Estimating the catechin concentrations of new shoots in green tea fields using ground-based hyperspectral imagery

Kang, Ye-Seong; Ryu, Chung Ho; Suguri, Masahiko; Park, Si‐Bum; Kishino, Shigenobu; Onoyama, Hiroyuki

doi:10.1016/j.foodchem.2021.130987

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…The results of monitoring the biochemical components of tea by hyperspectral imaging technology have been also reported. For example, in 2021, Ye et al used hyperspectral images to estimate the non-galloyl and galloyl types of catechins in new shoots of green tea, and the determination coefficient (R 2 ) of the estimation model can exceed 0.79 [ 24 ]. Yang et al established a model to quantitatively predict the main endoplasmic components of Congou black tea under a different fermentation time series [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

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Section: Introductionmentioning

confidence: 99%

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Mao

Wang

et al. 2022

Foods

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“…Baseline correction is mainly used to correct the baseline shift problem in spectroscopy due to measurement variations of spectroscopic instruments or changes in measurement environment parameters [ 39 ]. Baseline correction methods include multiple scattering correction (MSC), standard normal variation (SNV), detrending (DT), orthogonal signal correction (OSC), and moving average (MA) [ 40 , 41 , 42 , 43 , 44 ]. The MSC method is used to correct the baseline translation and offset phenomena of spectral data by ideal spectra, which can effectively eliminate the scattering phenomena generated by uneven particle distribution and particle size, thus enhancing the correlation between spectra and data [ 45 ].…”

Section: Hyperspectral Information Analysis Methods For Tea Fresh Lea...mentioning

confidence: 99%

“…The results showed that HSI combined with the variable selection method can be used as a fast and accurate method to predict carotenoid content. Kang et al determined EC, EGC, ECG, and EGCG of catechins in green tea new shoots using hyperspectral imaging [ 40 ]. The PLSR model was used, and with few exceptions, hyperspectral reflectance explained more than 79% of each catechin in the new shoots.…”

Section: Application Of Spectroscopic Techniques In Tea Fresh Leaf Qu...mentioning

confidence: 99%

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Tang,

Luo,

Yang

et al. 2023

Foods

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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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“…These images are then used to create quantitative models of tea quality, nutrients, and pigments using statistical analysis, machine learning, or deep learning algorithms. For instance, some studies have used multivariate analysis with hyperspectral data to predict polyphenols [47] , nitrogen [72] , chlorophyll [31] , and plant behaviors under different management practices [73] . Other studies have utilized various imaging systems to evaluate tea quality components or diagnose tea plant stress.…”

Section: Tea Plant Growth Monitoringmentioning

confidence: 99%

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

Cao,

Zhao,

et al. 2024

International Journal of Agricultural and Biological Engineering

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The tea plant is a valuable and evergreen crop that is extensively cultivated in China and many other countries. Currently, there is growing research interest in this plant. For the tea industry, it is crucial to develop rapid and non-invasive methods to evaluate tea plants in their natural environment. This article provides a comprehensive overview of non-invasive sensing techniques used for in-situ detection of tea plants. The topics covered include leaf, canopy, and field-level assessments, as well as statistical analysis techniques and characteristics specific to the research. Non-invasive testing technology is primarily used for monitoring and predicting tea pests and diseases, monitoring quality, and nutrients, determining tenderness and grade, identifying tea plant varieties, automatically detecting, and identifying tea buds, monitoring tea plant growth, and extracting tea garden areas through remote sensing. It also helps to evaluate planting suitability, assess disasters, and estimate yields. Additionally, the article examines the challenges and prospects of emerging techniques aimed at resolving the in-situ detection problem for tea plants. It can assist researchers and producers in comprehensively understanding the tea environment, quality characteristics, and growth process, thereby enhancing tea production quality, and fostering tea industry development.

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Estimating the catechin concentrations of new shoots in green tea fields using ground-based hyperspectral imagery

Cited by 13 publications

References 35 publications

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

Review of the application of in-situ sensing techniques to address the tea growth characteristics from leaf to field

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