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Hyperspectral images can provide useful biochemical information about tissue samples. Often, Fourier transform infrared (FTIR) images have been used to distinguish different tissue elements and changes caused by pathological causes. The spectral variation between tissue types and pathological states is very small and multivariate analysis methods are required to describe adequately these subtle changes. In this work, a strategy combining multivariate curve resolution-alternating least squares (MCR-ALS), a resolution (unmixing) method, which recovers distribution maps and pure spectra of image constituents, and K-means clustering, a segmentation method, which identifies groups of similar pixels in an image, is used to provide efficient information on tissue samples. First, multiset MCR-ALS analysis is performed on the set of images related to a particular pathology status to provide basic spectral signatures and distribution maps of the biological contributions needed to describe the tissues. Later on, multiset segmentation analysis is applied to the obtained MCR scores (concentration profiles), used as compressed initial information for segmentation purposes. The multiset idea is transferred to perform image segmentation of different tissue samples. Doing so, a difference can be made between clusters associated with relevant biological parts common to all images, linked to general trends of the type of samples analyzed, and sample-specific clusters, that reflect the natural biological sample-to-sample variability. The last step consists of performing separate multiset MCR-ALS analyses on the pixels of each of the relevant segmentation clusters for the pathology studied to obtain a finer description of the related tissue parts. The potential of the strategy combining multiset resolution on complete images, multiset segmentation and multiset local resolution analysis will be shown on a study focused on FTIR images of tissue sections recorded on inflamed and non-inflamed palatine tonsils.

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Chemometric techniques like Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) are used to explore, analyze and model relationships among different water quality parameters in wastewater treatment plants (WWTP). Different data sets generated by laboratory analysis and by an automatic multi-parametric monitoring system with a new designed optical device have been investigated for temporal variations on water quality parameters measured in the water influent and effluent of a WWTP over different time scales. The obtained results allowed the discovery of the more important relationships among the monitored parameters and of their cyclic dependence on time (daily, monthly and annual cycles) and on different plant management procedures. This study intended also the modeling and prediction of concentrations of several water components and parameters, especially relevant for water quality assessment, such as Dissolved Organic Matter (DOM), Total Organic Carbon (TOC) nitrate, detergent, and phenol concentrations. PLS models were built to correlate target concentrations of these constituents with UV spectra measured in samples collected at (1) laboratory conditions (in synthetic water mixtures); and at (2) WWTP conditions (in real water samples from the plant). Using synthetic water mixtures, specific wavelengths were selected with the aim to establish simple and reliable prediction models, which gave good relative predictions with errors of around 3-4% for nitrates, detergent and phenols concentrations and of around 15% for the DOM in external validation. In the case of nitrate and TOC concentrations modeling in real water samples from the effluent of the WWTP using the reduced spectral data set, results were also promising with low prediction errors (less than 20%).

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Polymorphism is often encountered in many crystalline compounds. To control the quality of the products, it is relevant knowing the potential presence of polymorph transformations induced by different agents, such as light exposure or temperature changes. Raman images offer a great potential to identify polymorphs involved in a process and to accurately describe this kind of solid-state transformation in the surface scanned. As a way of example, this work proposes the use of multiset analysis on the series of Raman hyperspectral images acquired during a thermal induced transformation of carbamazepine as the optimal way to extract useful information about polymorphic or any other kind of dynamic transformation among process compounds. Image multiset analysis, performed by using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), will furnish pure spectra and distribution maps of the compounds involved in the process and, hence, will allow the identification of polymorphs and, more important, the description of the process evolution at a global and local (pixel) level. Thus, process will be defined from a spatial point of view and by means of a set of global process profiles dependent on the process control variable. The results obtained confirm the power of this methodology and show the crucial role of the spatial information contained in the image (absent in conventional spectroscopy) for a correct process description.

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Water-soluble organic compounds (WSOCs), represented by anhydro-saccharides, dicarboxylic acids, and polyols, were analyzed by gas chromatography interfaced to mass spectrometry in extracts from 103 PM1 and 22 PM2.5 filter samples collected in an urban background and road site in Barcelona (Spain) and an urban background site in Los Angeles (USA), respectively, during 1-month intensive sampling campaigns in 2010. Both locations have similar Mediterranean climates, with relatively high solar radiation and frequent anti-cyclonic conditions, and are influenced by a complex mixture of emission sources. Multivariate curve resolution-alternating least squares analyses were applied on the database in order to resolve differences and similarities in WSOC compositions in the studied sites. Five consistent clusters for the analyzed compounds were obtained, representing primary regional biomass burning organic carbon, three secondary organic components (aged SOC, isoprene SOC, and α-pinene SOC), and a less clear component, called urban oxygenated organic carbon. This last component is probably influenced by in situ urban activities, such as food cooking and traffic emissions and oxidation processes.

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Fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) spectroscopy, coupled with chemometrics methods have been applied to the fast and non-destructive quantitative determination of solid non fat (SNF) content in raw milk. Partial least squares regression (PLS) and support vector machine (SVM) regression methods were used to model and predict SNF contents in raw milk based on FTIR spectral transmission measurements. Both methods, PLS and SVM, showed good performances in SNF prediction with relative prediction errors in the external validation of between 0.2% and 0.3% depending on the spectral range and regression method. Coefficient of determination of the global fit was always above 0.99. Since, the relative prediction errors were low, it can be concluded that FTIR-ATR with chemometrics can be used for accurate quantitative determinations of SNF contents in raw milk within the investigated calibration range of 79-100g/L. The proposed procedure is fast, non-destructive, simple and easy to implement.

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Hyperspectral images are analytical measurements that provide spatial and structural information. The spatial description of the samples is the specific asset of these measurements and the reason why they have become so important in (bio)chemical fields, where the microdistribution of sample constituents or the morphology or spatial pattern of sample elements constitute very relevant information. Often, because of the small size of the samples, the spatial detail provided by the image acquisition systems is insufficient. This work proposes a data processing strategy to overcome this instrumental limitation and increase the natural spatial detail present in the acquired raw images. The approach works by combining the information of a set of images, slightly shifted from each other with a motion step among them lower than the pixel size of the raw images. The data treatment includes the application of multivariate curve resolution (unmixing) multiset analysis to the set of collected images to obtain the distribution maps and spectral signatures of the sample constituents. These sets of maps are noise-filtered and compound-specific representations of all the relevant information in the pixel space and decrease the dimensionality of the original image from hundreds of spectral channels to few sets of maps, one per sample constituent or element. The information in each compound-specific set of maps is combined via a super-resolution post-processing algorithm, which takes into account the shifting, decimation, and point spread function of the instrument to reconstruct a single map per sample constituent with much higher spatial detail than that of the original image measurement.

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MCR-ALS is a resolution method that has been applied in many different fields, such as process analysis, environmental data and, recently, hyperspectral image analysis. In this context, the algorithm provides the distribution maps and the pure spectra of the image constituents from the sole information in the raw image measurement. Based on the distribution maps and spectra obtained, additional information can be easily derived, such as identification of constituents when libraries are available or quantitation within the image, expressed as constituent signal contribution. This work summarizes first the protocol followed for the resolution on two examples of kidney calculi, taken as representations of images with major and minor compounds, respectively. Image segmentation allows separating regions of images according to their pixel similarity and is also relevant in the biomedical field to differentiate healthy from non-healthy regions in tissues or to identify sample regions with distinct properties. Information on pixel similarity is enclosed not only in pixel spectra, but also in other smaller pixel representations, such as PCA scores. In this paper, we propose the use of MCR scores (concentration profiles) for segmentation purposes. K-means results obtained from different pixel representations of the data set are compared. The main advantages of the use of MCR scores are the interpretability of the class centroids and the compound-wise selection and preprocessing of the input information in the segmentation scheme.

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The well established risk assessment COMMPS methodology (Fraunhofer Institut, Schmallenberg, Germany) developed at a European basin scale to identify priority pollutants based on their exposure and effects, has been applied at a local scale along a double step process: (a) first, the corresponding adapted list of priority pollutants has been obtained from monitoring data at a regional scale; (b) second, a new site pollution risk index has been developed for the relative comparison of the chemical pollution status of the investigated geographical region. As a case study, representative of the Mediterranean area, the process has been applied to 17 Catalan rivers (NE Spain), using the monitoring data collected between 1997 and 2006 in 207 different sampling sites. After an appropriate selection procedure, 52 parameters were finally used for the calculation of the local priority substance and site risk indexes. From the exposure point of view the most relevant substances at the Catalan scale were volatile organohalogen compounds (VOX) and volatile aromatic hydrocarbons (toluene and xylene), whereas the priority substance risk index points to dibromomethane (volatile organohalogen compound) and benzo-a-anthracene (PAH) as the most concerning compounds. The local and European priority substance indexes have been compared, showing minor differences. Finally, site risk indexes have been calculated and utilized for comparison of the chemical status of different sites. They can be conveniently displayed in quality geographical maps and are considered a valuable tool for the environmental management and risk assessment of the region under study.

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This study presents the optimization and application of an analytical method based on the use of stir bar sorptive extraction (SBSE) gas chromatography coupled to mass spectrometry (GC-MS) for the ultra-trace analysis of POPs (Persistent Organic Pollutants) in Arctic ice. In a first step, the mass-spectrometry conditions were optimized to quantify 48 compounds (polycyclic aromatic hydrocarbons, brominated diphenyl ethers, chlorinated biphenyls, and organochlorinated pesticides) at the low pg/L level. In a second step, the performance of this analytical method was evaluated to determine POPs in Arctic cores collected during an oceanographic campaign. Using a calibration range from 1 to 1800 pg/L and by adjusting acquisition parameters, limits of detection at the 0.1-99 and 102-891 pg/L for organohalogenated compounds and polycyclic aromatic hydrocarbons, respectively, were obtained by extracting 200 mL of unfiltered ice water. alpha-hexachlorocyclohexane, DDTs, chlorinated biphenyl congeners 28, 101 and 118 and brominated diphenyl ethers congeners 47 and 99 were detected in ice cores at levels between 0.5 to 258 pg/L. We emphasise the advantages and disadvantages of in situ SBSE in comparison with traditional extraction techniques used to analyze POPs in ice.

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Multivariate data analysis methods are applied to study of the geographical and temporal distribution of nitric oxide (NO) in Catalonia (North-East Spain), measured during the period 2001-2006 in 50 sampling stations. Principal component analysis (PCA) and Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) were applied for that purpose. The simultaneous analysis of NO data from sampling stations showed that its geographical distribution was rather uniform during the period considered. When three individual sampling stations were considered (two urban sites and one rural location), three different temporal patterns were resolved, with marked daily-night changes mainly attributed to traffic and also, important winter-summer seasonal variations. A decreasing trend in the levels of NO has been also observed in recent years. Comparison with nitrogen dioxide (NO(2)) profiles shows that the daily variation is quite similar to the NO variation, however NO(2) displays very little oscillations along the seasons and no reduction of its concentration was observed in the last years, contrasting with NO tendencies. The use of MCR-ALS is confirmed to be a useful method to improve interpretability in atmospheric contamination studies. The use of non-negativity and trilinearity constraints is shown to provide improved interpretations of the different contamination patterns in environmental terms.

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The presence of organochlorine compounds (OCs)-namely hexachlorobenzene (HCB), hexachlorocyclohexanes (HCH), polychlorobiphenyls (PCBs #28, 52, 101, 118, 138, 153 and 180), dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethane (DDE)-was examined in various fish tissues (muscle and liver) sampled in 23 mountain lakes in Europe. The dependence of these organochlorine compounds on geographical parameters (altitude, longitude, latitude and temperature) and physiological parameters (lipid content, age, weight and size) was assessed. Principal components analysis (PCA) and partial least squares (PLS) models were used for the analyses. PCA results showed that organochlorine compound concentrations in fish tissues increased with increasing altitude and decreasing temperatures. This trend appeared to be more marked for the less volatile compounds. Some differences were found between the muscle and liver in the effects of the percentage of lipids on the accumulation of organochlorine compounds and the behaviour of HCB. Moreover, PCBs tended to accumulate more in liver rather than in muscle. PCA scores clearly differentiated samples according to the lake of origin. PLS models confirmed that temperature and altitude were the main factors influencing the accumulation of most organochlorine compounds in the lipids of the fish tissue.

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Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.

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Although alternating least squares algorithms have revealed extremely useful and flexible to solve multivariate curve resolution problems, other approaches based on non-linear optimization algorithms using non-linear constraints are possible. Once the subspaces defined by PCA solutions are identified, appropriate rotation and perturbation of these solutions can produce solutions fulfilling the constraints obeyed by the physical nature of the investigated systems. In order to perform such a rotation, an optimization algorithm based in the fulfillment of constraints and some examples of application in chemistry and environmental chemistry are given. It is shown that the solutions obtained either by alternating least squares or by the new proposed algorithm are rather similar and that they are both within the boundaries of the band of feasible solutions obtained by an algorithm previously developed to estimate them.

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Three sampling campaigns were carried out in rivers located at two hydrographic basins affected by urban and semi-urban areas around the Metropolitan area of A Coruña (ca. 500,000 inhabitants, NW-Spain) to study local and temporal variations of 21 physicochemical parameters (pH, conductivity, Cl-, SO4(2-), SiO2, Ca2+, Mg2+, Na+, K+, hardness, NO3(-), NO2(-), NH4(+), COD, PO4(3-), Zn2+, Cu2+, Mn2+, Pb2+, alkalinity and acidity) in 23 sampling points. The temporal evolution of the water quality was assessed by matrix augmentation principal components analysis (MA-PCA) and parallel factor analysis (PARAFAC). Moreover, classical principal components analysis (PCA) (one per sampling campaign) was applied with exploratory and comparison purposes. The first factor of the different studies comprised variables associated to the mineral content and it differentiated the samples according to their hydrographic basins. The second factor was mainly associated to organic matter, from domestic wastes and decomposition of natural debris. The temporal evolution of the water quality was mostly related to seasonal increments of the physicochemical parameters defining the decomposition of the organic matter. The three models applied (PCA, MA-PCA and PARAFAC) led to similar conclusions, nonetheless, MA-PCA excelled, since the refolding of scores provided more straightforward and convenient overview of sample time and geographical variations than individual PCA and it is more flexible and adaptable to environmental studies than PARAFAC.

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The interactions of actinomycin D (ACTD) with the oligonucleotides 5'-CAAAGCTTTG-3', 5'-CATGGCCATG-3' and 5'-TATGGCCATA-3' were investigated by means of acid-base titrations and mole-ratio and melting experiments monitored by molecular absorption and circular dichroism (CD) spectroscopies. For each experiment, CD and molecular absorption spectra were recorded at each point in the experiment, and later analyzed via appropriate multivariate data analysis methods. The study of the interactions between these oligonucleotides and ACTD at 25 degrees C showed the formation of an interaction complex with a stoichiometry of 1:1 (ACTD:duplex) and values for the log(formation constant) of 5.1+/-0.3, 6.4+/-0.2, and 5.6+/-0.2, respectively. An additional interaction complex at higher temperatures was also detected, which might be related to the single-stranded forms of the oligonucleotides.

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The utility and validity of toxicity tests for monitoring of wastewater treatment have been assessed. The evaluated acute toxicity tests have been Vibrio fischeri, Selenastrum capricornotum and Daphnia magna tests. The validation studies indicated that the acute toxicity tests can be considered as high sensitivity analytical tools to detect common environmental concentrations of the pollutants at concentration levels as low as ngl(-1). The toxicity tests showed to have discriminatory ability to distinguish between different degrees of toxicity, and the toxic specificity of the compounds on target organisms. Synergistic, additive or antagonistic effects were evaluated indicating the capacity of the toxicity test to assess the combined effects of chemicals in wastewaters. The reproducibility of these tests, calculated as relative standard deviation, is acceptable in the range of 5-22.3%. The application of multivariate date analysis proved that toxicity and chemical measures are complementary analytical tools for monitoring of wastewaters quality. The toxicity tests are useful analytical tools for screening of chemical analysis and as an early warning system to monitor the treatment of WWTPs. The use of single toxicity test or battery of tests is the best approach to evaluate the risk because they are reliable indices of the toxic impact of effluents in the aquatic environment. The toxicity tests were applied in the quality control of different European WWTPs.

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The nucleoside 2'-deoxyzebularine (K) was incorporated into the self-complementary dodecamer 5'-CGTACGKGTACG-3' by solid-phase 2-cyanoethylphosphoramidite chemistry using dimethoxytrityl (DMT) as the 5'-hydroxyl protecting group. Standard synthesis cycles using trichloroacetic acid and short ammonia treatment (50 degrees C for 30 min) were found to be the optimal conditions to obtain the desired dodecamer with minimum acid and basic degradation of the acid- and base-sensitive 2-pyrimidinone residue. The protonation equilibria of the K nucleoside and of the dodecamer at 37 degrees C were studied by means of spectroscopically monitored titrations. For the K nucleoside, a pK(a) value of 3.13 +/- 0.09 was obtained. For the dodecamer, four acid-base species were found in the pH range 2-12, with pK(a) values of 9.60 +/- 0.07, 4.46 +/- 0.16, and 2.87 +/- 0.19. Melting experiments were carried out to confirm the proposed acid-base concentration profiles. Finally, kinetic experiments were also carried out at several pH values to evaluate the stability of the K nucleoside and of the dodecamer. An increased stability was shown by the K nucleoside when incorporated into the dodecamer. Multivariate methods based on both hard- and soft-modeling were applied for the analysis of spectroscopic data, allowing the estimation of concentration profiles and pure spectra.

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A procedure is described for the complete resolution of concentration profiles of oligonucleotide triplexes as a function of pH and temperature. The pH and temperature ranges at which triplexes are present and the relative concentrations of all the species involved in acid-base and conformational equilibria are successfully estimated from Multivariate Curve Resolution analysis of UV absorbance spectra recorded along acid-base titrations and melting experiments of single stranded, hairpin and their mixtures. The dependence of formation constants upon pH was successfully estimated. The hairpin h26 (5'-GAAGGAGGAGA-TTTT-TCTCCTCCTTC-3'), and the single stranded oligonucleotides s11CT (5'-CTTCCTCCTCT-3'), s11AG (5'-AGAGGAGGAAG-3') and s11TG (5'-TGTGGTGGTTG-3') were synthesized and their protonation and conformational equilibria were studied in detail. The procedure was shown to be especially useful for the study of triplexes with a low hypochromism upon formation.

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The application of multivariate curve resolution-alternating least squares (MCR-ALS) to the quantitative analysis of different metal ion mixtures using voltammetric data is described. The performance of MCR-ALS was evaluated in the resolution and quantitation of overlapped voltammetric peaks obtained in the analysis of binary and quaternary mixtures of Cd(II), In(III), Pb(II) and Tl(I) metal ions by anodic stripping voltammetry. Quality assessment of qualitative and quantitative determinations was evaluated considering the effects of different constraints and of initial estimations. MCR-ALS results were validated and compared with those obtained by applying other well-established multivariate calibration methods, such as partial least squares (PLS) and direct classical least squares (DCLS) methods.

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The influence of metal ions (Na(+), Mg(2+) and Cd(2+)) on the thermal unfolding of phenylalanine transfer ribonucleic acid (tRNA(Phe)) was studied by UV spectroscopy-monitored melting experiments. Absorbance data were obtained during the unfolding process in the range 220-340 nm and later analyzed by a multivariate curve resolution approach (MCR-ALS) based on factor analysis. This procedure determines the number of spectroscopically distinct conformations present during the unfolding process and reveals their concentration profiles and pure spectra, without any initial assumption having to be made about the number of steps in the unfolding pathway. From the concentration profiles and pure spectra, information such as T(m) values can be recovered. The results were compared with those obtained previously in spectroscopic and calorimetric unfolding experiments, showing that the multivariate approach recovers information that complements that obtained in traditional spectroscopic melting experiments.

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