ArticleSensors (Basel, Switzerland)2020
Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
13 citing papers in PubMed.
- Characterizing the Effect of Simultaneous Enhancements of Reducing Gas Species on Figaro Taguchi Gas Sensor Resistance Response.ACS omega · 2024Article
- Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose.Micromachines · 2024Article
- DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath.Journal of clinical medicine · 2023Article
- Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection ofSensors (Basel, Switzerland) · 2023Article
- Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine.Sensors (Basel, Switzerland) · 2022Article
- A Review of Gas Measurement Set-Ups.Sensors (Basel, Switzerland) · 2022Review
- High Density Resistive Array Readout System for Wearable Electronics.Sensors (Basel, Switzerland) · 2022Article
- Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It toSensors (Basel, Switzerland) · 2021Article
- Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant.Sensors (Basel, Switzerland) · 2021Article
- Market Perspectives and Future Fields of Application of Odor Detection Biosensors within the Biological Transformation-A Systematic Analysis.Biosensors · 2021Article
- Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic OomycetesSensors (Basel, Switzerland) · 2021Article
- FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.