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

Mansour Rasekh,Alphus Dan Wilson,Hamed Karami,Marek Gancarz

doi:10.3390/chemosensors9060142

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).

Highlights

Introduction published maps and institutional affilUses of purified essential oils as additives in foods, cosmetics and fragrance industries have increased significantly in recent decades [1,2]
The spatial separation of data clusters, derived from herbal and fruit e-nose principal component analysis (PCA) plots of e-nose sensor-array outputs, provided good evidence to indicate that the MAU-9 e-nose was capable of differentiating between sample types among the two groups of essential oil volatile organic compounds (VOCs) emissions analyzed, those extracted from herbal leaves and fruits, due to significant differences in their volatile (VOC) content and chemical composition
The high precision of instrument performance, indicated by tight data clusters in PCA plots, suggested that the selected sensors in the sensor array were effective in detecting differences in VOC emissions from essential oils

Summary

Introduction

Uses of purified essential oils as additives in foods, cosmetics and fragrance industries have increased significantly in recent decades [1,2]. Essential oils are normally sold as highly concentrated, pure hydrophobic mixtures of natural chemicals extracted from medicinal herbal plants and fruits. 90% of the total worldwide production of essential oils is consumed by the food flavoring and perfume industries [3]. Natural sources of essential oils from plants consist of complex mixtures of volatiles that typically exist in low concentrations. Different methods such as hydro-distillation and centrifugation, steam distillation and cold press processes may be used to extract them [4]. Fragrance oils are manufactured (synthetic), petroleum-based products sold to mimic the aroma (scent) of real fruits such as banana, blueberry, cantaloupe, cranberry, kiwi, pineapple, iations

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Conclusion