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Abstract: A new chemoinformatic approach, called Mapping of Activity through Dichotomic Scores, is introduced. Its goal is the supervised projection of molecules, represented with strings of binary digits expressing the presence or absence of selected structural features, onto a novel 2‐dimensional space, which highlights regions of active (inactive) molecules of interest. At the same time, variables are projected onto a second 2‐dimensional space, which highlights those structural features that are more related to the molecular activity of interest. Unlike the classical weighting schemes used in substructural analysis, which consider the substructures independently of each other, the Mapping of Activity through Dichotomic Scores approach considers the interactions between pairs of substructures, that is, their frequencies of cooccurrence in the molecules. In this work, the theory is presented and elucidated, with an example dataset and in comparison with a benchmark fragment‐based scoring scheme. [ABSTRACT FROM AUTHOR]

This work presents the weighted power–weakness ratio (wPWR), a multivariate index for multi-criteria decision-making (MCDM) and ranking comparison. The index derives from the power–weakness ratio (PWR) originally proposed to select the strongest winner of tournaments, which has been re-adapted in this study to solve MCDM problems. Key features of wPWR are: (1) its multivariate character, (2) the ability to account simultaneously for the strengths and weaknesses of each element, (3) the possibility to weight criteria according to previous knowledge about the problem. In order to analyze wPWR, we selected three datasets available in scientific literature. The obtained wPWR scores and rankings were compared with four well-established techniques: Simple Average Ranking, Dominance, Reciprocal Rank Fusion and Kendall–Wei approach. Where rankings obtained by other techniques were available from literature, they were also included in the analysis. Results highlighted a correct correlation between wPWR and the other ranking measures, but also interesting differences that support its introduction in the field of MCDM and chemometrics. [ABSTRACT FROM AUTHOR]

The assessment of the classification performance can be based on class indices, such as sensitivity, specificity and precision, which describe the classification results achieved on each modelled class. However, in several situations, it is useful to represent the global classification performance with a single number. Therefore, several measures have been introduced in literature to deal with this assessment, accuracy being the most known and used. These metrics have been proposed to generally face binary classification tasks and can behave differently depending on the classification scenario. In this study, different global measures of classification performances are compared by means of results achieved on an extended set of real multivariate datasets. The systematic comparison is carried out through multivariate analysis. Further investigations are then derived on specific indices to understand how the presence of unbalanced classes and the number of modelled classes can influence their behaviour. Finally, this work introduces a set of benchmark values based on different random classification scenarios. These benchmark thresholds can serve as the initial criterion to accept or reject a classification model on the basis of its performance. [ABSTRACT FROM AUTHOR]

Abstract: A modified version of the Sequential Replacement (SR) algorithm for variable selection is proposed, featuring modern functionalities aimed to: 1) reduce the computational time; 2) estimate the real predictivity of the model; 3) identify models suffering from pathologies. This redesigned version was called Reshaped Sequential Replacement (RSR) algorithm. The RSR algorithm was applied to several datasets in regression and classification and was compared with the original SR method by means of a Design of Experiments (DoE). The DoE took into account the functions that affect the outcome of the search in terms of generated combinations of variables and time required for computation. The results were also compared with published models on the same datasets, taken as reference, and obtained by different variable selection methods. This latter comparison showed that the RSR algorithm managed to find good subsets of variables on all datasets, even though the reference models were not always found. When the reference model was not found the RSR algorithm returned comparable or better subsets of variables, evaluated in cross-validation. The DoE showed that the inclusion of the additional functions allowed to obtain models with equivalent or better performances in a decreased computational time compared to the original SR method. [Copyright &y& Elsevier]

Abstract: Kohonen maps (or Self Organizing Maps, SOMs) and Counterpropagation Artificial Neural Networks are two of the most popular Neural Networks proposed in literature and are increasing in uses and applications related to several multivariate chemical issues, since they can handle both supervised and unsupervised problems. This work deals with the presentation of the Kohonen and CP-ANN toolbox, that is a collection of MATLAB modules freely available via the Internet (http://www.disat.unimib.it/chm) for the calculation of the quoted models. A graphical user interface (GUI), which allows an easy model calculation and analysis of results, is also provided. The toolbox features are presented by reproducing the classification of a real multivariate dataset. This work is not an attempt to summarize the general applications of Self Organizing Maps, but to inform chemometricians and practitioners who are not skilled programmers of the existence of a user-friendly Matlab toolbox to develop unsupervised and supervised SOM models. [Copyright &y& Elsevier]

Atmospheric pollutants can originate the decay of historic paintings exposed to the outdoor elements. This is a cause of great concern, since such contaminants can produce physical-chemical alterations manifested initially in undesirable color change. This paper tests an unsupervised multivariate approach on discrete data color parameters in a pioneering study which combines spectrophotometric data and principal components analysis to detect unaesthetic color change on paint dosimeters in (semi)open-air monuments exposed long-term to the urban atmosphere of the city of Granada (South Spain). To this end the chromatic parameter of the CIEL*a*b* and CIEL*C*h* systems (L*, a*, b*, h*, C* and ΔE) were used as variables for subsequent multivariate analysis in order to determine the intrinsic color change trends. The aim is to evaluate the specific chromatic parameter(s) that cause the unaesthetic damage for each type of paint dosimeter, while also considering the influence of the binder (egg yolk/rabbit glue), the pigment (azurite, malachite and lapis lazuli) and for the first time, the grain size of the studied pigments (azurite). Results demonstrated that this approach is capable of discriminating samples on the basis of dosimeter composition, so enabling interpretation of their aging process. Azurite and lapis lazuli-laden dosimeters tended to turn green over time as a result of exposure to city air regardless of binder composition and location. By contrast, all malachite-laden dosimeters became bluer over time. Luminosity remained stronger in dosimeters prepared with collagen, an important parameter in binder discrimination. This information is also of great value for restoration purposes. [ABSTRACT FROM AUTHOR]

Abstract: Unsupervised variable reduction methods are intended for reducing the presence of redundancy and multicollinearity in the data. These are common issues when dealing with multivariate analysis associated to large number of variables. With respect to supervised selection, unsupervised reduction aims at selecting subsets of variables able to preserve information, but eliminating redundancy, noise and linearly or near-linearly dependent variables, without considering any dependent response. In this study, we propose the V-WSP algorithm for unsupervised variable reduction, which is a modification of the recently proposed WSP algorithm for design of experiments (DOE). Convergence, performances and comparison with several benchmark algorithms, as well as with other DOE strategies adapted to variable reduction, were evaluated on both simulated, benchmark and real QSAR datasets. The proposed algorithm demonstrated to converge to similar solutions with respect to other reduction strategies, with the advantage to be faster and simpler. [Copyright &y& Elsevier]

Abstract: In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure–Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks’ Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging. [Copyright &y& Elsevier]

Abstract: The purpose of the present work is to highlight potentiality of chemometric interpretation of consolidated instrumental food texture evaluations. In this study, back-extrusion tests were carried out on industrial-made whipped dairy creams. Mechanical profiles were obtained from 0 up to 49 storage days and were analyzed by considering the signal as a whole using basic chemometrics tools. In order to obtain the maximum content of information related to the mechanical properties of the dairy cream samples, the texture information carried on by mechanical profiles were quantified in terms of Principal Component Analysis. A threshold in whipped creams shelf-life was identified in terms of overall textural attributes and was equal to around 28 and 35 storage days, where a relevant variation of the mechanical properties was observed. Multivariate classification furnished objective and quantitative criteria for the shelf life mapping of the product, based on mechanical signals. Multivariate statistical analysis proved to be able to handle large datasets produced by a consolidated analytical technique and consequently appeared as a promising tool for studying the entire mechanical profiles. [Copyright &y& Elsevier]