optimality in the case of adaptive mesh refinement for a variety of molecular configurations. In particular, we show promising performance for the Born ion, Fasciculin 1, methanol, and a dipole, which highlights robustness of our approach. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010