Electronic sensor technologies in monitoring quality of tea: A review

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

Study on Key Technology for the Discrimination of Xihu Longjing Tea Grade by Electronic Tongue

International audienceElectronic tongue has the characteristics of sensitivity and instability. However the technical specification for its research has still not been formed. In this paper, it was introduced the key technology of electronic tongue qualitative discriminant analysis to micro-difference samples. The research objects were four different grades of Xihu Longjing Tea with less difference within small producing area in Hangzhou of Zhejiang Province in China. According to the research, the stability of equipment had not been shown until the fifth repetition for the same sample by the electronic tongue. Finally, the signal from seventh repetitive test was selected to represent the intelligent taste fingerprint for this sample. The fingerprints of electronic tongue collected at different days showed linearity drifting for the same sample. The special tea samples were set into each series of experiments to be considered as the reference sample. All samples’ signals were calibrated with the difference value between the reference sample fingerprint of corresponding test to the designated reference sample. Principal component analysis (PCA) results showed that the same grade samples were clustered, while the different grades samples were more dispersion and non-overlapping. Through Mahalanobis Distance and Residual Method, the four abnormal tea samples were rejected from highest and 1st grade respectively. The electronic tongue’s Longjing Tea Grade models were built by soft independent modeling of class analogy (SIMCA). The discrimination accuracy for tea sample grade were both 100% for correction set and prediction set. Through this study, it was established the technical specification and flow for the quick detection of tea by electronic tongue, which including the determination on intelligent taste spectrum repeatability performance, system error calibration for spectrum drifting, rejection for abnormal tea sample based on taste spectrum and establishment for the quality judgment model . The technical specification provides the theoretical base for reasonable use of electronic tongue