Article: Applications of Electronic Nose Coupled with Statistical and Intelligent Pattern Recognition Techniques for Monitoring Tea Quality: A Review
Agriculture (Basel). 2022 Sept. 01, v. 12, no. 9
2022
Abstract: Tea is the most widely consumed non-alcoholic beverage worldwide. In the tea sector, the high demand for tea has led to an increase in the adulteration of superior tea grades. The procedure of evaluating tea quality is difficult to assure the highest ... ...
| Abstract | Tea is the most widely consumed non-alcoholic beverage worldwide. In the tea sector, the high demand for tea has led to an increase in the adulteration of superior tea grades. The procedure of evaluating tea quality is difficult to assure the highest degree of tea safety in the context of consumer preferences. In recent years, the advancement in sensor technology has replaced the human olfaction system with an artificial olfaction system, i.e., electronic noses (E-noses) for quality control of teas to differentiate the distinct aromas. Therefore, in this review, the potential applications of E-nose as a monitoring device for different teas have been investigated. The instrumentation, working principles, and different gas sensor types employed for E-nose applications have been introduced. The widely used statistical and intelligent pattern recognition methods, namely, PCA, LDA, PLS-DA, KNN, ANN, CNN, SVM, etc., have been discussed in detail. The challenges and the future trends for E-nose devices have also been highlighted. Overall, this review provides the insight that E-nose combined with an appropriate pattern recognition method is a powerful non-destructive tool for monitoring tea quality. In future, E-noses will undoubtedly reduce their shortcomings with improved detection accuracy and consistency by employing food quality testing. |
|---|---|
| Keywords | adulterated products ; agriculture ; electronic nose ; food quality ; humans ; instrumentation ; non-alcoholic beverages ; quality control ; smell ; tea |
| Language | English |
| Dates of publication | 2022-0901 |
| Publishing place | Multidisciplinary Digital Publishing Institute |
| Document type | Article |
| ZDB-ID | 2651678-0 |
| ISSN | 2077-0472 |
| ISSN | 2077-0472 |
| DOI | 10.3390/agriculture12091359 |
| Database | NAL-Catalogue (AGRICOLA) |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.