Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system

A review: Integration of NIRS and chemometric methods for tea quality control-principles, spectral preprocessing methods, machine learning algorithms, research progress, and future directions

With the steady rise in tea production, the need for effective tea quality monitoring has become increasingly pressing. Traditional sensory evaluation and wet chemical detection methods are insufficient for real-time tea quality monitoring. As an emerging technology, near infrared spectroscopy (NIRS) offers numerous advantages, such as preserving sample integrity, generating objective results, and enabling rapid, straightforward assessments. These features make it an ideal choice for real-time tea quality testing. This paper systematically reviews the principles of NIRS, spectral preprocessing methods, statistical modeling techniques, and commonly used machine learning approaches. Furthermore, it provides an in-depth discussion of the research progress of NIRS in areas such as fresh tea leaf quality evaluation, rapid detection of tea-specific components, tea quality assessment and species identification, geographic traceability, development of NIRS equipment, and standardization. Future research directions in the tea field are also proposed. This review serves as a valuable resource for researchers aiming to understand the application and development of NIRS technology in the tea field. It offers insights to facilitate real-time tea quality monitoring and ultimately achieve intelligent quality control.

Rapid and real time detection of black tea rolling quality by using an inexpensive machine vison system

Rolling is a crucial step in black tea manufacturing. Rolling is achieved through a rolling machine, which achieves the processes of crushing, tearing, and curling. At present, the lack of intelligent detection for black tea rolling quality has limited the development of automation in the black tea industry. This study established qualitative and quantitative prediction models based on machine vision technology and chemometric methods for detecting black tea rolling quality. High-performance liquid chromatography (HPLC) was employed to quantify the contents of catechins and theaflavins (TFs). A qualitative discrimination model for rolling time was constructed using the DarkNet-53 convolutional neural network (DarkNet-53 CNN) with a dynamic learning rate, while a quantitative prediction model for TFs content was developed using the improved sparrow search algorithm optimized support vector regression (ISSA-SVR) during the black tea rolling process. The results indicated that, compared to other models, the DarkNet-53 CNN model presented in this study achieved superior discrimination of rolling time, attaining an overall accuracy of 97.82%. The ISSA-SVR model demonstrated considerable advantages in predictive performance, with an Rp value exceeding 0.9 and an RPD value surpassing 7.5. Therefore, this research introduces a low-cost and reliable method for rapid detection of black tea rolling quality for the first time.

Analysis of volatile compounds and vintage discrimination of raw Pu-erh tea based on GC-IMS and GC-MS combined with data fusion

Storage duration significantly influences the aroma profile of raw Pu-erh tea. To comprehensively investigate the differences in the volatile compounds across various vintages of raw Pu-erh teas and achieve the rapid classification of tea vintages, volatile compounds of raw Pu-erh tea with different years (2020-2023) were analyzed using a combination of gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-mass spectrometry (GC-MS). The datasets obtained from both techniques were integrated through low-level and mid-level data fusion strategies. Additionally, partial least squares discriminant analysis (PLS-DA) and random forest (RF) machine learning algorithms were applied to develop predictive models for the classification of tea storage durations. Consequently, GC-IMS and GC-MS identified 54 and 76 volatile compounds, respectively. Notably, the RF model, particularly when coupled with mid-level data fusion, exhibited exceptional predictive accuracy for tea storage time, reaching an accuracy of 100%. These findings provide a reference for elucidating the aroma characteristics of raw Pu-erh tea of different vintages and demonstrate that data fusion combined with machine learning has great potential for ensuring food quality.

Effects of Different Kinds of Fruit Juice on Flavor Quality and Hypoglycemic Activity of Black Tea

At present, the heavy bitter taste, poor flavor quality and low functional activity of summer and autumn tea are the bottleneck problems restricting the low utilization rate of summer and autumn tea resources. The research and development of new products of fruit-flavored black tea is conducive to expanding the utilization of summer and autumn tea resources. Different kinds of fruit juice were added during the fermentation and processing of classic black tea, such as bananas, apples, fragrant pear and Sydney pear, in this study. The effects of fruit juice on the flavor quality and amylase inhibitory activity of fruity black tea were researched. The sensory quality, flavor chemicals and α-amylase inhibitory activity were evaluated. The results showed that the sensory evaluation scores of black tea treated with fruit juice were significantly higher than those of black tea treated without fruit juice, especially the crown pear juice. The amylase inhibition rate of black tea treated with fruit juice was significantly higher than the control treated without fruit juice (p < 0.05). The sensory evaluation scores, polyphenol oxidase activity, water extract content, soluble sugar content, free amino acid content, theaflavin content, thearubigin content and inhibition rate of amylase activity of black tea treated with pear juice were significantly higher than those of the apple and banana juices (p < 0.05), especially crown pear juice. Tea polyphenol content and theaflavin content of black tea treated with added pear juice were significantly lower (p < 0.05) than the black tea control treated with added apple juice and banana juice, especially crown pear juice. The fruity black tea treated with crown pear juice had a redder broth, more pronounced sweet fruit aroma, sweet and mellow taste and reduced astringency. Therefore, the black tea treated with crown pear juice was preferred. The research hopes to provide a theoretical basis for the research of black tea quality control and the research of summer and autumn tea resources utilization technology.

Tea Quality: An Overview of the Analytical Methods and Sensory Analyses Used in the Most Recent Studies

Tea, one of the world's most consumed beverages, has a rich variety of sensory qualities such as appearance, aroma, mouthfeel and flavor. This review paper summarizes the chemical and volatile compositions and sensory qualities of different tea infusions including black, green, oolong, dark, yellow, and white teas based on published data over the past 4 years (between 2021 and 2024), largely focusing on the methodologies. This review highlights the relationships among the different processing methods of tea and their resulting chemical and sensory profiles. Environmental and handling factors during processing, such as fermentation, roasting, and drying are known to play pivotal roles in shaping the unique flavors and aromas of different types of tea, each containing a wide variety of compounds enhancing specific sensory characteristics like umami, astringency, sweetness, and fruity or floral notes, which may correlate with certain groups of chemical compositions. The integration of advanced analytical methods, such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS), with traditional sensory analysis techniques was found to be essential in the evaluation of the chemical composition and sensory attributes of teas. Additionally, emerging approaches like near-infrared spectroscopy (NIRS) and electronic sensory methods show potential in modern tea evaluation. The complexity of tea sensory characteristics necessitates the development of combined approaches using both analytical methods and human sensory analysis for a comprehensive and better understanding of tea quality.

Effect of fermentation temperature on the non-volatile components and in vitro hypoglycemic activity of Jinxuan black tea

Fermentation significantly influences the chemical composition of black tea, yet the effects of different fermentation temperatures on non-volatile components and their in vitro hypoglycemic activity are insufficiently studied. This research investigates how varying temperatures (20, 25, and 30°C) affect the bioactive profile and the inhibitory activity of Jinxuan black tea against α-glucosidase and α-amylase. Our results show that lower fermentation temperatures (20°C) lead to elevated levels of key bioactive compounds, including tea polyphenols (9.24%), soluble sugars (8.24%), thearubigins (7.17%), and theasinesin A (0.15%). These compounds correlate strongly with enhanced α-glucosidase inhibition (R = 0.76-0.97). Non-targeted metabolomic analysis revealed that 36 differential metabolites, including catechins, exhibited altered levels with increasing fermentation temperature. Notably, tea fermented at 20°C exhibited superior hypoglycemic activity, with α-glucosidase inhibition (IC50 = 14.00 ± 1.00 μg/ml) significantly outperforming α-amylase inhibition (IC50 = 2.48 ± 0.28 mg/ml). The findings of this research underscore the importance of fermentation temperature in optimizing the bioactive profile of black tea. It is proposed that recommendations for future processing or formulation should emphasize the use of lower fermentation temperatures, aimed at augmenting the health benefits linked to higher polyphenol content and stronger hypoglycemic activity.

Lightweight CNN combined with knowledge distillation for the accurate determination of black tea fermentation degree

Black tea is the second most common type of tea in China. Fermentation is one of the most critical processes in its production, and it affects the quality of the finished product, whether it is insufficient or excessive. At present, the determination of black tea fermentation degree completely relies on artificial experience. It leads to inconsistent quality of black tea. To solve this problem, we use machine vision technology to distinguish the degree of fermentation of black tea based on images, this paper proposes a lightweight convolutional neural network (CNN) combined with knowledge distillation to discriminate the degree of fermentation of black tea. After comparing 12 kinds of CNN models, taking into account the size of the model and the performance of discrimination, as well as the selection principle of teacher models, Shufflenet_v2_x1.0 is selected as the student model, and Efficientnet_v2 is selected as the teacher model. Then, CrossEntropy Loss is replaced by Focal Loss. Finally, for Distillation Loss ratios of 0.6, 0.7, 0.8, 0.9, Soft Target Knowledge Distillation (ST), Masked Generative Distillation (MGD), Similarity-Preserving Knowledge Distillation (SPKD), and Attention Transfer (AT) four knowledge distillation methods are tested for their performance in distilling knowledge from the Shufflenet_v2_x1.0 model. The results show that the model discrimination performance after distillation is the best when the Distillation Loss ratio is 0.8 and the MGD method is used. This setup effectively improves the discrimination performance without increasing the number of parameters and computation volume. The model's P, R and F1 values reach 0.9208, 0.9190 and 0.9192, respectively. It achieves precise discrimination of the fermentation degree of black tea. This meets the requirements of objective black tea fermentation judgment and provides technical support for the intelligent processing of black tea.

A comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis to systematically evaluate spice quality: Cinnamomum cassia as an example

Spices have long been popular worldwide. Besides serving as aromatic and flavorful food and cooking ingredients, many spices exhibit notable bioactivity. Quality evaluation methods are essential for ensuring the quality and flavor of spices. However, existing methods typically focus on the content of particular components or certain aspects of bioactivity. For a systematic evaluation of spice quality, we herein propose a comprehensive "quality-quantity-activity" approach based on portable near-infrared spectrometer and membership function analysis. Cinnamomum cassia was used as a representative example to illustrate this approach. Near-infrared spectroscopy and chemometric methods were combined to predict the geographical origin, cinnamaldehyde content, ash content, antioxidant activity, and integrated membership function value. All the optimal prediction models displayed good predictive ability (correlation coefficient of prediction > 0.9, residual predictive deviation > 2.1). The proposed approach can provide a valuable reference for the rapid and comprehensive quality evaluation of spices.

Application of visible near-infrared spectroscopy combined with colorimetric sensor array for the aroma quality evaluation in tencha drying process

The drying process is a critical stage in developing the aroma quality of tencha. In our research, visible near infrared (Vis-NIR) and colorimetric sensor array (Vis-NIR-CSA) were used for evaluating the aroma quality of tencha drying process. Vis-NIR recorded the spectral signal of CSA after the reaction in samples. Subsequently, the aroma quality was predicted by a combination of different data fusion strategies and classification and regression tree (CART) in tencha drying process. The high-level fusion strategy showed the best performance, with calibration and prediction set accuracy of 94.68% and 93.48%, respectively. The results indicated that Vis-NIR-CSA combined with high-level data fusion could be applied satisfactorily in the aroma quality evaluation of tencha. Moreover, pentanal was identified to be highly correlated with aroma quality during tencha drying process, which verified the sensor identification results. This study contributed to controlling good manufacturing practices and designing optimal tencha processing systems.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Portable NIR spectroscopy: the route to green analytical chemistry

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

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.

Improving the Accuracy of Saffron Adulteration Classification and Quantification through Data Fusion of Thin-Layer Chromatography Imaging and Raman Spectral Analysis

Agricultural crops of high value are frequently targeted by economic adulteration across the world. Saffron powder, being one of the most expensive spices and colorants on the market, is particularly vulnerable to adulteration with extraneous plant materials or synthetic colorants. However, the current international standard method has several drawbacks, such as being vulnerable to yellow artificial colorant adulteration and requiring tedious laboratory measuring procedures. To address these challenges, we previously developed a portable and versatile method for determining saffron quality using a thin-layer chromatography technique coupled with Raman spectroscopy (TLC-Raman). In this study, our aim was to improve the accuracy of the classification and quantification of adulterants in saffron by utilizing mid-level data fusion of TLC imaging and Raman spectral data. In summary, the featured imaging data and featured Raman data were concatenated into one data matrix. The classification and quantification results of saffron adulterants were compared between the fused data and the analysis based on each individual dataset. The best classification result was obtained from the partial least squares-discriminant analysis (PLS-DA) model developed using the mid-level fusion dataset, which accurately determined saffron with artificial adulterants (red 40 or yellow 5 at 2-10%, w/w) and natural plant adulterants (safflower and turmeric at 20-100%, w/w) with an overall accuracy of 99.52% and 99.20% in the training and validation group, respectively. Regarding quantification analysis, the PLS models built with the fused data block demonstrated improved quantification performance in terms of R² and root-mean-square errors for most of the PLS models. In conclusion, the present study highlighted the significant potential of fusing TLC imaging data and Raman spectral data to improve saffron classification and quantification accuracy via the mid-level data fusion, which will facilitate rapid and accurate decision-making on site.

Study on the Trend in Microbial Changes during the Fermentation of Black Tea and Its Effect on the Quality

The role of tea endophytes in black tea fermentation and their impact on black tea quality remain unclear. We collected fresh leaves of Bixiangzao and Mingfeng tea and processed them into black tea, while testing the biochemical composition of both the fresh leaves and the black tea. We also used high-throughput techniques, such as 16S rRNA, to analyze the dynamic changes in the microbial community structure and function during black tea processing in order to investigate the influence of dominant microorganisms on the quality of black tea formation. Our results showed that bacteria, such as Chryseobacterium and Sphingomonas, and Pleosporales fungi dominated the entire black tea fermentation process. Predicted functional analysis of the bacterial community indicated that glycolysis-related enzymes, pyruvate dehydrogenase, and tricarboxylic acid cycle-related enzymes were significantly elevated during the fermentation stage. Amino acids, soluble sugars, and tea pigment content also increased considerably during fermentation. Pearson's correlation analysis revealed that the relative bacterial abundance was closely related to the content of tea polyphenols and catechins. This study provides new insights into the changes in microbial communities during the fermentation of black tea and demonstrates understanding of the basic functional microorganisms involved in black tea processing.

Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information

BACKGROUND

Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines.

RESULTS

Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46.

CONCLUSION

The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman

Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model’s excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation.

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

Metabolomics combined with proteomics provides a novel interpretation of the compound differences among Chinese tea cultivars (Camellia sinensis var. sinensis) with different manufacturing suitabilities

Different tea cultivars differ in their manufacturing suitability. In this study, metabolomics and proteomics were applied to investigate the metabolite and protein differences in fresh leaves from 23 Chinese tea cultivars suitable for manufacturing green, white, oolong, and black teas. The combined analysis revealed 115 differential metabolites and significant differences in the biosynthesis pathways for amino acids, phenylpropanoids, flavonoids, and terpenoids, and in the peroxidases abundances among these four groups. Green tea cultivars had higher abundances of amino acids and amino acids biosynthesis-related enzymes but lower abundances of flavanols and flavonoids biosynthesis-related enzymes. Black tea cultivars presented higher abundances of flavanols, flavanol-O-glycosides, flavonoids biosynthesis-related enzymes, and peroxidases. Oolong tea cultivars showed higher abundances of enzymes involved in terpenoids biosynthesis. Our study provides a novel interpretation of the manufacturing suitability of tea cultivars from the perspective of both metabolites and proteins and will be helpful for cultivar breeding.

Rapid monitoring of black tea fermentation quality based on a solution-phase sensor array combined with UV-visible spectroscopy

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.

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.

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.