Coupling of air-assisted liquid–liquid microextraction method with partial least squares for simultaneous spectrophotometric determination of some preservatives

An air-assisted liquid–liquid microextraction method coupled with a multivariate calibration method, namely partial least squares (PLS), was developed for the extraction and simultaneous determination of benzoic acid (BA) and sorbic acid (SA) via a spectrophotometric approach. In this work, a two-step microextraction method was used. In the first step, analytes were extracted from acidic aqueous solution into octanol, as an organic solvent, and in the second step, the analytes were simultaneously back-extracted into an alkaline aqueous solution. The high absorption signal of octanol was the main reason to perform this back-extraction step. The effects of different parameters on the method efficiency were investigated; the parameters included extraction solvent volume, ionic strength of aqueous solution, pH, number of extraction cycles, and aqueous sample volume. Under optimum conditions, calibration graphs were seen to be linear over the range of 0.1–2.0 µg mL^?1 for the both analytes. Other analytical parameters were obtained as follows: Enrichment factors (EFs) were found to be 14.98 and 13.03, and limits of detection were determined to be 0.03 and 0.04 µg mL^?1 for BA and SA, respectively. As the last step, binary mixtures of the analytes were prepared and simultaneously extracted using the proposed method. Finally, PLS modeling was used for multivariate calibration of spectrophotometric data. It was successfully utilized for the analysis of the target analytes in real samples.     

Spectrophotometric simultaneous determination of ceratine, creatinine, and uric acid in real samples by orthogonal signal correction–partial least squares regression

Abstract  This work describes a quantitative spectroscopic method for the analysis of ternary mixtures of ceratine (CER), creatinine (CRE), and uric acid (UA) using multivariate data models based upon ultraviolet spectroscopy. By multivariate calibration methods, such as partial least squares regression, it is possible to obtain a model adjusted to the concentration values of the mixtures used in the calibration range. In this study, the calibration model is based on absorption spectra in the 200–260 nm range for 36 different mixtures of CER, CRE, and UA. The unrelated information was removed by the orthogonal signal correction (OSC) method and the results were proved. Evaluation of the prediction errors for the prediction set reveals the OSC-treated data give substantially lower root mean square error of prediction (RMSEP) values than original data. The RMSEP for CER, CRE, and UA with OSC were 1.1686, 0.2195, and 0.3726, and without OSC were 1.9057, 0.3482, and 0.6164, respectively. This procedure allows the simultaneous determination of CER, CRE, and UA in synthetic and real samples. Graphical abstract        

Simultaneous spectrophotometric determination of iron species using orthogonal signal correction–generalized partial least squares calibration method after vortex-assisted dispersive liquid–liquid microextraction

A vortex-assisted dispersive liquid–liquid microextraction method in combination with UV–Vis spectrophotometry was developed for the simultaneous extraction and determination of iron species. In this method, Fe²⁺ and Fe³⁺ were complexed with pyridine-2-amidoxime, neutralized through ion pair formation with sodium dodecyl sulfate, and extracted into the fine droplets of chloroform. After centrifugation, the absorbance of the extracted complexes was recorded in the wavelength range of 360–700 nm. The parameters affecting the extraction efficiency such as the pH, the type and volume of the extraction solvent, ligand concentration, and sample volume were optimized. The individual iron species was then determined by means of the orthogonal signal correction–generalized partial least squares method. Under the optimized conditions, the calibration curves were linear over the range of 2.0–100 and 3.0–200 µg L^?1 with detection limits of 0.4 µg L^?1 for Fe²⁺ and 0.8 µg L^?1 for Fe³⁺, respectively. The relative standard deviations for intra- and inter-day assays (n = 5) were 2.3 and 4.0 for Fe²⁺ at 50 µg L^?1 and 2.7 and 4.3 for Fe³⁺ at 30 µg L^?1, respectively. The enhancement factors of 77 and 69 were achieved for Fe²⁺ and Fe³⁺, respectively. The proposed method was successfully applied to the determination of iron species in water samples.     

Achieving second order advantage with multi-way partial least squares and residual bi-linearization with total synchronous fluorescence data of monohydroxy–polycyclic aromatic hydrocarbons in urine samples

An attractive approach to handle matrix interference in samples of unknown composition is to generate second- or higher-order data formats and process them with appropriate chemometric algorithms. Several strategies exist to generate high-order data in fluorescence spectroscopy, including wavelength time matrices, excitation–emission matrices and time-resolved excitation–emission matrices. This article tackles a different aspect of generating high-order fluorescence data as it focuses on total synchronous fluorescence spectroscopy. This approach refers to recording synchronous fluorescence spectra at various wavelength offsets. Analogous to the concept of an excitation–emission data format, total synchronous data arrays fit into the category of second-order data. The main difference between them is the non-bilinear behavior of synchronous fluorescence data. Synchronous spectral profiles change with the wavelength offset used for sample excitation. The work presented here reports the first application of total synchronous fluorescence spectroscopy to the analysis of monohydroxy-polycyclic aromatic hydrocarbons in urine samples of unknown composition. Matrix interference is appropriately handled by processing the data either with unfolded-partial least squares and multi-way partial least squares, both followed by residual bi-linearization.     

Direct determination of polymerized triglycerides in deep-frying olive oil by attenuated total reflectance–Fourier transform infrared spectroscopy using partial least squares regression

A partial least squares (PLS) regression model based on attenuated total reflectance–Fourier transform infrared spectra of heated olive oil samples has been developed for the determination of polymerized triacylglycerides (PTGs) generated during thermal treatment of oil. Three different approaches for selection of the spectral regions used to build the PLS model were tested and compared: (1) variable selection based on expert knowledge, (2) uninformative variable elimination PLS, and (3) interval PLS. Each of the three variable selection methods provided PLS models from heated olive oil samples with excellent performance for the prediction of PTGs in fried olive oils with comparable model statistics. However, besides a high coefficient of determination (R ² of 0.991) and low calibration, validation, and prediction errors of 1.14%, 1.21%, and 1.40% w/w, respectively, variable selection based on expert knowledge gave additionally almost identical low calibration (−0.0017% w/w) and prediction (−0.0023% w/w) bias. Furthermore, it was verified that the determination of PTGs was not influenced by the type of foodstuff fried in the olive oil.     

Progress and prospect of the zinc–iodine battery

The zinc–iodine battery has the advantages of high energy density and low cost owing to the flexible multivalence changes of iodine and natural abundance of zinc resources. Compared with the flow battery, it has simpler components and more convenient installation, yet it still faces challenges in practical applications. How to select suitable materials as the cathode and electrolyte to control the process of energy storage reaction and inhibit the self-transformation of by-products, together with the corrosion resistance of metallic zinc are crucial factors. Herein, the principles of the zinc–iodine flow battery and zinc–iodine battery are described, and the unprecedented progresses are highlighted. This mini review is anticipated to provide valuable guidance for the further development of the zinc–iodine battery.     

Local classification: Locally weighted–partial least squares-discriminant analysis (LW–PLS-DA)

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW–PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones.     

An emerging strategy for tracing the anti-erectile dysfunction components of raw and salt-processed semens of Astragali Complanati by combinatory liquid chromatography–mass spectrometry-based quantitative analysis,efficacy assessment on impotent rats,and partial least squares regression

Semens of Astragali Complanati own anti-erectile dysfunction effect; however, the components which contribute to the anti-erectile dysfunction effect remain unclear. This work raised a strategy that integrates liquid chromatography coupled mass spectrometry-based quantitative analysis, anti-erectile dysfunction assessment on impotent rats, and their relationship analysis for pinpointing anti-erectile dysfunction components from semens of Astragali Complanati. For simultaneous quantification of seven major components in raw and salt-processed semens of Astragali Complanati, an accurate and reliable liquid chromatography–mass spectrometry method was developed under multiple reaction monitoring mode. Of note, chloramphenicol was employed as the internal standard. The method showed good linearity and repeatability, where the recovery rates of each component ranged from 98.1 to 104.7%, and the precisions of intra- and interday were all within 3.4%. The method has been used for quantification of the seven major components in 10 batches of raw and salt-processed semens of Astragali Complanati. Then, the anti-erectile dysfunction effects of raw and salt-processed semens of Astragali Complanati were evaluated on impotent rats. Gray relationship analysis and partial least squares regression were combined for elucidating the relationship. As a result, complanatuside, astragalin, complanatoside B, and kaempferol were found to be responsible for anti-erectile dysfunction effect of Astragali Complanati.     

Simultaneous spectrophotometric determination of trace amounts of uranium, thorium, and zirconium using the partial least squares method after their preconcentration by α-benzoin oxime modified Amberlite XAD-2000 resin

A new solid phase extraction method for separation and preconcentration of trace amounts of uranium, thorium, and zirconium in water samples is proposed. The procedure is based on the adsorption of U(VI), Th(IV) and Zr(IV) ions on a column of Amberlite XAD-2000 resin loaded with α-benzoin oxime prior to their simultaneous spectrophotometric determination with Arsenazo III using orthogonal signal correction partial least squares method. The enrichment factor for preconcentration of uranium, thorium, and zirconium was found to be 100. The detection limits for U(VI), Th(IV) and Zr(IV) were 0.50, 0.54, and 0.48 μg L⁻¹, respectively. The precision of the method, evaluated as the relative standard deviation obtained by analyzing a series of 10 replicates, was below 4% for all elements. The practical applicability of the developed sorbent was examined using synthetic seawater, natural waters and ceramic samples.     

Solving chromatographic challenges in comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry using multivariate curve resolution–alternating least squares

Multivariate curve resolution–alternating least squares (MCR–ALS) analysis is proposed to solve chromatographic challenges during two-dimensional gas chromatography–time-of-flight mass spectrometry (GC?×?GC–TOFMS) analysis of complex samples, such as crude oil extract. In view of the fact that the MCR–ALS method is based on the fulfillment of the bilinear model assumption, three-way and four-way GC?×?GC–TOFMS data are preferably arranged in a column-wise superaugmented data matrix in which mass-to-charge ratios (m/z) are in its columns and the elution times in the second and first chromatographic columns are in its rows. Since m/z values are common for all measured spectra in all second-column modulations, unavoidable chromatographic challenges such as retention time shifts within and between GC?×?GC–TOFMS experiments are properly handled. In addition, baseline/background contributions can be modeled by adding extra components to the MCR–ALS model. Another outstanding aspect of MCR–ALS analysis is its extreme flexibility to consider all samples (standards, unknowns, and replicates) in a single superaugmented data matrix, allowing joint analysis. In this way, resolution, identification, and quantification results can be simultaneously obtained in a very fast and reliable way. The potential of MCR–ALS analysis is demonstrated in GC?×?GC–TOFMS analysis of a North Sea crude oil extract sample with relative errors in estimated concentrations of target compounds below 6.0 % and relative standard deviations lower than 7.0 %. The results obtained, along with reasonable values for the lack of fit of the MCR–ALS model and high values of the reversed match factor in mass spectra similarity searches, confirm the reliability of the proposed strategy for GC?×?GC–TOFMS data analysis.      

Comparison of performance of partial least squares regression,secured principal component regression,and modified secured principal component regression for determination of human serum albumin, γ-globulin and glucose in buffer solutions and in vivo blood glucose quantification by near-infrared spectroscopy

The performances of three multivariate analysis methods—partial least squares (PLS) regression, secured principal component regression (sPCR) and modified secured principal component regression (msPCR)—are compared and tested for the determination of human serum albumin (HSA), γ-globulin, and glucose in phosphate buffer solutions and blood glucose quantification by near-infrared (NIR) spectroscopy. Results from the application of PLS, sPCR and msPCR are presented, showing that the three methods can determine the concentrations of HSA, γ-globulin and glucose in phosphate buffer solutions almost equally well provided that the prediction samples contain the same spectral information as the calibration samples. On the other hand, when some potential spectral features appear in new measurements, sPCR and msPCR outperform PLS significantly. The reason for this is that such spectral features are not included during calibration, which leads to a degradation in PLS prediction performance, while sPCR and msPCR can improve their predictions for the concentrations of the analytes by removing the uncalibrated features from the original spectra. This point is demonstrated by successfully applying sPCR and msPCR to in vivo blood glucose measurements. This work therefore shows that sPCR and msPCR may provide possible alternatives to PLS in cases where some uncalibrated spectral features are present in measurements used for concentration prediction.     

Ultrafast quantitation of six quinolones in water samples by second-order capillary electrophoresis data modeling with multivariate curve resolution–alternating least squares

This paper presents the development of a capillary electrophoresis method with diode array detector coupled to multivariate curve resolution–alternating least squares (MCR-ALS) to conduct the resolution and quantitation of a mixture of six quinolones in the presence of several unexpected components. Overlapping of time profiles between analytes and water matrix interferences were mathematically solved by data modeling with the well-known MCR-ALS algorithm. With the aim of overcoming the drawback originated by two compounds with similar spectra, a special strategy was implemented to model the complete electropherogram instead of dividing the data in the region as usually performed in previous works. The method was first applied to quantitate analytes in standard mixtures which were randomly prepared in ultrapure water. Then, tap water samples spiked with several interferences were analyzed. Recoveries between 76.7 and 125 % and limits of detection between 5 and 18 μg L^?1 were achieved.     

A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.     

Multivariate curve resolution–alternating least squares (MCR-ALS) applied to spectroscopic data from monitoring chemical reactions processes

This paper overviews the application of multivariate curve resolution (optimized by alternating least squares) to spectroscopic data acquired by monitoring chemical reactions and other processes. The goals of the resolution methods and the principles for understanding their applications are described. Some of the problems arising from these evolving systems and the limitations of the multivariate curve resolution methods are also discussed. This article reviews most of the applications of multivariate curve resolution applied to reacting systems published between January 2000 and June 2007. Some basic papers dated before 2000 have also been included.