Institute of Chemistry, Faculty of Natural Sciences and Maths, Technical University of Berlin, Straße des 17. Juni 124, 10623 Berlin, Germany.
Institute of Materials Science, Faculty of Engineering, Kiel University, 24143 Kiel, Germany.
Biosensors (Basel). 2022 May 20;12(5):356. doi: 10.3390/bios12050356.
Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea.
茶是继水之后,世界上消费最频繁的饮料。茶叶的发酵在其质量中起着关键作用,通常使用实验室分析仪器和品茶师的嗅觉感知来监测。开发电子感应平台(ESP),如电子鼻(e-nose)、电子舌(e-tongue)和电子眼(e-eye),配备先进的数据处理算法,不仅可以准确加速基于消费者的茶叶感官质量评估,还可以为这种生物活性产品定义新标准,以满足全球市场需求。使用与多元统计相结合的电子信号的复杂数据集,从而有助于质量预测和区分。使用仿生 ESP 综述了最新的成果和现有解决方案,以解决未来的问题,并实现对茶叶及其产品感官化学特性的简单、准确的实时分析。这些先进的传感技术可以测量香气、味道和颜色特征,并将数据输入到数学分类算法中,根据价格、产地、采摘、发酵、储存时间、质量等级和掺假比例来区分不同的茶叶。虽然用于设计电子舌系统的伏安法和荧光传感器阵列正在出现,但电位电极更常用于监测茶叶的味道特征。使用特征级融合策略可以显著提高预测模型的效率和准确性,并伴随着茶叶感官特性和生化特征之间的模式识别关联。