Rapid differentiation of Piper methysticum (kava) plant parts using single point and imaging vibrational spectroscopy

Abstract

Piper methysticum Forst., commonly referred to as kava, has been used medicinally and recreationally by inhabitants of the South Pacific Islands for centuries. Kavalactones present in roots and aerial parts are regarded as the bioactive compounds responsible for the relaxant effects, and for mitigating anxiety and stress-related conditions. The development of pharmaceutical products containing root extracts led to a boom in kava sales in Europe in 1998. However, reported cases of toxicity led to the subsequent banning of kava products in several countries. This study was initiated to develop rapid, robust and alternative spectroscopic methods for quality control that can be implemented at the point of export, to authenticate the use of kava roots as legislated by the Vanuatu Amended Kava Act no. 6 (2015). Roots, peeled stems, and stump peelings samples (n = 47) were sourced from Fiji, Hawaii, Samoa, the Solomon Islands and Tonga. The sample extracts were analysed using ultra performance liquid chromatography coupled to a photodiode array detector and mass spectrometer (UHPLC-PDA/MS), while powdered material was analysed using spectroscopic techniques. These included single-point (near-infrared (NIR) and mid-infrared (MIR) spectroscopy), as well as an imaging (hyperspectral imaging). Principal component analysis of both the raw UPLC-MS and the quantitative UPLC-PDA data revealed chemical differences between the root and non-root samples. Kavain, methysticin and yangonin were identified as the compounds largely responsible for the chemical differences between the plant parts. Discriminant analysis models (OPLS-DA and PLS-DA) were developed for all the techniques, to reliably discriminate kava roots from non-roots. All the discriminant models indicated a good prediction ability (Q2XCum ≥ 60 %) and were successfully used to accurately identify external roots and non-root samples. However, hyperspectral imaging yielded superior results, with a prediction ability above 90 %. This technique can be automated and is capable of continuously scanning multiple samples, making it ideal for quality control.