Processing of NMR,UV, and IR spectrometric data prior to chemometric simulation by independent component and principal component analysis

We consider methods for the mathematical preprocessing of signals in the spectrometric analysis of multicomponent mixtures using chemometric algorithms aimed at adjusting the baseline, experimental noise, and random shift of spectral bands. Practical examples of using simple mathematical operations (scaling, centering, derivatization) are given. The effectiveness of algorithms is illustrated by a wide range of spectroscopic signals (electronic absorption, IR, and NMR spectra) combined with chemometric methods of principal component analysis and independent component analysis.     

Application of MATLAB package for the automation of the chemometric processing of spectrometric signals in the analysis of complex mixtures

We describe three types of automatic software for the chemometric processing of spectrometric data. The software was developed in the MATLAB working environment and includes data import, mathematical preprocessing, chemometric analysis, and generation of a report file. The software is designed to solve problems regarding identification of some components of multicomponent mixtures, determination of compounds with overlapping signals, and differentiation of samples by their spectral responses. To test the software, we present examples of spectrometric analyses of coffee, fruit juices, and alcoholic beverages using chemometric methods of independent component analysis (ICA) and partial least squares–discriminant analysis (PLS–DA). In particular, we simulated electronic absorption spectra for the identification of three artificial colors (E110, E102, and E122) in alcoholic beverages, NMR spectra for the simultaneous determination of five components (acetic acid, γ-aminobutyric acid, arginine, acetaldehyde, and proline) in orange juice without using reference standards, and NMR spectra of coffee samples to determine its varietal authenticity (Arabica or Robusta). The duration of automatic chemometric processing did not exceed 1 min per sample. The developed software can be optimized for other matrices and/or brands of spectrometers.     

高效液相色谱－核磁共振联用技术及其应用

高效液相色谱－核磁共振（ＨＰＬＣ－ＮＭＲ）在线联用技术是同时进行未知混合物的分离和结构鉴定的最好手段之一。详细介绍了在ＨＰＬＣ－ＮＭＲ联用技术中１Ｈ谱的分辨率、检测限和多重溶剂峰抑制的最新进展，并简要评述了其它分离方法与ＮＭＲ联用情况     

高效液相色谱－核磁共振联用技术及其应用

高效液相色谱－核共共振在线联用技术是同时进行未知混合物的分离和结构鉴定的量好手段之一。详细介绍了在ＨＰＬＣ－ＮＭＲ联用技术中Ｈ谱的分辨率，检测限手多重溶剂峰抑制的最新进展，并简要评述了其它分离方法与ＮＭＲ联用情况。     

A metabonomic investigation of hepatotoxicity using diffusion-edited 1H NMR spectroscopy of blood serum

It has been shown extensively, that chemometric investigations of 1H NMR spectra of rat urine taken from animals dosed with model toxins produce characteristic patterns of metabolic responses and that this permits the identification of biomarkers of toxic response and regeneration. To date, metabonomic methods have been mainly optimised for urine which contains mainly low molecular weight moieties, and thus a conventional 1-dimensional 1H NMR pulse sequence is an efficient means of obtaining information-rich data. In the case of biofluids such as blood plasma or serum, which contain a wide range of macromolecules the resonances of which can overlap with peaks from small molecule metabolites, the information giving rise to sample classification can be concealed in a conventional NMR spectrum andthis presents a different analytical challenge in terms of chemometric analysis of spectral profiles. Here, the use of other types of NMR data have been investigated and it is shown that by using spectra where the peak intensities are edited according to their molecular diffusion coefficients, it is possible to improve differentiation of control animals and those treated with the model hepatotoxin, alpha-naphthylisothiocyanate (ANIT). By using diffusion-edited spectroscopy, plasma lipid moieties are less attenuated than those from small endogenous metabolites and thus the toxin-induced changes to the lipoprotein profiles are more easily detectable.     

NMR spectroscopy evaluation of direct relationship between soils and molecular composition of red wines from Aglianico grapes

¹H NMR spectroscopy was employed to investigate the molecular quality of Aglianico red wines from the Campania region of Italy. The wines were obtained from three different Aglianico vineyards characterized by different microclimatic and pedological properties. In order to reach an objective evaluation of “terroir” influence on wine quality, grapes were subjected to the same winemaking procedures. The careful subtraction of water and ethanol signals from NMR spectra allowed to statistically recognize the metabolites to be employed in multivariate statistical methods: Principal Component Analysis (PCA), Discriminant Analysis (DA) and Hierarchical Clustering Analysis (HCA). The three wines were differentiated from each other by six metabolites: α-hydroxyisobutyrate, lactic acid, succinic acid, glycerol, α-fructose and β-d-glucuronic acid. All multivariate analyses confirmed that the differentiation among the wines were related to micro-climate, and carbonate, clay, and organic matter content of soils. Additionally, the wine discrimination ability of NMR spectroscopy combined with chemometric methods, was proved when commercial Aglianico wines, deriving from different soils, were shown to be statistically different from the studied wines. Our findings indicate that multivariate statistical elaboration of NMR spectra of wines is a fast and accurate method to evaluate the molecular quality of wines, underlining the objective relation with terroir.     

Chemometrics in pharmaceutical analysis: an introduction, review, and future perspectives

Chemometrics is the application of statistical and mathematical methods to analytical data to permit maximum collection and extraction of useful information. The utility of chemometric techniques as tools enabling multidimensional calibration of selected spectroscopic, electrochemical, and chromatographic methods is demonstrated. Application of this approach mainly for interpretation of UV-Vis and near-IR (NIR) spectra, as well as for data obtained by other instrumental methods, makes identification and quantitative analysis of active substances in complex mixtures possible, especially in the analysis of pharmaceutical preparations present in the market. Such analytical work is carried out by the use of advanced chemical instruments and data processing, which has led to a need for advanced methods to design experiments, calibrate instruments, and analyze the resulting data. The purpose of this review is to describe various chemometric methods in combination with UV-Vis spectrophotometry, NIR spectroscopy, fluorescence spectroscopy, electroanalysis, chromatographic separation, and flow-injection analysis for the analysis of drugs in pharmaceutical preparations. Theoretical and practical aspects are described with pharmaceutical examples of chemometric applications. This review will concentrate on gaining an understanding of how chemometrics can be useful in the modern analytical laboratory. A selection of the most challenging problems faced in pharmaceutical analysis is presented, the potential for chemometrics is considered, and some consequent implications for utilization are discussed. The reader can refer to the citations wherever appropriate.     

Proton nuclear magnetic resonance characterisation of glycosaminoglycans using chemometric techniques

Various types of glycosaminoglycans (GAGs) including heparins, chondroitin sulfates, dermatan sulfate and hyaluronic acid were studied from their proton nuclear magnetic resonance (1H NMR) spectra using chemometric techniques. Despite the complexity of the 1H NMR signals, data analysis using principal component analysis enabled the different GAG classes to be distinguished and permitted their classification according to their chemical structure. The analysis of the composition of the major disaccharide unit and other relevant chemical structures in the heparin samples was performed using partial least squares regression.     

Residual water suppression by indirect covariance NMR

Residual water solvent signals in 2D NMR experiments adversely affect appearance and subsequent analysis of spectra. A method for water suppression that is based on indirect covariance processing is described. It produces a symmetric spectrum with a water signal that is substantially decreased or completely absent. The method, which can be combined with other water suppression schemes, is demonstrated for 2D TOCSY, NOESY, and ROESY spectra of the protein, ubiquitin in aqueous solution.     

Improvements in lipid suppression for 1H NMR-based metabolomics: Applications to solution-state and HR-MAS NMR in natural and in vivo samples

Proton nuclear magnetic resonance (NMR) spectra of intact biological samples often show strong contributions from lipids, which overlap with signals of interest from small metabolites. Pioneering work by Diserens et al. demonstrated that the relative differences in diffusivity and relaxation of lipids versus small metabolites could be exploited to suppress lipid signals, in high-resolution magic angle spinning (HR-MAS) NMR spectroscopy. In solution-state NMR, suspended samples can exhibit very broad water signals, which are challenging to suppress. Here, improved water suppression is incorporated into the sequence, and the Carr-Purcell-Meiboom-Gill sequence (CPMG) train is replaced with a low-power adiabatic spinlock that reduces heating and spectral artefacts seen with longer CPMG filters. The result is a robust sequence that works well in both HR-MAS as well as static solution-state samples. Applications are also extended to include in vivo organisms. For solution-state NMR, samples containing significant amount of fats such as milk and hemp hearts seeds are used to demonstrate the technique. For HR-MAS, living earthworms (Eisenia fetida) and freshwater shrimp (Hyalella azteca) are used for in vivo applications. Lipid suppression techniques are essential for non-invasive NMR-based analysis of biological samples with a high-lipid content and adds to the suite of experiments advantageous for in vivo environmental metabolomics.     

Processing of high resolution magic angle spinning spectra of breast cancer cells by the filter diagonalization method

Proton nuclear magnetic resonance ((1)H NMR) spectroscopy for detection of biochemical changes in biological samples is a successful technique. However, the achieved NMR resolution is not sufficiently high when the analysis is performed with intact cells. To improve spectral resolution, high resolution magic angle spinning (HR-MAS) is used and the broad signals are separated by a T(2) filter based on the CPMG pulse sequence. Additionally, HR-MAS experiments with a T(2) filter are preceded by a water suppression procedure. The goal of this work is to demonstrate that the experimental procedures of water suppression and T(2) or diffusing filters are unnecessary steps when the filter diagonalization method (FDM) is used to process the time domain HR-MAS signals. Manipulation of the FDM results, represented as a tabular list of peak positions, widths, amplitudes and phases, allows the removal of water signals without the disturbing overlapping or nearby signals. Additionally, the FDM can also be used for phase correction and noise suppression, and to discriminate between sharp and broad lines. Results demonstrate the applicability of the FDM post-acquisition processing to obtain high quality HR-MAS spectra of heterogeneous biological materials.     

Quantification of casein phosphorylation with conformational interpretation using Raman spectroscopy

Raman spectroscopy is emerging as a powerful method for obtaining both quantitative and qualitative information from biological samples. One very interesting area of research, for which the technique has rarely been used, is the detection, quantification and structural analysis of post-translational modifications (PTMs) on proteins. Since Raman spectra can be used to address both of these questions simultaneously, we have developed near infrared Raman spectroscopy with appropriate chemometric approaches (partial least squares regression) to quantify low concentration (4 microM) mixtures of phosphorylated and dephosphorylated bovine alpha(s)-casein. In addition, we have used these data in conjunction with Raman optical activity (ROA) spectra and NMR to assess the structural changes that occur upon phosphorylation.     

Experimental and Chemometric Optimization to Enhance the Performance of Near-infrared Diffuse Reflectance Spectroscopy

Approaches for improving the performance of near-infrared diffuse reflectance spectroscopy (NIRDRS) have been extensively studied. The silver mirror was found to be a substrate to enhance the detection ability of this technique. For further optimization of the method, experimental and chemometric efforts were made in this study. Lysozyme samples of different concentrations were spotted on the silver mirror for measuring the spectra. Low-resolution spectra with repeated measurements were used for increasing the signal-to-noise ratio, and the chemometric methods of variable selection and nonlinear modeling were used to make the quantitative model more effective and accurate. The results show that the quality of the spectra was improved and a satisfactory quantitative model was obtained after optimization. The maximum deviation for the prediction samples is only 2.98?µg, which is much lower than the reported values in previous papers. Significant advances were obtained for NIRDRS for trace analysis.     

Monitoring the melt-extrusion transesterification of ethylene-vinylacetate copolymer by self-modeling curve resolution analysis of on-line near-infrared spectra

The transesterification of molten ethylene-vinylacetate (EVA) copolymers by octanol with sodium methoxide as catalyst in an extruder has been monitored by on-line near infrared (NIR) spectroscopy. A total of 60 NIR spectra were acquired for 37 min with the last spectrum recorded 31 min after the addition of octanol and catalyst was stopped. The experimental spectra show strong baseline fluctuations which are corrected for by multiplicative scatter correction (MSC). The chemometric methods of the orthogonal projection approach (OPA) and multivariate curve resolution (MCR) were used to resolve the spectra and to derive concentration profiles of the species. The detailed analysis reveals the absence of completely pure variables which leads to small errors in the calculation of pure spectra. The initial estimation of a concentration that is necessary as an input parameter for MCR also presents a non-trivial task. We obtained results that were not ideal but applicable for practical concentration control. They enable a fast monitoring of the process in real-time and resolve the spectra of the EVA copolymer and the ethylene-vinyl alcohol (EVAL) copolymer to be very close to the reference spectra. The chemometric methods used and the decomposed spectra are discussed in detail.     

Chemometric determination of the length distribution of single walled carbon nanotubes through optical spectroscopy

Current synthesis methods for producing single walled carbon nanotubes (SWCNTs) do not ensure uniformity of the structure and properties, in particular the length, which is an important quality indicator of SWCNTs. As a result, sorting SWCNTs by length is an important post-synthesis processing step. For this purpose, convenient analysis methods are needed to characterize the length distribution rapidly and accurately. In this study, density gradient ultracentrifugation was applied to prepare length-sorted SWCNT suspensions containing individualized surfactant-wrapped SWCNTs. The length of sorted SWCNTs was first determined by atomic force microscope (AFM), and their absorbance was measured in ultraviolet-visible near-infrared (UV-vis-NIR) spectroscopy. Chemometric methods are used to calibrate the spectra against the AFM-measured length distribution. The calibration model enables convenient analysis of the length distribution of SWCNTs through UV-vis-NIR spectroscopy. Various chemometric techniques are investigated, including pre-processing methods and non-linear calibration models. Extended inverted signal correction, extended multiplicative signal correction and Gaussian process regression are found to provide good prediction of the length distribution of SWCNTs with satisfactory agreement with the AFM measurements. In summary, spectroscopy in conjunction with advanced chemometric techniques is a powerful analytical tool for carbon nanotube research.     

Bioreactor monitoring with spectroscopy and chemometrics: a review

Biotechnological processes are crucial to the development of any economy striving to ensure a relevant position in future markets. The cultivation of microorganisms in bioreactors is one of the most important unit operations of biotechnological processes, and real-time monitoring of bioreactors is essential for effective bioprocess control. In this review, published material on the potential application of different spectroscopic techniques for bioreactor monitoring is critically discussed, with particular emphasis on optical fiber technology, reported for in situ bioprocess monitoring. Application examples are presented by spectroscopy type, specifically focusing on ultraviolet-visible, near-infrared, mid-infrared, Raman, and fluorescence spectroscopy. The spectra acquisition devices available and the major advantages and disadvantages of each spectroscopy are discussed. The type of information contained in the spectra and the available chemometric methods for extracting that information are also addressed, including wavelength selection, spectra pre-processing, principal component analysis, and partial least-squares. Sample handling techniques (flow and sequential injection analysis) that include transport to spectroscopic sensors for ex-situ on-line monitoring are not covered in this review.     

Gradient‐based solvent suppression methods on a benchtop spectrometer

Benchtop NMR emerges as an appealing alternative to widely extend the scope of NMR spectroscopy in harsh environments and for on‐line monitoring. Obviously, the use of low‐field magnets induces a dramatic reduction of the spectral resolution leading to frequent peak overlaps. This issue is even more serious because applications such as chemical process monitoring involve the use of non‐deuterated solvents, leading to intense and broad peaks overlapping with the signals of interest. In this article, we highlight the need for efficient suppression methods compatible with flowing samples, which is not the case of the common pre‐saturation approaches. Thanks to a gradient coil included in our benchtop spectrometer, we were able to implement modern and efficient solvent suppression blocks such as WET or excitation sculpting to deliver quantitative spectra in the conditions of the on‐line monitoring. While these methods are commonly used at high field, this is the first time that they are investigated on a benchtop setting. Their analytical performance is evaluated and compared under static and on‐flow conditions. The results demonstrate the superiority of gradient‐based methods, thus highlighting the relevance of implementing this device on benchtop spectrometers. The comparison of major solvent suppression methods reveals an optimum performance for the WET‐180‐NOESY experiment, both under static and on‐flow conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

Simultaneous quantitative resolution of atorvastatin calcium and fenofibrate in pharmaceutical preparation by using derivative ratio spectrophotometry and chemometric calibrations.

In the present work, five different spectrophotometric techniques for simultaneous determination of formulations containing atorvastatin calcium (ATOR) and fenofibrate (FENO) in various combinations are described. In ratio spectra derivative spectrophotometry, analytical signals were measured at wavelengths corresponding to either maximums or minimums for both drugs in first derivative spectra of ratio spectra obtained by using either spectrum as divisor. For the remaining four methods using chemometric techniques, namely, classical least squares (CLS), inverse least squares (ILS), principal component regression (PCR) and partial least squares (PLS), the calibrations were constructed by using the absorption data matrix corresponding to the concentration data matrix, with measurements in the range of 231 - 310 nm (Deltalambda = 1 nm) in their zero-order spectra. The linearity range was found to be 4 - 22 and 2 - 20 microg/ml for ATOR and FENO, respectively. The validity of the proposed methods was successfully assessed for analyses of both drugs in laboratory-prepared mixtures and in commercial tablet formulations.     

Chemometrics in near-infrared spectroscopy

Spectroscopy methods of chemical analysis are excellent for the application of chemometric methods, because the measurements at many different wavelengths provide inherently multivariate data. The chemist generally requires three categories of information from specimens under investigation: quantitative data, qualitative data, and fundamental information on the properties of the material. Spectroscopy has long been used for all three purposes; the recent application of chemometric algorithms has assisted greatly in these endeavors. Although there is some overlap, three chemometric methods correspond to the three types of information: multiple regression, discriminant analysis, and principal components analysis. The basis of these chemometric methods and some of their strengths and limitations in application to near-infrared spectroscopy are discussed.