E-nose Application With Chemometrics for Monitoring Kombucha Tea Fermentation Process
DOI:
https://doi.org/10.23887/jstundiksha.v12i1.50994Kata Kunci:
eNose, Sensor Gas, Kemometrik, Fermentasi, Teh Kombucha, LDAAbstrak
Kombucha tea is a fermented tea drink using Symbiotic Culture of Bacteria and Yeast (SCOBY), which is currently being widely produced and consumed because of its health benefits. Kombucha tea has a great opportunity on a global industrial scale, so it is necessary to monitor the production process. This paper uses system sensor gas array or eNose to distinguish volatiles delivered amid the maturation preparation and to think about the method stages using the linear discriminant analysis (LDA) strategy. The results obtained from LDA showed that the 1st to 6th day was the growth process of the SCOBY mushroom, while the 7th to 12th day was the ripening process of kombucha to be consumed. The stages of the kombucha fermentation process are classified using three classification methods, namely KNN, CART, and LDA. The results show the highest accuracy obtained by LDA, with an accuracy of 83.33%. These results can be agreed that eNose can be used as a measuring tool for monitoring the fermentation process and testing the quality of kombucha tea.
Referensi
Abu-Khalaf, N., & Masoud, W. (2022). Electronic Nose for Differentiation and Quantification of Yeast Species in White Fresh Soft Cheese. Applied Bionics and Biomechanics. https://doi.org/10.1155/2022/8472661.
Alvian Nugroho, A., Wijaya, W., Hendry, J., & Sumanto, B. (2022). Seleksi Fitur Aroma Teh Kombucha menggunakan ANN untuk Optimasi Kinerja Sistem E-nose. ELKOMIKA, 10(2), 334–349. https://doi.org/10.26760/elkomika.v10i2.334.
Bauer-Petrovska, B., & Petrushevska-Tozi, L. (2000). Mineral and water soluble vitamin content in the Kombucha drink. International Journal of Food Science and Technology, 35(2). https://doi.org/10.1046/j.1365-2621.2000.00342.x.
Bishop, P., Pitts, E. R., Budner, D., & Thompson-Witrick, K. A. (2022). Kombucha: Biochemical and microbiological impacts on the chemical and flavor profile. Food Chemistry Advances, 1. https://doi.org/10.1016/j.focha.2022.100025.
Blanc, P. J. (1996). Characterization of the tea fungus metabolites. Biotechnology Letters, 18(2). https://doi.org/10.1007/BF00128667.
Dutta, H., & Paul, S. K. (2019). Kombucha Drink: Production, Quality, and Safety Aspects. In Production and Management of Beverages. https://doi.org/10.1016/b978-0-12-815260-7.00008-0.
Hidayat, S. N., Nuringtyas, T. R., & Triyana, K. (2018). Electronic Nose Coupled with Chemometrics for Monitoring of Tempeh Fermentation Process. Proceedings - 2018 4th International Conference on Science and Technology,. ICST. https://doi.org/10.1109/ICSTC.2018.8528580.
Hotmaria Simanjuntak, D., & Lestari, D. (2016). FishtecH-Jurnal Teknologi Hasil Perikanan Karakteristik Kimia dan Aktivitas Antioksidan Kombucha dari Tumbuhan Apu-apu (Pistia stratiotes) Selama Fermentasi., 5(2), 123–133.
Inayah, S. N., Nurul, W., Jainudin Heremba, M., Samloy, Y., & Tuapattinaya, P. M. J. (2019). Uji Organoleptik Enhalus Tea Berdasarkan Cara Pengeringan Dan Tingkat Ketuaan Daun Secara Morfologi. Scie Map J, 1(2), 65–72.
Ita Purnami, K., Anom Jambe, A., & Wayan Wisaniyasa, N. (2018). Pengaruh Jenis Teh Terhadap Karakteristik Teh Kombucha. Itepa: Jurnal Ilmu Dan Teknologi Pangan, 7(2). https://doi.org/10.24843/itepa.2018.v07.i02.p01.
Jayabalan, R., Marimuthu, S., & Swaminathan, K. (2007). Changes in content of organic acids and tea polyphenols during kombucha tea fermentation. Food Chemistry, 102(1). https://doi.org/10.1016/j.foodchem.2006.05.032.
Kovacs, Z., Bodor, Z., Zaukuu, J. L. Z., Kaszab, T., Bazar, G., Tóth, T., & Mohácsi-Farkas, C. (2020). Electronic nose for monitoring odor changes of Lactobacillus species during milk fermentation and rapid selection of probiotic candidates. Foods, 9(11). https://doi.org/10.3390/foods9111539.
Kruk, M., Trząskowska, M., Ścibisz, I., & Pokorski, P. (2021). Application of the “scoby” and kombucha tea for the production of fermented milk drinks. Microorganisms, 9(1). https://doi.org/10.3390/microorganisms9010123.
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. In Applied Predictive Modeling. https://doi.org//10.1007/978-1-4614-6849-3.
Kumar, S. D., Narayan, G., & Hassarajani, S. (2008). Determination of anionic minerals in black and kombucha tea using ion chromatography. Food Chemistry, 111(3). https://doi.org/10.1016/j.foodchem.2008.05.012.
Laavanya, D., Shirkole, S., & Balasubramanian, P. (2021). Current challenges, applications and future perspectives of SCOBY cellulose of Kombucha fermentation. In Journal of Cleaner Production, 295. https://doi.org/10.1016/j.jclepro.2021.126454.
Laureys, D., Britton, S. J., & de Clippeleer, J. (2020). Kombucha Tea Fermentation: A Review. In Journal of the American Society of Brewing Chemists, 78(3). https://doi.org/10.1080/03610470.2020.1734150.
Lazaro, J. B., Ballado, A., Bautista, F. P. F., So, J. K. B., & Villegas, J. M. J. (2018). Chemometric data analysis for black tea fermentation using principal component analysis. In AIP Conference Proceedings (p. 2045). https://doi.org/10.1063/1.5080863.
Leal, J. M., Suárez, L. V., Jayabalan, R., Oros, J. H., & Escalante-Aburto, A. (2018). A review on health benefits of kombucha nutritional compounds and metabolites. CYTA - Journal of Food, 16(1). https://doi.org/10.1080/19476337.2017.1410499.
Li, Y.-X., Lai, H.-M., & Chen, C.-P. (2017). A Scientometric Review of the Current Status and Emerging Trends in Project-Based Learning. International Journal of Information and Education Technology, 7(8), 581–584. https://doi.org/10.18178/ijiet.2017.7.8.935.
Sanaeifar, A., Mohtasebi, S. S., Ghasemi-Varnamkhasti, M., & Ahmadi, H. (2016). Application of MOS based electronic nose for the prediction of banana quality properties. Measurement: Journal of the International Measurement Confederation, 82. https://doi.org/10.1016/j.measurement.2015.12.041.
Saputri, R. K., Al-bari, A., Nahdlatul, U., Sunan, U., & Bojonegoro, G. (2020). Pengaruh Konsumsi Teh dengan Tingkat Obesitas Mahasiswa Farmasi Universitas Nahdlatul Ulama Sunan Gir. ,. Jurnal Penjas Dan Farmasi, 3.
Seesaard, T., & Wongchoosuk, C. (2022). Recent Progress in Electronic Noses for Fermented Foods and Beverages Applications. Fermentation, 8(7), 302. https://doi.org/10.3390/fermentation8070302.
Sharma, P., Ghosh, A., , Tudu, B., Sabhapondit, S., Baruah, B. D., Tamuly, P., Bhattacharyya, N., & Bandyopadhyay, R. (2015). Monitoring the fermentation process of black tea using QCM sensor based electronic nose. Sensors and Actuators, B: Chemical, 219. https://doi.org/10.1016/j.snb.2015.05.013.
Sharmilan, T., Premarathne, I., Wanniarachchi, I., Kumari, S., & Wanniarachchi, D. (2020). Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process. In Journal of Sensors, 2020. https://doi.org/10.1155/2020/3073104
Sharmilan, T., Premarathne, I., Wanniarachchi, I., Kumari, S., & Wanniarachchi, D. (2022). Application of Electronic Nose to Predict the Optimum Fermentation Time for Low-Country Sri Lankan Tea. Journal of Food Quality. https://doi.org/10.1155/2022/7703352.
Triyana, K., Taukhid Subekti, M., Aji, P., Nur Hidayat, S., & Rohman, A. (2015). Development of Electronic Nose with Low-Cost Dynamic Headspace for Classifying Vegetable Oils and Animal Fats. Applied Mechanics and Materials, 771. https://doi.org/10.4028/www.scientific.net/amm.771.50.
Villarreal-Soto, S. A., Beaufort, S., Bouajila, J., Souchard, J. P., & Taillandier, P. (2018). Understanding Kombucha Tea Fermentation: A Review. In Journal of Food Science, 83(3), 580–588. https://doi.org/10.1111/1750-3841.14068.
Wakhid, S., Sarno, R., & Sabilla, S. I. (2022). The effect of gas concentration on detection and classification of beef and pork mixtures using E-nose. Computers and Electronics in Agriculture, 195. https://doi.org/10.1016/j.compag.2022.106838.
Wijaya, D. R., Sarno, R., & Zulaika, E. (2016). Sensor array optimization for mobile electronic nose: Wavelet transform and filter based feature selection approach. International Review on Computers and Software, 11(8). https://doi.org/10.15866/irecos.v11i8.9425.
Wu, Q. J., Dong, Q. H., Sun, W. J., Huang, Y., Wang, Q. Q., & Zhou, W. L. (2014). Discrimination of Chinese teas with different fermentation degrees by stepwise linear discriminant analysis (S-LDA) of the chemical compounds. Journal of Agricultural and Food Chemistry, 62(38). https://doi.org/10.1021/jf5025483.
Yu, D., & Gu, Y. (2021). A machine learning method for the fine-grained classification of green tea with geographical indication using a mos-based electronic nose. Foods, 10(4). https://doi.org/10.3390/foods10040795.
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Hak Cipta (c) 2022 Budi Sumanto, Yessi Idianingrum TW, Shafura Humaira, Ratna Lestari Budiani, Muhammad Arrofiq
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