Chemometric analysis of UV–visible spectral data for the differentiation of Dalbergia latifolia and Dalbergia sissoo woods

Abstract

Dalbergia latifolia and Dalbergia sissoo woods are economically valuable due to their high-quality timber. However, the overexploitation of D. latifolia has led to the inclusion of D. sissoo along with D. latifolia in the CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) list, which mandates regulated trade. Traditional wood identification methods, such as anatomical analysis, often fail to distinguish between these species. This study investigates the use of UV–visible spectroscopy combined with chemometric techniques - specifically principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) for the rapid and accurate differentiation of these two species. UV–visible spectral analysis of methanol extracts revealed distinct absorption peaks that facilitated the differentiation. The PCA, PLS-DA and LDA models demonstrated the effectiveness of this approach in distinguishing the two species' woods. This method offers a promising alternative for these Dalbergia species differentiation, providing a balance between speed, cost, and reliability. It is particularly valuable in situations where DNA barcoding or other high-precision techniques are impractical. The findings highlight the potential of UV–visible spectroscopy combined with multivariate analysis for timber differentiation and trade monitoring, contributing to conservation efforts.