Estimation of theaflavin content in black tea using electronic tongue

New insights into the effect of fermentation temperature and duration on catechins conversion and formation of tea pigments and theasinensins in black tea

BACKGROUND

The phenol oxidative pathway during fermentation remains unclear. To elucidate the effect of fermentation on phenol conversion, we investigated the effects of fermentation temperature and duration on the conversion of catechins and the formation of theasinensins (TSs), theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs).

RESULTS

During fermentation, TSs formation increased initially and then decreased. Long fermentation durations were unfavorable for liquor brightness (LB) and resulted in the production of large amounts of TRs and TBs. Low fermentation temperatures (20 °C and 25 °C) favored the maintenance of polyphenol oxidase activity and the continuous formation of TFs, TSs, and TRSI (a TRs fraction), resulting in better LB and liquor color. Higher temperatures (30 °C, 35 °C, and 40 °C) resulted in higher peroxidase activity, higher oxidative depletion rates of catechins, and excessive production of TRSII (a TRs fraction) and TBs. Analysis of the conversion pathway of polyphenolic compounds during fermentation showed that, during early fermentation, large amounts of catechins were oxidized and converted to TFs and theasinensin B. As fermentation progressed, considerable amounts of theaflavin-3'-gallate, theasinensin A, theaflavin-3-gallate, theaflavin-3,3'-digallate, and theasinensin C were produced and then converted to TRSI; in the final stage, TRSII and TBs were converted continuously.

CONCLUSION

Different fermentation temperature and duration combinations directly affected the type and composition of phenolic compounds. The key conditions for controlling phenolic compound conversion and fermentation direction were 60 or 90 min and 25 or 30 °C. Our study provides insights into the regulation of phenolic compound conversion during black tea fermentation. © 2021 Society of Chemical Industry.

Optimization of the factors affecting BT-2 black tea fermentation by observing their combined effects on the quality parameters of made tea using Response Surface Methodology (RSM)

This research work aimed to optimize the fermentation time, temperature, and relative humidity of the black tea produced from Bangladesh Tea 2 (BT-2) variety by observing their quality parameters. Total theaflavin (TF), thearubigin (TR), the ratio of TF: TR, total liquor color (TLC), high polymeric substances (HPS), and total phenolic content (TPC) were evaluated for quality measurements of BT-2 black tea. Response Surface Methodology (RSM) with Box-Behnken design (BBD) was applied to optimize fermentation time, temperature, and relative humidity as well as evaluate the effects of optimized conditions on the quality of made tea. The results obtained from the response surface optimization affirmed that under the optimum conditions of time (80.14 min), temperature (28.76 °C), and relative humidity (92.30%), the model showed the value of TF (0.69%), TR (5.57%), HPS (8.61%), TLC (3.05%), and TPC (7.95 GAE g/100g tea). Moreover, the optimized model found that the TF:TR value was 1:9.13, which is close to black tea's optimum quality. The values observed in experiments were highly congruent with the predicted value by the regression model. The Analysis of Variance (ANOVA) test revealed that the model was significant for TF, TR, HPS, TLC, TPC, and TF:TR values of prepared BT-2 black tea at different levels (p < 0.001 to p < 0.01). The composite desirability of the model was 0.93, which suggests that the developed model could be utilized effectively to maintain the quality parameters of BT-2 black tea during fermentation.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics

BACKGROUND

Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds.

RESULTS

Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples.

CONCLUSION

The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process

The theaflavin-to-thearubigin ratio (TF/TR) is an important parameter for evaluating the degree of fermentation and quality characteristics of Congou black tea. Near infrared (NIR) spectroscopy, one of the most promising techniques for evaluating large-scale tea processing quality, in association with chemometrics, can be used as a selection tool when a fast determination of the requested parameters is required. The aim of this work is to develop a unique model for the determination of TF/TR. First, 11 key wavelength variables were screened by synergy interval partial least-squares regression (SI-PLS) and competitive adaptive reweighted sampling (CARS). Based on these characteristic variables, a new extreme learning machine (ELM) combined with an adaptive boosting (ADABOOST) algorithm (ELM-ADABOOST) was applied to construct the nonlinear prediction model for TF/TR, and an independent external set was used for the validation. A determinate coefficient (R_p²) of 0.893, root mean square error of prediction (RMSEP) of 0.0044, RSD below 10%, and RPD above 3 were acquired in the prediction model. These results demonstrate that NIR can be used to rapidly determine the TF/TR value during fermentation, and it effectively simplify the model and improve the prediction accuracy when combined with the SI-CARS variable.

Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods

Fermentation is the key process to produce the special color of congou black tea. The machine vision technology is applied to detect the color space changes of black tea's color in RGB, Lab and HSV, and to find out its relevance to black tea's fermentation quality. And then the color feature parameter is used as input to establish physicochemical indexes (TFs, TRs, and TBs) and sensory features' linear and non-linear quantitative evaluation model. Results reveal that color features are significantly correlated to quality indices. Compared with the other two color models (RGB and HSV), CIE Lab model can better reflect the dynamic variation features of quality indices and foliage color information of black tea. The predictability of non-linear models (RF and SVM) is superior to PLS linear model, while RF model presents a slight advantage over the classic SVM model since RF model can better represent the quantitative analytical relationship between image information and quality indices. This research has proved that computer image color features and non-linear method can be used to quantitatively evaluate the changes of quality indices (e.g. sensory quality) and the pigment during black tea's fermentation. Besides, the test is simple, fast, and nondestructive.

Prediction of black tea fermentation quality indices using NIRS and nonlinear tools

Catechin content, the ratio of tea polyphenols and free amino acids (TP/FAA), as well as the ratio of theaflavins and thearubigins (TFs/TRs) are important biochemical indicators to evaluate fermentation quality. To achieve rapid determination of such biochemical indicators, synergy interval partial least square and extreme learning machine combined with an adaptive boosting algorithm, Si-ELM-AdaBoost algorithm, were used to establish quantitative analysis models between near infrared spectroscopy (NIRS) and catechin content and between TFs/TRs and TP/FAA, respectively. The results showed that prediction performance of the Si-ELM-AdaBoost mixed algorithm is superior than that of other models. The prediction results with root-mean-square error of prediction ranged from 0.006 to 0.563, the ratio performance deviation values exceeded 2.5, and predictive correlation coefficient values exceeded 0.9 in the prediction model of each biochemical indicator. NIRS combined with Si-ELM-AdaBoost mixed algorithm could be utilized for online monitoring of black tea fermentation. Meanwhile, the AdaBoost algorithm effectively improved the accuracy of the ELM model and could better approach the nonlinear continuous function.

Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue

In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

Assessment of taste attributes of peanut meal enzymatic-hydrolysis hydrolysates using an electronic tongue

Peanut meal is the byproduct of high-temperature peanut oil extraction; it is mainly composed of proteins, which have complex tastes after enzymatic hydrolysis to free amino acids and small peptides. The enzymatic hydrolysis method was adopted by using two compound proteases of trypsin and flavorzyme to hydrolyze peanut meal aiming to provide a flavor base. Hence, it is necessary to assess the taste attributes and assign definite taste scores of peanut meal double enzymatic hydrolysis hydrolysates (DEH). Conventionally, sensory analysis is used to assess taste intensity in DEH. However, it has disadvantages because it is expensive and laborious. Hence, in this study, both taste attributes and taste scores of peanut meal DEH were evaluated using an electronic tongue. In this regard, the response characteristics of the electronic tongue to the DEH samples and standard five taste samples were researched to qualitatively assess the taste attributes using PCA and DFA. PLS and RBF neural network (RBFNN) quantitative prediction models were employed to compare predictive abilities and to correlate results obtained from the electronic tongue and sensory analysis, respectively. The results showed that all prediction models had good correlations between the predicted scores from electronic tongue and those obtained from sensory analysis. The PLS and RBFNN prediction models constructed using the voltage response values from the sensors exhibited higher correlation and prediction ability than that of principal components. As compared with the taste performance by PLS model, that of RBFNN models was better. This study exhibits potential advantages and a concise objective taste assessment tool using the electronic tongue in the assessment of DEH taste attributes in the food industry.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.