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Summary: Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank. [ABSTRACT FROM AUTHOR]

BACKGROUND Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS: Two matrix statistical algorithms encompassing a gray‐level co‐occurrence matrix (GLCM) and a gradient co‐occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near‐infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION: This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND: Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms. RESULTS: A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second‐derivative (2‐Der), respectively. The optimal 2‐Der‐PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (RP) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g‐1, respectively. CONCLUSION: The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND: The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information. RESULTS: To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K‐nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible‐near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%. CONCLUSION: Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Summary: The effects of green tea powder (GTP) on the properties of hard red winter (HRW) flour and Chinese steamed bread were investigated. GTP was blended with HRW flour at levels of 0.00, 1.00, 2.00, 3.00 and 4.00 g GTP/100 g wheat flour. With the addition of GTP, the maximum torque of flour became stronger. The resilience and degree of green colour of steamed bread increased, while hardness and chewiness decreased, with the addition of GTP. The addition of 1.00% GTP did not significantly affect the specific volume, but remarkably influenced the hardness and resilience of steamed bread. In steamed bread formulated with GTP, the antioxidant activity was 0.84 mm TE per g at 1.00% of GTP and increased with GTP levels. The steamed bread had a pleasant flavour, and the degree of overall liking of steamed bread increased with GTP levels to the maximum used in this work. Green tea powder increased the antioxidant activity of steamed bread, and did not affect the quality of steamed bread with a pleasant tea flavour. [ABSTRACT FROM AUTHOR]

BACKGROUND Brick tea usually contains very high fluoride, which may affect human health. Biosorbents have received much attention for selective removal of fluoride because of low cost, environmental friendliness, and relative safeness. RESULTS In the present study, a highly selective fluoride tea waste based biosorbent, namely, aluminum (Al) oxide decorated tea waste (Tea-Al), was successfully prepared. The Tea-Al biosorbent was characterized by energy-dispersive spectrometry, Fourier transform infrared spectroscopy, powder X-ray diffraction and X-ray photoelectron spectroscopic analysis. The Tea-Al sample exhibited remarkably selective adsorption for fluoride (52.90%), but a weaker adsorption for other major constituents of brick tea infusion, such as catechins, polyphenols and caffeine, under the same conditions. Fluoride adsorption by Tea-Al for different times obeyed the surface reaction and adsorption isotherms fit the Freundlich model. In addition, the fluoride adsorption mechanism appeared to be an ion exchange between hydroxyl and fluoride ions. CONCLUSION Results from this study demonstrated that Tea-Al is a promising biosorbent useful for the removal of fluoride in brick tea infusion. © 2016 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

A total 206 samples of green, yellow, white, black, and Oolong teas were utilized to acquire hyperspectral imaging, and five tea categories were identified based on visible and near-infrared (NIR) hyperspectral imaging, combined with classification pattern recognition. The characteristic spectra were extracted from the region of interest (ROI), and the standard normal variate (SNV) method was preprocessed to reduce background noise. Four dominant wavelengths (589, 635, 670, and 783 nm) were selected by principal component analysis (PCA) as spectral features. Textural features were extracted by the Grey-level co-occurrence matrix (GLCM) from images at selected dominant wavelengths. Linear discriminant analysis (LDA), library support vector machine (Lib-SVM), and extreme learning machine (ELM) classification models were established based on full spectra, spectral features, textural features, and data fusion, respectively. Lib-SVM was the best model with the input data fusion, and the correct classification rate (CCR) achieved 98.39%. The results implied that visible and NIR hyperspectral imaging combined with Lib-SVM has the capability of rapidly and non-destructively classifying tea categories. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Changes in aroma characteristic of Keemun black tea during ripening were analyzed. • Fatty acid degradation products cause the rich aroma of Keemun Congou black tea. • The heat degradation pattern of major fatty acids in the tea was identified. Ripening refers to the process of chemical change during the refinement of Keemun black tea (KBT) and is crucial in the formation of Keemun Congou black tea's quality. In this study, the aroma composition of KBT during the ripening was analyzed. Sensomics indicated that ripening strengthened the coconut and fatty aroma of KBT and contributed to the decrease of green aroma substances, resulting in a shift of the overall aroma type of KBT to an integrated aroma profile, which was consistent with sensory evaluation. Changes in fatty acid content and the results of in vitro addition simulation tests confirmed that heat causes highly degradation of fatty acids into fatty aroma volatiles, which is a key driver of the formation of "Keemun aroma" quality. This study revealed the mechanism behind the formation of KBT's integrated "Keemun aroma" quality and the mode of thermal degradation of major fatty acids. [ABSTRACT FROM AUTHOR]

Roasting is a key process in the production of large-leaf yellow tea (LYT) and substantially affects the formation of its aroma. In order to investigate the effect of roasting method on the aroma of large-leaf yellow tea and to identify the potential causes. In this study, guided by molecular sensory science, the aroma components of charcoal-roasted, electric-roasted and drum-roasted LYTs were analyzed using headspace solid phase microextraction and solvent-assisted flavor evaporation in conjunction with gas chromatography-mass spectrometry, and the data were visualized and analyzed using SMICA, SPSS and Origin. The results of quantitative descriptive analysis revealed that rice crust, burnt, corn and floral aromas were significantly different between the three samples. The results of aroma extract dilution and odor activity value analyses revealed that 2,4,5-trimethyloxazole, 1-ethylpyrrole-2-carboxaldehyde, 2-ethyl-3,5-dimethylpyrazine, 3-ethyl-2,5-dimethylpyrazine, linalool, 2,3-diethyl-5-methylpyrazine, 3,5-diethyl-2-methylpyrazine, β -damascenone, and (E)- β -ionone were the main compounds responsible for differences in the aroma of LYT subjected to different roasting methods. Aroma recombination and addition experiments verified these findings. All in all, charcoal roasting enhances the rice crust and burnt aromas of LYT, and drum roasting facilitates the presentation of floral and corn aromas. This study provides a theoretical basis for the processing of high-quality LYT. [Display omitted] • Roasting methods affect the aroma quality of LYT. • CR can significantly enhance the rice crust aroma of LYT. • CR and ER contain 2.9 × and 3.1 × more volatiles than DR. • CR and DR have the highest and lowest heterocyclic compounds. • 9 compounds identified as key differential aroma-active compounds. [ABSTRACT FROM AUTHOR]

Fixation is a crucial step in the processing of Anjibai tea. In this study, gas chromatography tandem with mass spectrometry was used to analyze the aroma compositions of Anjibai tea across three types of fixation methods: roller-electrical fixation (REF), carding machine fixation (CMF), and roller-hot air fixation (RHF). Nine key volatile compounds were found responsible for Anjibai tea's aroma variations. Results from quantitative descriptive analyses, along with aroma recombination and omission tests, showed that linalool and geraniol significantly contribute to the formation of the medium flowery, fruity, and honey aromas in the REF samples; hexanal has a significant impact on the green aroma in the CMF samples; and 2-ethyl-3,6-dimethylpyrazine is an important aroma compound for the strong roasty aroma in the RHF samples. These findings provide an important theoretical foundation for improving the aroma quality of Anjibai tea and selecting the optimal fixation method. • Fixation method affects the aroma quality of Anjibai tea. • Nerolidol and geraniol are the most active aroma compounds in Anjibai tea. • Roller-electrical fixation significantly enhanced the flowery and honey aroma of Anjibai tea. [ABSTRACT FROM AUTHOR]