SEARCH · Geology Reports

Results for “Journal of Chemometrics”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

4 recordsLinked to original sources

USGS1996

Pattern recognition analysis and classification modeling of selenium-producing areas

Established chemometric and geochemical techniques were applied to water quality data from 23 National Irrigation Water Quality Program (NIWQP) study areas in the Western United States. These techniques were applied to the NIWQP data set to identify common geochemical processes responsible for mobilization of selenium and to develop a classification model that uses major-ion concentrations to identify areas that contain elevated selenium concentrations in water that could pose a hazard to water fowl. Pattern recognition modeling of the simple-salt data computed with the SNORM geochemical program indicate three principal components that explain 95% of the total variance. A three-dimensional plot of PC 1, 2 and 3 scores shows three distinct clusters that correspond to distinct hydrochemical facies denoted as facies 1, 2 and 3. Facies 1 samples are distinguished by water samples without the CaCO 3 simple salt and elevated concentrations of NaCl, CaSO 4 , MgSO 4 and Na 2 SO 4 simple salts relative to water samples in facies 2 and 3. Water samples in facies 2 are distinguished from facies 1 by the absence of the MgSO 4 simple salt and the presence of the CaCO 3 simple salt. Water samples in facies 3 are similar to samples in facies 2, with the absence of both MgSO 4 and CaSO 4 simple salts. Water samples in facies 1 have the largest selenium concentration (10 μg l −1 ), compared to a median concentration of 2·0 μg l −1 and less than 1·0 μg l −1 for samples in facies 2 and 3. A classification model using the soft independent modeling by class analogy (SIMCA) algorithm was constructed with data from the NIWQP study areas. The classification model was successful in identifying water samples with a selenium concentration that is hazardous to some species of water-fowl from a test data set comprised of 2,060 water samples from throughout Utah and Wyoming. Application of chemometric and geochemical techniques during data synthesis analysis of multivariate environmental databases from other national-scale environmental programs such as the NIWQP could also provide useful insights for addressing ‘real world’ environmental problems.

Journal of Chemometrics↗

USGS2014

A bootstrap estimation scheme for chemical compositional data with nondetects

The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided.

Journal of Chemometrics↗

USGS2023

Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser-induced breakdown spectroscopy

The illegal timber trade has significant impact on the survival of endangered tropical hardwood species like Dalbergia spp. (rosewood), a world-wide protected genus from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Due to increased threat to Dalbergia spp., and lack of action to reduce threats, port of entry analysis methods are required to identify Dalbergia spp. Handheld laser-induced breakdown spectroscopy (LIBS) has been shown to be capable of identifying species and establishing provenance of Dalbergia spp. and other tropical hardwoods, but analysis methods for this work have yet to be investigated in detail. The present work investigates five well-known algorithms—partial least squares discriminant analysis (PLS-DA), classification and regression trees (CART), k -nearest neighbor ( k -NN), random forest (RF), and support vector machine (SVM)—two training/test set sampling regimes, and data collection at two signal-to-noise (S/N) ratios to assess the potential for handheld LIBS analyses. Additionally, imbalanced classes are addressed. For this application, SVM and RF yield near identical results (though RF takes nearly 100 longer to compute), while the S/N ratio has a significant effect on model success assuming all else is equal. It was found that forming a training set with replicate low S/N analyses can perform as well as higher precision training sets for true prediction, even if the predicted samples have low signal to noise! This work confirms handheld LIBS analyzers can provide a viable method for classification of hardwood species, even within the same genus.

Journal of Chemometrics↗

USGS2012

Compression map, functional groups and fossilization: A chemometric approach (Pennsylvanian neuropteroid foliage, Canada)

Nearly all of the spectrochemical studies involving Carboniferous foliage of seed-ferns are based on a limited number of pinnules, mainly compressions. In contrast, in this paper we illustrate working with a larger pinnate segment, i.e., a 22-cm long neuropteroid specimen, compression-preserved with cuticle, the compression map. The objective is to study preservation variability on a larger scale, where observation of transparency/opacity of constituent pinnules is used as a first approximation for assessing the degree of pinnule coalification/fossilization. Spectrochemical methods by Fourier transform infrared spectrometry furnish semi-quantitative data for principal component analysis. The compression map shows a high degree of preservation variability, which ranges from comparatively more coalified pinnules to less coalified pinnules that resemble fossilized-cuticles, noting that the pinnule midveins are preserved more like fossilized-cuticles. A general overall trend of coalified pinnules towards fossilized-cuticles, i.e., variable chemistry, is inferred from the semi-quantitative FTIR data as higher contents of aromatic compounds occur in the visually more opaque upper location of the compression map. The latter also shows a higher condensation of the aromatic nuclei along with some variation in both ring size and degree of aromatic substitution. From principal component analysis we infer correspondence between transparency/opacity observation and chemical information which correlate with varying degree to fossilization/coalification among pinnules.

Nova Scotia↗