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The resolution and quantitation of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS was used to model the pure component spectra and to obtain concentration profiles of minor components, but MCR-ALS could not. [ABSTRACT FROM AUTHOR]

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. [ABSTRACT FROM AUTHOR]

Femtosecond laser-induced breakdown spectroscopy (fs-LIBS) has been used for the first time for quantitative determination of nutrients in plant materials from different crops. A highly heterogeneous population of 31 samples, previously analyzed by inductively coupled plasma optical emission spectroscopy, covering a wide range of matrices was interrogated. To tackle the analysis, laser-induced plasmas under argon atmosphere of pellets prepared from sieved cryogenically ground leaves were studied. Predictive functions based on univariate and multivariate modeling of optical emissions associated to macro- (Ca, Mg, and P) and micronutrients (Cu, Fe, Mn and Zn) were designed. Hierarchical cluster analysis was performed to select representative calibration ( n cal = 17) and validation ( n val = 14) datasets. The predictive performance of calibration functions over fs-LIBS data was compared with that attained on spectral information from nanosecond LIBS (ns-LIBS) operating at different wavelengths (1064 nm, 532 nm, and 266 nm). Findings established higher accuracy and less uncertainty on mass fractions quantification from fs-LIBS, whatever the modeling approach. Quality coefficients below 20% for the accuracy error on mass fractions’ prediction in unknown samples, and residual predictive deviations in general above 5, were obtained. In contrast, only multivariate modeling satisfactorily handled the non-linear variations of emissions in ns-LIBS, leading to 2-fold decrease in the root mean square error of prediction (RMSEP) of Ca, Mg, P, Cu, Fe, Mn and Zn in comparison with the univariate approach. But still, an averaged quality coefficient about 35% and residual predictive deviations below 3 were found. Similar predictive capabilities were observed when changing the laser wavelength. Although predicted values by ns-LIBS multivariate modeling exhibit better agreement with reference mass fractions as compared to univariate functions, fs-LIBS conducts better quantification of nutrients in plant materials since it is less dependent on the chemical composition of the matrices. [ABSTRACT FROM AUTHOR]

Highlights: [•] Femtosecond transient absorption spectroscopy investigating a complex photodynamic scheme. [•] Combining experiments obtained from two photo-active systems with complementary pathways. [•] Multiset hybrid hard- and soft-multivariate curve resolution incorporating reaction quantum yields. [Copyright &y& Elsevier]

Abstract: Extent-based kinetic identification is a kinetic modeling technique that uses concentration measurements to compute extents and identify reaction kinetics by the integral method of parameter estimation. This article considers the case where spectroscopic data are used together with a calibration model to predict concentrations. The calibration set is assumed to be constructed from reacting data that include pairs of concentration and spectral data. Alternatively, one can use the concentration- and spectral contributions of the reactions and mass transfers, which are obtained by pretreatment in reaction- and mass-transfer-variant form. The extent-based kinetic identification using concentrations predicted from spectroscopic data is illustrated through the simulation of both a homogeneous and a gas–liquid reaction system. [Copyright &y& Elsevier]

Abstract: Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. [Copyright &y& Elsevier]

Abstract: The c-kit oncogene shows a cytosine-rich DNA region upstream of the transcription initiation site which forms an i-motif structure at slightly acidic pH values (Bucek et al. ). In the present study, the pH-induced formation of i-motif – forming sequences 5′-CCC CTC CCT CGC GCC CGC CCG-3′ (ckitC1, native), 5′-CCC TTC CCT TGT GCC CGC CCG-3′ (ckitC2) and 5′-CCCTT CCC TTTTT CCC T CCC T-3′ (ckitC3) was studied by spectroscopic techniques, such as UV molecular absorption and circular dichroism (CD), in tandem with two multivariate data analysis methods, the hard modelling-based matrix method and the soft modelling-based MCR-ALS approach. Use of the hard chemical modelling enabled us to propose the equilibrium model, which describes spectral changes as functions of solution acidity. Additionally, the intrinsic protonation constant, K in, and the cooperativity parameters, ω c, and ω a, were calculated from the fitting procedure of the coupled CD and molecular absorption spectra. In the case of ckitC2 and ckitC3, the hard model correctly reproduced the spectral variations observed experimentally. The results indicated that folding was accompanied by a cooperative process, i.e. the enhancement of protonated structure stability upon protonation. In contrast, unfolding was accompanied by an anticooperative process. Finally, folding of the native sequence, ckitC1, seemed to follow a more complex mechanism. [ABSTRACT FROM AUTHOR]

Abstract: A direct spectrophotometric method for the determination of three artificial colors – amaranth, sunset yellow FCF and tartrazine – in beverages samples is proposed. The spectra were recorded between 359 and 600nm. The spectra of the samples (just filtrated), pure dyes (concentrations ranged between 0.01 and 1.8mgL−1 for amaranth, 0.08 and 4.4mgL−1 for sunset yellow and 0.04 and 1.8mgL−1 for tartrazine) and synthetic mixtures were disposed in a column-wise augmented data matrix. This kind of data structure, analyzed by multivariate curve resolution-alternating least squares (MCR-ALS) makes it possible to exploit the so called ‘second order advantage’. MCR-ALS algorithm was applied to the experimental data under the non-negativity and equality constraints. As a result, the concentration of each dye in the samples and their corresponding pure spectra were obtained. The results were validated using internal reference materials and no significant differences were found (α =5%) between the reference values and the ones obtained with the proposed method. The second order advantage made it possible to obtain unbiased results even in the presence of interferences. [Copyright &y& Elsevier]

Abstract: By employing the simple but effective principle ‘survival of the fittest’ on which Darwin''s Evolution Theory is based, a novel strategy for selecting an optimal combination of key wavelengths of multi-component spectral data, named competitive adaptive reweighted sampling (CARS), is developed. Key wavelengths are defined as the wavelengths with large absolute coefficients in a multivariate linear regression model, such as partial least squares (PLS). In the present work, the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength, CARS sequentially selects N subsets of wavelengths from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a calibration model. Next, based on the regression coefficients, a two-step procedure including exponentially decreasing function (EDF) based enforced wavelength selection and adaptive reweighted sampling (ARS) based competitive wavelength selection is adopted to select the key wavelengths. Finally, cross validation (CV) is applied to choose the subset with the lowest root mean square error of CV (RMSECV). The performance of the proposed procedure is evaluated using one simulated dataset together with one near infrared dataset of two properties. The results reveal an outstanding characteristic of CARS that it can usually locate an optimal combination of some key wavelengths which are interpretable to the chemical property of interest. Additionally, our study shows that better prediction is obtained by CARS when compared to full spectrum PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE) and moving window partial least squares regression (MWPLSR). [Copyright &y& Elsevier]

Abstract: This paper presents a Bayesian approach to the development of spectroscopic calibration models. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific optimization method, i.e. Bayesian evidence approximation, is utilized to estimate the model “hyper-parameters”. The relation of the proposed approach to the calibration models in the literature is discussed, including ridge regression and Gaussian process model. The Bayesian model may be modified for the calibration of multivariate response variables. Furthermore, a variable selection strategy is implemented within the Bayesian framework, the motivation being that the predictive performance may be improved by selecting a subset of the most informative spectral variables. The Bayesian calibration models are applied to two spectroscopic data sets, and they demonstrate improved prediction results in comparison with the benchmark method of partial least squares. [Copyright &y& Elsevier]

Abstract: In this work the ANOVA-PCA method is applied to a MIR spectroscopy dataset of carraghenan in order to evaluate which of the factors within its fixed effects experimental design are significant in relation to the residual error. The factors defined in the experimental design are concentration (1% and 2%), temperature (30, 40, 45, 50, and 60°C), day (1 and 2) and sample (20 samples, 3 repetitions). The two factors, concentration and temperature, were considered as significant and the main features related with its physico-chemical properties were identified. It is also of interest to acquire a better understanding of the interaction between concentration and temperature and its effect on the adhesion of gels onto the surface of contact. In fact, no significant interaction was found between the two factors, but it was shown that the factor temperature behaves in a non-linear way. As classification using the ANOVA-PCA procedure has not been developed until now, a new method is proposed for the classification of new samples in respect to the levels of each significant factor. [Copyright &y& Elsevier]

Abstract: Multivariate curve resolution coupled to alternating least-squares (MCR-ALS) has been employed to model kinetic-spectroscopic second-order data, with focus on the achievement of the important second-order advantage, under conditions of extreme spectral overlapping among sample components. A series of simulated examples shows that MCR-ALS can conveniently handle the studied analytical problem unlike other second-order multivariate calibration algorithms, provided matrix augmentation is implemented in the spectral mode instead of in the usual kinetic mode. The approach has also been applied to three experimental examples, which involve the determination of: (1) the antiparkinsonian carbidopa (analyte) in the presence of levodopa as a potential interferent, both reacting with cerium (IV) to produce the fluorescent species cerium (III) with different kinetics; (2) Fe(II) (analyte) in the presence of the interferent Zn(II), both catalyzing the oxidation of methyl orange with potassium bromate; and (3) tartrazine (analyte) in the presence of the interferent brilliant blue, both oxidized with potassium bromate, with the interferent leading to a product with an absorption spectrum very similar to tartrazine. The results indicate good analytical performance towards the analytes, despite the intense spectral overlapping and the presence of unexpected constituents in the test samples. [Copyright &y& Elsevier]

Abstract: We report the use of a calibration transfer strategy to correct for drift in the quantitative sensitivity of a portable quadrupole mass spectrometer (QMS) aimed at process monitoring applications. Gas mixtures of CH4/Ar/C2H6/CO2 were studied with calibration phase measurements made of the pure gases for a univariate analysis and of 40 multi-component mixtures for a multivariate approach. To evaluate calibrations, test set spectra of a CH4/Ar/C2H6/CO2 gas mixture were recorded bi-weekly over a period of 12 months. As part of the strategy a standard of pure argon was measured during both calibration and test phases so that correction factors could be calculated for each measurement day. It was shown that in the absence of a calibration transfer strategy quantifications of test set spectra could be inaccurate by more than an order of magnitude over 12 months. Furthermore, due to the effects of drift in the sensitivity over the 6 days required to record the training set in the calibration phase it was found that the multivariate analysis quantified test spectra less accurately than the univariate analysis. However, by applying the calibration transfer strategy across all measurements (both calibration and test phases) it was shown that the errors in prediction using the multivariate analysis previously seen after 2 weeks were not observed until approximately 12 months later. [Copyright &y& Elsevier]

Abstract: The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique. Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA). The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils. [Copyright &y& Elsevier]

Abstract: Ion mobility spectrometry based analysis has been found to provide a powerful and reliable tool as a portable gas detector of different chemical compounds and mixtures. The suitability of the spectrometer in food quality assay was studied in a case study, in which fish quality changes during cold storage were modelled. Hexane extracts of gills of vendace (Coregonus albula L.) were analysed by using ion mobility spectrometry. For reference, microbial counts of gills, ATP-breakdown products (K-value) of fish flesh, electrical properties (Torrymeter scores) of fish skin and sensory evaluation scores were determined. Principal component analysis (PCA) was utilised to create an index for quality by compressing the reference data. Relationship between the quality index and the spectral data were modelled with N-mode partial least squares (PLS). Thus, the ion mobility data have three-dimensional nature, since signals from six channels are recorded simultaneously. Reliability of the predictions and ion mobility measurements was of great interest in the development procedure of this novel analytical method. The multivariate statistical process control (MSPC) charts were used to determine the reliability of the ion mobility measurements. The chemical composition of gill extracts changed during the storage period as indicated by the ion mobility spectrum profiles. The dominant variation in the data was related to the length of storage of fish and to seasonal variation. The results of the case study revealed that the spectrometer, with the analytical method, and with chemometric tools form a promising tool for food quality assays. [Copyright &y& Elsevier]

Abstract: This is the first study introducing an NMR-based metabolomic approach to elucidate metabolism in the portal-drained viscera and liver. High-resolution 1H NMR spectroscopy was performed on blood plasma obtained from the mesenteric artery, the hepatic portal vein and the hepatic vein of steers before and after interruminal infusion of short-chain fatty acids (SCFA). Partial least squares regression discriminant analysis (PLS-DA) on 1H NMR spectra obtained for arterial and portal plasma samples revealed increases in the intensities of spectral regions that could mostly be assigned to the methyl group of propionate, β-hydroxybutyrate as well as acetate in portal plasma samples compared with arterial plasma samples. PLS-DA on portal and hepatic plasma samples showed decreases in the intensities of spectral regions corresponding to the regions where methyl and methylene groups of n-butyrate, propionate, iso-butyrate, valerate, iso-valerate as well as lactate are reported to have chemical shifts. In addition, spectral differences likely to reflect a hepatic excretion of β-hydroxybutyrate and glucose were observed between portal and hepatic samples. All findings were supported by traditional biochemical analyses. Consequently, the present study clearly demonstrated the great potential of the NMR-based metabolomic approach for elucidating splanchnic metabolism. In addition, prediction of metabolite concentrations from the 1H NMR spectra obtained on plasma samples was investigated using PLS, which in relation to use 1H NMR spectra quantitatively revealed promising results with R-values in the region of 0.87–0.98. [Copyright &y& Elsevier]

Abstract: Multivariate methods comprise of a group of chemometric tools allowing the analysis of different analytical data, i.e., spectroscopic, chromatographic obtained from multichannel detector systems. Second-way data are widely used in analytical applications in combination with multivariate calibration methods, but three- and higher-way data are yet not as widely applied. In complex biological samples, the employment of the three-way data is of special interest, as they may be combined with methods that exploit the second-order advantage allowing calculating individual concentrations of the analytes of interest in the presence of unknown interferences in untreated samples. A very sensitive and selective method is proposed, by coupling photoinduced fluorescence and multivariate analysis of the three-way data excitation-emission fluorescence matrices (EEMs), of the photoproducts obtained from UV irradiation of three fluoroquinolones: enoxacin (ENO), norfloxacin (NOR) and ofloxacin (OFLO). The application of a previous photoirrradiation process allows the determination of mixtures of ENO, NOR and OFLO, in urine samples at biological levels without sample pretreatments. The resolution ability of N-way partial least squares (N-PLS), parallel factor analysis (PARAFAC) and self weighted alternating trilinear decomposition (SWATLD), is compared with partial least squares (PLS) and unfolded-PLS (U-PLS), in the analysis of ENO, NOR and OFLO in human urine samples. [Copyright &y& Elsevier]

The objective of this paper is to illustrate how chemometrics can enhance the scope and power of flow injection analysis (FIA) by considering a few simple but representative cases where the ability of chemometrics to improve performance is not readily apparent. In principle, there are two phases when chemometrics can be usefully combined with FIA: first when developing an FIA method and, second, when treating raw data acquired from an FIA detection system. The most obvious application of chemometrics for the FIA practitioner is to use experimental design to replace the obsolete, but too often used one-variable-at-a-time approach when optimising an FIA method. Therefore, methods for screening variables and system optimisation are discussed. Raw data acquired from most FIA systems are first-order data, containing information about the dispersed sample plug. However, the information that is extracted when using FIA for routine purposes is of zero-order: predominantly peak height values. It is shown by a simple example that a chemometric approach in such cases can again provide additional useful information about the sample. First-order spectral data and second-order data more or less require a chemometrics approach for successful analysis, and examples of such applications are briefly discussed. [Copyright &y& Elsevier]

Progress in the analysis of multicomponent processes and mixtures relies on the combination of sophisticated instrumental techniques and suitable data analysis tools focused on the interpretation of the multivariate responses obtained. Despite the differences in compositional variation, complexity and origin, the raw measurements recorded in a multicomponent chemical system can be very often described with a simple model consisting of the composition-weighted sum of the signals of their pure compounds.Multivariate resolution methods have been the tools designed to unravel this pure compound information from the non-selective mixed original experimental output. The evolution of these chemometric approaches through the improvement of exploratory tools, the adaptation to work with complex data structures, the ability to introduce chemical and mathematical information in the algorithms and the better quality assessment of the results obtained is revisited. The active research on these chemometric area has allowed the successful application of these methodologies to chemical problems as complex and diverse as the interpretation of protein folding processes or the resolution of spectroscopic images. [Copyright &y& Elsevier]

Diffusion-ordered spectroscopy (DOSY) NMR is based on a pulse-field gradient spin-echo NMR experiment, in which components experience diffusion. Consequently, the signal of each component decays with different diffusion rates as the gradient strength increases, constructing a bilinear NMR data set of a mixture. By calculating the diffusion coefficient for each component, it is possible to obtain a two-dimensional NMR spectrum: one dimension is for the conventional chemical shift and the other for the diffusion coefficient. The most interesting point is that this two-dimensional NMR allows non-invasive “chromatography” to obtain the pure spectrum for each component, providing a possible alternative for LC-NMR that is more expensive and time-consuming. Potential applications of DOSY NMR include identification of the components and impurities in complex mixtures, such as body fluids, or reaction mixtures, and technical or commercial products, e.g. comprising polymers or surfactants.Data processing is the most important step to interpret DOSY NMR. Single channel methods and multivariate methods have been proposed for the data processing but all of them have difficulties when applied to real-world cases. The big challenge appears when dealing with more complex samples, e.g. components with small differences in diffusion coefficients, or severely overlapping in the chemical shift dimension. Two single channel methods, including SPLMOD and continuous diffusion coefficient (CONTIN), and two multivariate methods, called direct exponential curve resolution algorithm (DECRA) and multivariate curve resolution (MCR), are critically evaluated by simulated and real DOSY data sets. The assessments in this paper indicate the possible improvement of the DOSY data processing by applying iterative principal component analysis (IPCA) followed by MCR-alternating least square (MCR-ALS). [Copyright &y& Elsevier]