MatchaDB

Research

The Global Matcha Research Library

641 papers, trials, theses and preprints that study matcha, gathered from PubMed, OpenAlex and Europe PMC, tagged by study type and topic, and linked back to every source. We keep abstracts and citations; the full text stays with its publisher.

Tip: use "quotes" for an exact phrase, OR between alternatives, and a minus to leave a word out, for example fluoride OR pesticide. Related words are matched too, so “EGCG” also finds “epigallocatechin.”

Popular searchesL-theanineEGCG and catechinsCaffeineFocus and memoryStress and sleepWeight and metabolismGut healthContaminantsPesticidesShade-growingAroma and flavorTea ceremony

5 studies match for “Machine learning”

2026Journal articleFree to read

Identification of the Geographical Origins of Matcha Using Three Spectroscopic Methods and Machine Learning

Meryem Taskaya, Rikuto Akiyama, Mai Kanetsuna, et al. — AgriEngineering

… (NIR), and Fourier transform infrared (FT-IR) spectroscopy, were combined with machine learning algorithms to accurately identify the origin of Japanese matcha. FF data were analyzed using convolutional neural networks (CNNs), whereas NIR …

Randomized controlled trialAntioxidants and polyphenolsL-theanine and caffeineAuthenticity and analytical methodsCited 3×
2026Journal articleFree to read

Multimodal Sensor Fusion for Non-Destructive Tea Quality Evaluation: Deep Learning-Enabled Methods, Applications, and Challenges.

Hu X, Zhang M, Yang B, et al. — Foods (Basel, Switzerland)

… review summarizes recent advances in multimodal sensing integrated with deep learning for tea quality evaluation, with emphasis on sensor complementarity, data-fusion strategies, representative applications, and deployment-related …

ReviewProcessing, milling and qualityAuthenticity and analytical methodsMetabolism and weightCited 6×
2024Journal article

Comprehensive assessment of matcha qualities and visualization of constituents using hyperspectral imaging technology.

Hu Y, Yu H, Song X, et al. — Food research international (Ottawa, Ont.)

… product. In this study, hyperspectral imaging technology (HSI) combined with machine learning methods enabled the first visual in situ assessment of matcha quality. The physicochemical contents of matcha were determined chemically. …

Randomized controlled trialAntioxidants and polyphenolsL-theanine and caffeineProcessing, milling and qualityCited 17×
2025Journal articleFree to read

Detection and classification of microplastics in green tea using SERS with gold nanoparticle substrates integrating chemometrics and deep learning.

Joshi R, Adhikari S, Lee D, et al. — Current research in food science

… Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM)—with a deep learning approach namely one-dimensional convolutional neural network (1D-CNN). Using optimized preprocessing, PLS-DA achieved …

Chemical / analyticalSafety, contaminants and toxicityProcessing, milling and qualityAuthenticity and analytical methodsCited 11×
2024Journal articleFree to read

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

Qinghai He, Zhiyuan Liu, Xiaoli Li, et al. — Agriculture

… presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative …

Chemical / analyticalProcessing, milling and qualityAuthenticity and analytical methodsMetabolism and weightCited 4×

How this library is built

Every record comes from three open scholarly indexes — PubMed, OpenAlex and Europe PMC — searched for the word “matcha.” Records are merged by DOI and PubMed ID so each study appears once. A paper is kept only if matcha appears in its title, abstract, author keywords or subject terms and the text is about tea or food, which filters out the surname Matcha, software called Matcha, and the Scandinavian verb “matcha” (to match).

Study type and topic tags are assigned automatically from each paper's own title, abstract and subject terms using fixed keyword rules — no guesses, and papers without an abstract are marked as unclassified. Automatic tagging is imperfect, so treat tags as a way to narrow a search, not as a verdict on a paper's quality. A study being listed here is not an endorsement of its findings.

Coverage: this is every indexed paper that names matcha, not every study of green tea or its compounds (those are far more numerous). Papers that mention matcha only in passing are hidden by default. Work that never uses the word “matcha,” and publications outside these indexes, are not included. The library was last rebuilt October 20, 2026.

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