دانشگاه سمنان

 آمار

تعداد نشریات21
تعداد شماره‌ها691
تعداد مقالات10,035
تعداد مشاهده مقاله71,039,831
تعداد دریافت فایل اصل مقاله62,489,425
Amini Hoshiar, Mohammad, Jafarian, Sara, Rezaian, Ramezan, Sharifi Soltani, Mahdi. (1402). Handling the Impact of Uncertainties on Predicting the Quality Aspects of Doogh. هنرهای کاربردی, 3(4), 53-62. doi: 10.22075/mseee.2025.34738.1170
Mohammad Amini Hoshiar; Sara Jafarian; Ramezan Rezaian; Mahdi Sharifi Soltani. "Handling the Impact of Uncertainties on Predicting the Quality Aspects of Doogh". هنرهای کاربردی, 3, 4, 1402, 53-62. doi: 10.22075/mseee.2025.34738.1170
Amini Hoshiar, Mohammad, Jafarian, Sara, Rezaian, Ramezan, Sharifi Soltani, Mahdi. (1402). 'Handling the Impact of Uncertainties on Predicting the Quality Aspects of Doogh', هنرهای کاربردی, 3(4), pp. 53-62. doi: 10.22075/mseee.2025.34738.1170
Amini Hoshiar, Mohammad, Jafarian, Sara, Rezaian, Ramezan, Sharifi Soltani, Mahdi. Handling the Impact of Uncertainties on Predicting the Quality Aspects of Doogh. هنرهای کاربردی, 1402; 3(4): 53-62. doi: 10.22075/mseee.2025.34738.1170

Handling the Impact of Uncertainties on Predicting the Quality Aspects of Doogh

Journal of Modeling and Simulation in Electrical and Electronics Engineering
دوره 3، شماره 4 - شماره پیاپی 14، اردیبهشت 2024، صفحه 53-62    اصل مقاله (891.79 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22075/mseee.2025.34738.1170
نویسندگان
Mohammad Amini Hoshiar1؛ Sara Jafarian* 2؛ Ramezan Rezaian3؛ Mahdi Sharifi Soltani4
1Department of Food science & Technology, Noor Branch, Islamic Azad University, Noor, Iran.
2Department of Food Science & Technology, Noor Branch, Islamic Azad University, Noor, Iran.
3Department of Mathematics, Noor Branch, Islamic Azad University, Noor, Iran.
4Department of Veterinary, Chalous Branch, Islamic Azad University, Chalous, Iran.
تاریخ دریافت: 23 تیر 1403،  تاریخ بازنگری: 19 مهر 1403،  تاریخ پذیرش: 28 بهمن 1403 
چکیده
Support Vector Machines (SVMs) are a valuable tool in the food industry due to their ability to handle complex, nonlinear relationships between variables, even with limited datasets, high-dimensional data, and noisy data. This makes SVMs well-suited for applications such as food quality and safety assessment, sensory evaluation, process optimization, and food authentication. Accordingly, a new approach is introduced to predict different features of a traditional yogurt drink, also called doogh. The proposed model combines the principles of Support Vector Regression with fuzzy logic to handle uncertainty and approximate complex relationships between inputs (retentate, xanthan, and shelf-life) and target variables including viscosity, syneresis, color values, and total acceptability. The implemented approach is particularly useful when dealing with problems where the relationships are not easily captured by traditional mathematical models due to their non-linearity or imprecision. Also, it mitigates the limitations of data availability. The predictive ability of the proposed model has been evaluated in terms of MSE, R2, RMSE, and MAE when adding different noise levels. Additionally, the conditions necessary to attain optimized metric values have been found. At the optimum point, the viscosity, syneresis, L*, a*, b* and total acceptability are 19.70 mPa.s, 11.30%, 97.04, -1.43, 8.13, and 5.00, respectively. Besides, the findings indicate that samples containing 0.8% retentate, 0.4% xanthan, and a 31-day shelf-life exhibit the highest viscosity, while those with 0.6% retentate, 0.4% xanthan, and a 31-day shelf-life show the lowest syneresis. Moreover, samples with 0.7% retentate, 0.2% xanthan, and a 13-day shelf-life demonstrate the highest total acceptability.
کلیدواژه‌ها
Doogh, Fuzzy theory؛ Support Vector Regression (SVR)؛ Syneresis؛ Uncertainty
مراجع
  1. Azarikia F, Abbasi S (2010) On the stabilization mechanism of Doogh (Iranian yoghurt drink) by gum tragacanth. Food Hydrocolloids 24 (4):358-363
  2. Rahmati F, Mahjoorian A, Fazeli F, Ranjbar S (2023) Investigation of rheological, physicochemical, and sensorial properties of traditional low‐fat Doogh formulated. Food Science & Nutrition 11 (11):7218-7228
  3. Turkmen N, Akal C, Özer B (2019) Probiotic dairy-based beverages: A review. Journal of Functional Foods 53:62-75
  4. Tahsiri Z, Niakousari M, Khoshnoudi‐Nia S, Hosseini SMH (2017) Sensory evaluation of selected formulated milk barberry drinks using the fuzzy approach. Food science & nutrition 5 (3):739-749
  5. Mani-López E, Palou E, López-Malo A (2014) Probiotic viability and storage stability of yogurts and fermented milks prepared with several mixtures of lactic acid bacteria. Journal of Dairy Science 97 (5):2578-2590. doi:https://doi.org/10.3168/jds.2013-7551
  6. DeBruyne AN, Hekmat S (2024) The effects of fortification of yogurt with various functional flours on survival and growth of probiotic bacteria and sensory properties of the yogurt. Nutrition & Food Science 54 (3):597-612. doi:10.1108/NFS-11-2023-0257
  7. Beirami-Serizkani F, Hojjati M, Jooyandeh H (2021) The effect of microbial transglutaminase enzyme and Persian gum on the characteristics of traditional kefir drink. International Dairy Journal 112:104843
  8. Sworn G (2021) Xanthan gum. Handbook of hydrocolloids. Elsevier, pp 833-853
  9. Karagözlü C, Kınık Ö, Sibel Akalın A, Kesenkaş H, Dinkçi N (2024) Characterisation of probiotic fermented milks produced from goat milk containing low or high somatic cell counts. Mljekarstvo: časopis za unaprjeđenje proizvodnje i prerade mlijeka 74 (2):106-115
  10. Khoobbakht F, Khorshidi S, Bahmanyar F, Hosseini SM, Aminikhah N, Farhoodi M, Mirmoghtadaie L (2024) Modification of mechanical, rheological and structural properties of agar hydrogel using xanthan and locust bean gum. Food Hydrocolloids 147:109411
  11. Bak JH, Yoo B (2018) Intrinsic viscosity of binary gum mixtures with xanthan gum and guar gum: Effect of NaCl, sucrose, and pH. International journal of biological macromolecules 111:77-81
  12. Cheng Y, Wang B, Lv W, Zhong Y, Li G (2024) Effect of xanthan gum on physicochemical properties and 3D printability of emulsion-filled starch gels. Food Hydrocolloids 149:109613. doi:https://doi.org/10.1016/j.foodhyd.2023.109613
  13. Zhang J, Wang G, Liang Q, Cai W, Zhang Q (2019) Rheological and microstructural properties of gelatin B/tara gum hydrogels: Effect of protein/polysaccharide ratio, pH and salt addition. LWT 103:108-115. doi:https://doi.org/10.1016/j.lwt.2018.12.080
  14. Gorji EG, Mohammadifar MA, Ezzatpanah H (2011) Influence of gum tragacanth, Astragalus gossypinus, addition on stability of nonfat Doogh, an Iranian fermented milk drink. International Journal of Dairy Technology 64 (2):262-268
  15. Reig M, Vecino X, Cortina JL (2021) Use of membrane technologies in dairy industry: An overview. Foods 10 (11):2768
  16. Brazuelo A, Suarez E, Riera FA, Alvarez R, Igesias JR, Granda J (1995) Protein‐enriched yoghurt by ultrafiltration of skim‐milk. Journal of the Science of Food and Agriculture 69 (3):283-290
  17. Singh R, Rathod G, Meletharayil GH, Kapoor R, Sankarlal VM, Amamcharla JK (2022) Invited review: Shelf-stable dairy protein beverages—Scientific and technological aspects. Journal of Dairy Science 105 (12):9327-9346
  18. Amatayakul T, Sherkat F, Shah NP (2006) Physical characteristics of set yoghurt made with altered casein to whey protein ratios and EPS-producing starter cultures at 9 and 14% total solids. Food hydrocolloids 20 (2-3):314-324
  19. Tajkarimi M (2020) Food Safety and Quality Data Management Using Artificial Intelligence. Food Protection Trends 40 (6):464-467
  20. Ni X, Jiang Y, Zhang Y, Zhou Y, Zhao Y, Guo F, Wang H (2023) Identification of liquid milk adulteration using Raman spectroscopy combined with lactose indexed screening and support vector machine. International Dairy Journal 146:105751. doi:https://doi.org/10.1016/j.idairyj.2023.105751
  21. Amsaraj R, Ambade ND, Mutturi S (2021) Variable selection coupled to PLS2, ANN and SVM for simultaneous detection of multiple adulterants in milk using spectral data. International Dairy Journal 123:105172. doi:https://doi.org/10.1016/j.idairyj.2021.105172
  22. Lazim MA, Suriani M (2009) Sensory evaluation of the selected coffee products using fuzzy approach. World Academy of Science, Engineering and Technoly International Journal of Mathematical and Computational Sciences 50 (2):133-136
  23. Vishwakarma S, Mandliya S, Dalbhagat CG, Singh PK, Mishra HN (2023) Rheological modeling of marjoram fortified rice dough: Empirical and machine learning approach. Journal of Food Process Engineering 46 (8):e14387
  24. Zhou Z, Tian D, Yang Y, Cui H, Li Y, Ren S, Han T, Gao Z (2024) Machine learning assisted biosensing technology: An emerging powerful tool for improving the intelligence of food safety detection. Current Research in Food Science:100679
  25. Martins JA, Azevedo AM, de Almeida AC, da Silva LCR, Fernandes ACG, Valadares NR, Aspiazu I (2022) Fuzzy logic is a powerful tool for the automation of milk classification. Acta Scientiarum Technology
  26. Meena GS, Gupta VK, Khetra Y, Raghu HV, Parmar PT (2015) Fuzzy logic (Similarity Analysis) modelling for sensory evaluation of the market samples of Kheer Mohan. Indian J Dairy Sci 68 (4):326-333
  27. Alves V, dos Santos JM, Pinto E, Ferreira IM, Lima VA, Felsner ML (2024) Digital image processing combined with machine learning: A new strategy for brown sugar classification. Microchemical Journal 196:109604
  28. Abass T, Itua EO, Bature T, Eruaga MA (2024) Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis. World Journal of Advanced Research and Reviews 21 (3):823-828
  29. Zhang X, Li H, Mu W, Gecevska V, Zhang X, Feng J (2022) Sensory evaluation and prediction of bulk wine by physicochemical indicators based on PCA‐PSO‐LSSVM method. Journal of Food Processing and Preservation 46 (3):e16343
  30. Issaad FZ, Abdessemed A, Bouhedjar K, Bouyahmed H, Derdour M, Ouffroukh K, Fellak A, Dems MAS, Chihoub S, Bechlem R (2024) Classification of Algerian olive oils: Physicochemical properties, polyphenols and fatty acid composition combined with machine learning models. Journal of Food Composition and Analysis 125:105812
  31. Sun J, Yang F, Cheng J, Wang S, Fu L (2024) Nondestructive identification of soybean protein in minced chicken meat based on hyperspectral imaging and VGG16-SVM. Journal of Food Composition and Analysis 125:105713
  32. Singh R (2024) Implementation of User Rating Classification for Amazon FoodReview Dataset Using SVM and LSTM. International Journal of Intelligent Systems and Applications in Engineering
  33. Wang Y, Gu H-W, Yin X-L, Geng T, Long W, Fu H, She Y (2024) Deep leaning in food safety and authenticity detection: An integrative review and future prospects. Trends in Food Science & Technology:104396
  34. Ahmad I, Benjamin T-A (2023) Chapter 8 - Application of artificial intelligence and machine learning to food rheology. In: Ahmed J, Basu S (eds) Advances in Food Rheology and Its Applications (Second Edition). Woodhead Publishing, pp 201-219. doi:https://doi.org/10.1016/B978-0-12-823983-4.00004-2
  35. Boger DV, Tiu C (1974) Rheological properties of food products and their use in the design of flow systems. Food technology in Australia
  36. Yakoubi S (2024) Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration. Food Chemistry 452:139520. doi:https://doi.org/10.1016/j.foodchem.2024.139520
  37. Croonenberghs AP, Bongaerts D, Bouchez A, De Roos J, De Vuyst L (2024) Fruit beers, beers with or without a co-fermentation step with fruits. Current Opinion in Biotechnology 86:103081
  38. Sheikh H, Anand GG, Shivanna GB (2024) Fermentation: A Potential Strategy for Microbial Metabolite Production.
  39. Ma X, Wang Y, Liu Y, Li X, Wang F, Huang Y, Shi P, Brennan CS, Wang M (2024) Mechanisms and factors influencing the ability of lactic acid bacteria on reducing biogenic amines in fermented food: A mini-review. LWT:115890
  40. Lončar B, Pezo L, Iličić M, Kanurić K, Vukić D, Degenek J, Vukić V (2024) Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics. Foods 13 (4):548
  41. Yu H, Liu S, Qin H, Zhou Z, Zhao H, Zhang S, Mao J (2024) Artificial intelligence-based approaches for traditional fermented alcoholic beverages’ development: Review and prospect. Critical Reviews in Food Science and Nutrition 64 (10):2879-2889
  42. Urtubia A, León R, Vargas M (2021) Identification of chemical markers to detect abnormal wine fermentation using support vector machines. Computers & Chemical Engineering 145:107158. doi:https://doi.org/10.1016/j.compchemeng.2020.107158
  43. Hernández G, León R, Urtubia A (2016) Detection of abnormal processes of wine fermentation by support vector machines. Cluster Computing 19:1219-1225
  44. Xu M, Wang X, Xu Z, Wang Y, Jia P, Ding W, Dong S, Liu P (2024) Aroma quality characterization for Pixian broad bean paste fermentation by electronic nose combined with machine learning methods. Journal of Food Measurement and Characterization 18 (5):3363-3377
  45. Zheng P, Solomon Adade SY, Rong Y, Zhao S, Han Z, Gong Y, Chen X, Yu J, Huang C, Lin H (2024) Online System for Monitoring the Degree of Fermentation of Oolong Tea Using Integrated Visible–Near-Infrared Spectroscopy and Image-Processing Technologies. Foods 13 (11). doi:10.3390/foods13111708
  46. Nikravan L, Zamanpour S, Noori SMA (2024) Postbiotics: an innovative approach to increase shelf life and quality of foods. Nutrition & Food Science 54 (1):192-206
  47. Phimolsiripol Y, Suppakul P (2016) Techniques in shelf life evaluation of food products.
  48. Jimoh KA, Hashim N, Shamsudin R, Che Man H, Jahari M (2024) Optimization of computational intelligence approach for the prediction of glutinous rice dehydration. Journal of the Science of Food and Agriculture 104 (10):6208-6220
  49. Goyal K, Kumar P, Verma K (2024) Tomato ripeness and shelf-life prediction system using machine learning. Journal of Food Measurement and Characterization 18 (4):2715-2730
  50. Liang B, Li X, Yang M, Zhang Z, Ren J (2024) Stacking ensemble learning for gas sensor‐based detection of salmon freshness and shelf life. Journal of Food Process Engineering 47 (3):e14593
  51. Wang B, Yang H, Li L, Zhang S (2024) Non-Destructive Detection of Cerasus Humilis Fruit Quality by Hyperspectral Imaging Combined with Chemometric Method. Horticulturae 10 (5):519
  52. Bagnulo E, Strocchi G, Bicchi C, Liberto E (2024) Industrial food quality and consumer choice: Artificial intelligence-based tools in the chemistry of sensory notes in comfort foods (coffee, cocoa and tea). Trends in Food Science & Technology:104415
  53. Gharibzahedi SMT, Chronakis IS (2018) Crosslinking of milk proteins by microbial transglutaminase: Utilization in functional yogurt products. Food chemistry 245:620-632
  54. Afonso IM, Maia JM (1999) Rheological monitoring of structure evolution and development in stirred yogurt. Journal of Food Engineering 42 (4):183-190
  55. Ghosi Hoojaghan S, Sedaghati M, Mooraki N (2022) Characterization of Iranian Doogh enriched with gum Tragacanth and fennel extract (Foeniculum Vulgare). Journal of Agricultural Science and Technology 24 (6):1345-1356
  56. Janhøj T, Frøst MB, Ipsen R (2008) Sensory and rheological characterization of acidified milk drinks. Food Hydrocolloids 22 (5):798-806
  57. Khodashenas M, Jouki M (2020) Optimization of stabilized probiotic Doogh formulation by edible gums and response surface methodology: assessment of stability, viability and organoleptic attributes. Journal of Food Science and Technology 57:3201-3210
  58. Haji Ghafarloo M, Jouki M, Tabari M (2020) Production and characterization of synbiotic Doogh, a yogurt-based Iranian drink by gum Arabic, ginger extract and B. bifidum. Journal of food science and technology 57:1158-1166
آمار
تعداد مشاهده مقاله: 234
تعداد دریافت فایل اصل مقاله: 324


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب