119 research outputs found
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
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Advantages of soft versus hard constraints in Self-Modeling Curve Resolution problems. Penalty-alternating least squares (P-ALS) extension to multi-way problems
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
Spatial and Depth Profiling of Agricultural Formulations in Leaf Tissue Using LAESI Mass Spectrometry
Formulating agrochemical products involves combining
several chemical
components, including the active ingredient(s), to obtain a final
product with desirable efficacy. A formulated product incorporates
additional components to modulate properties that enhance the efficacy
of the active(s) by modifying physical properties such as viscosity,
hydrophobicity, miscibility, and others. In plants, understanding
the formulation’s ability to spread on tissues and penetrate
through the outer layer is critical in evaluating the efficacy of
the final product. We have previously demonstrated the use of mass
spectrometry imaging to determine spreadability. In this study, we
show that laser ablation electrospray mass spectrometry (LAESI-MS)
can be a valuable tool to assess the penetrability of formulations
into the leaf tissues by selectively sampling various layers of leaf
tissue by manipulating the laser intensity and analyzing the ablated
material using a mass spectrometer. Using this technique, we were
able to identify a formulation composition that can improve the penetration
and uptake of active ingredients
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