Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology

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Abstract
The frequent occurrence of adulterated or counterfeit plant products sold in worldwide commercial markets has created the necessity to validate the authenticity of natural plant-derived palatable products, based on product-label composition, to certify pricing values and for regulatory quality control (QC). The necessity to confirm product authenticity before marketing has required the need for rapid-sensing, electronic devices capable of quickly evaluating plant product quality by easily measurable volatile (aroma) emissions. An experimental MAU-9 electronic nose (e-nose) system, containing a sensor array with 9 metal oxide semiconductor (MOS) gas sensors, was developed with capabilities to quickly identify and classify volatile essential oils derived from fruit and herbal edible-plant sources. The e-nose instrument was tested for efficacy to discriminate between different volatile essential oils present in gaseous emissions from purified sources of these natural food products. Several chemometric data-analysis methods, including pattern recognition algorithms, principal component analysis (PCA), and support vector machine (SVM) were utilized and compared. The classification accuracy of essential oils using PCA, LDA and QDA, and SVM methods was at or near 100%. The MAU-9 e-nose effectively distinguished between different purified essential oil aromas from herbal and fruit plant sources, based on unique e-nose sensor array responses to distinct, essential-oil specific mixtures of volatile organic compounds (VOCs).

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