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).

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25–100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography–mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82–91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

Consumer demands for fresh foods with desired sensory properties. Lack of easy, cheap, and nondestructive methods to control the quality of fruit juices is one of the main challenges in the beverage industry. The present study has examined two types of natural and industrial juices. Machine olfaction with 9 metal oxide semiconductor (MOS) sensors was used for the experiments. Sensor response patterns were analyzed using artificial neural networks (ANN). PCR method was used to evaluate the discrimination power of the sensors. According to the results, the discrimination power of the sensors used in the electronic nose sensor array was determined, three of which were removed due to improper response power. The proposed method can be used to select the minimum and most effective number of MOS gas sensors to construct an electronic nose system to control the olfactory quality of different foods. The use of a minimum number of sensors reduces the cost of constructing an electronic nose system, decreases the volume of the processor data input, and consequently increases the classification accuracy. Therefore, the MOS-based electronic nose combined with the ANN can be an effective and highly efficient tool in the fast and nondestructive classification of pure and industrial fruit juices.