scholarly journals Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis‐NIR spectroscopy

2020 ◽  
Vol 8 (11) ◽  
pp. 5860-5874
Author(s):  
Alireza Sanaeifar ◽  
Xinyao Huang ◽  
Mengyuan Chen ◽  
Zhangfeng Zhao ◽  
Yifan Ji ◽  
...  
Keyword(s):  
Green Tea ◽  
Nir Spectroscopy ◽  
Nondestructive Monitoring

Measurement of the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion Based on near Infrared Spectroscopy

2011 ◽  
Vol 301-303 ◽  
pp. 1093-1097 ◽  
Author(s):  
Shi Rong Ai ◽  
Rui Mei Wu ◽  
Lin Yuan Yan ◽  
Yan Hong Wu
Keyword(s):  
Amino Acids ◽  
Infrared Spectroscopy ◽  
Correlation Coefficient ◽  
Green Tea ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Tea Polyphenols ◽  
Strong Absorption ◽  
Tea Infusion

This study attempted the feasibility to determine the ratio of tea polyphenols to amino acids in green tea infusion using near infrared (NIR) spectroscopy combined with synergy interval PLS (siPLS) algorithms. First, SNV was used to preprocess the original spectra of tea infusion; then, siPLS was used to select the efficient spectra regions from the preprocessed spectra. Experimental results showed that the spectra regions [7 8 18] were selected, which were out of the strong absorption of H2O. The optimal PLS model was developed with the selected regions when 6 PCs components were contained. The RMSEP value was equal to 0.316 and the correlation coefficient (R) was equal to 0.8727 in prediction set. The results demonstrated that NIR can be successfully used to determinate the ration of tea polyphenols to amino acids in green tea infusion.

scholarly journals Protected Geographical Indication Identification of a Chinese Green Tea (Anji-White) by Near-Infrared Spectroscopy and Chemometric Class Modeling Techniques

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Lu Xu ◽  
Peng-Tao Shi ◽  
Xian-Shu Fu ◽  
Hai-Feng Cui ◽  
Zi-Hong Ye ◽  
...  
Keyword(s):  
Green Tea ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Rapid Identification ◽  
Classification Performance ◽  
Chinese Market ◽  
White Tea ◽  
Modeling Techniques ◽  
Tea Plantations ◽  
Model Training

This paper reports a rapid identification method for a Chinese green tea with PGI, Anji-white tea, by class modeling techniques and NIR spectroscopy. 167 real and representative Anji-white tea samples were collected from 8 tea plantations in their original producing areas for model training. Another 81 non-Anji-white tea samples of similar appearance were collected from 7 important tea producing areas and used for validation of model specificity. Diffuse NIR spectra were measured with finely ground tea powders. OCPLS and SIMCA were used to describe the distribution of representative Anji-white tea objects and predict the authenticity of new objects. For data preprocessing, smoothing, derivatives, and SNV were applied to improve the raw spectra and classification performance. It is demonstrated that taking derivatives and SNV can improve classification accuracy and reduce the complexity of class models by removing spectral background and baseline. For the best models, the sensitivity and specificity were 0.886 and 0.951 for OCPLS, 0.886 and 0.938 for SIMCA with SNV spectra, respectively. Although it is difficult to perform an exhaustive analysis of all types of potential false objects, the proposed method can detect most of the important non-Anji-white teas in the Chinese market.

Identification of green tea origins by near-infrared (NIR) spectroscopy and different regression tools

2016 ◽  
Vol 60 (1) ◽  
pp. 84-90 ◽  
Author(s):  
XinGang Zhuang ◽  
LiLi Wang ◽  
Qi Chen ◽  
XueYuan Wu ◽  
JiaXiong Fang
Keyword(s):  
Green Tea ◽  
Near Infrared ◽  
Nir Spectroscopy

FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules

LWT ◽  
2009 ◽  
Vol 42 (5) ◽  
pp. 998-1002 ◽  
Author(s):  
V.R. Sinija ◽  
H.N. Mishra
Keyword(s):  
Green Tea ◽  
Nir Spectroscopy ◽  
Green Tea Powder

Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea

2021 ◽  
Vol 247 ◽  
pp. 119096
Author(s):  
Luqing Li ◽  
Shanshan Jin ◽  
Yujie Wang ◽  
Ying Liu ◽  
Shanshan Shen ◽  
...  
Keyword(s):  
Quality Control ◽  
Green Tea ◽  
Nir Spectroscopy
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