SmartAgro-Spectral: Teknik Pengukuran Kandungan Nitrit Pada Sarang Burung Walet Berbasis Spektral Menggunakan Metode Regresi Linier
I will put the dimension here
Abstract
Indonesia is the largest exporter of edible bird’s nest (EBN) to China, involving many EBN farmers from various regions in Indonesia. Therefore, a portable device is needed to rapidly and accurately measure the required quality of SBW to avoid rejection by Chinese buyers, which could result in significant losses. Consequently, for this purpose, an electronic instrument has been developed. smartAgro-Spectral is a microcontroller-based electronic instrument that measures nitrite content in edible bird’s nest (EBN) using linear regression method in machine learning calculations. This instrument can measure nitrite content based on the intensity of colors produced by EBN products. The coloring process is carried out by mixing EBW powder with Sulphanilamide solution and N-(1-naphthyl) Ethylenediamine Dihydrochloride (NED) solution. The concentration of EBN solution is normalized to values between 0.2 ppm and 0.7 ppm. The measurement process is carried out by emitting 18 waves of the light spectrum. The intensity of the 18 wavelengths of the measured light spectrum was selected based on the strong correlation between the intensity of the light spectrum and the value of nitrite content in the EBN product. The measurement results show that the intensity of the light spectrum that has a strong linear correlation is at wavelengths of 460 nm, 485 nm, 510 nm, 535 nm, and 610 nm. So, smartAgro-Spectral electronic instruments can be realized based on the intensity relationship of each wavelength through multiple linear regression analysis, and are able to linearly measure nitrite content in EBW products with a precision level of 99.85% and an accuracy rate of 99.85%.
Downloads
References
A. Mudan, D. Repplinger, J. Lebin, J. Lewis, R. Vohra, and C. Smollin, “Severe Methemoglobinemia and Death From Intentional Sodium Nitrite Ingestions,” J. Emerg. Med., vol. 59, no. 3, pp. e85–e88, 2020, doi: 10.1016/j.jemermed.2020.06.031.
Ö. Özdestan and A. Üren, “Nitrate and Nitrite Contents of Baby Foods,” Akad. Gıda, vol. 10, no. 4, pp. 11–18, 2012, [Online]. Available: http://www.academicfoodjournal.com.
K. Zengin, C. Atilgan, and E. Çaglarer, “Cost-effective colour sensor construction,” Majlesi J. Electr. Eng., vol. 12, no. 2, pp. 69–71, 2018.
S. Kabeer, P. Veeramani, S. Lakshmanan, and S. S. Nandhini, “Quality Detection of Beverage Using Sensor,” vol. 4, no. 8, pp. 123–127, 2021.
Lawless HT and Heymann H., Sensory evaluation of food: principles and practices, Vol. 2. Newyork: Springer US, 2010.
N. H. Jamalluddin, N. A. Tukiran, N. Ahmad Fadzillah, and S. Fathi, “Overview of edible bird’s nests and their contemporary issues,” Food Control, vol. 104, no. May, pp. 247–255, 2019, doi: 10.1016/j.foodcont.2019.04.042.
G. K. Chan, K. Y. Zhu, D. J. Chou, A. J. Guo, T. T. Dong, and K. W. Tsim, “Surveillance of nitrite level in cubilose: evaluation of removal method and proposed origin of contamination,” Food Control, vol. 34, no. 2, pp. 637–644, 2013.
Wolff IA and Wasserman AE, “Nitrates, nitrites, and nitrosamines,” Science (80-. )., vol. 177, no. 4043, pp. 15–9, 1972.
Z. C. F. Wong, G. K. L. Chan, T. T. X. Dong, and K. W. K. Tsim, “Origin of Red Color in Edible Bird’s Nests Directed by the Binding of Fe Ions to Acidic Mammalian Chitinase-like Protein,” J. Agric. Food Chem., vol. 66, no. 22, pp. 5644–5653, 2018, doi: 10.1021/acs.jafc.8b01500.
E. K. S. Shim, G. F. Chandra, and S. Y. Lee, “Thermal analysis methods for the rapid identification and authentication of swiftlet (Aerodramus fuciphagus) edible bird’s nest – A mucin glycoprotein,” Food Res. Int., vol. 95, pp. 9–18, 2017, doi: 10.1016/j.foodres.2017.02.018.
H. Min, W. Jia, X. F. Wang, Y. Zhao, and Y. T. Luo, “A polynomial piecewise constant approximation method based on dual constraint relaxation for segmenting images with intensity inhomogeneity,” Pattern Recognit., vol. 73, pp. 15–32, 2018, doi: 10.1016/j.patcog.2017.07.002.
X. Huang et al., “A low cost smart system to analyze different types of edible Bird’s nest adulteration based on colorimetric sensor array,” J. Food Drug Anal., vol. 27, no. 4, pp. 876–886, 2019, doi: 10.1016/j.jfda.2019.06.004.
S. Defit, G. W. Nurcahyo, P. Studi, and M. Ilmu, “Decision Support System in Identification of Swallow ’ s Nest Quality with Weighted Product Method,” vol. 3, no. 1, pp. 181–192, 2021.
C. D. Sachi Agrawal, Sakshi Jha, “Grading of Tomatoes Using Digital Image Processing on the Basis of Size,” Int. J. Res. Eng. Technol., vol. 05, no. 10, pp. 109–111, 2016, doi: 10.15623/ijret.2016.0510020.
Y. Dai, J. Cao, Y. Wang, Y. Chen, and L. Jiang, “A comprehensive review of edible bird’s nest,” Food Res. Int., vol. 140, no. November, p. 109875, 2021, doi: 10.1016/j.foodres.2020.109875.
A. Harjoko, A. Prahara, T. W. Supardi, I. Candradewi, R. Pulungan, and S. Hartati, “Image processing approach for grading tobacco leaf based on color and quality,” Int. J. Smart Sens. Intell. Syst., vol. 12, no. 1, pp. 1–10, 2019, doi: 10.21307/ijssis-2019-010.
G. Seiphepi, A. M. Zungeru, J. Gaboitaolelwe, C. Lebekwe, and B. Mtengi, “Automatic bell pepper colour detector and sorting machine,” Int. J. Eng. Res. Technol., vol. 13, no. 11, pp. 3156–3166, 2020, doi: 10.37624/ijert/13.11.2020.3156-3166.
Y. Hendrawan et al., “Deep Learning to Detect and Classify the Purity Level of Luwak Coffee Green Beans,” Pertanika J. Sci. Technol., vol. 30, no. 1, pp. 1–18, 2022, doi: 10.47836/pjst.30.1.01.
N. J. Watson et al., “Intelligent Sensors for Sustainable Food and Drink Manufacturing,” Front. Sustain. Food Syst., vol. 5, 2021, doi: 10.3389/fsufs.2021.642786.
N. S. Ayush Pratap, “Machine learning-based image processing in materials science and engineering: A review,” 2022, doi: https://doi.org/10.1016/j.matpr.2022.01.200.
Y. H. Yeo and K. S. Yen, “Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model,” Int. J. Eng. Technol. Innov., vol. 11, no. 2, pp. 135–145, 2021, doi: 10.46604/IJETI.2021.6891.
Y. Huo et al., “Towards Portable Large-Scale Image Processing with High-Performance Computing,” J. Digit. Imaging, vol. 31, no. 3, pp. 304–314, 2018, doi: 10.1007/s10278-018-0080-0.
K. Molane, M. Kharade, S. Bhavsar, S. Rathod, and S. Mishra, “Microcontroller Based Product Sorting by Using Colour Sensor,” Int. J. Progress. Res. Sci. Eng. , vol. 1, no. 6, pp. 69–73, 2020, [Online]. Available: https://www.journals.grdpublications.com/index.php/ijprse/article/view/183.
Á. G. Estévez-Pedraza, R. Martínez-Méndez, O. Portillo-Rodríguez, J. Rodríguez-Arce, and L. Parra-Rodríguez, “Portable electronic device to assess the human balance using a minimum number of sensors,” Biomed. Phys. Eng. Express, vol. 6, no. 1, 2020, doi: 10.1088/2057-1976/ab6858.
J. E. Gan and W. K. Lai, “Automated Grading of Edible Birds Nest Using Hybrid Bat Algorithm Clustering Based on K-Means,” 2019 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2019 - Proc., no. June, pp. 73–78, 2019, doi: 10.1109/I2CACIS.2019.8825077.
R. Hudaya et al., “Development of Spectral Sensors for Nitrite Content in Edible Bird’s Nest,” Proc. 2nd Int. Semin. Sci. Appl. Technol. (ISSAT 2021), vol. 207, no. Issat, pp. 88–92, 2021, doi: 10.2991/aer.k.211106.015.
Copyright (c) 2023 Rida Hudaya, Hepi Ludiyati, Feni Isdaryani, Muhamad Rafhi Rihadatus Syawal, Julian Harith Al Banny Hudaya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Pernyataan Hak Cipta dan Lisensi
Dengan mengirimkan manuskrip ke Journal of Applied Computer Science and Technology (JACOST), penulis setuju dengan kebijakan ini. Tidak diperlukan persetujuan dokumen khusus.
- Hak cipta pada setiap artikel adalah milik penulis.
- Penulis mempertahankan semua hak mereka atas karya yang diterbitkan, tak terbatas pada hak-hak yang diatur dalam laman ini.
- Penulis mengakui bahwa Journal of Applied Computer Science and Technology (JACOST) sebagai yang pertama kali mempublikasikan dengan lisensi Creative Commons Atribusi 4.0 Internasional (CC BY-SA).
- Penulis dapat memasukan tulisan secara terpisah, mengatur distribusi non-ekskulif dari naskah yang telah terbit di jurnal ini kedalam versi yang lain (misal: dikirim ke respository institusi penulis, publikasi kedalam buku, dll), dengan mengakui bahwa naskah telah terbit pertama kali pada Journal of Applied Computer Science and Technology (JACOST);
- Penulis menjamin bahwa artikel asli, ditulis oleh penulis yang disebutkan, belum pernah dipublikasikan sebelumnya, tidak mengandung pernyataan yang melanggar hukum, tidak melanggar hak orang lain, tunduk pada hak cipta yang secara eksklusif dipegang oleh penulis.
- Jika artikel dipersiapkan bersama oleh lebih dari satu penulis, setiap penulis yang mengirimkan naskah menjamin bahwa dia telah diberi wewenang oleh semua penulis bersama untuk menyetujui hak cipta dan pemberitahuan lisensi (perjanjian) atas nama mereka, dan setuju untuk memberi tahu rekan penulis persyaratan kebijakan ini. Journal of Applied Computer Science and Technology (JACOST) tidak akan dimintai pertanggungjawaban atas apa pun yang mungkin timbul karena perselisihan internal penulis.
Lisensi :
Journal of Applied Computer Science and Technology (JACOST) diterbitkan berdasarkan ketentuan Lisensi Creative Commons Atribusi 4.0 Internasional (CC BY-SA). Lisensi ini mengizinkan setiap orang untuk :.
- Berbagi — menyalin dan menyebarluaskan kembali materi ini dalam bentuk atau format apapun;
- Adaptasi — menggubah, mengubah, dan membuat turunan dari materi ini untuk kepentingan apapun.
Lisensi :
-
Atribusi — Anda harus mencantumkan nama yang sesuai, mencantumkan tautan terhadap lisensi, dan menyatakan bahwa telah ada perubahan yang dilakukan. Anda dapat melakukan hal ini dengan cara yang sesuai, namun tidak mengisyaratkan bahwa pemberi lisensi mendukung Anda atau penggunaan Anda.
-
BerbagiSerupa — Apabila Anda menggubah, mengubah, atau membuat turunan dari materi ini, Anda harus menyebarluaskan kontribusi Anda di bawah lisensi yang sama dengan materi asli.
