Study of the similarity between distribution maps of concentration in near-infrared spectroscopy chemical imaging obtained by different multivariate calibration approaches

Nowadays, near-infrared spectroscopy chemical imaging (NIR-CI) has been widely used in pharmaceutical analysis since it provides important surface information about the samples. In this work the information of NIR-CI at the pixel level was compared through calculation of the similarity between distribution maps of concentration obtained by different multivariate calibration approaches. The comparison was performed by using four different multivariate methods (MCR, MLR, CLS and PLS) in analysis of carbamazepine pharmaceutical formulations. For global determination, all models developed showed RMSEP below 1.9% (w/w) for active principal ingredient (API) and better than 4.6% (w/w) for excipients. Also, the distribution maps obtained by PLS, CLS and MCR showed great similarity for all compounds of the formulation as well with concentrations in the tablets. However, comparing the distribution maps obtained by MLR with those from the other chemometric tools, a lower similarity was observed. Thus, this fitted model does not ensure, by itself, that the images obtained are reliable or accurate. The paper also compares the distribution maps of concentrations obtained from all constituents present in the pharmaceutical formulation with their respective micrographs.     

Determining the quality of insulating oils using near infrared spectroscopy and wavelength selection

This study presents an analytical method for determining interfacial tension and relative density in insulating oils using near infrared spectrometry (NIR). Five different strategies of regression were evaluated: partial least squares (PLS) with significant regression coefficients selected by jack-knife algorithm; interval PLS (iPLS); multiple linear regression (MLR) with variable selection by genetic algorithm (MLR/GA), successive projections algorithm (MLR/SPA) and stepwise strategy (SR/MLR). The overall results point to MLR/SPA as the best modeling strategy. The strategy is simpler and uses fewer spectral variables.     

Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel

This paper evaluates analytical methods based on near infrared (NIR) and middle infrared (MIR) spectroscopy and multivariate calibration to monitor the stability of biodiesel. There was a focus on three parameters: oxidative stability index, acid number and water content. Ethylic and methylic biodiesel from different feedstocks were used in experiments of accelerated aging, in order to take into account the wide variety of oilseeds and feedstocks available in Brazil. Partial least squares (PLS) and multiple linear regression (MLR) models were developed. Different pre-processing techniques and spectral variable/regions selection algorithms were evaluated. For MLR models, the successive projection algorithm (SPA) was employed. Interval PLS (iPLS) and selection of variables taking into account the significant regression coefficients were used for PLS models. Results showed that both near and middle infrared regions, and all variable selection methods tested were efficient for predicting these three important quality parameters of B100, the root mean squares error of prediction (RMSEP) values being comparable to the reproducibility of the corresponding standard method for each property investigated.     

Data exploration on standard asphalt mix analyses

The purpose of our study was the evaluation of the most important factors that affect the volumetric and conventional mechanical properties of produced asphalt mix and the volumetric properties of built‐in asphalt layer. Asphalt mix design follows the standard procedure (Marshall procedure). We were interested not only in the quantity of bitumen specified by the Marshall procedure, but also in the quantity of stone aggregate fractions, temperatures of production and properties of bitumen that is used. The influence of these factors was investigated with several models. For the building of models we used 444 asphalt samples, analysed by one laboratory. To select the most important factors, several multiple linear regression (MLR) models, partial least squares (PLS) regression models and counterpropagation neural network models were made. Obtained models were tested with leave‐one‐out (LOO) and leave‐10%‐out cross‐validation procedures. The results of MLR and PLS models show that the independent variables are closely related. Among 21 variables there is only one found as less important. MLR and PLS models show better predictive ability than counterpropagation neural network models. The best MLR models will be employed for the preparation of the asphalt mix design (recipe) with some unknown material. Copyright © 2009 John Wiley & Sons, Ltd.     

Flow-batch technique for the simultaneous enzymatic determination of levodopa and carbidopa in pharmaceuticals using PLS and successive projections algorithm

An enzymatic flow-batch system with spectrophotometric detection was developed for simultaneous determination of levodopa [(S)-2 amino-3-(3,4-dihydroxyphenyl)propionic acid] and carbidopa [(S)-3-(3,4-dihydroxyphenyl)-2-hydrazino-2-methylpropionic acid] in pharmaceutical preparations. The data were analysed by univariate method, partial least squares (PLS) and a novel variable selection for multiple lineal regression (MLR), the successive projections algorithm (SPA). The enzyme polyphenol oxidase (PPO; EC 1.14.18.1) obtained from Ipomoea batatas (L.) Lam. was used to oxidize both analytes to their respective dopaquinones, which presented a strong absorption between 295 and 540 nm. The statistical parameters (RMSE and correlation coefficient) calculated after the PLS in the spectral region between 295 and 540 nm and MLR-SPA application were appropriate for levodopa and carbidopa. A comparative study of univariate, PLS, in different ranges, and MLR-SPA chemometrics models, was carried out by applying the elliptical joint confidence region (EJCR) test. The results were satisfactory for PLS in the spectral region between 295 and 540 nm and for MLR-SPA. Tablets of commercial samples were analysed and the results obtained are in close agreement with both, spectrophotometric and HPLC pharmacopeia methods. The sample throughput was 18 h(-1).     

The determination of the fatty acid content of sea buckthorn seed oil using near infrared spectroscopy and variable selection methods for multivariate calibration

This study proposes an analytical method for the simultaneous near infrared (NIR) spectrometric determination of palmitic, oleic, linoleic and linolenic acids in sea buckthorn seed oil. For this purpose, four different combinations of multivariate calibration methods and variable selections were evaluated: partial least squares (PLS) with full spectrum; PLS with uninformative variables elimination (UVE); PLS with competitive adaptive reweighted sampling (CARS); and multiple linear regression (MLR) with uninformative variable elimination combined with successive projections algorithm (UVE-SPA). An independent set of samples was employed to evaluate the performance of the resulting models. The UVE-SPA-MLR model developed with a few spectral variables provided the best results for each parameter. The values of relative errors of prediction (REP) from the UVE-SPA-MLR model for palmitic, oleic, linoleic and linolenic acids are 1.77%, 1.20%, 1.02% and 1.40%, respectively. These results indicate that this method is a feasible and fast method for the determination of the fatty acid content of sea buckthorn seed oil.     

Rapid determination of sucrose in chocolate mass using near infrared spectroscopy

This paper reports the results of a rapid method to determine sucrose in chocolate mass using near infrared spectroscopy (NIRS). We applied a broad-based calibration approach, which consists in putting together in one single calibration samples of various types of chocolate mass. This approach increases the concentration range for one or more compositional parameters, improves the model performance and requires just one calibration model for several recipes. The data were modelled using partial least squares (PLS) and multiple linear regression (MLR). The MLR models were developed using a variable selection based on the coefficient regression of PLS and genetic algorithm (GA). High correlation coefficients (0.998, 0.997, 0.998 for PLS, MLR and GA-MLR, respectively) and low prediction errors confirms the good predictability of the models. The results show that NIR can be used as rapid method to determine sucrose in chocolate mass in chocolate factories.     

Determination of Quercetin and Luteolin in Paprika Samples by Voltammetry and Partial Least Squares Calibration

Quercetin and luteolin are flavonoids with beneficious properties, which are present in paprika. In this work, both have been determined in paprika by using electrochemistry combined with chemometrics. The electrochemical oxidation mechanisms of both analytes have been studied through sampled direct current (DC) voltammetry, differential pulse voltammetry (DPV) and Square Wave Voltammetry (SWV), making use of a glassy carbon electrode. The final technique selected for the quantification was DPV due to its high repeatability with respect SWV. The chemical variables and the instrumental parameters were optimized and the final conditions employed were ethanol: water (20 : 80), 0.75 mol dm⁻³ of HCl, and a pulse amplitude of 50 mV. Due to the facts that oxidation potential of both analytes were quite similar, their DPV peaks were overlapped, and also because the analytes interaction during the electrochemical process causes a non‐additivity of the signals, they could not be quantified separately by direct measurement of peak intensity. For this reason, a chemometric algorithm was applied (partial least squares (PLS) regression in its modality PLS‐2). In the case of validation samples, appropriate sets of calibration and validation were built and good results were obtained. This methodology was applied to real paprika samples and the results were similar to those obtained with a HPLC method previously reported.     

Quantitative determination of acetylsalicylic acid and acetaminophen in tablets by FT-Raman spectroscopy

A procedure for quantitative determination of acetylsalicylic acid and acetaminophen in pharmaceuticals by PLS (partial least squares) and PCR (principal component regression) treatment of FT (Fourier transform)-Raman spectroscopic data is proposed. The proposed method was tested on powdered samples. Three chemometric models were built: the first, for samples consisting of an active substance diluted by lactose, starch and talc; the second, in which a simple inorganic salt was applied as an internal standard and additions were not taken into account; and the third, in which a model was constructed for a commercial pharmaceutical, where all constituents of the tablet were known. By utilising selected spectral ranges and by changing the chemometric conditions it is possible to carry out fast and precise analysis of the active component content in medicines on the basis of the simplified chemometric models. The proposed method was tested on five commercial tablets. The results were compared with data obtained by intensity ratio and pharmacopoeial methods. To appraise the quality of the models, the relative standard error of predictions (RSEPs) were calculated for calibration and prediction data sets. These were 0.7-2.0% and 0.8-2.3%, respectively, for the different PLS models. Application of these models to the Raman spectra of commercial tablets containing acetylsalicylic acid gave RSEP values of 1.3-2.0% and a mean accuracy of 1.2-1.7% with a standard deviation of 0.6-1.2%.     

Solving matrix effect,spectral overlapping and nonlinearity by generalized standard addition method coupled with radial basis functions–partial least squares: simultaneous determination of atorvastatin and amlodipine in urine

For simultaneous determination in conditions with spectral overlap and variation of matrix effects, coupling of the generalized standard addition method (GSAM) with the multivariate nonlinear method of radial basis function–partial least squares (RBF–PLS) was proposed. The nonlinearity caused by the GSAM used to correct matrix effects was studied, and principal component analysis was proposed for identifying it. In the method introduced, the whole sensor range can be used without the collinearity problem encountered in the application of GSAM with classical least squares (CLS), and calibration can be made for each analyte, separately. The introduced method was applied to determine amlodipine and atorvastatin in urine samples. The mean of the percent recoveries was between 95 and 101.12. The percent relative standard deviation values of the method were in most cases below 5%. The results of GSAM–RBF–PLS were compared with those obtained by GSAM–CLS and GSAM–PLS. Copyright © 2013 John Wiley & Sons, Ltd.     

Simultaneous spectrophotometric determination of minoxidil and tretinoin by the H-point standard addition method and partial least squares

A simple, sensitive and selective spectrophotometric method for simultaneous determination of tretinoin and minoxidil using partial least square (PLS) calibration and H-point standard addition method (HPSAM) is described. The results of the H-point standard addition method show that minoxidil and tretinoin can be determined simultaneously with the concentration ratio of tretinoin to minoxidil varying from 2: 1 to 1: 33 in mixed samples. A partial least squares multivariate calibration method for the analysis of binary mixtures of tretinoin and minoxidil was also developed. The total relative standard error for applying the PLS method to eleven synthetic samples in the concentration range of 0–10 μg mL⁻¹ tretinoin and 0–32 μg mL⁻¹ minoxidil was 2.59 %. Both proposed methods (PLS and HPSAM) were also successfully applied in the determination of tretinoin and minoxidil in several synthetic pharmaceutical solutions.     

支持向量机分类和回归用于肽的QSAR研究

使用支持向量机技术对两类肽化合物体系进行了分类和回归研究,并将其系统地与K最邻近法、多元线性回归、偏最小二乘、人工神经网络进行了比较。结果表明,对于小样本、非线性问题,支持向量机具有较强的稳定性能及泛化能力,在大多数情况下能够得到优于传统方法的建模效果。对于分类问题,支持向量机对训练集和测试集都达到了100%的分类正确率;对于回归问题,支持向量机虽对训练集样本拟合效果略低于人工神经网络,但对外部测试集却表现出较强的预测能力。     

Polynomial multivariate least‐squares regression for modeling nonlinear data applied to in‐depth characterization of chromatographic resolution

Well‐established, linear multivariate calibration methods such as multivariate least‐squares regression (MLR), principal component regression (PCR), or partial least squares (PLS) have two limitations: (i) measured data must be linearly related to the response variables and (ii) predictor variables x_{n = 1, …, N} cannot be coupled to each other. For evaluation of nonlinear data, however, these restrictions need to be overcome and thus polynomial multivariate least‐squares regression (PMLR or “response surfaces”) has been introduced here. PMLR is based on multivariate least squares but incorporates all combinations of predictor variables up to a user‐selected polynomial order (e.g., including u or v = 0). Because of the inclusion of such coupled terms and their powers, PMLR models are better adapted to model nonlinear data and can help to enhance the prediction step's accuracy and precision. PMLR has been based on MLR because it facilitates—unlike PCR or PLS—a physical and chemical interpretation of the predictors. Hence, the origins and the relevance of nonlinear and/or coupled predictors can be investigated. The details of the PMLR algorithm and its implementation are presented along with a method for model optimization utilizing gradients of response surfaces. Newly developed PMLR models up to quintic order have been applied to predict a chromatograph's peak resolution as a function of six‐instrument parameters. It has been demonstrated that PMLR is better capable than MLR and PCR to describe these nonlinear and coupled instrument parameters. In addition, the novel software tool has been utilized for model optimization to determine instrument parameters, which result in the best chromatographic resolution. Copyright © 2011 John Wiley & Sons, Ltd.     

New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. However, the repeatability of the number of selected variables was improved significantly.     

Solid phase extraction and HPLC determination of veterinary pharmaceuticals in wastewater

This work focuses on the application of SPE-HPLC analysis of important veterinary pharmaceuticals from different classes in highly complex wastewater matrix. The pharmaceutical investigated included three sulfonamides (sulfamethazine, sulfadiazine and sulfaguanidine), a sulfonamide synergist (trimethoprim), a tetracycline (oxytetracycline), a fluoroquinolone (enrofloxacine) and a β-lactame (penicillin G/procaine). The method involves pre-concentration and clean-up by solid phase extraction (SPE) using Oasis HLB extraction catridges. Final analysis of the selected pharmaceutical compounds was carried out by high-performance liquid chromatography (HPLC) coupled with diode array detector (DAD). Recoveries were ranged from 68.3 to 97.9% with relative standard deviation below 8.4%. Only for sulfaguanidine low recovery was obtained. Limits of quantification were in the range 1.5-100 μg/L depending on pharmaceutical. The described method was applied to the determination of pharmaceuticals in wastewater samples from pharmaceutical industry.