Sanislav Teodora, Mois George D, Zeadally Sherali, Folea Silviu, Radoni Tudor C, Al-Suhaimi Ebtesam A
Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
College of Communication and Information, University of Kentucky, Lexington, KY 40506-0224, USA.
Sensors (Basel). 2025 Jul 16;25(14):4437. doi: 10.3390/s25144437.
Food quality and safety are essential for ensuring public health, preventing foodborne illness, reducing food waste, maintaining consumer confidence, and supporting regulatory compliance and international trade. This has led to the emergence of many research works that focus on automating and streamlining the assessment of food quality. Electronic noses have become of paramount importance in this context. We analyze the current state of research in the development of electronic noses for food quality and safety. We examined research papers published in three different scientific databases in the last decade, leading to a comprehensive review of the field. Our review found that most of the efforts use portable, low-cost electronic noses, coupled with pattern recognition algorithms, for evaluating the quality levels in certain well-defined food classes, reaching accuracies exceeding 90% in most cases. Despite these encouraging results, key challenges remain, particularly in diversifying the sensor response across complex substances, improving odor differentiation, compensating for sensor drift, and ensuring real-world reliability. These limitations indicate that a complete device mimicking the flexibility and selectivity of the human olfactory system is not yet available. To address these gaps, our review recommends solutions such as the adoption of adaptive machine learning models to reduce calibration needs and enhance drift resilience and the implementation of standardized protocols for data acquisition and model validation. We introduce benchmark comparisons and a future roadmap for electronic noses that demonstrate their potential to evolve from controlled studies to scalable industrial applications. In doing so, this review aims not only to assess the state of the field but also to support its transition toward more robust, interpretable, and field-ready electronic nose technologies.
食品质量与安全对于保障公众健康、预防食源性疾病、减少食物浪费、维持消费者信心以及支持法规遵循和国际贸易至关重要。这促使许多专注于食品质量评估自动化和流程简化的研究工作涌现。在此背景下，电子鼻变得至关重要。我们分析了用于食品质量与安全的电子鼻开发的当前研究状况。我们查阅了过去十年在三个不同科学数据库中发表的研究论文，从而对该领域进行了全面综述。我们的综述发现，大多数研究工作使用便携式、低成本电子鼻，并结合模式识别算法，来评估某些明确界定的食品类别中的质量水平，在大多数情况下准确率超过90%。尽管取得了这些令人鼓舞的成果，但关键挑战依然存在，尤其是在使传感器对复杂物质的响应多样化、改善气味区分、补偿传感器漂移以及确保实际可靠性方面。这些局限性表明，一种完全模仿人类嗅觉系统灵活性和选择性的设备尚未出现。为了弥补这些差距，我们的综述推荐了一些解决方案，例如采用自适应机器学习模型以减少校准需求并增强抗漂移能力，以及实施数据采集和模型验证的标准化协议。我们引入了电子鼻的基准比较和未来路线图，展示了它们从对照研究发展到可扩展工业应用的潜力。通过这样做，本综述不仅旨在评估该领域的现状，还旨在支持其向更强大、可解释且适用于实际场景的电子鼻技术转型。
