Comparison of near-infrared and Raman spectroscopy for the determination of the density of polyethylene pellets

In this study, we compare near-infrared (NIR) and Raman spectroscopy for the determination of the density of linear low density polyethylene (PE) (in a pellet form). As generally known, Raman spectral features are more selective than those of NIR for most chemical samples. NIR spectroscopy has been more extensively used for the quantitative analysis of polymers, but Raman spectroscopy is the better choice as long as the problem of reproducibility of Raman measurements (especially for solid samples), mostly resulting from insufficient sample representation due to probing only localized chemical information and the sensitivity of sample placement with regard to the focal plane, can be overcome. To improve sample representation and reproducibility of Raman measurements, we have employed the wide area illumination (WAI) Raman scheme, capable of illuminating a laser onto a large sample area (28.3 mm²) for Raman spectral collection (a 6-mm laser spot with a focal length of 248 mm). Diffuse reflectance NIR spectra of PE pellets were collected using a sample moving system which allowed for the scanning of large areas. The prediction error was 0.0008 g cm⁻³ for Raman spectroscopy and 0.0011 g cm⁻³ for NIR spectroscopy. The harmonization of inherently selective Raman features and a reproducible spectral collection with correct sample representations using the WAI scheme led to an accurate determination of the density of the PE pellets.     

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L⁻¹ for Penicillin V and 0.32 g L⁻¹ for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L⁻¹ for Penicillin V and 0.15 g L⁻¹ for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.     

Near-infrared determination of active substance content in intact low-dosage tablets

Near-infrared (NIR) spectroscopy can be applied to determine the active substance content of tablets. Its great advantage lies in the minimal sample preparation required, which helps to reduce the potential for error. The aim of this study is to show the feasibility of this method on low-dosage tablets. The influence of various spectral pretreatments [standard normal variate (SNV), multiplicative scatter correction (MSC), second derivative (D2), orthogonal signal correction (OSC), separately and combined] and regression methods on prediction error are compared. Partial least square (PLS) regression provided better prediction than principal component regression (PCR). SNV was applied to the first data set and SNV and a second derivative to the second set to maximise model accuracy for quantifying the active substance of intact pharmaceutical products using diffuse reflectance NIR. The models yielded standard errors of prediction (SEP) of 0.1768 and 0.0682 mg for the two products. The experiments were conducted with two low-dosage pharmaceutical forms and results of NIR predictions were comparable to currently approved methods. Diffuse reflectance NIR has the potential to become a reliable and robust quality control method for determining active tablet content.     

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.     

TS-FF-AAS and multivariate calibration: A proposition for sewage sludge slurry sample analyses

This work describes a methodology for Cd and Pb determination in sewage sludge slurry samples using thermospray flame furnace atomic absorption spectrometry (TS-FF-AAS). The TS-FF-AAS system was equipped with a Ni tube atomizer placed on an oxidizing air/acetylene flame. Two multivariate calibration models based on partial least squares (PLS) were proposed using total peak profiles (recorded during 57 s). Metals concentration in the sewage sludge samples were from 1.87 to 6.26 mg kg⁻¹ for Cd and from 101 to 327 mg kg⁻¹ for Pb. The limits of detection and quantification were, respectively, 0.2 and 0.7 μg kg⁻¹ for Cd and 8 and 26 μg kg⁻¹ for Pb. These values were three times lower than the limits found when these metals were calibrated using linear calibration with aqueous standard solutions.     

Enzyme-assisted extraction and liquid chromatography mass spectrometry for the determination of arsenic species in chicken meat

Chicken is the most consumed meat in North America. Concentrations of arsenic in chicken range from μg kg⁻¹ to mg kg⁻¹. However, little is known about the speciation of arsenic in chicken meat. The objective of this research was to develop a method enabling determination of arsenic species in chicken breast muscle. We report here enzyme-enhanced extraction of arsenic species from chicken meat, separation using anion exchange chromatography (HPLC), and simultaneous detection with both inductively coupled plasma mass spectrometry (ICPMS) and electrospray ionization tandem mass spectrometry (ESIMS). We compared the extraction of arsenic species using several proteolytic enzymes: bromelain, papain, pepsin, proteinase K, and trypsin. With the use of papain-assisted extraction, 10 arsenic species were extracted and detected, as compared to 8 detectable arsenic species in the water/methanol extract. The overall extraction efficiency was also improved using a combination of ultrasonication and papain digestion, as compared to the conventional water/methanol extraction. Detection limits were in the range of 1.0–1.8 μg arsenic per kg chicken breast meat (dry weight) for seven arsenic species: arsenobetaine (AsB), inorganic arsenite (As^III), dimethylarsinic acid (DMA), monomethylarsonic acid (MMA), inorganic arsenate (As^V), 3-nitro-4-hydroxyphenylarsonic acid (Roxarsone), and N-acetyl-4-hydroxy-m-arsanilic acid (NAHAA). Analysis of breast meat samples from six chickens receiving feed containing Roxarsone showed the presence of (mean ± standard deviation μg kg⁻¹) AsB (107 ± 4), As^III (113 ± 7), As^V (7 ± 2), MMA (51 ± 5), DMA (64 ± 6), Roxarsone (18 ± 1), and four unidentified arsenic species (approximate concentration 1–10 μg kg⁻¹).     

Improved detection limit for ammonium/ammonia achieved by Berthelot's reaction by use of solid-phase extraction coupled to diffuse reflectance spectroscopy

The proposed procedure is based on the extraction of the indothylmol blue into C₁₈ solid-phase extraction (SPE) membranes and direct quantification on the membrane surface by diffuse reflectance spectroscopy. The analytical performance of the proposed method has been evaluated for standard solutions of ammonium using reflectance values, R, as well as the Kubelka-Munk function, F(R). The results have been compared with those obtained by the conventional method, which uses UV-vis absorption spectroscopy with a sensor-based method. The described methodology provided satisfactory linearity and reproducibility within the ammonium concentration intervals 25-250 μg L⁻¹ and 25-500 μg L⁻¹ when using R and F(R), respectively. The limit of detection was around 10 μg L⁻¹, which is markedly lower than that of the classical procedure and than those provided by Nessler and OPA/thiol fluorimetric methods. For air samples the linear interval expressed as μg of ammonia is 0.24-2.4 or 0.24-4.7 employing R or F(R), respectively. The effect of potential interferences such as metals and aliphatic amines has also been evaluated. Finally, the proposed methodology has been adapted to the determination of ammonia in air and water samples. The method can be also used as a detector support for visual estimation.     

Determination of total arsenic and arsenic (III) in phosphate fertilizers and phosphate rocks by HG-AAS after multivariate optimization based on Box-Behnken design

In the present paper, a procedure for the determination of total arsenic and arsenic (III) in phosphate fertilizers and phosphate rocks by slurry sampling (SS) with hydride generation atomic absorption spectrometry (HG-AAS) is proposed. Arsenic (III) is determinated directly and total arsenic is determinated after reduction reaction. The procedure was optimized for the flow rate of NaBH₄, NaBH₄ and hydrochloric acid concentrations using a full two-level factorial and also a Box-Behnken design. Slurry preparation with hydrochloric acid in an ultrasonic bath allowed the determination of arsenic (III) with limits of detection and quantification of 0.1 and 0.3 μg L⁻¹, respectively. The precision of results, expressed as relative standard deviation (RSD), was always lower than 3%. The accuracy of this method was confirmed by analysis of certified sediment reference materials, while the procedure also allows for calibration using aqueous external standards. This method (SS/HG-AAS) was used to determine total arsenic and arsenic (III) in two phosphate rock samples and two phosphate fertilizer samples. In these samples, total arsenic concentrations varied from 5.2 to 20.0 mg kg⁻¹, while As (III) concentrations varied from 2.1 to 5.5 mg kg⁻¹, in agreement with published values. All samples were also analyzed using acid digestion/HG-AAS. Both, a paired t-test and a linear regression model demonstrated no significant difference (95% CL) between the results obtained using these two sample preparation procedures.     

Optimization of a GFAAS method for determination of total inorganic arsenic in drinking water

The new 10 μg l⁻¹ arsenic standard in drinking water has been a spur to the search for reliable routine analytical methods with a limit of detection at the μg l⁻¹ level. These methods also need to be easy to handle due to the routine analyses that are required in drinking water monitoring. Graphite furnace atomic absorption spectrometry (GFAAS) meets these requirements, but the limit of detection is generally too high except for methods using a pre-concentration or separation step. The use of a high-intensity boosted discharge hollow-cathode lamp decreases the baseline noise level and therefore allows a lower limit of detection. The temperature program, chemical matrix modifier and thermal stabilizer additives were optimized for total inorganic arsenic determination with GFAAS, without preliminary treatment. The optimal furnace program was validated with a proprietary software. The limit of detection was 0.26 μg As l⁻¹ for a sample volume of 16 μl corresponding to 4.2 pg As. This attractive technique is rapid as 20 samples can be analysed per hour. This method was validated with arsenic reference solutions. Its applicability was verified with artificial and natural groundwaters. Recoveries from 91 to 105% with relative standard deviation <5% can be easily achieved. The effect of interfering anions and cations commonly found in groundwater was studied. Only phosphates and silicates (respectively at 4 and 20 mg l⁻¹) lead to significant interferences in the determination of total inorganic arsenic at 4 μg l⁻¹.     

Absolute quantitative analysis for sorbic acid in processed foods using proton nuclear magnetic resonance spectroscopy

An analytical method using solvent extraction and quantitative proton nuclear magnetic resonance (qHNMR) spectroscopy was applied and validated for the absolute quantification of sorbic acid (SA) in processed foods. The proposed method showed good linearity. The recoveries for samples spiked at the maximum usage level specified for food in Japan and at 0.13 g kg⁻¹ (beverage: 0.013 g kg⁻¹) were larger than 80%, whereas those for samples spiked at 0.063 g kg⁻¹ (beverage: 0.0063 g kg⁻¹) were between 56.9 and 83.5%. The limit of quantification was 0.063 g kg⁻¹ for foods (and 0.0063 g kg⁻¹ for beverages containing Lactobacillus species). Analysis of the SA content of commercial processed foods revealed quantities equal to or greater than those measured using conventional steam-distillation extraction and high-performance liquid chromatography quantification. The proposed method was rapid, simple, accurate, and precise, and provided International System of Units traceability without the need for authentic analyte standards. It could therefore be used as an alternative to the quantification of SA in processed foods using conventional method.     

Enhancing calibration models for non-invasive near-infrared spectroscopical blood glucose determination

Partial least-squares regression (PLS) and radial basis function (RBF) networks are used to compute calibration models for non-invasive blood glucose determination by NIR diffuse reflectance spectroscopy. A model computation shows that even extremely small deviations of the spectra induce increased prediction errors. Since the spectral contribution of blood glucose is much smaller than deviations resulting from the non-invasive measuring process a method based on Pearson’s correlation coefficient can be used for evaluating the quality of the recorded spectra during the prediction step. Another method is based on the leverage values from the hat matrix of the RBF network. Both methods lead to a significant decrease in prediction error.     

Application of multivariate techniques in the optimization of a procedure for the determination of bioavailable concentrations of Se and As in estuarine sediments by ICP OES using a concomitant metals analyzer as a hydride generator

A procedure has been developed for the determination of bioavailable concentrations of selenium and arsenic in estuarine sediments employing inductively coupled plasma optical emission spectrometry (ICP OES) using a concomitant metals analyzer device to perform hydride generation. The optimization of hydride generation was done in two steps: using a two-level factorial design for preliminary evaluation of studied factors and a Doehlert design to assess the optimal experimental conditions for analysis. Interferences of transition metallic ions (Cd²⁺, Co²⁺, Cu²⁺, Fe³⁺ and Ni²⁺) to selenium and arsenic signals were minimized by using higher hydrochloric acid concentrations. In this way, the procedure allowed the determination of selenium and arsenic in sediments with a detection limit of 25 and 30 μg kg⁻¹, respectively, assuming a 50-fold sample dilution (0.5 g sample extraction to 25 mL sample final volume). The precision, expressed as a relative standard deviation (% RSD, n = 10), was 0.2% for both selenium and arsenic in 200 μg L⁻¹ solutions, which corresponds to 10 μg g⁻¹ in sediment samples after acid extraction. Applying the proposed procedure, a linear range of 0.08-10 and 0.10-10 μg g⁻¹ was obtained for selenium and arsenic, respectively. The developed procedure was validated by the analysis of two certified reference materials: industrial sludge (NIST 2782) and river sediment (NIST 8704). The results were in agreement with the certified values. The developed procedure was applied to evaluate the bioavailability of both elements in four sediment certified reference materials, in which there are not certified values for bioavailable fractions, and also in estuarine sediment samples collected in several sites of Guanabara Bay, an impacted environment in Rio de Janeiro, Brazil.     

Determination of naphthalene-derived compounds in apples by ultra-high performance liquid chromatography-tandem mass spectrometry

Naphthylacetic acid, naphthyloxy acetic acid and naphthylacetamide belong to a group of synthetic substances known as “auxin-like” compounds which are used as growth regulators in vegetables and fruits due to their structure similarities with the indoleacetic acid, the most important plant auxin. This paper reports a selective, sensitive and fast ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC–MS/MS) method for the determination of naphthylacetamide (NAD) and the isomers (α and β) of naphthylacetic acid (NAA) and naphthyloxy acetic (NOA) acid in apple samples. A baseline separation between the respective isomers was achieved using an RP-Amide column with gradient elution. The UHPLC-MS/MS method developed, using electrospray and selected reaction monitoring (SRM) acquisition mode led to a reliable determination of these family of compounds in apple samples at low quantitation levels, down to 1.0 μg kg⁻¹ and 0.25 μg kg⁻¹ respectively. For confirmation of NAA accurate mass measurement is proposed giving at these conditions quantitation limits of 10 μg kg⁻¹ for this compound. The UHPLC-MS/MS method developed was used for the analysis of apple samples harvested in three different apple fields from Lleida (Spain) during the blooming period. NAD and NAA were found in samples collected during 4–5 weeks after application at concentrations between the quantification limits and 43 μg kg⁻¹ and 24 μg kg⁻¹, respectively.     

Three-way models and detection capability of a gas chromatography-mass spectrometry method for the determination of clenbuterol in several biological matrices: the 2002/657/EC European Decision

Clenbuterol has been extracted by mixed solid-phase extraction from two biological matrices (bovine hair and urine) and detected by GC/MS (selected ion monitoring (SIM) and full-SCAN modes). The analytical signal has been modelled with univariate and three-way models, namely DTLD, PARAFAC, PARAFAC2, Tucker3 and trilinear PLS. Since clenbuterol is a banned substance a comparative study of the capability of detection (CCβ, X₀=0) has been performed as a function of the sample (hair, 74 μg kg⁻¹ and urine, 0.36 μg l⁻¹), the mode in which the signals are monitored (SCAN, 283 μg kg⁻¹ and SIM, 74 μg kg⁻¹) and the statistical model (univariate, 283 μg kg⁻¹ and trilinear PLS, 20.91 μg kg⁻¹). The capability of detection has been calculated as stated in ISO 11843 and Decision 2002/657/EC setting in all cases the probabilities of false positive and of false negative at 0.05.The identification of the mass spectra must be done to confirm the presence of clenbuterol and has been carried out through PARAFAC. The correlation coefficient between the spectra estimated by PARAFAC and the library spectra is 0.96 (hair, SCAN mode) and 1.00 (hair and urine, SIM mode).The Decision 2002/657/EC advocates the use of independent mass fragments to identify banned compounds. These recommendations together with the effect of the number of ions registered on the capability of detection have lead us to select five uncorrelated fragments (86, 243, 262, 264 and 277) from the data set of 210 ions by hierarchical clustering of variables.     

Discrimination of ethylene/vinyl acetate copolymers with different composition and prediction of the content of vinyl acetate in the copolymers and their melting points by near-infrared spectroscopy and chemometrics

Near-infrared (NIR) diffuse reflectance spectra have been measured by use of a rotating drawer for pellets of 12 kinds of ethylene/vinyl acetate (EVA) copolymers with vinyl acetate (VA, the comonomer) varying in the 7–44 wt % range. They are unambiguously discriminated from one another by a score plot of the principal component analysis (PCA) Factor 1 and 2, based upon the NIR spectra pretreated by multiplicative scatter correction (MSC). Principal component (PC) weight loadings for Factor 1 show that the discrimination relies largely upon bands due to the overtone and combination modes arising from the VA unit. We have found one “outlier” in the score plot and elucidated its spectral characteristics based upon PC weight loadings for Factor 2. Partial least-squares (PLS) regression has been applied to propose calibration models which predict the VA content in EVA. The models have been prepared for three kinds of pretreatment, the first derivative, the second derivative, and MSC; and four kinds of wavelength regions. The NIR spectra in the 1100–2200 nm region after the MSC treatment has given the best correlation coefficient and standard error of prediction (SEP) of 0.998 and 0.70%, respectively. The calibration models, prepared by NIR diffuse reflectance spectroscopy for the pellet samples, are compared with previously reported models by NIR transmission spectroscopy for the flowing molten samples, and with those by Raman spectroscopy for the pellet samples. PLS regression has also allowed us to predict melting points of the copolymers with the correlation coefficient and SEP of 0.997 and 0.78°C, respectively. © 1998 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 36: 1529–1537, 1998     

Screening of humic and fulvic acids in estuarine sediments by near-infrared spectrometry

Diffuse reflectance near-infrared spectroscopy (NIR) combined with partial least squares (PLS) data treatment has been employed for the rapid and nondestructive determination of sedimentary humic substances. Forty one samples of surface estuarine sediments, taken during distinct seasonal periods from different locations across Ria de Arousa (northwest of Spain), were scanned at wavelengths from 833 to 2,976 nm (12,000 to 3,360 cm⁻¹). Twenty four samples were randomly selected, from previous hierarchical cluster analysis of their NIR spectra, for the calibration set, and the 17 remaining samples were assigned to the validation set. NIR spectra of calibration samples were correlated to measured values of humic acids (HAs) and fulvic acids (FAs), which ranged from 1.53 to 28.17 mg/g and from 0.37 to 2.45 mg/g, respectively, using PLS regression and multiplicative scattering correction on the raw and first-derivative NIR spectra, respectively. Low root mean square error of prediction values of 4.3 mg HA/g sediment and 0.25 mg FA/g sediment were obtained. Good residual prediction deviation values of 1.16 and 1.2 were obtained for HA and FA, respectively, allowing the PLS models built to be considered as appropriate tools for screening purposes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Determination of edible oil parameters by near infrared spectrometry

A chemometric method has been developed for the determination of acidity and peroxide index in edible oils of different types and origins by using near infrared spectroscopy (NIR) measurements. Different methods for selecting the calibration set, after an hierarchical cluster analysis, were applied. After discrimination of olive oils from maize, seed and sunflower, the prediction capabilities of partial least squares (PLS) multivariate calibration of NIR data were evaluated. Several preprocessing alternatives (first derivative, multiplicative scatter correction, vector normalization, constant offset elimination, mean centering and standard normal variate) were investigated by using the root mean square error of validation (RMSEV) and prediction (RMSEP), as control parameters. Under the best conditions studied, the validation set provides RMSEP values of 0.034 and 0.037% (w/w) for acidity in (I) olive oil group and (II) sunflower, seed and maize oils group. RMSEP values for peroxide in both sample groups, expressed as mequiv. O₂ kg⁻¹, were, respectively 1.87 and 0.79. The limit of detection of the methodology developed was 0.03% for acidity in both groups of edible oils (I and II), and 0.9 and 0.8 mequiv. O₂ kg⁻¹ for peroxide in the olive oil and other edible oils groups, respectively. In fact, the methodology developed is proposed for direct acidity quantification and for the screening of peroxide index in edible oils, requiring less than 30 s per sample without any previous treatment.     

Determination of trace amounts of lead, arsenic, nickel and cobalt in high-purity iron oxide pigment by inductively coupled plasma atomic emission spectrometry after iron matrix removal with extractant-contained resin

Inductively coupled plasma atomic emission spectrometry (ICP-AES) was applied to the determination of lead, arsenic, nickel and cobalt in high-purity iron oxide pigment. Samples were dissolved with hydrochloric acid and hydrogen peroxide. The digest was passed through a column, which was packed with a polymer resin containing a neutral organophosphorus extractant, tri-n-butylphosphate. Iron was sorbed selectively on the resin and the analytes of interest passed through the column, allowing the effective separation of them from the iron matrix. Conditions of separation were optimized. The detection limits (3σ) in solution were 10, 40, 7 and 5 μg L⁻¹, and in pigment were 0.2, 0.8, 0.14 and 0.1 mg kg⁻¹ for lead, arsenic, cobalt and nickel, respectively. The recoveries ranged from 95% to 107% when sample digests were spiked with 5 μg of the analytes of interest, and relative standard deviations (n = 6) were 1.5-17.6% for the determination of the spiked samples. The method was successfully applied to the determination of trace amounts of these elements in high-purity iron oxide pigment samples.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.     

Characterization of estuarine sediments by near infrared diffuse reflectance spectroscopy

It has been developed a partial least squares near infrared (PLS-NIR) method for the determination of estuarine sediment physicochemical parameters. The method was based on the chemometric treatment of first order derivative reflectance spectra obtained from samples previously lyophilized and sieved through a lower than 63 μm grid. Spectra were scanned from 833 to 2976 nm, averaging 36 scans per spectrum at a resolution of 8 cm⁻¹, using chromatographic glass vials of 9.5 mm internal diameter as measurement cells. Models were built using reference data of 31 samples selected through the use of a hierarchical cluster analysis of NIR spectra of sediments obtained from the Ria de Arousa estuary and prediction parameters were established from a validation set of 50 samples of the same area. pH, redox potential (Eh), carbon (C), nitrogen (N) and hydrogen (H) content together with Sn, Pb, Cd, As, Sb and total Cr and also acid soluble, reducible and oxidable Cr fractions were employed as characteristic parameters of the studied sediments. Standard error of prediction values for C and N content were of the order of 4 and 1.3 mg g⁻¹ for H. Prediction errors for pH and Eh were 0.15 units and 37 mV, respectively, thus indicating the good prediction capabilities of the method. Regarding trace metal concentrations PLS-NIR provided prediction error levels for unknown samples around 20% for Sn, Pb, As and Sb and root mean square errors of prediction around 40% for concentration levels of 400 ng g⁻¹ Cd and 100 μg g⁻¹ Cr. For the different extractable fractions of Cr the residual prediction deviation varied from 1.3 to 1.7 but relative errors found for samples of the validation set were only useful for screening purposes.