Area correlation constraint for the MCR−ALS quantification of cholesterol using EEM fluorescence data: A new approach

This work demonstrates the use of a new additional constraint for the Multivariate Curve Resolution−Alternating Least Squares (MCR−ALS) algorithm called “area correlation constraint”, introduced to build calibration models for Excitation Emission Matrix (EEM) data. We propose the application of area correlation constraint MCR−ALS for the quantification of cholesterol using a simulated data set and an experimental data system (cholesterol in a ternary mixture). This new constraint includes pseudo-univariate local regressions using the area of resolved profiles against reference values during the alternating least squares optimization, to provide directly accurate quantifications of a specific analyte in concentration units. In the two datasets investigated in this work, the new constraint retrieved correctly the analyte and interference spectral profiles and performed accurate estimations of cholesterol concentrations in test samples. This the first study using the proposed area constraint using EEM measurements. This new constraint approach emerges as a new possibility to be tested in general cases of second-order multivariate calibration data in the presence of unknown interferents or in more involved higher order calibration cases.     

Classical univariate calibration and partial least squares for quantitative analysis of brass samples by laser-induced breakdown spectroscopy

In this work we compare the analytical results obtained by traditional calibration curves (CC) and multivariate Partial Least Squares (PLS) algorithm when applied to the LIBS spectra obtained from ten brass samples (nine standards of known composition and one ‘unknown’). Both major (Cu and Zn) and trace (Sn, Pb, Fe) elements in the sample matrix were analyzed. After the analysis, the composition of the ‘unknown’ sample, measured by X-ray Fluorescence (XRF) technique, was revealed. The predicted concentrations of major elements obtained by rapid PLS algorithms are in very good agreement with the nominal concentrations, as well as with those obtained by the more time-consuming CC approach. A discussion about the possible effects leading to discrepancies of the results is reported. The results of this study open encouraging perspectives towards the development of cheap LIBS instrumentation which would be capable, despite the limitations of the experimental apparatus, to perform fast and precise quantitative analysis on complex samples.     

A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents

This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet–visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense.     

Simultaneous determination of antazoline and naphazoline by the net analyte signal standard addition method and spectrophotometric technique

A novel net analyte signal standard addition method (NASSAM) was used for simultaneous determination of the drugs anthazoline and naphazoline. The NASSAM can be applied for determination of analytes in the presence of known interferents. The proposed method is used to eliminate the calibration and prediction steps of multivariate calibration methods; the determination is carried out in a single step for each analyte. The accuracy of the predictions against the H-point standard addition method is independent of the shape of the analyte and interferent spectra. The net analyte signal concept was also used to calculate multivariate analytical figures of merit, such as LOD, selectivity, and sensitivity. The method was successfully applied to the simultaneous determination of anthazoline and naphazoline in a commercial eye drop sample.     

A novel second-order standard addition analytical method based on data processing with multidimensional partial least-squares and residual bilinearization

In the presence of analyte-background interactions and a significant background signal, both second-order multivariate calibration and standard addition are required for successful analyte quantitation achieving the second-order advantage. This report discusses a modified second-order standard addition method, in which the test data matrix is subtracted from the standard addition matrices, and quantitation proceeds via the classical external calibration procedure. It is shown that this novel data processing method allows one to apply not only parallel factor analysis (PARAFAC) and multivariate curve resolution-alternating least-squares (MCR-ALS), but also the recently introduced and more flexible partial least-squares (PLS) models coupled to residual bilinearization (RBL). In particular, the multidimensional variant N-PLS/RBL is shown to produce the best analytical results. The comparison is carried out with the aid of a set of simulated data, as well as two experimental data sets: one aimed at the determination of salicylate in human serum in the presence of naproxen as an additional interferent, and the second one devoted to the analysis of danofloxacin in human serum in the presence of salicylate.     

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 calibration of spectra obtained by Laser Induced Breakdown Spectroscopy of plutonium oxide surrogate residues

Laser Induced Breakdown Spectroscopy (LIBS) was used to determine elemental concentration of plutonium oxide surrogate (cerium oxide) residue for monitoring the fabrication of lanthanide borosilicate glass. Quantitative analysis by LIBS is affected by the severe limitation of variation in the induced plasma due to changes in the matrix. Multivariate calibration was applied to LIBS data to predict the concentrations of Ce, Cr, Fe, Mo, and Ni. A total of 18 different samples were prepared to compare calibration from univariate data analysis and from multivariate data analysis. Multivariate calibration was obtained using Principal Component Regression (PCR) and Partial Least Squares (PLS). Univariate calibration was obtained from background-corrected atomic emission lines. Calibration results show improvement in the coefficient of determination from 0.87 to 0.97 for Ce compared to univariate calibration. The root mean square error also reduced from 7.46 to 2.93%. A similar trend was obtained for Cr, Fe, Mo, and Ni also. These results clearly demonstrate the feasibility of using LIBS for online process monitoring in a hazardous waste management environment.     

Improvement of residual bilinearization by particle swarm optimization for achieving the second‐order advantage with unfolded partial least‐squares

The combination of unfolded partial least‐squares (U‐PLS) with residual bilinearization (RBL) provides a second‐order multivariate calibration method capable of achieving the second‐order advantage. RBL is performed by varying the test sample scores in order to minimize the residues of a combined U‐PLS model for the calibrated components and a principal component model for the potential interferents. The sample scores are then employed to predict the analyte concentration, with regression coefficients taken from the calibration step. When the contribution of multiple potential interferents is severe, particle swarm optimization (PSO) helps in preventing RBL to be trapped by false minima, restoring its predictive ability and making it comparable to the standard parallel factor (PARAFAC) analysis. Both simulated and experimental systems are analyzed in order to show the potentiality of the new technique. Copyright © 2007 John Wiley & Sons, Ltd.     

Determination of Maneb Residues,in Tomatoes,by Spectrophotometric Method of Acid Hydrolysis,Through Multivariate Calibration

ABSTRACT

In this work, a chemometric method was applied through multivariate calibration, PLS (Partial Least Squares), to establish the analysis of ethylenebisdithiocarbamates residues in tomatoes samples by the hydrolysis method. The algorithm used to implement the PLS in the MatLab environment on IBM-compatible personal computer, was obtained from chemometrics package PLS_ToolBox. In samples with elevated levels of Maneb the univariate calibration showed similar results to the multivariate calibration. However, in samples with lower levels of residues increases occurred in the order of 15 to 47% in the levels detected by the multivariate calibration. In addition, there was a significant decrease in the standard deviations, in relation to those obtained, when the method of univariate calibration was used. The levels of contamination by Maneb found in tomatoes samples were below the maximum established by the Brazilian legislation.     

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.     

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.     

Chemometric-assisted MIP-optosensing system for the simultaneous determination of monoamine naphthalenes in drinking waters

In the present work a chemometric-assisted molecularly imprinted polymer (MIP)-fluorescence optosensing system has been developed for determining monoamines naphthalene compounds in drinking waters. The use of chemometrics for processing flow injection analysis with MIP fluorescence optosensor data allowed the simultaneous determination of the principal monoamine naphthalene compounds 1-naphthylamine (1-NA) and 2-naphthylamine (2-NA) even in presence of potential interferent 1-naphthalenemethylamine (1-NMA). Classical chemometrics tools such as partial least-squares (PLS-1), as well as second-order algorithms like multiway PLS (N-PLS) and unfolded PLS (U-PLS), were successfully applied, assisting fluorescence emission spectra at a fixed excitation wavelength or excitation-emission fluorescence matrices (EEM), respectively, when interferents are considered in the calibration set. The combinations of both N-PLS and U-PLS with residual bilinearization (RBL), achieving the second-order advantage, were satisfactory applied for the simultaneous determination of the main monoamine naphthalene compounds in drinking water, in the presence of a potential interferent without sample pretreatment, even when the later is not modeled in calibration set. Predictive ability, accuracy, figures of merit, as well as advantages and disadvantages of the different strategies were discussed.     

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.     

Determination of Total Sugar in Tobacco by Near-Infrared Spectroscopy and Wavelet Transformation-Based Calibration

Based on wavelet transformation (WT) and mutual information (MI), a simple and effective procedure is proposed for multivariate calibration of near-infrared spectroscopy. In such a procedure, the original spectra of the training set are first transformed into a set of wavelet representations by wavelet prism transform. Then, the MI value between each wavelet coefficient variable and the dependent variable is calculated, resulting in a MI spectrum; by retaining a subset set of coefficients with higher MI, an update training set consisting of wavelet coefficients is obtained and reconstructed/converted back to the original domain. Based on this, a partial least square (PLS) model can be constructed and optimized. The optimal wavelet and decomposition level are determined by experiment. A NIR quantitative problem involving the determination of total sugar in tobacco is used to demonstrate the overall performance of the proposed procedure, named RPLS, meaning PLS in reconstructed original domain coupled with MI-induced variable selection in wavelet domain (RPLS). Three kinds of procedures, that is, conventional full-spectrum PLS in original domain (FPLS), PLS in original domain coupled with MI-induced variable selection (OPLS), and direct PLS in MI-based wavelet coefficients (WPLS), are used as reference. The result confirms that it can build more accurate and robust calibration models without increasing the complexity.     

Standardization of SPE signals in multicomponent analysis of three benzimidazolic pesticides by spectrofluorimetry

Piecewise direct standardization (PDS) is applied to multivariate standardization of fluorescence signals using partial least squares (PLS) and principal component regression (PCR) as the calibration models. The multivariate standardization was used to transfer spectra obtained after a step of solid phase extraction (SPE) to spectra registered in pure solvent in the determination of carbendazim, fuberidazole and thiabendazole in water samples. The influential parameters, such as tolerance, window size and the number of samples of the standardization subset were optimized by means of the root mean squared error of prediction (RMSEP). Similar RMSEP values were obtained by PLS and PCR using the optimized influential parameters in the standardization. However, better predictions of the compounds were obtained in test set by the PLS model.     

Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apple fruit

Non-destructive, rapid, instrumental tools in fruit production are required for predicting the optimum harvest window and monitoring fruit quality during shelf life. The degree of chlorophyll degradation is a sensitive indicator for fruit maturation and ageing. Adequate indices for chlorophyll prediction by means of non-destructive spectral analysis have been studied in the fields of photosynthesis research and remote sensing developments. However, an evaluation of these indices and multivariate linear regression models does not exist so far for predicting the fruit chlorophyll content.Spectral transmittance recordings in the visible wavelength range were carried out on apple fruit Malus domestica Borkh. ‘Elstar’ (n=99) and ‘Jonagold’ (n=117). The fruit chlorophyll a content of ‘Elstar’ apples was measured wet-chemically and predicted by means of the specific indices: NDVI, Tr₆₉₈/Tr₇₆₀, red-edge, TrII on Tr′(λ), TrII, and RVSI with the correlation coefficients of determination R²=0.84, 0.81, 0.75, 0.81, 0.15, and 0.67, respectively. Partial least-squares (PLS) calibration models were built using calibrated spectra (630-730 nm), first derivative of spectra and second derivative of spectra yielding multivariate correlation coefficients of determination R²=0.81, 0.86, and 0.92, respectively. Similar results were found for ‘Jonagold’ apple fruit. Linear regression of indices and PLS calibration models were empirically tested on the chlorophyll a content of apple fruit measuring the same cultivars grown in a different seasons and growing locations. In the case of ‘Elstar’ apple fruit, correlation coefficients of fruit chlorophyll a content analysed wet-chemically as well as fruit maturation measured as calendar weeks were similar to those in the calibration experiment. Application of linear regression equations of indices and PLS models on spectra of ‘Jonagold’ apple fruit led to less accurate results for those methods, which use wavelengths above 720 nm as indicative range.     

Chemometrics: the issues of measurement and modelling

In this paper, two spectral data sets have been used to illustrate the importance of maintaining chemical information whilst generating predictive multivariate calibration models. The first data set is based on 26 duplicate UV/VIS spectra for four meal ions (Fe, Ni, Co, Cu) present at varying concentrations in aqueous solution. Spectra were collected across the range 180–800 nm at a resolution of 3.5 nm generating 211 data points for each sample. Calibration was carried out using multiple linear regression (MLR) and a K-matrix approach to demonstrate the advantages the latter method has in describing real spectral features. In addition, the limitation of MLR in accommodating noise and spectral overlap in the data is also illustrated. The second data set based on NIR spectroscopy, was generated using a four-level 2 factor Factorial design strategy and consisted of two additives present at a range of concentrations in an aqueous caustic system, with the spectra being collected over the range 10,000–3000 cm⁻¹. Whilst a conventional partial least squares (PLS) model was applied to the data, it was through the use of variable selection (VS) prior to PLS and the application of weighted ridge regression (WRR) techniques that the need to develop chemometric methodology which intuitively reflected chemical information has been demonstrated. The results will also illustrate how a poorly designed experimental design protocol and missing data can limit the performance of the calibration models generated. The aims of this paper are not to prescribe ideal calibration methodology but rather to demonstrate the relevance of selecting multivariate calibration methodology that relates more to the chem rather than just the metrics in chemometrics.     

叠加多元校正分析

基于多模型(模型融合)建模的思想,开发了两种新的叠加多元校正分析算法:叠加PCR(PLS)多元校正分析和叠加移动窗口PCR(PLS)多元校正分析。与一般的多模型建模方法不同的是其通过赋予光谱数据中的不同部分不同权重叠加子多元校正模型。因此,其可以通过权重调节或选择变量。在消除光谱数据中常见的冗余信息的同时,避免信息遗漏的缺点,并最终提高模型的稳健性,简化了模型。对于这两个新的算法,尽管其具体步骤不同,但仍取得了相似的预测结果。本文通过两套近红外光谱文献数据计算验证了这两个新方法的优越性。