Toward robust QSPR models: Synergistic utilization of robust regression and variable elimination

Widely used regression approaches in modeling quantitative structure-property relationships, such as PLS regression, are highly susceptible to outlying observations that will impair the prognostic value of a model. Our aim is to compile homogeneous datasets as the basis for regression modeling by removing outlying compounds and applying variable selection. We investigate different approaches to create robust, outlier-resistant regression models in the field of prediction of drug molecules' permeability. The objective is to join the strength of outlier detection and variable elimination increasing the predictive power of prognostic regression models. In conclusion, outlier detection is employed to identify multiple, homogeneous data subsets for regression modeling.     

Oxidation in Acidic Medium of Lignins from Agricultural Residues

Agricultural residues as sugarcane straw and bagasse are burned in boilers for generation of energy in sugar and alcohol industries. However, excess of those by-products could be used to obtain products with higher value. Pulping process generates cellulosic pulps and lignin. The lignin could be oxidized and applied in effluent treatments for heavy metal removal. Oxidized lignin presents very strong chelating properties. Lignins from sugarcane straw and bagasse were obtained by ethanol–water pulping. Oxidation of lignins was carried out using acetic acid and Co/Mn/Br catalytical system at 50, 80, and 115 °C for 5 h. Kinetics of the reaction was accomplished by measuring the UV-visible region. Activation energy was calculated for lignins from sugarcane straw and bagasse (34.2 and 23.4 kJ mol⁻¹, respectively). The first value indicates higher cross-linked formation. Fourier-transformed infrared spectroscopy data of samples collected during oxidation are very similar. Principal component analysis applied to spectra shows only slight structure modifications in lignins after oxidation reaction.     

Analysis of lignin from archaeological waterlogged wood by direct exposure mass spectrometry (DE-MS) and PCA evaluation of mass spectral data

The chemical characterisation of waterlogged archaeological wood is of fundamental importance to understand the degradation processes undergone by wooden objects and consequently to develop suitable consolidation and conservation procedures. Lignin extracted from archaeological waterlogged wood samples was characterized using direct exposure electron ionisation mass spectrometry (DE-MS). DE-MS achieves a mass spectral fingerprint of the sample in a few minutes, avoiding any chemical pre-treatment and requiring only few micrograms of material.Mass spectral data were put in relation to the chemical composition of lignin and evaluated by means of principal component analysis (PCA). The preliminary results, presented in this study, demonstrate the feasibility and the potential of DE-MS as a reproducible and rapid screening method for archaeological waterlogged wood samples.     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

利用毛细管电泳和主成分分析判断海洛因地理来源

对在中国境内缴获的海洛因样本进行地理来源判断.采用动态涂层毛细管电泳技术(CE)对300份海洛因样本进行生物碱定量检测,并利用主成分分析建立二维教学模型.实现了对中国境内缴获的海洛因样本地理来源的推断.     

Dispersion polymerization of 2-hydroxyethyl methacrylate (HEMA) using siloxane-based surfactant in supercritical carbon dioxide and in compressed liquid dimethyl ether

The free radical dispersion polymerization of 2-hydroxyethyl methacrylate (HEMA) has been carried out in supercritical carbon dioxide (scCO₂) and compressed liquid DME using several surfactants. The polymerization are performed in the presence of fluorine-based poly(3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-heptadecafluorodecyl acrylate) [poly(HDFDA)], poly(3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-heptadecafluorodecyl methacrylate) [poly(HDFDMA)], or poly(HDFDMA-co-MMA) and siloxane-based PDMS-g-pyrrolidonecarboxylic acid (Monasil PCA™) or PDMS modified surfactants, SS-5050K™ and KF6017™ as polymerization surfactants. When scCO₂ was used as a polymerization medium, the PHEMA were heavily agglomerated. However, the spherical and relatively uniform poly(2-hydroxyethyl methacrylate) (PHEMA) particles could be produced even at 20 bar, with a narrow particle size distribution in compressed liquid DME. It was observed that fluorine-based surfactants were not a good surfactant as siloxane-based surfactants for the dispersion polymerization of HEMA. The average particle size of PHEMA was shown to be dependent on the type of the surfactant, the amount of the surfactant and initiator added to the system. The effect of two continuous phases, which are scCO₂ and compressed liquid DME, as a polymerization medium, the surfactant types and the concentration, initiator concentration, and monomer concentration on the morphology and size of the polymer particles was also investigated.     

Rapid multivariate analysis of 3D ToF‐SIMS data: graphical processor units (GPUs) and low‐discrepancy subsampling for large‐scale principal component analysis

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth‐profiles in XPS, AES and SIMS. For large images or three‐dimensional (3D) imaging depth‐profiles, PCA has been difficult to apply until now simply because of the size of the matrices of data involved. In a recent paper, we described two algorithms, random vector 1 (RV1) and random vector 2 (RV2), that improve the speed of PCA and allow datasets of unlimited size, respectively. In this paper, we now apply the RV2 algorithm to perform PCA on full 3D time‐of‐flight SIMS data for the first time without subsampling. The dataset we process in this way is a 128 × 128 pixel depth‐profile of 120 layers, each voxel having a 70 439 value mass spectrum associated with it. This forms over a terabyte of data when uncompressed and took 27 h to process using the RV2 algorithm using a conventional windows desktop personal computer (PC). While full PCA (e.g. using RV2) is to be preferred for final reports or publications, a much more rapid method is needed during analysis sessions to inform decisions on the next analytical step. We have therefore implemented the RV1 algorithm on a PC having a graphical processor unit (GPU) card containing 2880 individual processor cores. This increases the speed of calculation by a factor of around 4.1 compared with what is possible using a fast commercially available desktop PC having central processing units alone, and full PCA is performed in less than 7 s. The size of the dataset that can be processed in this way is limited by the size of the memory on the GPU card. This is typically sufficient for two‐dimensional images but not 3D depth‐profiles without sampling. We have therefore examined efficient sampling schemes that allow a good approximate solution to the PCA problem for large 3D datasets. We find that low‐discrepancy series such as Sobol series sampling gives more rapid convergence than random sampling, and we recommend such methods for routine use. Using the GPU and low‐discrepancy series together, we anticipate that any time‐of‐flight SIMS dataset, of whatever size, can be efficiently and accurately processed into PCA components in a maximum of around 10 s using a commercial PC with a widely available GPU card, although the longer RV2 approach is still to be preferred for the presentation of final results, such as in published papers. Copyright © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd     

基于主元分析与动态时间弯曲的故障诊断方法及应用研究

轴承是工程实际中常用而又极易损坏的部件,特别是对其早期微弱响应的辨识,具有重要的社会价值和意义。为提高运转轴承的安全可靠性和可维护性,提出了基于主元分析与动态时间弯曲距离的故障诊断方法,它可以准确对早期微弱动态响应辨识、诊断。该方法首先将典型故障样本信号与待测信号小波去噪并EMD分解,并对若干固有模态分量主元分析求取主元,然后对主元分量进行分析,获得相关特征值组成特征向量,计算待测信号与已知故障样本信号特征向量的弯曲距离,弯曲距离越小表明两信号越相似,从而辨识故障。此外,还可将其应用于转子、碰磨、齿轮故障诊断中,工程应用实例表明该方法可以准确故障分类,高效故障诊断。     

Chemometric analysis of the water purification process data

The aim of this work was to show usefulness of chemometric analysis in processing of the data describing production of drinking water in the Silesian region of Poland. Water samples have been collected within the period of 1 year and the quality of water was characterized by a number of physical, chemical and microbiological parameters. Principal component analysis (PCA) and STATIS (Structuration des Tableaux A Trois Indices de la Statistique) were employed to obtain the knowledge about the complete water treatment process. PCA makes it possible to uncover seasonal changes influencing the water treatment process. In particular, it was found out that the salt content, hardness and conductivity of water tend to obtain higher levels in winter rather than in summer, and the relatively lower acidity is also to be expected in winter. The sensory quality of water is considerably improved over the consecutive purification steps. Complementary information about the individual technological units of the process is gained with the STATIS approach. The obtained results show that the water produced by the two independent filtering branches of the water plant is of similar quality and the prescribed quality characteristics of drinking water are fulfilled.     

Multivariate image analysis of a set of FTIR microspectroscopy images of aged bovine muscle tissue combining image and design information

In this paper we present an algorithm for analysing sets of FTIR microscopic images of tissue sections. The proposed approach allows one to investigate sets of many FTIR tissue images both with respect to sample information (variation from image to image) and spatial variations of tissues (variation within the image). The algorithm is applied to FTIR microscopy images of beef loin muscles containing myofibre and connective tissue regions. The FTIR microscopy images are taken of sub-samples from five different beef loin muscles that were aged for four different lengths of time. The images were investigated regarding variation due to the ageing length and due to the homogeneity of the connective tissue regions. The presented algorithm consists of the following main elements: (1) pre-processing of the spectra to overcome large quality differences in FTIR spectra and differences due to scatter effects, (2) identification of connective tissue regions in every image, (3) labelling of every connective tissue spectrum with respect to its location in the connective tissue region, and (4) analysis of variations in the FTIR microscopic images in regard to ageing time and pixel position of the spectra in the connective tissue region. Important spectral parameters characterising collagen and proteoglycan structure were determined. Figure Effective optical path length estimated by EMSC