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BACKGROUND Tea waste is the residue that remains after tea leaves have been extracted by hot water to obtain water-soluble components. The waste contains a re-usable energy substrate and nutrients which may pollute the environment if they are not dealt with appropriately. Other agricultural wastes have been widely studied as substrates for cultivating mushrooms. In the present study, we cultivated oyster mushroom using tea waste as substrate. To study the feasibility of re-using it, tea waste was added to the substrate at different ratios in different experimental groups. Three mushroom strains (39, 71 and YOU) were compared and evaluated. Mycelia growth rate, yield, biological efficiency and growth duration were measured. RESULTS Substrates with different tea waste ratios showed different growth and yield performance. The substrate containing 40-60% of tea waste resulted in the highest yield. CONCLUSION Tea waste could be used as an effective and economic substrate for oyster mushroom cultivation. This study also provided a useful way of dealing with massive amounts of tea waste. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Caffeine (1, 3, 7-trimethylxanthine) and theobromine (3, 7-dimethylxanthine) are the major purine alkaloids in plants, e.g. tea (Camellia sinensis) and coffee (Coffea arabica). Caffeine is a major component of coffee and is used widely in food and beverage industries. Most of the enzymes involved in the caffeine biosynthetic pathway have been reported previously. Here, we demonstrated the biosynthesis of caffeine (0.38 mg/L) by co-expression of Coffea arabica xanthosine methyltransferase (CaXMT) and Camellia sinensis caffeine synthase (TCS) in Saccharomyces cerevisiae. Furthermore, we endeavored to develop this production platform for making other purine-based alkaloids. To increase the catalytic activity of TCS in an effort to increase theobromine production, we identified four amino acid residues based on structural analyses of 3D-model of TCS. Two TCS1 mutants (Val317Met and Phe217Trp) slightly increased in theobromine accumulation and simultaneously decreased in caffeine production. The application and further optimization of this biosynthetic platform are discussed. [ABSTRACT FROM AUTHOR]

The effect of ultrasonic treatment was evaluated to develop an efficient preparation method of cellulose with controllable molecular weight (Mw) and preferable functional properties. Our results showed that the physical approach could prominently reduce the mean Mw of cellulose from 68.07 to 6.82 KDa via 30‐hr ultrasonic treatment. The morphology and structure of the samples were characterized using Fourier transform infrared spectroscopy, X‐ray diffraction, scanning electron microscope (SEM), and laser diffraction, indicating a slight change in structure and notable modification in morphology caused by ultrasonic treatment. Interestingly, the ultrasonic‐treated cellulose exhibited much better physicochemical properties. For example, with a 30‐hr ultrasonic treatment, the cholesterol‐binding capacities of cellulose were increased about 2.42‐fold at pH 7.0 and 1.96‐fold at pH 2.0. The findings demonstrated that cellulose treated by ultrasonic processing could have a great potential for applications in the biomedical and food industries. Practical applications: As a major source of insoluble fiber, cellulose has already been recommended for its possible benefits to human health, leading to numerous efforts for its applications in food industry and biomedical engineering. In this work, we successfully controlled Wm of cellulose via ultrasonic treatment, and the ultrasonic‐treated cellulose exhibited improved functional and physicochemical properties. The positive effect of ultrasound may be useful for functional modification and utilization of cellulose. [ABSTRACT FROM AUTHOR]

• Estimation of Congou black tea quality by an artificial lipid membrane taste sensor. • Optimizing taste features using ant colony optimization. • Multivariate analyses of quality categories and taste characteristics are performed. • The ACO-LS-SVM model achieves the desired predictive performance. The taste attribute is an important indicator to estimate the quality and rank of black tea. It is crucial to prevent fraud and avoid economic losses. This study creatively proposed the assessment of Congou black tea quality based on an electronic tongue system combined with the ant colony optimization (ACO) algorithm and stoichiometry. Firstly, the electronic tongue could effectively present the taste quality of black tea, capturing characteristic potential signals from 700 samples of seven different qualities and converting the signals into nine relative characteristic taste values. Then, the taste indicators from different artificial lipid membrane sensors were optimized using the ACO method. Finally, the discriminant models based on the extreme learning machine, support vector machine, partial least-squares discriminant analysis, and least squares-support vector machine (LS-SVM) were developed using the optimized taste characteristics for the assessment of black tea quality. Results indicated that the LS-SVM model, which was created using the five taste features, could obtain better predictive outcomes based on the generalization performance of the model. In the prediction set, the correct discriminant rate was 99.14%. The overall results demonstrate that the electronic tongue sensor array has potential application prospects for the assessment of Congou black tea products in the actual production process. [ABSTRACT FROM AUTHOR]

• Smartphone-based micro NIRS was used to collect spectrum of green tea samples. • A multilayer algorithmic model was designed to explain the obtained spectral data. • Simplified models were established by using three wavelength selection algorithms. • A low-cost and in-situ method was proposed to detect adulteration in green tea. Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea. [ABSTRACT FROM AUTHOR]

• We firstly used CDs to identify eight grades of Dianhong black tea. • We firstly increased recognition rate by adding cobalt ions. • All materials we used are laboratory-synthesized, and operation is convenient. We established a novel Dianhong black tea grades discriminant analytic technique based on a fluorescence image along with carbon quantum dots (CDs) as fluorescent probes. Different grades of Dianhong black tea contain different various amounts of tea polyphenols. Tea polyphenols can quench the fluorescent intensity of CDs, resulting in different fluorescent peaks; Dianhong black tea grades can then be discriminated through the use of principal component analysis and Bayesian analysis. Compared with the additional data processing required in other methods, the advantage of our method is that the fluorescence curve can be used directly, and it achieves satisfactory results. We firstly used CDs combined with chemometrics to identify eight grades of Dianhong black tea, and we also provide a new method that improves the identification rate using nanotechnology to avoid performing complex data processing. [ABSTRACT FROM AUTHOR]

Fermentation is a key black tea processing step and makes an important contribution to quality formation. Current approaches to fermentation monitoring are costly or laboratory-based. Here, we first evaluated the potential of at-line computer vision for detecting fermentation quality in a tea factory. A self-built industrial camera was used to collect tea samples at various fermentation durations. The correlations of color variables that were extracted from the images with key quality indicators in the tea samples were verified. Subsequently, partial least-squares regression models based on the color variables showed high prediction accuracy with residual prediction deviation values of 4.13, 3.53, and 3.39 for catechins, theaflavins and chlorophylls, respectively. Finally, the spatial and temporal distributions of indicators during fermentation were mapped to visualize the fermentation quality. This study realized low-cost, at-line and real-time detection for black tea fermentation, which provides technical support for the industrial and intelligent production of black tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV-visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3'-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Withering is one of the most critical steps in the processing of black tea. The degree of withering affects the aroma quality of the finished tea. In this study, we used a pH indicator-based colorimetric sensor array in combination with hyperspectral imaging to intelligently evaluate the withering degree. After analyzing the difference between images taken before and after the reaction of pH indicators with withered leaves, six pH indicators were selected to build a sensor array. Then, the hyperspectral image of each pH indicator was obtained at wavelengths between 400 and 1000 nm. Nonlinear support vector machine (SVM) and least-squares (LS) SVM models were established to determine the degree of withering. Results revealed that the spectral information from single pH indicator failed to accurately evaluate the withering degree. The LS-SVM model achieved satisfactory discriminant results with the low-level data fusion of six pH indicators followed by principal component analysis for dimensionality reduction. The optimal model yielded accuracies of 93.75% and 90.00% for the calibration and prediction sets, respectively. The results indicated that colorimetric sensor array in combination with hyperspectral imaging can effectively determine the withering degree, thus providing a novel method for the intelligent processing of food and tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In this study, a turn-on nanosensor for detecting Hg 2+ was developed based on the fluorescence resonance energy transfer (FRET) between long-strand aptamers-functionalized upconversion nanoparticles (UCNPs) and short-strand aptamers-functionalized gold nanoparticles (GNPs). In the absence of Hg 2+ , FRET between UCNPs and GNPs occurred because of the specific matching between two aptamers, resulting in the fluorescence quenching of UCNPs. In the presence of Hg 2+ , long-stranded aptamers fold back into a hairpin structure due to the stable binding interactions between Hg 2+ and thymine, leading to the release of GNPs from UCNPs, resulting in the quenched fluorescence restoration. Under the optimized conditions, the nanosensor achieved a linear detection range of 0.2-20 μM and a low detection limit (LOD) of 60 nM. Meanwhile, it showed good selectivity and has been applied to detecting Hg 2+ in tap water and milk samples with good precision.