A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets

Abstract

Context: Quality Control (QC) has been constantly an essential concern in many fields like food industry production, medical drugs, environmental protection, and so on. An odor or flavor, as a global fingerprint, can be implemented as a non-invasive mechanism for quality assurance. This computer-based approach can assure accurate detection and precise identification of the product quality or manufactured goods.Objective: This paper aims to achieve a systematic review about e-nose by introducing the achievements made by researchers in this area, to summarize their findings, to provide motivations and challenges to new researchers in the field of e-nose. Methods:The articles that were being utilized in the e-nose field were systematically achieved using three search engines: The online library of IEEE Explore, Web of Science and Science Direct for time span of 7 years (from 2013 to 2020). Both medical literature reviews and technical reviews were considered in the criteria of the research for wider understanding in the field of e-nose. The articles were categorized according to the objective of the research and projected into four classes. Upon completion of screening process 333 research papers using the exclusion and inclusion conditions, as the final set 54 articles were selected. Results:The taxonomy of this research was classified into four categories. The first one included the suggested methods that introduced the utilization of the e-nose for classification purposes (9/54 papers). The second category comprises the methods related to the development of e-nose (24/54 papers). The third one included the review studies about the e-nose (8/54 papers). The fourth group comprises comparative studies and evaluation (13/54 papers).Discussion: This systematic review contributes for a clearer understanding and a full insight in the e-nose research field by surveying and categorizing pertinent research efforts.

Volatile Organic Compounds, Evaluation Methods and Processing Properties for Cooked Rice Flavor

“…( 2019 ), Yu et al ( 2021 ) HS-SPME Low detection limit, relative high sensitivity, simplicity, speed, wider compound coverage, and higher throughput, simple operation and fast test speed, basically no solvent, little environmental pollution, less sample consumption Sulfur and sulfide compounds cannot be detected (De Giovanni and Marchetti. 2020 ) Zeng et al ( 2008 ) and Zeng et al ( 2009 ) E-nose Low cost, accurate, fast, reliable and portble Data collection are tedious and labor intensive, data collection from different sources, the e-nose performance is highly affected by temperature modulation (Al-Dayyeni et al 2021 ; Lei and Zhang. 2015 ) Chen et al ( 2021 ) and Jana et al ( 2015 ) GC-O-MS Identification of key aroma-active compounds accurately, capable of illustrate relationship between odorants and sensory properties Limited scope of application, need to train professionals to operate (Song and Liu.…”

Progress of Research on the Application of Nanoelectronic Smelling in the Field of Food

“…Because each flour has unique aromatic properties, it is important to understand it in order to obtain the desired flavor compounds. Danielle Laure Taneyo Saa et al [ 52 ] applied nanoelectronic smelling to study the volatile aspects of different bakery products made using both mature and immature grains and transformed by fermented dough of the genus Lactobacillus. The results show that nanoelectronic smelling can distinguish between doughs composed of two types of flour, and the study verifies that, as a first step in the baking process, a rapid analysis can be performed using nanoelectronic smelling to verify the aromatic compounds of each flour and to control whether the flour is the correct type.…”

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

“…The electronic nose (E-nose) is a rapid detection instrument that has been rising in recent years [9,10]. The instrument is composed of a sensor array with several different sensors and some machine learning algorithms, which are capable of recognizing simple or complex odors [11,12]. Given their rapid recognition ability while still being compact and lightweight, this technique has been attracting extensive attention in the pesticide detection field [13].…”