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.     

Kinetic spectrophotometric method for simultaneous determination of selenium and tellurium using partial least squares calibration

A kinetic-spectrophotometric method for the simultaneous determination of Se(IV) and Te(IV) is described. The method is based upon the catalytic effect of these cations on the reaction of toluidine blue with sulfide. Partial least squares calibration method was employed for the data manipulation and analysis. The concentrations were varied between 0.02-0.24 and 0.01-0.08 μg/ml for Se(IV) and Te(IV), respectively. Cross-validation method was used to select the optimum number of factors (NF). The root mean square errors of difference for selenium and tellurium were 1.2 and 1.7 μg/ml, respectively. Application of the method to artificial samples and several mixtures of standard solutions of Se(IV) and Te(IV) were performed and satisfactory results were obtained.     

Stacked partial least squares regression analysis for spectral calibration and prediction

Two novel algorithms which employ the idea of stacked generalization or stacked regression, stacked partial least squares (SPLS) and stacked moving‐window partial least squares (SMWPLS) are reported in the present paper. The new algorithms establish parallel, conventional PLS models based on all intervals of a set of spectra to take advantage of the information from the whole spectrum by incorporating parallel models in a way to emphasize intervals highly related to the target property. It is theoretically and experimentally illustrated that the predictive ability of these two stacked methods combining all subsets or intervals of the whole spectrum is never poorer than that of a PLS model based only on the best interval. These two stacking algorithms generate more parsimonious regression models with better predictive power than conventional PLS, and perform best when the spectral information is neither isolated to a single, small region, nor spread uniformly over the response. A simulation data set is employed in this work not only to demonstrate this improvement, but also to demonstrate that stacked regressions have the potential capability of predicting property information from an outlier spectrum in the prediction set. Moisture, oil, protein and starch in Cargill corn samples have been successfully predicted by these new algorithms, as well as hydroxyl number for different instruments of terpolymer samples including and excluding an outlier spectrum. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimisation of partial least squares regression calibration models in near-infrared spectroscopy: a novel algorithm for wavelength selection

A novel optimisation algorithm is presented for full spectrum calibration models in near-infrared (NIR) spectroscopy. The algorithm is used to investigate the affect of removing continuous spectral regions on parameters critical to the validity of the model (e.g. explained variance, bias etc.) and ultimately identify and remove problem areas of the spectrum. As an example of its application, this paper shows how to optimise partial least squares regression (PLSR) calibration models for predicting moisture content within an intact pharmaceutical product and how problems due to changes in the nature of samples since setting up the original model may be eliminated. On application of two validated calibration models to a new set of samples unacceptable results were obtained for bias (-0.26 and -0.21% m/m moisture content) between the NIR predicted values and the true values (Karl Fischer analysis). The optimisation algorithm identified small regions of the spectrum, which if included in development of the models contributed significant bias to the final prediction. On removal of these problem regions the calibration models were found to be equally accurate and precise, but with the added advantage of robustness to a variable region of the sample spectrum (bias reduced to -0.05 and -0.09% m/m).     

Mutlivariate calibration with Raman data using fast principal component regression and partial least squares methods

Linear and non-linear calibration methods (principal component regression (PCR), partial least squares regression (PLS), and neural networks (NN)) were applied to a slightly non-linear Raman data set. Because of the large size of this data set, recently introduced linear calibration methods, specifically optimised for speed, were also used. These fast methods achieve speed improvement by using the Lanczos decomposition for the singular value decomposition steps of the calibration procedures, and for some of their variants, by optimising the models without cross-validation (CV). Linear methods could deal with the slight non-linearity present in the data by including extra components, therefore, performing comparably to NNs. The fast methods performed as well as their classical equivalents in terms of precision in prediction, but the results were obtained considerably faster. It, however, appeared that CV remains the most appropriate method for model complexity estimation.     

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.     

Simultaneous kinetic-spectrophotometric determination of levodopa and benserazide by bi- and three-way partial least squares calibration

A procedure for the simultaneous kinetic-spectrophotometric determination of levodopa (I) and benserazide (II), from their oxidation reaction with KIO(4) in an acidic medium, is described. Both species instantly oxidize, giving rise to compounds which present maximum values of absorbance close to 400 nm. In the presence of an excess of the oxidizing agent, the levodopa derivative evolves to form the corresponding aminochrome (lambda(m)=480 nm), while the benserazide derivative decomposes to yield colorless compounds. The appearance of new compounds, with absorption bands in the region of 500-700 nm, is additionally seen upon adding the oxidizing agent to a mixture of I and II. These compounds also evolve decomposing and forming colorless products. In spite of the complexity of the system studied, the calibration by bi-linear partial least squares (PLS) as well as by three-way partial least squares (nPLS) permit the quantification of both analytes with a precision on the order of 0.7% for levodopa and of 1.5% for benserazide. nPLS also allows for the qualitative interpretation of the phenomena which occur. The proposed method is applied to the quantification of I and II in the commercial, pharmaceutical preparation Madopar, using high performance liquid chromatography (HPLC) as the analytical reference technique.     

Simultaneous fluorimetric determination of acetylsalicylic acid metabolites in urine by partial least squares multivariate calibration

A method is described for the simultaneous determination of the main urinary acetylsalicylic acid (aspirin) metabolites, salicyclic, salicyluric and gentisic acids, based on their native fluorescence. The urine was extracted into diethyl ether in acid medium, and back-extracted with glycine/sodium hydroxide buffer solution at pH 9.4. A comparative study of the results found using the excitation, the emission and the combination of the excitation plus the emission spectral data, as analytical signals, was performed. The data set, composed of the excitation plus the emission spectra, was selected as the analytical signal. The optimum wavelengths to record the excitation (_em=444 nm) and the emission spectra (_ex=323 nm) were selected to maximize the contribution from gentisic acid, which is the minor urinary metabolite. Partial least squares (PLS-1) multivariate calibration was then applied for the determination. Recovery values from urine samples spiked with salicyclic, salicyluric and gentisic acids varied from 90.1 to 97.6% (mean 93.6%), from 90.0 to 110% (mean 97.9%) and from 89.9 to 104.7% (mean 98.5%), respectively.     

Simultaneous fluorimetric determination of acetylsalicylic acid metabolites in urine by partial least squares multivariate calibration

A method is described for the simultaneous determination of the main urinary acetylsalicylic acid (aspirin) metabolites, salicyclic, salicyluric and gentisic acids, based on their native fluorescence. The urine was extracted into diethyl ether in acid medium, and back-extracted with glycine/sodium hydroxide buffer solution at pH 9.4. A comparative study of the results found using the excitation, the emission and the combination of the excitation plus the emission spectral data, as analytical signals, was performed. The data set, composed of the excitation plus the emission spectra, was selected as the analytical signal. The optimum wavelengths to record the excitation (lambda(em)=444 nm) and the emission spectra (lambda(ex)=323 nm) were selected to maximize the contribution from gentisic acid, which is the minor urinary metabolite. Partial least squares (PLS-1) multivariate calibration was then applied for the determination. Recovery values from urine samples spiked with salicyclic, salicyluric and gentisic acids varied from 90.1 to 97.6% (mean 93.6%), from 90.0 to 110% (mean 97.9%) and from 89.9 to 104.7% (mean 98.5%), respectively.     

Simultaneous fluorometric determination of nalidixic acid and 7-hydroxymethylnalidixic acid by partial least squares calibration

The resolution of binary mixtures of nalidixic acid (NA) and 7-hydroxymethylnalidixic acid (OH-NA) has been accomplished by partial least squares (PLS) and principal component regression (PCR) multivariate calibration. The method of determination is based on the fluorescence emission of these compounds in the presence of gamma-cyclodextrin (gamma-CD). The formation of the inclusion compounds gives rise to an increase of the fluorescence emission compared to aqueous solution. The total luminescence information of the compounds has been used to optimize the spectral data set to perform the calibration. A comparison between the predictive ability of three multivariate calibration methods, PLS-1, PLS-2 and PCR, on three spectral data sets, excitation, 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 NA and OH-NA in urine. Recovery values from urine samples containing (NA) and (OH-NA) range from 91 to 103% (mean 97%), and from 92 to 105% (mean 99%), respectively.     

Comparison between ordinary least squares regression and weighted least squares regression in the calibration of metals present in human milk determined by ICP-OES

In this study we compared the use of ordinary least squares and weighted least squares in the calibration of the method for analyzing essential and toxic metals present in human milk by ICP-OES, in order to avoid systematic errors in the measurements used. Human milk samples were provided by maternity clinic Odete Valadares and digested by means of a high-performance microwave (MW) oven. Evaluation of plasma short and long-term stability was made using a solution of digested milk (1:50) with 2.0 mg L⁻¹ Mg in HNO₃ 2% (v/v). The detection power resulted to be at or below the μg L⁻¹ level, whilst the precision expressed as relative standard deviation R.S.D. was almost always equal to or better than 3.3%. Certified reference material Infant Formula (NIST SRM 1846) was used to assess the accuracy of the proposed method, which proved to be accurate and precise. Recovery rates were in the range of 83-117%. Aqueous calibration was carried out for each element under study.     

Flow-injection chemiluminescence simultaneous determination of cobalt(II) and copper(II) using partial least squares calibration

A flow-injection chemiluminescence (CL) system is proposed for simultaneous determination of Co²⁺ and Cu²⁺ using partial least squares (PLS) calibration. This method is based on the fact that both Co²⁺ and Cu²⁺ catalyse the CL reaction of luminol-H₂O₂, and that their kinetic characteristics of Co²⁺ and Cu²⁺ are significantly different in the luminol-H₂O₂ system. The CL intensity was measured and recorder at different reaction times of luminol-H₂O₂Co²⁺Cu²⁺, and the obtained data were processed by the chemometric approach of partial least squares. The experimental calibration set was composed of 16 sample solutions using an orthogonal calibration design for two component mixtures. The proposed method offers the potential advantages of high sensitivity, simplicity and rapidity for Co²⁺ and Cu²⁺ determination, and was successfully applied to the simultaneous determination of both analytes in real water sample. The present paper demonstrated that the simultaneous determination of two metal ions without any prior separation has been possible using flow-injection CL system.     

Solid-phase extraction and simultaneous spectrophotometric determination of trace amounts of Co, Ni and Cu using partial least squares regression

A novel sensitive and simple method for rapid extraction, preconcentration and determination of cobalt, nickel and copper as their 1-(2-pyridilazo)-2-naphthol (PAN) complexes using polytetrafluoroethylene filter as solid phase and multivariate calibration of spectrophotometric data is presented. The analytical wavelengths of 400-700 nm were chosen and the experimental calibration matrix for partial least squares (PLS) was designed with 21 samples of 5.90-41.25, 0.30-29.35 and 0.64-41.30 ng ml⁻¹ for cobalt, nickel and copper, respectively. The cross-validation method was used for selecting the number of components. The root-mean-square errors of predictions (RMSEPs) were between 0.48 and 1.37 ng ml⁻¹. In this work we could reach preconcentration factors of 100 or even higher by using polytetrafluoroethylene as solid phase which is cheap and can be used in a wide range of pH, flow rates and for many times. The proposed method was successfully applied to the simultaneous determination of Co, Ni and Cu in tap and pit water samples.     

Voltammetric behavior and determination of tocopherols with partial least squares calibration: analysis in vegetable oil samples

The voltammetric behavior of Vitamin E in the presence of olive oil is studied at the glassy carbon electrode, in a hexane-ethanol medium, with diverse techniques: sampled DC, differential pulse, and square-wave voltammetry. The influence of such variables as the hexane-ethanol proportion, sulfuric acid concentration, and instrumental parameters is studied. Separate voltammetric peaks are obtained for α-tocopherol and δ-tocopherol, but the peaks for β-tocopherol and γ-tocopherol overlap. For the simultaneous determination of α-, β+γ-, and δ-tocopherols in vegetable oils by the PLS-1 multivariate calibration method, the results using sampled DC and DPV voltammograms are compared. The DPV voltammograms are found to be the best data set. The proposed method is applied to the determination of the tocopherols in different vegetable oil samples. The olive oil samples needed a prior cleaning stage by solid-phase extraction on silica cartridges. The results are very acceptable.     

Spectrophotometric simultaneous determination of cobalt, copper and nickel using nitroso-R-salt in alloys by partial least squares

Partial least squares modeling as a powerful multivariate statistical tool applied to spectrophotometric simultaneous determination of cobalt, copper, and nickel in aqueous solutions. The concentration range for cobalt, copper and nickel were 0.4-2.6, 0.6-3.4, 0.5-5.5 ppm, respectively. The experimental calibration set was composed with 36 sample solutions using a mixture design for three component mixtures. The absorption spectra were recorded from 470 to 600 nm. The effect of pH on the sensitivity and selectivity was studied according to net analyte signal (NAS). The values of root mean square difference (RMSD) for cobalt, copper and nickel using partial least squares (PLS) were 0.0192, 0.0263 and 0.0446 ppm, respectively. The effects of various cations and anions were investigated. The method was used to determination of cobalt, copper and nickel in two sample alloys based on copper, nickel and cobalt (known as cunico) and based on cobalt, nickel and iron (known as conife).     

Analysis of interferents by means a D-optimal screening design and calibration using partial least squares regression in the spectrophotometric determination of Cr(VI)

Using a central composite design, the signal of the process for the spectrophotometric determination of hexavalent chromium (λ = 543 nm) is maximised and its variability minimised using as complexing agent 1,5-diphenylcarbazide in sufficiently acid medium. To analyse the interference of various analytes (Mo(VI), V(V), Fe(III) and Mn(VII)) on the Cr(VI) as a function of concentration of interferent, a factorial design was prepared at three levels of each (zero, medium and high concentration), which implies performing 81 determinations. However, a D-optimal design with just nine experiments is sufficiently good to estimate the model proposed.The interference of these metals makes it impossible to determine Cr(VI) when they are present in the sample. To avoid prior separation steps, a multivariate regression by partial least squares, PLS, is proposed to calibrate the Cr(VI) in the presence of these analytes varying the concentration of the Cr(VI) between 0.1 and 0.9 μg ml⁻¹ and that of the interferents between 3 and 5 μg ml⁻¹. The average errors obtained were 4.5% and 3.29% fitted and in prediction, respectively, with a standard error in prediction (RMSEP) of 0.016% presenting absence of both constant and proportional bias.The detection limit with the PLS regression in the presence of interferents is 0.1 μg ml⁻¹ with a probability of false positive equal to 5% and less than 5% for false negative. The capability of detection is similar to that obtained with the univariate calibration (absorbance at 543 nm) in absence of interferents.With the PLS regression it is possible to discriminate 0.085 μg ml⁻¹ of Cr(VI) in a sample with 0.5 μg ml⁻¹ of Cr(VI) with probabilities of false compliance and false non-compliance equal to 0.05. For the univariate calibration without interferents, it was established at 0.0971 μg ml⁻¹ of Cr(VI) for the same nominal concentration.In relation to interference of V(V), Fe(III) and Mn(VII), the PLS calibration could be an efficient alternative to the separation step for Cr(VI) spectrophotometric determination using 1,5-diphenylcarbazide.     

Simultaneous determination of domperidone and cinnarizine in a binary mixture using derivative spectrophotometry,partial least squares and principle component regression calibration

This work is concerned with the simultaneous determination of domperidone maleate (DOM) and cinnarizine (CINN) in a binary mixture form, without previous separation, by two different techniques. The first method is the application of derivative spectrophotometry where the linearity range and percentage recoveries for DOM and CINN were 2.5-30 micro g mL(-1), 5-25 micro g mL(-1) and 100.06+/-1.157, 99.93+/-1.377, respectively. The second method depends on the application of partial least squares (PLS) and principle component regression (PCR) models. A training set consisting of 10 mixtures containing 5-20 micro g mL(-1) for each component was used for the construction of the PCR and PLS models. These models were used after their validation for the prediction of the concentration of DOM and CINN in their mixtures. The proposed procedures were successfully applied for the simultaneous determination of both drugs in laboratory prepared mixtures and in commercial tablet preparations. The validity of the proposed methods was assessed by applying the standard addition technique where the percentage recovery of the added standard was found to be 99.98+/-0.297 and 99.84+/-0.700 for DOM and CINN, respectively, using the derivative spectrophotometric method and 100.29+/-0.398 and 100.11+/-0.363 for DOM and CINN, respectively, using the PLS and PCR methods.The proposed procedures are rapid, simple, require no preliminary separation steps and can be used for routine analysis of both drugs in quality control laboratories.     

Quantification of brain lipids by FTIR spectroscopy and partial least squares regression

Brain tissue is characterized by high lipid content. Its content decreases and the lipid composition changes during transformation from normal brain tissue to tumors. Therefore, the analysis of brain lipids might complement the existing diagnostic tools to determine the tumor type and tumor grade. Objective of this work is to extract lipids from gray matter and white matter of porcine brain tissue, record infrared (IR) spectra of these extracts and develop a quantification model for the main lipids based on partial least squares (PLS) regression. IR spectra of the pure lipids cholesterol, cholesterol ester, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, galactocerebroside and sulfatide were used as references. Two lipid mixtures were prepared for training and validation of the quantification model. The composition of lipid extracts that were predicted by the PLS regression of IR spectra was compared with lipid quantification by thin layer chromatography.     

Simultaneous determination of Sb(III) and Sb(V) by partial least squares regression

A method for simultaneous spectrophotometric determination of Sb(III) and Sb(V) using multivariate calibration method is proposed. This method is based on the development of the reaction between the analytes and pyrogallol red at pH 2.00. The selection of variables was studied. A series of synthetic solutions containing different concentrations of Sb(III) and Sb(V) were used to check the prediction ability of the partial least squares model. The calibration curves were linear over the range of 0.3-3.4 and 0.3-3.0 μg ml⁻¹ for Sb(III) and Sb(V), respectively. The detection limits were 0.177 and 0.200 μg ml⁻¹ for Sb(III) and Sb(V), respectively.     

beta-Correction as a preprocessing method for partial least squares in simultaneous determination of zinc and lead

An error analysis of predicted values using spectral correction matrix and partial least squares (PLS) modeling is applied for the determination of Zn²⁺ and Pb²⁺ with methylthymol blue (MTB) as a metallochromic indicator. The concentration ranges for Pb²⁺ and Zn²⁺ in standard solution sets are 0.5-5.2 and 0.1-2.5 μg ml⁻¹, respectively. The experimental calibration set was composed of 20 sample solutions using a random design for two component mixtures. The absorption spectra were recorded from 400 to 700 nm. The two wavelengths, which exert the minimum error in prediction of two metal ion concentrations, are chosen according to an error analysis of different pairs of wavelengths. The effect of the pH on the sensitivity in determination of Zn²⁺ and Pb²⁺ using MTB was studied in order to choose the optimum pH (pH=6) for determination. The values of root mean square difference (RMSD) for lead and zinc using β-correction partial least squares were 0.0977 and 0.1266, respectively. The effect of diverse ions and several experimental parameters were studied. The method was used for the determination of lead and zinc in alloy samples.