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Development of a portable electronic nose for the classification of tea quality based on tea dregs aroma Cover

Development of a portable electronic nose for the classification of tea quality based on tea dregs aroma

International Journal on Smart Sensing and Intelligent Systems
Volume 17 (2024): Issue 1 (January 2024)
By: Adi Djoko Guritno,  Agus Harjoko,  Megita Ryanjani Tanuputri,  Diyah Utami Kusumaning Putri and  Nur Achmad Sulistyo Putro  
Open Access
|Jul 2024

References

  1. V. Sai, P. Chaturvedula, and I. Prakash, “The aroma, taste, color and bioactive constituents of tea,” Journal of Medicinal Plants Research, vol. 5, no. 11, pp. 2110–2124, 2011, [Online]. Available: http://www.academicjournals.org/JMPR
    Search in Google ScholarBack to article
  2. D. Kalauni, B. Joshi, and A. Joshi, “Production, marketing, and future prospects of Nepali orthodox tea,” Cogent Food Agric, vol. 6, no. 1, p. 1757227, Jan. 2020, doi: 10.1080/23311932.2020.1757227.
    Open DOISearch in Google ScholarBack to article
  3. A. Kumar, H. Singh, S. Sharma, and A. Kumar, “Color Analysis of Black Tea Liquor using Image Processing Techniques,” International Journal of Electronics & Communication Technology, vol. 2, no. 3, pp. 292–296, 2011.
    Search in Google ScholarBack to article
  4. N. Bhattacharyya et al., “Monitoring of black tea fermentation process using electronic nose,” J Food Eng, vol. 80, no. 4, pp. 1146–1156, Jun. 2007, doi: 10.1016/j.jfoodeng.2006.09.006.
    Open DOISearch in Google ScholarBack to article
  5. G. W. Sanderson, “The Chemistry of Tea and Tea Manufacturing,” in Structural and functional aspects of phytochemistry: Recent advance in phytochemistry, V. C. Runeckles and T. C. Tso, Eds., 1st ed. New York: Academic Press, 1972, pp. 247–317.
    Search in Google ScholarBack to article
  6. D. Saikia, P. K. Boruah, and U. Sarma, “Development And Implementation Of A Sensor Network To Monitor Fermentation Process Parameter In Tea Processing,” International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 3, pp. 1254–1270, Jan. 2022, doi: 10.21307/ijssis-2017-704.
    Open DOISearch in Google ScholarBack to article
  7. M. Kawakami, S. N. Ganguly, J. Banerjee, and A. Kobayashi, “Aroma Composition of Oolong Tea and Black Tea by Brewed Extraction Method and Characterizing Compounds of Darjeeling Tea Aroma,” J Agric Food Chem, vol. 43, no. 1, pp. 200–207, Jan. 1995, doi: 10.1021/jf00049a037.
    Open DOISearch in Google ScholarBack to article
  8. X. Pang et al., “Development of regression model to differentiate quality of black tea (Dianhong): correlate aroma properties with instrumental data using multiple linear regression analysis,” Int J Food Sci Technol, vol. 47, no. 11, pp. 2372–2379, Nov. 2012, doi: 10.1111/j.1365-2621.2012.03112.x.
    Open DOISearch in Google ScholarBack to article
  9. C. Schuh and P. Schieberle, “Characterization of the Key Aroma Compounds in the Beverage Prepared from Darjeeling Black Tea: Quantitative Differences between Tea Leaves and Infusion,” J Agric Food Chem, vol. 54, no. 3, pp. 916–924, Feb. 2006, doi: 10.1021/jf052495n.
    Open DOISearch in Google ScholarBack to article
  10. C. Wang, S. Lv, Y. Wu, M. Lian, X. Gao, and Q. Meng, “Study of aroma formation and transformation during the manufacturing process of Biluochun green tea in Yunnan Province by HS-SPME and GC-MS,” J Sci Food Agric, vol. 96, no. 13, pp. 4492–4498, Oct. 2016, doi: 10.1002/jsfa.7663.
    Open DOISearch in Google ScholarBack to article
  11. C. Wang et al., “A comparative study of volatile components in Dianhong teas from fresh leaves of four tea cultivars by using chromatography-mass spectrometry, multivariate data analysis, and descriptive sensory analysis,” Food Research International, vol. 100, pp. 267–275, Oct. 2017, doi: 10.1016/j.foodres.2017.07.013.
    Open DOISearch in Google ScholarBack to article
  12. A. Akuli et al., “A Machine Vision System For Estimation Of Theaflavins And Thearubigins In Orthodox Black Tea,” International Journal on Smart Sensing and Intelligent Systems, vol. 9, no. 2, pp. 709–731, Jan. 2016, doi: 10.21307/ijssis-2017-891.
    Open DOISearch in Google ScholarBack to article
  13. M. F. Njoman et al., “The vulnerability of human sensory evaluation and the promising senses instrumentation,” British Food Journal, vol. 119, no. 10, pp. 2145–2160, 2017, doi: 10.1108/BFJ-10-2016-0505.
    Open DOISearch in Google ScholarBack to article
  14. X. Pang et al., “Development of regression model to differentiate quality of black tea (Dianhong): correlate aroma properties with instrumental data using multiple linear regression analysis,” Int J Food Sci Technol, vol. 47, no. 11, pp. 2372–2379, Nov. 2012, doi: 10.1111/J.1365-2621.2012.03112.X.
    Open DOISearch in Google ScholarBack to article
  15. M. Ghasemi-Varnamkhasti, C. Apetrei, J. Lozano, and A. Anyogu, “Potential use of electronic noses, electronic tongues and biosensors as multisensor systems for spoilage examination in foods,” Trends Food Sci Technol, vol. 80, pp. 71–92, Oct. 2018, doi: 10.1016/j.tifs.2018.07.018.
    Open DOISearch in Google ScholarBack to article
  16. R. Gutierrez-Osuna, H. T. Nagle, B. Kermani, and S. S. Schiffman, “Signal Conditioning and Preprocessing,” in Handbook of Machine Olfaction, Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2004, pp. 105–132. doi: 10.1002/3527601597.ch5.
    Open DOISearch in Google ScholarBack to article
  17. J. W. Gardner and P. N. Bartlett, “A brief history of electronic noses,” Sens Actuators B Chem, vol. 18, no. 1–3, pp. 210–211, Mar. 1994, doi: 10.1016/0925-4005(94)87085-3.
    Open DOISearch in Google ScholarBack to article
  18. S. Kiani, S. Minaei, and M. Ghasemi-Varnamkhasti, “Real-time aroma monitoring of mint (Mentha spicata L.) leaves during the drying process using electronic nose system,” Measurement, vol. 124, pp. 447–452, Aug. 2018, doi: 10.1016/j.measurement.2018.03.033.
    Open DOISearch in Google ScholarBack to article
  19. M. Tohidi, M. Ghasemi-Varnamkhasti, V. Ghafarinia, M. Bonyadian, and S. S. Mohtasebi, “Development of a metal oxide semiconductor-based artificial nose as a fast, reliable and non-expensive analytical technique for aroma profiling of milk adulteration,” Int Dairy J, vol. 77, pp. 38–46, Feb. 2018, doi: 10.1016/j.idairyj.2017.09.003.
    Open DOISearch in Google ScholarBack to article
  20. A. James, A. Seth, and S. C. Mukhopadhyay, “IoT enabled sensor node: a tutorial paper,” International Journal on Smart Sensing and Intelligent Systems, vol. 13, no. 1, pp. 1–18, Jan. 2020, doi: 10.21307/ijssis-2020-022.
    Open DOISearch in Google ScholarBack to article
  21. A. Aryal, Y. Liao, P. Nattuthurai, and B. Li, “The emerging big data analytics and IoT in supply chain management: a systematic review,” Supply Chain Management, vol. 25, no. 2. Emerald Group Holdings Ltd., pp. 141–156, Feb. 24, 2020. doi: 10.1108/SCM-03-2018-0149.
    Open DOISearch in Google ScholarBack to article
  22. C. Ganeshkumar, S. K. Jena, A. Sivakumar, and T. Nambirajan, “Artificial intelligence in agricultural value chain: review and future directions,” J Agribus Dev Emerg Econ, 2021, doi: 10.1108/JADEE-07-2020-0140.
    Open DOISearch in Google ScholarBack to article
  23. Z. Ye, Y. Liu, and Q. Li, “Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods,” Sensors, vol. 21, no. 22, p. 7620, Nov. 2021, doi: 10.3390/s21227620.
    Open DOISearch in Google ScholarBack to article
  24. M. Xu, J. Wang, and L. Zhu, “The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics,” Food Chem, vol. 289, pp. 482–489, Aug. 2019, doi: 10.1016/j.foodchem.2019.03.080.
    Open DOISearch in Google ScholarBack to article
  25. R. Zhi, L. Zhao, and D. Zhang, “A Framework for the Multi-Level Fusion of Electronic Nose and Electronic Tongue for Tea Quality Assessment,” Sensors, vol. 17, no. 5, p. 1007, May 2017, doi: 10.3390/s17051007.
    Open DOISearch in Google ScholarBack to article
  26. Y. Dai, R. Zhi, L. Zhao, H. Gao, B. Shi, and H. Wang, “Longjing tea quality classification by fusion of features collected from E-nose,” Chemometrics and Intelligent Laboratory Systems, vol. 144, pp. 63–70, May 2015, doi: 10.1016/j.chemolab.2015.03.010.
    Open DOISearch in Google ScholarBack to article
  27. H. Rouabeh, S. Gomri, and M. Masmoudi, “The design and validation of a fast and low-cost multi-purpose electronic nose for rapid gas identification,” Sensor Review, vol. 42, no. 6, pp. 613–630, Nov. 2022, doi: 10.1108/SR-01-2022-0019.
    Open DOISearch in Google ScholarBack to article
  28. N. Benabdellah, K. Hachami, M. Bourhaleb, and N. Benazzi, “IDENTIFICATION OF TWO TYPES OF ROTTEN MEAT USING AN ELECTRONIC NOSE FOR FOOD QUALITY CONTROL,” International Journal on Smart Sensing and Intelligent Systems, vol. 10, no. 3, pp. 1–23, Jan. 2017, doi: 10.21307/ijssis-2017-229.
    Open DOISearch in Google ScholarBack to article
  29. M. Xu, J. Wang, and L. Zhu, “Tea quality evaluation by applying E-nose combined with chemometrics methods,” J Food Sci Technol, vol. 58, no. 4, pp. 1549–1561, Apr. 2021, doi: 10.1007/s13197-020-04667-0.
    Open DOISearch in Google ScholarBack to article
  30. B. Tudu, B. Kow, N. Bhattacharyya, and R. Bandyopadhyay, “Normalization techniques for gas sensor array as applied to classification for black tea,” International Journal on Smart Sensing and Intelligent Systems, vol. 2, no. 1, pp. 176–189, Jan. 2009, doi: 10.21307/ijssis-2017-344.
    Open DOISearch in Google ScholarBack to article
  31. N. K. Chauhan and K. Singh, “A Review on Conventional Machine Learning vs Deep Learning,” in 2018 International Conference on Computing, Power and Communication Technologies (GUCON), IEEE, Sep. 2018, pp. 347–352. doi: 10.1109/GUCON.2018.8675097.
    Open DOISearch in Google ScholarBack to article
  32. R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: review, opportunities and challenges,” Brief Bioinform, vol. 19, no. 6, pp. 1236–1246, Nov. 2018, doi: 10.1093/bib/bbx044.
    Open DOISearch in Google ScholarBack to article
  33. U. SIRIPATRAWAN and P. JANTAWAT, “A novel method for shelf life prediction of a packaged moisture sensitive snack using multilayer perceptron neural network,” Expert Syst Appl, vol. 34, no. 2, pp. 1562–1567, Feb. 2008, doi: 10.1016/j.eswa.2007.01.008.
    Open DOISearch in Google ScholarBack to article
  34. Badan Standardisasi Nasional, “Standar Nasional Indonesia: Teh Hitam,” 2016, [Online]. Available: www.bsn.go.id
    Search in Google ScholarBack to article
  35. H. T. Nagle, R. Gutierrez-Osuna, B. G. Kermani, and S. S. Schiffman, “Environmental Monitoring,” in Handbook of Machine Olfaction, Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2004, pp. 419–444. doi: 10.1002/3527601597.ch17.
    Open DOISearch in Google ScholarBack to article
  36. Hecht-Nielsen, “Theory of the backpropagation neural network,” in International Joint Conference on Neural Networks, IEEE, 1989, pp. 593–605 vol.1. doi: 10.1109/IJCNN.1989.118638.
    Open DOISearch in Google ScholarBack to article
DOI: https://doi.org/10.2478/ijssis-2024-0019 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Jun 26, 2023
|
Published on: Jul 20, 2024
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
electronic nose,
tea dregs aroma,
multilayer perceptron,
machine learning
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2024 Adi Djoko Guritno, Agus Harjoko, Megita Ryanjani Tanuputri, Diyah Utami Kusumaning Putri, Nur Achmad Sulistyo Putro, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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