Partial least squares modeling of combined infrared, H NMR and C NMR spectra to predict long residue properties of crude oils

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding ¹H and ¹³C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (D_LR), viscosity (V_LR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, ¹³C NMR and ¹H NMR spectra and on 3 sets of merged spectra, i.e., IR + ¹H NMR, IR + ¹³C NMR and IR + ¹H NMR + ¹³C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, ¹³C NMR and ¹H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input ¹³C NMR or ¹H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site.     

Partial least squares modeling of combined infrared, 1H NMR and 13C NMR spectra to predict long residue properties of crude oils

Research has been carried out to determine the potential of partial least squares (PLS) modeling of mid-infrared (IR) spectra of crude oils combined with the corresponding ¹H and ¹³C nuclear magnetic resonance (NMR) data, to predict the long residue (LR) properties of these substances. The study elaborates further on a recently developed and patented method to predict this type of information from only IR spectra. In the present study, PLS modeling was carried out for 7 different LR properties, i.e., yield long-on-crude (YLC), density (D_LR), viscosity (V_LR), sulfur content (S), pour point (PP), asphaltenes (Asph) and carbon residue (CR). Research was based on the spectra of 48 crude oil samples of which 28 were used to build the PLS models and the remaining 20 for validation. For each property, PLS modeling was carried out on single type IR, ¹³C NMR and ¹H NMR spectra and on 3 sets of merged spectra, i.e., IR + ¹H NMR, IR + ¹³C NMR and IR + ¹H NMR + ¹³C NMR. The merged spectra were created by considering the NMR data as a scaled extension of the IR spectral region. In addition, PLS modeling of coupled spectra was performed after a Principal Component Analysis (PCA) of the IR, ¹³C NMR and ¹H NMR calibration sets. For these models, the 10 most relevant PCA scores of each set were concatenated and scaled prior to PLS modeling. The validation results of the individual IR models, expressed as root-mean-square-error-of-prediction (RMSEP) values, turned out to be slightly better than those obtained for the models using single input ¹³C NMR or ¹H NMR data. For the models based on IR spectra combined with NMR data, a significant improvement of the RMSEP values was not observed neither for the models based on merged spectra nor for those based on the PCA scores. It implies, that the commonly accepted complementary character of NMR and IR is, at least for the crude oil and bitumen samples under study, not reflected in the results of PLS modeling. Regarding these results, the absence of sample preparation and the straightforward way of data acquisition, IR spectroscopy is preferred over NMR for the prediction of LR properties of crude oils at site.     

Near-infrared spectroscopy quantitative determination of Pefloxacin mesylate concentration in pharmaceuticals by using partial least squares and principal component regression multivariate calibration

Pefloxacin mesylate, a broad-spectrum antibacterial fluoroquinolone, has been widely used in clinical practice. Therefore, it is very important to detect the concentration of Pefloxacin mesylate. In this research, the near-infrared spectroscopy (NIRS) has been applied to quantitatively analyze on 108 injection samples, which was divided into a calibration set containing 89 samples and a prediction set containing 19 samples randomly. In order to get a satisfying result, partial least square (PLS) regression and principal components regression (PCR) have been utilized to establish quantitative models. Also, the process of establishing the models, parameters of the models, and prediction results were discussed in detail. In the PLS regression, the values of the coefficient of determination (R²) and root mean square error of cross-validation (RMSECV) of PLS regression are 0.9263 and 0.00119, respectively. For comparison, though applying PCR method to get the values of R² and RMSECV we obtained are 0.9685 and 0.00108, respectively. And the values of the standard error of prediction set (SEP) of PLS and PCR models are 0.001480 and 0.001140. The result of the prediction set suggests that these two quantitative analysis models have excellent generalization ability and prediction precision. However, for this PFLX injection samples, the PCR quantitative analysis model achieved more accurate results than the PLS model. The experimental results showed that NIRS together with PCR method provide rapid and accurate quantitative analysis of PFLX injection samples. Moreover, this study supplied technical support for the further analysis of other injection samples in pharmaceuticals.     

Prediction of properties of biodiesel-diesel blends using spectrofluorimetry and multivariate calibration

Regressions based on fluorescence spectroscopy were developed to provide relatively inexpensive and rapid measurements of the concentration, viscosity, and specific gravity of biodiesel-diesel blends. The methods involved obtaining a mathematical model from spectrofluorimetric data and data from a given property (concentration, dynamic viscosity, or specific gravity) using partial least squares (PLS) regression, which was then applied as a model for predicting properties of interest. The predicted concentrations, dynamic viscosities, and specific gravities of the biodiesel-diesel blends were compared with actual values and agreed reasonably well with the obtained results. The models showed high correlation between real and predicted values. The R-square values near 1 indicated excellent model accuracy for predicting concentrations, specific gravities, and dynamic viscosities of biodiesel-diesel blends. The residual distribution did not follow a trend with respect to the predicted variables, indicating an excellent fit to the data.      

Butene concentration prediction in ethylene/propylene/1-butene terpolymers by FT-IR spectroscopy through multivariate statistical analysis and artificial neural networks

This paper reports the development of calibration models for quality control in the production of ethylene/propylene/1-butene terpolymers by the use of multivariate tools and FT-IR spectroscopy.1-Butene concentration prediction is achieved in terpolymers by coupling FT-IR spectroscopy to multivariate regression tools. A dataset of 26 terpolymers (14 coming from a constrained experimental design for mixtures, plus 12 terpolymers used for external validation) was analysed by FT-IR spectroscopy. An internal method of “Polimeri Europa” plant, based on ¹³C NMR spectroscopy is used to determine the percentage of 1-butene in the samples. Then, different multivariate tools are used for 1-butene concentration prediction based on the FT-IR spectra recorded. Different multivariate calibration methods were explored: principal component regression (PCR), partial least squares (PLS), stepwise OLS regression (SWR) and artificial neural networks (ANNs). The model obtained by back-propagation neural networks turned out to be the best one. The performances of the BP-ANN model were further improved by variable selection procedures based on the calculation of the first derivative of the network.The proposed approach allows the monitoring in real time of the polymer synthesis and the estimation of the characteristics of the product attainable from the concentration of 1-butene.     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Simultaneous Kinetic‐Spectrophotometric Determination of Hydrazine and its Derivatives by Partial Least Squares and Principle Component Regression Methods

Simultaneous kinetic‐spectrophotometric determination of a ternary mixture of hydrazine (HZ) and its derivatives by principal component regression (PCR) and partial least squares (PLS) calibration is described. The methods were based on the difference observed in the reduction rate of iron(III) with HZ, thiosemicarbazide (TSCZ) and phenylhydrazine (PHZ) in the presence of 2,2′‐bipyridine (Bpy). The colored complex of [Fe(Bpy)₃]²⁺ was formed in sodium dodecyl sulfate (SDS) as micellar media, and then monitored at 520 nm. The results showed that simultaneous determination of HZ, TSCZ and PHZ could be performed in their concentration ranges of 1.0–70.0, 0.2–6.0 and 0.1–10.0 μg mL^?1, respectively. The root mean squares errors of prediction (RMSEP) of HZ, TSCZ and PHZ were 0.719, 0.164 and 0.105 (for PLS) 0.788, 0.166 and 0.993 (for PCR), respectively. Both methods (PCR and PLS) were validated using a set of synthetic sample mixtures and then applied for simultaneous determination of HZ, TSCZ and PHZ in water samples.     

Genetic‐Algorithm‐Based Wavelength Selection in Multicomponent Spectrophotometric Determination by PLS: Application on Ascorbic Acid and Uric Acid Mixture

Genetic algorithm (GA) is a suitable method for selecting wavelengths for partial least squares (PLS) calibration of mixtures with almost identical spectra without loss of prediction capacity using the spectrophotometric method. In this study, the concentration model is based on absorption spectra in the range of 200‐320 nm for 25 different mixtures of ascorbic acid (AA) and uric acid (UA). The calibration curve was linear over the concentration range of 1‐15 and 2‐16 μg mL^?1 for ascorbic acid and uric acid, respectively. The root mean square deviation (RMSD) for ascorbic acid and uric acid with GA and without GA were 0.3071 and 0.3006, 0.3971 and 0.7063, respectively. The proposed method was successfully applied to the simultaneous determination of both analytes in human serum and urine samples.     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Multicomponent Determination of Organophosphorus Pesticides in Grain Samples by Linear Sweep Stripping Voltammetry and Multivariate Calibration

Abstract

Organophosphorus pesticides, such as parathion methyl (PTM), fenitrothion (FT), parathion (PT), and isocarbophos (ICP), have sensitive but overlapped voltammetric peaks with peak potentials ?309, ?364, ?317, and ?480 mV, respectively, in Britton‐Robinson buffer of pH 4.8 by application of linear sweep stripping voltammetry (LSSV). In this work, two multivariate calibration methods, partial least squares (both PLS‐1 and PLS‐2), and principal component regression (PCR), were applied to quantitatively resolve the overlapping voltammogram of the mixtures of these four pesticides. The prediction results obtained from a set of independent test samples showed that PLS‐1 method performed better prediction ability than PLS‐2 and PCR methods. The proposed method was successfully applied to the determination of these four pesticides in grain samples after a pre‐extraction step with a solvent of acetone.     

Estimation of the composition of heparin mixtures from various origins using proton nuclear magnetic resonance and multivariate calibration methods

A multivariate calibration method for the characterization of heparin samples based on the analysis of (1)H nuclear magnetic resonance (NMR) spectral data is proposed. Heparin samples under study consisted of two-component or four-component mixtures of heparins from porcine, ovine and bovine mucosae and bovine lung. Although the (1)H NMR spectra of all heparin types were highly overlapping, each origin showed some particular features that could be advantageously used for the quantification of the components. These features mainly concerned the anomeric H, which appeared in the range 5.0-5.7 ppm and the peaks of acetamidomethyl protons at 2.0-2.1 ppm. The determination of the percentage of each heparin class depended on these differences and was carried out using partial least squares regression (PLS) as a calibration method. Prior to the PLS analysis, the spectral data were standardized using the internal standard peak (sodium 4,4-dimethyl-4-silapentanoate- 2,2,3,3- d (4), TSP) as the reference. The quantification of each heparin type in the samples using PLS models built with 4 or 5 components was satisfactory, with an overall prediction error ranging from 3% to 10%.     

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Simultaneous Kinetic‐Potentiometric Determination of Hydrazine and Thiosemicarbazide by Partial Least Squares and Principle Component Regression Methods

Simultaneous determination of hydrazine (HZ) and thiosemicarbazide (TSC) by partial least squares (PLS) and principle component regression (PCR) was carried out based on kinetic data of novel potentiometry. The rate of chloride ion production in reaction of HZ and TSC with N‐chlorosuccinimide (NCS) was monitored by a chloride ion‐selective electrode. The experimental dada shows not only the good ability of ion‐selective electrodes (ISEs) as detectors for the direct determination of chloride ions but also for simultaneous kinetic‐potentiometric analysis using chemometrics methods. The methods are based on the difference observed in the production rate of chloride ions. The results show that simultaneous determination of HZ and TSC can be performed in their concentration ranges of 0.7‐20.0 and 0.5‐20.0 μg mL^?1, respectively. The total relative standard error for applying PLS and PCR methods to 9 synthetic samples in the concentration ranges of 0.8‐10 μg mL^?1 of TSC and 1.0‐12.0 μg mL^?1 of HZ was 4.62 and 4.98, respectively. The effects of certain foreign ions upon the reaction rate were determined for the assessment of the selectivity of the method. Both methods (PLS and PCR) were validated using a set of synthetic sample mixtures and then applied for simultaneous determination of HZ and TSC in water samples.     

Determination of methyl ester contents in biodiesel blends by FTIR-ATR and FTNIR spectroscopies

Partial last square regression (PLS) and artificial neural network (ANN) combined to FTIR-ATR and FTNIR spectroscopies have been used to design calibration models for the determination of methyl ester content (%, w/w) in biodiesel blends (methyl ester + diesel). Methyl esters were obtained by the methanolysis of soybean, babassu, dende, and soybean fried oils. Two sets of samples have been used: Group I, binary mixtures (diesel + one kind of methyl ester), corresponding to 96 biodiesel blends (0–100%, w/w), and Group II, quaternary mixtures (diesel + three types of methyl esters), corresponding to 60 biodiesel blends (0–100%, w/w). The PLS results have shown that the FTNIR model for Group I is more precise and accurate (±0.02 and ±0.06%, w/w). In the case of Group II the PLS models (FTIR-ATR and FTNIR) have shown the same accuracies, while the ANN/FTNIR models has presented better performance than the ANN/FTIR-ATR models. The best accuracy was achieved by the ANN/FTNIR model for diesel determination (0.14%, w/w) while the worthiest was that of dende ANN/FTIR-ATR model (0.6%, w/w). Precisions in Group II analysis ranged from 0.06 to 0.53% (w/w) and coefficients of variation were better than 3% indicating that these models are suitable for the determination of diesel–biodiesel blends composed of methyl esters derived from different vegetable oils.     

A NMR method for the analysis of mixtures of alkanes with different deuterium substitutions

¹³C NMR at 125.76 MHz with ¹H and ²H decoupling, ²H NMR at 76.77 MHz with ¹H decoupling, and ¹H NMR at 500.14 MHz with ²H decoupling were employed as analytical tools to study the complex mixtures of deuterated ethanes resulting from the catalytic H–D exchange of normal ethane with gas-phase deuterium in the presence of a platinum foil. Reference samples consisting of 1:1 binary mixtures of pure normal ethane and ethane-d_n (n=1–6) were used to identify the peak positions in the ¹³C, ²H, and ¹H NMR spectra due to each individual isotopomer, and the effect of isotopic substitution on the chemical shifts was determined in each case. While the NMR of all three nuclei worked well for the identification of the individual components of the 1:1 standard mixtures, both ¹H and ²H NMR suffered from inadequate resolution when studying complex reaction mixtures because of the broadening of the lines due to ¹H–¹H (¹H NMR) and ²H–²H (²H NMR) couplings. ¹³C NMR was therefore determined to be the method of choice for the quantitative analysis of the reaction mixtures. Using the ¹³C NMR results, a correlation that takes into account the primary and secondary isotope substitution effects on chemical shifts was deduced. This equation was used for the identification of the individual components of the mixtures, and integration of the individual observed resonances was then employed for quantification of their composition. This study shows that ¹³C NMR with ¹H and ²H decoupling is a viable procedure for studying mixtures of deuterated ethanes. Furthermore, the additivity of the isotopic effects on chemical shifts and the transferability of the values obtained with ethane to other molecules makes this approach general for the analysis of other isotopomer mixtures.     

Evolving window zone selection method followed by independent component analysis as useful chemometric tools to discriminate between grapefruit juice, orange juice and blends

This study investigates the use of high resolution ¹H NMR as a suitable alternative to the standard chromatographic method for the determination of adulteration of orange juice (Citrus sinensis) with grapefruit juice (Citrus paradisi) based on flavonoid glycoside content. Fifty-nine orange juices (OJ), 23 grapefruit juices (GJ) and 10 blends (OG), obtained from local retail outlets were used to assess the performance of the ¹H NMR method. The work presented here introduces the Evolving Window Zone Selection (EWZS) function that holds promise for the automatic detection of spectral regions tailored to discriminate predefined groups. This technique was applied on the pre-processed ¹H NMR spectra of the 92 juices. Independent Component Analysis (ICA) is a good alternative to Principal Component Analysis (PCA) for recovering linearly-mixed unobserved multidimensional independent signals and has been used in this study to build supervised models that classify the samples into three categories, OJ, GJ, OG. The regions containing the known flavonoid glycoside markers were selected as well as another zone containing the signals of sucrose, α-glucose and other components that were tentatively attributed. ICA was applied on three different groups of selected variables and showed good results for both discrimination and interpretation of the signals. Up to 97.8% of the juices were correctly attributed. This method gave better results than the commonly used PCA method. In addition, the time required to carry out the ¹H NMR analysis was less than half the time of the standard chromatographic method.     

Genetic Algorithm Applied to Selection of Wavelength in Partial Least Squares for Simultaneous Spectrophotometric Determination of Nitrophenol Isomers

Abstract

Ternary mixtures of nitrophenol isomers have been simultaneously determined in synthetic and real matrix by application of genetic algorithm and partial least squares model. All factors affecting the sensitivity were optimized and the linear dynamic range for determination of nitrophenol isomers found. The simultaneous determination of nitrophenol mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares modeling was used for the multivariate calibration of the spectrophotometric data. A genetic algorithm is a suitable method for selecting wavelength for PLS calibration of mixtures with almost identical spectra without loss prediction capacity. The experimental calibration matrix was designed by measuring the absorbance over the range 300–520 nm for 21 samples of 1–20 µg mL^?1, 1–20 µg mL^?1, and 1–10 µg mL^?1 of m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol, respectively. The root mean square error of prediction for m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol with genetic algorithms and without genetic algorithms were 0.3732, 0.5997, 0.3181 and 0.7309, 0.9961, 1.0055, respectively. The proposed method was successfully applied for the determination of m‐nitrophenol, o‐nitrophenol, and p‐nitrophenol in synthetic and water samples.     

Intimate blending of binary polymer systems from their common cyclodextrin inclusion compounds

A procedure for the formation of intimate blends of three binary polymer systems polycarbonate (PC)/poly(methyl methacrylate) (PMMA), PC/poly(vinyl acetate) (PVAc) and PMMA/PVAc is described. PC/PMMA, PC/PVAc, and PMMA/PVAc pairs were included in γ‐cyclodextrin (γ‐CD) channels and were then simultaneously coalesced from their common γ‐CD inclusion compounds (ICs) to obtain intimately mixed blends. The formation of ICs between polymer pairs and γ‐CD were confirmed by wide‐angle X‐ray diffraction (WAXD), fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC). It was observed [solution ¹H nuclear magnetic resonance (NMR)] that the ratios of polymers in coalesced PC/PMMA and PC/PVAc binary blends are significantly different than the starting ratios, and PC was found to be preferentially included in γ‐CD channels when compared with PMMA or PVAc. Physical mixtures of polymer pairs were also prepared by coprecipitation and solution casting methods for comparison. DSC, solid‐state ¹H NMR, thermogravimetric analysis (TGA), and direct insertion probe pyrolysis mass spectrometry (DIP‐MS) data indicated that the PC/PMMA, PC/PVAc, and PMMA/PVAc binary polymer blends were homogeneously mixed when they were coalesced from their ICs. A single, common glass transition temperature (T_g) recorded by DSC heating scans strongly suggested the presence of a homogeneous amorphous phase in the coalesced binary polymer blends, which is retained after thermal cycling to 270 °C. The physical mixture samples showed two distinct T_gs and ¹H T_1ρ values for the polymer components, which indicated phase‐separated blends with domain sizes above 5 nm, while the coalesced blends exhibited uniform ¹H spin‐lattice relaxation values, indicating intimate blending in the coalesced samples. The TGA results of coalesced and physical binary blends of PC/PMMA and PC/PVAc reveal that in the presence of PC, the thermal stability of both PMMA and PVAc increases. Yet, the presence of PMMA and PVAc decreases the thermal stability of PC itself. DIP‐MS observations suggested that the degradation mechanisms of the polymers changed in the coalesced blends, which was attributed to the presence of molecular interactions between the well‐mixed polymer components in the coalesced samples. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 2578–2593, 2005     

Chemometric determination of arsenic and lead in untreated powdered red paprika by diffuse reflectance near-infrared spectroscopy

It has been evaluated the potential of near-infrared (NIR) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a way for non-destructive measurement of trace elements at μg kg⁻¹ level in foods, with neither physical nor chemical pre-treatment. Predictive models were developed using partial least-square (PLS) multivariate approaches based on first-order derivative spectra. A critical comparison of two spectral pre-treatments, multiplicative signal correction (MSC) and standard normal variate (SNV) was also made. The PLS models built after using SNV provided the best prediction results for the determination of arsenic and lead in powdered red paprika samples. Relative root-mean-square error of prediction (RRMSEP) of 23% for both metals, arsenic and lead, were found in this study using 20 well characterized samples for calibration and 13 additional samples as validation set. Results derived from this study showed that NIR diffuse reflectance spectroscopy combined with the appropriate chemometric tools could be considered as an useful screening tool for a rapid determination of As and Pb at concentration level of the order of hundred μg kg⁻¹.     

Partial least squares X-ray fluorescence determination of trace elements in sediments from the estuary of Nerbioi-Ibaizabal River

The feasibility of partial least squares (PLS) regression modeling of X-ray fluorescence (XRF) spectra of estuarine sediments has been evaluated as a tool for rapid trace element content monitoring. Multivariate PLS calibration models were developed to predict the concentration of Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn in sediments collected from different locations across the estuary of the Nerbioi-Ibaizabal River (Metropolitan Bilbao, Bay of Biscay, Basque Country). The study was carried out on a set of 116 sediment samples, previously lyophilized and sieved with a particle size lower than 63 μm. Sample reference data were obtained by inductively coupled plasma mass spectrometry. 34 samples were selected for building PLS models through a hierarchical cluster analysis. The remaining 82 samples were used as a test set to validate the models. Results obtained in the present study involved relative root mean square errors of prediction varying from 21%, for the determination of Pb at hundreds μg g⁻¹ level, up to 87%, for Ni determination at little tens μg g⁻¹ level. An average prediction error of ±37% for the 14 elements under study was obtained, being in all cases mean differences between predicted and reference results of the same order than the standard deviation of three replicates from a same sample. Residual predictive deviation values obtained ranged from 1.1 to 3.9.