Prediction of total extracted oil and major terpenes in leaves of Kunzea ambigua using near infrared reflectance spectroscopy

Abstract

The therapeutic potential of Kunzea ambigua essential oil is experiencing increased market demand. However, commercial production is currently limited by variability of wild-harvested feedstock so that managed production from high yielding clones is desired. This study explored the utility of near infrared (NIR) reflectance spectroscopy to predict total extracted oil and the major terpenes from intact and ground K. ambigua leaves. Partial least squares regression enabled accurate prediction of total extracted oil and the concentration of α-pinene, globulol and viridiflorol but not α-terpineol (the major components). This was the case for both ground and intact leaf samples, though the coefficients of determination (r2) values were lower for the intact leaf samples (r2: 0.59 to 0.71, ratio of residual prediction to the deviation, RPD: 1.4-5.0, RMSEP: 0.53-2.27 mg/g) as compared to the ground samples (r2: 0.72-0.88; RPD: 1.9-8; RMSEP: 0.32-1.40 mg/g). In contrast, a NIR predictive model was produced for ground but not intact samples for 1,8-cineole (r2: 0.72; RPD: 1.9; RMSEP: 0.42 mg/g). Though the predictive models developed in this study were less accurate and less precise than for the intact samples, this technology could still be immediately useful and allow for rapid identification and screening for superior K. ambigua genotypes from native vegetation for both total extracted oil and preferred chemical profiles.