Evaluation of green tea sensory quality via process characteristics and image information

Comparison of Pivot Profile (PP), Rate-All-That-Apply (RATA), and Pivot-CATA for the sensory profiling of commercial Chinese tea products

Rapid sensory profiling methods relying on consumers' perceptions are getting prevalent and broadly utilized by labs and companies to supersede conventional sensory profiling methodologies. Till now, various intensity-based sensory methods such as the newly proposed Pivot-Check-All-That-Apply (CATA) are limitedly developed and compared. In this investigation, Pivot Profile (PP), Rate-All-That-Apply (RATA), and Pivot-CATA methods were applied and validated using tea consumers and commercial Chinese tea products as samples. Data from three approaches were collected, analyzed by correspondence analysis (CA), and used to compare the three methods assessing the panel assessment process, sensory maps, confidence ellipses, and practical applications. Pivot-CATA exhibited a high similarity with RATA (RV = 0.873), and a lower similarity with PP (RV = 0.629). Of the three intensity-related methods, confidence ellipses on the RATA sensory map were the smallest and overlapped the least. However, Pivot-CATA consumed less time in collecting data and its questionnaire was more friendly to participants compared with PP and made the difference in intensity of samples more noticeable to the participants than RATA due to the existence of the pivot sample. Its experimental versatility also allows for a wide range of applications, indicating that the Pivot-CATA is an approach with great promise for routine use.

Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level

Hitherto, the intelligent detection of black tea fermentation quality is still a thought-provoking problem because of one-side sample information and poor model performance. This study proposed a novel method for the prediction of major chemical components including total catechins, soluble sugar and caffeine using hyperspectral imaging technology and electrical properties. The multielement fusion information were used to establish quantitative prediction models. The performance of model using multielement fusion information was better than that of model using single information. Subsequently, the stacking combination model using fusion data combined with feature selection algorithms for evaluating the fermentation quality of black tea. Our proposed strategy achieved better performance than classical linear and nonlinear algorithms, with the correlation coefficient of the prediction set (R_p) for total catechins, soluble sugar and caffeine being 0.9978, 0.9973 and 0.9560, respectively. The results demonstrated that our proposed strategy could effectively evaluate the fermentation quality of black tea.

Tea authentication and determination of chemical constituents using digital image-based fingerprint signatures and chemometrics

Tea (Camellia sinensis) fraud has been frequently identified and involves tampering with the labelling of inferior products or without geographical origin certification and even mixing them with superior quality teas to mask an adulteration. Consequently, economic losses and health damage to consumers are observed. Thus, a Chemometrics-assisted Color Histogram-based Analytical System (CACHAS) was employed a simple, cost-effective, reliable, and green analytical tool to screen the quality of teas. Data-Driven Soft Independent Modeling of Class Analogy was used to authenticate their geographical origin and category simultaneously, recognizing correctly all Argentinean and Sri Lankan black teas and Argentinean green teas. For the determination of moisture, total polyphenols, and caffeine, Partial Least Squares obtained satisfactory predictive abilities, with values of root mean squared error of prediction (RMSEP) of 0.50, 0.788, and 0.25 mg kg^-1, r_pred of 0.81, 0.902, and 0.81, and relative error of prediction (REP) of 6.38, 9.031, and 14.58%., respectively. CACHAS proved to be a good alternative tool for environmentally-friendly non-destructive chemical analysis.

Identifying the “Dangshan” Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image

The “Dangshan” pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of “Dangshan” pears. This paper employs the merging of NIRS features and image features for the detection of “Dangshan” pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the “Dangshan” pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.

Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.

Exploring The Relative Astringency of Tea Catechins and Distinct Astringent Sensation of Catechins and Flavonol Glycosides via an In Vitro Assay Composed of Artificial Oil Bodies

Artificial oil bodies covered by a recombinant surface protein, caleosin fused with histatin 3 (a major human salivary peptide), were employed to explore the relative astringency of eight tea catechins. The results showed that gallate-type catechins were more astringent than non-gallate-type catechins, with an astringency order of epicatechin gallate > epigallocatechin gallate > gallocatechin gallate > catechin gallate > epigallocatechin > epicatechin > gallocatechin > catechin. As expected, the extension of brewing time led to an increase in catechin content in the tea infusion, thus elevating tea astringency. Detailed analysis showed that the enhanced proportion of gallate-type catechins was significantly higher than that of non-gallate-type catechins, indicating that tea astringency was elevated exponentially, rather than proportionally, when brewing time was extended. Rough surfaces were observed on artificial oil bodies when they were complexed with epigallocatechin gallate (a catechin), while a smooth surface was observed on those complexed with rutin (a flavonol glycoside) under an atomic force microscope and a scanning electron microscope. The results indicate that catechins and flavonol glycosides induce the sensation of rough (puckering) and smooth (velvety) astringency in tea, respectively.

Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors

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.

A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

Methylglyoxal-hydroimidazolones (MG-Hs) instead of Nɛ-(carboxymethyl)-l-lysine (CML) is the major advanced glycation end-product during drying process in black tea

This study was to examine the formation of advanced glycation end-products (AGEs) in black tea during drying process at 90, 120, and 150 °C for 1 h. Nine AGEs including Nɛ-(carboxyethyl)-l-lysine (CEL), Nɛ-(carboxymethyl)-l-lysine (CML), three isomers of methylglyoxal-hydroimidazolones (MG-Hs), three isomers of glyoxal-hydroimidazolones (GO-Hs), and argpyrimidine were quantified by using HPLC-MS/MS with isotope-labelled internal standard. Results showed that each AGE during the drying process of 150 °C was significantly higher (p < 0.05) than at 90 and 120 °C, and argpyrimidine was only found in the treatment of 150 °C. MG-H1/3 was first quantified as the major AGE during drying at 120-150 °C, the content respectively reached to (39.66 ± 2.61) μg/g and (58.88 ± 1.76) μg/g after 1 h drying, where CML content only had (19.86 ± 1.02) μg/g and (23.71 ± 1.40) μg/g. This study indicated that arginine derived-AGEs are the key components of black tea AGEs.

Applicability of multifunctional preprocessing device for simultaneous estimation of spreading of green tea, withering of black tea and shaking of oolong tea

BACKGROUND

Preprocessing technologies of fresh tea leaves have a great influence on tea quality. A multifunctional preprocessing device for tea raw materials has been designed and utilized as a novel item of equipment to synchronously meet the process needs of spreading of green tea, withering of black tea and shaking of oolong tea.

RESULTS

The preprocessing parameters of fresh tea leaves for spreading of green tea, withering of black tea and shaking of oolong tea were optimized by orthogonal experiments. Sensory assessment combined with statistical tools was employed as an analytical method to evaluate the pretreatment effect of processing different sorts of tea. The range analysis and variance analysis of tea sensory evaluation combined with chemical components (total polyphenols, free amino acids and soluble sugar) showed that A₃ B₂ C₃ (70%, 25 °C, 8 h), A₁ B₃ C₁ (60%, 28 °C, 18 h) and A₁ B₁ C₃ (5 min, 20 °C, discontinuous leaf turning c) were considered to be the optimum schemes for the best pretreatment conditions of the above three major processing types of tea, respectively. The verification experiment of the proposed schemes was performed with satisfactory performance.

CONCLUSION

This study demonstrated that a multifunctional preprocessing device for fresh tea leaves can be successfully applied to simultaneously estimate spreading of green tea, withering of black tea and shaking of oolong tea. © 2019 Society of Chemical Industry.

Effects of withering on the main physical properties of withered tea leaves and the sensory quality of congou black tea

To explore the relationship between the moisture content of withered tea leaves and their physical properties (i.e., elasticity, plasticity, flexibility, and texture) during withering, texture analyzer was employed to test the elasticity and flexibility of withered tea leaves with different moisture contents. The texture was evaluated by computer vision technology. The withered tea leaves with different moisture contents were used to process congou black tea, which was then subjected to sensory evaluation. Results showed that good elasticity, optimal flexibility, and plasticity were achieved when the moisture content of the withered tea leaves of Fudingdabai comprising two leaves and one bud varied arranging from 65.51 to 61.48%. The sensory evaluation of congou black tea revealed that moderate withering was better than long-term withering and that both moderate and long-term withering were better than no withering during processing. The moisture content was significantly correlated with the flexibility and plasticity of the withered tea leaves. Fresh tea leaves undergoing moderate withering with moisture content of 65.51-61.48% to process congou black tea, good tea shape and liquor color were achieved. This study provided new evidence that the moisture content of withered tea leaves significantly affected the quality of black tea.

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

A Comprehensive Insight on the Health Benefits and Phytoconstituents of Camellia sinensis and Recent Approaches for Its Quality Control

Tea, Camellia sinensis, which belongs to the family Theaceae, is a shrub or evergreen tree up to 16 m in height. Green tea is very popular because of its marked health benefits comprising its anticancer, anti-oxidant, and antimicrobial activities, as well as its effectiveness in reducing body weight. Additionally, it was recognized by Chinese people as an effective traditional drink required for the prophylaxis against many health ailments. This is due to the complex chemical composition of green tea, which comprises different classes of chemical compounds, such as polyphenols, alkaloids, proteins, minerals, vitamins, amino acids, and others. The beneficial health effects of green tea ultimately led to its great consumption and increase its liability to be adulterated by either low-quality or non-green tea products with concomitant decrease in activity. Thus, in this review, green tea was selected to highlight its health benefits and phytoconstituents, as well as recent approaches for its quality-control monitoring that guarantee its incorporation in many pharmaceutical industries. More research is needed to find out other more biological activities, active constituents, and other simple and cheap techniques for its quality assurance that ascertain the prevention of its adulteration.

Characterization of Volatile Compounds and Sensory Analysis of Jasmine Scented Black Tea Produced by Different Scenting Processes

Jasmine scented black tea is widely popular in China and the scenting process is the most crucial issue affecting its aroma quality. This study aimed to analyze the influence of different processes on aroma quality and the specific contribution of volatiles to aroma quality. To achieve it, the headspace solid-phase microextraction method combined with gas-chromatography mass-spectrometry was used to identify and quantify the volatile compounds, and further collected the characteristics according to odor activity values or contents. The quantitative descriptive analysis (QDA) and traditional sensory analysis (TSA) were used to evaluate its aroma quality and the correlation analysis between QDA and characteristic aroma compounds was carry out to analyze the specific contribution of characteristics to sensory quality. The GC/MS results showed that 130 aroma components and 20 characteristics were identified in 6 tea samples and more new volatile components appeared and less original volatile components disappeared in traditional scenting process (TSP) compared with continuous scenting process (CSP). The QDA and TSA results showed that TSP samples had better "overall" and "jasmine" than CSP samples. The correlation analysis between QDA and characteristics showed that nerolidol and methylbenzoate were related to "overall," linalool and methyl anthranilate to "jasmine," and decanal and benzeneacetaldehyde to "sweet floral," "roasted," and "fermented." This study suggested that TSP is a favorable scenting process for black tea, and QDA is an objective and digital sensory evaluation method for jasmine scented black tea. PRACTICAL APPLICATION: Compare the effect of scenting technology on the aroma quality of jasmine scented black tea based on sensory analysis and chemical analysis, and suggested a favorable scenting process for processing jasmine scented black tea with better qualities. Explore the correlation between aroma composition and specific aroma properties to provide some useful information about guiding the scenting process to acquire a product with target aroma qualities. And the application of QDA for evaluation of jasmine scented black tea was better for consumers to understand the quality attributes of product.

Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea

Fourier transform near-infrared spectroscopy (FT-NIR) coupled to chemometric algorithms such as back propagation (BP)-AdaBoost and synergy interval partial least square (Si-PLS) were deployed for the rapid prediction taste quality and taste-related components in black tea. Eight main taste-related components were determined via chemical analysis and Pearson correlations. The achieved chemical results of the eight taste-related components in black tea infusion were predicted based on 160 tea samples obtained from different countries. Prediction results revealed BP-AdaBoost models gave superior predictions, with all the correlation coefficients of the prediction set (Rp) > 0.76, and the root mean square error values of the prediction set (RMSEP) < 1.7% compared with Si-PLS models (0.71 ≤ Rp ≤ 0.94, 0.08% ≤ RMSEP ≤ 1.73%). This implies that FT-NIR combined to BP-AdaBoostis capable of being deployed for the rapid evaluation of black tea taste quality and taste-related components content simultaneously.

Sensory quality evaluation for appearance of needle-shaped green tea based on computer vision and nonlinear tools

Tea is one of the three greatest beverages in the world. In China, green tea has the largest consumption, and needle-shaped green tea, such as Maofeng tea and Sparrow Tongue tea, accounts for more than 40% of green tea (Zhu et al., 2017). The appearance of green tea is one of the important indexes during the evaluation of green tea quality. Especially in market transactions, the price of tea is usually determined by its appearance (Zhou et al., 2012). Human sensory evaluation is usually conducted by experts, and is also easily affected by various factors such as light, experience, psychological and visual factors. In the meantime, people may distinguish the slight differences between similar colors or textures, but the specific levels of the tea are hard to determine (Chen et al., 2008). As human description of color and texture is qualitative, it is hard to evaluate the sensory quality accurately, in a standard manner, and objectively. Color is an important visual property of a computer image (Xie et al., 2014; Khulal et al., 2016); texture is a visual performance of image grayscale and color changing with spatial positions, which can be used to describe the roughness and directivity of the surface of an object (Sanaeifar et al., 2016). There are already researchers who have used computer visual image technologies to identify the varieties, levels, and origins of tea (Chen et al., 2008; Xie et al., 2014; Zhu et al., 2017). Most of their research targets are crush, tear, and curl (CTC) red (green) broken tea, curly green tea (Bilochun tea), and flat-typed green tea (West Lake Dragon-well green tea) as the information sources. However, the target of the above research is to establish a qualitative evaluation method on tea quality (Fu et al., 2013). There is little literature on the sensory evaluation of the appearance quality of needle-shaped green tea, especially research on a quantitative evaluation model (Zhou et al., 2012; Zhu et al., 2017).