Abbasi, S. & Rahimi, S. (2006). Investigating the effect of concentration, temperature, viscosity and rotation speed on the flow behavior of Iranian Tragacanth gum solution. Iranian Journal of Food Sciences and Industries, 2(4): (in Persian). https://fsct.modares.ac.ir.
Anon. (2001). Tragacanth Gum. Metals and arsenic specifications revised at the 57th JECFA. https://cybercolloids.net/information/technical-articles/tragacanth-gum-jecfa-specification/
Balaghi, S., Mohammadifar, M., & Zargaraan, A. (2010). Physicochemical and rheological characterization of gum tragacanth exudates from six species of Iranian Astragalus. Food Biophysics, 5(1), 59e71. DOI: 10.1007/s11483-009-9144-5
Barak, S., Mudgil, D., & Taneja, S. (2020). Exudate gums: Chemistry, properties and food applications–a review. Journal of the Science of Food and Agriculture, 100(7), 2828–2835. DOI: 10.1002/jsfa.10302
Boamah, P. O., Afoakwah, N. A., Onumah, J., Osei, E. D. & Mahunu, G. K. (2023). Physicochemical properties, biological properties and applications of gum tragacanth-a review. Carbohydrate Polymer Technologies and Applications 5 (2023) 100288. DOI: 10.1016/j.carpta.2023.100288
Chew, A. K., Sender, M., Kaplan, Z., Chandrasekaran, A., Chief Elk, J., Browning, A. R., Shaun Kwak, H., Halls, M. d. & Afzal, M. A. F. (2024). Advancing material property prediction: using physics-informed machine learning models for viscosity. DOI: 10.26434/chemrxiv-2023-1qfw8-v2
Haroon, K., Arafeh, A., Martin, P., Rodgers, T., Mendoza, C. & Baker, M. (2019). Use of inline near-infrared spectroscopy to predict the viscosity of shampoo using multivariate analysis. International Journal of Cosmetic Science, 2019, 41, 346–356. DOI: 10.1111/ics.12536
Imanmehr, A & Heidarisoltanabadi, M. (2024). The effect of mechanical processing methods on the color and viscosity properties of Tragacanth gum (In persian). Journal of Research in Mechanics of Agricultural Machinery. 13(2), 79-86. DOI: 10.22034/JRMAM.2024.14735.702
Lee, S., Jeong, S. & Lee, S. (2026). Hyperspectral imaging-based classification and viscosity prediction of hydrocolloids using convolutional neural network and ensemble models. Journal of Food Engineering. Volume 409. https://doi.org/10.1016/j.jfoodeng.2025.112902
Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., & Borodulin, A. (2025). Boosting-Based Machine Learning Applications in Polymer Science: A Review. Polymers 2025, 17, 499. DOI: 10.3390/polym17040499
Ninchan, B., Chauywongyart, P., Utapong, T., & Thamrongsiripak, N. (2024). Effect of Gamma Irradiation on Depolymerization and Property Changes of Gum Tragacanth. https://doi.org/10.1155/ijbm/8875341
Petkovska, J., Geskovski, N., Markovi´c, D., Dimova, V., Mirakovski, D., Radeti´c, M. & Jordanov, I. (2024). Chitosan-pectin multilayer coating with anthocyanin grape dye as pH indicating wound dressing: Synthesis and characterization. Carbohydrate Polymer Technologies and Applications 7 (2024) 100438. DOI: 10.1016/j.carpta.2024.100438
Qi, H., Li, H., Chen, L., Chen, F., Luo, J. & Zhang, C. (2024). Hyperspectral Imaging Using a Convolutional Neural Network with Transformer for the Soluble Solid Content and pH Prediction of Cherry Tomatoes. Foods 2024, 13, 251. https://doi.org/10.3390/foods13020251
Qiu, L., Zhang, M., Mujumdar, A. S. Chang, L. (2022). Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq “Crimsin Glory” H.T.) petals during infrared drying. Food Chemistry Volume 369, 1 February 2022, 130951. DOI: 10.1016/j.foodchem.2022.130951
Qomshi Bozorg, P., Wahabi, M. & Fazilati, M. (2012). Qualitative investigation of white gossip (Astragalus gossypinus Fischer.) in the western region of Isfahan province. Scientific-research quarterly of medicinal and aromatic plants. 27(4): 668-680. (in Persian). DOI: 10.22092/ijmapr.2012.4516
Tavakol, M. & Mohammadifar, M. A. (2017). A review on gum tragacanth and its biomedical applications. Nashrieh Shimi va Mohandesi Shimi Iran (NSMSI), 36(84): 1-20. (in Persian). https://www.nsmsi.ir/article_29721_en.html
Xuan,W., Zhao, C., Ma, Z., Liu, Z. & Zhang, J. (2025) Data-driven machine learning methods to predict the viscosity-temperature characteristics for gasification slags. Fuel. Volume 398. https://doi.org/10.1016/j.fuel.2025.135287
Zahedi, A. H. & Vedadi, I. (1979). Effect of Gum Tragacanth size reduction on DTA and TG behavior and its relationship to solution viscosity. Thermochimica Acta, 34: 281-286. https://doi.org/10.1016/0040-6031(79)87118-X
Zhang, W., Kasun, L. C., Wang, Q. J., Zheng, Y. & Lin, Z. (2022). A Review of Machine Learning for Near-Infrared Spectroscopy. Sensors 2022, 22, 9764.
