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Journals / Gazi University Journal of Science / 2026 / Volume: 38 Issue: 3

Evaluations of Comet Assay Data Through Statistical Analysis, Machine Learning, and Multi-Criteria Decision-Making Methods for Genotoxic Potential of Food Sweeteners

Fatma Ünal(Gazi Üniversitesi)0000-0002-7468-6186
Muhlis Özdemir(GAZİ ÜNİVERSİTESİ)0000-0002-4921-8209
Deniz Yüzbaşıoğlu(GAZİ ÜNİVERSİTESİ)0000-0003-2756-7712
Sevcan Mamur(GAZİ ÜNİVERSİTESİ)0000-0002-8615-5331
Ece Avuloğlu Yılmaz(AMASYA ÜNİVERSİTESİ, AMASYA TEKNİK BİLİMLER MESLEK YÜKSEKOKULU)0000-0002-5164-3431
Fatma Okuş(GAZİ ÜNİVERSİTESİ)0000-0001-7648-9584
Ece Akbaş(GAZİ ÜNİVERSİTESİ)0000-0002-4978-3638
Reşat Kasap(GAZI UNIVERSITY)0000-0002-9306-3101
Büşra Güneş(GAZİ ÜNİVERSİTESİ)0009-0007-8043-1888
Pages
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Publication Date
2025-09-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings highlight the impact of combining ML and MCDM methods for a more intensive genotoxicity evaluation, providing precious insights into food safety regulations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Although the genotoxic effects of food sweeteners have been studied previously, there is still a lack of application using an integrated approach that combines statistical analysis, Machine Learning (ML), and Multi-Criteria Decision Methods (MCDM) in depth to reveal the DNA-damaging potential of food sweeteners (D-Sorbitol (DS) and Xylitol (XYL)), both alone and in combination (DSX).
📊
Key Findings & Data:Kruskal-Wallis and One-Way ANOVA tests were used to identify significant differences in DNA damage.
💡
Conclusion & Impact:These findings highlight the impact of combining ML and MCDM methods for a more intensive genotoxicity evaluation, providing precious insights into food safety regulations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Comet assay · DNA damage · Statistical analysis

Abstract

Although the genotoxic effects of food sweeteners have been studied previously, there is still a lack of application using an integrated approach that combines statistical analysis, Machine Learning (ML), and Multi-Criteria Decision Methods (MCDM) in depth to reveal the DNA-damaging potential of food sweeteners (D-Sorbitol (DS) and Xylitol (XYL)), both alone and in combination (DSX). A dataset on comet assay observations for DNA damage (tail length, tail intensity, and tail moment) was collected from previous studies. Kruskal-Wallis and One-Way ANOVA tests were used to identify significant differences in DNA damage. K-Means and Hierarchical Clustering lead to grouping additives of genotoxic effects, while MOORA and TOPSIS ranked toxicity levels. The findings of MCDM showed that XYL_1000 caused the highest DNA damage (0.726683 and 0.382296). The combination of DS and XYL (DSX_M8) exhibited higher toxicity (0.715258 and 0.37281) compared to their treatments, whereas DSX_M1 revealed the least damaging effect (0.235688 and 0.0324946). This is the first study using this approach. These findings highlight the impact of combining ML and MCDM methods for a more intensive genotoxicity evaluation, providing precious insights into food safety regulations.

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Cite This Article

Ünal, F., Özdemir, M., Yüzbaşıoğlu, D., Mamur, S., Yılmaz, E. A., Okuş, F., Akbaş, E., Kasap, R., Güneş, B. (2025). Evaluations of Comet Assay Data Through Statistical Analysis, Machine Learning, and Multi-Criteria Decision-Making Methods for Genotoxic Potential of Food Sweeteners. Gazi University Journal of Science, Volume: 38 Issue: 3. https://doi.org/10.35378/gujs.1581396

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References (71)

  1. Wu, L., Zhang, C., Long, Y., Chen, Q., Zhang, W., Liu, G., “Food additives: From functions to analytical methods”, Critical Reviews in Food Science and Nutrition, 62(30): 8497-8517, (2022). DOI: https://doi.org/10.1080/10408398.2021.1929823
  2. Antonik, N., Janda, K., Jakubczyk, K., “Characteristics of sweeteners used in foods and their effects on human health”, Pomeranian Journal of Life Sciences, 66(3): 57-65, (2020). DOI: https://doi.org/10.21164/pomjlifesci.723
  3. Chen, L., Zhang, Y., Zhou, Y., Shi, D., Feng, XS., “Sweeteners in food samples: An update on pretreatment and analysis techniques since 2015”, Food Chemistry, 408: 135248, (2023). DOI: https://doi.org/10.1016/j.foodchem.2022.135248
  4. Avuloglu-Yılmaz, E., Yuzbaşıoglu, D., Unal, F., “Investigating in vitro genotoxic effects of sweetener xylitol”, KSU Journal of Agriculture and Nature, 25(6): 1315-1325, (2022). DOI: https://doi.org/10.18016/ksutarimdoga.vi.99382
  5. Yin, KJ., Xie, DY., Zhao, L., Fan, G., Ren, JN., Zhang, LL., Pan, SY., “Effects of different sweeteners on behavior and neurotransmitters release in mice”, Journal of Food Science and Technology, 57: 113-121, (2020). DOI: https://doi.org/10.1007/s13197-019-04036-6
  6. Hernández-Pérez, AF., Jofre, FM., de Souza Queiroz, S., de Arruda, PV., Chandel, AK., de Almeida Felipe, MDG., Biotechnological Production of Bioactive Compounds, Verma, ML., Chandel, AK., Elsevier, 261-292, (2020).
  7. Rice, T., Zannini, EK., Arendt, E., Coffey, AA., “A review of polyols-biotechnological production, food applications, regulation, labeling, and health effects”, Critical Reviews in Food Science and Nutrition, 60(12): 2034-2051, (2020). DOI: https://doi.org/10.1080/10408398.2019.1625859
  8. Cardoso, FS., Araujo-Lima, CF., Aiub, CA., Felzenszwalb, I., “Exposure to sorbitol during lactation causes metabolic alterations and genotoxic effects in rat offspring”, Toxicology Letters, 260: 36-45, (2016). DOI: https://doi.org/10.1016/j.toxlet.2016.08.018
  9. Roze, M., Crucean, D., Diler, G., Rannou, C., Catanéo, C., Jonchère, C., Le-Bail, A., Le-Bail, P., “Impact of maltitol and sorbitol on technological and sensory attributes of biscuits”, Foods, 10(11): 2545, (2021). DOI: https://doi.org/10.3390/foods10112545
  10. Zhang, W., Chen, J., Chen, Q., Wu, H., Mu, W., “Sugar alcohols derived from lactose: lactitol, galactitol, and sorbitol”, Applied microbiology and Biotechnology, 104: 9487-9495, (2020). DOI: https://doi.org/10.1007/s00253-020-10929-w
  11. Awuchi, CG., Echeta, KC., “Current developments in sugar alcohols: Chemistry, nutrition, and health concerns of sorbitol, xylitol, glycerol, arabitol, inositol, maltitol, and lactitol”, International Journal of Advanced Academic Research, 5(11): 1-33, (2019).
  12. Dasgupta, D., Ahuja, V., Singh, R., More, S., Mudliar, S., Kumar, M., “Food-grade xylitol production from corncob biomass with acute oral toxicity studies”, World Journal of Microbiology and Biotechnology, 39(4): 102, (2023). DOI: https://doi.org/10.1007/s11274-023-03542-2
  13. Mäkinen, KK., "Gastrointestinal disturbances associated with the consumption of sugar alcohols with special consideration of xylitol: scientific review and instructions for dentists and other healthcare professionals", International Journal of Dentistry, 2016(1): 967907, (2016). DOI: https://doi.org/10.1155/2016/5967907
  14. FDA., “Generally Recognized as Safe (GRAS)”, U.S. Food & Drug Administration, (2017).
  15. Recoules, C., Touvier, M., Pierre, F., Audebert, M. “Evaluation of the toxic effects of food additives, alone or in mixture, in four human cell models”, Food and Chemical Toxicology, 196: 115198, (2025). DOI: https://doi.org/10.1016/j.fct.2024.115198.
  16. Moriconi, E., Feraco, A., Marzolla, V., Infante, M., Lombardo, M., Fabbri, A., Caprio, M., “Neuroendocrine and metabolic effects of low-calorie and non-calorie sweeteners”, Frontiers in Endocrinology, 11: 444, (2020). DOI: https://doi.org/10.3389/fendo.2020.00444
  17. Collins, A., et al., “Measuring DNA modifications with the comet assay: A compendium of protocols”, Nature Protocols, 18(3): 929-989, (2023). DOI: https://doi.org/10.1038/s41596-022-00754-y
  18. Erikel, E., Yuzbasioglu, D., Unal, F., “Genotoxic and antigenotoxic potential of amygdalin on isolated human lymphocytes by the comet assay”, Journal of Food Biochemistry, 44(10): e13436, (2020). DOI: https://doi.org/10.1111/jfbc.13436
  19. Žegura, B., Filipič, M., “Optimization in Drug Discovery”. Yan, Z., Caldwell, GW., Humana Press, 301-313, (2004).
  20. Anderson, D., Dhawan, A., Laubenthal, J., “Genotoxicity Assessment. Methods in Molecular Biology”, Dhawan A, Bajpayee M, Humana, 259-274, (2019).
  21. Azqueta, A., Ladeira, C., Giovannelli, L., Boutet-Robinet, E., Bonassi, S., Neri, M., Gajski, G., Duthie, S., Del Bo, C., Riso, P., Koppen, G., Basaran, N., Collins, A., Møller, P., “Application of the comet assay in human biomonitoring: An hCOMET perspective”, Mutation Research/Reviews in Mutation Research, 78: 108288, (2020). DOI: https://doi.org/10.1016/j.mrrev.2019.108288
  22. Ceppi, M., Smolkova, B., Staruchova, M., Kazimirova, A., Barancokova, M., Volkovova, K., Collins, A., Kocan, A., Dzupinkova, Z., Horska, A., Buocikova, V., Tulinska, J., Liskova, A., Mikusova, ML., Krivosikova, Z., Wsolova, L., Kuba, D., Rundén-Pran, E., El Yamani, N., Longhin., E., Dusinska, M., “Genotoxic effects of occupational exposure to glass fibres-A human biomonitoring study”, Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 885: 503572, (2023). DOI: https://doi.org/10.1016/j.mrgentox.2022.503572
  23. Marciano, LP., Costa, LF., Cardoso, NS., Freire, J., Feltrim, F., Oliveira, GS., Paula, FBA., Silvério, ACP., Martins, I., “Biomonitoring and risk assessment of human exposure to triazole fungicides”, Regulatory Toxicology and Pharmacology, 147: 105565, (2024). DOI: https://doi.org/10.1016/j.yrtph.2024.105565
  24. Fan, D., Yang, H., Li, F., Sun, L., Di, P., Li, W., Tang, Y., Liu, G., “In silico prediction of chemical genotoxicity using machine learning methods and structural alerts”. Toxicology Research, 7: 211-220, (2018). DOI: https://doi.org/10.1039/c7tx00259a
  25. Sizochenkoa, N., Syzochenko, M., Fjodorova, N., Rasulev, B., Leszczynski, J., “Evaluating genotoxicity of metal oxide nanoparticles: Application of advanced supervised and unsupervised machine learning techniques”, Ecotoxicology and Environmental Safety, 185: 109733, (2019). DOI: https://doi.org/10.1016/j.ecoenv.2019.109733
  26. Xu, J-L., Lin, X., Gowen. AA., “Combining machine learning with meta-analysis for predicting cytotoxicity of micro-and nanoplastics”, Journal of Hazardous Materials Advances, 8: 100175, (2022). DOI: https://doi.org/10.1016/j.hazadv.2022.100175
  27. Ji, Z., Guo, W., Wood, EL., Liu, J., Sakkiah, S., Xu, X., Patterson, TA., Hong, H., “Machine Learning Models for Predicting Cytotoxicity of Nanomaterials”, Chemical Research and Toxicology, 35: 125-139, (2022). DOI: https://doi.org/10.1021/acs.chemrestox.1c00310
  28. Atila, Ü., Baydilli, YY., Sehirli, E., Turan, MK., “Classification of DNA damages on segmented comet assay images using convolutional neural network”, Computer Methods and Programs in Biomedicine 186: 105192, (2020). DOI: https://doi.org/10.1016/j.cmpb.2019.105192
  29. Hong, Y., Han, H.J., Lee, H., Lee, D., Ko, J., Hong, Z. yu., Lee, J Y., Seok, J H., Lim, H. S., Son, W. C., Sohn, I., “Deep learning method for comet segmentation and comet assay image analysis”, Scientific Reports, 10: 18915, (2020). DOI: https://doi.org/10.1038/s41598-020-75592-7
  30. Grenet, I., Yin, Y., Comet, JP., Gelenbe, E., “Artificial neural networks and machine learning”, 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 335-345, (2018).
  31. Çelik, Ö., Altunaydın, SS., “A research on machine learning methods and its applications”, Journal of Educational Technology and Online Learning, 1(3): 25-40, (2018). DOI: https://doi.org/10.31681/jetol.457046
  32. FDA., “Artificial Intelligence and Machine Learning (AI/ML) for Drug Development” https://www.fda.gov/about-fda/center-drug-evaluation-and-research-cder/artificial-intelligence-drug-development. Access date: 08.08.2025
  33. Guo W., Liu J., Dong F., Song M., Li Z., Khan MKH., Patterson TA., Hong H. “Review of machine learning and deep learning models for toxicity prediction”, Experimental Biology and Medicine, 248: 1952-1973, (2023). DOI: https://doi.org/10.1177/15353702231209421
  34. Fan, D., Yang, H., Li, F., Sun, L., Di, P., Li, W., Tang, Y., Liu, G., “In silico prediction of chemical genotoxicity using machine learning methods and structural alerts”, Toxicology Research, 7(2): 211-220, (2018). DOI: https://doi.org/10.1039/c7tx00259a
  35. Yadav, S., Soni, A., “Application of MOORA and TOPSIS for genotoxicity assessment: A decision-making approach”, Journal of Hazardous Materials, 374: 245-253, (2019).
  36. Sanabria, P., Lumbaque, EC., Becker, RW., Jachstet, LA., Scunderlick, D., Ruiz-Padillo, A., Sirtori, C., “Use of multi-criteria ranking method for environmental risk assessment of antineoplastic agents and their transformation products”, Journal of Environmental Chemical Engineering, 11:109588, (2023). DOI: https://doi.org/10.1016/j.jece.2023.109588
  37. Singh, NP., McCoy, MT., Tice, RR., Schneider, EL., “A simple technique for quantitation of low levels of DNA damage in individual cells”, Experimental Cell Research, 175: 184-191, (1988). DOI: https://doi.org/10.1016/0014-4827(88)90265-0
  38. Avuloglu Yilmaz, E., Yuzbasioglu, D., Unal, F., “Investigation of genotoxic effect of octyl gallate used as an antioxidant food additive in in vitro test systems”, Mutagenesis, 38(3): 151-159, (2023). DOI: https://doi.org/10.1093/mutage/gead005
  39. Yuzbasioglu, D., Dilek, UK., Erikel, E., Unal, F., “Antigenotoxic effect of hyperoside against Mitomycin C and hydrogen peroxide-induced genotoxic damage on human lymphocytes”, Toxicology in Vitro, 90: 105604, (2023). DOI: https://doi.org/10.1016/j.tiv.2023.105604
  40. Gunes, B., “Gıda Tatlandırıcıları Olan D-Sorbitol ve D-Sorbitol + Ksilitol Karışımının İnsan Lenfositlerinde in vitro Genotoksik Etkileri”, Yüksek Lisans Tezi (Turkish), Gazi Üniversitesi, Fen Bilimleri Enstitüsü, (2024).
  41. Unal, F., Saygılı, Y., Akbas, E., “Genetic Toxicology”, F Unal, D Yuzbasioglu, Nobel Academic Publishing, (Turkish), 313-340, (2022).
  42. Akbas, E., Unal, F., Yuzbasioglu, D., “Cellular toxicities of gadolinium‐based contrast agents used in magnetic resonance imaging”, Journal of Applied Toxicology, 43: 958-972, (2023). DOI: https://doi.org/10.1002/jat.4416
  43. Özdemir, M., “R ile Programlama ve Makine Öğrenmesi”, Nobel Akademik Yayıncılık, 280, (2020a).
  44. Özdemir, M., “R ile Programlama ve Makine Öğrenmesi”, Nobel Akademik Yayıncılık, (2020b).
  45. Core Team R. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, (2023).
  46. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K., “Cluster: Cluster analysis basics and extensions. R package version 2.1.4. 2022. For new features, see the ’Changelog’ file (in the package source)” https://CRAN.R-project.org/package=cluster
  47. Kassambara, A., Mundt, F., “Factoextra: Extract and visualize the results of multivariate data analyses. R Package Version 1.0.7.” (2020). https://CRAN.R-project.org/package=factoextra
  48. Auguie, B., Antonov, A., “gridExtra: Miscellaneous functions for “Grid” Graphics. R Package Version 2.3.” (2017). https://CRAN.R-project.org/package=gridExtra
  49. Gu, Z., Gu, L., Eils, R., Schlesner, M., Brors, B., “Circlize implements and enhances circular visualization in R. Bioinformatics”, 30(19): 2811-2812, (2014). DOI: https://doi.org/10.1093/bioinformatics/btu393
  50. Wickham, H., “ggplot2: Elegant Graphics for Data Analysis”, Springer-Verlag New York, (2016).
  51. Çelikbilek, Y., Özdemir, M., “Çok Kriterli Karar Verme Yöntemleri Açıklamalı ve Karşılaştırmalı Sağlık Bilimleri Uygulamaları”, Nobel Akademik Yayıncılık, 337, (2020a).
  52. Çelikbilek, Y., Özdemir, M., “Çok Kriterli Karar Verme Yöntemleri Açıklamalı ve Karşılaştırmalı Sağlık Bilimleri Uygulamaları”, Nobel Akademik Yayıncılık, 375, (2020b).
  53. Brauers, WK., Zavadskas, EK., “The MOORA method and its application to privatization in a transition economy”, Control Cybern, 35(2):445-469, (2006).
  54. Hwang, CL., Yoon, K. "TOPSIS (technique for order preference by similarity to ideal solution)-a multiple attribute decision making, w: Multiple attribute decision making-methods and applications, a state-of-the-at survey." Berlin: Springer Verlag 128 (1981): 140.
  55. The MathWorks Inc. MATLAB version: 23.2.0.2489961 (R2023b), The MathWorks Inc. (2023). https://www.mathworks.com
  56. Rodríguez-Belenguer, P., Piñana, JL., Sánchez-Montañés, M., Soria-Olivas, E., Martínez-Sober, M., Serrano-López, AJ., “A machine learning approach to identify groups of patients with hematological malignant disorders”, Computer Methods and Programs in Biomedicine, 246: 108011, (2024). DOI: https://doi.org/10.1016/j.cmpb.2024.108011
  57. EFSA., “Scientific opinion on genotoxicity testing strategies applicable to food and feed safety assessment”, EFSA Journal, 9(9): 2379, (2011).
  58. FDA., “Guidance for industry and other stakeholds toxicological principles for the safety assessment of food ingredients redbook”, (2000). https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-industry-and-other-stakeholders-redbook-2000 link ekledim hocam
  59. Fındıklı, Z., Türkoğlu, Ş., “Determination of the effects of some artificial sweeteners on human peripheral lymphocytes using the comet assay”, Journal of Toxicology and Environmental Health Sciences, 6(8): 147-153, (2014). DOI: https://doi.org/10.5897/JTEHS2014.0313
  60. Metwaly, A., Aboul-Enein, A., Abd-Allah, A., Hanafy, E., “Do synthetic food additives possess higher genotoxic effects than natural ones?”, Bioscience Research, 15(4): 3329-3336, (2018).
  61. De Oliveira, et al. “Evaluation of cytotoxic and mutagenic effects of two artificial sweeteners by using eukaryotic test systems”, African Journal of Biotechnology, 16(11): 547-551, (2017). DOI: https://doi.org/10.5897/AJB
  62. Mukhopadhyay, M., Mukherjee, A., Chakrabarti, J., “In vivo cytogenetic studies on blends of aspartame and acesulfame-K”, Food and Chemical Toxicology, 38(1): 75-77, (2000). DOI: https://doi.org/10.1016/S0278-6915(99)00115-5
  63. Benhusein, G., Mutch, E., Aburawi, S., Williams, F., “Genotoxic effect induced by hydrogen peroxide in human hepatoma cells using comet assay”, Libyan Journal of Medicine, 5: 4637, (2020). DOI: https://doi.org/10.3402/ljm.v5i0.4637
  64. Okus, F., Yuzbasioglu, D., Unal, F., “Molecular docking study of frequently used food additives for selected targets depending on the chromosomal abnormalities they cause”, Toxicology, 5: 153716, (2024). DOI: https://doi.org/10.1016/j.tox.2023.153716
  65. Çelikbilek, Y., Tüysüz, F., “An in-depth review of theory of the TOPSIS method: An experimental analysis”, Journal of Management Analytics, 7(2): 281-300, (2020). DOI: https://doi.org/10.1080/23270012.2020.1748528
  66. Brauers, WKM., Zavadskas, EK., “Robustness of MULTIMOORA: A method for multi-objective optimization”, Informatica, 23(1):1-25, (2012). DOI: https://doi.org/10.15388/Informatica.2012.346
  67. Sevim, F., Ugurluoglu Aldogan, E., “Evaluation of health systems performance of OECD countries using MOORA method”, Journal of Health Management, 26(1): 172-183, (2024). DOI: https://doi.org/10.1177/09720634231215131
  68. Zyoud, SH., Fuchs-Hanusch, D., “A bibliometric-based survey on AHP and TOPSIS techniques”, Expert Systems with Applications, 78: 158-181, (2017). DOI: https://doi.org/10.1016/j.eswa.2017.02.016
  69. Blaauboer, BJ., Boobis, AR., Bradford, B., Cockburn, A., Constable, A., Daneshian, M., Edwards, G., Garthoff, JA., Jeffery, B., Krul, C., Schuermans, J., “Considering new methodologies in strategies for safety assessment of foods and food ingredients”, Food and Chemical Toxicology, 91: 19-35, (2016). DOI: https://doi.org/10.1016/j.fct.2016.02.019
  70. Lo Piparo, EL., Worth, A., Manibusan, M., Yang, C., Schilter, B., Mazzatorta, P., Jacobs, MN., Steinkellner, H., Mohimont, L., “Use of computational tools in the field of food safety”, Regulatory Toxicology and Pharmacology, 60(3): 354-362, (2011). DOI: https://doi.org/10.1016/j.yrtph.2011.05.003
  71. Brauers, WKM., Zavadskas, EK., “Robustness of MULTIMOORA: A method for multi-objective optimization”, Informatica, 23(1):1-25, (2012). DOI: https://doi.org/10.15388/Informatica.2012.346