Integration of Commercial Screen‐printed Electrodes into a Voltammetric Electronic Tongue for the Analysis of Aminothiols

A voltammetric sensor array (or electronic tongue) is developed for the simultaneous quantification of cysteine, glutathione and homocysteine without need of previous separation. It is based on the integration of three commercial screen‐printed electrodes (gold curated at high and low temperature and carbon modified with carbon nanotubes). Linear sweep voltammograms measured simultaneously by all three sensors are processed by Partial Least Squares (PLS) regression and different variables selection algorithms such as Genetic Algorithm and interval‐Partial Least Squares. The method was applied to synthetic mixtures and successfully validated, with correlation coefficients of prediction (R_p²) of 0.9542, 0.9429 and 0.9589 for cysteine, glutathione, and homocysteine respectively.     

Comparison of Chemometric and Chromatographic Methods to Obtain Kinetic Parameters for Textile Dyes during a Biodegradation Process

The concentrations of three industrial-grade textile dyes were determined in a mixture after degradation by the fungus Ganoderma sp, by using the methods of UV-Vis spectrophotometry associated with Partial Least Squares regression and HPLC and comparing the results obtained from both methods. Using the concentrations calculated from the two methods, a kinetic study of the biodegradation mediated by the fungus was performed. The rate constants and the activation energies for this transformation were obtained for each dye in the mixture. The concentration of Remazol Blue R ESP could be determined by the HPLC method, and the value obtained was comparable with the result using the Partial Least Squares regression method. The Partial Least Squares regression method offers advantages over the HPLC method for the quantification of dyes in textile effluents, as it provides the kinetic parameters of the biodegradation reaction.     

Comparison of different calibration methods for the determination by FT-NIR spectroscopy of the hydroxyl number in polyester resins

Different calibration methods have been applied for the determination of the Hydroxyl Number in polyester resins, namely Partial Least Squares (PLS), Principal Component Regression (PCR), Ordinary Least Squares with selection of the variables by genetic algorithm (OLS-GEN) and back-propagation Artificial Neural Networks (BP-ANN). The predictive ability of the regression models was estimated by splitting the dataset in training and test sets by application of the Kohonen self-organising maps. The linear methods (OLS-GEN, PLS and PCR) showed comparable results while artificial neural networks provided the best results both in fitting and prediction.     

Multivariate analysis of Cd(II), In(III), Tl(I) and Pb(II) in mixtures using square wave anodic stripping voltammetry

Accurate qualitative and quantitative results were obtained by the application of parameter estimation methods, viz. Classical Least Squares ;CLS', Inverse Least Squares ;ILS' and Kalman Filter ;KF' algorithms. These methods were used to separate strongly overlapping electrochemical peaks produced by binary, ternary and quaternary mixtures of traces of cited poisonous heavy metals stripped from the hanging mercury drop electrode in an acetate-bromide electrolyte using the square wave anodic stripping voltammetry. The analysis was achieved using a single standard addition, the concentrations studied were down to 50 nM and molar ratios up to 1:6 for binary mixtures. A statistical analysis of the results was reported. The method was applied for the ultratrace analysis of the cited cations in a sample of sodium hydrogen carbonate AR.     

On heteroaromaticity of nucleobases. Bond lengths as multidimensional phenomena

Three hundred and nine carbon-carbon, carbon-nitrogen, and carbon-oxygen pi-bond lengths in high precision crystal structures of 31 purine and pyrimidine nucleobases were related to the Pauling pi-bond order, its analogues corrected to crystal packing effects, the numbers of non-hydrogen atoms around the bond, and the sum of atomic numbers of the bond atoms. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) demonstrated that the bond lengths in the nucleobases are three-dimensional phenomenon, characterized by nine distinct classes of bonds. Bond lengths predicted by Linear Regression models, Pauling Harmonic Potential Curves, Multiple Linear Regression, Principal Component, and Partial Least Squares Regression were compared to those calculated by molecular mechanics, semiempirical, and ab initio methods using PCA-HCA procedure on the calculated bond lengths, statistical parameters, and structural aromaticity indices. Incorporation of crystal packing effects into bond orders makes multivariate models to be competitive to semiempirical results, while further improvement of quantum chemical calculations can be achieved by geometry optimization of molecular clusters.     

Determination of thiamine HCl and pyridoxine HCl in pharmaceutical preparations using UV-visible spectrophotometry and genetic algorithm based multivariate calibration methods

Simultaneous determination of binary mixtures pyridoxine hydrochloride and thiamine hydrochloride in a vitamin combination using UV-visible spectrophotometry and classical least squares (CLS) and three newly developed genetic algorithm (GA) based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The sample data set contains the UV-visible spectra of 30 synthetic mixtures (8 to 40 microg/ml) of these vitamins and 10 tablets containing 250 mg from each vitamin. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the two components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of <0.01 and 0.43 microg/ml for all the four methods. The SEP values for the tablets were in the range of 2.91 and 11.51 mg/tablets. A comparison of genetic algorithm selected wavelengths for each component using GR method was also included.     

A nonlinear partial least squares algorithm using quadratic fuzzy inference system

We introduce a new nonlinear partial least squares algorithm ‘Quadratic Fuzzy PLS (QFPLS)’ that combines the outer linear Partial Least Squares (PLS) framework and the Takagi–Sugeno–Kang (TSK) fuzzy inference system. The inner relation between the input and the output PLS score vectors is modeled by a quadratic TSK fuzzy inference system. The performance of the proposed QFPLS method is tested and compared against four other well‐known partial least squares methods (Linear PLS (LPLS), Quadratic PLS (QPLS), Linear Fuzzy PLS (LFPLS), and Neural Network PLS (NNPLS)) on various different types of randomly generated test data. QFPLS outperformed competitors based on two comparison measures: the output variables cumulative per cent variance captured by the PLS latent variables and the root mean‐square error of prediction (RMSEP). Copyright © 2009 John Wiley & Sons, Ltd.     

基于RF_AdaBoost模型的血液种属鉴别算法

针对人类和非人类血液种属鉴别对无损、 高效分析方法的需求, 结合随机森林(Random Forest)和AdaBoost(Adaptive Boosting Algorithm)算法, 提出了一种血液种属鉴别方法(RF_AdaBoost). 该方法将RF作为AdaBoost的弱分类器, 以达到提高模型鉴别准确度, 增强模型鲁棒性的目的. 采用RF、 支持向量机(SVM)、 极限学习机(ELM)、 核极限学习机(KELM)、 堆栈自编码网络(SAE)、 反向传播网络(BP)、 主成分分析-线性判别法(PCA-LDA)及偏最小二乘判别分析(PLS-DA)与RF_AdaBoost模型进行对比, 以不同规模血液拉曼光谱数据训练集进行鉴别实验评估其性能. 结果表明, 随着训练样本的增加, RF_AdaBoost鉴别准确度最高达100%, 预测标准偏差趋于0. 与其它模型相比, RF_AdaBoost具有较高的分类准确度及较强的稳定性, 为血液种属的鉴别工作提供了新方法.     

木薯品质分析的近红外光谱模型建立及其应用研究

应用近红外光谱法(NIRS)建立木薯中淀粉、水分定量分析的近红外光谱数学模型,探讨了修正偏最小二乘法(MPLS)、偏最小二乘法(PLS)以及主成分回归法(PCR)等优化处理对定标模型的影响,确定了修正偏最小二乘法(MPLS)是建立模型最适合的数学方法。并对模型预测结果的准确性进行了评价。结果表明:验证集样品的化学值和近红外预测值拟合存在较好的线性关系,相关性显著。淀粉模型预测标准偏差(Sep)为0.850,系统偏差(Bias)为-0.095,相关系数(r)为0.971。水分模型预测标准偏差(Sep)为0.075,系统偏差(Bias)为0.007,相关系数(r)为0.980。淀粉、水分定量分析的NIRS数学模型具有较高的预测准确性,可应用于木薯批量收购中的品质等分析。     

Classification of GC‐MS measurements of wines by combining data dimension reduction and variable selection techniques

Different classification methods (Partial Least Squares Discriminant Analysis, Extended Canonical Variates Analysis and Linear Discriminant Analysis), in combination with variable selection approaches (Forward Selection and Genetic Algorithms), were compared, evaluating their capabilities in the geographical discrimination of wine samples. Sixty‐two samples were analysed by means of dynamic headspace gas chromatography mass spectrometry (HS‐GC‐MS) and the entire chromatographic profile was considered to build the dataset. Since variable selection techniques pose a risk of overfitting when a large number of variables is used, a method for coupling data dimension reduction and variable selection was proposed. This approach compresses windows of the original data by retaining only significant components of local Principal Component Analysis models. The subsequent variable selection is then performed on these locally derived score variables. The results confirmed that the classification models achieved on the reduced data were better than those obtained on the entire chromatographic profile, with the exception of Extended Canonical Variates Analysis, which gave acceptable models in both cases. Copyright © 2008 John Wiley & Sons, Ltd.     

Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy

The application of supervised pattern recognition methodology is becoming important within chemistry. The aim of the study is to compare classification method accuracies by the use of a McNemar’s statistical test. Three qualitative parameters of sugar beet are studied: disease resistance (DR), geographical origins and crop periods. Samples are analyzed by near-infrared spectroscopy (NIRS) and by wet chemical analysis (WCA). Firstly, the performances of eight well-known classification methods on NIRS data are compared: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN) method, Soft Independent Modeling of Class Analogy (SIMCA), Discriminant Partial Least Squares (DPLS), Procrustes Discriminant Analysis (PDA), Classification And Regression Tree (CART), Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) neural network are computed. Among the three data sets, SIMCA, DPLS and PDA have the highest classification accuracies. LDA and KNN are not significantly different. The non-linear neural methods give the less accurate results. The three most accurate methods are linear, non-parametric and based on modeling methods. Secondly, we want to emphasize the power of near-infrared reflectance data for sample discrimination. McNemar’s tests compare classification developed with WCA or with NIRS data. For two of the three data sets, the classification results are significantly improved by the use of NIRS data.     

Modelling the quality of enantiomeric separations using Mutual Information as an alternative variable selection technique

This paper uses Mutual Information as an alternative variable selection method for quantitative structure-property relationships data. To evaluate the performance of this criterion, the enantioselectivity of 67 molecules, in three different chiral stationary phases, is modelled. Partial Least Squares together with three commonly used variable selection techniques was evaluated and then compared with the results obtained when using Mutual Information together with Support Vector Machines. The results show not only that variable selection is a necessary step in quantitative structure-property relationship modelling, but also that Mutual Information associated with Support Vector Machines is a valuable alternative to Partial Least Squares together with correlation between the explanatory and the response variables or Genetic Algorithms. This study also demonstrates that by producing models that use a rather small set of variables the interpretation can be also be improved.     

Genetic programming as an analytical tool for non-linear dielectric spectroscopy.

By modelling the non-linear effects of membranous enzymes on an applied oscillating electromagnetic field using supervised multivariate analysis methods, Non-Linear Dielectric Spectroscopy (NLDS) has previously been shown to produce quantitative information that is indicative of the metabolic state of various organisms. The use of Genetic Programming (GP) for the multivariate analysis of NLDS data recorded from yeast fermentations is discussed, and GPs are compared with previous results using Partial Least Squares (PLS) and Artificial Neural Nets (NN). GP considerably outperforms these methods, both in terms of the precision of the predictions and their interpretability.     

Resolution of ternary mixtures of nitrofurantoin, furazolidone and furaltadone by application of Partial Least Squares analysis to the differential pulse polarographic signals

The abilities of the Partial Least Squares (PLS) methods in the resolution of ternary mixtures of organic compounds (furaltadone, furazolidone and nitrofurantoin) by using their differential pulse polarographic (DPP) signals are reported. The applicability of these methods to resolve very overlapped peaks whose E(p) also changes with concentration is demonstrated. The analysis of both synthetic and real samples has been made with satisfactory results. The relative error of prediction (REP) is 8.7% for FD, 7.7% for FZ and 6.7% for NF by application of the PLS-2 method.     

Dual stacked partial least squares for analysis of near-infrared spectra

A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications.     

Model building in multivariate additive partial least squares splines via the GCV criterion

In the literature, much effort has been put into modeling dependence among variables and their interactions through nonlinear transformations of predictive variables. In this paper, we propose a nonlinear generalization of Partial Least Squares (PLS) using multivariate additive splines. We discuss the advantages and drawbacks of the proposed model, building it via the generalized cross validation criterion (GCV) criterion, and show its performance on a real dataset and on simulated datasets in comparison to other methods based on splines. Copyright © 2009 John Wiley & Sons, Ltd.     

MATLAB语言在光谱定量分析中的应用

利用MATLAB语言实验紫外-可见吸收光谱法和近红外漫反射光谱法的定量分析数据的处理，着重阐述了偏最小二乘法的多元校正过程。该方法简便、实用，简化并优化了计算过程，效率高，数值稳定性好。     

A review of the main factors influencing the FT-IR-PLS abilities exemplified with petrochemical qualimetric applications

Quality control usually involves monitoring several variables directly related with industrial necessities using univariate tests. One powerful alternative is to link multivariate analytical techniques and multivariate chemometrics. In this way, Fourier Transform Infrared spectroscopy and Partial Least Squares regression are used to discuss and review several advantages and drawbacks encountered in using such combination in industrial facilities. Typical drawbacks are selection of data pretreatment, errors in reference methods, selection of calibration and validation sets and model-aging. This review is exemplified with petrochemical applications although other fields are also considered (mainly when dealing with data pretreatment).