Concentration dependent red shift: qualitative and quantitative investigation of motor oils by synchronous fluorescence scan

Synchronous fluorescence scan (SFS) has been described as a successful technique to characterize Motor oils like diesel, petrol, kerosene, 2T oil and Mobil. The concentration dependent investigation of Motor oils shows a red shift in lambda(SFS)(max). Using red shift of lambda(SFS)(max), a method has been developed to quantify Motor oil in the concentration range 5-100% v/v. The concentration dependent overall rate of energy transfer of Motor oil gives a unique behavioral change according to the oil type and SFS is a simpler spectroscopic method to qualitatively differentiate between heavy and light oil. The molecular interaction of polycyclic aromatic compounds (PACs) in fluorophoric mixtures like resonance energy transfer and self-quenching via solvent collision has been clearly explained by SFS method. Effect of solvent and external quencher molecule on Motor oils has also been studied. Nitrobenzene is found to be a selective quencher for PACs of Motor oils.     

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.     

Recent developments in multi-component synchronous fluorescence scan analysis

The ability to analyse complex multi-component mixtures without resorting to tedious separation procedures is extremely useful for routine analysis. Single-wavelength fluorescence measurement is limited in its ability to analyse complicated multi-component samples when they have severely overlapping emission and/or excitation spectra. This can be overcome by using synchronous fluorescence scan (SFS), where overlapping of spectra can be minimized. The selectivity of SFS can still be increased by taking derivative spectrum, applying different multivariate methods, selective fluorescence quenching, three-dimensional synchronous measurement or using some of these procedures in combination. Recent developments in various synchronous fluorescence methods for analysis of multi-component systems are discussed in this review.     

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.     

Comparison of near-infrared and mid-infrared spectroscopy for the determination of distillation property of kerosene

Near-infrared (NIR) and mid-infrared (MIR) spectroscopy have been compared and evaluated for the determination of the distillation property of kerosene with the use of partial least squares (PLS) regression. Since kerosene is a complex mixture of similar hydrocarbons, both spectroscopic methods will be best evaluated with this complex sample matrix. PLS calibration models for each percent recovery temperature have been developed by using both NIR and MIR spectra without spectral pretreatment. Both methods have shown good correlation with the corresponding reference method, however NIR provided better calibration performance over MIR. To rationalize the improved calibration performance of NIR, spectra of the same kerosene sample were continuously collected and the corresponding spectral reproducibility was evaluated. The greater spectral reproducibility including signal-to-noise ratio of NIR led to the improved calibration performance, even though MIR spectroscopy provided more qualitative spectral information. The reproducibility of measurement, signal-to-noise ratio, and richness of qualitative information should be simultaneously considered for proper selection of a spectroscopic method for quantitative analysis.     

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.     

Two Reliable Simple Relationships between Flash Points of Hydrocarbon Kerosene Fuels and Their Molecular Structures

This study presents two new reliable simple correlations for predicting flash point of kerosene hydrocarbons using multiple linear regression method. The methodology assumes that the flash point of kerosene fuels can be expressed as a function of elemental composition and several structural parameters. The proposed correlations have determination coefficients of 0.910 and 0.977. Also, the first model has root mean square deviation (RMSD) and the average absolute deviations (AAD) of 10.6 and 8.2 K, respectively, for 111 kerosene fuels with different molecular structures as training set. The RMSD and AAD for the second improved model are 5.39 and 4.33 K, respectively. The predictive power of two correlations is checked using a cross validation method. (R² = 0.977, Q²_Ext = 0.975, and Q²_LMO = 0.979). Also, these correlations give good predictions for further 25 kerosene fuels as test set. The proposed model can also be applied for designing novel kerosene fuels.     

Supervised principal components: a new method for multivariate spectral analysis

The supervised principal components (SPC) method was proposed by Bair and Tibshirani for statistics regression problems where the number of variables greatly exceeds the number of samples. This case is extremely common in multivariate spectral analysis. The objective of this research is to apply SPC to near‐infrared and Raman spectral calibration. SPC is similar to traditional principal components analysis except that it selects the most significant part of wavelength from the high‐dimensional spectral data, which can reduce the risk of overfitting and the effect of collinearity in modeling according to a semi‐supervised strategy. In this study, four conventional regression methods, including principal component regression, partial least squares regression, ridge regression, and support vector regression, were compared with SPC. Three evaluation criteria, coefficient of determination (R²), external correlation coefficient (Q²), and root mean square error of prediction, were calculated to evaluate the performance of each algorithm on both near‐infrared and Raman datasets. The comparison results illustrated that the SPC model had a desirable ability of regression and prediction. We believe that this method might be an alternative method for multivariate spectral analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

一类用于多元光度分析的小波基主成分回归法

通过将变尺度小波分解滤噪与特征信息提取相结合,提出一类新的多元光度分析算法－小波基主成分回归（ＰＣＲＷ）方法。该法可有效地减小主成分向量残留噪声所引 误差显著提高多元校正准确性。将其用于分析氯霉素、醋酸对塞米松、尼泊金乙脂体系、得到满意的回收率,与ＰＣＲ法相比,分析结果的总平均相对误差从３．３８％降低到０．８３％。     

Spectrophotometric determination of hydrocortisone, nystatin and oxytetracycline in synthetic and pharmaceutical preparations based on various univariate and multivariate methods

Two spectrophotometric methods are described and applied to resolve ternary mixtures of the corticosteroid hydrocortisone (HYD) and the antibiotics nystatin (NYS) and oxytetracycline (OXY). The simultaneous determination of these three compounds was firstly accomplished by a derivative method using the “ratio spectrum-zero crossing derivative” and then by multivariate methods partial least squares (PLS)-1, -2 and principal component regression (PCR). Multivariate calibration methods provide, specially PLS-2 in this case, a clear example of the high resolving powder of these techniques. The two described procedures do not require any separation step. Repeatability and reproducibility studies were achieved over two series of 10 standards for each compound showing no significant differences at 95% confidence level in the four spectrophotometric methods. A comparison of the derivative and multivariate calibration results obtained in pharmaceutical formulations was performed resulting in agreement of the values obtained and the results was confirm by a high-pressure liquid chromatography (HPLC) method.     

Generation and mid-IR measurement of a gas-phase to predict security parameters of aviation jet fuel

The worldwide use of kerosene as aviation jet fuel makes its safety considerations of most importance not only for aircraft security but for the workers’ health (chronic and/or acute exposure). As most kerosene risks come from its vapours, this work focuses on predicting seven characteristics (flash point, freezing point, % of aromatics and four distillation points) which assess its potential hazards. Two experimental devices were implemented in order to, first, generate a kerosene vapour phase and, then, to measure its mid-IR spectrum. All the working conditions required to generate the gas phase were optimised either in a univariate or a multivariate (SIMPLEX) approach. Next, multivariate prediction models were deployed using partial least squares regression and it was found that both the average prediction errors and precision parameters were satisfactory, almost always well below the reference figures.     

基于分子参数的药物小肠吸收预测模型

选择100个化合物作为数据集,随机选取其中80个为训练集,其他分子为验证集,并为每个化合物分子计算了30个参数.通过采用五种不同多元线性回归分析方法对其训练模拟,建立了数学模型,并用验证集检验了所建模型的预测能力.结果发现向后筛选法为最优小肠吸收建模方法.由该法所建模型的统计结果良好(R2>0.80),应用于验证集时也表现出较强预测能力.该模型确定了对小肠吸收影响较大的分子参数,有助于指导进一步的新药筛选和开发.     

Butene concentration prediction in ethylene/propylene/1-butene terpolymers by FT-IR spectroscopy through multivariate statistical analysis and artificial neural networks

This paper reports the development of calibration models for quality control in the production of ethylene/propylene/1-butene terpolymers by the use of multivariate tools and FT-IR spectroscopy.1-Butene concentration prediction is achieved in terpolymers by coupling FT-IR spectroscopy to multivariate regression tools. A dataset of 26 terpolymers (14 coming from a constrained experimental design for mixtures, plus 12 terpolymers used for external validation) was analysed by FT-IR spectroscopy. An internal method of “Polimeri Europa” plant, based on ¹³C NMR spectroscopy is used to determine the percentage of 1-butene in the samples. Then, different multivariate tools are used for 1-butene concentration prediction based on the FT-IR spectra recorded. Different multivariate calibration methods were explored: principal component regression (PCR), partial least squares (PLS), stepwise OLS regression (SWR) and artificial neural networks (ANNs). The model obtained by back-propagation neural networks turned out to be the best one. The performances of the BP-ANN model were further improved by variable selection procedures based on the calculation of the first derivative of the network.The proposed approach allows the monitoring in real time of the polymer synthesis and the estimation of the characteristics of the product attainable from the concentration of 1-butene.     

Simultaneous spectrophotometric determination of cobalt and nickel by partial least square regression in micellar media

Diphenycarbazone has been used for the simultaneous determination of cobalt and nickel by partial least square regression method. DPC complexes of cobalt and nickel at pH 7-10 are of pink color, which are soluble in TX-100 micellar media. A partial least square multivariate calibration method for the analysis of binary mixtures of cobalt and nickel was developed. The total relative standard error for applying the PLS method was calculated. The accuracy and reproducibility of the determination method for various known amounts of Co(II) and Ni(II) in their binary mixtures were tested. The effects of diverse ions on the determination of cobalt and nickel to investigate the selectivity of the method were also studied. The proposed method was applied to the synthetic binary mixtures, alloys and pharmaceutical samples.     

Application of multivariate curve resolution alternating least squares (MCR-ALS) to the quantitative analysis of pharmaceutical and agricultural samples

Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.     

Multivariate linear regression with missing values

This contribution presents and discusses an efficient algorithm for multivariate linear regression analysis of data sets with missing values. The algorithm is based on the insight that multivariate linear regression can be formulated as a set of individual univariate linear regressions. All available information is used and the calculations are explicit. The only restriction is that the independent variable matrix has to be non-singular. There is no need for imputation of interpolated or otherwise guessed values which require subsequent iterative refinement.     

基于小波系数的近红外光谱局部建模方法与应用研究

局部建模方法使用与预测样本相似的样本建立模型,可解决光谱响应与浓度之间的非线性问题,扩大模型的适用范围,提高预测准确度。采用小波变换进行数据压缩并利用小波系数之间的欧氏距离作为光谱相似性的判据,实现了近红外光谱定量分析的局部建模方法,避免了样本之间的依赖性。将所建立的方法用于烟草样品中氯含量的测定,100次重复计算得到的预测集均方根误差(RMSEP)平均值为0.0665,标准偏差(σ)为0.0045,优于全局建模和基于主成分的局部建模方法。     

Orthogonal signal correction-partial least squares method for simultaneous spectrophotometric determination of cypermethrin and tetramethrin

The simultaneous determination of cypermethrin and tetramethrin mixtures by using spectrophotometric method is a difficult problem in analytical chemistry, due to spectral interferences. By multivariate calibration methods, such as partial least squares (PLS) regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. Orthogonal signal correction (OSC) is a preprocessing technique used for removing the information unrelated to the target variables based on constrained principal component analysis. OSC is a suitable preprocessing method for partial least squares calibration of mixtures without loss of prediction capacity using spectrophotometric method. In this study, the calibration model is based on absorption spectra in the 200-350 nm range for 25 different mixtures of cypermethrin and tetramethrin. Calibration matrices were containing 0.1-12.9 and 0.1-13.8 microg mL(-1) for cypermethrin and tetramethrin, respectively. The RMSEP for cypermethrin and tetramethrin with OSC and without OSC were 0.0884, 0.0614 and 0.2915, 0.2309, respectively. This procedure allows the simultaneous determination of cypermethrin and tetramethrin in synthetic and real samples good reliability of the determination was proved.     

Simultaneous determination of phenobarbital and phenytoin in tablet preparations by multivariate spectrophotometric calibration

The use of multivariate spectrophotometric calibration for the simultaneous determination of the active components of antiepileptic tablets is presented. The resolution of binary mixtures of phenobarbital and phenytoin has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy, with no interference from tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap presented by the studied compounds. A statistical measure for the spectral overlap is proposed.     

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.