Artificial noses, or electronic noses, are emerging as transformative tools for aroma profiling in food and beverage analysis. With greater consistency and precision than the human nose, they provide insights that ensure product quality and authenticity.
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Introduction
The quality of food and beverage products heavily relies on their sensory appeal, with aroma playing a critical role. Sensory analysis is generally used to determine the various aromatic characteristics of products such as coffee and wine. However, this method can be imprecise, lack reproducibility, and be highly subjective. Hence, there is a strong interest in developing human sense-like sensors.
Aroma profiling and authenticity verification are essential for the food and beverage industry, as aroma is often an indicator of freshness, quality, and product origin. For instance, a particular coffee blend may have a unique aroma profile based on the region it was grown in, and a subtle deviation from this profile can indicate an adulteration or substitution.1
An artificial nose (or electronic nose, e-nose) is a device equipped with sensors that detect and analyze the volatile compounds typically responsible for aroma. By mimicking the human nose, artificial noses capture complex aroma profiles that can be used to assess quality, verify authenticity, and maintain product consistency.
What Methods Are Used in Beverage Contaminant Analysis?
How Artificial Noses Work
Artificial noses operate by detecting and interpreting volatile organic compounds (VOCs) in food or beverage products using a multi-stage process involving chemical sensors, data processing, and advanced machine learning algorithms.
Most e-nose sensors are made from materials such as metal-oxide semiconductors (typically SnO2 or TiO2) or conducting polymers based on materials such as pyrrole, aniline, thiophene, and acetylene deposited onto ceramic substrates.
Each sensor reacts to specific VOCs, generating a response pattern. By placing several sensors with distinct sensitivities in a single device, artificial noses can capture a complex aroma fingerprint for each product.
Once VOCs are detected, the artificial nose processes the data to create a distinct profile of the product’s aroma. These aroma fingerprints provide a baseline for comparison in quality control and authenticity assessments.
The data generated are manipulated and interpreted using machine learning models that can recognize subtle differences and identify patterns linked to specific qualities or defects.
This process enables artificial noses to learn and adapt, becoming more precise in detecting spoilage, identifying product origin, and distinguishing between authentic and adulterated products. The most used statistical approaches include support vector machines, multivariate statistical analysis, and artificial neural networks.
Fruit Volatile Analysis Using Electronic Nose l Protocol Preview
Applications in Food and Beverage Analysis
Artificial noses have a wide variety of applications in food and beverage. In quality control, they can detect spoilage, oxidation, or changes in flavor consistency. In dairy processing, the detection of off-flavors may indicate spoilage long before a human tester could.
In winemaking, artificial noses are commonly used to detect wine spoilage. They have also been used for the evaluation of wine and grape quality by discriminating between grape musts at different degrees of ripeness, which would be challenging to achieve using a sensory panel.2
Smoke contamination (a phenomenon that occurs when grapes are exposed to smoke from nearby fire incursions or forest fires) is associated with smoke-derived volatile products such as guaiacol, 4-methylguaiacol, syringol, and cresol.
Models to assess near real-time smoke contamination levels have been developed by integrating e-noses and artificial intelligence. Such methods can help the implementation of strategies to minimize smoke taint in wines following bushfires, saving harvests and preventing financial losses.3
Artificial noses are also used to verify product authenticity by comparing aroma profiles with known standards. This is particularly valuable for products with protected geographical indications, like wines, coffee blends, and olive oil, where aroma profiles are tied to the region of origin.4
The ability to monitor aroma changes over time also makes artificial noses effective for shelf-life testing. By tracking aroma profiles as a product ages, artificial noses provide insights into how long a product maintains its optimal quality.
Advantages of Artificial Noses in Food Science
Artificial noses offer several advantages over traditional sensory analysis methods, making them an appealing option in food science. They can provide rapid results, often within minutes, compared to the time-consuming process of organizing human sensory panels.
Unlike sensory analysis which is affected by the psychological state and condition of human testers, leading to inconsistencies, artificial noses provide an objective, repeatable measure of aroma profiles, reducing variability and providing greater precision and reliability.
They are also highly sensitive tools for quality control, being able to detect subtle variations that human noses might miss. This sensitivity is crucial for detecting off-flavors or adulterations that could impact consumer perception.
The Role of Voltammetry in Ensuring Food Quality and Safety
Challenges and Future Potential
Although artificial noses are developing rapidly, they face some challenges. Especially when dealing with complex products, sensor sensitivity can vary. In addition, the initial costs for high-quality devices can be prohibitive.
Nevertheless, ongoing advances in sensor technology and machine learning are helping to address these limitations, with the development of faster, smaller and more reproducible devices, together with better data analysis systems.5
In the future, artificial noses could become even more integrated with AI-driven systems, enhancing their accuracy and versatility. Future developments may include applications in personalized nutrition, allowing consumers to choose products based on their aroma preferences, or real-time feedback systems in grocery stores, offering quality ratings to buyers.
Conclusion
Artificial noses are invaluable tools, providing objective, consistent, and sensitive methods for evaluating aroma profiles. They are increasingly used in quality control and authenticity verification in food and beverage products.
Ongoing advancements in sensor materials, machine learning, and artificial intelligence are addressing current challenges. As technology evolves, artificial noses are expected to more closely replicate the human sensory system and play an even greater role in food and beverage analysis.
References
- Jang, M., Bae, G., Kwon, Y. M., Cho, J. H., Lee, D. H., Kang, S., Yim, S., Myung, S., Lim, J., Lee, S. S., Song, W. & An, K. S. (2024). Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System. Adv Sci (Weinh), 11, e2308976.10.1002/advs.202308976.
- Aleixandre, M., Santos, J. P., Sayago, I., Cabellos, J. M., Arroyo, T. & Horrillo, M. C. (2015). A wireless and portable electronic nose to differentiate musts of different ripeness degree and grape varieties. Sensors (Basel), 15, 8429-43.10.3390/s150408429.
- Fuentes, S., Summerson, V., Gonzalez Viejo, C., Tongson, E., Lipovetzky, N., Wilkinson, K. L., Szeto, C. & Unnithan, R. R. (2020). Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach. Sensors, 20, 5108. Available: https://www.mdpi.com/1424-8220/20/18/5108
- Alfieri, G., Modesti, M., Riggi, R. & Bellincontro, A. (2024). Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry. Sensors, 24, 2293. Available: https://www.mdpi.com/1424-8220/24/7/2293
- Rabehi, A., Helal, H., Zappa, D. & Comini, E. (2024). Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review. Applied Sciences, 14, 4506. Available: https://www.mdpi.com/2076-3417/14/11/4506
Further Reading