Roberto Todeschini

Publications (103) View all

• Article: Chemometric analysis of gas chromatography with flame ionisation detection chromatograms: a novel method for classification of petroleum products.

  N J Nielsen, D Ballabio, G Tomasi, R Todeschini, J H Christensen
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  ABSTRACT: Most oil characterisation procedures are time consuming, labour intensive and utilise only part of the acquired chemical information. Oil spill fingerprinting with multivariate data processing represents a fast and objective evaluation procedure, where the entire chromatographic profile is used. Methods for oil classification should be robust towards changes imposed on the spill fingerprint by short-term weathering, i.e. dissolution and evaporation processes in the hours following a spill. We propose a methodology for the classification of petroleum products. The method consists of: chemical analysis; data clean-up by baseline removal, retention time alignment and normalisation; recognition of oil type by classification followed by initial source characterisation. A classification model based on principal components and quadratic discrimination robust towards the effect of short-term weathering was established. The method was tested successfully on real spill and source samples.
  Journal of chromatography. A 03/2012; 1238:121-7. · 4.19 Impact Factor
• Source

  Available from: Alessio Ippolito

  Article: Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits.

  A Ippolito, R Todeschini, M Vighi
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  ABSTRACT: Assessing the sensitivity of different species to chemicals is one of the key points in predicting the effects of toxic compounds in the environment. Trait-based predicting methods have proved to be extremely efficient for assessing the sensitivity of macroinvertebrates toward compounds with non specific toxicity (narcotics). Nevertheless, predicting the sensitivity of organisms toward compounds with specific toxicity is much more complex, since it depends on the mode of action of the chemical. The aim of this work was to predict the sensitivity of several freshwater macroinvertebrates toward three classes of plant protection products: organophosphates, carbamates and pyrethroids. Two databases were built: one with sensitivity data (retrieved, evaluated and selected from the U.S. Environmental Protection Agency ECOTOX database) and the other with biological traits. Aside from the "traditional" traits usually considered in ecological analysis (i.e. body size, respiration technique, feeding habits, etc.), multivariate analysis was used to relate the sensitivity of organisms to some other characteristics which may be involved in the process of intoxication. Results confirmed that, besides traditional biological traits, related to uptake capability (e.g. body size and body shape) some traits more related to particular metabolic characteristics or patterns have a good predictive capacity on the sensitivity to these kinds of toxic substances. For example, behavioral complexity, assumed as an indicator of nervous system complexity, proved to be an important predictor of sensitivity towards these compounds. These results confirm the need for more complex traits to predict effects of highly specific substances. One key point for achieving a complete mechanistic understanding of the process is the choice of traits, whose role in the discrimination of sensitivity should be clearly interpretable, and not only statistically significant. The final publication is available at link.springer.com
  Ecotoxicology 03/2012; 21(2):336-52. · 2.36 Impact Factor
• Article: Self organizing maps for analysis of polycyclic aromatic hydrocarbons 3-way data from spilled oils.

  R Fernández-Varela, M P Gómez-Carracedo, D Ballabio, J M Andrade, V Consonni, R Todeschini
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  ABSTRACT: In this paper, the application of a new method based on self-organizing maps (SOM; termed MOLMAP, molecular map of atom-level properties) to handle 3-way data generated in a monitoring environmental study is presented. The study comprised 50 polycyclic aromatic hydrocarbons (PAHs) analyzed in samples derived from the weathering of six oil products (four crude oils and two fuel oils) spilled under controlled conditions for about 4 months. MOLMAP yielded useful information on each mode of the data cube: weathering samples, spilled oil products, and PAHs. Thus, the different behaviors of the six oils were ascertained, along with their particular evolution on time, and their weathering patterns were studied in terms of the original PAHs. Thus, the two heaviest products (two fuel oils) were characterized by two neurons whose more relevant weights were associated to heavy PAHs, as C(1)-fluoranthene, C(2)-fluoranthene, benzo(ghi)perylene, and dibenz(ah)anthracene. The six spilled products were projected on different regions on both the MOLMAP-SOM and a subsequent principal components analysis (PCA) scatter plot, developed using the so-called MOLMAP-scores. Besides, it was possible to further differentiate between unweathered, or slightly weathered, samples and the most weathered ones. The more relevant PAHs characterizing those samples were assessed studying the weights of the neurons in which the samples got projected.
  Analytical Chemistry 04/2010; 82(10):4264-71. · 5.86 Impact Factor
• Source

  Available from: Davide Ballabio

  Article: Evaluation of model predictive ability by external validation techniques

  V. Consonni, D. Ballabio, R. Todeschini
  Journal of Chemometrics. 01/2010; 24.
• Source

  Available from: Davide Ballabio

  Article: Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 1. Theory and simple chemometric applications.

  R Todeschini, D Ballabio, V Consonni, A Manganaro, A Mauri
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  ABSTRACT: So far, similarity/diversity of objects has been widely studied in different research fields and a number of distance measures to estimate diversity between objects have been proposed. However, not much interest has been addressed to analysis of how diverse are configurations of objects in two different multivariate spaces. Since computerisation and automation nowadays lead to a large availability of information, it is apparent that a system could be described in different ways and, consequently, methods for comparison of the different viewpoints are required. These methods, for instance, may be usefully applied to Quantitative Structure-Activity Relationship (QSAR) studies. In this field, several thousands of molecular descriptors have been proposed in the literature and different selections of descriptors define different chemical spaces that need to be compared. Moreover, variable selection techniques such as Genetic Algorithms, Simulated Annealing, and Tabu Search are widely used to process available information in order to select optimal QSAR models. When more than one optimal model results, the problem arising is how to compare these models to find out whether they are really diverse or based on descriptors explaining almost the same information. In this paper, novel indices are proposed to measure similarity/diversity between pairs of data sets by the aid of the variable cross-correlation matrix.
  Analytica chimica acta 09/2009; 648(1):45-51. · 4.31 Impact Factor