Essential oils (EOs) are natural products produced by plants, with many applications all over the world and have become a fundamental part of everyday life. For instance, EOs are increasingly used in foods, cosmetics products, and in agrochemical industry as pesticides. With increasing demand of EOs, many cases of adulteration of EOs with cheap and artificial oils have been widely reported. Adulteration of EOs has economic implications for a food and cosmetic industry, with potential health implications to the customers and public safety. The overreaching goal of this study is the development of rapid, non-destructive and low cost analytical technique for the quality assurance of EOs. Specifically, a combined use of a Fourier transform infrared spectroscopy (FTIR) and multivariate partial-least-square (PLS-1) regression was used for analysis of lemon eucalyptus oil (LEO) adulterated with extra virgin olive oil (EVOO). Samples of LEO were adulterated with EVOO at a concentration range of 10 to 99 % w/w. The FTIR of the adulterated LEO samples were measured over a 600-3400cm<sup>-1</sup> range and subjected to a PLS-1 regression analysis. The developed PLS-1 regression resulted in a correlation coefficient (R<sup>2</sup>) of 0.9996. In addition, the model was able to accurately predict the compositions of the adulterated LEO samples with a root-mean-square-relative-percent error (RMS%RE) of prediction of 3.18%. The score plot of the PLS-1 regression analysis was further used for pattern recognition of the adulterated LEO samples. The simplicity, low-cost, and the obtained high accuracy of this protocol are attractive, with potential real-world applications in quality control and quality assurance of essential oils in foods, cosmetics, and agrochemical industry. The detailed results of the FTIR, PLS-1 regression and the results of the validation study conducted for LEO and other essential oils will be presented.

Determination of adulterated lemon eucalyptus oil compositions by fourier transform infrared spectroscopy and multivariate partial-least-regression analysis

251st National Meeting (2016)

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