Determination of acetonitrile and ethanol in water by guided microwave spectroscopy with multivariate calibration

The feasibility of using guided microwave spectroscopy (GMS) utilizing the frequency range 0.25-3.20 GHz, was combined with multivariate calibration for the determination of acetonitrile or ethanol concentration in water. A wide range of different concentrations was used (up to 30% v/v). Partial least squares (PLS) and weighted ridge regression (WRR) was applied to generate a model for prediction, based upon the microwave spectra. A high level of collinearity was observed in both of the sample data sets and this was reduced by background subtraction. The prediction ability for the two types of regression models were found to be comparable with the percentage error of prediction (PEP) being approximately 2.5% for the acetonitrile samples and 1.1% for ethanol samples.     

Vapor–liquid equilibrium of systems containing alcohols,water, carbon dioxide and hydrocarbons using SAFT

The statistical associating fluid theory (SAFT) equation of state is employed for the correlation and prediction of vapor–liquid equilibrium (VLE) of eighteen binary mixtures. These include water with methane, ethane, propane, butane, propylene, carbon dioxide, methanol, ethanol and ethylene glycol (EG), ethanol with ethane, propane, butane and propylene, methanol with methane, ethane and carbon dioxide and finally EG with methane and ethane. Moreover, vapor–liquid equilibrium for nine ternary systems was predicted. The systems are water/ethanol/alkane (ethane, propane, butane), water/ethanol/propylene, water/methanol/carbon dioxide, water/methanol/methane, water/methanol/ethane, water/EG/methane and water/EG/ethane. The results were found to be in satisfactory agreement with the experimental data except for the water/methanol/methane system for which the root mean square deviations for pressure were 60–68% when the methanol concentration in the liquid phase was 60 wt.%.     

Optimisation of partial least squares regression calibration models in near-infrared spectroscopy: a novel algorithm for wavelength selection

A novel optimisation algorithm is presented for full spectrum calibration models in near-infrared (NIR) spectroscopy. The algorithm is used to investigate the affect of removing continuous spectral regions on parameters critical to the validity of the model (e.g. explained variance, bias etc.) and ultimately identify and remove problem areas of the spectrum. As an example of its application, this paper shows how to optimise partial least squares regression (PLSR) calibration models for predicting moisture content within an intact pharmaceutical product and how problems due to changes in the nature of samples since setting up the original model may be eliminated. On application of two validated calibration models to a new set of samples unacceptable results were obtained for bias (-0.26 and -0.21% m/m moisture content) between the NIR predicted values and the true values (Karl Fischer analysis). The optimisation algorithm identified small regions of the spectrum, which if included in development of the models contributed significant bias to the final prediction. On removal of these problem regions the calibration models were found to be equally accurate and precise, but with the added advantage of robustness to a variable region of the sample spectrum (bias reduced to -0.05 and -0.09% m/m).     

A rapid quantitative assay of intact paracetamol tablets by reflectance near-infrared spectroscopy

Near-infrared (NIR) reflectance spectroscopy was used to determine rapidly and non-destructively the content of paracetamol in bulk batches of intact Sterwin 500 mg tablets by collecting NIR spectra in the range 1100-2500 nm and using a multiple linear regression calibration method. The developed NIR method gave results comparable to the British Pharmacopoeia 1993 UV assay procedure, the standard errors of calibration and prediction being 0.48% and 0.71% m/m, respectively. The method showed good repeatability, the standard deviation and coefficient of variation for six NIR assays on the same batch on the same day being 0.14 and 0.16% m/m, respectively, while measurements over six consecutive days gave 0.31 and 0.36% m/m, respectively. Applying the calibration to a parallel test set gave a mean bias of -0.22% and a mean accuracy of 0.45%. The developed method illustrates how the full potential of NIR can be utilised and how the ICH guidelines which recommend the validation of linearity, range, accuracy and precision for pharmaceutical registration purposes can be applied. Duplicate determinations on bulk batches could be performed in under 2 min, allowing the potential use of the method on-line for real time monitoring of a running production process.     

The potential of Raman spectroscopy for characterisation of the fatty acid unsaturation of salmon

Raman spectroscopy has been evaluated for characterisation of the degree of fatty acid unsaturation (iodine value) of salmon (Salmo salar). The Norwegian Quality Cuts from 50 salmon samples were obtained, and the samples provided an iodine value range of 147.8-170.0 g I₂/100 g fat, reflecting a normal variation of farmed salmon. Raman measurements were performed both on different spots of the intact salmon muscle, on ground salmon samples as well as on oil extracts, and partial least squares regression (PLSR) was utilised for calibration. The oil spectra provided better iodine value predictions than the other data sets, and a correlation coefficient of 0.87 with a root mean square error of cross-validation of 2.5 g I₂/100 g fat was achieved using only one PLSR component. The ground samples provided comparable results, but at least two PLSR components were needed. Higher prediction errors were obtained from Raman spectra of intact salmon muscle, and this may partly be explained by sampling uncertainties in the relation between Raman measurements and reference analysis. All PLSR models obtained were based on chemically sound regression coefficients, and thus information regarding fatty acid unsaturation is readily available from Raman spectra even in systems with high contents of protein and water. The accuracy, the robustness and the low complexity of the PLSR models obtained suggest Raman spectroscopy as a promising method for rapid in-process control of the degree of unsaturation in salmon samples.     

低场核磁结合偏最小二乘回归预测含油污泥水和油含率

快速准确分析处理过程中含油污泥的含水率和含油率有助于现场评价其原油回收效率和优化处理工艺参数。以Dean-Stark装置测定的含油污泥样品的含水率和含油率作为参考值,利用低场核磁共振结合偏最小二乘回归法建立了样品含水率和含油率校正集模型,考察了回波衰减曲线和横向弛豫时间T2曲线对校正集模型性能的影响。结果表明,采用前者建立的校正集模型性能优于后者;在此基础上,建立了31个样品的含水率和含油率通用校正集模型,其含水率和含油率模型的决定系数(R2)分别为0.965 7和0.978 5,校正标准差(RMSECV)分别为2.73%和2.22%。利用3个不同批次采集的HZ-OS样品对该模型进行验证,对于含水率和含油率模型,其验证集R2分别为0.914 1和0.924 7,预测标准差(RMSEP)分别为1.85%和2.04%,与RMSECV值比较接近,说明该模型的稳定性较好,可用于准确分析样品的含水率和含油率。     

Application of transmission near-infrared spectroscopy to uniformity of content testing of intact steroid tablets

Transmission near-infrared (NIR) spectroscopy was used for the rapid and non-destructive determination of the content of a hormone steroid in single intact tablets. Tablets produced for clinical trial purposes containing 5, 10, 15, 20 and 30 mg (2.94, 5.88, 8.82, 11.76 and 17.64% m/m, respectively) were used to develop calibration models without the need to specially prepare any out of specification tablets. Reference values for the individual tablets used in the NIR calibration models and test set were measured by reversed-phase high performance liquid chromatography (HPLC). Partial least squares regression using standard normal variate transformed second-derivative spectra over the range 800 to 1040 nm gave the optimum calibration model with a standard error of calibration of 0.52 mg per tablet. Measurements of an independent test set gave comparable results (standard error of prediction 0.31 mg per tablet). Measurement errors for a single tablet (RSD < 2.5% for a given active level) were sufficiently small to allow the procedure to be applied to pharmacopoeial uniformity of content testing of batches of these tablets and permitted the non-destructive testing of 30 tablets in under 20 min as compared to 6 h by HPLC.     

Simultaneous determination of methanol and ethanol in gasoline using NIR spectroscopy: effect of gasoline composition

Near infrared (NIR) spectroscopy was employed for simultaneous determination of methanol and ethanol contents in gasoline. Spectra were collected in the range from 714 to 2500 nm and were used to construct quantitative models based on partial least squares (PLS) regression. Samples were prepared in the laboratory and the PLS regression models were developed using the spectral range from 1105 to 1682 nm, showing a root mean square error of prediction (RMSEP) of 0.28% (v/v) for ethanol for both PLS-1 and PLS-2 models and of 0.31 and 0.32% (v/v) for methanol for the PLS-1 and PLS-2 models, respectively. A RMSEP of 0.83% (v/v) was obtained for commercial samples. The effect of the gasoline composition was investigated, it being verified that some solvents, such as toluene and o-xylene, interfere in ethanol content prediction, while isooctane, o-xylene, m-xylene and p-xylene interfere in the methanol content prediction. Other spectral ranges were investigated and the range 1449-1611 nm showed the best results.     

Determination of water in lubricating oils by mid- and near-infrared spectroscopy

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy were used to determine water in lubricating oils with high additive contents that introduce large errors in determinations by the Karl-Fischer and hydride methods. MIR spectra were obtained in the attenuated total reflectance (ATR) mode and exhibited water specific band absorption in the region 3100–3700cm^–1, which facilitated calibration. Multivariate (partial least-squares regression, PLSR) and univariate calibration (based on peak height and band area as independent variables) were tested. Both led to errors of prediction less than 5%. NIRS determinations rely on absorbance and first-derivative spectra, in addition to two different types of multivariate calibration,viz. inverse multiple linear regression (MLR) and partial least-squares regression (PLSR). Both approaches gave similar results, with errors of prediction less than 2%.For none of the proposed approaches any sample pretreatment for recording spectra is required.     

Determination of total sulfur in diesel fuel employing NIR spectroscopy and multivariate calibration

A method for sulfur determination in diesel fuel employing near infrared spectroscopy, variable selection and multivariate calibration is described. The performances of principal component regression (PCR) and partial least square (PLS) chemometric methods were compared with those shown by multiple linear regression (MLR), performed after variable selection based on the genetic algorithm (GA) or the successive projection algorithm (SPA). Ninety seven diesel samples were divided into three sets (41 for calibration, 30 for internal validation and 26 for external validation), each of them covering the full range of sulfur concentrations (from 0.07 to 0.33% w/w). Transflectance measurements were performed from 850 to 1800 nm. Although principal component analysis identified the presence of three groups, PLS, PCR and MLR provided models whose predicting capabilities were independent of the diesel type. Calibration with PLS and PCR employing all the 454 wavelengths provided root mean square errors of prediction (RMSEP) of 0.036% and 0.043% for the validation set, respectively. The use of GA and SPA for variable selection provided calibration models based on 19 and 9 wavelengths, with a RMSEP of 0.031% (PLS-GA), 0.022% (MLR-SPA) and 0.034% (MLR-GA). As the ASTM 4294 method allows a reproducibility of 0.05%, it can be concluded that a method based on NIR spectroscopy and multivariate calibration can be employed for the determination of sulfur in diesel fuels. Furthermore, the selection of variables can provide more robust calibration models and SPA provided more parsimonious models than GA.     

Effect of pharmaceutically acceptable glycols on the stability of the liquid crystalline gels formed by Poloxamer 407 in water

The ability of poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO) block copolymers (Poloxamers) to form "gels" (lyotropic liquid crystalline structures) in water is of interest to pharmaceutical applications. In such applications the presence of polar organic solvents is often desirable or required. The effect of such solvents on the stability of lyotropic liquid crystalline gels formed by PEO-PPO-PEO block copolymers was assessed by studying the phase behavior and structure in ternary isothermal (25 degrees C) systems of pharmaceutical interest consisting of Poloxamer 407 (EO(100)PO(70)EO(100)), water, and one of the following solvents (referred to here collectively as "glycols"): glycerol, propylene glycol, ethanol, polyethylene glycol 400, and glucose. Small-angle X-ray scattering was employed to establish the structure of the liquid crystals obtained and to determine their characteristic length scales. The stability range of the liquid crystalline gel phases in the systems studied was found to vary with the glycol type. For example, the micellar cubic structure can accommodate about 0.85:1 parts glucose per part water (in terms of weight) and up to as much as 5.5:1 parts propylene glycol per part water. A correlation between the glycol effects on the stability of the liquid crystalline phases and glycol physiochemical characteristics such as octanol/water partition coefficient or solubility parameter is proposed.     

Structure and dynamics of a sponge phase in the methyl delta-aminolevulinate/monoolein/water/propylene glycol system

The structural effect caused by the addition of up to 16% (w/w) of the hydrochloride salt of delta-aminolevulinic acid (ALA, HOOC-CH2-CH2-CO-CH2-NH2HCl) or its methyl ester (m-ALA) to the sponge phase formed of monoolein/water/propylene glycol was investigated by means of crossed polarizers, small angle X-ray diffraction (SAXD) and nuclear magnetic resonance diffusometry (NMRD). Inspection with crossed polarizers revealed that additions of 4-16% (w/w) m-ALA transformed the isotropic bicontinuous sponge phase partly (4-10%) or completely (13 and 16%) into an anisotropic lamellar phase, indicating that m-ALA has a flattening effect on the bilayer curvature. The addition of 16% (w/w) ALA did not show any effect on the sponge phase. By addition of water to the anisotropic m-ALA samples, isotropic liquids were re-formed. The SAXD data for the isotropic liquids showed a diffuse Bragg peak and the NMRD self-diffusion coefficients for the drug (m-ALA) and the components of the original sponge phase (monoolein, water and propylene glycol) were shown to be essentially constant for 0-16% (w/w) added m-ALA. These results confirmed the hypothesis that the re-formed isotropic phases were indeed sponge phases. Water, for example, showed a diffusion coefficient of 3.1-3.9x10(-10)m(2)s(-1) in the sponge phase, compared to 5.3-5.7 x 10(-10)m2s(-1) in relevant water/propylene glycol solutions or 2.3 x 10(-9)m2s(-1) in pure water. The reduction can be explained as a consequence of the microstructure (congruent monoolein bilayer) of the sponge phase and of the viscosity effect caused by propylene glycol and m-ALA.     

Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk

This paper proposes the use of the least-squares support vector machine (LS-SVM) as an alternative multivariate calibration method for the simultaneous quantification of some common adulterants (starch, whey or sucrose) found in powdered milk samples, using near-infrared spectroscopy with direct measurements by diffuse reflectance. Due to the spectral differences of the three adulterants a nonlinear behavior is present when all groups of adulterants are in the same data set, making the use of linear methods such as partial least squares regression (PLSR) difficult. Excellent models were built using LS-SVM, with low prediction errors and superior performance in relation to PLSR. These results show it possible to built robust models to quantify some common adulterants in powdered milk using near-infrared spectroscopy and LS-SVM as a nonlinear multivariate calibration procedure.     

Simultaneous Determination of Oil and Water in Soybean by LF-NMR Relaxometry and Chemometrics

A fast, non-destructive and eco-friendly method was developed to simultaneously determine the oil and water contents of soybean based on low field nuclear magnetic resonance(LF-NMR) relaxometry combined with chemometrics, such as partial least squares regression(PLSR). The Carr-Purcell-Meiboom-Gill(CPMG) magnetization decay data of ten soybean samples were acquired by LF-NMR and directly applied to the PLSR analysis. Calibration models were established via PLSR with full cross-validation based on the reference values obtained by the Soxhlet extraction method for measuring oil and oven-drying method for measuring water. The results indicate that the calibration models are satisfactory for both oil and water determinations; the root mean squared errors of cross-validation(RMSECV) for oil and water are 0.2285% and 0.0178%, respectively. Furthermore, the oil and water contents in unknown soybean samples were predicted by the PLSR models and the results were compared with the reference values. The relative errors of the predicted oil and water contents were in ranges of 1.25%-4.96% and 0.44%-2.49%, respectively. These results demonstrate that the combination of LF-NMR relaxometry with chemometrics shows great potential for the simultaneous determination of contents of oil and water in soybean with high accuracy.     

中药材三七提取液近红外光谱的支持向量机回归校正方法

提出近红外光谱的支持向量机回归校正建模方法.以中药材三七渗漉提取液为实际分析对象,对其近红外光谱数据进行预处理和主成分分析后,用支持向量机回归算法建立人参皂苷R_g1,R_b1和R_d以及三七总皂苷的近红外光谱校正模型.以R_g1,R_b1和R_d的HPLC测定值及三七总皂苷的比色法测定值为参照,将本文方法与偏最小二乘回归和径向基神经网络建模方法相比较,结果表明,本文所建模型的预测准确性优于后两者,可推广应用于中药提取过程的近红外光谱分析.     

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.     

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV–Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.     

Evolving factor analysis-based method for correcting monitoring delay in different batch runs for use with PLS: On-line monitoring of a transesterification reaction by ATR-FTIR

In this work, the base-catalyzed transesterification of soybean oil with ethanol was monitored on-line using mid-infrared spectroscopy (MIRS) and the yield of fatty acid ethyl esters (biodiesel) was obtained by (1)H NMR spectroscopy. The MIRS monitoring carried out for 12min, was performed using a cylindrical internal reflectance cell of PbSe in the range of 3707-814cm(-1) with eight co-added scans. Two different data treatment strategies were used: in the first, the models were built using the raw data and in the other, evolving factor analysis (EFA) was used to overcome the sensor time delay due to the on-line analysis, producing significantly better results. In addition, models based on partial least squares (PLS) using three batches for calibration and another for validation were compared with models with just one batch for calibration and three for validation. The models were compared between each other using root mean square error of prediction (RMSEP) and root mean square prediction difference (RMSPD), obtaining relative errors under 3%.     

Quantification of paracetamol in intact tablets using near-infrared transmittance spectroscopy.

Production batch samples of paracetamol tablets and specially prepared out-of-specification batches covering the range 90-110% of the stated amount (500 mg) were analysed by the BP official UV assay and by NIR transmittance spectroscopy. NIR measurements were made on 20 intact tablets from each batch, scanned five times each (10 min measurement time per batch) over the spectral range 6000-11,520 cm-1. An average spectrum was calculated for each batch. Partial least squares (PLS) regression models were set up using a calibration set (20 batches) between the NIR response and the reference tablet paracetamol content (UV). Various pre-treatments of the spectra were examined; the smallest relative standard error of prediction (0.73%) was obtained using the first derivative of the absorbance over the full spectrum. Only two principal components were required for the PLS model to give a good relationship between the spectral information and paracetamol content. Applying this model to the validation set (15 batches) gave a mean bias of -0.08% and a mean accuracy of 0.59% with relative standard deviations of 0.75 and 0.44%, respectively. The proposed method is non-destructive and therefore lends itself to on-line/at-line production control purposes. The method is easy to use and does not require a knowledge of the mass of the tablets.