Simultaneous determination of three fluoroquinolones by linear sweep stripping voltammetry with the aid of chemometrics

A linear sweep stripping voltammetric (LSSV) method has been researched and developed for simultaneous quantitative determination of mixtures of three antibiotic drugs, ofloxacin, norfloxacin and ciprofloxacin. It relies on reductive reaction of the antibiotics at a mercury electrode in a Britton-Robinson buffer (pH 3.78). The voltammograms of these three compounds overlap strongly, and show non-linear character. Thus, it is difficult to analyse the compounds individually in their mixtures. In this work, chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN) were applied for the simultaneous determination of these compounds. The prediction performance of the calibration models constructed on the basis of these methods was compared. It was shown that satisfactory quantitative results were obtained with the use of the RBF-ANN calibration model relative prediction error (RPE_T) of 8.1% and an average recovery of 101%. This method is able to accommodate non-linear data quite well. The proposed analytical method based on LSSV was applied for the analysis of ofloxacin, norfloxacin and ciprofloxacin antibiotics in bird feedstuffs and their spiked samples, as well as in eye drops with satisfactory results.     

广义二面角在定量结构/活性相关分析中的应用

化合物的结构与其活性具有相关性 .建立相关性需要由结构提取特征 ,并运用这些特征作为自变量去构造数学模型 ,以便预测未知的化合物及其活性 .目前 ,在诸多 3D- QSAR方法中 ,应用最为广泛的是 Co MFA( Comparasive Molecular Field Analysis)方法 [1～ 3] ,但新近的研究发现 ,Co MFA方法对于某类化合物 (如苯胺类化合物 )适应性较差 ,但分子在三维空间投影面积及量化参数的引入可大大改善所得结果[4 ] .本文从另一角度 (即广义二面角 )研究了分子在三维空间的形状 ,得到了满意的结果 .1　实验部分本文选用 34个 1 - [( 2 - Hydroxy…     

Liquid chromatographic determination of quinolones in water and human urine samples after microextraction by packed sorbent

A method for the simultaneous determination of quinolones in water and urine samples by microextraction in a sorbent-packed syringe (MEPS) with LC is described. MEPS is a new miniaturized SPE technique that can be used with chromatographic instruments without any modifications. In MEPS, approximately 1 mg of the solid packing material is inserted into a syringe (100-250 microL) as a plug. Sample preparation takes place on the packed bed. The new method is promising, easy to use, economical, and rapid. The determination of quinolones in groundwater and urine was performed using MEPS as a sample preparation method with LC-UV determination. Four quinolone antibiotics--enrofloxacin, enoxacin, danofloxacin, and nalidixic acid--in groundwater and urine samples were used as analytes. The extraction recovery was found to be between 64.9 and 98.9%. The results showed high correlation coefficients (R2 > 0.992) for all of the analytes within the calibration range. The LOQ was between 0.091 and 0.315 ng/mL.     

Polarography and artificial neural network for the simultaneous determination of nalidixic acid and its main metabolite (7-hydroxymethylnalidixic acid)

Nalidixic acid (NA) and its main metabolite, 7-hydroxymethylnalidixic acid (OH-NA), are simultaneously determined by applying artificial neural networks (ANNs), to their square wave voltammetric signals. The scores of a PCR model, built with the voltammetric data of a set of standard samples, recorded between −0.70 and −1.0 V, are used as training set for the net for each compound. The trained nets (ANNs) are used for the simultaneous determination of NA and OH-NA in urine. The recovery values are comprised between 91 and 109% for NA and between 82 and 112% for OH-NA, being these results better than the results obtained by application of partial least squares (PLS) multivariate calibration.     

Precision limits and interval estimation in the calibration of 1-hydroxypyrene in urine and hexachlorbenzene in water,applying the regression triplet procedure on chromatographic data

A method for the determination of 1-hydroxypyrene in urine and hexachlorbenzene in water applying the regression triplet in the calibration procedure of chromatographic data has been applied. The detection limit and quantification limit are currently calculated on the basis of the standard deviation of replicate analyses at a single concentration. However, since the standard deviation depends on concentration, these single-concentration techniques result in limits that are directly dependent on spiking concentration. A more rigorous approach requires first careful attention to the three components of the regression triplet (data, model, method), examining (1) the data quality of the proposed model, (2) the model quality and (3) the least-squares method to be used for fulfilment of all least-squares assumptions. For high-performance liquid chromatography determination of 1-hydroxypyrene in urine and gas chromatography analysis of hexachlorbenzene in water, this paper describes the effects of deviations from five basic assumptions The paper considers the correction of deviations: identifying influential points, namely, outliers, the calibration task depends on the regression model used, and the least-squares method is based on the assumptions of the normality of the errors, homoscedasticity and the independence of errors. Results show that the approach developed provides improved estimates of analytical limits and that the single-concentration approaches currently in wide use are seriously flawed.     

Resolution of overlapping capillary electrophoresis peaks by using chemometric analysis: quantification of the components in compound reserpine tablets

The application of multivariate curve resolution with alternating least squares (MCR-ALS) methods to second-order data from capillary electrophoresis with diode array detector (CE-DAD) is reported. Initial qualitative solutions obtained by evolving factor analysis (EFA) and pure-variable detection method can be further optimized by a simultaneous analysis of multiple electrophoresis run data with ALS regression. While unknown samples are analyzed simultaneously against the corresponding standards in different composition ratios, the exact amounts of common components in different CE runs can be determined by the traditional calibration curve method, and quantification can thus be achieved. The above methods are applied to the determination of the components in compound reserpine tablets in overlapping peaks from CE. The quantification results are compared with those of the first derivative of the electropherogram method and artificial neural network (ANN) method.     

Simultaneous determination of aniline and cyclohexylamine by principal component artificial neural networks.

A specterophotometric method for simultaneous determination of aniline and cyclohexylamine using principal component artificial neural networks is proposed. This method is based on the reactions involving aniline and/or cyclohexylamine, with bis(acetylacetoneethylendiamine)tributylphosphine cobalt(III) perchlorate as a complexing reagent. A nonionic surfactant, Triton X-100, was used for dissolving the complexes and intensifying the signals. The absorption data were based on the spectra registered in the range of 350 - 550 nm. An artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Sigmoid transfer functions were used in the hidden and output layers to facilitate nonlinear calibration. The predictive ability of artificial neural networks was examined for the determination of aniline and cyclohexylamine in synthetic mixtures.     

Different approaches to multivariate calibration of nonlinear sensor data

In this study, different approaches to the multivariate calibration of the vapors of two refrigerants are reported. As the relationships between the time-resolved sensor signals and the concentrations of the analytes are nonlinear, the widely used partial least-squares regression (PLS) fails. Therefore, different methods are used, which are known to be able to deal with nonlinearities present in data. First, the Box–Cox transformation, which transforms the dependent variables nonlinearly, was applied. The second approach, the implicit nonlinear PLS regression, tries to account for nonlinearities by introducing squared terms of the independent variables to the original independent variables. The third approach, quadratic PLS (QPLS), uses a nonlinear quadratic inner relationship for the model instead of a linear relationship such as PLS. Tree algorithms are also used, which split a nonlinear problem into smaller subproblems, which are modeled using linear methods or discrete values. Finally, neural networks are applied, which are able to model any relationship. Different special implementations, like genetic algorithms with neural networks and growing neural networks, are also used to prevent an overfitting. Among the fast and simpler algorithms, QPLS shows good results. Different implementations of neural networks show excellent results. Among the different implementations, the most sophisticated and computing-intensive algorithms (growing neural networks) show the best results. Thus, the optimal method for the data set presented is a compromise between quality of calibration and complexity of the algorithm.Electronic Supplementary Material Supplementary material is available for this article at     

Confirmatory and quantitative analysis using experimental design for the extraction and liquid chromatography-UV, liquid chromatography-mass spectrometry and liquid chromatography-mass spectrometry/mass spectrometry determination of quinolones in turkey muscle

The aim of this work is to established methods for determination of quinolones (ciprofloxacin, danofloxacin, enrofloxacin, difloxacin and flumequine), regulated by European Union, and sarafloxacin in turkey muscle. An experimental design has been applied for the optimization of the factors that influence the extraction of quinolones from turkey muscle in order to determine the experimental conditions for their extraction with high recoveries. Liquid chromatography with ultraviolet detection (LC-UV), liquid chromatography-mass spectrometry (LC-MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS) have been used for the simultaneous quantification of quinolones antibiotics in turkey muscle. The proposed methods have been validated according to the Food Drugs Administration guideline and presents the limit of quantification below the maximum residue limits established by the European Union for quinolones in turkey muscle. The methods developed have been applied to quantification of enrofloxacin and its main metabolite ciprofloxacin in samples of turkey muscle obtained from animals treated with enrofloxacin.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

Determination of physical properties of bitumens by use of near-infrared spectroscopy with neural networks. Joint modelling of linear and non-linear parameters

The fact that bitumens behave as non-Newtonian fluids results in non-linear relationships between their near-infrared (NIR) spectra and the physico-chemical properties that define their consistency (viz. penetration and viscosity). Determining such properties using linear calibration techniques [e.g. partial least-squares regression (PLSR)] entails the previous transformation of the original variables by use of non-linear functions and employing the transformed variables to construct the models. Other properties of bitumens such as density and composition exhibit linear relationships with their NIR spectra. Artificial neural networks (ANNs) enable modelling of systems with a non-linear property-spectrum relationship; also, they allow one to determine several properties of a sample with a single model, so they are effective alternatives to linear calibration methods. In this work, the ability of ANNs simultaneously to determine both linear and non-linear parameters for bitumens without the need previously to transform the original variables was assessed. Based on the results, ANNs allow the simultaneous determination of several linear and non-linear physical properties typical of bitumens.     

基于独立分量和神经网络的近红外多组分分析方法

采用小波变换对光谱数据进行压缩,用独立分量分析(ICA)方法提取近红外光谱数据矩阵的独立成分和相应的混合矩阵,再用BP神经网络对混合矩阵和实测浓度矩阵进行建模,提出了基于独立分量分析-神经网络回归(ICA-NNR)的近红外分析建模方法。进一步研究了独立分量数和网络中间隐层的神经元数对模型性能的影响,经优化后的ICA-NNR模型在相关系数与均方根误差两个指标上均优于直接用光谱矩阵作为输入所建立的模型。本方法用于玉米中水分、淀粉、蛋白质3种主要成分含量的同时测定,检验样品集的化学检测值与近红外预测值的相关系数分别达到:淀粉r=0.971,蛋白质r=0.976,水分r=0.975。     

人工神经网络在纸浆卡伯值光学定量分析中的应用

卡伯值 (硬度 )是纸浆的重要质量指标 ,是制浆过程控制的关键参数 .目前的测量方法包括化学分析法和光学分析法两大类型 ,国内大多数的制浆造纸厂采用离线的传统化学分析法来测定纸浆的卡伯值 ,需要比较长的时间 .而光学分析法因具有实时性好、精度和可靠性高等优点 ,已逐步用于卡伯值的在线测量和控制 .研究 [1] 发现 ,在 460～ 580 nm的可见光谱范围内 ,蒸煮液吸光度的变化可以表征纸浆中木素含量的变化 .本文将可见分光光谱技术应用于蒸煮液中木素含量的在线测量 ,根据蒸煮液在所选波段的吸光度来预测纸浆的卡伯值 ,建立纸浆卡伯值与蒸煮…     

Simultaneous determination of thiocyanate and salicylate by a combined UV-spectrophotometric detection principal component artificial neural network

A modified principle component artificial neural network (PC-ANN) model is developed for simultaneous determination of thiocyanate and salycilate concentration after passing through the bulk of a liquid membrane by tri-phenyl benzyl phosphonium chloride. All calibration, and test samples data were obtained using UV-Vis spectrophotometer. In this way, a modified PC-ANN consisting of three layers of nodes was trained by combination of Bayesian-Levenberg-Marquardt as training rule. Sigmoid and liner transfer functions were used in the hidden and output layers respectively to facilitate nonlinear calibration. The model could accurately estimate the concentration of components with acceptable precision and accuracy, for mixtures. The PC-ANN model exhibits a good ability for the simultaneous determination of the thiocyanate and salycilate in concentration range 0.5 x 10(-4) mol.l(-1) up to 5.0 x 10(-4) mol.l(-1) with Root Mean square error (2.22% and 2.20%, for thiocyanate and salycilate, respectively) and high correlation coefficients (R2= 0.998 or greater). Results obtained with modified trained PC-ANN were compared with stepwise linear regression (SMLR) model. Validation of the two models shows a better ability in estimation of the modified PC-ANN as compared with the SMLR model (MSRE given are 3.12%, 6.31%.).     

Rapid determination of quinolones in cosmetic products by ultra high performance liquid chromatography with tandem mass spectrometry

This study developed an improved analytical method for the simultaneous quantification of 13 quinolones in cosmetics by ultra high performance liquid chromatography combined with ESI triple quadrupole MS/MS under the multiple reaction monitoring mode. The analytes were extracted and purified by using an SPE cartridge. The limits of quantification ranged from 0.03 to 3.02 μg/kg. The precision for determining the quinolones was <19.39%. The proposed method was successfully developed for the determination of quinolones in real cosmetic samples.     

小波潜变量回归和广义回归神经网络同时测定三组分混合物

本文采用小波潜变量回归（WLVR）方法，同时测定重叠的光谱信号。结合小波阈值法和主组分分析（PCA）改进除噪质量。八个误差判据用于推断因子数目。潜变量由小波处理过的信号投影到正交基矢量而获得。广义回归神经网络（GRNN）被应用于多组分同时测定。依据算法原理编制了三个程序（PWMRA、PWLVR和PGRNN）执行有关计算。三个方法（WLVR、LVR（潜变量回归）和GRNN）同时测定三组分混合物，获得满意的结果。     

The effect of influential data, model and method on the precision of univariate calibration

Building a calibration model with detection and quantification capabilities is identical to the task of building a regression model. Although commonly used by analysts, an application of the calibration model requires at first careful attention to the three components of the regression triplet (data, model, method), examining (a) the data quality of the proposed model; (b) the model quality; (c) the LS method to be used or a fulfillment of all least-squares assumptions. This paper summarizes these components, describes the effects of deviations from assumptions and considers the correction of such deviations: identifying influential points is the first step in least-squares model building, the calibration task depends on the regression model used, and finally the least squares LS method is based on assumptions of normality of errors, homoscedasticity, independence of errors, overly influential data points and independent variables being subject to error. When some assumptions are violated, the ordinary LS is inconvenient and robust M-estimates with the iterative method of reweighted least-squares must be used. The effects of influential points, heteroscedasticity and non-normality on the calibration precision limits are also elucidated. This paper also considers the proper construction of the statistical uncertainty expressed as confidence limits predicting an unknown concentration (or amount) value, and its dependence on the regression triplet. The authors' objectives were to provide a thorough treatment that includes pertinent references, consistent nomeclature, and related mathematical formulae to show by theory and illustrative examples those approaches best suited to typical problems in analytical chemistry. Two new algorithms, calibration and linear regression written in s-plus and enabling regression triplet analysis, the estimation of calibration precision limits, critical levels, detection limits and quantification limits with the statistical uncertainty of unknown concentrations, form the goal of this paper.     

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%...     

Quantification of butanol and ethanol in aqueous phases by reflectometric interference spectroscopy--different approaches to multivariate calibration

This paper presents several methods for analysis of data from reflectometric interference spectroscopic measurements (RIfS) of water samples. The set-up consists of three sensors with different polymer layers. Mixtures of butanol and ethanol in water were measured from 0 to 12,000 ppm each. The data space was characterized by principal component analysis (PCA). Calibration and prediction were achieved by multivariate methods, e.g. multiple linear regression (MLR), partial least squares (PLS) with additional predictors, and quadratic partial least squares (Q-PLS), and by use of artificial neural networks. Artificial neural networks gave the best results of all the calibration methods used. Calibration and prediction of the concentration of the two analytes by artificial neural nets were robust and the set-up could be reduced to only two sensors without deterioration of the prediction.     

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.