Electronic Nose Technologies in Quality Assessment

Electronic nose systems mimic mammalian olfaction by deploying arrays of semi-selective gas sensors to detect complex mixtures of volatile compounds. Advances in sensor materials, miniaturisation and data analytics have driven their adoption in quality-control settings across food and beverage production, agriculture, environmental monitoring and industrial manufacturing. These instruments offer rapid, non-invasive assessment of freshness, spoilage, contamination and geographical provenance, often replacing or complementing conventional chromatographic methods. Integration of machine-learning algorithms has bolstered accuracy in classification and prediction tasks, while portable and modular designs have extended applications beyond the laboratory to in-field and on-line process monitoring.

Current challenges include improving sensor stability, selectivity and reproducibility, as well as establishing standardised protocols for sampling and data processing. Future developments are expected to centre on novel nanostructured materials, advanced pattern-recognition techniques and multi-sensor fusion strategies, all aimed at enhancing sensitivity and resilience in real-world conditions.

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Electronic Nose Technologies in Quality Assessment publication trend

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Technical terms

Electronic nose (e-nose): An instrument that analyses volatile compounds using an array of chemical sensors and pattern-recognition software to generate an “olfactory” fingerprint of a sample.

Electronic tongue (e-tongue): A sensor system analogous to the e-nose, designed to detect non-volatile compounds in liquids through an array of electrochemical or potentiometric sensors.

Sensor array: A group of sensors with differing sensitivities and response profiles, used collectively to capture multidimensional chemical information.

Pattern-recognition algorithm: A computational method, including statistical and machine-learning techniques, for classifying sensor outputs and identifying characteristic signal patterns.

Volatile organic compounds (VOCs): Organic chemicals that readily vapourise at ambient temperature, often responsible for aroma and odour profiles in quality-assessment contexts.

Convolutional neural network (CNN): A deep-learning architecture employing convolutional layers to automatically extract hierarchical features from input data, used here for gas classification tasks.

References

1. Electronic Nose and Its Applications: A Survey. Machine Intelligence Research (2019).
2. Gas Classification Using Deep Convolutional Neural Networks. Sensors (2018).
3. Electronic Noses and Tongues: Applications for the Food and Pharmaceutical Industries. Sensors (2011).
4. Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment. Sensors (2017).
5. A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment. Sensors (2017).