ArticleSensors (Basel, Switzerland)2022
Portable Electronic Nose Based on Digital and Analog Chemical Sensors for 2,4,6-Trichloroanisole Discrimination.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Smart Chemical Sensors for Monitoring and Detection of Spoilage in Fermented and Non-Fermented Food Products.Sensors (Basel, Switzerland) · 2026Review
- Presence-gated VOC sensing for urban search and rescue applications.Scientific reports · 2026Article
- A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics.Biosensors · 2024Review
- Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.Sensors (Basel, Switzerland) · 2024Review
- Efficient and Accurate Analog Voltage Measurement Using a Direct Sensor-to-Digital Port Interface for Microcontrollers and Field-Programmable Gate Arrays.Sensors (Basel, Switzerland) · 2024Article
- Elephants and algorithms: a review of the current and future role of AI in elephant monitoring.Journal of the Royal Society, Interface · 2023Review
- Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader.Micromachines · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
2,4,6-trichloroanisole (TCA) is mainly responsible for cork taint in wine, which causes significant economic losses; therefore, the wine and cork industries demand an immediate, economic, noninvasive and on-the-spot solution. In this work, we present a novel prototype of an electronic nose (e-nose) using an array of digital and analog metal-oxide gas sensors with a total of 31 signals, capable of detecting TCA, and classifying cork samples with low TCA concentrations (≤15.1 ng/L). The results show that the device responds to low concentrations of TCA in laboratory conditions. It also differentiates among the inner and outer layers of cork bark (81.5% success) and distinguishes among six different samples of granulated cork (83.3% success). Finally, the device can predict the concentration of a new sample within a ±10% error margin.
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Registered trials
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