Rapid prediction of yellow tea free amino acids with hyperspectral images

Bao-hua, Yang; Gao, Yuan; Li, Hongmin; Ye, Shengbo; He, Hongqing; Xie, Shenru

doi:10.1371/journal.pone.0210084

Cited by 12 publications

(9 citation statements)

References 42 publications

(38 reference statements)

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“…To improve the stability of the model, a total of 457 bands of 944-1688 nm were selected for further analysis. Successive projections algorithm (SPA) [40] was used to extract 1106 and 1375 nm as feature wavelengths for hyperspectral imaging. Time domain and frequency domain features were further extracted for hyperspectral images corresponding to feature wavelengths.…”

Section: Feature Extraction and Feature Selection From Hsimentioning

confidence: 99%

“…Therefore, it is necessary to study the spatial features of tea to make up for the lack of information. To date, hyperspectral imaging technology has been used to improve the evaluation of tea components, including polyphenols [37][38][39], amino acid [40], and catechins [41], due to the advantages of simultaneous acquisition of spatial image information and spectral information of the analyte. However, it remains unclear whether it is possible to improve the estimation model of the polyphenol content of cross-category tea based on the fusion features of different sensors.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Yang

Wang

et al. 2019

Sensors

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Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea.

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Section: Feature Extraction and Feature Selection From Hsimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Yang

Wang

et al. 2019

Sensors

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“…The hyperspectral image acquisition system used in this experiment that included a spectral imager ( Imspector V17E, Spectral Imaging Ltd., Oulu, Finland) and a camera as a CCD camera (IPX-2M30, Imperx Inc., Boca Raton, FL, USA), two 150W halogen lamps (3900, Illumination Technologies Inc., New York, USA), one data acquisition black box, reflective linear tube and electronically controlled displacement platform (MTS120, Beijing Optical Instrument Factory, China), Image acquisition and analysis software (Spectral Image Software, Isuzu Optics Corp., Taiwan, China). The four tungsten halogen lamps of reflected light source were evenly distributed on the ring bracket in the dark box, and the light source is irradiated in a direction of 45 • with respect to the vertical direction [24]. HIS is shown in Figure 2.…”

Section: B Hyperspectral Imaging Systemmentioning

confidence: 99%

“…At present, feature extraction has been the premise and basis for hyperspectral data processing [13]. As another feature of the hyperspectral image, the texture was often used in the tea field because of its rotational invariance and resistance to noise, reflecting the visual characteristics of homomorphism in the image [24]. In particular, the grayscale co-occurrence matrix was used to study the spatial correlation properties of gray scale.…”

Section: Introductionmentioning

confidence: 99%

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A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion

et al. 2019

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Polyphenols are one of the important ingredients determining the quality of tea, which play an important role in affecting tea quality standards and quality control. At present, NIR spectroscopy technology has been widely used in tea quality detection to achieve good results. However, due to the lack of spatial features, it is difficult to meet the accuracy requirements to comprehensively judge the quality of both inside and outside of tea. With the development of hyperspectral imaging system (HIS), although progress has been made in the estimation of tea texture based on gray level co-occurrence matrix (GLCM) extraction, there are still some obstacles in practical applications. When the resolution is low, the texture features of the image are not significantly different, and a small number of features cannot more fully represent the image features, resulting in lower model accuracy. When the resolution is higher, the increase in the dimension of the feature leads to a more complex model. Therefore, it is necessary to extract multi-scale features from the hyperspectral image while preserving the original information of the hyperspectral image. In this study, a multi-feature fusion method is proposed to improve the accuracy of tea polyphenols content estimation. The process includes multi-scale wavelet decomposition, and feature fusion based on wavelet coefficient features, GLCM texture features and wavelet texture features. Experiments were carried out on five kinds of yellow tea by comparing different models based on different features, including partial least squares regression (PLSR) and support vector regression (SVR). Results indicated that the model based on multi-feature fusion is more accurate than the individual indicators, and models based on SVR perform better than PLSR.

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Research progress on the application of hyperspectral imaging techniques in tea science

Liang

Zhou

Ling

et al. 2023

Journal of Chemometrics

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Hyperspectral imaging technology combines two‐dimensional imaging and spectral technology, which can simultaneously obtain spatial and spectral information of the object to be measured and is an advanced technical method. With the development of science and technology, the detection of tea has also been continuously improved, and it has developed in the direction of being nondestructive, fast, real‐time, and accurate. In this paper, the principle of hyperspectral imaging technology is introduced, and according to research on hyperspectral imaging technology in the nondestructive rapid detection of tea in the past 5 years, the application of hyperspectral imaging technology in the detection of tea biochemical components, accurate classification, determination of mildew degree, and stress monitoring and the application progress in planting production management are analyzed. Additionally, the main challenges existing in the current research are analyzed, and future application prospects are proposed to provide a reference for the application and promotion of hyperspectral imaging technology in the actual production of tea.

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Rapid prediction of yellow tea free amino acids with hyperspectral images

Cited by 12 publications

References 42 publications

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery

A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion

Research progress on the application of hyperspectral imaging techniques in tea science

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