Advanced Chemometrics Research Articles

A simplified clustering method for novice narcotic chemists

Statistical classification remains the most useful statistical tool for forensic chemists to assess the relationships between samples. Many clustering techniques such as principal component analysis and hierarchical cluster analysis have been employed to analyze chemical data for pattern recognition. Due to the feeble foundation of this statistics knowledge among novice drug chemists, a tetrahedron method was designed to simulate how advanced chemometrics operates. In this paper, the development of the graphical tetrahedron and computational matrices derived from the possible tetrahedrons are discussed. The tetrahedron method was applied to four selected parameters obtained from nine illicit heroin samples. Pattern analysis and mathematical computation of the differences in areas for assessing the dissimilarity between the nine tetrahedrons were found to be user-convenient and straightforward for novice cluster analysts.

Virtually Nonexistent Correlation between the OH Stretching Frequency and the Instantaneous Geometry in the Short Hydrogen Bond of Sodium Hydrogen Bis(sulfate): Advanced Chemometrics Analysis

We examined the correlation between the dynamically sampled anharmonic frequency of the OH stretching motion and the corresponding instantaneous geometric parameters associated with the structure of crystalline sodium hydrogen bis(sulfate), which is a benchmark system with an extremely short hydrogen bond. We analyzed the trajectory obtained by a conventional Car-Parrinello molecular dynamics simulation, followed by an a posteriori quantization of the proton motion. For statistical analysis we applied the established methodologies of multiple linear regression, principal component analysis, principal component regression, and Kohonen neural networks. No simple correlation scheme between the OH stretching frequency and any particular geometry parameter (or their combination) was found. In comparison to the established correlation schemes (e.g., Mikenda and Novak) that consider a series of systems, our study provides a complementary insight into the nature of hydrogen bonding of a single system, in the sense that it considers the important aspects of fluctuations of the environment and the resulting broadening of the OH stretching band, which cannot be adequately assessed by experiment. The absence of appreciable correlations gives strong evidence of the extreme complexity of short hydrogen bonding.

Highly sensitive terahertz spectroscopy in microsystem

We herein present a microfluidic system dedicated to THz spectroscopy of aqueous solutions. This device is able to reach a state of the art sensitivity of 5 mg mL−1, while requiring only small sample quantities and a low power source. Hydration of BSA, lysozyme and chymotrypsine proteins is studied inside microchannels and hydration numbers are computed, showing that the developed system is well suited for quantitative analysis. Moreover, coupled with advanced chemometrics algorithms, this system can become a tool for fundamental research and for the understanding of biochemical processes. Here, the hydration shell structure is discussed by chemometric analysis of the measured absorption spectra. Two spectral behaviours are observed and can be explained by Molecular Dynamics simulations. This methodology could be considered as a key element of a lab-on-a-chip for biological liquid metrology.

Pattern recognition and classification of sediments according to their metal content using chemometric tools. A case study: The estuary of Nerbioi-Ibaizabal River (Bilbao, Basque Country)

Chemometrics are increasingly used in environmental monitoring studies, but are still far from being accepted as routine tools by field specialists. The multivariate character of usually highly correlated environmental data recommends the use of advanced chemometrics as part of the analytical methodology in order to get information on the basic structure of data. In this work, we have applied a battery of non-supervised (Principal Component Analysis (PCA)) and supervised ( k-Nearest Neighbour ( k-NN), Soft Independent Modelling of Class Analogies (SIMCA), Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)) multivariate techniques on a specific environmental dataset. The dataset consists on the concentration of 14 elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn) in 95 sediments collected at eight different locations of the estuary of the Nerbioi-Ibaizabal River (Bilbao, Basque Country) during 12 sampling campaigns conducted every 3 months between 2005 and 2008. The study aims to present a simple methodology of general applicability which may result in a flexible and practical tool to assess chemical pollution in sediments of a given specific site.

Exploiting the diagnostic potential of biomolecular fingerprinting with vibrational spectroscopy

There is immense clinical need for techniques that can detect the biochemical changes associated with pre-malignancy. The ideal diagnostic test would provide rapid, non-invasive diagnosis at the point of care with high throughput and without prior tissue processing. Over the past decade vibrational spectroscopy techniques have demonstrated their ability to provide non-destructive, rapid, clinically relevant diagnostic information. Biochemical fingerprints of tissues measured using Raman and infrared spectroscopy analysed in conjunction with advanced chemometrics have shown great potential in the diagnostic assessment of biological material. Development of Raman probes is enabling the potential of in vivo clinical measurements to be realised. A novel probe design has been evaluated in clinical studies to identify and classify the subtle pre-malignant biochemical changes related to the carcinogenesis process. Exciting recent developments have enabled the probing of tissue samples at depth with huge potential for breast and prostate cancer diagnostics. Furthermore, the potential of vibrational spectroscopy to provide prognostic information is tantalising. Raman spectral data acquired on oesophageal biopsy samples analysed in conjunction with patient outcome data has shown the power of spectral biomolecular fingerprinting in predicting the outcome of patients with high-grade dysplasia in Barrett's oesophagus. Raman mapping can also be used to analyse thin tissue sections on calcium fluoride slides enabling the distribution of tissue constituents to be realised. The spectral data acquired effectively enables multiplexing of digital tissue stains since a whole array of information is gathered simultaneously. Technological developments are bringing the technologies closer to the clinical reality of spectral pathology and high-throughput non-destructive measurement with high resolution.

Theory of Midinfrared Absorption Microspectroscopy: I. Homogeneous Samples

Midinfrared (IR) microspectroscopy is widely employed for spatially localized spectral analyses. A comprehensive theoretical model for the technique, however, has not been previously proposed. In this paper, rigorous theory is presented for IR absorption microspectroscopy by using Maxwell's equations to model beam propagation. Focusing effects, material dispersion, and the geometry of the sample are accounted to predict spectral response for homogeneous samples. Predictions are validated experimentally using Fourier transform IR (FT-IR) microspectroscopic examination of a photoresist. The results emphasize that meaningful interpretation of IR microspectroscopic data must involve an understanding of the coupled optical effects associated with the sample, substrate properties, and microscopy configuration. Simulations provide guidance for developing experimental methods and future instrument design by quantifying distortions in the recorded data. Distortions are especially severe for transflection mode and for samples mounted on certain substrates. Last, the model generalizes to rigorously consider the effects of focusing. While spectral analyses range from examining gross spectral features to assessing subtle features using advanced chemometrics, the limitations imposed by these effects in the data acquisition on the information available are less clear. The distorting effects are shown to be larger than noise levels seen in modern spectrometers. Hence, the model provides a framework to quantify spectral distortions that may limit the accuracy of information or present confounding effects in microspectroscopy.

Combining operando spectroscopy with experimental design, signal processing and advanced chemometrics: State-of-the-art and a glimpse of the future

The present contribution reviews the operando spectroscopy programme in the area of homogeneous catalysis that exists at the Institute of Chemical and Engineering Sciences (ICES). This review first considers the importance of experimental design and then addresses a number of signal processing issues. Band-target entropy minimization (BTEM), an algorithm designed for pure component spectral estimation, is the most important signal processing tool discussed. A homogeneous catalytic example using BTEM analysis, as well as other quantitative and kinetic analyses is shown. In addition to spectroscopic measurements, other in situ sensor measurements and their analyses are also discussed. The determination of individual physico-chemical properties of the constituents present allows insight into solute–solute interactions, etc. This review is based on a plenary lecture presented at Operando III.

Nickel-catalyzed isomerization of 2-methyl-3-butenenitrile to 3-pentenenitrile: A kinetic study using in situ FTIR-ATR spectroscopy

The isomerization of 2-methyl-3-butenenitrile (2M3BN) to 3-pentenenitrile (3PN) was followed using operando FTIR spectroscopy. The spectra were analyzed comprehensively to obtain kinetic profiles from the different band dynamics. Several spectral regions were transformed to their second derivative in order to improve the peak resolution. The dynamics in the spectra were calculated and normalized to the end conversion towards 3PN determined by GC. Similar kinetic profiles were obtained from the dynamics of different bands. Furthermore, calculations on the DFT level were performed for 2M3BN and trans- and cis-3PN in order to help identifying the corresponding bands in the spectra of the reaction mixture. An average conversion profile was calculated from different bands of the substrate and the product, applying a “ quasi-multivariate analysis” technique to correlate different band dynamics. This approach was validated using advanced chemometrics. These profiles obtained by IR spectroscopic analysis for the formation of 3PN and the consumption of 2M3BN showed a zero order kinetic.

Vibrational spectroscopy: a clinical tool for cancer diagnostics

Vibrational spectroscopy techniques have demonstrated potential to provide non-destructive, rapid, clinically relevant diagnostic information. Early detection is the most important factor in the prevention of cancer. Raman and infrared spectroscopy enable the biochemical signatures from biological tissues to be extracted and analysed. In conjunction with advanced chemometrics such measurements can contribute to the diagnostic assessment of biological material. This paper also illustrates the complementary advantage of using Raman and FTIR spectroscopy technologies together. Clinical requirements are increasingly met by technological developments which show promise to become a clinical reality. This review summarises recent advances in vibrational spectroscopy and their impact on the diagnosis of cancer.

Laser-Induced Breakdown Spectroscopy of Composite Samples: Comparison of Advanced Chemometrics Methods

Laser-induced breakdown spectroscopy is used to measure chromium concentration in soil samples. A comparison is carried out between the calibration curve method and two chemometrics techniques: partial least-squares regression and neural networks. The three quantitative techniques are evaluated in terms of prediction accuracy, prediction precision, and limit of detection. The influence of several parameters specific to each method is studied in detail, as well as the effect of different pretreatments of the spectra. Neural networks are shown to correctly model nonlinear effects due to self-absorption in the plasma and to provide the best results. Subsequently, principal components analysis is used for classifying spectra from two different soils. Then simultaneous prediction of chromium concentration in the two matrixes is successfully performed through partial least-squares regression and neural networks.

Rapid analysis of high-dimensional bioprocesses using multivariate spectroscopies and advanced chemometrics.

There are an increasing number of instrumental methods for obtaining data from biochemical processes, many of which now provide information on many (indeed many hundreds) of variables simultaneously. The wealth of data that these methods provide, however, is useless without the means to extract the required information. As instruments advance, and the quantity of data produced increases, the fields of bioinformatics and chemometrics have consequently grown greatly in importance. The chemometric methods nowadays available are both powerful and dangerous, and there are many issues to be considered when using statistical analyses on data for which there are numerous measurements (which often exceed the number of samples). It is not difficult to carry out statistical analysis on multivariate data in such a way that the results appear much more impressive than they really are. The authors present some of the methods that we have developed and exploited in Aberystwyth for gathering highly multivariate data from bioprocesses, and some techniques of sound multivariate statistical analyses (and of related methods based on neural and evolutionary computing) which can ensure that the results will stand up to the most rigorous scrutiny.