Nonlinear Multivariate SAR of Lepidoptera Pheromones

Abstract The combined use of the nonlinear mapping method with correspondence factor analysis allowed to derive interesting structure-chemoreception relationships in Lepidoptera. A chemotaxonomy of insects based on their responses to pheromones was also proposed.     

Nonlinear Neural Mapping Analysis of the Adverse Effects of Drugs

Abstract

Numerous drugs have been identified as presenting adverse effects towards the driving of vehicles. A large set of these drugs was compiled and classified into ten categories. Nonlinear neural mapping (N2M) was used to derive a typology of these molecules and also to link their adverse effects to therapeutic categories and structural information.     

Multivariate Analysis of the First 10 MEIC Chemicals

Abstract

Nonlinear mapping coupled to powerful graphical tools was used to compare the texicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. The obtained results clearly underline the usefulness of our methodological approach for the comparison of the different endpoints and the selection of a battery of in vitro toxicity tests allowing to estimate the possible harmful effects of chemicals in vivo.     

New synthesis of (Z)-5- and (Z)-7-monoene components of insect sex pheromones of the Lepidoptera order

A new procedure was developed for the synthesis of (Z)-5- and (Z)-7-monoene components of sex pheromones of Lepidoptera insects based on cometathesis of readily accessible cycloocta-1,5-diene and ethylene. Published inIzvestiya Akademii Nauk. Seriya Khimicheskaya, No. 7, pp. 1304–1307, July, 2000.     

Multivariate Association of Graph-Theoretic Variables and Physicochemical Properties

Abstract

We used canonical correlation analysis to examine the multivariate association between two distinct data sets commonly measured or calculated for approximately 600 chemicals: (1) measured or calculated values of select physieochemical properties (i.e., K _ow, boiling point, heat of vaporization, molecular weight, water solubility, molecular volume, hydrogen bonding potential, and vapor pressure) and (2) calculated algorithmically-derived variables (i.e., topological and neighborhood indices derived from graph theory). Canonical correlation analysis identified eight highly significant associations between linear combinations of graph-theoretic variables and linear combinations of physicochemical properties. The set of graph theoretic variables was significantly related to all physieochemical properties, explaining 55% to 99% of the variation in these properties.     

用多变量统计分析方法辨识肺癌患者分类中的关键元素(英文)

用逐步判别、主成分分析和聚类方法研究了根据血清和毛发样品中元素含量对正常人和肺癌患者分类中的关键元素．用主成分分析的结果表明,在肺癌患者与正常人的分类中,血清中的Ｃａ,Ｃｒ,Ｃｕ,Ｐ和Ｚｎ是关键元素,而毛发中的Ａｌ,Ｂ,Ｃｒ,Ｐ和Ｓｒ是关键元素．对于正常人和癌症患者元素之间的欧氏距离不同     

Monitoring chemical impacts on cell cultures by means of image analyses

Many chemical processes are involved in the interactions of living cells with their environment; however, monitoring such processes often requires sophisticated analyzers. In this study, a sensing strategy based on imaging techniques has been developed to (i) enable cell discrimination based on their physical appearance such as size and shape and (ii) to build predictive models that relate the measured cell appearance to chemical parameters in their environment. Both goals aim at innovative and straightforward sensing strategies for analyzing cell–environment interactions. Image analyses offer several advantages such as the use of simpler, more robust sensors and the omission of extensive sample/sensor preparations. Imaging can analyze numerous cells and thus gains a culture representative insight rather than a potentially nonrepresentative single‐cell response. As a proof‐of‐principle application, different species of microalgae cells have been exposed to various nutrient conditions. Microalgae are known to sensitively adapt to changing nutrient conditions and could potentially become biological “probes” for chemical shifts in ecosystems. Because of considerable spreads of cell size and shapes within one class, size and shape distributions have been derived from visible images of cell cultures. It is shown that the novel image analyses are capable of discriminating different cell species based on their cell shapes and sizes. It is also demonstrated that in conjunction with the recently introduced, nonlinear multivariate “predictor surfaces”, the nutrient availability has a quantifiable impact on the cell size distributions. In this application, predictor surfaces are somewhat more precise than partial least squares. Copyright © 2012 John Wiley & Sons, Ltd.     

Multivariate analysis of a data matrix containing A-DNA and B-DNA dinucleoside monophosphate steps: Multidimensional Ramachandran plots for nucleic acids

A method to construct the equivalent of multidimensional Ramachandran plots for nucleic acids on the basis of singular value decomposition (SVD) is presented. For this purpose, a data matrix containing 244 DNA dinucleoside monophosphate steps, represented by nine torsion angles, was decomposed into a score and loading matrix. It is shown that biplots, containing both score points and loading vectors, provide a simple tool to interpret the principles of DNA class separation. Scores separate the data matrix into one A-DNA class, two different B-DNA classes, and one so-called crankshaft class. Loading vectors correlate torsion angles. The projections of scores on loading vectors indicate which torsion angles play a dominant role in DNA class separation. The results of the biplots are supported by (simple) physical interpretations. From a three-dimensional score space the nine original torsion angles can be reconstructed. Hence, the potential to create the multidimensional equivalent of a Ramachandran plot is available; that is, forbidden and accessible regions in the reduced space reflect these same regions in the nine-dimensional original space. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 695–715, 1998     

SPME-GC-MS and PTR-ToF-MS Techniques for the Profiling of the Metabolomic Pattern of VOCs and GC-MS for the Determination of the Cannabinoid Content of Three Cultivars of Cannabis sativa L. Pollen

Considering the large number of volatile molecules that characterize Cannabis sativa L., adequate investigation supported by the application of robust and effective analytical methods is essential to better understand the impact of these low- and medium-molecular-weight molecules on the entire phytocomplex. This work aimed to characterize the volatile fraction of the chemical profile of three different cultivars of Cannabis sativa L. pollen, grown in Italy, which were thoroughly investigated by the application of two complementary techniques: SPME-GC-MS and PTR-ToF-MS. Furthermore, in order to provide more information on the chemical profile of the matrices under study, the cannabinoid content of the hexane extracts was also measured by GC-MS. Until now, no similar study, in terms of survey techniques applied, has been performed on C. sativa pollen. The obtained results showed a high content of volatile molecules, which differentiated the three matrices. The data relating to the content of cannabinoids were also interesting as they showed that one of the three cultivars was richer than the others. Finally, an in-depth statistical survey was performed to better compare the investigated samples and identify the molecules that most contribute to differentiating them. The findings of this study may be useful for integrating the compositional information on C. sativa L.     

糖尿病人微量元素谱的多元分析

胜多元分析了糖尿病患者样品中微量元素，了解微量元素与糖尿病的关系，并提出了新见解。用ＩＣＰ－ＡＥＳ测定糖尿病患者血、发中１８种微量和宏量元素 ；结果经多元分析处理，找到血、发共有的相关链：Ｍｎ－Ｎｉ－Ｃｕ－ＳｒＴｉ，它与患者年龄、性别、样品无关，看来它提供了机体的特殊信息。     

Exploration of the Interrelationship within Biomass Pyrolysis Liquid Composition Based on Multivariate Analysis

The diverse utilization of pyrolysis liquid is closely related to its chemical compositions. Several factors affect PA compositions during the preparation. In this study, multivariate statistical analysis was conducted to assess PA compositions data obtained from published paper and experimental data. Results showed the chemical constituents were not significantly different in different feedstock materials. Acids and phenolics contents were 31.96% (CI: 25.30–38.62) and 26.50% (CI: 21.43–31.57), respectively, accounting for 58.46% (CI: 46.72–70.19) of the total relative contents. When pyrolysis temperatures range increased to above 350 °C, acids and ketones contents decreased by more than 5.2-fold and 1.53-fold, respectively, whereas phenolics content increased by more than 2.1-fold, and acetic acid content was the highest, reaching 34.16% (CI: 25.55–42.78). Correlation analysis demonstrated a significantly negative correlation between acids and phenolics (r² = −0.43, p < 0.001) and significantly positive correlation between ketones and alcohols (r² = 0.26, p < 0.05). The pyrolysis temperatures had a negative linear relationship with acids (slope = −0.07, r² = 0.16, p < 0.001) and aldehydes (slope = −0.02, r² = 0.09, p < 0.05) and positive linear relationship with phenolics (slope = 0.04, r² = 0.07, p < 0.05). This study provides a theoretical reference of PA application.     

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF‐SIMS data. Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF‐SIMS peak lists using up‐binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time‐of‐flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low‐density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF‐SIMS data. Artificial neural networks, in the form of self‐organising maps, are introduced and provide a non‐linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions. This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.     

Rational Design of a Fluorescent Chromophore as a Calcium Receptor via DFT and Multivariate Approaches

Computational and experimental approaches were adopted to utilize a chromophore diglycolic functionalized fluorescein derivative as a Ca²⁺ receptor. Fluorescein diglycolic acid (Fl-DGA, 1) was synthesized and used in multivariate determination of Ca²⁺ and K⁺. Full-structure computation shows that the complexes of 1 and Ca²⁺ have comparable energies regardless of additional interaction with lactone moiety. The initial formation of diglycolic-Ca²⁺ complex followed by macrocyclization is thermodynamically disfavored. A U-shaped pre-organized 1 allows Ca²⁺ to interact simultaneously with diglycolic and lactone motifs. Both motifs actively participate in Ca²⁺ recognition and the eleven methylene units in the undecyl arm provides excellent flexibility for reorganization and optimum interaction. Principal component analysis (PCA) of computational molecular properties reveals a simple method in evaluating motifs for cation recognition. Fragment models support full-structure results that negative charge causes significant structural changes, but do not reproduce the full extent of C-O bond breaking observed in the latter. Experimental optical responses show that 1 is selective towards Ca²⁺ and discriminates against K⁺ and Mg²⁺. PCA of emission intensities affords distinct clusters of 0.01, 0.1 and 1 mM Ca²⁺ and K⁺, and suggests applicability of this technique for simultaneous determination of cationic plant macronutrients in precision agriculture and a wide variety of other applications.     

Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth profiles in X‐ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS). These methods have proved equally useful in fundamental studies as in applied work where speed of interpretation is very valuable. Until now these methods have been difficult to apply to very large datasets such as spectra associated with 2D images or 3D depth‐profiles. Existing algorithms for computing PCA matrices have been either too slow or demanded more memory than is available on desktop PCs. This often forces analysts to ‘bin’ spectra on much more coarse a grid than they would like, perhaps even to unity mass bins even though much higher resolution is available, or select only part of an image for PCA analysis, even though PCA of the full data would be preferred. We apply the new ‘random vectors’ method of singular value decomposition proposed by Halko and co‐authors to time‐of‐flight (ToF)SIMS data for the first time. This increases the speed of calculation by a factor of several hundred, making PCA of these datasets practical on desktop PCs for the first time. For large images or 3D depth profiles we have implemented a version of this algorithm which minimises memory needs, so that even datasets too large to store in memory can be processed into PCA results on an ordinary PC with a few gigabytes of memory in a few hours. We present results from ToFSIMS imaging of a citrate crystal and a basalt rock sample, the largest of which is 134GB in file size corresponding to 67 111 mass values at each of 512 × 512 pixels. This was processed into 100 PCA components in six hours on a conventional Windows desktop PC. © 2015 The Authors. Surface and Interface Analysis published by John Wiley & Sons Ltd.     

Multivariate statistical analysis of non‐mass‐selected ToF‐SIMS data

Cluster LMIGs are now regarded as the standard primary ion guns on time‐of‐flight secondary ion mass spectrometers (ToF‐SIMS). The ToF‐SIMS analyst typically selects a bombarding species (cluster size and charge) to be used for material analysis. Using standard data collection protocols where the analyst uses only a single primary bombarding species, only a fraction of the ion‐beam current generated by the LMIG is used. In this work, we demonstrate for the first time that it is possible to perform ToF‐SIMS analysis when all of the primary ion intensity (clusters) are used; we refer to this new data analysis mode as non‐mass‐selected (NMS) analysis. Since each of the bombarding species has a different mass‐to‐charge ratio, they strike the sample at different times, and as a result, each of the bombarding species generates a spectrum. The resulting NMS ToF‐SIMS spectrum contains contributions from each of the bombarding species that are shifted in time. NMS spectra are incredibly complicated and would be difficult, if not impossible, to analyze using univariate methodology. We will demonstrate that automated multivariate statistical analysis (MVSA) tools are capable of rapidly converting the complicated NMS data sets into a handful of chemical components (represented by both spectra and images) that are easier to interpret since each component spectrum represents a unique and simpler chemistry. Copyright © 2008 John Wiley & Sons, Ltd.     

Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo

Multivariate analysis of thin‐layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the ‘PRISMA’ optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet‐converted TLC image and 2,2‐diphynyl‐picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x‐ and y‐variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC‐MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.     

Utilizing AgNPt-SALDI to Classify Edible Oils by Multivariate Statistics of Triacylglycerol Profile

Edible oils are valuable sources of nutrients, and their classification is necessary to ensure high quality, which is essential to food safety. This study reports the establishment of a rapid and straightforward SALDI-TOF MS platform used to detect triacylglycerol (TAG) in various edible oils. Silver nanoplates (AgNPts) were used to optimize the SALDI samples for high sensitivity and reproducibility of TAG signals. TAG fingerprints were combined with multivariate statistics to identify the critical features of edible oil discrimination. Eleven various edible oils were discriminated using principal component analysis (PCA). The results suggested the creation of a robust platform that can examine food adulteration and food fraud, potentially ensuring high-quality foods and agricultural products.     

Design of Nonlinear Model‐Based Control Using Bifurcation Analysis for Solution Polymerizations Carried Out in Lumped‐Distributed Reactors

In order to implement nonlinear control, nonlinear system identification must be performed, however, there are open questions concerning this field of process control, for example, experimental planning, model structure selection, parameter estimation, and validation. Therefore, the study of nonlinear model identification is a relevant unsolved problem that needs to be handled for nonlinear control synthesis. This paper presents the use of bifurcation theory, dynamic and stability analysis for nonlinear identification, and control of polymerization reactors. Peroxide‐initiated styrene‐solution polymerization reactors (lumped‐distributed) are investigated: batch, continuous stirred‐tank reactor (CSTR), and tubular reactors. Open and closed loop analyses are carried out using jacket temperature and weight average molecular weight setpoints as the bifurcation parameters. Phenomenological mathematical models, neural network nonlinear models, and an experimental data from a polymerization unit are employed for validating the proposed methodology in order to implement confident nonlinear controllers.     

双脉冲激光诱导击穿光谱结合多元定标法定量分析大豆油中的铅

应用双脉冲激光诱导击穿光谱(DP-LIBS)对大豆油中的铅(Pb)含量进行检测。配制9个大豆油样品,采用一定规格圆柱形桐木对样品中Pb进行富集,然后通过Ava-Spec二通道高精度光谱仪采集其LIBS光谱信号。根据样品的LIBS谱线图和美国国家标准技术研究所(NIST)原子光谱数据库,确定选用CaⅡ393.284 nm,CaⅡ396.752 nm,NⅡ399.399 nm和PbⅠ405.685 nm的特征谱线强度作为自变量,得到Pb含量的多元线性回归定量分析模型,并通过方差分析和t检验验证分析模型的可行性。结果表明,采用Pb元素直接定标法得到的平均相对误差约为16%,拟合度R2为0.981 8;采用多元线性回归模型得到的平均相对误差为7.25%,拟合度R2为0.997 1,3个检验样品的相对误差均在合理范围内。采用多元校正分析模型可以充分利用光谱中的有效信息,降低基体效应的影响,从而提高LIBS分析的准确性。