Analysis of paint cross-sections: a combined multivariate approach for the interpretation of µATR-FTIR hyperspectral data arrays.

The present research is aimed at introducing a suitable approach for the exploitation of the hyperspectral data obtained by µATR-FTIR analyses of paint cross-sections. The application of principal component analysis for chemical mapping is well-established, even if a very limited number of applications to µFTIR data have been reported so far in the field of analytical chemistry for cultural heritage. Moreover, in many cases, chemometric tools are under-utilized and the outcomes under-interpreted. As a consequence, results and conclusions may be considerably compromised. In an attempt to overcome such drawbacks, the present work is proposing a comprehensive and efficient procedure based on an interactive brushing approach, which combines the structural information of the score scatter plots and the spatial information of the principal component (PC) score maps. In particular, the study demonstrates not only how the multivariate approach may provide more information than the univariate one, but also how the integration of different chemometric tools may allow a more comprehensive interpretation of the results with respect to the studies up to now reported in the literature. The examination of the average spectral profile of each score cluster, jointly with the loading analysis, is functional to characterize each area investigated on the basis of its spectral features. A multivariate comparison with spectra of standard compounds, projected in the PC score space, helps in supporting the chemical identification. The approach was validated on two real case studies.

Differentiation of green tea samples by chiral CD-MEKC analysis of catechins content.

A chiral CD-MEKC method, enantioselective for catechin and gallocatechin, was developed, validated and applied to the analysis of tea samples. The method was addressed to the fast and simultaneous quantitation of the most represented and biologically important green tea catechins and methylxanthines. The CD-MEKC was based on SDS as surfactant (90 mM) and hydroxypropyl-beta-CD (25 mM) as chiral selector, under acidic conditions (25 mM borate-phosphate buffer, pH 2.5). The method was first applied to study the thermal epimerisation of epi-structured catechins, (-)-epicatechin and (-)-epigallocatechin, to non-epi-structured (-)-catechin and (-)-gallocatechin. The latter compounds, being non-native molecules, were for the first time regarded as useful phytomarkers of tea samples subjected to thermal treatment. The proposed method was applied to the analysis of more than twenty tea samples of different geographical origins (China, Japan, Ceylon), having undergone different storage conditions and manufacturing processes. Finally, factor analysis was used to visualise the useful information contained in the data set, showing that it was possible to distinguish tea samples on the basis of their different contents of native and non-native catechins.

Direct quantification of test bacteria in synthetic water-polluted samples by square wave voltammetry and chemometric methods.

A home-made microelectrode array, based on reticulated vitreous carbon, was used as working electrode in square wave voltammetry experiments to quantify the bacterial load of Escherichia coli ATCC 13706 and Pseudomonas aeruginosa ATCC 27853, chosen as test microorganisms, in synthetic samples similar to drinking water (phosphate buffer). Raw electrochemical signals were analysed with partial least squares regression coupled to variable selection in order to correlate these values with the bacterial load estimated by aerobic plate counting. The results demonstrated the ability of the method to detect even low loads of microorganisms in synthetic water samples. In particular, the model detects the bacterial load in the range 3-2,020 CFU ml(-1) for E. coli and in the range 76-155,556 CFU ml(-1) for P. aeruginosa.

Selection of background electrolyte for CZE analysis by a chemometric approach. Part I. Separation of a mixture of acidic non-steroidal anti-inflammatory drugs.

This paper is the first part of the presentation of a chemometric approach for the rapid selection of a suitable background electrolyte (BGE) in CZE analysis of small drug molecules. The strategy is based on principal component analysis and experimental design. In this first section, the approach is applied to the analysis of a mixture of six arylpropionic anti-inflammatory drugs. Initially, 222 possible aqueous background electrolytes (objects) were characterized using as descriptors pH, conductivity, ionic strength and relative viscosity. In order to allow the dissociation of the acidic analytes, this original data set was reduced to 154 background electrolytes with pH values higher than or equal to 5. Principal component analysis made it possible to graphically represent the new set of objects, described by the four variables, in a two-dimensional space. Among these electrolytes, Kennard-Stone algorithm selected ten objects to be tested by CZE, covering homogeneously principal component space. CZE analyses were carried out with the selected electrolytes, and 0.1 M borax was identified as the most suitable one for the specified application. Finally, the characteristics of the analysis were finely tuned by means of a response surface study, which allowed the best conditions to be determined: borax concentration, 0.09 M; methanol, 6% (v/v); temperature, 24 degrees C, voltage, 20 kV. Applying these conditions, a baseline resolution among the six compounds was obtained in less than 10 min.

Selection of background electrolyte for CZE analysis by a chemometric approach. Part II. Separation of a mixture of basic beta-blocker drugs.

In the first part of this study a chemometric approach to choose a suitable background electrolyte for CZE analysis was introduced. Two hundred and twenty-two possible electrolytes were previously characterized by means of the descriptors pH, conductivity, ionic strength and relative viscosity and the approach was applied to the separation of a mixture of acidic drugs. In this second part, another application concerning the analysis of basic drugs is presented. The test mixture was made of eight beta-blocker drugs. According to the basic nature of the analytes, the original data set was reduced to a new subset of 117 objects with pH less than or equal to 7, and after computing principal components the new set of objects was represented in a two-dimensional space. Ten objects to be tested in CZE, capable of covering homogeneously the principal component space, were selected by means of Kennard-Stone algorithm. The data set was further reduced around the BGEs which gave the best results, and a new set of electrolytes to be tested was selected. Using pH 4 citrate buffer, an electropherogram with baseline resolution was obtained in 10 min. A Doehlert design was run to further reduce analysis time, and applying the optimized conditions (voltage, 23 kV; temperature, 26 degrees C) the separation was obtained in about 7 min.

Has your ancient stamp been regummed with synthetic glue? A FT-NIR and FT-Raman study.

The potential of FT-NIR and FT-Raman spectroscopies to characterise the gum applied on the backside of ancient stamps was investigated for the first time. This represents a very critical issue for the collectors' market, since gum conditions heavily influence stamp quotations, and fraudulent application of synthetic gum onto damaged stamp backsides to increase their desirability is a well-documented practice. Spectral data were processed by exploratory pattern recognition tools. In particular, application of principal component analysis (PCA) revealed that both of the spectroscopic techniques provide information useful to characterise stamp gum. Examination of PCA loadings and their chemical interpretation confirmed the robustness of the outcomes. Fusion of FT-NIR and FT-Raman spectral data was performed, following both a low-level and a mid-level procedure. The results were critically compared with those obtained separately for the two spectroscopic techniques.

The potential of spectral and hyperspectral-imaging techniques for bacterial detection in food: A case study on lactic acid bacteria.

Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest. In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham. Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470-5880cm(-1)) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500-5800cm(-1) region, and a FT-MIR microscopy operating in the 4000-675cm(-1) region. Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.

Direct thermal extraction and gas chromatographic-mass spectrometric determination of volatile compounds of extra-virgin olive oils.

The instrumental performances of a Thermo Desorption-Cooled Injection System coupled with a gas chromatography-mass spectrometer (GC-MS) were improved by a Plackett-Burman experimental design for the direct thermal extraction of volatile compounds from extra-virgin olive oils. The obtained experimental conditions were applied to the analysis of samples from West Liguria (cv. Taggiasca > or = 90%) and Spain (cv. Arbequina), which shared such similar sensorial features that Taste Panel did not distinguish them. Principal component analysis (PCA) was then applied to the experimental data. Three linear combinations of the amounts of the lipoxygenase oxidation products proved to be decisive and sufficient in the differentiation of the two groups of samples.

Classification and class-modeling of "Riviera Ligure" extra-virgin olive oil using chemical-physical parameters.

The Protected Designation of Origin (PDO) "Riviera Ligure" for extra-virgin olive oils from Liguria specifies three additional geographical mentions corresponding to three different geographical areas. To obtain a complete characterization of this typical Italian product, 217 samples of olive oils produced in this North Italian region during 1998/99 and 1999/2000 were analyzed. For each sample 31 variables were determined by chemical-physical analyses, and the data were subjected to a multivariate statistical analysis. For the 1998/99 crop, characterized by favorable climatic conditions, class-models of the three geographical areas were obtained with good predictive ability, also considering the influence of the month of olive harvesting. The oil samples from the 1999/2000 crop were similarly studied, but bad climatic conditions and a widespread Dacus oleae infestation leveled out the peculiar features of the oils produced in the three areas.

Deconvolution of UV-VIS spectroelectrochemical thin-layer data for an EE electrochemical process.

A deconvolution method, based on Fixed Size Window Evolving Factor Analysis (FSWEFA) has been developed to analyse the spectroelectrochemical sequence of spectra obtained in OTTLE electrodes for EE electrochemical processes. The analysis of the data permits to reconstruct the concentration profiles, the spectra of the intermediates and an optical analogue of the i vs E curve.

Characterisation of mucilages extracted from seven Italian cultivars of flax.

The chemical composition, physicochemical, functional and sensory properties of mucilages, extracted from seven Italian flax cultivars, were evaluated. All samples were composed of neutral and acidic sugars, with a low protein content. From the NMR data, a rhamnogalacturonan backbone could be inferred as a common structural feature for all the mucilages, with some variations depending on the cultivar. All the suspensions showed a poor stability, which was consistent with a low zeta potential absolute value. The viscosity seemed to be positively correlated with the neutral sugars and negatively with the amount of proteins. Functional properties were dependent on the cultivar. The sensory analysis showed that most mucilages are tasteless. All these outcomes could support the use of flaxseed mucilages for industrial applications. In particular, Solal and Festival cultivars could be useful as thickeners, due to their high viscosity, while Natural, Valoal and Kaolin as emulsifiers for their good surface-active properties.

Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment.

The relationships between sensory attribute and analytical measurements, performed by electronic tongue (ET) and near-infrared spectroscopy (NIRS), were investigated in order to develop a rapid method for the assessment of umami taste. Commercially available umami products and some aminoacids were submitted to sensory analysis. Results were analysed in comparison with the outcomes of analytical measurements. Multivariate exploratory analysis was performed by principal component analysis (PCA). Calibration models for prediction of the umami taste on the basis of ET and NIR signals were obtained using partial least squares (PLS) regression. Different approaches for merging data from the two different analytical instruments were considered. Both of the techniques demonstrated to provide information related with umami taste. In particular, ET signals showed the higher correlation with umami attribute. Data fusion was found to be slightly beneficial - not so significantly as to justify the coupled use of the two analytical techniques.

Partial least squares density modeling (PLS-DM) - a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy.

A new class-modeling method, referred to as partial least squares density modeling (PLS-DM), is presented. The method is based on partial least squares (PLS), using a distance-based sample density measurement as the response variable. Potential function probability density is subsequently calculated on PLS scores and used, jointly with residual Q statistics, to develop efficient class models. The influence of adjustable model parameters on the resulting performances has been critically studied by means of cross-validation and application of the Pareto optimality criterion. The method has been applied to verify the authenticity of olives in brine from cultivar Taggiasca, based on near-infrared (NIR) spectra recorded on homogenized solid samples. Two independent test sets were used for model validation. The final optimal model was characterized by high efficiency and equilibrate balance between sensitivity and specificity values, if compared with those obtained by application of well-established class-modeling methods, such as soft independent modeling of class analogy (SIMCA) and unequal dispersed classes (UNEQ).

Multivariate strategies for screening evaluation of harmful drinking.

BACKGROUND: A chemometric class modeling strategy (unequal dispersed classes [UNEQ]) is applied for the first time to evaluate harmful alcohol drinking within large population screening programs, in comparison with traditional strategies of data interpretation. Five inexpensive indirect biomarkers (aspartate aminotransferase, alanine aminotransferase, γ-glutamyltransferase, mean corpuscular volume and carbohydrate-deficient transferrin) were determined in blood samples from 423 patients, classified as low-risk or harmful drinkers, according to their ethanol consumption. RESULTS: The multivariate UNEQ approach remarkably improves the diagnostic performances of indirect biomarkers in harmful drinking evaluation, leading to reliable decision rules, with few doubtful classifications to be reviewed through complex confirmation procedures. CONCLUSION: This UNEQ model represents an innovative general approach for clinical evaluation that efficiently extracts the information content present in each biomarker to provide a new synthetic multivariate parameter, to be directly used in diagnostic protocols.

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine.

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction - the boxcar signal transfer (BST) - was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.

Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques.

The current study presents an application of near infrared spectroscopy for identification and quantification of the fraudulent addition of barley in roasted and ground coffee samples. Nine different types of coffee including pure Arabica, Robusta and mixtures of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 wt% of barley. D-optimal design was applied to select 100 and 30 experiments to be used as calibration and test set, respectively. Partial least squares regression (PLS) was employed to build the models aimed at predicting the amounts of barley in coffee samples. In order to obtain simplified models, taking into account only informative regions of the spectral profiles, a genetic algorithm (GA) was applied. A completely independent external set was also used to test the model performances. The models showed excellent predictive ability with root mean square errors (RMSE) for the test and external set equal to 1.4% w/w and 0.8% w/w, respectively.

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification.

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated. Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together). In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression. Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression. Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.

Low total normal motile count values are associated with increased sperm disomy and diploidy rates in infertile patients.

This study was undertaken to evaluate the possibility of identifying men at increased risk of sperm aneuploidy and diploidy on the bases of specific cut-off values of the total normal motile count (TNMC). Twenty-seven consecutive, unselected male patients referred to our Unit were studied: 11 patients with normal sperm parameters (group A) suffering from unexplained infertility and 16 infertile patients with abnormal sperm parameters (group B). Disomy rates for chromosomes 1, 4, 8, 12, 18, X and Y were ascertained for each patient by means of triple and double fluorescence in situ hybridization (FISH) experiments. Both univariate and multivariate statistical analyses by principal component analysis (PCA) were performed for comparisons between sperm aneuploidy rates and semen quality (TNMC). TNMC scores in the two groups were significantly different (23.5 x 10(6) and 1.52 x 10(6), in groups A and B, respectively, p = 0.00002). In general, higher sperm disomy rates were noted for all chromosomes in group B compared with group A. Statistical significance was observed for disomy 1, total disomy rate (3.36% vs. 1.38%), and diploidy (0.49% vs. 0.19%) (p < 0.01). For disomy 4 and 8, differences resulted close to significance. PCA clearly showed how independent variables were inter-related. Infertile men with TNMC < 2 x 10(6) (male factor) were found to be at increased risk for sperm aneuploidy and diploidy. Multivariate analysis by PCA resulted as a useful method to visualize the information of the data sets on a bi-dimensional plot considering all the patients and all the variables at the same time.