ArticleSensors (Basel, Switzerland)2018
Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Smart Chemical Sensors for Monitoring and Detection of Spoilage in Fermented and Non-Fermented Food Products.Sensors (Basel, Switzerland) · 2026Review
- Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods.Biosensors · 2025Article
- Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor.Materials (Basel, Switzerland) · 2024Review
- Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.Sensors (Basel, Switzerland) · 2024Review
- A Study on E-Nose System in Terms of the Learning Efficiency and Accuracy of Boosting Approaches.Sensors (Basel, Switzerland) · 2024Article
- Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry.Sensors (Basel, Switzerland) · 2023Review
- Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning.ACS sensors · 2023Article
- Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data.Nutrients · 2023Article
- Performance evaluation of E-nose and E-tongue combined with machine learning for qualitative and quantitative assessment of bear bile powder.Analytical and bioanalytical chemistry · 2023Article
- Can Electronic Nose Replace Human Nose?-An Investigation of E-Nose Sensor Responses to Volatile Compounds in Alcoholic Beverages.ACS omega · 2023Article
- A Machine Learning Method for the Quality Detection of Base Liquor and Commercial Liquor Using Multidimensional Signals from an Electronic Nose.Foods (Basel, Switzerland) · 2023Article
- Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types.Sensors (Basel, Switzerland) · 2023Article
- Could Collected Chemical Parameters Be Utilized to Build Soft Sensors Capable of Predicting the Provenance, Vintages, and Price Points of New Zealand Pinot Noir Wines Simultaneously?Foods (Basel, Switzerland) · 2023Article
- Prediction of Ammonia Concentration in a Pig House Based on Machine Learning Models and Environmental Parameters.Animals : an open access journal from MDPI · 2022Article
- Methodology for Quantifying Volatile Compounds in a Liquid Mixture Using an Algorithm Combining B-Splines and Artificial Neural Networks to Process Responses of a Thermally Modulated Metal-Oxide Semiconductor Gas Sensor.Sensors (Basel, Switzerland) · 2022Article
- An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans.Micromachines · 2022Article
- Artificial Olfactory Neuron for an In-Sensor Neuromorphic Nose.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.Foods (Basel, Switzerland) · 2022Article
- An Overview of Sensory Characterization Techniques: From Classical Descriptive Analysis to the Emergence of Novel Profiling Methods.Foods (Basel, Switzerland) · 2022Review
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)-were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.
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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.