ReviewFoods (Basel, Switzerland)2023
Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology.
Review in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Model for Identifying the Fermentation Degree of Tieguanyin Oolong Tea Based on RGB Image and Hyperspectral Data.Foods (Basel, Switzerland) · 2026Article
- A lightweight hybrid transformer approach for hyperspectral imaging-based drought tolerance evaluation in tea plants.Plant methods · 2025Article
- Optimizing chlorophyll content prediction in tea leaves via spectral transformations and deep learning.BMC plant biology · 2025Article
- Non-Destructive Sensing of Tea Pigments in Black Tea Rolling Process.Foods (Basel, Switzerland) · 2025Article
- Terahertz Spectroscopy for Food Quality Assessment: A Comprehensive Review.Foods (Basel, Switzerland) · 2025Review
- Overview of Deep Learning and Nondestructive Detection Technology for Quality Assessment of Tomatoes.Foods (Basel, Switzerland) · 2025Review
- Differences in abundance and functional intensity of characteristic microorganisms of tea plant rhizosphere soils contribute to the differentiation of tea quality in different rocky zones.Frontiers in microbiology · 2025Article
- Prediction of the Quality of Anxi Tieguanyin Based on Hyperspectral Detection Technology.Foods (Basel, Switzerland) · 2024Article
- Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis.Foods (Basel, Switzerland) · 2024Article
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.
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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 Socratic graph.