Independent components in spectroscopic analysis of complex mixtures

We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to quantitative and qualitative analysis of UV absorption spectra of several non-trivial mixture types. Both methods use the concept of statistical independence and aim at the reconstruction of minimally dependent components from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a veterinary drug. Both MICLA and SNICA were able to recover concentrations and individual spectra with minimal errors comparable with instrumental noise. In most cases their performance was similar to or better than that of other chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA. These results suggest that the ICA methods used in this study are suitable for real life applications. Data used in this paper along with simple matlab codes to reproduce paper figures can be found at http://www.klab.caltech.edu/~kraskov/MILCA/spectraComment: 22 pages, 4 tables, 6 figure

Using statistical and artificial neural networks to predict the permeability of loosely packed granular materials

Well-known analytical equations for predicting permeability are generally reported to overestimate this important property of porous media. In this work, more robust models developed from statistical (multivariable regression) and Artificial Neural Network (ANN) methods utilised additional particle characteristics [‘fines ratio’ (x50/x10) and particle shape] that are not found in traditional analytical equations. Using data from experiments and literature, model performance analyses with average absolute error (AAE) showed error of ~40% for the analytical models (Kozeny–Carman and Happel–Brenner). This error reduces to 9% with ANN model. This work establishes superiority of the new models, using experiments and mathematical techniques

Multivariate curve resolution of time course microarray data

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements

Stable Isotope Tracking of Endangered Sea Turtles: Validation with Satellite Telemetry and δ15N Analysis of Amino Acids

Effective conservation strategies for highly migratory species must incorporate information about long-distance movements and locations of high-use foraging areas. However, the inherent challenges of directly monitoring these factors call for creative research approaches and innovative application of existing tools. Highly migratory marine species, such as marine turtles, regularly travel hundreds or thousands of kilometers between breeding and feeding areas, but identification of migratory routes and habitat use patterns remains elusive. Here we use satellite telemetry in combination with compound-specific isotope analysis of amino acids to confirm that insights from bulk tissue stable isotope analysis can reveal divergent migratory strategies and within-population segregation of foraging groups of critically endangered leatherback sea turtles (Dermochelys coriacea) across the Pacific Ocean. Among the 78 turtles studied, we found a distinct dichotomy in δ15N values of bulk skin, with distinct “low δ15N” and “high δ15N” groups. δ15N analysis of amino acids confirmed that this disparity resulted from isotopic differences at the base of the food chain and not from differences in trophic position between the two groups. Satellite tracking of 13 individuals indicated that their bulk skin δ15N value was linked to the particular foraging region of each turtle. These findings confirm that prevailing marine isoscapes of foraging areas can be reflected in the isotopic compositions of marine turtle body tissues sampled at nesting beaches. We use a Bayesian mixture model to show that between 82 and 100% of the 78 skin-sampled turtles could be assigned with confidence to either the eastern Pacific or western Pacific, with 33 to 66% of all turtles foraging in the eastern Pacific. Our forensic approach validates the use of stable isotopes to depict leatherback turtle movements over broad spatial ranges and is timely for establishing wise conservation efforts in light of this species’ imminent risk of extinction in the Pacific

Equilibrium modeling of mixtures of methanol and water

An understanding of the species that form in mixtures of alcohol and water is important for their use in liquid chromatography applications. In reverse-phase liquid chromatography the retention of solutes on a chromatography column is influenced by the composition of the mobile phase, and in the case of alcohol and water mobile phases, the amount of free alcohol and water present. Previous and similar modeling studies of methanol (MeOH) and water mixtures by near-infrared (NIR) spectroscopy have found up to four species present including free MeOH and water and MeOH and water complexes formed by hydrogen bonding associations. In this work an equilibrium model has been applied to NIR measurements of MeOH and water mixtures. A high-performance liquid chromatography (HPLC) pump was coupled to an NIR flow cell to produce a gradual change in mixture composition. This resulted in a greater mixture resolution than has been achieved previously by manual mixture preparation. It was determined that five species contributed to the data. An equilibria model consisting of MeOH, MeOH H2O, MeOH(H2O) (log K-H2O(MeOH) = 0.10 +/- 0.03), MeOH(H2O)(4) (log K-4H2O(MeOH) = -2.14 +/- 0.08), and MeOH(H2O)(9) (log K-9H2O(MeOH) = -8.6 +/- 0.1) was successfully fitted to the data. The model supports the results of previous work and highlights the progressive formation of MeOH and water complexes that occur with changing mixture composition. The model also supports that mixtures of MeOH and water are not simple binary mixtures and that this is responsible for observed deviations from expected elution behavior

Determination of the ethylene oxide content of polyether polyols by low-field H-1 nuclear magnetic resonance spectrometry

Methods have been developed and compared for the analysis of a glycerol-based polyether polyol using a low-field, medium-resolution NMR spectrometer, with an operating frequency of 29 MHz for 1 H. Signal areas in the time and frequency domains were used to calculate the ethylene oxide (EO) content of individual samples. The time domain signals (free induction decay) were analysed using a new version of the direct exponential curve resolution algorithm (FID-DECRA). Direct analysis of the H-1 NMR FF spectra gave percentage EO concentrations of reasonable accuracy (average percentage error of 1.3%) and precision (average RSD of 1.8%) when compared with results derived from high-field C-13 NMR spectrometry. The direct FID-DECRA method showed a negative bias (-0.8+/-0.12% w/w) in the estimation of percentage EO concentration, but the precision (average RSD of 0.9%) was twice as good as that of direct spectral analysis. When the 13C NMR analysis was used as a reference method for univariate calibration of the 1 H NMR procedures, the best accuracy (average percentage error of 0.5%) and precision (average RSD of 0.6%) were obtained using FID-DECRA, for EO concentrations in the range 14.8-15.5% w/w. An additional advantage of FID-DECRA is that the analytical procedure could be automated, which is particularly desirable for process analysis. (C) 2002 Elsevier Science B.V. All rights reserved