The Influence of Skewed Measurement Distribution Commonly Found in Analytical Geochemistry

Abstract

Transformation of data with skew distributions into symmetrie ones is an important aid by evaluation of histograms and accentuates class groupings in multivariate principal components analysis.     

Systematic Errors in the Determination of Trace Metals,Part II. Memory Effects of Quartz Vessels Used for Sample Preparation in the Determination of Ultra-Trace Levels of Platinum

The influence of different cleaning procedures applied to quartz vessels used for sample preparation on the Pt contents in blank solutions was studied. The platinum concentration was determined by inductively coupled plasma mass spectrometry. The strong dependence of the memory effect of the quartz crucible on the previously stored Pt content of the sample was observed. It was shown that cleaning a quartz crucible previously used for Pt determinations with hot nitric acid was insufficient. When heating the vessels with a mixture of nitric, perchloric and hydrochloric acid, considerable amounts of Pt are released. In the case of new crucibles, the platinum contents in blanks were very low after cleaning with hot nitric acid.     

概率主成分分析联合支持向量机的前列腺SELDI-TOF质谱数据分析方法研究

基于前列腺癌检测中获取的表面增强激光解吸/离子化飞行时间质谱(SELDI-TOF-MS)数据,提出一种概率主成分分析(PPCA)联合支持向量机(SVM)的分类方法。对临床322例血清样本的质谱数据进行特征提取,以随机选取训练样本集(225例)构造SVM判别模型,对剩余样本集(97例)进行测试。采用均方根误差、识别率与预测率指标,将所构造的PPCA-SVM模型分别与偏最小二乘(Partial least squares,PLS)和PCA-SVM模型进行比较,发现PLS模型的识别率和预测率分别为90.92%和76.38%,PCA-SVM模型分别为99.23%和84.63%,而PPCA-SVM模型分别为99.01%和90.41%。因此SELDI-TOF-MS技术结合PPCA-SVM在样品分类中具有准确、重复性好等优点,为前列腺癌早期诊断提供了一种新方法。     

Systematic Errors in the Determination of Trace Metals by GFAAS Technique,Part I

The behaviour of solutions containing 7 metals in trace range (pH=2) prepared in borosilicate glass and polyethylene containers and transferred into commercial polyethylene–polypropylene and home-made quartz autosampler cups for measurements were studied using graphite furnace atomic absorption spectrometry. The different interactions between metal solution and vessel material were observed for the metals studied. The most significant losses of chromium and nickel occurred on a polyethylene surface, while the highest absorbance signals were registered from quartz containers. Higher absorbance signals from quartz containers were also obtained for cadmium and copper solutions. Contrary to these results, the absorbance signals for lead solutions were lowest when measured from quartz vessels. The container material had no influence on the analytical signals of cobalt and manganese.Received January 15, 2003; accepted April 15, 2003 published online August 22, 2003     

Arsenic Speciation in Marine Sediments: Effects of Redox Potential and Reducing conditions

Abstract

The speciation of arsenic in the environment is among others controlled by reduction, methylation and oxidation processes and therefore influenced by the prevailing redox conditions. In this study we have analyzed sediments taken from La Coruña estuary in the north west of Spain. Inorganic (trivalent and pentavalent) and the organic (MMA and DMA) arsenic speciation is related to Eh, Fe and Mn load. The various of the arsenic species concentration and other parameters was analyzed at different depths in some of the sampling points. Low arsenic concentrations (1–10 μg·g^–1) were found. In spite of oxidising conditions (Eh values between 31–96 mV), most of the samples showed a higher As(V) percentage than As (III). Principal component analysis was made to see a sample groups and the results showed that speciation depends on reducing conditions (Eh and Mn).     

Assessment of the impact of a phosphatic fertilizer plant on the adjacent environment using fuzzy logic

The impact of a phosphatic fertilizer plant on the adjacent environment was examined. Selected rare earth elements, heavy metals and metalloids were determined in substrates and products, waste by-product, and grass and soil samples. Concentration gradients of elements in grass and soil samples along the southerly and easterly directions were examined and compared with the content of interior soil and grass samples, substrates, and products. Results were compared with available data on soil permissible element concentration levels. Two fuzzy principal component analysis (FPCA) methods for robust estimation of principal components were applied and compared with classical PCA. The efficiency of the new algorithms is illustrated. The investigation explored the impact of the plant on the adjacent environment. The most reliable results, in good agreement with types of samples, were produced using the FPCA-O algorithm     

Sources of Errors Associated with the Determination of PAH and PCB Analytes in Water Samples

Abstract

This work describes the problems occurring in routine determination of polyaromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) in water. On the basis of experiments carried out, sources of analyte losses in a particular step of the analytical procedure were identified and assessed. Primary attention has been paid to evaluation of the?following:

• degree of adsorption of analytes from the group of PCBs and PAHs on the walls of glass vessels used during sample pretreatment and storage

• influence of excess of solvent evaporation from extracts on analyte recoveries

• influence of material of extraction column (glass, polyethylene) on analyte recovery during the solid extraction process.

     

Impact of the R292K Mutation on Influenza A (H7N9) Virus Resistance towards Peramivir: A Molecular Dynamics Perspective

In March 2013, a novel avian influenza A (H7N9) virus emerged in China. By March 2021, it had infected more than 1500 people, raising concerns regarding its epidemic potential. Similar to the highly pathogenic H5N1 virus, the H7N9 virus causes severe pneumonia and acute respiratory distress syndrome in most patients. Moreover, genetic analysis showed that this avian H7N9 virus carries human adaptation markers in the hemagglutinin and polymerase basic 2 (PB2) genes associated with cross-species transmissibility. Clinical studies showed that a single mutation, neuraminidase (NA) R292K (N2 numbering), induces resistance to peramivir in the highly pathogenic H7N9 influenza A viruses. Therefore, to evaluate the risk for human public health and understand the possible source of drug resistance, we assessed the impact of the NA-R292K mutation on avian H7N9 virus resistance towards peramivir using various molecular dynamics approaches. We observed that the single point mutation led to a distorted peramivir orientation in the enzyme active site which, in turn, perturbed the inhibitor’s binding. The R292K mutation induced a decrease in the interaction among neighboring amino acid residues when compared to its wild-type counterpart, as shown by the high degree of fluctuations in the radius of gyration. MM/GBSA calculations revealed that the mutation caused a decrease in the drug binding affinity by 17.28 kcal/mol when compared to the that for the wild-type enzyme. The mutation caused a distortion of hydrogen bond-mediated interactions with peramivir and increased the accessibility of water molecules around the K292 mutated residue.     

Classification of Wines Based on Combination of ^1H NMR Spectroscopy and Principal Component Analysis

A combination of ^1H nuclear magnetic resonance （NMR） spectroscopy and principal component analysis （PCA） has shown the potential for being a useful method for classification of type, production origin or geographic origin of wines. In this preliminary study, twenty-one bottled wines were classified/separated for their location of production in Shacheng, Changli and Yantai, and the types of the blended, medium dry, dry white and dry red wines, using the NMR-PCA method. The wines were produced by three subsidiary companies of an enterprise according to the same national standard. The separation was believed to be mainly due to the fermentation process for different wines and environmental variations, such as local climate, soil, underground water, sunlight and rainfall. The major chemicals associated with the separation were identified.     

Multivariate Statistical Assessment of Air Quality: A Case Study

The present paper deals with the application of several chemometrical methods (cluster and principal components analysis, source apportioning on absolute principal components scores) to an aerosol data collection from Unterloibach, Austria. It is shown that seven latent factors explaining almost 80% of the total variance are responsible for the data structure and are conditionally identified as secondary aerosol, mineral dust, oil burning, lead smelter, coal burning, salt and fertilizer emission sources. Furthermore, the contribution of each identified source to the formation of the particle total mass and chemical compounds total concentration is calculated. Thus, a reliable assessment of the air quality in the region is performed. The requirements of the sustainability concept for ecological indicators in this case is easily transformed into a multivariate statistical problem taking into account not separate indicators but the specific multivariate nature of aerosol pollution.     

The Atomic Partial Charges Arboretum: Trying to See the Forest for the Trees

Atomic partial charges are among the most commonly used interpretive tools in quantum chemistry. Dozens of different ‘population analyses’ are in use, which are best seen as proxies (indirect gauges) rather than measurements of a ‘general ionicity’. For the GMTKN55 benchmark of nearly 2,500 main-group molecules, which span a broad swathe of chemical space, some two dozen different charge distributions were evaluated at the PBE0 level near the 1-particle basis set limit. The correlation matrix between the different charge distributions exhibits a block structure; blocking is, broadly speaking, by charge distribution class. A principal component analysis on the entire dataset suggests that nearly all variation can be accounted for by just two ‘principal components of ionicity’: one has all the distributions going in sync, while the second corresponds mainly to Bader QTAIM vs. all others. A weaker third component corresponds to electrostatic charge models in opposition to the orbital-based ones. The single charge distributions that have the greatest statistical similarity to the first principal component are iterated Hirshfeld (Hirshfeld-I) and a minimal-basis projected modification of Bickelhaupt charges. If three individual variables, rather than three principal components, are to be identified that contain most of the information in the whole dataset, one representative for each of the three classes of Corminboeuf et al. is needed: one based on partitioning of the density (such as QTAIM), a second based on orbital partitioning (such as NPA), and a third based on the molecular electrostatic potential (such as HLY or CHELPG).     

Influence of Triazole Pesticides on Wine Flavor and Quality Based on Multidimensional Analysis Technology

Triazole pesticides are widely used to control grapevine diseases. In this study, we investigated the impact of three triazole pesticides—triadimefon, tebuconazole, and paclobutrazol—on the concentrations of wine aroma compounds. All three triazole pesticides significantly affected the ester and acid aroma components. Among them, paclobutrazol exhibited the greatest negative influence on the wine aroma quality through its effect on the ester and acid aroma substances, followed by tebuconazole and triadimefon. Qualitative and quantitative analysis by solid-phase micro-extraction gas chromatography coupled with mass spectrometry revealed that the triazole pesticides also changed the flower and fruit flavor component contents of the wines. This was attributed to changes in the yeast fermentation activity caused by the pesticide residues. The study reveals that triazole pesticides negatively impact on the volatile composition of wines with a potential undesirable effect on wine quality, underlining the desirability of stricter control by the food industry over pesticide residues in winemaking.     

核分段逆回归集成线性判别分析用于质谱数据分类

针对高维小样本质谱数据在构造模型时易产生的过拟合现象、变量间的严重共线性、及结构与性质间的非线性关系,采用了核分段逆回归(KSIR)特征提取集成线性判别分析(LDA)新技术。首先以KSIR算法完成质谱数据的非线性特征提取,然后在由新特征矢量张成的低维空间构造样本类别的线性判别函数,负责各样本个体类别的判定。将KSIR-LDA方法应用于软饮料的质谱数据分类,结果表明:该方法不仅适应质谱数据与性质间的非线性关系,而且可以更少、解释能力更强的特征变量取得更高的分类精度,并能实现在低维特征空间对数据的解释及可视化。     

用系统聚类分析法与ALKNN法进行地质、合金样品分类研究

运用系统聚类分析、ALKNN等模式识别方法对地质样品、合金样品进行了分类研究，并从分类方法、浓度、强度、特征值等方面考察了影响样品识别率的因素。     

Chemometrics on Microchips: Towards the Classification of Wines

The coupling of a capillary electrophoresis (CE) microchip with electrochemical detection with a chemometric approach was used for the classification of wines. Cabernet Sauvignon wine samples from 3 different geographical regions were used for verifying the discriminating power of the microchip electrophoretic technique using high separation voltage (3000 V) with analysis time of only 60 seconds. Electrophoretic data were transformed into initial 14 variables, concerning peak's area and peak's height. Principal component analysis was applied to optimize the information given by the electrophoretic data sets. Results of classification using LDA and CART were promising and the preliminary model was able to classify samples on the basis of their geographical origin. While the coupling of microchips with chemometrics approaches is illustrated for a former tentative of the classification of wines it could be readily extended to a wide range of other important applications.     

Classification of Congeneric and QSAR of Homologous Antileukemic S–Alkylcysteine Ketones

Based on a set of six vector properties, the partial correlation diagram is calculated for a set of 28 S-alkylcysteine diazomethyl- and chloromethyl-ketone derivatives. Those with the greatest antileukemic activity in the same class correspond to high partial correlations. A periodic classification is performed based on information entropy. The first four characteristics denote the group, and the last two indicate the period. Compounds in the same period and, especially, group present similar properties. The most active substances are situated at the bottom right. Nine classes are distinguished. The principal component analysis of the homologous compounds shows five subclasses included in the periodic classification. Linear fits of both antileukemic activities and stability are good. They are in agreement with the principal component analysis. The variables that appear in the models are those that show positive loading in the principal component analysis. The most important properties to explain the antileukemic activities (50% inhibitory concentration Molt-3 T-lineage acute lymphoblastic leukemia minus the logarithm of 50% inhibitory concentration Nalm-6 B-lineage acute lymphoblastic leukemia and stability k) are ACD logD, surface tension and number of violations of Lipinski’s rule of five. After leave-m-out cross-validation, the most predictive model for cysteine diazomethyl- and chloromethyl-ketone derivatives is provided.     

Spectrophotometric Evaluation of Acidity Constants of Diprotic Acids: Errors Involved as a Consequence of an Erroneous Choice of the Limit Absorbances

Abstract

The erroneous choice of the limit absorbances of a diprotic acid leads to the inaccurate acidity constants to be evaluated. The errors thus implied are calculated for two hypothetical systems. In addition, two practical systems, isonicotinic acid and 1,3-bis [(2-pyridyl)methyleneamino]guanidine have also been the subject of study having had their acidity constants evaluated accurately.     

Cross‐validation in PCA models with the element‐wise k‐fold (ekf) algorithm: theoretical aspects

Cross‐validation has become one of the principal methods to adjust the meta‐parameters in predictive models. Extensions of the cross‐validation idea have been proposed to select the number of components in principal components analysis (PCA). The element‐wise k‐fold (ekf) cross‐validation is among the most used algorithms for principal components analysis cross‐validation. This is the method programmed in the PLS_Toolbox, and it has been stated to outperform other methods under most circumstances in a numerical experiment. The ekf algorithm is based on missing data imputation, and it can be programmed using any method for this purpose. In this paper, the ekf algorithm with the simplest missing data imputation method, trimmed score imputation, is analyzed. A theoretical study is driven to identify in which situations the application of ekf is adequate and, more importantly, in which situations it is not. The results presented show that the ekf method may be unable to assess the extent to which a model represents a test set and may lead to discard principal components with important information. On a second paper of this series, other imputation methods are studied within the ekf algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterization,Classification, and Authentication of Polygonatum sibiricum Samples by Volatile Profiles and Flavor Properties

The importance of monitoring key aroma compounds as food characteristics to solve sample classification and authentication is increasing. The rhizome of Polygonatum sibiricum (PR, Huangjing in Chinese) has great potential to serve as an ingredient of functional foods owing to its tonic effect and flavor properties. In this study, we aimed to characterize and classify PR samples obtained from different processing levels through their volatile profiles and flavor properties by using electronic nose, electronic tongue, and headspace gas chromatography-mass spectrometry. Nine flavor indicators (four odor indicators and five taste indicators) had a strong influence on the classification ability, and a total of 54 volatile compounds were identified in all samples. The traditional Chinese processing method significantly decreased the contents of aldehydes and alkanes, while more ketones, nitrogen heterocycles, alcohols, terpenoids, sulfides, and furans/pyrans were generated in the processing cycle. The results confirmed the potential applicability of volatile profiles and flavor properties for classification of PR samples, and this study provided new insights for determining the processing level in food and pharmaceutical industries based on samples with specific flavor characteristics.     

Bayesian latent variable regression via Gibbs sampling: methodology and practical aspects

Bayesian latent variable regression (BLVR) aims to utilize all available information for empirical modeling via a Bayesian framework. Such information includes prior knowledge about the underlying variables, model parameters and measurement error distributions. This paper improves upon the existing optimization‐based BLVR (BLVR‐OPT) method [1] by developing a sampling‐based Bayesian latent variable regression (BLVR‐S) method that relies on Gibbs sampling. Use of the sampling‐based framework not only provides point estimates, but its ability to generate samples that represent the posterior distribution of the unknown variables, also readily provides error bounds. Features and advantages of this method are demonstrated via examples based on simulated data and real Near‐Infrared (NIR) spectroscopy data. Practical aspects of Bayesian modeling such as determining when the extra computation may be worth the effort are addressed by an empirical study of the effects of the amount of training data and signal to noise ratio (SNR). The benefits of BLVR seem to be most significant when the number of measurements is limited and when noise in output variables is relatively large. Copyright © 2007 John Wiley & Sons, Ltd.