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Two-dimensional (2D) electrophoresis is the most wide spread technique for the separation of proteins in biological systems. This technique produces 2D maps of high complexity, which creates difficulties in the comparison of different samples. The method proposed in this paper for the comparison of different 2D maps can be summarised in four steps: (a) digitalisation of the image; (b) fuzzyfication of the digitalised map in order to consider the variability of the two-dimensional electrophoretic separation; (c) decoding by principal component analysis of the previously obtained fuzzy maps, in order to reduce the system dimensionality; (d) classification analysis (linear discriminant analysis), in order to separate the samples contained in the dataset according to the classes present in said dataset. This method was applied to a dataset constituted by eight samples: four belonging to healthy human lymph-nodes and four deriving from non-Hodgkin lymphomas. The amount of fuzzyfication of the original map is governed by the sigma parameter. The larger the value, the more fuzzy theresulting transformed map. The effect of the fuzzyfication parameter was investigated, the optimal results being obtained for sigma = 1.75 and 2.25. Principal component analysis and linear discriminant analysis allowed the separation of the two classes of samples without any misclassification.

An apparatus to dry aqueous dispersions of solid lipid nanoparticles (SLNs) was designed. Optimal running conditions were evaluated to obtain minimum process time and produce dried SLNs characterized by small size variation. To achieve process optimization, SLN average diameter, SLNs polydispersity index, and drying time were related to three operative variables: process temperature, SLN concentration in the original aqueous dispersions, and nitrogen flow rate as the physical means of the drying process. An experimental design procedure and a multicriteria optimization method, targeting desirability functions, enabled us to obtain the optimal conditions for all responses. Drying time, average diameter, and polydispersity index of dried SLN batches were more favorable than those obtained by freeze-drying identical SLN aqueous dispersions with the same initial nanoparticle concentration.

The optimisation of the formulation of a commercial bubble bath was performed by chemometric analysis of Panel Tests results. A first Panel Test was performed to choose the best essence, among four proposed to the consumers; the best essence chosen was used in the revised commercial bubble bath. Afterwards, the effect of changing the amount of four components (the amount of primary surfactant, the essence, the hydratant and the colouring agent) of the bubble bath was studied by a fractional factorial design. The segmentation of the bubble bath market was performed by a second Panel Test, in which the consumers were requested to evaluate the samples coming from the experimental design. The results were then treated by Principal Component Analysis. The market had two segments: people preferring a product with a rich formulation and people preferring a poor product. The final target, i.e. the optimisation of the formulation for each segment, was obtained by the calculation of regression models relating the subjective evaluations given by the Panel and the compositions of the samples. The regression models allowed to identify the best formulations for the two segments ofthe market.

An efficient method for the simultaneous determination of methyl tert.-butyl ether, tert.-butyl alcohol, benzene, toluene, ethylbenzene and xylene isomers in groundwater by purge-and-trap-gas chromatography–mass spectrometry was developed and validated. Experimental design was used to investigate the effects of temperature of extraction, time of extraction and percentage of salt added to the water samples. Regression models and desirability functions were applied to find the experimental conditions providing the highest global extraction yield. Validation was carried out in terms of limits of detection (LOD), limits of quantitation (LOQ), linearity and precision. LOD values ranging from 2.6 to 23 ng l−1 were achieved, whereas linearity was statistically verified over two orders of magnitude for each compound. Precision was evaluated testing two concentration levels. Good results were obtained both in terms of intra-day repeatability and intermediate precision: RSD% lower than 4.5% at the highest concentration and lower than 13% at the lowest one were calculated for intra-day repeatability. A groundwater sample suspected of contamination by leaking underground petroleum storage tanks was analysed and some of the analytes were detected and quantitated.

The Carlo Alberto Canal connects Bormida and Tanaro rivers in Piedmont (ITALY). It was created for irrigation purposes but since its waters are suspected to be polluted, a sampling campaign was performed by the ARPA of Alessandria. The physico-chemical parameters analysed along 3 years (1998-2000) were investigated by multivariate chemometric methods. PCA showed that the waters situation depends heavily on the sampling period. Also a Kohonen self-organising map confirmed the clustering observed, providing insights into the causes of the clusterisation. New samplings are now being performed and a larger set of environmental variables is determined on each sample.

Sixty-eight samples of wines from Piedmont (Italy) were analysed to determine their content of volatile compounds, using the solid-phase microextraction (SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS). Samples were from five groups of wines: Barolo, Barbaresco, Nebbiolo d'Alba, Roero and Langhe Nebbiolo, all produced from the Nebbiolo grape in the Langhe and Roero areas (province of Cuneo, Piedmont) but differing in vintage (respectively, 3 years, 2 years, 1 year, 8 months and few months) and production zone. Thirty-five analytes were identified; peak area data, corrected for internal standard, were used for pattern recognition treatments. Principal components analysis, hierarchical cluster analysis, Kohonen self organising map, stepwise linear discriminant analysis and soft independent modelling of class analogy were applied to the data, revealing a good separation between the five groups. A main factor, strictly connected to wine vintage, was identified and found to be related to some analytes.

Pancreatic cancer (PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies: (1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and (2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques (proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this field are discussed.

Experimental factorial design was used to evaluate the influence of two factors involved in producing cholesteryl butyrate (chol-but) solid lipid nanospheres (SLN), microemulsion formulation and microemulsion/water ratio, on the effect of the SLN on the proliferation of NIH-H460, a non-small-cell lung carcinoma; six experimental settings were tested. The cells were treated with scalar concentrations of cholesteryl butyrate (from 0.008 to 1.000 mM) for each experimental condition; NIH-H460 cell growth was inhibited in all cases. The best experimental setting provided complete inhibition at 0.125 mM chol-but, while at the same concentration sodium butyrate provided only 38% inhibition.

Direct gas chromatographic methods to analyse phenylurea pesticides are discouraged by the thermal instability of these compounds, that in conventional hot splitless inlet systems leads to extensive and irreproducible formation of isocyanates and amines. However a careful control of the operating conditions, like the inlet temperature, the pressure and the presence of suitable chemical additives (as acetic acid, low-molecular-mass amines, organic anhydrides) can either: (i) minimise the thermal decomposition enabling the direct GC-MS analysis of phenylureas, or (ii) lead to reproducible conversion to isocyanates. Experimental design was employed to study the effect of the experimental variables on the thermal transformation of phenylurea pesticides in splitless inlet system. Two strategies were alternatively optimised: (i) the minimisation of degradation reactions to increase the signal of phenylureas; (ii) the maximisation of the degradation to isocyanates that are in turn determined. The maximal yields in isocyanate were obtained with high inlet temperatures, low carrier flows in the injection phase and the presence of acetic anhydride. By contrast, the use of relatively low inlet temperatures, high carrier flows during the injection and the presence of an amine maximise the response of the parent compounds.

An apparatus was designed to prepare solid lipid nanospheres (SLN), potential colloidal therapeutic system obtained by dispersing a warm oil-in-water (o/w) microemulsion in cold water. The apparatus, consisting mainly of a thermostated aluminium chamber and a pneumatic piston, permitted to disperse through a needle up to 100 ml of warm microemulsion and to vary the temperature, the dispersing rate and the drop size of the warm o/w microemulsion. Experimental design was applied to study the effect of four experimental factors, such as chamber temperature, piston pressure, needle gauge and volume of dispersing water, on average diameter and polydispersity index of SLN and on dispersing time of microemulsion (the time required for the microemulsion to drip completely from the apparatus). The results showed that temperature and pressure play the most important roles depending on the needle gauge used. In particular, the smallest SLN were obtained using high temperature and pressure values and a small needle gauge.

The identification of biomarkers is one of the leading research areas in proteomics. When biomarkers have to be searched for in spot volume datasets produced by 2D gel-electrophoresis, problems may arise related to the large number of spots present in each map and the small number of samples available in each class (control/pathological). In such cases multivariate methods are usually exploited together with variable selection procedures, to provide a set of possible biomarkers: they are however usually aimed to the selection of the smallest set of variables (spots) providing the best performances in prediction. This approach seems not to be suitable for the identification of potential biomarkers since in this case all the possible candidate biomarkers have to be identified to provide a general picture of the "pathological state'': in this case exhaustivity has to be preferred to provide a complete understanding of the mechanisms underlying the pathology. We propose here a ranking and classification method, "Ranking-PCA'', based on Principal Component Analysis and variable selection in forward search: the method selects one variable at a time as the one providing the best separation of the two classes investigated in the space given by the relevant PCs. The method was applied to an artificial dataset and a real case-study: Ranking-PCA exhaustively identified the potential biomarkers and provided reliable and robust results

Marengo, E. and Todeschini, R., 1993. Linear discriminant hierarchical clustering: a modeling and cross-validable divisive clustering method. Chemometrics and Intelligent Laboratory Systems, 19: 43–51. A simple modification of the linear discriminant classification tree (LDCT) (R. Todeschini and E. Marengo, Linear discriminant classification trees, Chemometrics and Intelligent Laboratory Systems, 16 (1992) 25-35) method is used to deal with clustering problems in the framework of a divisive hierarchical strategy. The method produces a mathematical model which allows the validation of the obtained clusters by using a cross-validation technique; simple parameters can then be estimated to assess the significance of the clusters. The performance of linear discriminant hierarchical clustering has been tested on seven data sets where the class assignments are known.

A new algorithm for optimal, distance based, experimental design is presented. The proposed approach, in contrast to other optimality criteria, does not require any preliminary hypothesis about a regression model. The best set of experiments is defined through a fast exchange algorithm where, in each cycle, a substitution is selected to provide the maximum increase of the minimum distance between the currently selected experiments. Such an algorithm provides a final uniform distribution of the experiments selected from the set of allowed candidates.

Leardi, R., Marengo, E. and Todeschini, R., 1991. A new procedure for the visual inspection of multivariate data of different geographic origins. Chemometrics and Intelligent Laboratory Systems , 12: 181–187. A new display method to be used for data of different geographic origin is presented. A geographic map of the region is shown, the zone corresponding to each sample being in a colour which is derived from the sum of the fundamental colours which have been associated with the variables (original or transformed). Three real examples show that the interpretation of the resulting plots is much easier compared to dealing with classical loading and score plots, both concerning the differences between the objects as well as the relative importance of the variables.

Passage sur echangeur d'anions de traces d'Al complexe par du tiron et spectrometrie d'emission

A new approach to the statistical treatment of 2D-maps has been developed. This method is based on the use of fuzzy logic and allows to take into consideration the typical low reproducibility of 2D-maps. In this approach the signal corresponding to the presence of proteins on the 2D-maps is substituted with probability functions, centred on the signal itself. The standard deviation of the bidimensional gaussian probability function employed to blur the signal allows to assign different uncertainties to the two electrophoretic dimensions. The effect of changing the standard deviation and the digitalisation resolution are investigated.

The control and monitoring of an industrial process is performed in this paper by the multivariate control charts. The process analysed consists of the bottling of the entire production of 1999 of the sparkling wine "Asti Spumante". This process is characterised by a great number of variables that can be treated with multivariate techniques. The monitoring of the process performed with classical Shewhart charts is very dangerous because they do not take into account the presence of functional relationships between the variables. The industrial process was firstly analysed by multivariate control charts based on Principal Component Analysis. This approach allowed the identification of problems in the process and of their causes. Successively, the SMART Charts (Simultaneous Scores Monitoring And Residual Tracking) were built in order to study the process in its whole. In spite of the successful identification of the presence of problems in the monitored process, the Smart chart did not allow an easy identification of the special causes of variation which casued the problems themselves.

Contamination of water resources is one of the major problems to be faced for environment preservation and sustainability. The monitoring of target compounds based on mass spectrometry and selected reaction monitoring mode is often insufficient to definitely assess the quality of surface water. Also potentially harmful non-target pollutants simultaneously present must be taken into account. Liquid chromatography coupled with tandem mass spectrometry is suitable to obtain complete information on water composition. Hybrid mass spectrometers such as triple quadrupole/linear ion trap, hybrid quadrupole/time-of-flight and linear ion trap/orbitrap analyzers should be used. Here, we review ultra-high performance liquid chromatography coupled with mass spectrometry methods developed for post-target and non-target screening analysis of water emerging contaminants, such as pesticides and their degradation products, pharmaceuticals and drug side-reaction products, surfactants and illicit drugs. The major points are the following: (1) the possibility of performing retrospective analysis only by high-resolution mass analyzer; (2) the compatibility of mass analyzer with ultra-high performance liquid chromatography; (3) the use of deconvolution software to detect unknowns; and (4) the limited availability of library database.

A method of ion-interaction chromatography (IIR-RP-HPLC) for the simultaneous separation of 21 polar aromatic sulfonates is developed and optimised by a chemometric treatment. The analytes are: 1-naphthalene sulfonic acid (1-NS), 2-naphthalene sulfonic acid, 2-amino-1-naphthalene sulfonic acid (2-A-1-NS), 5-amino-2-naphthalene sulfonic acid (5-A-2-NS), 8-amino-2-naphthalene sulfonic acid (8-A-1-NS), 1-amino-5-naphthalene sulfonic acid (1-A-5-NS), 4-amino-1-naphthalene sulfonic acid (4-A-1-NS), 6-hydroxy-2-naphthalene sulfonic acid (6-H-2-NS), 4-hydroxy-1-naphthalene sulfonic acid (4-H-1-NS), 6-amino-4-hydroxy-2-naphthalene sulfonic acid (6-A-4-H-NS), 6-amino-1-hydroxy-3-naphthalene sulfonic acid (6-A-1-H-3-NS), 3-amino-2,7-naphthalene disulfonic acid (3-A-2,7-NdS), 7-amino-1,3-naphthalene disulfonic acid (7-A1,3-NdS), 2-amino-1,5-naphthalene disulfonic acid (2-A-1,5-NdS), 1-hydroxy-3,6-naphthalene disulfonic acid (1-H-3,6-NdS), 2-hydroxy-3,6-naphthalene disulfonic acid (2-H-3,6-NdS), 3-nitrobenzene sulfonic acid (3-NBS), 4-phenol sulfonic acid (4-PS), 4-hydroxy-3-nitrobenzene sulfonic acid (4-H-3-NBS), 1,2-benzene disulfonic acid (1,2-BdS), 2,6-anthraquinone disulfonic acid (2,6-AntdS). The factors optimised are: (i) the alkyl chain length of the ion-interaction reagent, (ii) the ion-interaction reagent concentration, (iii) the acetonitrile concentration and (iv) the pH of the mobile phase. An Fractional Factorial design, together with a star design are used to simultaneously study the effect of the variables and of their interactions. The retention times resulted to be not homogeneously distributed in the variable domain: in particular, for all the analytes considered, the use of nonylamine as the IIR caused unexpectedly large retention times. In these conditions, the linear regression algorithm failed in building good predictive models. The problem was solved by using the Box–Cox transformation, that considers other possible dependencies, besides the linear one, between the response (retention time tR) and the variables. In particular, a response 1/tR and 1 t R permitted to better fit the data and to build reliable predictive models. In turn, the treatment of these models allowed us to locate the experimental conditions to separate, with good resolution, the 21 polar aromatic sulfonates in a total analysis time lower than 37 min.

The study investigates the natural photodegradation pathway of mono-chloroanilines in river waters, with the aim to identify the predominant photoproducts formed. At this purpose a new sensitive on-line SPE HPLC–MS/MS method has been developed with LOQ values equal or lower than the legal threshold concentration levels allowed for mono-chloroanilines in waters. The degradation processes of o-, m- and p-chloroaniline have been investigated subjecting their solutions, prepared both in ultrapure and in river water, to sun light irradiation simulated by a solar box system. The SPE HPLC–MS/MS methodology allowed to evaluate the degradation kinetics, to identify the predominant photodegradation products and to propose the chemical structures. Two photoproducts (aniline and 3-aminophenol), for which standards are available, have also been quantified.