The Potential of Cassava Starch to Improve the Quality of Artificial Nori Made from Green Grass Jelly Leaves and Papaya

Abstract

Artificial nori is a dry sheet food product resembling nori made from materials other than seaweed. In this study, nori was made from green grass jelly and papaya. The quality of artificial nori can be influenced by its binding material. Cassava starch has potential as a binder in making analog nori. This study aims to determine the potential of cassava starch to improve the quality of artificial nori made from green grass jelly leaves in terms of moisture content, total dissolved solids, water absorption capacity, texture, and sensory quality. The addition of 5% cassava starch to artificial nori made from grass jelly and papaya leaves reduced the total dissolved solids, water absorption capacity and hard texture of artificial nori, but had no effect on other sensory properties. Cassava starch has the potential to improve the texture of artificial nori so that it is drier but not too brittle.

Country : Indonesia

1 Sri Mulyani2 Setya Budi M Abduh3 Nurwantoro4 Lutfindar Rizki

  1. Associate Professor, Department of Agriculture, Universitas Diponegoro, Central Java, Indonesia
  2. Assistant Professor, Department of Agriculture, Universitas Diponegoro, Central Java, Indonesia
  3. Associate Professor, Department of Agriculture, Universitas Diponegoro, Central Java, Indonesia
  4. Student, Department of Agriculture, Universitas Diponegoro, Central Java, Indonesia

IRJIET, Volume 8, Issue 4, April 2024 pp. 1-7

doi.org/10.47001/IRJIET/2024.804001

References

  1. Erman, A. Dilo, and P. Havinga, “A virtual infrastructure based on honeycomb tessellation for data dissemination in multi-sink mobile wireless sensor networks,” EURASIP J. Wireless Commun. Netw., vol. 2012, no. 17, pp. 1–54, 2012.
  2. Kinalis, S. Nikoletseas, D. Patroumpa, and J. Rolim, “Biased sink mobility with adaptive stop times for low latency data collection in sensor networks,” Inf. Fusion, vol. 15, pp. 56–63, Jan. 2014.
  3. W. Khan, A. H. Abdullah, M. H. Anisi, and J. I. Bangash, “A comprehensive study of data collection schemes using mobile sinks in wireless sensor networks,” Sensors, vol. 14, no. 2, pp. 2510–2548, 2014.
  4. Nazir and H. Hasbullah, “Mobile sink based routing protocol (MSRP) for prolonging network lifetime in clustered wireless sensor network,” in Proc. Int. Conf. Comput. Appl. Ind. Electron. (ICCAIE), pp. 624–629, Dec. 2010.
  5. E. B. Hamida and G. Chelius, “Strategies for data dissemination to mobile sinks in wireless sensor networks,” IEEE Wireless Commun., vol. 15, no. 6, pp. 31–37, Dec. 2008.
  6. Chalermek, R. Govindan, and D. Estrin, “Directed diffusion: A scalable and robust communication paradigm for sensor networks,” in Proc. ACM SIGMOBILE Int. Conf. Mobile Computer Network (MOBICOM), pp. 56–67, 2000.
  7. M. Di Francesco, S. K. Das, and G. Anastasi, “Data collection in wireless sensor networks with mobile elements,” ACM Trans. Sensor Netw., vol. 8, no. 1, pp. 1–31, Aug. 2011.
  8. S. R. Gandham, M. Dawande, R. Prakash, and S. Venkatesan, “Energy efficient schemes for wireless sensor networks with multiple mobile base stations,” in Proc. IEEE Global Telecommun. Conf. (GLOBECOM), vol. 1. pp. 377–381, Dec. 2003.
  9. T. Banerjee, B. Xie, J. H. Jun, and D. P. Agrawal, “Increasing lifetime of wireless sensor networks using controllable mobile cluster heads,” Wireless Commun. Mobile Comput., vol. 10, no. 3, pp. 313–336, Mar. 2010.
  10. T.S. Chen, H.-W. Tsai, Y.-H. Chang, and T.-C. Chen, “Geographic converge cast using mobile sink in wireless sensor networks,” Comput. Commun., vol. 36, no. 4, pp. 445–458, Feb. 2013.
  11. W. M. Aioffi, C. A. Valle, G. R. Mateus, and A. S. da Cunha, “Balancing message delivery latency and network lifetime through an integrated model for clustering and routing in wireless sensor networks,” Comput. Netw., vol. 55, no. 13, pp. 2803–2820, Sep. 2011.