Ultrasonic-assisted extraction and automated determination of catalase and lipase activities in bovine and poultry livers using a digital movie-based flow-batch analyzer.

An ultrasonic reactor (UR) was developed and coupled to a digital movie-based flow-batch analyzer (DM-FBA) for the ultrasonic-assisted extraction (UAE) and fast determination of catalase and lipase activities in bovine and poultry livers. The lab-made UR mainly consisted of a borosilicate glass container and a piezoelectric disc. The DM-FBA mainly consisted of a webcam, an ultrasonic actuator controller, a peristaltic pump, six solenoid valves, a valve driver, a mixing chamber, a magnetic stirrer, an Arduino Mega 2560, and a personal computer. This setup, named UR-DM-FBA, was controlled by custom software. Ultrasound (US) frequency, US power, sonication time, and concentration of extraction agent were optimized using the Taguchi method. Experiments at silent conditions (mechanical stirring at 1500 rpm) were carried out to evaluate extraction efficiency. Optimized parameters for the UAE of catalase were US frequency of 30 kHz, 2.0 mL of Triton X-100, sonication time of 270 s, and US power of 10.8 W. For the UAE of lipase, the optimized parameters were US frequency of 20 kHz, 0.30 mL of triethanolamine, sonication time of 270 s, and US power of 18 W. Catalase and lipase activities obtained with the UR were, on average, 1.9 × 103% and 2.0 × 103% higher than those obtained at silent conditions, respectively, which indicates that that the lab-made UR was capable of extracting these enzymes more efficiently. Determinations using the UR-DM-FBA were highly accurate (relative error ranging from -1.98% to 1.96% for bovine catalase, -0.65% to 0.76% for bovine lipase, -2.03 to 2.08% for poultry catalase, and -0.55% to 0.64% for poultry lipase) and precise (overall coefficient of variation <0.02% for bovine and poultry catalase and <0.2% for bovine and poultry lipase). Results obtained with the proposed system and reference methods were in good agreement according to the paired t-test (95% confidence level). High sampling rates (>69 h-1) and low sample/reagent consumption (<1.6 mL) were also obtained. Due to the highly efficient UAE, the proposed system can be applied for fast and accurate quantification of lipase and catalase in biological samples with low waste generation.

A robotic magnetic nanoparticle solid phase extraction system coupled to flow-batch analyzer and GFAAS for determination of trace cadmium in edible oils without external pretreatment.

A lab-made magnetic-mechanical robotic (MMR) system coupled to a flow-batch analyzer (FBA) for magnetic nanoparticles solid phase extraction (MSPE) is presented. As an illustrative application, an NMR-FBA couple was connected to a graphite furnace atomic absorption spectrometer (GFAAS) for quantification of trace cadmium in edible oils. Factors affecting MSPE, such as the amount of adsorbent, the type, concentration and volume of the eluent and elution time were studied. Under the optimized experimental conditions, the interferents studied did not reveal a significant change in the analytical response, indicating that proposed method is selective. The sampling rate, characteristic mass, working linear range, limits of detection (LOD), and sensitivity were 10h-1, 0.18pg, 0.05-1.0µgkg-1, 0.006µgL-1, and 0.4197, respectively. An enrichment factor of 9 was achieved using a 2.5mL oil sample. In order to evaluate the accuracy, a certified reference material was analyzed by the proposed and a reference method. The values obtained were compared with the one provided from the manufacturer and no statistically significant differences were observed among three values at a confidence level of 95% using paired t-test. In addition, the precision intra-day and inter day of the proposed method and the robustness were assessed and again no statistically significant differences were observed at a confidence level of 95%. The use of a microcolumn to immobilize the MNPs is not needed with the proposed MMR-FBA-GFAAS system, thus avoiding the well-known problem of non-uniform packing of the MNPs presented in previous flow-based automatic methods. Despite a high organic load of edible oils, the method developed is simple, robust and presents satisfactory analytical features when compared with others that have been reported in the literature, suggesting that it is a potentially useful alternative to determine trace analytes in viscous matrices without external pretreatment.

Two-dimensional linear discriminant analysis for classification of three-way chemical data.

The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power. However, despite its promising performance in image processing tasks, the 2D-LDA algorithm has not yet been used in applications involving chemical data. The present paper bridges this gap by investigating the use of 2D-LDA in classification problems involving three-way spectral data. The investigation was concerned with simulated data, as well as real-life data sets involving the classification of dry-cured Parma ham according to ageing by surface autofluorescence spectrometry and the classification of edible vegetable oils according to feedstock using total synchronous fluorescence spectrometry. The results were compared with those obtained by using the spectral data with no feature extraction, U-PLS-DA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. In the simulated data set, all methods yielded a correct classification rate of 100%. However, in the Parma ham and vegetable oil data sets, better classification rates were obtained by using 2D-LDA (86% and 100%), compared with no feature extraction (76% and 77%), U-PLS-DA (81% and 92%), PARAFAC-LDA (76% and 86%) and TUCKER3-LDA (86% and 93%).

Unfolded partial least squares/residual bilinearization combined with the Successive Projections Algorithm for interval selection: enhanced excitation-emission fluorescence data modeling in the presence of the inner filter effect.

The use of the successive projections algorithm (SPA) for elimination of uninformative variables in interval selection, and unfold partial least squares regression (U-PLS) modeling of excitation-emission matrices (EEM), when under the inner filter effect (IFE) is reported for first time. Post-calibration residual bilinearization (RBL) was employed against events of unknown components in the test samples. The inner filter effect can originate changes in both the shape and intensity of analyte spectra, leading to trilinearity losses in both modes, and thus invalidating most multiway calibration methods. The algorithm presented in this paper was named iSPA-U-PLS/RBL. Both simulated and experimental data sets were used to compare the prediction capability during: (1) simulated EEM; and (2) quantitation of phenylephrine (PHE) in the presence of paracetamol (PAR) (or acetaminophen) in water samples. Test sets containing unexpected components were built in both systems [a single interference was taken into account in the simulated data set, while water samples were added with varying amounts of ibuprofen (IBU), and acetyl salicylic acid (ASA)]. The prediction results and figures of merit obtained with the new algorithm were compared with those obtained with U-PLS/RBL (without intervals selection), and with the well-known parallel factors analysis (PARAFAC). In all cases, U-PLS/RBL displayed better EEM handling capability in the presence of the inner filter effect compared with PARAFAC. In addition, iSPA-U-PLS/RBL improved the results obtained with the full U-PLS/RBL model, in this case demonstrating the potential of variable selection.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization.

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL.

Automatic microemulsion preparation for metals determination in fuel samples using a flow-batch analyzer and graphite furnace atomic absorption spectrometry.

The principal thermodynamic advantages of using microemulsions over standard emulsions for flow metal analysis are the greatly increased analyte stability and emulsive homogeneity that improve both the ease of sample preparation, and the analytical result. In this study a piston propelled flow-batch analyzer (PFBA) for the determination of Cu, Cr and Pb in gasoline and naphtha by graphite furnace atomic absorption spectrometry (GF AAS) was explored. Investigative phase modeling for low dilution was conducted both for gasoline and naphtha microemulsions. Rheological considerations were also explored including a mathematical flow derivation to fine tune the system's operational parameters, and the GF AAS coupling. Both manual and automated procedures for microemulsion preparation were compared. The results of the paired t test at a 95% confidence level showed no significant differences between them. Further recovery test results confirmed a negligible matrix effect of the sample on the analyte absorption signals and an efficient stabilization of the samples (with metals) submitted to microemulsion treatment. The accuracy of the developed procedure was attested by good recovery percentages in the ranges of 100.0±3.5% for Pb in the naphtha samples, and 100.2±3.4% and 100.7±4.6% for Cu and Cr, respectively in gasoline samples.

A monosegmented flow-batch system for slow reaction kinetics: spectrophotometric determination of boron in plants.

This work introduces the monosegmented flow-batch (MSFB) analysis concept. This system combines favourable characteristics of both flow-batch and the monosegmented analysers, allowing use of the flow-batch system for slow reaction kinetics without impairing sensitivity or sampling throughput. The MSFB was evaluated during spectrophotometric determination of boron in plant extracts, which is a method that involves a slow reaction between boron and azomethine-H. All calibration solutions were prepared in-line, and all analytical processes completed by simply changing the operational parameters in the MSFB control software. The limit of detection was estimated at 0.008 mg L(-1). The measurements could be performed at a rate of 120 samples per hour with satisfactory precision. The proposed MSFB was successfully applied to analyse 10 plant samples and the results are in agreement with the reference method at a 95% level of confidence.

Near-infrared spectrometric determination of dipyrone in closed ampoules.

The present paper proposes an analytical method for fast near-infrared (NIR) determination of dipyrone in injectable formulations with a nominal content of 50.0%mv(-1) without violation of the ampoule. For this purpose, two multivariate calibration methods are evaluated, namely Partial-Least-Squares (PLS) and Multiple Linear Regression (MLR) with variable selection by the Successive Projections Algorithm (SPA). The resulting models comprised four latent variables (PLS) and five spectral variables (MLR-SPA). Appropriate predictions were obtained in both cases, with RMSEP values of 0.39 (PLS) and 0.35%mv(-1) (MLR-SPA) and correlation coefficients of 0.9970 (PLS) and 0.9975 (MLR-SPA) for a calibration range of 40-60%mv(-1). No systematic error was observed and no significant differences were found between the predicted and reference values, according to a paired t-test at 95% confidence level.

Kinetics independent spectrometric analysis using non-linear calibration modelling and exploitation of concentration gradients generated by a flow-batch system for albumin and total protein determination in blood serum.

An automatic method for kinetics independent spectrometric analysis is proposed in this study. It uses a non-linear calibration model that explores concentration gradients generated by a flow-batch analyser (FBA) for the samples, dye, and the single standard solution. The procedure for obtaining the gradients of the dye and standard solution is performed once at the beginning of analysis. The same procedure is applied thereafter for each sample. For illustration, the proposed automatic methodology was applied to determine total protein and albumin in blood serum by using the Biuret and Bromocresol Green (BCG) methods. The measurements were made by using a laboratory-made photometer based on a red and green bicolour LED (Light-Emitting Diode) and a phototransistor, coupled to a "Z" form flow cell. The sample throughput was about 50 h(-1) for albumin and 60 h(-1) for total protein, consuming about 7 microL of sample, 2.6 mL of BCG and 1.2 mL of biuret reagents for each determination. Applying the paired t-test for results from the proposed analyser and the reference method, no statistic differences at 95% confidence level were found. The absolute standard deviation was usually smaller than 0.2 g dL(-1). The proposed method is valuable for the determination of total protein and albumin; and can also be used in other determinations where kinetic effects may or may not exist.

A method for determination of COD in a domestic wastewater treatment plant by using near-infrared reflectance spectrometry of seston.

This paper proposes a method for determination of chemical oxygen demand (COD) in domestic wastewater. The proposed method is based on near-infrared reflectance (NIRR) measurements of seston collected from wastewater samples by filtration. The analysis does not require any special reagent, catalyst or solvent. Inherent baseline and noise features present in NIRR spectra are removed by a Savitzky-Golay derivative procedure followed by wavelet denoising. The resulting wavelet approximation coefficients are used for partial-least-squares modelling and subsequent prediction of COD values in new samples. The model is calibrated by using COD values obtained according to the American Public Health Association (APHA) reference method. The proposed method is applied to effluent samples from the anaerobic ponds of the Mangabeira municipal wastewater treatment plant in the city of João Pessoa (Paraíba, Brazil). By comparing the NIRR prediction results with the APHA reference values, a root-mean-square error of prediction (RMSEP) of 19 mg O2 L(-1) and a correlation of 0.97 were obtained. Such results are deemed adequate in view of the joint estimate of the standard error of the reference method, which was calculated as 21 mg O2 L(-1).

A linear semi-infinite programming strategy for constructing optimal wavelet transforms in multivariate calibration problems.

A novel strategy for the optimization of wavelet transforms with respect to the statistics of the data set in multivariate calibration problems is proposed. The optimization follows a linear semi-infinite programming formulation, which does not display local maxima problems and can be reproducibly solved with modest computational effort. After the optimization, a variable selection algorithm is employed to choose a subset of wavelet coefficients with minimal collinearity. The selection allows the building of a calibration model by direct multiple linear regression on the wavelet coefficients. In an illustrative application involving the simultaneous determination of Mn, Mo, Cr, Ni, and Fe in steel samples by ICP-AES, the proposed strategy yielded more accurate predictions than PCR, PLS, and nonoptimized wavelet regression.