Performance Analysis of MAU-9 Electronic-Nose MOS Sensor Array Components and ANN Classification Methods for Discrimination of Herb and Fruit Essential Oils

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

doi:10.3390/chemosensors9090243

Abstract

The recent development of MAU-9 electronic sensory methods, based on artificial olfaction detection of volatile emissions using an experimental metal oxide semiconductor (MOS)-type electronic-nose (e-nose) device, have provided novel means for the effective discovery of adulterated and counterfeit essential oil-based plant products sold in worldwide commercial markets. These new methods have the potential of facilitating enforcement of regulatory quality assurance (QA) for authentication of plant product genuineness and quality through rapid evaluation by volatile (aroma) emissions. The MAU-9 e-nose system was further evaluated using performance-analysis methods to determine ways for improving on overall system operation and effectiveness in discriminating and classifying volatile essential oils derived from fruit and herbal edible plants. Individual MOS-sensor components in the e-nose sensor array were performance tested for their effectiveness in contributing to discriminations of volatile organic compounds (VOCs) analyzed in headspace from purified essential oils using artificial neural network (ANN) classification. Two additional statistical data-analysis methods, including principal regression (PR) and partial least squares (PLS), were also compared. All statistical methods tested effectively classified essential oils with high accuracy. Aroma classification with PLS method using 2 optimal MOS sensors yielded much higher accuracy than using all nine sensors. The accuracy of 2-group and 6-group classifications of essentials oils by ANN was 100% and 98.9%, respectively.

Highlights

IntroductionElectronic-nose (e-nose) devices, used in the food industry for quality control (QC) of animal and plant-based products, generally consist of an array of electrochemical sensors used in combination with machine-learning methods, such as through artificial neural networks (ANN), pattern-recognition algorithms, and various statistical data-evaluation systems collectively capable of detecting aroma emissions from organic food sources [1,2]
Access to the most important sensors can play a significant role in the data processing stage because additional variables in data sometimes can lead to problems such as over analysis of the data
We have provided evidence to demonstrate that e-nose sensor-array optimization, through a statistical evaluation of individual sensor performance, based on single-sensor contributions to sample discriminations, is an effective approach to improve on overall e-nose performance and accuracy

Summary

Introduction

Electronic-nose (e-nose) devices, used in the food industry for quality control (QC) of animal and plant-based products, generally consist of an array of electrochemical sensors used in combination with machine-learning methods, such as through artificial neural networks (ANN), pattern-recognition algorithms, and various statistical data-evaluation systems collectively capable of detecting aroma emissions from organic food sources [1,2]. E-nose devices usually contain an array of non-specific, cross-reactive chemical sensors that have been used to detect complex food volatiles, consisting of unique combinations of volatile organic compounds (VOCs), and provide specific chemical, aroma signature patterns (smellprints) representative of the VOC-emissions being analyzed from various food sources [3,4,5]. The development of e-nose gas-sensor array technologies are effective analytical tools for assessing food quality and detecting distinct mixtures of volatile emissions from food products [8,13]

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