Determination of quinolone antimicrobial agents in strongly overlapped peaks from capillary electrophoresis using multivariate calibration methods

This paper describes a new procedure for the determination of quinolones ciprofloxacin and sarafloxacin in chicken muscle samples. It is based on a previously developed capillary zone electrophoresis (CZE) separation, in which all the quinolones regulated by EU Council Regulation number 2377/90 could be separated. However, as ciprofloxacin and sarafloxacin coelute in the CZE run and they have strongly overlapped spectra, separation between them is not possible.To overcome this problem, we have used a multivariate calibration procedure (partial least square regression (PLS-2)), applied to the spectra obtained at the maximum of the electrophoretic peaks, by using a diode array detector. The method has been validated by a combination of pure standards and fortified blank chicken muscle extracts. The recoveries obtained in the validation set were 101±6 and 93±6% for sarafloxacin and ciprofloxacin, respectively. The method has been also applied to chicken muscle samples, fortified at concentration levels between 100 and 350 μg kg⁻, corresponding to values near the maximum residue level (MRL) regulated by the European Community.     

Multivariate analytical sensitivity in the determination of selenium, copper, lead and cadmium by stripping voltammetry when using soft calibration

A new approach to the multivariate sensitivity concept based on the determination of the capability of discrimination of a method of analysis is shown. Thus the analytical sensitivity is defined in this work by the analyte concentration that a analytical method is able to discriminate, which implies the estimation of the ‘false noncompliance’ and ‘false compliance’. In this approach the estimation of the multivariate analytical sensitivity is independent of scale factors and calibration models, and allows one to study the behavior of a analytical method for several concentrations and matrix. The estimation of this parameter in the simultaneous determination of selenium, copper, lead and cadmium by stripping voltammetry when using soft calibration is carried out, showing that different multivariate analytical sensitivities are obtained for each metal.     

A novel algorithm for linear multivariate calibration based on the mixed model of samples

We present a novel algorithm for linear multivariate calibration that can generate good prediction results. This is accomplished by the idea of that testing samples are mixed by the calibration samples in proper proportion. The algorithm is based on the mixed model of samples and is therefore called MMS algorithm. With both theoretical support and analysis of two data sets, it is demonstrated that MMS algorithm produces lower prediction errors than partial least squares (PLS2) model, has similar prediction performance to PLS1. In the anti-interference test of background, MMS algorithm performs better than PLS2. At the condition of the lack of some component information, MMS algorithm shows better robustness than PLS2.     

Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods

Most multivariate calibration methods require selection of tuning parameters, such as partial least squares (PLS) or the Tikhonov regularization variant ridge regression (RR). Tuning parameter values determine the direction and magnitude of respective model vectors thereby setting the resultant predication abilities of the model vectors. Simultaneously, tuning parameter values establish the corresponding bias/variance and the underlying selectivity/sensitivity tradeoffs. Selection of the final tuning parameter is often accomplished through some form of cross-validation and the resultant root mean square error of cross-validation (RMSECV) values are evaluated. However, selection of a “good” tuning parameter with this one model evaluation merit is almost impossible. Including additional model merits assists tuning parameter selection to provide better balanced models as well as allowing for a reasonable comparison between calibration methods. Using multiple merits requires decisions to be made on how to combine and weight the merits into an information criterion. An abundance of options are possible. Presented in this paper is the sum of ranking differences (SRD) to ensemble a collection of model evaluation merits varying across tuning parameters. It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired. Essentially, the user’s preference for the degree of balance between bias and variance ultimately decides the merits used in SRD and hence, the tuning parameter values ranked lowest by SRD for automatic selection. The SRD process is also shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection. Because SRD evaluates consistency across multiple merits, decisions on how to combine and weight merits are avoided. To demonstrate the utility of SRD, a near infrared spectral data set and a quantitative structure activity relationship (QSAR) data set are evaluated using PLS and RR.     

Voltammetric determination of dopamine in the presence of ascorbic and uric acids using partial least squares regression: determination of dopamine in human urine and plasma

A new differential pulse voltammetric method for dopamine determination at a bare glassy carbon electrode has been developed. Dopamine, ascorbic acid (AA) and uric acid (UA) usually coexist in physiological samples. Because AA and UA can be oxidized at potentials close to that of DA it is difficult to determine dopamine electrochemically, although resolution can be achieved using modified electrodes. Additionally, oxidized dopamine mediates AA oxidation and the electrode surface can be easily fouled by the AA oxidation product. In this work a chemometrics strategy, partial least squares (PLS) regression, has been applied to determine dopamine in the presence of AA and UA without electrode modification. The method is based on the electrooxidation of dopamine at a glassy carbon electrode in pH 7 phosphate buffer. The dopamine calibration curve was linear over the range of 1–313 μM and the limit of detection was 0.25 μM. The relative standard error (RSE %) was 5.28%. The method has been successfully applied to the measurement of dopamine in human plasma and urine.      

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%...     

Adsorptive stripping square wave voltammetry (Ad-SSWV) accomplished with second-order multivariate calibration determination of fenitrothion and its metabolites in river water samples

A method, using stripping square wave voltammetry (Ad-SSWV), for the simultaneous determination of fenitrothion (FEN) and its metabolites: fenitrooxon (OXON) and 3-methyl-4-nitrophenol (3-MET) in environmental samples is reported. All three compounds produce, at mercury electrode (HMDE), an electrochemical signal due to an adsorptive-reductive process. The electrochemical approach shows a very high overlap degree for FEN and OXON voltammograms, however the adsorption kinetic profile could be used as an additional differential variable between both analytes. Second-order multivariate calibration has been tested to solve the mixture of the three compounds. The second-order assayed methods were parallel factor analysis (PARAFAC), unfolded partial least squares (U-PLS), multidimensional partial least squares (N-PLS) and the latest ones were used in combination with the residual bilinearization procedure RBL. U-PLS/RBL model was stated as the best second-order algorithm for the simultaneous determination of these three compounds up to 50 ng mL⁻¹ for each analyte. The detection limits and recovery values were 1.6 ng mL⁻¹ and 92 ± 7% for FEN; 3.7 ng mL⁻¹ and 101 ± 9% for OXON and 0.6 ng mL⁻¹ and 97 ± 8% for 3-MET.     

Simultaneous determination of halide and thiocyanate ions by potentiometric precipitation titration and multivariate calibration

Halide and thiocyanate ions can be determined by a precipitation titration with silver nitrate as the titrant, and the end-point can be evaluated by a potentiometric method, in which generally a silver indicator electrode is used as the indicator electrode and a double-junction Ag–AgCl electrode as the reference electrode. However, when mixtures of halide and thiocyanate are titrated, it is difficult to determine these components individually for there are overlapping steps in the potentiometric titration curves, especially in the case that there are obvious differences between concentrations of the components. In this paper, the linear equation for the potentiometric precipitation titration of a mixture of halide and thiocyanate ions was developed and it was then used for determining the components in the mixtures simultaneously with the aid of multivariate calibration methods. By application of this model, 27 synthetic mixtures with three- and four-component combinations of chloride, bromide, iodide and thiocyanate with low concentration levels from 1.8×10⁻⁴ to 6.2×10⁻⁴ mol l⁻¹ were analyzed and acceptable results were obtained.     

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.     

Bayesian linear regression and variable selection for spectroscopic calibration

This paper presents a Bayesian approach to the development of spectroscopic calibration models. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific optimization method, i.e. Bayesian evidence approximation, is utilized to estimate the model “hyper-parameters”. The relation of the proposed approach to the calibration models in the literature is discussed, including ridge regression and Gaussian process model. The Bayesian model may be modified for the calibration of multivariate response variables. Furthermore, a variable selection strategy is implemented within the Bayesian framework, the motivation being that the predictive performance may be improved by selecting a subset of the most informative spectral variables. The Bayesian calibration models are applied to two spectroscopic data sets, and they demonstrate improved prediction results in comparison with the benchmark method of partial least squares.     

Speciation of antimony by adsorptive stripping voltammetry using pyrogallol

This paper describes a new procedure for the determination of Sb (III) and Sb (V) by differential pulse adsorptive stripping voltammetry (DPAdSV) using pyrogallol as a complexing agent. The selection of the experimental conditions was made using experimental design methodology. The detection limits obtained were 1.03 × 10⁻¹⁰ and 9.48 × 10⁻⁹ mol dm⁻³ for Sb (III) and Sb (V), respectively.In order to carry out the simultaneously determination of both antimony species a partial least squares regression (PLS) is employed to resolve the voltammetric signals from mixtures of Sb (III) and Sb (V) in the presence of pyrogallol. The relative error in absolute value is less than 0.5% when concentrations of several mixtures are calculated. Moreover, the solution is analyzed for any possible effects of foreign ions. The procedure is successfully applied to the speciation of antimony in pharmaceutical preparations and water samples.     

Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry

Near-infrared (NIR) spectroscopy in conjunction with chemometric techniques allows on-line monitoring in real time, which can be of considerable use in industry. If it is to be correctly used in industrial applications, generally some basic considerations need to be taken into account, although this does not always apply. This study discusses some of the considerations that would help evaluate the possibility of applying multivariate calibration in combination with NIR to properties of industrial interest. Examples of these considerations are whether there is a relation between the NIR spectrum and the property of interest, what the calibration constraints are and how a sample-specific error of prediction can be quantified. Various strategies for maintaining a multivariate model after it has been installed are also presented and discussed.     

Determination of arsenic in gold samples using matrix exchange differential pulse stripping voltammetry

A method comprising matrix exchange differential pulse stripping voltammetry (DPSV) at a gold film electrode has been proposed for the determination of small quantities of arsenic in pure gold. A wall-jet cell (WJC) and an on-line deoxygenation system were used to facilitate matrix exchange. The gold(I) cyanide complex was formed to avoid gold deposition on the electrode together with the arsenic. The pH of the sample solutions were adjusted to 3, as alkaline solutions gold(I) cyanide produced interference and the uncomplexed cyanide led to passivation of the gold film electrode. Matrix exchange electrolytes consisting of 4 mol l⁻¹ hydrochloric acid or a combination of 2 mol l⁻¹ sulphuric acid and 0.2 mol l⁻¹ hydrochloric acid could be utilised. Arsenic concentrations as low as 0.1 mg l⁻¹, could readily be detected in a gold matrix with a 60 s deposition time. While, cobalt and silver did not interfere with the arsenic determination, copper interfered even when present at similar concentrations to that of arsenic.     

Partial least squares-based multivariate spectral calibration method for simultaneous determination of beta-carboline derivatives in Peganum harmala seed extracts

The partial least squares regression method has been applied for simultaneous spectrophotometric determination of harmine, harmane, harmalol and harmaline in Peganum harmala L. (Zygophyllaceae) seeds. The effect of pH was optimized employing multivariate definition of selectivity and sensitivity and best results were obtained in basic media (pH > 9). The calibration models were optimized for number of latent variables by the cross-validation procedure. Determinations were made over the concentration range of 0.15-10 μg mL⁻¹. The proposed method was validated by applying it to the analysis of the β-carbolines in synthetic quaternary mixtures of media at pH 9 and 11. The relative standard errors of prediction were less than 4% in most cases. Analysis of P. harmala seeds by the proposed models for contents of the β-carboline derivatives resulted in 1.84%, 0.16%, 0.25% and 3.90% for harmine, harmane, harmaline and harmalol, respectively. The results were validated against an existing HPLC method and it no significant differences were observed between the results of two methods.     

Electrochemical solid phase micro-extraction and determination of salicylic acid from blood samples by cyclic voltammetry and differential pulse voltammetry

The electrochemical solid phase micro-extraction of salicylic acid (SA) at graphite-epoxy-composed solid electrode surface was studied by cyclic voltammetry. SA was oxidized electrochemically in pH 12.0 aqueous solution at 0.70 V (vs. saturated calomel electrode) for 7 s. The oxidized product shows two surface-controlled reversible redox couples with two proton transferred in the pH range of 1.0∼6.0 and one proton transferred in the pH range of 10.0∼13.0 and is extracted on the electrode surface with a kinetic Boltzman function of i _p = 3.473–4.499/[1 + e^{(t − 7.332)/6.123}] (χ ² = 0.00285 μA). The anodic peak current of the extracted specie in differential pulse voltammograms is proportional to the concentration of SA with regression equation of i _p = −5.913 + 0.4843 c (R = 0.995, SD = 1.6 μA) in the range of 5.00∼200 μM. The detection limit is 5.00 μM with RSD of 1.59% at 60 μM. The method is sensitive and convenient and was applied to the detection of SA in mouse blood samples with satisfactory results.     

Reproducibility,complementary measure of predictability for robustness improvement of multivariate calibration models via variable selections

In multivariate calibration with the spectral dataset, variable selection is often applied to identify relevant subset of variables, leading to improved prediction accuracy and easy interpretation of the selected fingerprint regions. Until now, numerous variable selection methods have been proposed, but a proper choice among them is not trivial. Furthermore, in many cases, a set of variables found by those methods might not be robust due to the irreproducibility and uncertainty issues, posing a great challenge in improving the reliability of the variable selection. In this study, the reproducibility of the 5 variable selection methods was investigated quantitatively for evaluating their performance. The reproducibility of variable selection was quantified by using Monte-Carlo sub-sampling (MCS) techniques together with the quantitative similarity measure designed for the highly collinear spectral dataset. The investigation of reproducibility and prediction accuracy of the several variable selection algorithms with two different near-infrared (NIR) datasets illustrated that the different variable selection methods exhibited wide variability in their performance, especially in their capabilities to identify the consistent subset of variables from the spectral datasets. Thus the thorough assessment of the reproducibility together with the predictive accuracy of the identified variables improved the statistical validity and confidence of the selection outcome, which cannot be addressed by the conventional evaluation schemes.     

Simultaneous determination of sorbic and benzoic acids in commercial juices using the PLS-2 multivariate calibration method and validation by high performance liquid chromatography

A new method to determine a mixture for preserving sorbic and benzoic acids in commercial juices is proposed. The PLS-2 model was obtained preparing 40 standard solutions adding concentration of sorbic and benzoic acid to filtered natural juices of apple, lemon, orange and grapefruit. The concentration of analytes in the commercial samples was evaluated using the obtained model by UV spectral data. The PLS-2 method was validated by high performance liquid chromatography (HPLC), finding a relative error less than 12% between the PLS-2 and HPLC methods in all cases.     

Using variable combination population analysis for variable selection in multivariate calibration

Variable (wavelength or feature) selection techniques have become a critical step for the analysis of datasets with high number of variables and relatively few samples. In this study, a novel variable selection strategy, variable combination population analysis (VCPA), was proposed. This strategy consists of two crucial procedures. First, the exponentially decreasing function (EDF), which is the simple and effective principle of ‘survival of the fittest’ from Darwin’s natural evolution theory, is employed to determine the number of variables to keep and continuously shrink the variable space. Second, in each EDF run, binary matrix sampling (BMS) strategy that gives each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, model population analysis (MPA) is employed to find the variable subsets with the lower root mean squares error of cross validation (RMSECV). The frequency of each variable appearing in the best 10% sub-models is computed. The higher the frequency is, the more important the variable is. The performance of the proposed procedure was investigated using three real NIR datasets. The results indicate that VCPA is a good variable selection strategy when compared with four high performing variable selection methods: genetic algorithm–partial least squares (GA–PLS), Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV). The MATLAB source code of VCPA is available for academic research on the website: http://www.mathworks.com/matlabcentral/fileexchange/authors/498750.     

偏最小二乘法在多组分阳极溶出伏安分析中的应用—铜,锑,铋的同时测定

本文利用微分脉冲阳极溶出伏安法对微量铜、锑、铋混合溶液进行同进分析，实验在盐酸－氯化钠介质中进行，悬汞电极作工作电极，在－４００ｍＶ处富集电解５ｍｉｎ，然后由－４００ｍＶ向０Ｖ作微分脉冲溶出伏安扫描，扫描速度为２ｍＶ／ｓ，脉冲振幅为４０ｍＶ，由于计算机采集溶出伏安数据后采用偏最小二乘法计算并建立数学模型，然后据此模型对未知溶液进行定量分析，获得了较好的结果。     

Variable selection in multivariate calibration based on clustering of variable concept

Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.