AIMC Topic: Tea

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Storage life prediction and quality discrimination of instant green tea: Integrating computer vision, electronic nose, and electronic tongue.

Food chemistry
Tea storage is a critical determinant in determining the quality of tea products. This study systematically investigated the quality alterations of instant green tea during storage and developed an intelligent evaluation method by integrating compute...

Prediction of changes in suitable habitats for tea plants in China's four major tea-producing regions based on machine learning models.

PloS one
Under the background of ongoing global climate warming, clarifying the spatiotemporal dynamics of suitable habitats for tea plants and their potential impact on forest ecosystems is essential for promoting sustainable tea industry development and eco...

Deep learning-based fine-tuning transfer improves the generalizability of tea component prediction using miniature near-infrared spectroscopy.

Food chemistry
Model accuracies for predicting tea components using near-infrared spectroscopy are often constrained by sample type. Herein, spectral data from four tea types (green, black, oolong, and yellow) were collected using a low-cost, miniature near-infrare...

Advances and Outlook of Tea Polyphenol Chemistry: A Review.

Journal of agricultural and food chemistry
Tea is one of the most widely consumed beverages globally, whereas tea polyphenols are always receiving the most attention in scientific research. In past decades, the polyphenols, their derivatives, and oligomers and polymers which possess potential...

Intelligent geographical origin traceability of Pu-erh tea based on multispectral feature fusion.

Food chemistry
To achieve accurate origin traceability of Pu-erh tea, this study proposes a deep learning method based on multispectral fusion. By collecting Raman and near-infrared spectral data from five major origins, an improved ECA-ResNet network structure was...

Improvement method for tea leaf moisture content prediction using VIS-NIR spectrum based on transfer learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Moisture significantly affects tea plants' growth and quality. Traditional methods of leaf moisture detection are usually destructive to samples, slow and labour-intensive. In this study, visible-near infrared (VIS-NIR) spectroscopy was used to detec...

Classification of Lu'an Gua Pian tea before and after Qingming Festival using HPLC-DAD analysis: a comparison of different data analysis strategies.

Analytical methods : advancing methods and applications
Lu'an Gua Pian tea (LAGP) is a traditional Chinese historical tea and one of the top ten famous teas in China. The price of LAGP from the same place of origin varies greatly in the market depending on the harvest time, with the LAGP harvested before ...

Machine learning assisted nanozyme sensor array for accurate identification and discrimination of flavonoids in healthy tea.

Food chemistry
Identifying flavonoids in herbs is of great significance for elucidating their biological activity and pharmacological effects. However, distinguishing and detecting multiple flavonoids simultaneously remains a challenge. Here, an innovative citric a...

Quantitative non-volatile sensometabolome of Longjing tea and discrimination of taste quality by sensory analysis, large-scale quantitative metabolomics and machine learning.

Food chemistry
Study on quantitative non-volatile sensometabolome of Longjing tea remains lacked. Herein, the taste and molecular features of 42 Longjing tea samples were analyzed by sensory quantitative analysis and quantitative metabolomics. A comprehensive lands...

Differentiation of black tea according to country of origin using the μ-CTE/TD/GC-MS method combined with decision tree-optimizable neural network analysis.

Journal of the science of food and agriculture
BACKGROUND: Accurate discrimination of the country of origin of teas is critical to determine their actual commercial value, to meet consumer preferences, and to ensure compliance with labeling regulations. Therefore, in this study, we developed a ne...