Application of Spectrophotometer and Photocolorimetric Method from Mobile Phone for Soybean Meal Rawness Measurement
Article Sidebar
Main Article Content
Abstract
This study aimed to create a detection method for measuring rawness of soybean meals using a spectrophotometer and photocolorimetric method. A relationship between the percentage of the rawness of soybeans was studied by the detection of urease content using phenol red as an indicator. Absorbance values from the spectrophotometer, and color values generated by the Adobe Photoshop software from the picture of IOS and Android mobile phones were then measured. The results showed that the percentage of the rawness of soybeans was positively correlated with absorbance values from the spectrophotometer (r = 0.942;P < 0.01), (y = 33.738x - 13.236;R² = 0.886). In addition, the percentage of the rawness of soybeans was negatively correlated with the yellow (b*) values from IOS (r = -0.955;P < 0.01), (y = -0.3001x + 12.645;R² = 0.912). Furthermore, the percentage of the rawness of soybeans was negatively correlated with the green (G) values from Android (r = -0.964;P < 0.01), (y = -0.1119x + 18.395;R² = 0.929). Moreover, the percentage of the rawness of soybeans was negatively correlated with the green (G) values from Mobile (r = -0.941;P < 0.01), (y = -0.1093x + 17.727;R² = 0.886). The rawness of soybeans by spectrophotometer and photocolorimetric methods were then compared. This experimental design was a completely randomized design (CRD) consisting of 4 treatments (spectrophotometer analysis and photocolorimetric method from IOS Android, and mobile phones), (10 replications per treatment). The results showed a statistically significant difference between a method for determining rawness in soybean meal based on spectrophotometer analysis and a photocolorimetric method (P < 0.01). The results from the spectrophotometer analysis method for measuring the rawness of soybean meal produced were close to the actual rawness value and can be used to inspect the quality of soybean meal.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
King Mongkut's Agricultural Journal
References
Ayilara, M. S., Olanrewaju, O. S., Babalol, O. O. & Odeyemi, O. (2020). Waste management through composting: challenges and potentials. Sustainability. 12, 4456.
Caprita, R., Caprita, A., Ilia, G., Cretescu, I. & Simulescu, V. O. (2010). Laboratory procedures for assessing quality of soybean meal. In Proceedings of the World Congress on Engineering and Computer Science 2010. San Francisco: Internaitional association of engineer, USA.
Chang, B. Y. (2012). Smartphone-based chemistry instrumentation: digitization of colorimetric measurements. Bulletin of the Korean Chemical Society. 33(2), 549-552.
Choodum, A., Parabun, K., Klawach, N., Daeid, N. N., Kanatharana, P. & Wongniramaikul, W. (2014). Real time quantitative colorimetric test for methamphetamine detection using digital and mobile phone technology. Forensic Science International. 235, 8-13.
Dhruv, A., Surendra, K. G. P., Devanshi, Y. A., Manoj, K. S., Rahul, S. & Amit, G. (2016). Shade selection: spectrophotometer vs digital camera – a comparative in-vitro study. Annals of Prosthodontics & Restorative Dentistry. 2(3), 73-78.
Firdaus, M. L., Alwi, W., Trinoveldi, F., Rahayu, I., Rahmidar, L. & Warsito, K. (2014). Determination of chromium and iron using digital image-based colorimetry. Procedia Environmental Sciences. 20, 298-304.
Franco, M. O. K, Suarez, W. T., Maia, M. V. & Santos, V. B. (2017). Smartphone application for methanol determination in sugar cane spirits employing digital image-based method. Food Analysis Methods. 10, 2102-2109.
Gabor, K. & Péter, E. (2015). Implementation of mobile phones in education. Research in Pedagogy. 5, 98-108.
Georgieva-Nikolova, M. & Stoykova, V. (2020). Application of mobile devices in distance learning. In The 15th international conference on virtual learning ICVL. (pp. 272-277). University of Bucharest, Bucharest, Romania.
Girolami, A., Napolitano, F., Faraone, D. & Braghieri, A. (2013). Measurement of meat color using a computer vision system. Meat Science. 93, 111-118.
Gómez-García, M., Soto-Varela, R., Morón-Marchena, J. & del Pino-Espejo, M. (2020). Using mobile devices for educational purposes in compulsory secondary education to improve student’s learning achievements. Sustainability. 12, 3724.
Hamathulin, A., Simla, S., Boontang, S. & Inchuen, S. 2012. Relationship between color value and anthocyanin content in purple waxy corn germplasm. Khon Kaen AGR. J. 40 (Suppl.4), 59-64. (in Thai).
Is-haak, J., Taparhudee, W., joongjaraunsuk, R., biakaew, T. & Pandee, T. (2015). Application of a webcam and image processing techniquesfor measuring total ammonia and nitrite concentrationsin fresh water. RMUTSB Acadamic Journal. 2(3), 129-136. (in Thai).
Khajarern, J. (2017). Manual of feed microscopic and quality control. 2nd ed. Khon Kean: Thai Society of Animal Nutritionists National Farmers Council of Thailand.
Liu, F., Cheng, X., Miu, J., Li, X., Yin, R., Wang, J. & Qu, Y. (2021). Application of different methods to determine urease activity in enzyme engineering experiment and production. E3S Web of Conferences. 251, 02057.
Mendoza, F. & Aguilera, J. M. (2004). Application of image analysis for classification of ripening bananas. Journal of Food Science. 69, 471-477.
Nopparatmaitree, M., Montirach, P., Chotnipat, S., Raksasiri, B. V., Glinubon, J., Arkaphati, C. & Chaowalit, O. (2022). The development of photocolorimetric methodology for determining egg yolk color of laying hens as learning platform during the COVID-19 outbreak situation. Khon Kaen Agriculture Journal. 50(1), 62-75. (in Thai).
Nopparatmaitree, M., Sritulanond, S., Khunkhao, S., Konchan, N., Srapothong, A., Chotnipat, S., Raksasiri, B. V., Glinubon, J., Arkaphati, C. & Chaowalit, O. (2021). The photocolorimetric methodology as an alternative tool for chicken meat color assessment. Journal Mahanakorn Veterinary Medicine. 16(1), 147-158.
Nopparatmaitree, M., Wongwean, P., Riab-Roi, P., Chotnipat, S., Pootaeng-On, Y., Glinubon, J., Srakaew, W., Arkaphati, C. & Chaowalit, O. (2023). Development of technique for measuring non protein nitrogen in soybean meal by spectrophotometer and photocolorimetric methodology. Khon Kaen Agriculture Journal. 51(Suppl.1), 31-39. (in Thai).
R Core Team. R. (2020). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
Rocha, C., Durau, J. F., Barrilli, L. N. E., Dahlke, F., Maiorka, P. & Maiorka, A. (2014). The effect of raw and roasted soybeans on intestinal health, diet digestibility, and pancreas weight of broilers. Journal of Applied Poultry Research. 23(1), 71-79.
Saad, A. G., Iberahim, A., & El-Bialee, N. (2016). Internal quality assessment of tomato fruits using image colour analysis. Agricultural Engineering International: CIGR Journal. 18, 339-352.
SAS. (1996). SAS User’s guide: a basic version 6. 4th edition. North Carolina: SAS intitute inc, USA.
Shane K. K., Landmark, J. D. & Stickle, D. F. (2006). Demonstration of absorbance using digital color image analysis and colored solutions. Journal of Chemical Education. 4(83), 644-646.
Steel, R. G. D. & Torrie, J. H. (1992). Principles and Procedure Statistics. 2nd ed. Singapore: McGraw-Hill Book Co., Inc, Singapore.
Tanong, K., Jatuporn, C., Suvanvihok, V. & Seerasarn, N. (2021). Forecasting import demand for soybean meal in thailand using box-jenkins method. Journal of Hunan University (Natural Sciences). 48(5), 58-65.
Thaenthanee, W., Dechoswang, K. & Pongpiachan, P. (2006). Soybean rawness measurement by spectrophotometer. Kamphaengsaen Academic Journal. 4(1), 8-14. (in Thai).
Thongchai, W., Chammui, Y., Poolprasert, P. & Thongchai, S. (2019). Using a digital image camera and colorimetric analysis for simple detecting soil quality in Nam Dok Mai mango orchards. Agricultural Science Journal. 50(2), 211-223. (in Thai).
Trinderup, C. H. & Kim, Y. H. B. (2015). fresh meat color evaluation using a structured light imaging system. Food Research International. 71, 100-107.
Zlatev, Z. & Baycheva, S. (2017). Application of educational technical tools for analysis the color of essential oils from white oregano. In Proceedings of the 12th International Conference on Virtual learning (ICVL 2017). (pp. 144-144). Sibiu: University of Bucharest, “Lucian Blaga” University of Sibiu in cooperation with SIVECO Romania.
