ArticleSensors (Basel, Switzerland)2020
Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- AI-Enabled Next-Generation Dairy Systems: From Sensors to Smart Processing.Food science & nutrition · 2026Review
- Embedded MOX-Based Volatilomic Sensing for Real-Time Classification of Plant-Based Milk Beverages.Sensors (Basel, Switzerland) · 2026Article
- Data-Driven Soft Sensing for Raw Milk Ethanol Stability Prediction.Sensors (Basel, Switzerland) · 2026Article
- Evaluation of flavor quality of commercial butter: A systematic comparison of sweet cream and cultured butter.Food chemistry: X · 2025Article
- Advances in predicting human olfactory perception: from data acquisition to computational models.RSC advances · 2025Review
- Quantification of Volatile Compounds in Mixtures Using a Single Thermally Modulated MOS Gas Sensor with PCA-ANN Data Processing.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety.Sensors (Basel, Switzerland) · 2025Review
- Statistical Optimization and Analysis of Factors Maximizing Milk Productivity.Animals : an open access journal from MDPI · 2025Article
- Classification of Plant-Based Drinks Based on Volatile Compounds.Foods (Basel, Switzerland) · 2024Article
- Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor.Materials (Basel, Switzerland) · 2024Review
- Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables.Foods (Basel, Switzerland) · 2024Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry.Sensors (Basel, Switzerland) · 2023Review
- Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication.NPJ science of food · 2023Article
- Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types.Sensors (Basel, Switzerland) · 2023Article
- 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
- Application of an Electronic Nose and HS-SPME/GC-MS to Determine Volatile Organic Compounds in Fresh Mexican Cheese.Foods (Basel, Switzerland) · 2022Article
- Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.Molecules (Basel, Switzerland) · 2021Article
- An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring.Sensors (Basel, Switzerland) · 2021Review
- Nanoengineering Approaches Toward Artificial Nose.Frontiers in chemistry · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R
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