Dual amperometric biosensor device for analysis of binary mixtures of phenols by multivariate calibration using partial least squares

A simple and reliable method for rapid evaluation of mixtures of phenolic compounds (phenol/chlorophenol, cathecol/phenol, cresol/chlorocresol and phenol/cresol) using a dual amperometric device is described. This new approach is based on the difference between the sensitivity of laccase and tyrosinase for different phenolic compounds. A multichannel potentiostat was used to monitor simultaneously laccase- and tyrosinase-based biosensors, and the data were treated using the partial least squares (PLS) chemometric algorithm. This system showed an excellent efficiency for the resolution of the phenolic mixtures. For example, in the phenol/chlorophenol mixture it was studied the determination of individual species in a concentration range from 1.0×10⁻⁶ to 10.0×10⁻⁶ mol l⁻¹ obtaining relative standard deviations of 3.5 and 3.1% for phenol and chlorophenol, respectively. The excellent correlation between the estimated and the real concentrations can also be observed by the correlation coefficients (0.9958 and 0.9981 for phenol and chlorophenol, respectively). These results show that proposed methodology can be successfully employed to the simultaneous determination of phenolic compounds in mixtures, even in more diluted solutions.     

Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data

Variable selection using a genetic algorithm is combined with partial least squares (PLS) for the prediction of additive concentrations in polymer films using Fourier transform-infrared (FT-IR) spectral data. An approach using an iterative application of the genetic algorithm is proposed. This approach allows for all variables to be considered and at the same time minimizes the risk of overfitting. We demonstrate that the variables selected by the genetic algorithm are consistent with expert knowledge. This very exciting result is a convincing application that the algorithm can select correct variables in an automated fashion.     

Simultaneous spectrophotometric determination of four preservatives in foodstuffs by multivariate calibration and artificial neural networks

Benzoic acid(BA),methylparaben(MP),propylparaben(PP)and sorbic acid(SA)are food preservatives,and they have well defined UV spectra.However,their spectra overlap seriously,and it is difficult to determine them individually from their mixtures without preseparation.In this paper,seven different chemometric approaches were applied to resolve the overlapping spectra and to determine these compounds simultaneously.With respect to the criteria of%relative prediction error(RPE)and%recovery, principal component...     

Net analyte signal-based simultaneous determination of antazoline and naphazoline using wavelength region selection by experimental design-neural networks

Net analyte signal (NAS)-based multivariate calibration methods were employed for simultaneous determination of anthazoline and naphazoline. The NAS vectors calculated from the absorbance data of the drugs mixture were used as input for classical least squares (CLS), principal component and partial least squares regression PCR and PLS methods. A wavelength selection strategy was used to find the best wavelength region for each drug separately. As a new procedure, we proposed an experimental design-neural network strategy for wavelength region optimization. By use of a full factorial design method, some different wavelength regions were selected by taking into account different spectral parameters including the starting wavelength, the ending wavelength and the wavelength interval. The performance of all the multivariate calibration methods, in all selected wavelength regions for both drugs, was evaluated by calculating a fitness function based on the root mean square error of calibration and validation. A three-layered feed-forward artificial neural network (ANN) model with back-propagation learning algorithm was employed to model the nonlinear relationship between the spectral parameters and fitness of each regression method. From the resulted ANN models, the spectral regions in which lowest fitness could be obtained were chosen. Comparison of the results revealed that the net NAS-PLS resulted in lower prediction error than the other models. The proposed NAS-based calibration method was successfully applied to the simultaneous analyses of anthazoline and naphazoline in a commercial eye drop sample.     

Simultaneous spectrophotometric determination of ethinylestradiol and levonorgestrel by partial least squares and principal component regression multivariate calibration

Two spectrophotometric methods for the determination of Ethinylestradiol (ETE) and Levonorgestrel (LEV) by using the multivariate calibration technique of partial least square (PLS) and principal component regression (PCR) are presented. In this study the PLS and PCR are successfully applied to quantify both hormones using the information contained in the absorption spectra of appropriate solutions. In order to do this, a calibration set of standard samples composed of different mixtures of both compounds has been designed. The results found by application of the PLS and PCR methods to the simultaneous determination of mixtures, containing 4–11 μg ml⁻¹ of ETE and 2–23 μg ml⁻¹ of LEV, are reported. Five different oral contraceptives were analyzed and the results were very similar to that obtained by a reference liquid Chromatographic method.     

A kinetic spectrophotometric method for the determination of ternary mixtures of reducing sugars with the aid of artificial neural networks and multivariate calibration

A differential spectrophotometric method has been developed for the simultaneous quantitative determination of glucose (GLU), fructose (FRU) and lactose (LAC) in food samples. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K₃Fe(CN)₆) as the oxidant. The reaction data were recorded at the analytical wavelength (420 nm) of the K₃Fe(CN)₆ spectrum. Since the kinetic runs of glucose, fructose and lactose overlap seriously, the condition number was calculated for the data matrix to assist with the optimisation of the experimental conditions. Values of 80 °C and 1.5 mol l⁻¹ were selected for the temperature and concentration of sodium hydroxide (NaOH), respectively. Linear calibration graphs were obtained in the concentration range of 2.96-66.7, 3.21-67.1 and 4.66-101 mg l⁻¹ for glucose, fructose and lactose, respectively. Synthetic mixtures of the three reducing sugar were analysed, and the data obtained were processed by chemometrics methods, such as partial least square (PLS), principal component regression (PCR), classical least square (CLS), back propagation-artificial neural network (BP-ANN) and radial basis function-artificial neural network (RBF-ANN), using the normal and the first-derivative kinetic data. The results show that calibrations based on first-derivative data have advantages for the prediction of the analytes and the RBF-ANN gives the lowest prediction errors of the five chemometrics methods. Following the validation of the proposed method, it was applied for the determination of the three reducing sugars in several commercial food samples; and the standard addition method yielded satisfactory recoveries in all instances.     

Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection

An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).     

Model selection for partial least squares calibration and implications for analysis of atmospheric organic aerosol samples with mid‐infrared spectroscopy

In developing partial least squares calibration models, selecting the number of latent variables used for their construction to minimize both model bias and model variance remains a challenge. Several metrics exist for incorporating these trade‐offs, but the cost of model parsimony and the potential for underfitting on achievable prediction errors are difficult to anticipate. We propose a metric that penalizes growing model variance against decreasing bias as additional latent variables are added. The magnitude of the penalty is scaled by a user‐defined parameter that is formulated to provide a constraint on the fractional increase in root mean square error of cross‐validation (RMSECV) when selecting a parsimonious model over the conventional minimum RMSECV solution. We evaluate this approach for quantification of four organic functional groups using 238 laboratory standards and 750 complex atmospheric organic aerosol mixtures with mid‐infrared spectroscopy. Parametric variation of this penalty demonstrates that increase in prediction errors due to underfitting is bounded by the magnitude of the penalty for samples similar to laboratory standards used for model training and validation. Imposing an ensemble of penalties corresponding to a 0–30% allowable increase in RMSECV through sum of ranking differences leads to the selection of a model that increases the actual RMSECV up to 20% for laboratory standards but achieves an 85% reduction in the mean error in predicted concentrations for environmental mixtures. Partial least squares models developed with laboratory mixtures can provide useful predictions in complex environmental samples, but may benefit from protection against overfitting. © 2015 The Authors. Journal of Chemometrics published by John Wiley & Sons Ltd.     

Spectrophotometric determination of metal ions in electroplating solutions in the presence of EDTA with the aid of multivariate calibration and artificial neural networks

Metal ions such as Co(II), Ni(II), Cu(II), Fe(III) and Cr(III), which are commonly present in electroplating baths at high concentrations, were analysed simultaneously by a spectrophotometric method modified by the inclusion of the ethylenediaminetetraacetate (EDTA) solution as a chromogenic reagent. The prediction of the metal ion concentrations was facilitated by the use of an orthogonal array design to build a calibration data set consisting of absorption spectra collected in the 370-760 nm range from solution mixtures containing the five metal ions earlier. With the aid of this data set, calibration models were built based on 10 different chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), artificial neural networks (ANN) and others. These were tested with the use of a validation data set constructed from synthetic solutions of the five metal ions. The analytical performance of these chemometrics methods were characterized by relative prediction errors and recoveries (%). On the basis of these results, the computational methods were ranked according to their performances using the multi-criteria decision making procedures preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). PLS and PCR models applied to the spectral data matrix that used the first derivative pre-treatment were the preferred methods. They together with ANN-radial basis function (RBF) and PLS were applied for analysis of results from some typical industrial samples analysed by the EDTA-spectrophotometric method described. DPLS, DPCR and the ANN-RBF chemometrics methods performed particularly well especially when compared with some target values provided by industry.     

Simultaneous determination of potassium guaiacolsulfonate, guaifenesin, diphenhydramine HCl and carbetapentane citrate in syrups by using HPLC-DAD coupled with partial least squares multivariate calibration

A simple and rapid analytical procedure was proposed for the determination of chromatographic peaks by means of partial least squares multivariate calibration (PLS) of high-performance liquid chromatography with diode array detection (HPLC-DAD). The method is exemplified with analysis of quaternary mixtures of potassium guaiacolsulfonate (PG), guaifenesin (GU), diphenhydramine HCI (DP) and carbetapentane citrate (CP) in syrup preparations. In this method, the area does not need to be directly measured and predictions are more accurate. Though the chromatographic and spectral peaks of the analytes were heavily overlapped and interferents coeluted with the compounds studied, good recoveries of analytes could be obtained with HPLC-DAD coupled with PLS calibration. This method was tested by analyzing the synthetic mixture of PG, GU, DP and CP. As a comparison method, a classsical HPLC method was used. The proposed methods were applied to syrups samples containing four drugs and the obtained results were statistically compared with each other. Finally, the main advantage of HPLC-PLS method over the classical HPLC method tried to emphasized as the using of simple mobile phase, shorter analysis time and no use of internal standard and gradient elution.     

Simultaneous fluorimetric determination of glyphosate and its metabolite, aminomethylphosphonic acid, in water, previous derivatization with NBD-Cl and by partial least squares calibration (PLS)

The reactions of 4-chloro-7-nitrobenzofurazan (NBD-Cl) with glyphosate (GLY) and with its main metabolite, aminomethylphosphonic acid (AMPA), have been studied. The resolution of binary mixtures of glyphosate and aminomethylphosphonic acid has been accomplished by partial least squares (PLS) multivariate calibration. The method of determination is based on the fluorescence emission of the derivatives formed in presence of NBD-Cl at 90 °C, in methanol and in basic medium. The dynamic ranges of the methods were comprised between 10 and 150 μg l⁻¹ for GLY and between 10 and 200 μg l⁻¹ for AMPA, being the detection limits 2 and 5.4 μg l⁻¹ for GLY and AMPA, respectively. The total luminiscence information of the derivatives has been used to optimize the spectral data set to perform the calibration, by analysis of the three-dimensional excitation-emission matrices. A comparison between the predictive ability of the multivariate calibration method, partial least squares type 1 (PLS-1), on two spectral data sets, emission and synchronous spectra, has been performed. The PLS-1 method, applied to the emission spectra, has been selected as optimum. The proposed method has been applied to the simultaneous determination of GLY and AMPA in river water. For concentrations ranging from 100 to 600 μg l⁻¹ of each compound in the samples, analytical recoveries range from 83 to 94% for GLY and from 104 to 120% for AMPA.