ArticleBiosensors2023
Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors.
Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 39 citations in OpenAlex.
- Multidimensional Quality Assessment of Guizhou Teas Integrating Thermogravimetric, Amino Acid, and Trace Element Analyses.Food science & nutrition · 2026Article
- High-Accuracy Prediction of Chunmee Tea Grade via DeepSpectra Model and Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2026Article
- Hydrogel In-Tape Electronic Tongue.ACS applied electronic materials · 2025Article
- Differences in the Quality Components of Wuyi Rock Tea and Huizhou Rock Tea.Foods (Basel, Switzerland) · 2024Article
- Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach.Sensors (Basel, Switzerland) · 2024Article
- The Cultivar Effect on the Taste and Aroma Substances of Hakka Stir-Fried Green Tea from Guangdong.Foods (Basel, Switzerland) · 2023Article
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.