Chemometric data analysis for black tea fermentation using principal component analysis

LazaroJr., J. B.; Ballado, Alejandro H.; Bautista, F. P. F.; So, Jai-Hyun; Villegas, Josefina M.

doi:10.1063/1.5080863

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Innovative technologies were developed by several research groups using the electronic eye method [5,11,[13][14][15][16][17][18], the electronic tongue method [19][20][21][22], and the electronic nose method [23][24][25][26][27][28][29][30][31][32][33][34][35] that aim at minimizing human intervention. At present, the electronic nose methods are often utilized to classify different types of tea or different tea grades [24,26,31,35]. e e-nose systems developed so far are based on metal oxide gas sensors, which are inherently difficult to adapt to the high-humid environment of tea manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea

Sharmilan

Premarathne

Wanniarachchi

et al. 2022

Journal of Food Quality

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The fermentation stage is vital during the black tea manufacturing process to produce the best-quality tea. The oxidation of tea biochemical compounds results in the appearance of characteristic smell peaks during the fermentation stage. These subtle changes in tea aroma are hard to detect unless one is a trained personnel. Here for the first time, we applied e-nose to monitor the fermentation process of Sri Lankan low-country tea. In this study, detection of smell peaks during fermentation was conducted by a custom-made e-nose (Digi-Nose) with four gas sensors. Singular value decomposition (SVD) is applied to eliminate the noise and dimensionality reduction in the sensor responses observed. The prediction of the time of appearance of smell peaks was conducted with a support-vector machine (SVM). Finally, theaflavin content with time was compared to validate the optimum fermentation times observed with an e-nose.

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Section: Introductionmentioning

confidence: 99%

Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea

Sharmilan

Premarathne

Wanniarachchi

et al. 2022

Journal of Food Quality

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“…During the process, catechin compounds react with oxygen during oxidation to produce two compounds, namely theaflavins (TF) and thearubigins (TR). These compounds determine the aroma and taste of the tea (Lazaro et al, 2018). Also, fermentation changes the tea colour to coppery brown and causes a fruity smell.…”

Section: Introductionmentioning

confidence: 99%

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

Kimutai

Ngenzi

Ngoga

et al. 2021

J. Sens. Sens. Syst.

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Abstract. Tea (Camellia sinensis) is one of the most consumed drinks across the world. Based on processing techniques, there are more than 15 000 categories of tea, but the main categories include yellow tea, Oolong tea, Illex tea, black tea, matcha tea, green tea, and sencha tea, among others. Black tea is the most popular among the categories worldwide. During black tea processing, the following stages occur: plucking, withering, cutting, tearing, curling, fermentation, drying, and sorting. Although all these stages affect the quality of the processed tea, fermentation is the most vital as it directly defines the quality. Fermentation is a time-bound process, and its optimum is currently manually detected by tea tasters monitoring colour change, smelling the tea, and tasting the tea as fermentation progresses. This paper explores the use of the internet of things (IoT), deep convolutional neural networks, and image processing with majority voting techniques in detecting the optimum fermentation of black tea. The prototype was made up of Raspberry Pi 3 models with a Pi camera to take real-time images of tea as fermentation progresses. We deployed the prototype in the Sisibo Tea Factory for training, validation, and evaluation. When the deep learner was evaluated on offline images, it had a perfect precision and accuracy of 1.0 each. The deep learner recorded the highest precision and accuracy of 0.9589 and 0.8646, respectively, when evaluated on real-time images. Additionally, the deep learner recorded an average precision and accuracy of 0.9737 and 0.8953, respectively, when a majority voting technique was applied in decision-making. From the results, it is evident that the prototype can be used to monitor the fermentation of various categories of tea that undergo fermentation, including Oolong and black tea, among others. Additionally, the prototype can also be scaled up by retraining it for use in monitoring the fermentation of other crops, including coffee and cocoa.

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“…Innovative technologies were developed by several research groups using an electronic eye method which analyzes images of tea leaves during fermentation to notice color changes [3,[12][13][14][15][16][17]. The second method involves an electronic nose system which detects aroma levels of tea particles during fermentation [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Recent studies focused on the combination of electronic eye and electronic nose methods [32][33][34][35] which are aimed at minimizing human intervention of observing color changes and smelling of tea particles.…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process

et al. 2020

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“Tea” is a beverage which has a unique taste and aroma. The conventional method of tea manufacturing involves several stages. These are plucking, withering, rolling, fermentation, and finally firing. The quality parameters of tea (color, taste, and aroma) are developed during the fermentation stage where polyphenolic compounds are oxidized when exposed to air. Thus, controlling the fermentation stage will result in more consistent production of quality tea. The level of fermentation is often detected by humans as “first” and “second” noses as two distinct smell peaks appear during fermentation. The detection of the “second” aroma peak at the optimum fermentation is less consistent when decided by humans. Thus, an electronic nose is introduced to find the optimum level of fermentation detecting the variation in the aroma level. In this review, it is found that the systems developed are capable of detecting variation of the aroma level using an array of metal oxide semiconductor (MOS) gas sensors using different statistical and neural network techniques (SVD, 2-NM, MDM, PCA, SVM, RBF, SOM, PNN, and Recurrent Elman) successfully.

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Chemometric data analysis for black tea fermentation using principal component analysis

Cited by 5 publications

References 7 publications

Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea

Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea

An internet of things (IoT)-based optimum tea fermentation detection model using convolutional neural networks (CNNs) and majority voting techniques

Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process

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