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

Fatma Ünal; Muhlis Özdemir; Deniz Yüzbaşıoğlu; Sevcan Mamur; Ece Avuloğlu Yılmaz; Fatma Okuş; Ece Akbaş; Reşat Kasap; Büşra Güneş

Makale detayları

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

Yazarlar:

Fatma Ünal | Muhlis Özdemir | Deniz Yüzbaşıoğlu | Sevcan Mamur | Ece Avuloğlu Yılmaz | Fatma Okuş | Ece Akbaş | Reşat Kasap | Büşra Güneş

Yayıncı:

Gazi University

Ana Başlıklar:

Genetics (Other)

Dergi:

Gazi University Journal of Science Vol.38 Issue 3, pp.1480-1501

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356583

pISSN:

2147-1762

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Fatma Ünal | Muhlis Özdemir | Deniz Yüzbaşıoğlu | Sevcan Mamur | Ece Avuloğlu Yılmaz | Fatma Okuş | Ece Akbaş | Reşat Kasap | Büşra Güneş

Yayıncı:

Gazi University

Ana Başlıklar:

Genetics (Other)

Dergi:

Gazi University Journal of Science Vol.38 Issue 3, pp.1480-1501

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356583

pISSN:

2147-1762

Comet assay

DNA damage

Statistical analysis

Machine learning

Multi-criteria decision-making

Sayı

Makale detayları

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

Yazarlar: Fatma Ünal | Muhlis Özdemir | Deniz Yüzbaşıoğlu | Sevcan Mamur | Ece Avuloğlu Yılmaz | Fatma Okuş | Ece Akbaş | Reşat Kasap | Büşra Güneş

Yayıncı: Gazi University

Ana Başlıklar: Genetics (Other)

Dergi: Gazi University Journal of Science Vol.38 Issue 3, pp.1480-1501

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356583

pISSN: 2147-1762

Comet assay

DNA damage

Statistical analysis

Machine learning

Multi-criteria decision-making

Sayı

Yazarlar:

Fatma Ünal | Muhlis Özdemir | Deniz Yüzbaşıoğlu | Sevcan Mamur | Ece Avuloğlu Yılmaz | Fatma Okuş | Ece Akbaş | Reşat Kasap | Büşra Güneş

Yayıncı:

Gazi University

Ana Başlıklar:

Genetics (Other)

Dergi:

Gazi University Journal of Science Vol.38 Issue 3, pp.1480-1501

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356583

pISSN:

2147-1762