Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

Simultaneous Multivariate Spectrophotometric Analysis of Binary and Ternary Mixtures of Sulfamethoxazole,Trimethoprim and Phenazopyridine in Tablets

ABSTRACT

The use of multivariate spectrophotometric calibration is reported for the analysis of tablets containing the antibiotics sulfamethoxazole and trimethoprim, and a combination of the former two drugs with the analgesic phenazopyridine. The resolution of these mixtures has been accomplished without prior separation, derivatisation or use of nonaqueous solvents, with the aid of partial least-squares (PLS-1) regression analysis of electronic absorption spectral data. The analytes have been simultaneously determined with high accuracy and precision, and with no interference from tablet excipients.     

Simultaneous spectrophotometric determination of drugs in pharmaceutical preparations using multiple linear regression and partial least-squares regression, calibration and prediction methods

Two new methods for the simultaneous determination of acetylsalicylic acid, acetaminophen and caffeine based on total absorbance measurements and their processing by multiple linear regession and partial least-squares regression are proposed. The concentration ranges used to construct the calibration matrix were 4.0-12.0, 2.0-10.0 and 0.9-6.0 μg ml⁻¹ for acetylsalicylic acid, acetaminophen and caffeine respectively. The proposed methods were validated by using a set of synthetic sample mixtures and subsequently applied to the determination of the three active principles in three different pharmaceutical preparations.     

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.     

GA-ACE: Alternating conditional expectations regression with selection of significant predictors by genetic algorithms

The non-linear regression technique known as alternating conditional expectations (ACE) method is only applicable when the number of objects available for calibration is considerably greater than the number of considered predictors. Alternating conditional expectations regression with selection of significant predictors by genetic algorithms (GA-ACE), the non-linear regression technique presented here, is based on the ACE algorithm but introducing several modifications to resolve the applicability limitations of the original ACE method, thus facilitating the practical implementation of a very interesting calibration tool. In order to overcome the lack of reliability displayed by the original ACE algorithm when working on data sets characterized by a too large number of variables and prior to the development of the non-linear regression model, GA-ACE applies genetic algorithms as a variable selection technique to select a reduced subset of significant predictors able to accurately model and predict a considered variable response. Furthermore, GA-ACE actually provides two alternative application approaches, since it allows either the performance of prior data compression computing a number of principal components to be subsequently subjected to GA-selection, or working directly on original variables.In this study, GA-ACE was applied to two real calibration problems, with a very low observation/variable ratio (NIR data), and the results were compared with those obtained by several linear regression techniques usually employed. When using the GA-ACE non-linear method, notably improved regression models were developed for the two response variables modeled, with root mean square errors of the residuals in external prediction (RMSEP) equal to 11.51 and 6.03% for moisture and lipid contents of roasted coffee samples, respectively. The improvement achieved by applying the new non-linear method introduced is even more remarkable taking into account the results obtained with the best performance linear method (IPW-PLS) applied to predict the studied responses (14.61 and 7.74% RMSEP, respectively).     

Characterization and analysis of polymorphs by near-infrared spectrometry

The polymorphic purity of drug is of high pharmaceutical interest as it often dictates its bioavailability. In this work, we developed a rapid, efficient method for the characterization and determination of azithromycin polymorphs using near-infrared (NIR) spectrometry. The drug is characterized by comparison with a NIR spectral library that permits one to determine whether the amount of crystalline form contained in an amorphous azithromycin sample exceeds allowed levels. While the crystalline form is a hydrate, the amorphous form is anhydrous; however, the absorption of a small amount of moisture by the drug reduces the spectral differences between the two forms and hinders the establishment of an accurate calibration model. In this work, we determined the crystalline form by using a partial least-squares regression model (PLS1) for calibration and examined the influence of factors such as spectral treatment, wavelength range and moisture content on the results. The high correlation between the spectra for the two forms enabled the development of a PLS2 model for determining both species jointly. The proposed method was validated with a view to its subsequent use in the analytical control of azithromycin.     

Simultaneous Determination of Timolol Maleate and Pilocarpine Hydrochloride in Ophthalmic Solutions by First Derivative UV Spectrophotometry and PLS-1 Multivariate Calibration

ABSTRACT

The use of UV spectrophotometry (first-derivative/zero-crossing and zero-order spectra/multivariate calibration) is reported for the analysis of two miotic agents in ophthalmic solutions. The resolution of these mixtures has been accomplished without prior separation or derivatisation by using: 1) first-derivative measurements at two appropriate zero-crossing points: λ₁ = 222 nm, where the absorption corresponding to excipients is negligible, and λ₂ = 307 nm, where the contribution of pilocarpine and excipients to the overall absorption is negligible, and 2) partial least squares (PLS-1) regression analysis of zero-order spectral data. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy, and with no interference from ophthalmic solution excipients.     

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.     

Fast spectrophotometric determination of fluoride in ground waters by flow injection using partial least-squares calibration

The presence of sulphate constitutes a serious interference in the usual zirconium lake-based spectrophotometric method for the determination of fluoride in water. In this report, full spectral data have been recorded for the zirconium lake of 2-(parasulfophenylazo)-1,8-dihydroxy-3,6-naphthalene-disulfonate (SPADNS) in the simultaneous presence of fluoride and sulphate, as obtained with a flow injection system with a diode-array detector. The information has been processed with partial least-squares (PLS) multivariate calibration. Adequate modeling using a sixteen-sample calibration set allows fluoride to be determined in ground waters by the automated flow injection method, even in the presence of sulphate in concentrations up to 1000 mg l⁻¹. In the calibration range 0-1.50 mg l⁻¹ for fluoride, the limit of detection is 0.1 mg l⁻¹. The fluoride contents in real samples, as determined with the present method, were satisfactorily compared with those provided by ion selective potentiometry.     

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...     

Analysis of amino acids in complex samples by using voltammetry and multivariate calibration methods

A voltammetric method is proposed for the simultaneous determination of tryptophan, cysteine, and tyrosine using multivariate calibration techniques. Various electrodes and voltammetric techniques were explored to ascertain the optimum measurement strategy. Among them, differential pulse voltammetry (DPV) with a Pt electrode was selected as analytical technique since it provided a suitable compromise between sensitivity and reproducibility while allowing the oxidation peaks of the three compounds to be reasonably discriminated. The sensitivity of DPV with Pt electrode for Trp standards was 8.4×10⁻² A l mol⁻¹, the repeatability 3.7% and the detection limit below 10⁻⁷ M. The lack of full selectivity of the voltammetric data was overcome using multivariate calibration methods on the basis of the differences in the voltammetric waves of each compound. The accuracy of predictions was evaluated preliminarily from the analysis of three-component synthetic mixtures. Subsequently, this method was applied to the analysis of oxidizable amino acids in feed samples. Results obtained were in good concordance with those given by the standard method using an amino acid analyzer.     

利用混合线性分析分光光度法同时测定微量铜、锌、镍

研究以5 Br PADAP为显色剂,用混合线性分析分光光度法同时测定铜、锌、镍。在pH8.2的硼砂 盐酸缓冲溶液中,Cu2 、Zn2 、Ni2 分别与5 Br PADAP形成稳定的配合物,其吸收光谱相互重叠。用一种新的数学模型,即混合线性分析进行光谱分辨,各组分的纯光谱则用最小二乘法求得。讨论了反应条件、测量波长的选择等因素的影响。铜、锌、镍的标准回收率分别为95.0%～102.5%、94.2%～101.3%和96.2%～104.2%。应用于铝合金中铜、锌、镍的同时测定。     

Simultaneous voltammetric determination of levodopa, carbidopa and benserazide in pharmaceuticals using multivariate calibration

An analytical methodology based on differential pulse voltammetry (DPV) on a glassy carbon electrode and the partial least-squares (PLS-1) algorithm for the simultaneous determination of levodopa, carbidopa and benserazide in pharmaceutical formulations was developed and validated. Some sources of bi-linearity deviation for electrochemical data are discussed and analyzed. The multivariate model was developed as a ternary calibration model and it was built and validated with an independent set of drug mixtures in presence of excipients, according with manufacturer specifications. The proposed method was applied to both the assay and the uniformity content of two commercial formulations containing mixtures of levodopa-carbidopa (10:1) and levodopa-benserazide (4:1). The results were satisfactory and statistically comparable to those obtained by applying the reference Pharmacopoeia method based on high performance liquid chromatography. In conclusion, the methodology proposed based on DPV data processed with the PLS-1 algorithm was able to quantify simultaneously levodopa, carbidopa and benserazide in its pharmaceuticals formulations using a ternary calibration model for these drugs in presence of excipients. Furthermore, the model appears to be successful even in the presence of slight potential shifts in the processed data, which have been taken into account by the flexible chemometric PLS-1 approach.     

Selectivity for glucose,glucose-6-phosphate,and pyruvate in ternary mixtures from the multivariate analysis of near-infrared spectra

Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000–5,000 cm⁻¹) for a set of 60 standard solutions maintained at 37 °C. These standard solutions are composed of randomized concentrations (0.5–30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose. Figure Combination near-infrared spectroscopy allows selective analytical measurements for glucose, glucose-6-phosphate, and pyruvate in ternary mixtures owing to the uniqueness of the individual absorption spectra for each solute     

Determination of quinolone antimicrobial agents in strongly overlapped peaks from capillary electrophoresis using multivariate calibration methods

This paper describes a new procedure for the determination of quinolones ciprofloxacin and sarafloxacin in chicken muscle samples. It is based on a previously developed capillary zone electrophoresis (CZE) separation, in which all the quinolones regulated by EU Council Regulation number 2377/90 could be separated. However, as ciprofloxacin and sarafloxacin coelute in the CZE run and they have strongly overlapped spectra, separation between them is not possible.To overcome this problem, we have used a multivariate calibration procedure (partial least square regression (PLS-2)), applied to the spectra obtained at the maximum of the electrophoretic peaks, by using a diode array detector. The method has been validated by a combination of pure standards and fortified blank chicken muscle extracts. The recoveries obtained in the validation set were 101±6 and 93±6% for sarafloxacin and ciprofloxacin, respectively. The method has been also applied to chicken muscle samples, fortified at concentration levels between 100 and 350 μg kg⁻, corresponding to values near the maximum residue level (MRL) regulated by the European Community.     

Simultaneous Determination of Acetaminophen and Phenobarbital in Suppositories by Partial Least-Squares Spectrophotometric Calibration

Abstract

The use of partial least-squares spectrophotometric calibration for the simultaneous determination of suppositories in a multicomponent formulation is presented. This method is applied to the deternination of acetaminophen and phenobarbital in suppository preparations. The results show that these components in a molar ratio of about 61:1 in suppositories have been determined simultaneously with high precision.     

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.     

Direct and simultaneous spectrofluorometric determination of naproxen and salicylate in human serum assisted by chemometric analysis

A direct method for the simultaneous determination of naproxen and salicylate in human serum is reported, based on a combination of spectrofluorometric measurements with two multivariate calibration techniques: partial least-squares (PLS-1) and the novel net analyte preprocessing (NAP). The method is rapid, selective and sensitive, and is based on the measurement of the fluorescence spectra of NH₃ alkalinized whole human sera at the excitation wavelength of 315 nm. It can be applied within the ranges of concentrations 50-200 ng ml⁻¹ for naproxen and 100-300 ng ml⁻¹ for salicylate. The employed chemometric techniques have been compared on the basis of the statistical indicators for calibration and validation. Reproducibility and interference studies in abnormal sera have also been carried out.     

Radial basis function network calibration model for near-infrared spectra in wavelet domain using a genetic algorithm

Near-infrared (NIR) spectrometry is now widely used in various fields and great attention is paid to the application of it to addressing complex problems, which brings about the need for the calibration of systems that fail to exhibit satisfactional linear relationship between input-output data. In this work we present a novel method to build a multivariate calibration model for NIR spectra, i.e. genetic algorithm-radial basis function network in wavelet domain (WT-GA-RBFN), which combines the advantages of wavelet transform and genetic algorithm. The variable selection is accomplished in two stages in wavelet domain: at the first stage, the variables are pre-selected (compressed) by variance and at the second stage the variables are further reduced by a special designed GA. The proposed method is illustrated through presenting its application to three NIR data sets in different fields and the comparison to PLS model.