Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed
Nema prikaza
Autori
Rajković, Dragana![](/themes/Mirageriofh/images/orcid.png)
Marjanovic Jeromela, Ana
Pezo, Lato
![](/themes/Mirageriofh/images/orcid.png)
Lončar, Biljana
![](/themes/Mirageriofh/images/orcid.png)
Grahovac, Nada
Kondic Spika, Ankica
Članak u časopisu (Objavljena verzija)
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Metapodaci
Prikaz svih podataka o dokumentuApstrakt
With the aid of models used in artificial intelligence, a wide range of data can be processed quickly with high accuracy. The quality of rapeseed oil from 40 genotypes cultivated during four consecutive years was analysed. Two machine learning techniques (artificial neural network - ANN, and random forest regression - RFR) were applied for the modelling of fatty acids content (C16:0; C18:0; C18:1; C18:2; C18:3 and C22:1), alpha-tocopherol, gamma-tocopherol and total tocopherols, according to the data of production year and winter rapeseed genotype. The developed models exerted high-quality anticipation features, showing high r2 during the training cycle. The best fit between the modelled and measured traits for ANN model was observed for erucic acid content. RFR modelling for all fatty acids was more effective than ANN model, with the highest precision for palmitic, stearic, and oleic fatty acids (r2>0.9). This study emphasized the possibility of using ANN and RFR models to model winte...r rapeseed quality traits.
Ključne reči:
Tocopherols / Rapeseed / Quality traits / Mathematical modelling / Machine learning / Fatty acidsIzvor:
Journal of Food Composition and Analysis, 2023, 115Finansiranje / projekti:
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200032 (Naučni institut za ratarstvo i povrtarstvo, Novi Sad) (RS-MESTD-inst-2020-200032)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200051 (Institut za opštu i fizičku hemiju, Beograd) (RS-MESTD-inst-2020-200051)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200134 (Univerzitet u Novom Sadu, Tehnološki fakultet) (RS-MESTD-inst-2020-200134)
DOI: 10.1016/j.jfca.2022.105020
ISSN: 0889-1575
WoS: 000902071800006
Scopus: 2-s2.0-85141940976
Institucija/grupa
Institut za opštu i fizičku hemijuTY - JOUR AU - Rajković, Dragana AU - Marjanovic Jeromela, Ana AU - Pezo, Lato AU - Lončar, Biljana AU - Grahovac, Nada AU - Kondic Spika, Ankica PY - 2023 UR - https://riofh.iofh.bg.ac.rs/handle/123456789/984 AB - With the aid of models used in artificial intelligence, a wide range of data can be processed quickly with high accuracy. The quality of rapeseed oil from 40 genotypes cultivated during four consecutive years was analysed. Two machine learning techniques (artificial neural network - ANN, and random forest regression - RFR) were applied for the modelling of fatty acids content (C16:0; C18:0; C18:1; C18:2; C18:3 and C22:1), alpha-tocopherol, gamma-tocopherol and total tocopherols, according to the data of production year and winter rapeseed genotype. The developed models exerted high-quality anticipation features, showing high r2 during the training cycle. The best fit between the modelled and measured traits for ANN model was observed for erucic acid content. RFR modelling for all fatty acids was more effective than ANN model, with the highest precision for palmitic, stearic, and oleic fatty acids (r2>0.9). This study emphasized the possibility of using ANN and RFR models to model winter rapeseed quality traits. T2 - Journal of Food Composition and Analysis T1 - Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed VL - 115 DO - 10.1016/j.jfca.2022.105020 UR - conv_1066 ER -
@article{ author = "Rajković, Dragana and Marjanovic Jeromela, Ana and Pezo, Lato and Lončar, Biljana and Grahovac, Nada and Kondic Spika, Ankica", year = "2023", abstract = "With the aid of models used in artificial intelligence, a wide range of data can be processed quickly with high accuracy. The quality of rapeseed oil from 40 genotypes cultivated during four consecutive years was analysed. Two machine learning techniques (artificial neural network - ANN, and random forest regression - RFR) were applied for the modelling of fatty acids content (C16:0; C18:0; C18:1; C18:2; C18:3 and C22:1), alpha-tocopherol, gamma-tocopherol and total tocopherols, according to the data of production year and winter rapeseed genotype. The developed models exerted high-quality anticipation features, showing high r2 during the training cycle. The best fit between the modelled and measured traits for ANN model was observed for erucic acid content. RFR modelling for all fatty acids was more effective than ANN model, with the highest precision for palmitic, stearic, and oleic fatty acids (r2>0.9). This study emphasized the possibility of using ANN and RFR models to model winter rapeseed quality traits.", journal = "Journal of Food Composition and Analysis", title = "Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed", volume = "115", doi = "10.1016/j.jfca.2022.105020", url = "conv_1066" }
Rajković, D., Marjanovic Jeromela, A., Pezo, L., Lončar, B., Grahovac, N.,& Kondic Spika, A.. (2023). Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed. in Journal of Food Composition and Analysis, 115. https://doi.org/10.1016/j.jfca.2022.105020 conv_1066
Rajković D, Marjanovic Jeromela A, Pezo L, Lončar B, Grahovac N, Kondic Spika A. Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed. in Journal of Food Composition and Analysis. 2023;115. doi:10.1016/j.jfca.2022.105020 conv_1066 .
Rajković, Dragana, Marjanovic Jeromela, Ana, Pezo, Lato, Lončar, Biljana, Grahovac, Nada, Kondic Spika, Ankica, "Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed" in Journal of Food Composition and Analysis, 115 (2023), https://doi.org/10.1016/j.jfca.2022.105020 ., conv_1066 .