Multivariate calibration techniques applied to the spectrophotometric analysis of one-to-four component systems

The UV spectrophotometric analysis of a multicomponent mixture containing paracetamol, caffeine, tripelenamine and salicylamide by using multivariate calibration methods, such as principal component regression (PCR) and partial least-squares regression (PLS), was described. The calibration set was based on 47 reference samples, consisting of quaternary, ternary, binary and single-component mixtures, with the aim to develop models able to predict the concentrations of unknown samples containing as many as one-to-four components. The calibration models were optimized by an appropriate selection of the number of factors as well as wavelength ranges to be used for building up the data matrix and excluding any information about the interfering excipients included in pharmaceutics. The PCR and PLS models were compared and their predictive performance was inferred by a successful application to the assays of synthetic mixtures and pharmaceutical formulations.     

Simultaneous Spectrophotometric Determination of Aspirin,Paracetamol, Caffeine,and Chlorphenamine from Pharmaceutical Formulations Using Multivariate Regression Methods

This paper presents a simultaneous spectrophotometric determination of aspirin, paracetamol, caffeine, and chlorphenamine from commercial pharmaceutical products using principal component regression and partial-least squares regression. The concentration of the training set was established employing a partial factorial calibration design at four levels. Several quality parameters and recovery values obtained on authentic samples illustrated excellent performance characteristics concerning the goodness of fit and the accuracy and precision of prediction. Eight pharmaceutical formulations containing at least two of these four mentioned active ingredients and diverse electuaries were successfully analyzed. The obtained results were also validated by high-performance liquid chromatography.     

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.     

Influence of mixture design on multivariate prediction of PAHs in mixture spectra

This work describes a simulation study aimed at establishing the impact of mixture design on the prediction ability of PLS regression models. Data sets are formed by multiplying UV absorbance spectra of 12 PAHs by their concentration profiles. In these case studies, either all possible mixtures of 1-12 components are used or randomly chosen selections of the mixtures. The effects of the number of samples and the number of concentration levels in the mixture designs on the results of the calibration are assessed. Comparisons are made between models formed using orthogonal fractional factorial mixture designs and those based on random designs. The applicability limits of the orthogonal designs are analysed in terms of actual concentration ranges of individual components in the mixtures.     

Simultaneous determination of organotin compounds by hydride generation-gas phase molecular absorption spectrometry

This study describes the determination of ternary mixtures of dimethyltin chloride (DMT), trimethyltin chloride (TMT) and monobutyltin chloride (BT) by hydride generation-gas phase molecular absorption spectrometry and the application of different chemometric methods: principal components regression (PCR) and partial least squares (PLS). The two methods are applied to the absorption spectra of mixtures of DMT, TMT and BT. Two different experimental designs are tested for the mixtures, a triangular design and a central composite design. The models obtained from the triangular design offer the best prediction results. The effects of the number of working wavelengths and the number of factors included in the calibration model is studied and a different behaviour is seen for each compound and calibration model. The methods are applied to the analysis of artificial aqueous samples containing different concentrations of DMT, TMT and BT species. No significant differences are observed between the calibration models investigated.     

Quantification of the intensity of virgin olive oil sensory attributes by direct coupling headspace-mass spectrometry and multivariate calibration techniques

The main sensory defects of virgin olive oils (rancid, vinegary, winey, muddy sediment, musty and vegetable water) and one positive attribute (fruity) characteristic of three monovarietal extra virgin olive oils (Arbequina, Picual and Frantoio) have been quantified using the direct coupling headspace-mass spectrometry. The results obtained were compared with those provided by the panel test for the same samples. Taking into account that no chromatographic separation exists, multivariate calibration techniques (partial least squares, PLS, and principal components regression, PCR) were used to create the appropriate models. The best results, in terms of standard error of prediction and prediction residual error sum of squares were obtained by PLS and therefore it was used for the prediction of a new set of samples with the above-mentioned positive and negative attributes at different concentration levels. The samples were also assessed by the panel test and good correlations were obtained in all cases. In order to extend the applicability of the model with the time, a multiplicative calibration transfer was used. The benefit of this approach was found to be more marked for the negative than the positive attributes.     

Determination of ascorbic acid in pharmaceutical preparations by near infrared reflectance spectroscopy

We developed a method for determination of ascorbic acid in pharmaceutical preparations containing various excipients by using near infrared diffuse reflectance spectroscopy and two different calibration methods, viz. stepwise multiple linear regression (SMLR) and partial least-squares (PLS) regression, which provided comparable results and resulted in prediction errors of 1-2%. However, the PLS method provided somewhat better results with the more complex samples.     

Evaluation of selectivity and robustness of near-infrared glucose measurements based on short-scan Fourier transform infrared interferograms

The selectivity and robustness of near-infrared (near-IR) calibration models based on short-scan Fourier transform (FT) infrared interferogram data are explored. The calibration methodology used in this work employs bandpass digital filters to reduce the frequency content of the interferogram data, followed by the use of partial least-squares (PLS) regression to build calibration models with the filtered interferogram signals. Combination region near-IR interferogram data are employed corresponding to physiological levels of glucose in an aqueous matrix containing variable levels of alanine, sodium ascorbate, sodium lactate, urea, and triacetin. A randomized design procedure is used to minimize correlations between the component concentrations and between the concentration of glucose and water. Because of the severe spectral overlap of the components, this sample matrix provides an excellent test of the ability of the calibration methodology to extract the glucose signature from the interferogram data. The robustness of the analysis is also studied by applying the calibration models to data collected outside of the time span of the data used to compute the models. A calibration model based on 52 samples collected over 4 days and employing two digital filters produces a standard error of calibration (SEC) of 0.36 mM glucose. The corresponding standard errors of prediction (SEP) for data collected on the 5th (18 samples) and 7th (10 samples) day are 0.42 and 0.48 mM, respectively. The interferogram segment used for the analysis contained only 155 points. These results are compatible with those obtained in a conventional analysis of absorbance spectra and serve to validate the viability of the interferogram-based calibration.     

Assessment of dentifrice adulteration with diethylene glycol by means of ATR-FTIR spectroscopy and chemometrics

A direct and fast method for determination of the adulterant diethylene glycol (DEG) in toothpaste and gel dentifrices combining attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy with partial least squares (PLS) regression has been proposed. Considering the high heterogeneity of dentifrices available in the market, the possibility of reducing the number of calibration samples for PLS was evaluated. Similar prediction performance was achieved by both employing a large calibration set of 20 dentifrices spiked with different amounts of DEG and a reduced calibration set of seven ones selected by means of hierarchical cluster analysis (HCA). The feasibility of using the simple calibration model to predict DEG adulteration in a wide variety of unknown dentifrice samples increases the applicability of the proposed method. With this approach, DEG was quantified with a root mean squared error of prediction value of 1.1% for a validation set of 40 different dentifrices containing DEG in the range 0–16% (w:w).     

偏最小二乘回归用于近红外光谱分析的稳健策略

偏最小二乘法（PLS）在近红外光谱（NIR）定量分析中占有重要地位,但预测结果往往容易受到样本分组和奇异样本等因素的影响,稳健性不强。多模型PLS (EPLS）方法在模型稳健性上得到提高,然而它无法识别样本中存在的奇异样本。为了同时提高模型的预测准确性和稳健性,本文提出了一种根据取样概率重新取样的多模型PLS方法,称为稳健共识PLS（RE-PLS）方法。该方法通过迭代赋权偏最小二乘法(IRPLS)计算样本回归残差得到每个校正集样本的取样概率,然后根据样本的取样概率来选择训练子集建立多个PLS模型,最后将所有PLS模型的预测结果平均作为最终预测结果。该方法用于两种不同植物样品的近红外光谱建模,并与传统的PLS及EPLS方法进行比较。结果表明该方法可以有效的避免校正集中奇异样本对模型的影响,同时可以提高预测精确度和稳健性。对于含有较多奇异样本的,复杂近红外光谱烟草实际样本,利用简单PLS或者EPLS方法建模预测效果不是很理想,而RE-PLS凭借其独特优势则有望在这种复杂光谱定量分析中得到广泛的应用。     

Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.     

Quantitative Analysis of Catalpol in Chinese Patent Medicine Lixin Pill by Near-infrared Diffuse Reflectance Spectroscopy

Lixin pill is a typical Chinese patent medicine with anti-rheumatic heart disease activity that has been widely used in clinical practice. Therefore it is very important to detect the concentration of catalpol, as the main component of the active ingredient. Near-infrared reflectance（NIR） spectroscopy was used to study the content of catalpol in the unprocessed Chinese patent medicine of Lixin pills. NIR is applied to quantitatively analyze 77 sam- ples, which were randomly divided into a calibration set containing 61 samples and a prediction set containing 16 samples. To get a satisfying result, partial least squares（PLS） regression was utilized to establish quantitative models. In PLS regression, the values of coefficient of determination（R2） and root mean square error of cross-validation （RMSECV） of PLS regression are 0.9419 and 0.0216, respectively. The process of establishing model, parameters of model, and prediction results were also discussed in detail（root mean square error of prediction is 0.0164）. The over- all results show that NIR spectroscopy can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in the Chinese patent medicine of Lixin pills. The prediction set suggests that this quantitative analysis model has excellent generalization ability and prediction precision. Accordingly, the result can provide tech- nical support for the further analysis of catalpol in unprocessed Lixin pill. Moreover, this study supplied technical support for the further analysis of other Chinese patent medicine samples.     

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).     

PARAFAC and PLS applied to spectrophotometric determination of tetracycline in pharmaceutical formulation and biological fluids

A simple and rapid analytical procedure was proposed for determination of tetracycline in pharmaceutical formulation, urine and plasma based on chemometrics methods and spectrophotometric measurements. The calibration set was constructed with twenty solutions in concentration range 0.25-13.00 microg ml(-1) for tetracycline. The procedure was repeated at nine different pH values. Partial least squares (PLS) models were built at each pH and used to determinate a set of synthetic tetracycline solutions. The best model was obtained at pH 8.00 (PLS-PH8). Parallel factor analysis (PARAFAC) model was applied to a three-way array constructed using all the pH data sets and enabled better results. The capabilities of the method for the analysis of real samples were evaluated by determination of tetracycline in pharmaceutical formulations and biological fluids with satisfactory results.     

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks

This study compares the performance of partial least squares (PLS) regression analysis and artificial neural networks (ANN) for the prediction of total anthocyanin concentration in red-grape homogenates from their visible-near-infrared (Vis-NIR) spectra. The PLS prediction of anthocyanin concentrations for new-season samples from Vis-NIR spectra was characterised by regression non-linearity and prediction bias. In practice, this usually requires the inclusion of some samples from the new vintage to improve the prediction. The use of WinISI LOCAL partly alleviated these problems but still resulted in increased error at high and low extremes of the anthocyanin concentration range. Artificial neural networks regression was investigated as an alternative method to PLS, due to the inherent advantages of ANN for modelling non-linear systems. The method proposed here combines the advantages of the data reduction capabilities of PLS regression with the non-linear modelling capabilities of ANN. With the use of PLS scores as inputs for ANN regression, the model was shown to be quicker and easier to train than using raw full-spectrum data. The ANN calibration for prediction of new vintage grape data, using PLS scores as inputs, was more linear and accurate than global and LOCAL PLS models and appears to reduce the need for refreshing the calibration with new-season samples. ANN with PLS scores required fewer inputs and was less prone to overfitting than using PCA scores. A variation of the ANN method, using carefully selected spectral frequencies as inputs, resulted in prediction accuracy comparable to those using PLS scores but, as for PCA inputs, was also prone to overfitting with redundant wavelengths.     

Simultaneous Quantification of Three Polymorphic Forms of Carbamazepine using Raman Spectroscopy and Multivariate Calibration

Carbamazepine is a pharmaceutical product used to treat epilepsy and bipolar disorder. Some active pharmaceutical ingredients, such as carbamazepine, present polymorphism that may alter the bioavailability. Consequently, the determination of different polymorphic forms has become important for the pharmaceutical industry. In this work, polymorphic forms were synthesized and characterized by differential scanning calorimetry and X-ray diffraction. Raman spectroscopy was used to quantify mixtures of the three common polymorphic forms of carbamazepine. A ternary mixture design was used to create the calibration set of ten samples and six levels of concentration for each polymorph. Partial least squares was performed to build the prediction models. Ten spectra were obtained to obtain representative Raman spectra of the mixtures. The calibration models were built using the average spectra, and an external set of samples was used to evaluate the models. The partial least squares model gave a root mean square error of prediction of 6.2% for carbamazepine I, 6.8% for carbamazepine III, and 11.6% for carbamazepine dihydrate. The results showed that good results were obtained for the solid state characterization of the mixtures of polymorphs using a fast strategy for simultaneous analysis.     

Standardization of SPE signals in multicomponent analysis of three benzimidazolic pesticides by spectrofluorimetry

Piecewise direct standardization (PDS) is applied to multivariate standardization of fluorescence signals using partial least squares (PLS) and principal component regression (PCR) as the calibration models. The multivariate standardization was used to transfer spectra obtained after a step of solid phase extraction (SPE) to spectra registered in pure solvent in the determination of carbendazim, fuberidazole and thiabendazole in water samples. The influential parameters, such as tolerance, window size and the number of samples of the standardization subset were optimized by means of the root mean squared error of prediction (RMSEP). Similar RMSEP values were obtained by PLS and PCR using the optimized influential parameters in the standardization. However, better predictions of the compounds were obtained in test set by the PLS model.     

Development of a univariate calibration model for pharmaceutical analysis based on NIR spectra

Near-infrared spectroscopy (NIRS) has been widely used in the pharmaceutical field because of its ability to provide quality information about drugs in near-real time. In practice, however, the NIRS technique requires construction of multivariate models in order to correct collinearity and the typically poor selectivity of NIR spectra. In this work, a new methodology for constructing simple NIR calibration models has been developed, based on the spectrum for the target analyte (usually the active principle ingredient, API), which is compared with that of the sample in order to calculate a correlation coefficient. To this end, calibration samples are prepared spanning an adequate concentration range for the API and their spectra are recorded. The model thus obtained by relating the correlation coefficient to the sample concentration is subjected to least-squares regression. The API concentration in validation samples is predicted by interpolating their correlation coefficients in the straight calibration line previously obtained. The proposed method affords quantitation of API in pharmaceuticals undergoing physical changes during their production process (e.g. granulates, and coated and non-coated tablets). The results obtained with the proposed methodology, based on correlation coefficients, were compared with the predictions of PLS1 calibration models, with which a different model is required for each type of sample. Error values lower than 1-2% were obtained in the analysis of three types of sample using the same model; these errors are similar to those obtained by applying three PLS models for granules, and non-coated and coated samples. Based on the outcome, our methodology is a straightforward choice for constructing calibration models affording expeditious prediction of new samples with varying physical properties. This makes it an effective alternative to multivariate calibration, which requires use of a different model for each type of sample, depending on its physical presentation.     

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 Spectrofluorimetric Determination of Paracetamol and Caffeine in Pharmaceutical Preparations in Solid‐Phase Using Partial Least Squares Multivariate Calibration

Abstract

Partial least‐squares algorithm (PLS)‐1 was used for the solid‐phase spectrofluorimetric determination of paracetamol (PA) and caffeine (CF) in pharmaceutical formulations. In despite of the closely overlapping spectral bands, the method allows the simultaneous quantification and sample preparation prior to analysis is not required. The calibration set consisted of 96 samples with 100–400 mg/g^?1 PA plus 10–65 mg/g^?1 CF; another set of 25 samples was used for external validation. Agreement between predicted and experimental concentrations was fair (r=0.993 and 0.964 for PA and CF models). Prediction performance was evaluated in terms of the coefficient of variability (CV), relative predictive determination (RPD), and ratio error range (RER). The PLS‐1 model was used for the determination of PA and CF in pharmaceutical formulations.