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.     

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.     

偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定荆为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合.结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%...     

Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms

The aim of this study was to evaluate evolutionary variable selection methods in improving the classification of ¹H nuclear magnetic resonance (NMR) metabonomic profiles, and to identify the metabolites that are responsible for the classification. Human plasma, urine, and saliva from a group of 150 healthy male and female subjects were subjected to ¹H NMR-based metabonomic analysis. The ¹H NMR spectra were analyzed using two pattern recognition methods, principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA), to identify metabolites responsible for gender differences. The use of genetic algorithms (GA) for variable selection methods was found to enhance the classification performance of the PLS-DA models. The loading plots obtained by PCA and PLS-DA were compared and various metabolites were identified that are responsible for the observed separations. These results demonstrated that our approach is capable of identifying the metabolites that are important for the discrimination of classes of individuals of similar physiological conditions.     

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 spectrophotometric determination of tartrazine, patent blue V, and indigo carmine in commercial products by partial least squares and principal component regression methods

Two multivariate calibration methods, partial least squares (PLS-1) and principal component regression (PCR) were proposed and successfully applied to the simultaneous determination of three dyes, tartrazine (T) (E-102), patent blue V (P) (E-131), and indigo carmine (I) (E-132) in mixtures by ultraviolet-visible absorption spectrophotometry. Calibration models were evaluated by internal validation (prediction of dyes concentration in its own designed training set of calibration), by cross-validation (obtaining statistical parameters that show the efficiency for a calibration fit model), and by external validation over 19 synthetic mixtures of the three dyes in different ratios containing 2.4-17.6 mg l(-1) of T, 1.6-5.6 mg l(-1) of P, and 3.2-17.5 mg/l(-1) of I, with recoveries between 93.5 and 103.1% and over three commercial products, in which the proposed calibration models were satisfactorily applied without separation step. Repeatability and reproducibility studies (with the Students's and F tests) were achieved over two series of nine standards for each dye, showing no significant differences at 95% confidence level.     

Simultaneous spectrophotometric determination of Tartrazine, Sunset Yellow and Ponceau 4R in commercial products by partial least squares and principal component regression multivariate calibration methods

Three multivariate calibration methods, partial least squares (PLS-1 and PLS-2) and principal component regression (PCR), were proposed and successfully applied to the simultaneous determination of three dyes, Tartrazine (E-102), Sunset Yellow (E-110) and Ponceau 4R (E-124) in mixtures by ultraviolet-visible absorption spectrophotometry. The designed and optimized training set of calibration was applied to the determination of the three dyes in several synthetic mixtures, containing 1.6–20.0 mg/L of Tartrazine, 3.2–40.0 mg/L of Sunset Yellow and 3.2– 36.0 mg/L of Ponceau 4R. 94.5–105.3% recovery values were obtained. Three commercial foods that contained the three dyes were also satisfactorily analyzed without separation step. The results obtained by the application of the three chemometric approaches in the commercial products are discussed and compared with those obtained by an HPLC method and very similar values were found by all methods. Repeatability and reproducibility studies (with the Student’s and F tests) were achieved over two series of nine standards for each dye, showing no significant differences at the 95% confidence level. Received: 27 October 1997 / Revised: 9 January 1998 / Accepted: 24 January 1998     

Simultaneous spectrophotometric determination of Tartrazine, Sunset Yellow and Ponceau 4R in commercial products by partial least squares and principal component regression multivariate calibration methods

Three multivariate calibration methods, partial least squares (PLS-1 and PLS-2) and principal component regression (PCR), were proposed and successfully applied to the simultaneous determination of three dyes, Tartrazine (E-102), Sunset Yellow (E-110) and Ponceau 4R (E-124) in mixtures by ultraviolet-visible absorption spectrophotometry. The designed and optimized training set of calibration was applied to the determination of the three dyes in several synthetic mixtures, containing 1.6–20.0 mg/L of Tartrazine, 3.2–40.0 mg/L of Sunset Yellow and 3.2– 36.0 mg/L of Ponceau 4R. 94.5–105.3% recovery values were obtained. Three commercial foods that contained the three dyes were also satisfactorily analyzed without separation step. The results obtained by the application of the three chemometric approaches in the commercial products are discussed and compared with those obtained by an HPLC method and very similar values were found by all methods. Repeatability and reproducibility studies (with the Student’s and F tests) were achieved over two series of nine standards for each dye, showing no significant differences at the 95% confidence level.     

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.     

Near-infrared imaging for quantitative analysis of active component in counterfeit dimethomorph using partial least squares regression

Near-infrared (NIR) imaging systems simultaneously record spectral and spatial information. Near-infrared imaging was applied to the identification of (E,Z)-4-(3-(4-chlorophenyl)-3-(3,4-dimethoxyphenyl)acryloyl)morpholine (dimethomorph) in both mixed samples and commercial formulation in this study. The distributions of technical dimethomorph and additive in the heterogeneous counterfeit product were obtained by the relationship imaging (RI) mode. Furthermore, a series of samples which consisted of different contents of uniformly distributed dimethomorph were prepared and three data cubes were generated for each content. The spectra extracted from these images were imported to establish the partial least squares model. The model??s evaluating indicators were: coefficient of determination (R ²) 99.42 %, root mean square error of calibration (RMSEC) 0.02612, root mean square error of cross-validation (RMSECV) 0.01693, RMSECV_mean 0.03577, relative standard error of prediction (RSEP) 0.01999, and residual predictive deviation (RPD) 15.14. Relative error of prediction of the commercial formulation was 0.077, indicating the predicted value correlated with the real content. The chemical value reconstruction image of dimethomorph formulation products was calculated by a MATLAB program. NIR microscopy imaging here manifests its potential in identifying the active component in the counterfeit pesticide and quantifying the active component in its scanned image.     

Multivariate analysis of remote laser-induced breakdown spectroscopy spectra using partial least squares,principal component analysis,and related techniques

Quantitative analysis with laser-induced breakdown spectroscopy traditionally employs calibration curves that are complicated by chemical matrix effects. These chemical matrix effects influence the laser-induced breakdown spectroscopy plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, laser-induced breakdown spectroscopy calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis techniques are employed to analyze the laser-induced breakdown spectroscopy spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis and Soft Independent Modeling of Class Analogy are employed to generate a model and predict the rock type of the samples. These Multivariate Analysis techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.     

主成分-偏最小二乘动力学分光光度法同时测定农药两组分

对氨基甲酸酯杀虫剂残杀威（PRO）和异丙威（ISO）的速差动力学光度法同时测定进行了研究。残杀威和异丙威均能在碱性条件下发生水解，水解生成的酚盐，均可以与对氨基苯酚及高碘酸钾混合物发生反应生成兰色化合物，且这是一个反应速率适中的动力学反应。本实验在538nm-700nm采集多个时间点下多个波长的动力学－－吸收光谱数据，构成量测矩阵。采用主成分－偏最小二乘法（PC－PLS）对测定数据进行了解析。本文对环境水样中残杀威和异丙威的含量进行了测定。取得了较好的分析结果。从而提出了一种易于实现，准确度高的残杀威和异丙威的同时测定新方法。     

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.     

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L⁻¹ for Penicillin V and 0.32 g L⁻¹ for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L⁻¹ for Penicillin V and 0.15 g L⁻¹ for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.     

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.     

Analysis of elements in wine using near infrared spectroscopy and partial least squares regression

The use of visible (VIS) and near infrared spectroscopy (NIRS) to measure the concentration of elements in Australian wines was investigated. Both white (n=32) and red (n=94) wine samples representing a wide range of varieties and regions were analysed by inductively coupled plasma mass spectrometry (ICP-MS) for the concentrations of calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), sodium (Na), sulphur (S), iron (Fe), boron (B) and manganese (Mn). Samples were scanned in transmittance mode (1mm path length) in a monochromator instrument (400-2500nm). The spectra were pre-treated by second derivative and standard normal variate (SNV) prior to developing calibration models using partial least squares (PLS) regression method with cross-validation. The highest coefficients of determination in cross-validation (R(val)(2)) and the lowest errors of cross-validation (SECV) were obtained for Ca (0.90 and 9.80mgL(-1)), Fe (0.86 and 0.65mgL(-1)) and for K (0.89 and 147.6mgL(-1)). Intermediate R(val)(2) (<0.80) and SECV were obtained for the other minerals analysed. The results showed that some macro- and microelements present in wine might be measured by VIS-NIRS spectroscopy.     

Fluorometric determination of mixtures of quinolones by means of partial least squares and neural networks.

Multivariate calibration methods (partial least squares calibration, back propagation multilayer perceptrons networks, radial basis functions and generalized regression neural networks) were applied to the simultaneous fluorometric quantification of levofloxacin, garenoxacin and grepafloxacin, without previous separation steps. A data matrix was obtained by registering the emission spectra of mixtures of the three quinolones in urine (with concentrations ranging over 0.00-0.40 microg mL-1 for each quinolone) with a 283 nm excitation at pH 4.0. The generalized regression neural network model proved to be the most adequate model for simultaneous quantification of the three quinolones in urine samples.     

Optimisation of sensitivity in the multi-elemental determination of 83 isotopes by ICP-MS as a function of 21 instrumental operative conditions by modified simplex, principal component analysis and partial least squares

The optimisation of the sensitivity in the ICP-MS determination of 83 isotopes, as a function of 21 operative parameters was performed by generating an initial experimental design that was used to define, by principal component analysis, the multi-criteria target function. The first PC, which contained an overall evaluation of the signal intensity of all isotopes, was used to rank the experiments. The modified simplex optimisation technique was then applied on the ranked experiments. The increase in signal intensity was, on the average, 3.9 times for the isotopes considered for the simplex procedure. When finally convergence was achieved, a PLS regression model calculated on the available experiments allowed to investigate the effect played by each factor on the experimental response. Simplex and PCA proved to be extremely effective to obtain the optimisation and to generate the multi-criteria target function: they can be suggested as an automatic method to perform the optimisation of the instrumental operative conditions.     

Determination of carbohydrates present in Saccharomyces cerevisiae using mid-infrared spectroscopy and partial least squares regression

A fast and simple method to control variations in carbohydrate composition of Saccharomyces cerevisiae, baker's yeast, during fermentation was developed using mid-infrared (mid-IR) spectroscopy. The method allows for precise and accurate determinations with minimal or no sample preparation and reagent consumption based on mid-IR spectra and partial least squares (PLS) regression. The PLS models were developed employing the results from reference analysis of the yeast cells. The reference analyses quantify the amount of trehalose, glucose, glycogen, and mannan in S. cerevisiae. The selection and optimization of pretreatment steps of samples such as the disruption of the yeast cells and the hydrolysis of mannan and glycogen to obtain monosaccharides were carried out. Trehalose, glucose, and mannose were determined using high-performance liquid chromatography coupled with a refractive index detector and total carbohydrates were measured using the phenol–sulfuric method. Linear concentration range, accuracy, precision, LOD and LOQ were examined to check the reliability of the chromatographic method for each analyte.