Comparison of multianalyte proficiency test results by sum of ranking differences, principal component analysis, and hierarchical cluster analysis

Sum of ranking differences (SRD) was applied for comparing multianalyte results obtained by several analytical methods used in one or in different laboratories, i.e., for ranking the overall performances of the methods (or laboratories) in simultaneous determination of the same set of analytes. The data sets for testing of the SRD applicability contained the results reported during one of the proficiency tests (PTs) organized by EU Reference Laboratory for Polycyclic Aromatic Hydrocarbons (EU-RL-PAH). In this way, the SRD was also tested as a discriminant method alternative to existing average performance scores used to compare mutlianalyte PT results. SRD should be used along with the z scores-the most commonly used PT performance statistics. SRD was further developed to handle the same rankings (ties) among laboratories. Two benchmark concentration series were selected as reference: (a) the assigned PAH concentrations (determined precisely beforehand by the EU-RL-PAH) and (b) the averages of all individual PAH concentrations determined by each laboratory. Ranking relative to the assigned values and also to the average (or median) values pointed to the laboratories with the most extreme results, as well as revealed groups of laboratories with similar overall performances. SRD reveals differences between methods or laboratories even if classical test(s) cannot. The ranking was validated using comparison of ranks by random numbers (a randomization test) and using seven folds cross-validation, which highlighted the similarities among the (methods used in) laboratories. Principal component analysis and hierarchical cluster analysis justified the findings based on SRD ranking/grouping. If the PAH-concentrations are row-scaled, (i.e., z scores are analyzed as input for ranking) SRD can still be used for checking the normality of errors. Moreover, cross-validation of SRD on z scores groups the laboratories similarly. The SRD technique is general in nature, i.e., it can be applied to any experimental problem in which multianalyte results obtained either by several analytical procedures, analysts, instruments, or laboratories need to be compared. [Figure not available: see fulltext.] © 2013 Springer-Verlag Berlin Heidelberg

Comparison of comet assay parameters for estimation of genotoxicity by sum of ranking differences

Genotoxic potential of waters in six rivers and reservoirs from Serbia was monitored in different tissues of chub (Squalius cephalus L. 1758) with the alkaline comet assay. The comet assay, or a single cell gel electrophoresis, has a wide application as a simple and sensitive method for evaluating DNA damage in fish exposed to various xenobiotics in the aquatic environment. Three types of cells, erythrocytes, gills and liver cells were used for assessing of DNA damage. Images of randomly selected cells were analyzed with a fluorescence microscope Leica and image analysis by software (Comet Assay IV Image analysis system, PI, UK). Three parameters (tail length - l, tail intensity - i and Olive tail moment – m) were analyzed on 1750 nuclei per cell type. The procedure for sum of ranking differences (SRD) was implemented to compare different types of cells and different parameters for estimation of DNA damage. Regarding our nine different estimations of genotoxicity: tail length, intensity and moment in erythrocytes (rel, rei, rem), liver cells (rll, rli, rlm) and gill cells (rgl, rgi, rgm) SRD procedure has shown that the Olive tail moment and tail intensity are (almost) equally good parameters; the SRD value was lower for the tail moment and tail intensity than for tail length in case of all types of cells. The least reliable parameter was rel; close to the borderline case were rei, rll, and rgl (~5% probability of random ranking)

Modeling of the acute toxicity of benzene derivatives by complementary QSAR methods

A data set containing acute toxicity values (96-h LC50) of 69 substituted benzenes for fathead minnow (Pimephales promelas) was investigated with two Quantitative Structure- Activity Relationship (QSAR) models, either using or not using molecular descriptors, respectively. Recursive Neural Networks (RNN) derive a QSAR by direct treatment of the molecular structure, described through an appropriate graphical tool (variable-size labeled rooted ordered trees) by defining suitable representation rules. The input trees are encoded by an adaptive process able to learn, by tuning its free parameters, from a given set of structureactivity training examples. Owing to the use of a flexible encoding approach, the model is target invariant and does not need a priori definition of molecular descriptors. The results obtained in this study were analyzed together with those of a model based on molecular descriptors, i.e. a Multiple Linear Regression (MLR) model using CROatian MultiRegression selection of descriptors (CROMRsel). The comparison revealed interesting similarities that could lead to the development of a combined approach, exploiting the complementary characteristics of the two approaches