A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples.

A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

Comparison of macro and micro Raman measurement for reliable quantitative analysis of pharmaceutical polymorphs.

This work reports on the use of micro- and macro-Raman measurements for quantification of mebendazole (MBZ) polymorphs A, B, and C in mixtures. Three Raman spectrophotometers were studied with a laser spot size of 3, 80 and 100 µm and spectral resolutions of 3.9, 9 and 4 cm-1, respectively. The samples studied were ternary mixtures varying the MBZ polymorphs A and C from 0 to 100% and polymorph B from 0 to 30%. Partial Least Squares (PLS) regression models were developed using the pre-processing spectra (2nd derivative) of the ternary mixtures. The best performance was obtained when the macro-Raman configuration was applied, obtaining RMSEP values of 1.68%, 1.24% and 2.03% w/w for polymorphs A, B, and C, respectively. In general, micro-Raman presented worst results for MBZ polymorphs prediction because the spectra obtained with this configuration does not represent the bulk proportion of mixtures, which have different particle morphologies and sizes. In addition, the influence of these particle features on micro-Raman measurements was also studied. Finally, the results demonstrated that reliable analytical quantifying of MBZ polymorphs can be reached using a laser with wider area illuminated, thus enabling acquisition of more reproductive and representative spectra of the mixtures.

Multivariate quantification of mebendazole polymorphs by terahertz time domain spectroscopy (THZ-TDS).

This work presents an analytical method based on terahertz-time domain spectroscopy (THz-TDS) and partial least squares (PLS) regression models to quantify mebendazole (MBZ) polymorphs (forms A, B and C) in pharmaceutical raw material. Mebendazole polymorphs A, B and C were quantified with RMSEP values of 1.5% w/w, 1.2% w/w and 1.8% w/w, respectively. The limits of detection (LOD) ranges obtained with the best PLS regression models were 2.7-4.3% w/w, 2.9-4.0% w/w and 2.4-3.1% w/w, for polymorphs A, B and C, respectively. This analytical performance is better than those for the methods described in the literature using near (NIR) and middle (MIR) infrared spectroscopies. The main advantage of THz spectroscopy is its ability to access directly information related to crystal lattices. According to the results, the developed method is a powerful technique for the quantification of MBZ polymorphs in raw material. This methodology can be implemented as a Process Analytical Technology (PAT) tool for quality control of pharmaceutical feedstock.

iHEART: a miniaturized near-infrared in-line gas sensor using heart-shaped substrate-integrated hollow waveguides.

A novel heart-shaped substrate-integrated hollow waveguide (hiHWG) was integrated with a near-infrared micro-spectrometer (µNIR) for sensing natural gases, resulting in an ultra-compact near-infrared gas sensing system - iHEART. The iHEART system was evaluated using two different µNIR spectrometers, and the performance was compared with a laboratory NIR spectrometer for gas analysis based on an acousto-optic tunable filter (AOTF). The spectral data were pre-processed using the 1(st) derivative Savitzky-Golay algorithm, and then used for establishing multivariate regression models based on partial least squares (PLS). The root mean square errors of prediction (RMSEPs) obtained for major components of natural gas with both iHEART systems were similar to those associated with the AOTF spectrophotometer combined with a conventional long-path measurement cell. It was demonstrated that the iHEART system has significant potential for the development of compact in-line gas sensing systems, thus facilitating monitoring of (petro)chemically relevant processes and products. However, the flexibility and modularity of the system also allows tailoring iHEART to a wide range of other relevant analytical measurement scenarios requiring short response times and minute gas sample volumes.

Determination of detergent and dispensant additives in gasoline by ring-oven and near infrared hypespectral imaging.

A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.

A microfluidic device with integrated fluorimetric detection for flow injection analysis.

This work describes the development of flow analysis microsystems with integrated fluorimetric detection cells. Channels (width of 300-540 microm and depth of 200-590 microm) were manufactured by deep-UV lithography in urethane-acrylate (UA) resin. Plastic optical fibers (diameter of 250 microm) were coupled to a 2.0-mm-long detection channel in order to guide the excitation radiation from an LED (470 nm) and collect the emitted radiation at a right angle towards a photomultiplier. A single-line miniaturized system, with a total internal volume of 10.4 microL, was evaluated by means of standard fluorescein solutions (0.53-2.66 micromol L(-1), pH 8.5). The analytical signals presented a linear relationship in the concentration range studied, with a relative standard deviation of 1.9% (n = 5), providing a detection limit of 0.37 micromol L(-1) and an analytical frequency of 60 samples/h, using a flow rate of 60 microL min(-1). Optical microscopy images and videos acquired in real time for the hydrodynamic injection of 130 and 320 nL of sample solutions indicated the good performance of the proposed sampling strategy. Another microsystem with a total internal volume of 38 microL was developed, incorporating a confluence point for two solutions. This device was applied to the determination of the total concentration of Ca(2+) and Mg(2+) in commercial mineral waters using the calcein method. Microscopy images and videos demonstrated the mixing efficiency of the solutions in the microchannels. A linear relationship was observed for the analytical signal in the Ca(2+) concentration range from 25 to 125 micromol L(-1), with relative standard deviations of 3.5%. The analysis of mineral waters with the proposed system provided results that did not differ significantly from those obtained by the EDTA titration method at a confidence level of 95%. These results demonstrate the viability of developing micro flow injection systems with an integrated fluorimetric detection cell.

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition.

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.

Construction and evaluation of a flow injection micro-analyser based on urethane-acrylate resin.

A flow injection micro-analyser with an integrated injection device and photometric detection is described. Channels measuring 205-295 microm depth by 265-290 microm maximum width were manufactured by deep UV lithography on two layers of urethane-acrylate oligomers-based photoresist. Hypodermic syringe needles (450 microm diameter) were connected to the channels for introduction of solutions into the system. Plastic optical fibres were connected to the ends of a 5.0 mm long channel, in order to conduct the light from and to a homemade photometer. The device has a total volume of 7.0 microL and three different sample volumes (0.09, 0.22 and 0.30 microL) can be inserted into the system by choosing the appropriate loop of the hydrodynamic injection approach. The micro-analyser, designed as a single line manifold, was evaluated by determining chloride in waters (mercuric thiocyanate method), and chromium (VI) in wastewater and total chromium in metallic alloys (diphenylcarbazide method). For chloride determination two micro-pumps were employed to impel the solutions, while for chromium determination this task was performed by a conventional peristaltic pump. The results obtained in all determinations did not differ significantly from the reference methods at a confidence level of 95%. In the chloride determination, a flow rate of 50 microL min(-1) was used, providing a sample frequency of 45 injection h(-1), generating ca. 0.7 mg of Hg(II) after an 8-h working day (ca. 20 mL of solution). This result suggests the potential of the micro-analyser towards the reduction of waste, following the philosophy of Green Chemistry.

Assessment of infrared spectroscopy and multivariate techniques for monitoring the service condition of diesel-engine lubricating oils.

This paper presents two methodologies for monitoring the service condition of diesel-engine lubricating oils on the basis of infrared spectra. In the first approach, oils samples are discriminated into three groups, each one associated to a given wear stage. An algorithm is proposed to select spectral variables with good discriminant power and small collinearity for the purpose of discriminant analysis classification. As a result, a classification accuracy of 93% was obtained both in the middle (MIR) and near-infrared (NIR) ranges. The second approach employs multivariate calibration methods to predict the viscosity of the lubricant. In this case, the use of absorbance measurements in the NIR spectral range was not successful, because of experimental difficulties associated to the presence of particulate matter. Such a problem was circumvented by the use of attenuated total reflectance (ATR) measurements in the MIR spectral range, in which an RMSEP of 3.8cSt and a relative average error of 3.2% were attained.

Silicone sensing phase for detection of aromatic hydrocarbons in water employing near-infrared spectroscopy.

The use of silicone for detection of aromatic hydrocarbons in water using near-infrared spectroscopy is proposed. A sensing phase of poly(dimethylsiloxane) (PDMS) was prepared, and a rod of this material was adapted to a transflectance probe for measurements from 850 to 1800 nm. Deionized water samples contaminated separately with known amounts of benzene, toluene, ethylbenzene, and m-xylene were used for evaluation of the PDMS sensing phase, and measurements were made in a closed reactor with constant stirring. Equilibrium states were obtained after 90, 180, 360, and 405 min for benzene, toluene, ethylbenzene, and m-xylene, respectively. The PDMS sensing phase showed a reversible response, presenting linear response ranges up to 360, 290, 100, and 80 mg L(-1), with detection limits of 8.0, 7.0, 2.6, and 3.0 mg L(-1) for benzene, toluene, ethylbenzene, and m-xylene, respectively. Reference spectra obtained with different rods showed a relative standard deviation of 0.5%, indicating repeatability in the sensing phase preparation. A relative standard deviation of 6.7% was obtained for measurements performed with six different rods, using a 52 mg L(-1) toluene aqueous solution. The sensing phase was evaluated for identification of sources of contamination of water in simulated studies, employing Brazilian gasoline type A (without ethanol), gasoline type C (with 25% of anhydrous ethanol), and diesel fuel. Principal component analysis was able to classify the water in distinct groups, contaminated by gasoline A, gasoline C, or diesel fuel.

Chloride-selective membrane electrodes and optodes based on an indium(III) porphyrin for the determination of chloride in a sequential injection analysis system.

Two quasi-independent methods for potentiometric and optical determination of chloride were simultaneously implemented in a flow system, providing real-time assessment of the quality of results. A potentiometric and an optical polymeric membrane doped with the same indium(III) octaethyl-porphyrin were used as sensor ionophore. The working mechanism and the analytical characteristics of these porphyrin-based sensors with respect to dynamic range, selectivity, repeatability and lifetime are discussed. These sensors, utilised as detectors in a flow system, were applied for the analysis of chloride in pharmaceutical solutions. The quality of the results obtained was evaluated by comparison with those provided by the reference method and no significant statistical differences at the 95% confidence level were observed. The simultaneous attainment of two measurements permitted the standardisation of results in real time and the detection of failures in the procedure.

Determination of phosphate in natural water employing a monosegmented flow system with simultaneous multiple injection.

A monosegmented flow system was employed for the determination of low contents of phosphate in natural water. In this approach, sample and reagents are simultaneously injected to a PTFE reaction coil where the homogenization of the mixture occurs while the monosegment is pumped forwards the photometric detector. The proposed system was evaluated by determining phosphate ion, based on the reaction of association between molybdophosphate and malachite green. It was evaluated individually the best concentration of the reagent solution in relation to blank signal (absorbance of the blank) and the sensitivity of the method. A factorial design was proposed to explain the contribution of each component on the formation of the ion association complex, evaluating the individual contributions as well as the synergistic and antagonistic effects. With the established conditions, phosphorous is determined in the concentration range of 5.0-75mug P PO(4)(3-)l(-1) (r=0.9992), with a detection limit of 0.70mug P PO(4)(3-)l(-1) and a relative standard deviation of 2% (20mug P PO(4)(3-)l(-1), n=8). The proposed method has a sampling frequency of 72h(-1).

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration.

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.

Design and development of a multichannel potentiometer for monitoring an electrode array and its application in flow analysis.

A versatile potentiometer that works with electrode arrays in flow injection and/or monosegmented flow systems is described. The potentiometer is controlled by a microcomputer that allows individual, sequential multiplexed or random accesses to eight electrodes while employing only one reference electrode. The instrument was demonstrated by monitoring an array of seven flow-through ion-selective electrodes for Ag+ and for three electrodes for Cl(-), Ca2+ and K+. The figures of merit of the individual and multiplexed (summed) readings of the electrode array were compared. The absolute standard deviation of the measurements made by summing the potential of two or more electrodes was maintained constant, thus improving the precision of the measurements. This result shows that an attempt to combine the signals of the electrodes to produce a more intense signal in the Hadamard strategy is feasible and accompanied by a proportional improvement in the precision of individual measurements. The preliminary tests suggest that the system can allow for 270 determinations per hour, with a linear range from 1.0 x 10(-2) to 1.0 x 10(-4) mol l(-1) for the three different analytes. Detection limits were estimated as 3.1 x 10(-5), 3.0 x 10(-6) and 1.0 x 10(-5) mol l(-1) for Cl(-), Ca2+ and K+, respectively.