Monitoring the fermentation process of black tea using QCM sensor based electronic nose

Sharma, Prolay; Ghosh, Arunangshu; Tudu, Bipan; Sabhapondit, Santanu; Baruah, Binoti Devi; Tamuly, Pradip; Bhattacharyya, Nabarun; Bandyopadhyay, Rajib

doi:10.1016/j.snb.2015.05.013

Cited by 91 publications

(44 citation statements)

References 22 publications

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“…A non-destructive technique that has been used for analysis of tea processes and quality monitoring is e-nose (Yaroshenko et al, 2014) (Sharma et al, 2015). E-nose devices usually include an array of metal oxide sensors which respond to the amount of biochemical volatiles coming into contact with the corresponding sensor surface to explain the chemical profile (Bhattacharyya et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products

Mishra

Nordon

Tschannerl

et al. 2018

Journal of Food Engineering

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Tea is the most consumed manufactured drink in the world. In recent years, various high end analytical techniques such as high-performance liquid chromatography have been used to analyse tea products. However, these techniques require complex sample preparation, are time consuming, expensive and require a skilled analyst to carry out the experiments. Therefore, to support rapid and non-destructive assessment of tea products, the use of near infrared (NIR) (950-1760 nm) hyperspectral imaging (HSI) for classification of six different commercial tea products (oolong, green, yellow, white, black and Pu-erh) is presented. To visualise the HSI data, linear (principal component analysis (PCA) and multidimensional scaling (MDS)) and non-linear (t-distributed stochastic neighbour embedding (t-SNE) and isometric mapping (ISOMAP)) data visualisation methods were compared. t-SNE provided separation of the six commercial tea products into three groups based on the extent of processing: minimally processed, oxidised and fermented. To perform the classification of different tea products, a multi-class error-correcting output code (ECOC) model containing support vector machine (SVM) binary learners was developed. The classification model was further used to predict classes for pixels in the HSI hypercube to obtain the classification maps. The SVM-ECOC model provided a classification accuracy of 97.41±0.16 % for the six commercial tea products. The methodology developed provides a means for rapid, non-destructive, in situ testing of tea products, which would be of considerable benefit for process monitoring, quality control, authenticity and adulteration detection.

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Section: Introductionmentioning

confidence: 99%

Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products

Mishra

Nordon

Tschannerl

et al. 2018

Journal of Food Engineering

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“…[9][10][11] However, information concerning the effect of baking is limited. Therefore, a detailed investigation into the change in the aroma components in Dianhong black tea before and after baking is essential.…”

Section: Introductionmentioning

confidence: 99%

Aroma formation in Dianhong black tea: Effects of baking

Qiu

Wang

et al. 2017

International Journal of Food Properties

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Baking is the last, and yet most essential, step in the formation of the final distinct flavour of most black tea products. In this study, the influence of baking on the volatile components of Dianhong black tea, which is one of the most popular black teas in China, was investigated. As a very fast, effective, and simple method for collecting volatiles, headspace solid-phase microextraction (HS-SPME) with a 65 µm PDMS/DVB fibre was used to extract the volatile compounds of Dianhong black tea, and the volatile components were further separated and identified by gas chromatography-mass spectrometry (GC-MS). The results showed that the peak areas of alcohols, aldehydes, esters, and ketones were increased, but that those of some alkanes, acids, and nitrogen compounds did not demonstrate an obvious increase after baking. The differences generated by baking process actually result in the distinct flavour of most black teas and can be used as a model for analysing the chemical composition of special flavours, and for quality improvement and process control. ARTICLE HISTORY

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“…Electronic nose (e-nose) merupakan instrumen berbasis sensor-sensor gas kimia tak selektif (unselected), tergabung dalam sebuah larik sensor gas, digunakan untuk mendeteksi aroma (odor) sederhana maupun kompleks [5]. Implementasi e-nose telah banyak dilakukan seperti prediksi kualitas pisang [6], pemantauan proses fermentasi teh hitam [7], pemantauan kualitas daging sapi [8]. Khusus sampel konsentrasi aroma rendah, seperti teh, diperlukan pemanasan agar senyawa dan unsur pembentuk aroma yang masih banyak teperangkap pada fase padat berubah ke fase gas.…”

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“…Khusus sampel konsentrasi aroma rendah, seperti teh, diperlukan pemanasan agar senyawa dan unsur pembentuk aroma yang masih banyak teperangkap pada fase padat berubah ke fase gas. Selama ini, penentuan suhu pemanas sampel teh yang dilakukan oleh masing-masing peneliti masih terbatas pada informasi nilai suhu yang diterapkan, seperti pemanasan teh hitam menggunakan lampu pijar halogen (35,0 W) dengan set suhu (60,0 ± 3,0) °C [9], fermentasi teh yang dipanaskan pada suhu (30,0 ± 10,0) °C [7], pengenalan pola aroma beberapa jenis teh dengan suhu pemanas (28,0 ± 2,0) °C [10]. Disamping itu, Lelono et.…”

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Suhu Pemanas Sampel Optimal Untuk Klasifikasi Teh Hitam Menggunakan Electronic Nose

Lelono

Triyana

2019

Indonesian J. Electron. Instrum. Syst.

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The optimization of heating temperature of black tea samples for the measurement of aroma with electronic nose (e-nose) has been successfully performed. Sample heating is done because black tea has a low aroma intensity and easily lost. However, the selection of such temperature should be selective because it can result in damage to the aroma of the sample. Therefore, temperature optimization needs to be done so that the resulting sensor response comes from the transformation of the undamaged aroma.The method used to obtain the optimum heating temperature by analyzing the sensor response of the aroma transformation that is captured by e-nose. Consistency and pattern changes formed from the sensor response are used as a comparison of optimal heating temperature selection. The measured sample varied in temperature (30 - 60 °C) so that the resulting sensor response was observed. Change in patterns indicate the aroma has been burning. After optimal temperature is obtained then black tea (50 gr) Broken Orange Pokoe, Broken Pokoe II and Bohea with a total sample of 300 bags were measured with e-nose. For further analysis, the result of classification by method of Principal Component Analysis (PCA) as proof of sample heating temperature optimization successfully done.The experimental results show optimal sample heating for black tea 3 quality 40 - 45 °C. After then with the third PCA the sample can be classified up to 92.5% of the total data variant. This indicates the aroma of tea is relatively constant and there is no pattern change.

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Monitoring the fermentation process of black tea using QCM sensor based electronic nose

Cited by 91 publications

References 22 publications

Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products

Near-infrared hyperspectral imaging for non-destructive classification of commercial tea products

Aroma formation in Dianhong black tea: Effects of baking

Suhu Pemanas Sampel Optimal Untuk Klasifikasi Teh Hitam Menggunakan Electronic Nose

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