Analysis of lipoproteins using 2D diffusion-edited NMR spectroscopy and multi-way chemometrics

This study represents the first application of multi-way calibration by N-PLS and multi-way curve resolution by PARAFAC to 2D diffusion-edited ¹H NMR spectra. The aim of the analysis was to evaluate the potential for quantification of lipoprotein main- and subfractions in human plasma samples. Multi-way N-PLS calibrations relating the methyl and methylene peaks of lipoprotein lipids to concentrations of the four main lipoprotein fractions as well as 11 subfractions were developed with high correlations (R = 0.75-0.98). Furthermore, a PARAFAC model with four chemically meaningful components was calculated from the 2D diffusion-edited spectra of the methylene peak of lipids. Although the four extracted PARAFAC components represent molecules of sizes that correspond to the four main fractions of lipoproteins, the corresponding concentrations of the four PARAFAC components proved not to be correlated to the reference concentrations of these four fractions in the plasma samples as determined by ultracentrifugation. These results indicate that NMR provides complementary information on the classification of lipoprotein fractions compared to ultracentrifugation.     

Chemometrics assisted spectrophotometric determination of pyridine in water and wastewater

The paper reports a direct method for the determination of pyridine in water and wastewater samples based on ultraviolet spectrophotometric measurements using multi-way modeling techniques. Parallel factor analysis (PARAFAC) and multi-way partial least squares (N-PLS) regression methods were employed for the decomposition of spectra and quantification of pyridine. The study was carried out in the pH range of 1.0-12.0 and concentration range of 0.67-51.7 μg mL⁻¹ of pyridine. Both the three-way PARAFAC and tri-PLS1 models successfully predicted the concentration of pyridine in synthetic (spiked) river water and field wastewater samples. The mean recovery obtained from PARAFAC regression model were 97.39% for the spiked and 99.84% for the field wastewater samples, respectively. The sensitivity and precision of the method for pyridine determination were 0.58% and 5.95%, respectively. The N-PLS regression model yielded mean recoveries of 99.29% and 100.18% for the spiked and field wastewater samples, respectively. The prediction accuracy of the methods was evaluated through the root mean square error of prediction (RMSEP). For PARAFAC, it was 0.65 and 0.82 μg mL⁻¹ for spiked river water and field wastewater samples, respectively, while for N-PLS, it was 0.25 and 0.37 μg mL⁻¹, respectively. Both the PARAFAC and N-PLS methods, thus, yielded satisfactory results for the prediction of pyridine concentration in water and wastewater samples.     

Determination of doxorubicin in human plasma by excitation-emission matrix fluorescence and multi-way analysis

Direct determination of doxorubicin (DXR), a cytotoxic anthracycline antibiotic, in human plasma was accomplished based on excitation-emission matrix (EEM) fluorescence measurements and multi-way chemometric methods based on parallel factor analysis (PARAFAC) and N-PLS. Several different procedures, such as residual analysis, core consistency diagnostic (CONCORDIA) and split-half analysis were employed to determine the correct number of factors in PARAFAC. These procedures converged to a choice of two factors, attributed to DXR and to the sum of two fluorescence species present in the plasma. Sample PARAFAC loadings were employed to build a regression model against concentration, resulting in a RMSECV of 0.060 μg ml⁻¹. N-PLS using two factors produced a RMSECV of 0.045 μg ml⁻¹. Figures of merit (FOM), such as sensitivity (SEN), selectivity (SEL) and limit of detection (L_D) were determined for both PARAFAC and N-PLS.     

Vibrational overtone combination spectroscopy (VOCSY)—a new way of using IR and NIR data

This work explores a novel method for rearranging 1st order (one-way) infra-red (IR) and/or near infra-red (NIR) ordinary spectra into a representation suitable for multi-way modelling and analysis. The method is based on the fact that the fundamental IR absorption and the first, second, and consecutive overtones of NIR absorptions represent identical chemical information. It is therefore possible to rearrange these overtone regions of the vectors comprising an IR and NIR spectrum into a matrix where the fundamental, 1st, 2nd, and consecutive overtones of the spectrum are arranged as either rows or columns in a matrix, resulting in a true three-way tensor of data for several samples. This tensorization facilitates explorative analysis and modelling with multi-way methods, for example parallel factor analysis (PARAFAC), N-way partial least squares (N-PLS), and Tucker models. The vibrational overtone combination spectroscopy (VOCSY) arrangement is shown to benefit from the “order advantage”, producing more robust, stable, and interpretable models than, for example, the traditional PLS modelling method. The proposed method also opens the field of NIR for true peak decomposition—a feature unique to the method because the latent factors acquired using PARAFAC can represent pure spectral components whereas latent factors in principal component analysis (PCA) and PLS usually do not.     

Classification of weathered petroleum oils by multi-way analysis of gas chromatography-mass spectrometry data using PARAFAC2 parallel factor analysis

The application of multi-way parallel factor analysis (PARAFAC2) is described for the classification of different kinds of petroleum oils using GC-MS. Oils were subjected to controlled weathering for 2, 7 and 15 days and PARAFAC2 was applied to the three-way GC-MS data set (MSxGCxsample). The classification patterns visualized in scores plots and it was shown that fitting multi-way PARAFAC2 model to the natural three-way structure of GC-MS data can lead to the successful classification of weathered oils. The shift of chromatographic peaks was tackled using the specific structure of the PARAFAC2 model. A new preprocessing of spectra followed by a novel use of analysis of variance (ANOVA)-least significant difference (LSD) variable selection method were proposed as a supervised pattern recognition tool to improve classification among the highly similar diesel oils. This lead to the identification of diagnostic compounds in the studied diesel oil samples.     

Three-way partial least-squares/residual bilinearization study of second-order lanthanide-sensitized luminescence excitation-time decay data: analysis of benzoic acid in beverage samples

Lanthanide-sensitized luminescence excitation-time decay matrices were employed for achieving the second-order advantage using as chemometric algorithms parallel factor analysis (PARAFAC) and multidimensional partial least-squares with residual bilinearization (N-PLS/RBL). The second-order data were measured for a calibration set of samples containing the analyte benzoic acid in the concentration range from 0.00 to 5.00 mg L⁻¹, for a validation set containing the analyte and the potential interferent saccharin (in the range 0.00–6.00 mg L⁻¹), and for real samples of beverages containing benzoic acid as preservant, saccharin, and other potentially interfering compounds. All samples were treated with terbium(III), trioctylphosphine oxide as a synergistic ligand, and contained a suitable imidazol buffer, in order to ensure maximum intensity of the luminescence signals. The results indicate a slightly better predictive ability of the newly introduced N-PLS/RBL procedure over standard PARAFAC, both in what concerns the comparison with nominal analyte concentrations in the validation sample set and with results provided by the reference high-performance liquid chromatographic technique for the real sample set.     

Identification of gasoline adulteration using comprehensive two-dimensional gas chromatography combined to multivariate data processing

A method to detect potential adulteration of commercial gasoline (Type C gasoline, available in Brazil and containing 25% (v/v) ethanol) is presented here. Comprehensive two-dimensional gas chromatography with flame ionization detection (GCxGC-FID) data and multivariate calibration (multi-way partial least squares regression, N-PLS) were combined to obtain regression models correlating the concentration of gasoline on samples from chromatographic data. Blends of gasoline and white spirit, kerosene and paint thinner (adopted as model adulterants) were used for calibration; the regression models were evaluated using samples of Type C gasoline spiked with these solvents, as well as with ethanol. The method was also checked with real samples collected from gas stations and analyzed using the official method. The root mean square error of prediction (RMSEP) for gasoline concentrations on test samples calculated using the regression model ranged from 3.3% (v/v) to 8.2% (v/v), depending on the composition of the blends; in addition, the results for the real samples agree with the official method. These observations suggest that GCxGC-FID and N-PLS can be an alternative for routine monitoring of fuel adulteration, as well as to solve several other similar analytical problems where mixtures should be detected and quantified as single species in complex samples.     

Enzyme activity measurement via spectral evolution profiling and PARAFAC

The recent advances in multi-way analysis provide new solutions to traditional enzyme activity assessment. In the present study enzyme activity has been determined by monitoring spectral changes of substrates and products in real time. The method relies on measurement of distinct spectral fingerprints of the reaction mixture at specific time points during the course of the whole enzyme catalyzed reaction and employs multi-way analysis to detect the spectral changes. The methodology is demonstrated by spectral evolution profiling of Fourier Transform Infrared (FTIR) spectral fingerprints using parallel factor analysis (PARAFAC) for pectin lyase, glucose oxidase, and a cellulase preparation.     

Photoinduced fluorimetric determination of folic acid and 5-methyltetrahydrofolic acid in serum using the kinetic evolution of the emission spectra accomplished with multivariate second-order calibration methods

Second-order multivariate calibration methods in combination with a continuous flow system, which allows for the continuous on-line irradiation of the analytes, have been employed for the determination of folic acid and its main metabolite 5-methyltetrahydrofolic acid in serum samples. An experimental central composite design, together with response surface methodology, has been used to find the optimum instrumental variables to perform the photochemical reaction. The time evolution of the emission spectra of the generated photoproducts, in the range 330-540 nm, after irradiation at 275 nm for 20 min, provided the three-way data set employed. On the basis of the differences on the kinetic rates of the photoreaction of both analytes, direct determination of the compounds in human plasma has been accomplished. The second-order methods assayed were parallel factor analysis (PARAFAC), self-weighted alternating trilinear decomposition (SWATLD), and unfolded partial least-squares (U-PLS), multidimensional partial least-squares (N-PLS), and bilinear least-squares (BLLS), all three in combination with the residual bilinearization procedure (RBL).     

PARAFAC and PARAFAC2 calibration models for antihypertensor Nifedipine quantification

Two-way data structures were obtained by acquiring UV-vis spectra as function of the time of the alkaline hydrolysis reaction of the antihypertensor Nifedipine in dimethylsulfoxide (DMSO). Sets of three-way data structures were obtained from the analysis of solutions with different concentrations of Nifedipine generated by standard additions to DMSO, Nifedipine standard and a pharmaceutical formulation. PARAFAC and PARAFAC2 methods were used in the analysis of these multi-way data structures and calibration models were developed for Nifedipine quantification in pharmaceutical formulations. For all the three-way data structures a better model fit was found with the PARAFAC2 suggesting that the experimental data sets have deviations from trilinearity. The best concentrations estimations were found with the PARAFAC2 model in the analysis of a [concentration × time (s) × wavelength (nm)] three-way data structure which allows the quantification of Nifedipine in pharmaceutical formulations.     

Detecting outlying samples in a parallel factor analysis model

To explore multi-way data, different methods have been proposed. Here, we study the popular PARAFAC (Parallel factor analysis) model, which expresses multi-way data in a more compact way, without ignoring the underlying complex structure. To estimate the score and loading matrices, an alternating least squares procedure is typically used. It is however well known that least squares techniques suffer from outlying observations, making the models useless when outliers are present in the data. In this paper, we present a robust PARAFAC method. Essentially, it searches for an outlier-free subset of the data, on which we can then perform the classical PARAFAC algorithm. An outlier map is constructed to identify outliers. Simulations and examples show the robustness of our approach.     

Selective detection of the structural changes upon photoreactions of several redox cofactors in photosystem II by means of light-induced ATR-FTIR difference spectroscopy

Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was applied for the first time to detect the structural changes upon photoreactions of redox cofactors in photosystem II (PSII). The PSII-enriched membranes from spinach were adsorbed on the surface of a silicon prism, and FTIR measurements of various redox cofactors were performed for the same sample but under different conditions by exchanging buffers in a flow cell. Light-induced FTIR difference spectra upon redox reactions of the oxygen-evolving Mn cluster, the primary quinone electron acceptor QA, the redox-active tyrosine YD, the primary electron acceptor pheophytin, and the primary electron donor chlorophyll P680 were successively recorded in buffers including different redox reagents and inhibitors. All of these cofactors remained active in the PSII membranes on the silicon surface, and the resultant spectra were basically identical to those previously recorded by the conventional transmission method. These ATR-FTIR measurements enable accurate comparison between reactions of different active sites in a single PSII sample. The present results demonstrated that the ATR-FTIR spectroscopy is a useful technique for investigation of the reaction mechanism of PSII.     

A review on advanced oxidation processes: From classical to new perspectives coupled to two- and multi-way calibration strategies to monitor degradation of contaminants in environmental samples

This paper offers a critical review from classical to new perspectives of advanced oxidation processes (AOPs) coupled to two- and multi-way calibration strategies based on multivariate curve resolution – alternating least-squares (MCR-ALS) and parallel factory analysis (PARAFAC) with various analytical techniques to monitor the degradation of contaminants in environmental samples. It focuses on the generation of highly reactive hydroxyl (HO•) radicals (classical AOPs with emphasis on Fenton, photo-Fenton and ozonation processes) and emerging reactive sulphate (SO₄^•−) radicals (new perspectives of AOPs) for effective degradation of recalcitrant compounds. Other new perspectives of AOPs were also addressed, namely semiconductor photocatalysis (TiO₂/UV), combination of processes involving at least one AOP (hybrid or single-step processes and sequential or two-step processes), novel advanced electrochemical oxidation technologies (electro-Fenton and electro-photo-Fenton) and nanocatalytic heterogeneous Fenton technology with high specific surface area. Literature reports since 2008 for real applications in the environmental remediation based on AOPs (from classical to new perspectives) coupled to PARAFAC and MCR-ALS with first-, second- and third-order data were reviewed and the improvements obtained were briefly discussed. The two- and multi-way calibration strategies allow one the successful decomposition of first-, second- and third-order data collected from different analytical techniques. Therefore, the respective profiles obtained allowed qualitative (spectral profiles) and quantitative (concentration profiles) analysis of complex samples during the degradation of contaminants through the second-order advantage. Finally, trends of future research directions for AOPs coupled to various analytical techniques and advanced chemometric models were provided.     

荧光动力学二阶校正法定量分析人血浆样中去甲肾上腺素

提出了荧光动力学结合二阶校正算法实现人血浆样中去甲肾上腺素的间接定量测定新方法. 去甲肾上腺素本身荧光较弱, 在碱性溶液中可以被氧化生成强荧光化合物. 利用这一特性, 在pH值为9.06的硼酸缓冲液作用下采用铁氰化钾为氧化剂、抗坏血酸为抗氧化剂研究这一氧化反应过程. 设定激发波长为390 nm, 在发射波长为439～550 nm的范围内测定一段时间内连续时间点的该动力学反应中间物的荧光光谱, 构建三维响应数据阵, 然后运用三线性分解算法进行解析. 组分数N取3时, 采用基于平行因子分析(PARAFAC)算法的二阶校正法获得的平均回收率(AR)为(102.0±4.1)%, 预测残差平方根(RMSEP)为0.0197; 采用基于满秩平行因子分析(FRA-PARAFAC)算法的二阶校正法获得的平均回收率(AR)和预测残差平方根(RMSEP)分别为(102.4±4.0)%和0.0207. 两种算法可以得到相似且满意的结果.     

Multiway chemometric decomposition of EEM of fluorescence of CdTe quantum dots obtained as function of pH

The effect of the pH (from 3 to 10) on the excitation emission matrices (EEMs) of fluorescence of CdTe quantum dots (QDs), capped with mercaptopropionic acid (MPA), were analyzed by multiway decomposition methods of parallel factor analysis (PARAFAC), a variant of the parallel factor analysis method (PARAFAC2) and multivariate curve resolution alternating least squares (MCR-ALS). Three different sized CdTe QDs with emission maximum at 555 nm (QDa), 594 nm (QDb) and 628 nm (QDc) were selected for analysis. The three-way data structures composed of sets of EEMs obtained as function of the pH (EEMs, pH) do not have a trilinear structure. A marked deviation to the trilinearity is observed in the emission wavelength order—the emission spectra suffers wavelength shift as the pH is varied. The pH-induced variation of the fluorescence properties of QDs is described with only one-component PARAFAC2 or MCR-ALS models—other components are necessary to model scattering and/or other background signals in (EEMs, pH) data structures. Bigger sized QDs are more suitable tools for analytical methodologies because they show higher Stokes shifts (resulting in simpler models) and higher pH range sensitivity. The pH dependence of the maximum wavelength of the emission spectra is particularly suitable for the development of QDs/EEMs wavelength-encoded pH sensor bioimaging or biological label methodologies when coupled to multiway chemometric decomposition.     

Study on difference between epidermis, phloem and xylem of Radix Ginseng with near-infrared and infrared spectroscopy coupled with principal component analysis

Ginseng is a precious traditional Chinese herbal medicine. Different parts of ginseng are deemed to have different medicinal values and properties. Rapid and non-destructive methods, such as diffuse reflectance near-infrared spectroscopy (DR-NIR), Fourier transform infrared spectroscopy with attenuated total reflectance (ATR-FTIR), were used to evaluate the differences of epidermis, phloem and xylem in ginseng, respectively. Samples were grounded into 200-mesh fine powder or cut into slices with about 2 mm thickness for DR-NIR and ATR-FTIR spectra measurement, respectively. To explore the classifications between different parts of ginseng, the spectra of DR-NIR and ATR-FTIR were pretreated to calculate first derivative and then was analyzed with principal component analysis (PCA). The PCA results of DR-NIR spectra indicate that epidermis and xylem part of ginseng have distinct difference, and even different positions of epidermis or xylem part show regular and gradual differences. ATR-FTIR spectra directly show that three parts of ginseng are different at 2920, 2852, 1736 and 925.7 cm⁻¹ peaks, especially for epidermis of ginseng. The PCA results of ATR-FTIR spectra yield clear classifications of the three parts of ginseng.     

Multi-way partial least squares modeling of water quality data

A 10 years surface water quality data set pertaining to a polluted river was analyzed using partial least squares (PLS) regression models. Both the unfold-PLS and N-PLS (tri-PLS and quadri-PLS) models were calibrated through leave-one out cross-validation method. These were applied to the multivariate, multi-way data array with a view to assess and compare their predictive capabilities for biochemical oxygen demand (BOD) of river water in terms of their relative mean squares error of cross-validation, prediction and variance captured. The sum of squares of residuals and leverages were computed and analyzed to identify the sites, variables, years and months which may have influence on the constructed model. Both the tri- and quadri-PLS models yielded relatively low validation error as compared to unfold-PLS and captured high variance in model. Moreover, both of these methods produced acceptable model precision and accuracy. In case of tri-PLS the root mean squares errors were 1.65 and 2.17 for calibration and prediction, respectively; whereas these were 2.58 and 1.09 for quadri-PLS. At a preliminary level it seems that BOD can be predicted but a different data arrangement is needed. Moreover, analysis of the scores and loadings plots of the N-PLS models could provide information on time evolution of the river water quality.     

Application of multi-way data analysis on excitation-emission spectra for plant identification

The ability to distinguish among diets fed to Damascus goats using excitation-emission luminescence spectra was investigated. These diets consisted of Medicago sativa L. (alfalfa), Trifolium spp. (clover), Pistacia lentiscus, Phyllirea latifolia and Pinus brutia. The three-dimensional luminescence response surface from phosphate buffered saline (PBS) extracts of each material was analyzed using muti-way analysis chemometric tools (MPCA) and parallel factor analysis (PARAFAC). Using three principal components, the spectra from each diet material were distinguished. Additionally, fecal samples from goats fed diets of either alfalfa or clover hays were investigated. The application of MPCA and PARAFAC to these samples using models derived from the pre-digested diet materials was strongly suggestive of the utility of similarly derive training samples for the elucidation of botanical diet composition for animals.     

Parallel factor (PARAFAC) kernel analysis of temperature- and composition-dependent NMR spectra of poly(lactic acid) nanocomposites

The parallel factor (PARAFAC) kernel matrix to analyze a sample system stimulated by more than one type of perturbation is described. PARAFAC kernel is a quantitative representation of the synchronicity and asynchronicity observed within the PARAFAC score matrices generated by carrying out two-dimensional (2D) correlation analyses. Thus, kernel matrix representation provides more intuitively understandable interpretation to the conventional PARAFAC trilinear model. In this study, the utility of PARAFAC kernel is demonstrated by the study of poly(lactic acid)-nanocomposite undergoing a structural change depending on the temperature as well as the clay content in the sample. Seemingly complicated variation of nuclear magnetic resonance (NMR) spectra induced by the change in the temperature and clay content are readily analyzed by the multiple-perturbation 2D correlation spectroscopy and PARAFAC kernel. PARAFAC kernel revealed that crystalline and amorphous structures of the PLA substantially undergo thermal deformation, and these variations are also influenced by the presence of the clay.