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Back-propagation artificial neural networks (BP-ANN) are applied for modeling hydroxyl number and acid value of a set of 62 samples of polyester resins from their near infrared (NIR) spectra. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR) and partial least squares (PLS). The set of available samples is split into: (i) a training set, for models calculation; (ii) a test set, for setting the correct number of latent variables in PCR and PLS and for selecting the end point of the training phase of BP-ANN; (iii) a “production set” of samples, which are predicted to evaluate the models predictive ability. This approach guarantees that the predictive ability of the models is evaluated by genuine predictions. BP-ANN resulted always better than the classical PCR and PLS, from the point of view of the predictive ability. The study of the breakdown number of experiments to include in the training set showed instead that this factor does influence PCR and PLS at a lesser degree than what happens for BP-ANN. The latter approach requires a larger number of experiments for obtaining good results. The choice of optimal training sets is efficiently performed by Kohonen self-organizing maps (SOMs). It can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for monitoring the polyesterification of dicarboxylic acids with diols by predicting the acid and hydroxyl numbers directly along the process line.

The principles of quality control and multivariate statistical analysis were applied to the monitoring of the conservation state of wooden works of art. Three degradation processes (exposure to a wet atmosphere, to an acidic attack, and to UV light) were simulated on the surface of wooden boards of the XVI century and were monitored by the use of Raman spectroscopy. The resulting spectra were treated by principal component analysis, which allowed reduction of the system dimensionality. The relevant principal components were used to build multivariate control charts, namely, the Shewhart, CUSUM, and SMART (simultaneous scores monitoring and residual tracking) control charts. The Shewhart charts allowed identification of the effects due to the degradation processes. The CUSUM charts permitted identifcation of the exact moment in which the degradation treatment was started. The SMART charts provided a synthetic description of the conservation state of the wooden surface by means of only two charts: the T2 Hotelling and the DModX charts.

A full 23 experimental design was applied to identify conditions of cheesemaking which will minimise the contents of four biogenic amines (putrescine, cadaverine, histamine and tyramine) in cheese. The variables of the study were represented by: (1) pasteurisation of milk, (2) type of bacterial strain and (3) curd temperature. Each experiment was a process of cheesemaking. To evaluate the pure experimental error, each experiment was repeated three times for a total of 24 cheeses. Samples of each cheese, after 30 days of ripening, were analysed for amine contents by HPLC. The conditions for minimising the formation of biogenic amines were different for each amine, confirming that different bacteria and metabolic pathways were involved.

Sixty-eight samples of red wines were analysed with inductively coupled plasma–mass spectrometry (ICP–MS). Fifty-six elements were determined, mainly metal ions at trace level concentrations. The set of samples was constituted by five classes of wines obtained from Nebbiolo grape, produced in different zones inside the province of Cuneo (Piedmont, Italy). On the basis of the elements distribution, chemometric techniques of discriminant analysis and principal components analysis were applied to distinguish the five classes. A good separation was obtained among the wines, with respect to their geographical provenance. The results obtained show that the determination of the elemental composition could be a fast way to characterise and classify wine samples.

Three-way PCA has been applied to proteomic pattern images to identify the classes of samples present in the dataset. The developed method has been applied to two different datasets: a rat sera dataset, constituted by five samples of healthy Wistar rat sera and five samples of nicotine-treated Wistar rat sera; a human lymph-node dataset constituted by four healthy lymph-nodes and four lymph-nodes affected by a non-Hodgkin's lymphoma. The method proved to be successful in the identification of the classes of samples present in both of the groups of 2D-PAGE images, and it allowed us to identify the regions of the two-dimensional maps responsible for the differences occurring between the classes for both rat sera and human lymph-nodes datasets. Keywords: three-way principal component analysis • proteomics • multivariate analysis • 2D-maps

Three chromatographic methods are considered for the determination in Solanaceae of auxino-similar phytodrugs, so called because their structure resembles an auxine plant hormone. The phytodrugs studied were: 2,4-dichlorophenoxyacetic acid, 2,4-dichlorophenoxypropionic acid, 2,4-dichlorophenoxybutyric acid, 2-methyl-4-chlorophenoxyacetic acid, 2,4,5-trichlorophenoxyacetic acid, naphthylacetic acid and 2-naphthyloxyacetic acid. Three chromatographic methods, respectively based on ion-interaction HPLC, GC–MS with intra-injector derivatisation and GC–MS with pre-injection derivatisation, were developed, optimised and validated. A comparative discussion of the advantages/disadvantages of the methods suggests a strategy for their preferential use, that is essentially a function of the matrix complexity.

This article deals with the photodegradation of sulforhodamine B, a dye widely used in nonpermanent tattoos. Degradation evidence was obtained from both aqueous and sweat-simulating solutions of the dye after 9 days of Solarbox irradiation. The identification of the degradation products was achieved using a nontarget approach. For this purpose, a micro liquid chromatography method coupled with tandem high-resolution mass spectrometry was developed. In addition, the chemical structures of five degradation products and two dye impurities were elucidated. The degradation products were the same for both types of solution, whereas the degradation rate of the dye in sweat-simulating solution was slightly faster than that in aqueous solution. The method was also applied to samples of tattooed pigskin subjected to irradiation, in order to better simulate the irradiation effects on the dye used on the skin. None of the degradation products found in the sulforhodamine B solutions were identified in the degraded tattooed pigskin samples, but a new signal at m/z 637.3051 (positive ionization) was found, and the structure of the corresponding molecule was elucidated. The mutagenicity of the photodegradation products was evaluated using a quantitative structure-activity relationship approach, which gave negative results for all the structures elucidated. Graphical Abstract Comparison between tattoed pigskin before and after photodegration process. Strategies for the identification of sulforhodamine B degradation products.

Defects in genes encoding ribosomal proteins cause Diamond Blackfan Anemia (DBA), a red cell aplasia often associated with physical abnormalities. Other bone marrow failure syndromes have been attributed to defects in ribosomal components but the link between erythropoiesis and the ribosome remains to be fully defined. Several lines of evidence suggest that defects in ribosome synthesis lead to “ribosomal stress” with p53 activation and either cell cycle arrest or induction of apoptosis. Pathways independent of p53 have also been proposed to play a role in DBA pathogenesis. We took an unbiased approach to identify p53-independent pathways activated by defects in ribosome synthesis by analyzing global gene expression in various cellular models of DBA. Ranking-Principal Component Analysis (Ranking-PCA) was applied to the identified datasets to determine whether there are common sets of genes whose expression is altered in these different cellular models. We observed consistent changes in the expression of genes involved in cellular amino acid metabolic process, negative regulation of cell proliferation and cell redox homeostasis. These data indicate that cells respond to defects in ribosome synthesis by changing the level of expression of a limited subset of genes involved in critical cellular processes. Moreover, our data support a role for p53-independent pathways in the pathophysiology of DBA., Highlights • Ribosomopathies such as DBA are caused by ribosome dysfunction that activates p53. • p53-independent pathways may suggest possible treatments for DBA. • Expression analysis was performed in three p53-null models of DBA. • Genes involved in apoptosis and cell redox homeostasis were especially affected. • DBA is due to cumulative effects of p53-dependent and independent pathways.

An ion-interaction high-performance liquid chromatography (HPLC)-diode-array detection method is developed and optimized for the separation of typical antimicrobial agents used in cosmetics and hygiene products. The most used preservatives contain different molecular structures, different functionalities, and are characterized by different chemical properties. Organic acids, alkyl esters of benzoic acids, alkyl p-hydroxy benzoic acids (parabens), phenol derivatives, and carbanilides represent the most used preservatives, and are often present in multicomponent mixtures. In order to develop a multicomponent method to be used in quality control analysis, the ion-interaction reagent reversed-phase HPLC technique seems to be particularly suitable, because it allows for the simultaneous separation of acidic, basic, and neutral species. The experimental conditions of the method are developed by OVAT (one variable at a time) treatment and further optimized by a multivariate approach based on a Simplex algorithm that works on a desirability function targeted to maximize the resolution in a multicomponent mixture. The new method proposed that is able to simultaneously separate fourteen preservatives is applied in the analysis of commercial products.

This study presents an example of method validation, applied to an IIR-RP-HPLC method, developed for the separation of nine PPPs (Priority Pollutant Phenols). The parameters of both primary validation (accuracy, precision, response linearity, detection. and quantitation limits,) and of secondary validation (method ruggedness) are evaluated. The glossary used in literature for ruggedness and robustness is discussed. The robustness test is carried out with respect to five chromatographic conditions, both depending on analyst accuracy (concentration of the ion-interaction reagent, concentration of the organic modifier, and pH of the mobile phase) and on instrumentation (column temperature and flow-rate). The effect of the experimental factors is studied by a fractional factorial design and mathematical models are built, that correlate the chromatographic retention to the experimental factors and to their interactions. The cross-validated models can usefully be employed to evaluate for each studied variable t...

This paper shows how a porous graphitized stationary (PGC) phase can be modified to work in RP-HPLC ion-interaction reagent (IIR) mode. Aqueous solutions of alkylammonium o-phosphate salts are used as IIRs and the analytes are the inorganic anions, iodide, iodate, bromide, bromate, nitrite, and nitrate. Comparisons with the results obtained under the same conditions for an ODS stationary phase show that the use of the PGC column permits extension, to IIR chromatography, in the pH range of only acidic values.

To optimize the ion-interaction chromatographic separation of nine chloroaniline isomers, the effect on retention of six experimental parameters is investigated by means of multivariate analysis. The factors considered are the organic modifier concentration in the mobile phase; the length of the alkyl chain of the alkylammoniumion salts used as the ion-interaction reagents (IIRs); the concentration of IIRs; the pH of the mobile phase, the flow-rate and the ionic strength. The use of fractional factorial and star designs allowed one to draw out useful information on the retention mechanism involved and to build a model characterized by both descriptive and predictive ability. Concerning descriptions, the results suggest a retention mechanism mainly based on reversed-phase partition, while the main role of the alkylamine (used as IIR) seems to mask the activity of the residual silanol groups on the stationary phase. As a result efficiency is improved. For prediction purposes, the regression models allow the optimization of the chromatographic separation, as regards both resolution and total analysis time. The study allowed one to develop a method able to separate the nine mono- and dichloroanilines in a total analysis time within 66 min and with detection limits ranging from 4.0 to 21.0 μg/l.

Sites and objects of cultural heritage - from art to ancient inscriptions to ruins - are under constant attack by time and the environment. While much is known about how material components change from laboratory-based artificial aging, very little is known about the process or rates of change of actual objects and sites in situ. Reflectance Transformation Imaging (RTI) is a quantitative method that captures surface normals. In our case, it provides detailed information on the geometry of the object surface. We show that RTI can be quantified for use as a method for measuring change in cultural heritage objects. The past decade has seen the rapid evolution and application of computational photography methods to document important works of human heritage, from art and architecture to archives and archaeology. The next logical step involves defining just how reproducible and precise these methods can be to use them to measure rates of change for important works of cultural heritage. The need is to move to calibrated, quantitative image datasets for reproducible imaging. We measure the precision of computed surface normals , which define the basic repeatability of RTI. Our results show that the average included solid angle for RTI sensitivity fitted to the Hemispherical Harmonics (HSH) polynomial function is 0.003 steradians (3 sigma), while the older Polynomial texture map (PTM) method is much less sensitive (0.5 steradians). The absolute sensitivity of the method is the minimum variation of the normal that can be statistically considered a change of the object. It is calculated considering the average value of the normal of each single pixel. The solid angle of the cone of variation represents the statistical limit (3 *σ). Analysis of multiple RTI data sets from objects that have changed between image capture sessions results in a map of change that can easily be evaluated by conservators.

The study deals with the identification of the degradation products formed by simulated sunlight photoirradiation in a commercial beverage that contains Allura Red AC dye. An UHPLC-MS/MS method, that makes use of hybrid triple quadrupole/linear ion trap, was developed. In the identification step the software tool information dependent acquisition (IDA) was used to automatically obtain information about the species present and to build a multiple reaction monitoring (MRM) method with the MS/MS fragmentation pattern of the species considered. The results indicate that the identified degradation products are formed from side-reactions and/or interactions among the dye and other ingredients present in the beverage (ascorbic acid, citric acid, sucrose, aromas, strawberry juice, and extract of chamomile flowers). The presence of aromatic amine or amide functionalities in the chemical structures proposed for the degradation products might suggest potential hazards to consumer health.

The use of doping agents, once restricted to professional athletes, has nowadays become a problem of public health, since it also concerns young people and non-competing amateurs in different sports. The use is also diffused in social life for improving physical appearance and enhancing performance and even dietary supplements assumed to improve performance often contain anabolic steroids. While decades ago the so-called " classical doping agents" (like stimulants and narcotics) were used, to-day anabolic steroids are more widely diffused. Anabolic steroids are synthetic substances prepared by introducing modifications in the molecular structure of testosterone, the main natural androgenic anabolic steroid that forms in testes interstitial cells. The first report concerning the use of anabolic steroids by an athlete who searched for increased weight and power dates 1954. In 1974 the misuse of anabolic steroids in sports was banned by the International Olympic Committee and control tests were implemented in 1976 Montreal Olympic Games through radioimmunoassay analysis: the technique, however, only allows for unspecific detection of a limited number of exogenous steroids. Over the years, always new doping substances are synthesized and, as a consequence, the list of prohibited compounds is continuously updated and new suitable analytical methods for their detection and determination in biological matrices are continuously required. In doping control analysis the knowledge of steroid metabolism pathway in human body is of primary importance and the analytical methods must permit the simultaneous detection and determination not only of the forbidden precursor agents but also of their metabolites. In addition, the potential presence and amount in the biological samples of species that can interfere in the analysis should be evaluated. Also the several anabolic steroids, specifically designed to circumvent doping control, put on the market have been incorporated in the list of the prohibited substances of the World Anti-Doping Agency (WADA). In WADA list steroids figure in three main classes, namely anabolic steroids, corticosteroids and substances with anti-estrogenic properties. It must be strongly reminded that assumption of doping agents not only leads to athletes the possible failing of doping tests but causes important health risk and WADA prohibited list establishes criteria to highlight the alteration of the natural steroid profile caused by exogenous administration. Doping control analyses are generally performed in urine, a matrix that provides a prolonged detection time window, and less often in blood, serum, plasma, hair, saliva, and nails. To identify the chemical structures of anabolic steroids the use of mass spectrometry detection is very advantageous. Gas chromatography-mass spectrometry (GC-MS) techniques allowed for the development of comprehensive screening methods. GC-MS methods are sensitive and robust but present the disadvantages of time-consuming sample pretreatment, that is often based on hydrolysis and derivatisation reactions. Liquid chromatography-mass spectrometry (LC-MS) methods have been successfully used to identify and determinate steroids in different matrices, as well as to study their metabolisms. Nowadays, automatic rapid ultra high performance liquid chromatography (UHPLC) tandem mass spectrometry has become the technique of choice for steroid analysis. Due to its generally higher speed, sensitivity, reproducibility and specificity with respect to HPLC, it can be used to simultaneously separate and determinate multi component steroid mixtures. The technique is of huge interest to separate conjugates anabolic androgenic steroids, as it allows efficiency enhancement due to the small particle (sub-2. μm) column packing, which provides high peak capacity within analysis times even 5-10 fold shorter than conventional HPLC methods. Modern multiplex instruments can analyze thousands of samples per month so that, notwithstanding the generally high instrumental costs, the cost of the individual assay is affordable. In addition, the improved specificity and resolution offered by time-of-flight or quadrupole time-of-flight mass spectrometry allow their application in doping control analysis or in steroid profiling for accurate and sensitive full mass range acquisition. Aim of the present review is to consider, compare and discuss the applications of the UHPLC/MS methods present in literature for the identification and determination of forbidden steroids and their metabolites in human biological matrices

The broad group of biogenic amines includes polyamines and catecholamines, whose presence in human tissues and biological fluids can give important diagnostic information and act as marker of many pathologies. In particular, polyamines are involved in cancer cell growth while catecholamines act as neurotransmitters and hormones. Their simultaneous determination in biological tissues and fluids is therefore an important task. A high-performance liquid chromatography tandem mass spectrometry method is presented here for the simultaneous determination in urine of 16 biogenic amines: adrenaline (epinephrine), agmatine, cadaverine, dopamine, histamine, 3-methoxytyramine, noradrenaline (norepinephrine), norephedrine, octopamine, 2-phenylethylamine, putrescine, serotonin, spermidine, spermine, tryptamine, and tyramine. The method does not require any derivatization step. To guarantee the maximum of sensitivity, the mass spectrometer works in selected reaction monitoring mode, monitoring for each analyte the two most intensive transitions. Method validation includes the evaluation of limits of detection (that range from 0.3 to 6.6 μg L(-1)), limits of quantitation (that range from 1.0 to 21.9 μg L(-1)), linearity range (three orders of magnitude), recovery, intra- and inter-day precision on both concentration, and retention time. Recovery (R) is shown not to depend on the analyte concentration: the average R percent ranges from 72.9 to 100.0 %. Particular attention is devoted to the matrix effect and the correlated phenomena of ion enhancement or suppression in mass spectrometry detection.

Biomarkers discovery is a discipline achieving increasing importance since it provides diagnostic/prognostic markers and may permit to investigate and understand the mechanism of development of the pathology, possibly suggesting new biomolecular therapeutic targets. Biomarkers discovery in proteomics is hampered by the use of high-throughput techniques providing a great number of candidates among which the true biomarkers have to be searched for. Moreover, often a small number of samples are available. Two main problems arise when biomarkers have to be searched for in such datasets: 1) the identification of reliable markers, avoiding false positives due to chance correlations; 2) the exhaustive identification of all candidate markers, to obtain a complete snapshot of the effect investigated. Biomarkers can be identified by two approaches: classical monovariate methods, where each biomarker is considered as independent (Student’s t-test, Mann-Whitney test etc.) or multivariate methods, able to take into consideration the correlation structure of the data (i.e. interactions). These last ones are certainly to be preferred and should achieve the best compromise between the best predictive ability (accomplished through the use of variable selection procedures and exhaustivity. Here, we review the most recent applications of multivariate methods for the identification of biomarkers in proteomics with particular regard to the statistical methods exploited.

To address the growing interest of consumers for information on the provenance of foodstuffs, the production chain of world renowned Moscato d’Asti white wine has been studied using the distribution of lanthanides as chemical markers. From soil to must, upon every stage of the chain, samples have been taken and analysed with ICP-MS in order to verify whether the original fingerprint of soil is maintained or not along the chain. Results of this traceability study show clearly that lanthanides fingerprint is kept unaltered in the passage soil-grapes-must, while fractionation occurs upon wine clarifying with bentonites. The second part of the work involves a study on 102 samples of Moscato d’Asti musts in order to verify how they reflect the features of the different geographical zones where they come from, and to build a basis to be able to identify possible adulterations performed by addition of foreign musts.

The influence of twenty five different alcohols on the formation of warm oil-in-water (O/W) microemulsions was investigated. Selected concentrations of each alcohol were added to fixed amounts of stearic acid, Tween 20 and water at 65 ° C. Fifteen alcohols formed microemulsions, at least at one of the concentrations. A pattern recognition study was performed to elucidate the activities of the alcohols by Principal Component Analysis (PCA). Linear Discriminant Analysis (LDA) was used to classify them. Two classification functions, obtained for alcohols forming / not forming microemulsions, suggest that the formation of warm O/W microemulsion is linked to the nature and the dimension/lipophilicity of the alcohol.

This paper reports, for the first time, the concentrations of selected phthalates in drinking water consumed in Portugal. The use of bottled water in Portugal has increased in recent years. The main material for bottles is polyethylene terephthalate (PET). Its plasticizer components can contaminate water by leaching, and several scientific studies have evidenced potential health risks of phthalates to humans of all ages. With water being one of the most essential elements to human health and because it is consumed by ingestion, the evaluation of drinking water quality, with respect to phthalate contents, is important. This study tested seven commercial brands of bottled water consumed in Portugal, six PET and one glass (the most consumed) bottled water. Furthermore, tap water from Lisbon and three small neighbor cities was analyzed. Phthalates (di-n-butyl phthalate ester (DnBP), bis(2-ethylhexyl) phthalate ester (DEHP), and di-i-butyl phthalate ester (DIBP)) in water samples were quantified (PET and glass) by means of direct immersion solid-phase microextraction and ionic liquid gas chromatography associated with flame ionization detection or mass spectrometry due to their high boiling points and water solubility. The method utilized in this study showed a linear range for target phthalates between 0.02 and 6.5 μg L(-1), good precision and low limits of detection that were between 0.01 and 0.06 μg L(-1), and quantitation between 0.04 and 0.19 μg L(-1). Only three phthalates were detected in Portuguese drinking waters: dibutyl (DnBP), diisobutyl (DIBP), and di(ethylhexyl) phthalate (DEHP). Concentrations ranged between 0.06 and 6.5 μg L(-1) for DnBP, between 0.02 and 0.16 μg L(-1) for DEHP, and between 0.1 and 1.89 μg L(-1) for DIBP. The concentration of DEHP was found to be up to five times higher in PET than in glass bottled water. Surprisingly, all the three phthalates were detected in glass bottled water with the amount of DnBP being higher (6.5 μg L(-1)) than in PET bottled water. These concentrations do not represent direct risk to human health. Regarding potable tap water, only DIBP and DEHP were detected. Two of the cities showed concentration of all three phthalates in their water below the limits of detection of the method. All the samples showed phthalate concentrations below 6 μg L(-1), the maximum admissible concentration in water established by the US Environmental Protection Agency. The concentrations measured in Portuguese bottled waters do not represent any risk for adult's health.

This chapter describes a new software for the generation of simulated realistic sodium dodecyl sulfate two-dimensional polyacrylamide gel electrophoresis (SDS 2D-PAGE) images. In order to choose the simulation strategy to provide realistic 2D-PAGE maps the statistical characteristics of such images were taken into account, such as the distributions of sizes, intensities, and volumes of the spots. Also, the low reproducibility typical of replicated SDS 2D-PAGE maps of the same sample was simulated. This approach can be used to generate simulated datasets useful in the development and performance evaluation of new classification and/or image analysis algorithms applied to two-dimensional electrophoresis datasets, given the usually small number of experimental replications available.

To study proteomic changes involved in tenderization of bovine Longissimus dorsi four Charolaise heifers and four Charolaise bull's muscles were sampled at slaughter after early and long ageing (2-4°C for 12 and 26days respectively). Descriptive sensory evaluation of samples were performed and their tenderness evaluated by Warner-Bratzler shear force test. Protein composition of fresh muscle and of meat aged was analysed by cartesian and polar 2-D electrophoresis. Student's t-test and Ranking-PCA analyses were performed to detect proteomic modulation, and the selected protein spots were identified by nano-HPLC-Chip MS/MS. This research has demonstrated that there are no differences between proteomic patterns of male and females Longissimus dorsi muscle, and that the extension of ageing beyond 12days, did not brings any concrete advantage in terms of sensory quality. Furthermore, the data presented here demonstrated that meat maturation caused changes of the abundance of proteins involved in metabolic, structural, and stress related processes.

This chapter describes a new software for the generation of simulated realistic sodium dodecyl sulfate two-dimensional polyacrylamide gel electrophoresis (SDS 2D-PAGE) images. In order to choose the simulation strategy to provide realistic 2D-PAGE maps the statistical characteristics of such images were taken into account, such as the distributions of sizes, intensities, and volumes of the spots. Also, the low reproducibility typical of replicated SDS 2D-PAGE maps of the same sample was simulated. This approach can be used to generate simulated datasets useful in the development and performance evaluation of new classification and/or image analysis algorithms applied to two-dimensional electrophoresis datasets, given the usually small number of experimental replications available.

Hierarchical grid transformation is a powerful approach to SDS 2DPAGE maps warping. The hierarchy of the warping transformation is able to model both global and local deformations of the gels and the algorithm can be stopped when a certain degree of accuracy in the image alignment is obtained. The numerical optimization of the position of the nodes of the grid that are responsible for the image warping is a multivariate task that can be solved efficiently using Genetic Algorithms. The use of Genetic Algorithms ensures that an optimal position of the nodes can be defined with a low computational cost with respect to other methods. The optimal positions of the nodes of the grid can be successfully used for defining a good warping of the gels.

The study deals with the identification of the degradation products formed by simulated sunlight photoirradiation in a beverage that contains Allura Red AC (E129) dye. An UHPLC-MS/MS method that makes use of high resolution quadrupole-time-of-flight mass spectrometer, was developed. For the identification of the degradation products the software tool Information Dependent Acquisition (IDA) was used to automatically obtain information about the high resolution MS and MS/MS spectra of the species present. Accurate mass data were essential for the identification of the appropriate elemental composition: the elucidation of unknown chemical structures was obtained by using powerful software to mine the recorded chromatogram. The identified degradation products are formed from side-reactions and/or from the interactions taking place among the dye and other ingredients present in the beverage (i.e. ascorbic acid, citric acid, sucrose, aromas, strawberry juice and extract of chamomile flowers). The presence of aromatic amine or amide groups in the chemical structures of the degradation products suggests the potential hazard for the consumer health.

New theoretical molecular indices are defined. They contain information about the whole molecular structure in terms of size, shape, symmetry and atom distribution. These indices are calcualted from the (x, y, z) co‐ordinates of a molecule within different weighting schemes in a straightforward manner and represent a very general approach to describe molecules, molecular fragments, macromolecules and molecular conformations in a unitary conceptual framework. Their interpretability is quite evident and is defined by the same mathematical properties as the algorithm used for their calculation. Examples on the total surface area, toxicity of PCDD and PCDF and reaction rate of catalysed reactions show a high modelling power of these indices. Copyright © 1994 John Wiley & Sons, Ltd.

A new approach for monitoring the state of conservation of cultural heritage objects surfaces is being developed. The technique utilizes multi-spectral imaging, multivariate analysis and statistical process control theory for the automatic detection of a possible deterioration process, its localization and identification, and the wavelengths most sensitive to detecting this before the human eye can detect the damage or potential degradation changes occur. A series of virtual degradation analyses were performed on images of parchment in order to test the proposed algorithm in controlled conditions. The spectral image of a Dead Sea Scroll (DSS) parchment, IAA (Israel Antiquities Authority) inventory plate # 279, 4Q501 Apocryphal Lamentations B, taken during the 2008 Pilot of the DSS Digitization Project, was chosen for the simulation.

Four-component non-aqueous microemulsions, containing lecithin, taurodeoxycholic acid, ethyl oleate and 1,2-propylene glycol have been studied with chemometric techniques to emphasize the role of the components on the release of a drug. Since microemulsions can be obtained only for particular proportions of the constituents, their realm of existence was determined by using Doehlert experimental design. Successively, a mixture design technique was applied to select the set of microemulsions for the measurement of the diffusion rate of a model lipophilic drug, retinol, through a hydrophilic membrane. The drug permeability was modelled as a function of the mixture composition by partial least squares (PLS). The results emphasize the role of the cosurfactant and the oil phase of the system on the drug permeation behaviour from the waterless microemulsion.

The aim of this work is to investigate the performance of multivariate classification techniques like partial least squares–discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) when using Zernike moments as global image descriptors, in the classification of sodium dodecyl sulphate (SDS) two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) maps affected by different levels of deformation. Synthetic sets of images simulating real SDS 2D-PAGE maps were analysed in controlled conditions to obtain information on the robustness and limits of applicability of the classification techniques operating on the basis of a given image decomposition method.

We propose an integrated approach, obtained by the combination of multivariate statistics and proteomics, useful to isolate candidate biomarkers for the evaluation of grape ripening. We carried out a comparative 2-DE analysis of grape skins collected in three moments of ripening and analyzed the spot volume dataset through the application of principal component analysis followed by forward stepwise-linear discriminant analysis. This technique allowed to discriminate veraison, quite mature and mature samples, and to sort the matched spots according to their significance. We identified 36 spots showing high discriminating coefficients through liquid chromatography - electrospray ionization - tandem mass spectrometry (LC-ESI-MS/MS). Most of them were involved in biotic and abiotic stress responses indicating these enzymes as good candidate markers of berry ripening. These evidences hint at a likely developmental role of these proteins, in addition to their reported activity in stress events. Restricting the same statistical analysis to the samples belonging to the two last stages, it was indicated that this approach can clearly distinguish these close and similar phases of berry development. Taken all together, these results bear out that the employment of the combination of 2-DE and multivariate statistics is a reliable tool in the identification of new protein markers for describing the ripening phases and to assess the overall quality of the fruit.

A rapid on-line solid phase extraction ultra high performance liquid chromatography tandem mass spectrometry method was developed for the identification and quantitation of nine perfluorinated compounds in matrices of environmental, biological and food interest. Pre-treatment, solid phase extraction, chromatographic and mass detection conditions were optimised, in order to apply the whole methodology to the analysis of different matrices. Particular attention was devoted to the evaluation of matrix effect and the correlated phenomena of ion enhancement or suppression in mass spectrometry detection. LOD and LOQ range from 3 to 15 ng L−1 and from 10 to 50 ng L−1, respectively. Method detection limits (MDLs) were also calculated for each kind of matrix. The recovery, evaluated for each analyte, does not depend on analyte concentration in the explored concentration range: average R ¯ % values are always greater than 82.9%. In the whole, the results obtained for samples of river waters, blood serum, blood plasma, and fish confirm the ubiquitous presence of perfluorinated compounds, as recently denounced by many sources.

The study deals with a fast (analysis times of around 11 min) simultaneous identification and quantification in human urine of 42 drugs (21 therapeutic and 21 of abuse), through an automated online solid phase extraction ultra high performance liquid chromatography method coupled with tandem mass spectrometry (SPE UHPLC-MS/MS). In the method validation, particular attention was devoted to the matrix effect, through matrix-matched calibration in blank urine, suitably diluted. For all the abuse drugs investigated, the limit of quantitation (LOQ) values are lower than the legal threshold concentration levels, making the method suitable for routine control. The whole procedure was applied in the analysis of urine of patients positive to the I level screening test.

Mussels (Mytilus galloprovincialis) are sentinel species routinely used for monitoring the health status of coastal waters. Mussel’s circulatory fluid (haemolymph) contains secreted proteins as well as proteins coming from the damaged tissue, which could be useful as indicators of risk associated with exposure to environmental contaminants. In this work, we developed a simple procedure for rapid screening of mussel’s haemolymph proteins based on the matrix assisted laser-desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and chemometric data analysis. To overcome the difficulties related to relatively high dilution of proteins and the presence of salts, an easy and reproducible technique of solid-phase protein extraction was applied, followed by direct elution of sample on the MALDI-target plate. After preliminary optimization of analytical parameters, a sensitive MALDI TOF mass-spectrometry was capable to effectively resolve the low molecular weight haemolymph proteins thanks to their rapid ionization in the < 20 kDa m/z region. Finally, MALDI MS peptide profiling was applied to haemolymph samples of copper - exposed mussels to detect lost and/or gained ions within the MALDI spectra and variations of ions intensity upon treatment. The possibility to use the developed method to monitor the changes in mussel’s haemolymph peptides/proteins profile and to identify new biomarkers related to pathological states of organisms is discussed.

Todeschini, R. and Marengo, E., 1992. Linear discriminant classification tree: a user-driven multi-criteria classification method. Chemometrics and Intelligent Laboratory Systems. 16:25–35. A classification method, linear discriminant classification tree (LDCT), has been developed with particular attention to problem-driven solutions. It consists in the joint application of linear discriminant analysis (LDA) and classification tree methods. The population of each node is partitioned into two groups and classified using LDA which allows the introduction of multivariate binary classifiers. Thus the resulting classification trees are usually characterized by low complexity and ready interpretability. Several different trees can be obtained from the same data set: each tree can be cross-validated and a choice made on the basis of different criteria. This flexibility makes LDCT a really problem-driven classification method. Eight real data sets were used to test the method, and in all cases the results were good.

Ion-interaction chromatographic data were treated by chemometric methods in order to optimize resolution and analysis time. A mixture of nitrite, nitrate, and 1,4- 1,3-, and 1,2-phenylenediamine at average 0.5 ppm levels was considered. The effect on retention of alkyl chain length of the ammonium salt used as the interaction reagent, its concentration, and flow rate were studied

Marengo, E. and Todeschini, R., 1991. A fast method for the calculation of partial least squares coefficients. Chemometrics and Intelligent Laboratory Systems , 12: 117–120. Partial least squares (PLS) has proved to be more effective than ordinary least squares (OLS) in the study of complex systems. However, its use is made difficult by the splitting of the information into several latent variables. In an effort to overcome this difficulty, PLS coefficients which are analogues of the OLS have been introduced to provide an easy and immediate method to interpret numbers. This paper presents a method for the fast calculation of PLS coefficients. It is based on the use of PLS models to predict the response at proper points of the original variable space. The predicted responses directly render the PLS coefficients.

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.

The optimisation of the sensitivity in the ICP-MS determination of 83 isotopes, as a function of 21 operative parameters was performed by generating an initial experimental design that was used to define, by principal component analysis, the multi-criteria target function. The first PC, which contained an overall evaluation of the signal intensity of all isotopes, was used to rank the experiments. The modified simplex optimisation technique was then applied on the ranked experiments. The increase in signal intensity was, on the average, 3.9 times for the isotopes considered for the simplex procedure. When finally convergence was achieved, a PLS regression model calculated on the available experiments allowed to investigate the effect played by each factor on the experimental response. Simplex and PCA proved to be extremely effective to obtain the optimisation and to generate the multi-criteria target function: they can be suggested as an automatic method to perform the optimisation of the instrumental operative conditions.

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions. The theoretical basis of each procedure is briefly introduced, together with a review of the most interesting applications present in recent literature.

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back-propagation artificial neural network (BP-ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP-ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

Computer-assisted methods for improving research and production in a food-flavour company are described. Methods of experimental design are used in planning experiments. Special programs are used for identification of components through correlation of mass spectra and gas chromatographic retention indices, and for the design of new synthetic routes. In flavour creation, a data base containing qualitative and quantitative information on flavours, relevant legislation and organoleptic properties of components, can be searched selectively in attempts to improve flavours and to identify better combinations of substances. A computer-assisted production system is described which ensures complete qualitative and quantitative control.

A chemometric approach based on experimental design and desirability functions was used to develop and validated a method for the determination of some metals of biological concern by a fast sequential ICP-AES. The elements considered are Al, Fe, Mn, Zn, Li, Na and K. The experimental design was used to investigate the effects of three instrumental most crucial parameters, such as sheath gas flow rate, pump speed and auxiliary gas flow rate. In order to improve the multielemental analysis speed, although a sequential instrument allows the use of a separate parameter set for each wavelength, regression models and desirability functions were applied to find the experimental conditions providing the highest global sensitivity. Validation was performed in terms of limits of detection (LOD), limits of quantitation (LOQ), linearity, precision and recovery. By using the 167.02 nm wavelength, aluminium LOD was 0.5 microg L(-1) while the highest LOD was found for K (65 microg L(-1)). A linear range of at least three orders of magnitude was statistically demonstrated for each element. Precision was evaluated by testing two concentration levels, and good results in terms of intra-day repeatability were obtained, with R.S.D. values lower than 4.1% at the lowest concentration level. Lacking a suitable certified reference material, trueness was estimated using the recovery rate on fortified samples. The validated method was then used in the quantification of the elements considered in a serum sample.

The present review attempts to cover a number of methods that have appeared in the last few years for performing quantitative proteome analysis. However, due to the large number of methods described for both electrophoretic and chromatographic approaches, we have limited this review to conventional two-dimensional (2-D) map analysis which couples orthogonally a charge-based step (isoelectric focusing) to a size-based separation step (sodium dodecyl sulfate-electrophoresis). The first and oldest method applied to 2-D map data reduction is based on statistical analysis performed on sets of gels via powerful software packages, such as Melanie, PDQuest, Z3 and Z4000, Phoretix and Progenesis. This method calls for separately running a number of replicas for control and treated samples. The two sets of data are then merged and compared via a number of software packages which we describe. In addition to commercially-available systems, a number of home made approaches for 2-D map comparison have been recently described and are also reviewed. They are based on fuzzyfication of the digitized 2-D gel image coupled to linear discriminant analysis, three-way principal component analysis or a combination of principal component analysis and soft-independent modeling of class analogy. These statistical tools appear to perform well in differential proteomic studies.

This work is an extension of a method for monitoring the conservation state of pigmented surfaces presented in a previous paper. A cotton canvas painted with an organic pigment (Alizarin) was exposed to UV light in order to evaluate the effects of the applied treatment on the surface of the sample. The conservation state of the pigmented surface was monitored with ATR-FT-IR spectroscopy and multivariate control charts. The IR spectra were analysed by principal component analysis (PCA) and the relevant principal components (PCs) were used for constructing multivariate Shewhart, cumulative sums (CUSUM) and simultaneous scores monitoring and residuals tracking (SMART) control charts. These tools were successfully applied for the identification of the presence of relevant modifications occurring on the surface of the sample. Finally, with the aim to more deeply investigate what happened to the sample surface during the UV exposure, a PCA of the residuals matrix of degradation analyses only, not present in the previous paper, was performed. This analysis produced interesting results concerning the identification of the processes taking place on the irradiated surface.

A new method has been developed for monitoring the degradation of paintings. Two inorganic pigments (ultramarine blue and red ochre) were blended with linseed oil and spread on canvas. Each canvas was subjected to simulated accelerated ageing in the presence of typical degradation agents (UV radiation and acidic solution). Periodically the painted surfaces were analysed by FT-Raman, to investigate the status of the surface. The data obtained were analysed by principal component analysis (PCA). Finally the Shewhart and cumulative sum control charts based on the relevant principal components (PC) and the so called scores monitoring and residuals tracking (SMART) charts were built. The method based on the use of PC to describe the process was found to enable identification of the presence of relevant modification occurring on the surface of the samples studied.

Different calibration methods have been applied for the determination of the Hydroxyl Number in polyester resins, namely Partial Least Squares (PLS), Principal Component Regression (PCR), Ordinary Least Squares with selection of the variables by genetic algorithm (OLS-GEN) and back-propagation Artificial Neural Networks (BP-ANN). The predictive ability of the regression models was estimated by splitting the dataset in training and test sets by application of the Kohonen self-organising maps. The linear methods (OLS-GEN, PLS and PCR) showed comparable results while artificial neural networks provided the best results both in fitting and prediction.

Particular attention are recently receiving antimicrobial agents added as preservatives in hygiene and cosmetics commercial products, since some of them are suspected to be harmful to the human health. The preservatives used belong to different classes of chemical species and are generally used in their mixtures. Multi-component methods able to simultaneously determinate species with different chemical structure are therefore highly required in quality control analysis. This paper presents an ion interaction RP-HPLC method for the simultaneous separation of the 20 typical antimicrobial agents most used in cosmetics and hygiene products, that are: benzoic acid, salicylic acid, 4-hydroxybenzoic acid, methyl-, ethyl-, propyl-, butyl-, benzyl-benzoate, methyl-, ethyl-, propyl-, butyl-, benzyl-paraben, o-phenyl-phenol, 4-chloro-m-cresol, triclocarban, dehydroacetic acid, bronopol, sodium pyrithione and chlorhexidine. For the development of the method and the optimization of the chromatographic conditions, an experimental design was planned and models were built by the use of artificial neural network to correlate the retention time of each analyte to the variables and their interactions. The neuronal models developed showed good predictive ability and were used, by a grid search algorithm, to optimize the chromatographic conditions for the separation of the mixture.

In this paper, principal component analysis (PCA) is applied to a spot quantity dataset comprising 435 spots detected in 18 samples belonging to two different cell lines (Paca44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of the study was the identification of the differences occurring between the proteomic patterns of the two investigated cell lines and the evaluation of the effect of the drug Trichostatin A on the protein content of the cells. PCA turned out to be a successful tool for the identification of the classes of samples present in the dataset. Moreover, the loadings analysis allowed the identification of the differentially expressed spots, which characterise each group of samples. The treatment of both the cell lines with Trichostatin A therefore showed an appreciable effect on the proteomic pattern of the treated samples. Identification of some of the most relevant spots was also performed by mass spectrometry.