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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: 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]

Tea quality is often evaluated by experienced tea tasters; however, their assessments are subjective, being influenced by their individual physiological and psychological condition. Herein, we fabricated a colorimetric sensor array-based artificial olfactory system for sensing the quality of Chinese green tea. First, the colorimetric sensors array was man-made using printing 12 chemically responsive dyes (9 porphyrins, metalloporphyrins and 3 pH indicators) on silica-gel flat plate. The plate was exposed to volatile organic compounds, and the colour changes in each sample were obtained by distinguishing between the images of sensor array before and after contact with tea sample. The values of colour composition changes were extracted from the dyes’ colour sections. Multivariate calibrations were applied through principal component analysis and back propagation artificial neural network (BP-ANN) for modelling. The optimum BP-ANN model was obtained with nine principal components, and the discrimination rate was equal to 85% and 86% in the calibration and prediction sets, respectively. We thus conclude that the low cost colorimetric sensor array-based artificial olfactory technique has great potential for application in intelligent evaluation of the quality of green tea. [ABSTRACT FROM AUTHOR]

Fixation is the key step of green tea processing, usually with moisture content as the quality evaluation indicator. Near-infrared (NIR) spectroscopy has been widely used in tea moisture detection but is mostly realized in the laboratory, and online detection in tea processing is limited. This study explores the possibility of calibration transfer from lab-based hyperspectral imaging (HSI) to online NIR to enable the rapid detection of moisture during the fixation step. Raw spectral data were preprocessed, and direct standardization (DS) and partial least-squares regression (PLS) were used for data calibration and modeling, respectively. The results showed that there were observable differences in the spectral data of the same sample obtained by different instruments. The prediction model based on HSI failed to be transferred directly to the online NIR and has a large root mean square error of prediction (RMSEP) value. Spectral preprocessing with a standard normal variate (SNV) transformation reduced the differences between the instruments. When 40 standard samples were used, the established transferable SNV-DS-PLS model based on HSI achieved the optimal prediction performance with a correlation coefficient of prediction (Rp), RMSEP, and ratio of prediction to deviation (RPD) of 0.94, 2.24%, and 2.76, respectively, for the 50 validated tea samples obtained from online NIR. In conclusion, the model transfer across instruments was successfully realized, and tea moisture detection was brought from the laboratory to the tea factory, which provided a reference model for online moisture detection for other agricultural products. • Calibration transfer from HSI to online NIR for tea processing was optimized. • Online detection of moisture during green tea fixation was achieved. • Model built based on HSI fails to be transferred directly to the online NIR. • Transferable SNV-DS-PLS model gave the optimal performance for moisture. [ABSTRACT FROM AUTHOR]

Abstract: Theanine, an amino acid contained in green tea, is known to possess many pharmacological functions. In this paper, we investigated the absorption of theanine in the human intestinal epithelium, using a Caco-2 monolayer model. Different concentrations of either pure theanine or green tea extracts were administered to Caco-2 cells. The theanine content in the samples was analysed by high-performance liquid chromatography, coupled with fluorescence detection. Cell permeation was also measured. The data revealed that the transport of pure theanine occurred in a manner consistent with passive diffusion. Surprisingly, pure theanine showed good absorption, whereas theanine in the green tea extract was poorly absorbed in the Caco-2 cell model. Furthermore, the transport of theanine in green tea extract in the basolateral (BL) to apical (AP) direction was much greater than that in the AP–BL direction, suggesting that green tea components profoundly affect the trans-epithelial transport of theanine in this Caco-2 cell model. [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]

Tea storage period is difficult to be assessed by traditional sensory and chemical analysis methods. In this research, hyperspectral imaging (HSI) covering the spectral range of 400–1000 nm was combined with chemometric methods to predict the storage period of green tea samples. Tea samples were incubated under two storage temperatures of 4 °C (Sample set 1) and 25 °C (Sample set 2) for 360 days. The reflectance spectra were acquired every 90 days. Based on the preprocessed full spectra, partial least squares-discriminant analysis (PLS-DA)- and support vector machine (SVM)-based models had acceptable outputs for the two sample sets with an accuracy of over 90%. In addition, regression coefficients (RCs) of PLS-based models, second derivative (2-Der), and successive projections algorithm (SPA) were applied to select the optimal wavelengths. Satisfactory results were achieved by SVM-based models on the basis of optimal wavelengths. The SPA-SVM- and 2-Der-SVM-based models had the best performance to discriminate the storage period of the two sample sets with the prediction accuracies of 98% and 98%, respectively. The results demonstrated that HSI could be used to evaluate the storage period of green tea, providing an alternative for a real-time monitoring of tea quality. [ABSTRACT FROM AUTHOR]

Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (−)-epigallocatechin, (−)-epigallocatechin 3-gallate, (−)-epicatechin, and (−)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea. Unlabelled Image • Established a rapid and nondestructive testing method for instant green tea • Evaluated feasibility of the portable NIR spectrometer compared to benchtop instruments • Optimized modeling process by applying spectral pretreatment and variable selection • Provided a novel method for the mass production and quality control of instant green tea [ABSTRACT FROM AUTHOR]

A simple, rapid and low-cost analytical method was employed for simultaneous determination of bioactive constituents and antioxidant capability of green tea. The strategy was based on swarm intelligence algorithms with partial least squares (PLS) such as simulated annealing PLS (SA-PLS), ant colony optimization PLS (ACO-PLS), genetic algorithm PLS (GA-PLS), and synergy interval PLS (Si-PLS) coupled with Near-infrared (NIR) spectroscopy. These algorithms were independently applied to select informative spectral variables and improve the prediction of green tea components. Results showed that NIR combined with SA-PLS and Si-PLS had a strong correlation coefficient with the wet-chemical methods for predicting epigallocatechin gallate (R p 2 = 0.97); epigallocatechin (R p 2 = 0.97); epicatechin gallate (R p 2 = 0.96); epicatechin (R p 2 = 0.91); catechin (R p 2 = 0.98); caffeine (R p 2 = 0.96); theanine (R p 2 = 0.93); and antioxidant capability (R p 2 = 0.80) in green tea. Our results revealed the potential utilization of NIR spectroscopy coupled with SA-PLS and Si-PLS algorithms as an effective and robust technique to simultaneously predict active constituents and antioxidant capability of green tea. • Swarm intelligence algorithms were applied to predict green tea constituents. • Tea bioactive compounds and antioxidant capability models were improved. • PLS, enhanced by Si, GA, SA and ACO proved feasible to quantify tea constituents. • NIR coupled with SA-PLS and Si-PLS exhibited best predictive ability. [ABSTRACT FROM AUTHOR]

From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R p) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea. Unlabelled Image • Highly identification of keemun black tea rank based on cognitive spectroscopy. • Feature information were selected by GA, SPA, CARS and SFLA. • Cognitive models based on keemun black tea grades were developed using LSSVM, BPNN and RF. • The SFLA approach achieved steady predictive results. • The CARS-LSSVM model with the best predictive performance was proposed. [ABSTRACT FROM AUTHOR]