Geology topics

Karl S. Booksh

Publications and source records attributed to Karl S. Booksh.

5 recordsLinked to original sources

USGS2026

Stacked machine learning for timber identification with laser-induced breakdown spectroscopy (LIBS)

This study presents a new approach to wood species identification using laser-induced breakdown spectroscopy (LIBS) combined with stacked machine learning techniques. The research analyzed 700 samples comprising nine Dalbergia species and nine additional tropical timber species, utilizing a handheld LIBS analyzer. A stacking methodology was developed by integrating three support vector machine (SVM) models with different kernel functions (linear, polynomial, and radial) in a one-versus-all (OVA) configuration. These SVM outputs were then combined using a partial least squares discriminant analysis (PLS-DA) meta-learner. Through PCA-based variable selection, the dimensionality was reduced from 23 401 to wavelengths while maintaining classification accuracy. The stacking approach achieved a Cohen's kappa value of 0.8671 in the validation set, significantly outperforming traditional flat classifiers. Variable importance analysis revealed calcium, magnesium, and barium as crucial elements for species differentiation, with their concentrations reflecting environmental conditions and geographical origins. This research demonstrates the potential of combining LIBS spectroscopy with advanced machine learning techniques for rapid, non-invasive timber identification, which can support efforts against illegal logging and enforcement of international trade regulations.

Applied Spectroscopy↗

USGS2025

Determination of provenance soil type using inductively coupled plasma mass spectrometry (ICP-MS) analyses of Pinus ponderosa ash

This study demonstrates the feasibility of determining soil provenance from tree ash composition using elemental analysis and chemometric techniques. To date, no published studies have applied chemometric approaches to classify ash for provenance determination following forest fires. In this work, Pinus ponderosa ash was analyzed to distinguish samples based on soil type and geographic location. Pinus ponderosa , a widely distributed pine species in the western United States where wildfires are prevalent, was selected as a model system. Needles were collected from trees grown in five distinct soil types across northern Arizona and Colorado, then dry-ashed under controlled conditions. Classification was performed using three preprocessing techniques and five machine learning algorithms, including hierarchical modeling structures to optimize separation. Partial least squares discriminant analysis (PLS-DA) following a Box-Cox transformation yielded the highest classification accuracy, achieving a prediction kappa value of 0.98 for soil type identification. However, classification performance decreased when distinguishing both soil type and geographic location, indicating that additional variability may influence predictive accuracy in broader applications. These findings highlight the potential of inductively coupled plasma mass spectrometry (ICP-MS) and machine learning for post-wildfire forensic analysis and environmental monitoring.

Applied Spectroscopy↗

USGS2025

Geographic determination of Pinus ponderosa using DART TOFMS, ICP-MS, and LIBS handheld analyzer

Due to legal requirements on international imports, it is important for law enforcement and regulatory agencies to identify the geographical provenance of timber. Current methods for geographic identification utilize data generated by direct analysis in real time time-of-flight mass spectrometry (DART TOFMS), genetics, and isotope-ratio mass spectrometry (IRMS), but identification methods based on genetics and IRMS data require months to years to create usable databases. This study used machine learning algorithms to compare the results of DART TOFMS, inductively coupled plasma mass spectrometry (ICP-MS), and a handheld laser-induced breakdown spectroscopy (LIBS) analyzer for use in geographic identification of five populations of Pinus ponderosa spaced between 14 to 72 km apart. The results of the study showed comparable performances from machine learning algorithms applied to the ICP-MS and LIBS data with accuracy and kappa values over 90% while the DART TOFMS had an accuracy of 76% and a kappa value of 70%. This study demonstrated that data from the LIBS handheld analyzer is a viable and intriguing alternative to ICP-MS and DART TOFMS analyses in generating training databases and further indicates that trace elemental analysis via ICP-MS is a promising method for generating databases used to identify the origin of timber.

California, Oregon↗

USGS2023

Supervised discretization for decluttering classification models

Presented here is the first demonstration of supervised discretization to ‘declutter’ multivariate classification data in chemical sensor applications. The performance of multivariate classification models is often limited by the non-informative chemical variance within each target class; decluttering methods seek to reduce within-class variance while retaining between-class variance. Supervised discretization is shown to declutter classes in a manner that is superior to the state-of-the-art External Parameter Orthogonalization (EPO) by constructing a more parsimonious model with fewer parameters to optimize and is, consequently, less susceptible to overfitting and information loss. The comparison of supervised discretization and EPO is performed on three classification applications: X-ray fluorescence spectra of pine ash where the pine was grown in three distinct soil types, laser induced breakdown spectroscopy of colored artisanal glasses, and laser induced breakdown spectroscopy of exotic hardwood species.

Analyst↗

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↗