Using mixture models for bump-hunting in the results of proficiency tests

The interpretation of the results of proficiency tests by the use of mixture models is described. The data are interpreted as a sample from a mixture of several normal populations. The calculation of the statistics (the means, variances and proportions of each component) is accomplished by means of the ‘EM’ algorithm. The method has several advantages over those previously advanced, principally that the algorithm is fast and easy to execute. Examples from proficiency testing are discussed.     

Ultrafast far-infrared dynamics probed by terahertz pulses: a frequency-domain approach. II. Applications

We present data obtained by time-resolved terahertz spectroscopy in selected semiconducting and molecular systems exhibiting subpicosecond far-infrared dynamics. We use a frequency-domain method which eliminates the influence of instrumental functions and artifacts due to frequency mixing and yields a two-dimensional transient conductivity of the photoexcited sample. This technique enables improving the attainable experimental time resolution and allows a simple qualitative interpretation of the results without a priori modeling. The quantitative interpretation is based on the time-dependent Drude and damped harmonic oscillator models.     

Spectroscopic and structural elucidation of L-tyrosine-containing dipeptides valyl-tyrosine and tyrosyl-alanine: solid-state IR-LD spectroscopy, quantum chemical calculations and vibrational analysis

L-Tyrosine-containing dipeptides valyl-tyrosine (H-Val-Tyr-OH) and tyrosyl-alanine (H-Tyr-Ala-OH) are characterized structurally by means of quantum chemical ab initio calculations and solid-state linear-dichroic infrared (IR-LD) spectroscopy. The IR-characteristic bands are assigned by application of reducing-difference procedure for polarized IR-spectra interpretation. Infrared data obtained are supported as well by the made vibrational analysis. The structures of both peptides are predicted on the basis of conformational analysis and structural information, obtained by the shown IR-spectroscopic tool.     

A kinetic investigation of removal of chromium from aqueous solutions with a strong cation exchange resin

A kinetic investigation was performed with an ion exchange resin for chromium. A strong cation exchange resin (Amberlite IR 120) was used for removal of chromium. The effects of concentration, resin amount, and stirring speed on kinetics were investigated. The metal concentration range studied was between 5 to 160 mg/dm³ (the amount of solution was 4 dm³), the resin amount range was between 5 to 20 mg, and the stirring speed range was between 1000 to 3500 rpm. Equilibrium experiments were performed for calculation of separation factor. Kinetic studies were done using a Kressman-Kitchener stirrer reactor system and the results were compared with existing kinetic models. Two models, Nernst-Plank film diffusion control model (fdc) and solid phase diffusion control model (pdc), respectively were identified, and the dependence of the rate on parameters such as solution concentration, resin amount, stirring speed, etc. was examined for each of them. The interpretation of these data shows that the system is probably controlled by both film and particle diffusion. Correspondence: Sevgi Kocaoba, Department of Chemistry, Faculty of Art and Science, Yildiz Technical University, Davutpasa Cad. No: 127, 34210 Davutpasa-Istanbul, Turkey.     

Photochemistry and chemometrics—An overview

Photochemistry has made significant contributions to our understanding of many important natural processes as well as the scientific discoveries of the man-made world. The measurements from such studies are often complex and may require advanced data interpretation with the use of multivariate or chemometrics methods. In general, such methods have been applied successfully for data display, classification, multivariate curve resolution and prediction in analytical chemistry, environmental chemistry, engineering, medical research and industry. However, in photochemistry, by comparison, applications of such multivariate approaches were found to be less frequent although a variety of methods have been used, especially with spectroscopic photochemical applications. The methods include Principal Component Analysis (PCA; data display), Partial Least Squares (PLS; prediction), Artificial Neural Networks (ANN; prediction) and several models for multivariate curve resolution related to Parallel Factor Analysis (PARAFAC; decomposition of complex responses). Applications of such methods are discussed in this overview and typical examples include photodegradation of herbicides, prediction of antibiotics in human fluids (fluorescence spectroscopy), non-destructive in- and on-line monitoring (near infrared spectroscopy) and fast-time resolution of spectroscopic signals from photochemical reactions. It is also quite clear from the literature that the scope of spectroscopic photochemistry was enhanced by the application of chemometrics.To highlight and encourage further applications of chemometrics in photochemistry, several additional chemometrics approaches are discussed using data collected by the authors. The use of a PCA biplot is illustrated with an analysis of a matrix containing data on the performance of photocatalysts developed for water splitting and hydrogen production. In addition, the applications of the Multi-Criteria Decision Making (MCDM) ranking methods and Fuzzy Clustering are demonstrated with an analysis of water quality data matrix. Other examples of topics include the application of simultaneous kinetic spectroscopic methods for prediction of pesticides, and the use of response fingerprinting approach for classification of medicinal preparations. In general, the overview endeavours to emphasise the advantages of chemometrics’ interpretation of multivariate photochemical data, and an Appendix of references and summaries of common and less usual chemometrics methods noted in this work, is provided.     

Artificial intelligence and machine learning for targeted energy storage solutions

With the application of machine learning to large-material data sets, models are being developed that allow us to better predict novel materials with designed properties. Advances in artificial intelligence and its subclasses, as well as compute infrastructure, are making it possible to rapidly compute material properties, to access time/length scales and chemical spaces beyond the current capabilities of density functional theory and to outperform humans in interpretation and characterization of the data. This review highlights the latest developments in the field with special interest to energy storage materials.     

Electrochemical impedance spectroscopy on biofilm electrodes – conclusive or euphonious?

Electrochemical impedance spectroscopy (EIS) is a versatile tool that is also exploited to study bioelectrochemical systems and biofilm electrodes. EIS can be used to examine characteristics of biofilm electrodes, which are not accessible by direct current measurements such as biofilm resistance and biofilm capacitance. EIS in microbial electrochemistry is sometimes applied superficially or evaluation of presented data is not comprehensive due to misinterpretation or missing data validation. This hinders a more widespread application of this method, not only for determination of specific biofilm electrode parameters but also from a more practical perspective, e.g. as tool for in situ condition monitoring of biofilm electrodes. We discuss how a careful choice of the experimental set-up, as well as extraordinary diligent EIS data interpretation using electrical equivalent circuit models can lead to conclusive data and meaningful insights. We illustrate the special challenges of studying biofilm electrodes on the example of graphite anodes. We provide an initial guidepost on how to use EIS on biofilm electrodes that requires several preconditions, careful choice of experimental parameters and, nearly mandatory for novices similar to us, the consultation of experienced operators of EIS.     

Solvent effects upon reactions between ions: hexacyanoferrate(II)-peroxodisulphate and sulphite-hexacyanoferate(III)

Summary Different approaches to the interpretation of solvent effects on reactions between ionic reactants are analysed, taking as a basis the kinetic data corresponding to the sulphite-hexacyanoferrate(III) and peroxodisulphate-hexacyanoferrate(III) oxidations. It is concluded that the approach based on the use of solvent parameters is the more promising, although knowledge of the transfer chemical potentials of the reactants may also be useful in the interpretation of kinetic behaviour.     

MISER chromatography (multiple injections in a single experimental run): the chromatogram is the graph

An experimental approach for rapid analysis and convenient interpretation of multiparallel experiments is described. Conventional approaches use a series of individual chromatographic runs to produce integrated peak area data, which are stored in individual data files, then transferred to a spreadsheet program and graphed to allow interpretation of experimental results. A simpler and more direct approach utilizes multiple injections within a single chromatographic run to produce a continuous trace of chromatograms, which can often provide a direct visual readout of experimental outcome without the need for peak integration, data transfer, or graphing. In this approach, the chromatogram itself serves as the graph whereby the outcome of the multiparallel experiment can be discerned. The utility of the technique is greatly enhanced by the use of compound-specific detection technologies such as mass spectrometry or chiroptical spectroscopy, and can benefit from experimental designs that facilitate the direct interpretation of results.     

Data modelling with neural networks: Advantages and limitations

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.     

Deconvolution of Molecular Weight Distributions Using Dynamic Flory‐Schulz Distributions

Summary: The deconvolution of molecular weight distributions (MWDs) may be useful for obtaining information about the polymerization kinetics and properties of catalytic systems. However, deconvolution techniques are normally based on steady‐state assumptions and very little has been reported about the use of non‐stationary approaches for the deconvolution of MWDs. In spite of this, polymerization reactions are often performed in batch or semi‐batch modes. For this reason, dynamic solutions are proposed here for simple kinetic models and are then used for deconvolution of actual MWD data. Deconvolution results obtained with dynamic models are compared to deconvolution results obtained with the standard stationary Flory‐Schulz distributions. For coordination polymerizations, results show that dynamic MWD models are able to describe experimental data with fewer catalytic sites, which indicates that the proper interpretation of the reaction dynamics may be of fundamental importance for kinetic characterization. On the other hand, reaction dynamics induced by modification of chain transfer agent concentration seem to play a minor role in the shape of the MWD in free‐radical polymerizations.

This Figure illustrates that MWDs obtained at unsteady conditions should not be deconvoluted with standard steady‐state Flory‐Schulz distributions.     

Carbon-13 chemical shift anisotropy in DNA bases from field dependence of solution NMR relaxation rates

Knowledge of (13)C chemical shift anisotropy (CSA) in nucleotide bases is important for the interpretation of solution-state NMR relaxation data in terms of local dynamic properties of DNA and RNA. Accurate knowledge of the CSA becomes particularly important at high magnetic fields, prerequisite for adequate spectral resolution in larger oligonucleotides. Measurement of (13)C relaxation rates of protonated carbons in the bases of the so-called Dickerson dodecamer, d(CGCGAATTCGCG)(2), at 500 and 800 MHz (1)H frequency, together with the previously characterized structure and diffusion tensor yields CSA values for C5 in C, C6 in C and T, C8 in A and G, and C2 in A that are closest to values previously reported on the basis of solid-state FIREMAT NMR measurements, and mostly larger than values obtained by in vacuo DFT calculations. Owing to the noncollinearity of dipolar and CSA interactions, interpretation of the NMR relaxation rates is particularly sensitive to anisotropy of rotational diffusion, and use of isotropic diffusion models can result in considerable errors.     

Application of orthogonal signal correction to minimise the effects of physical and biological variation in high resolution 1H NMR spectra of biofluids

1H nuclear magnetic resonance (NMR)-based metabonomics is a well-established technique used to analyse and interpret complex multiparametric metabolic data, and has a wide number of applications in the development of pharmaceuticals. However, interpretation of biological data can be confounded by extraneous variation in the data such as fluctuations in either experimental conditions or in physiological status. Here we have shown the novel application of a data filtering method, orthogonal signal correction (OSC), to biofluid NMR data to minimise the influence of inter- and intra-spectrometer variation during data acquisition, and also to minimise innate physiological variation. The removal of orthogonal variation exposed features of interest in the NMR data and facilitated interpretation of the derived multivariate models. Furthermore, analysis of the orthogonal variation provided an explanation of the systematic analytical/biological changes responsible for confounding the original NMR data.     

Damped torsional oscillator model for polymer molecules

At present the widely used model for explaining viscoelastic and dielectric properties of polymer solutions is that of Rouse and Bueche. Here the polymer molecule is considered as an array of Gaussian subunits, each of which acts as an entropy spring. The motion of these segments is damped by the viscous drag of the surrounding solvent (RB model). An alternative model is presented, in which the segments are torsional oscillators consisting of two or three backbone links, and the damping is due to hindered internal rotation (DTO model). The mathematical treatment of these two models is essentially identical, but the physical interpretation of the constants used is very different. The DTO model has previously been applied by one of us to the interpretation of viscoelastic data. It is here applied to the interpretation of dielectric loss data. It is shown that dielectric measurements in dilute solution should very readily discriminate between the two approaches. Finally it is shown that the relaxation time computed from the DTO model is in closer agreement with published NMR data on poly(propylene oxide) 2025, than is the RB relaxation time. The postulates of the DTO model appear to be confirmed for this low molecular weight polymer. An even more sensitive distinction should be available by studies of the relaxation time as a function of polymer concentration.     

General purpose interactive physico-chemical property exploration

There have been many tools and methods developed to investigate structure-activity (SAR) and structure-property (SPR) relationships. Many of these tools are fully or almost fully automated and attempt to predict various properties of molecules. Even with these tools, there is still an urgent need to provide a simple and useful methodology so a consumer of these various models, such as a medicinal chemist or molecular modeler, can learn how various properties of small molecules relate to their structure. This paper describes a new viewing and navigation paradigm called the Spiral View, which can be used to facilitate the interpretation of structure-property relationships. Examples of how this tool proves useful in the human interpretation of these relationships are drawn from the fields of experimental toxicity, LogP, and biological activity data drawn from various sources.     

Application of nitrogen stable isotope analysis in size-based marine food web and macroecological research

Interacting human and environmental pressures influence the structure and dynamics of marine food webs. To describe and predict the effects of these pressures, theoretical advances need to be supported by a capacity to validate the underlying models and assumptions. Here, we review recent applications of nitrogen stable isotope analysis in marine food web and macroecological research, with a focus on work that has paralleled a resurgence of interest in the development and application of size-based models. Nitrogen stable isotope data have been used to estimate intra- and inter-specific variation in trophic level, predator-prey size ratios, transfer efficiency, food chain length, relationships between predator and prey species diversity and the dynamics of energy use. Many of these estimates have contributed to the development, testing and parameterisation of food web and ecosystem models, some of which have been used to establish baselines for assessing the scale of human impacts. The interpretation of results depends on assumed fractionation but, when supported by sensitivity analyses and experimental validation, nitrogen stable isotope data provide valuable insights into the structuring of marine communities and ecosystems.     

Novel software for the assignment of peaks from tandem mass spectrometry spectra of synthetic polymers

Novel software has been developed to aid the interpretation of tandem mass spectrometry (MS/MS) data from synthetic polymers. The software is particularly focused toward aiding the end-group determination of these materials by significantly speeding up the interpretation process. This allows information on the initiator and/or chain transfer agents, used to generate the polymer, and the mechanism of termination to be inferred from the data much more rapidly. The software allows the validity of hypothesized structures to be rapidly tested by automatically annotating the data file using previously proposed fragmentation rules for synthetic polymers. Low-energy collision-induced dissociation (CID) data from methacrylate, styrene, and polyether oligomers are used as example data for the software. Exact-mass CID information was used to aid the understanding of the dissociation mechanism of the polymers. The software can use exact-mass data to provide more confidence in the results. The MS/MS results indicate that the fragmentation pathways are those previously proposed for these polymers.     

An approach to the interpretation of backpropagation neural network models in QSAR studies

An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes.