Discrimination and polyphenol compositions of green teas with seasonal variations based on UPLC-QTOF/MS combined with chemometrics

He, Guangyun; Hou, Xue; Han, Mei; Qiu, Shiting; Liu, Ying; Qin, Shudi; Chen, Xi

doi:10.1016/j.jfca.2021.104267

Cited by 32 publications

(10 citation statements)

References 37 publications

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“…The tea plant ( Camellia sinensis (L.)) is a perennial evergreen economic forest crop that is widely planted in tropical and subtropical regions of the world, especially in the south of China [ 1 , 2 , 3 ]. The soil in these areas is generally highly leachable and strongly acidic, resulting in a lack of nutrients in tea garden soil [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Tea Plant Varieties with High- and Low-Nutrient Efficiency on Nutrients in Degraded Soil

Ruan

Song

et al. 2023

Plants

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Tea plants are widely planted in tropical and subtropical regions globally, especially in southern China. The high leaching and strong soil acidity in these areas, in addition to human factors (e.g., tea picking and inappropriate fertilization methods) aggravate the lack of nutrients in tea garden soil. Therefore, improving degraded tea-growing soil is urgently required. Although the influence of biological factors (e.g., tea plant variety) on soil nutrients has been explored in the existing literature, there are few studies on the inhibition of soil nutrient degradation using different tea plant varieties. In this study, two tea plant varieties with different nutrient efficiencies (high-nutrient-efficiency variety: Longjing43 (LJ43); low-nutrient-efficiency variety: Liyou002 (LY002)) were studied. Under a one-side fertilization mode of two rows and two plants, the tea plant growth status, soil pH, and available nutrients in the soil profiles were analyzed, aiming to reveal the improvement of degraded soil using different tea varieties. The results showed that (1) differences in the phenotypic features of growth (such as dry tea yield, chlorophyll, leaf nitrogen (N), phosphorus (P), and potassium (K) content) between the fertilization belts in LJ43 (LJ43-near and LJ43-far) were lower than those in LY002. (2) RDA results showed that the crucial soil nutrient factors which determine the features of tea plants included available P, slowly available K, and available K. Moreover, acidification was more serious near the fertilization belt. The pH of the soil near LJ43 was higher than that near LY002, indicating an improvement in soil acidification. (3) Soil nutrient heterogeneity between fertilization belts in LJ43 (LJ43-near and LJ43-far) was lower than in LY002. In conclusion, the long-term one-side fertilization mode of two rows and two plants usually causes spatial heterogeneities in soil nutrients and aggravates soil acidification. However, LJ43 can reduce the nutrient heterogeneities and soil acidification, which is probably due to the preferential development of secondary roots. These results are helpful in understanding the influence of tea plant variety on improving soil nutrients and provide a relevant scientific reference for breeding high-quality tea varieties, improving the state of degraded soil and maintaining soil health.

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Section: Introductionmentioning

confidence: 99%

Effects of Tea Plant Varieties with High- and Low-Nutrient Efficiency on Nutrients in Degraded Soil

Ruan

Song

et al. 2023

Plants

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“…18 Mass spectrometry has also been used to discriminate green tea based on avonoids' seasonal variation. 19 The colorimetric sensor array (i.e. optoelectronic nose) approach was pioneered by Rakow and Suslick and refers to cross-responsive colorimetric reagents based on non-specic chemical interactions that generate a unique response prole.…”

Section: Introductionmentioning

confidence: 99%

“…18 Mass spectrometry has also been used to discriminate green tea based on flavonoids' seasonal variation. 19…”

Section: Introductionmentioning

confidence: 99%

Paper-based colorimetric sensor array for the rapid and on-site discrimination of green tea samples based on the flavonoid composition

2022

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“…Headspace GC/MS can only detect volatile components with less than optimal parallel precision. He et al developed a pattern recognition method to identify seasonal changes in green tea based on UPLC-QTOF/MS and chemometrics 7 . QTOF can provide high-resolution spectrograms.…”

Section: Introductionmentioning

confidence: 99%

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Chang

et al. 2022

Sci Rep

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Maojian is one of China’s traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

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Discrimination and polyphenol compositions of green teas with seasonal variations based on UPLC-QTOF/MS combined with chemometrics

Cited by 32 publications

References 37 publications

Effects of Tea Plant Varieties with High- and Low-Nutrient Efficiency on Nutrients in Degraded Soil

Effects of Tea Plant Varieties with High- and Low-Nutrient Efficiency on Nutrients in Degraded Soil

Paper-based colorimetric sensor array for the rapid and on-site discrimination of green tea samples based on the flavonoid composition

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

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