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.

Assessing utility of handheld laser induced breakdown spectroscopy as a means of Dalbergia speciation.

Many species of Dalbergia are prized hardwoods, generally referred to as 'Rosewood,' and used in high-end products due to their distinctive hue and scent. Despite more than 58 species of Dalbergia being listed as endangered in Appendix 1 of The Convention on International Trade in Endangered Species of Fauna and Flora (CITES), the illegal logging and trade of this timber is ongoing. In this work, a handheld laser induced breakdown spectrometer (LIBS) was used to analyze seven Dalbergia species and two other exotic hardwood species to evaluate the ability of handheld LIBS for rapid classification of Dalbergia in the field.