1. Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array.
- Author
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Lin H, Zhang K, Guo J, Kwadzokpui BA, Adade SYS, and Chen Q
- Subjects
- Smell, Camellia sinensis chemistry, Humans, Tea chemistry, Volatile Organic Compounds analysis, Colorimetry methods, Odorants analysis, Taste, Support Vector Machine
- Abstract
Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
- Published
- 2024
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