Scientists in China have developed a rapid test to identify adulteration of agarwood - a botanical found in expensive perfumes and traditional Chinese medicines.
The fragrant wood from the Aquilaria sinensis plant - which is endemic to China but becoming scarce due to habitat loss - is frequently subject to economically-motivated adulteration as demand is outstripping supply, according to the researchers from Beijing and Tsinghua Universities and China's Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology.
The scientists have shown that a technique known as Fourier transform infrared (FT-IR) spectroscopy - coupled with a statistical technique called two-dimensional (2D) correlation analysis - can differentiate between genuine and adulterated samples by examining the relative levels of resins in the wood.
High-grade agarwood is one of the mosty expensive natural raw materials in the world, costing up to $100,000 per kilo, according to an article in the Flavour and Fragrance Journal. Adulterated products are widely sold for as little as $100/kg.
The resinous wood is known as Aquilariae Lignum Resinatum (ALR) is used in TCM products used to treat menstrual and hormonal imbalances and incontinence, and has a monograph in the Chinese Pharmacopoeia (ChP).
ALR is formed when the tree is infected with a particular type of mould - which gives the normally colourless and odourless resin its characteristic aroma. That aroma means oil from agarwood - which is also known as oud - finds its way perfume products, while the wood itself is collected in China as a form of natural sculpture.
FT-IR spectroscopy is non-destructive - critical when dealing with such an expensive material - and less prone to subjective bias than techniques such as macroscopic and microscopic observation, write the researchers in the Journal of Molecular Structure (15 November 2016).
The technique is also quicker than physicochemical identification methods such as thin layer chromatography which is "not suitable for rapid analysis of a large number of samples."