Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed
Authors
Rajković, DraganaMarjanović-Jeromela, Ana
Pezo, Lato
Lončar, Biljana
Grahovac, Nada
Kondić-Špika, Ankica
Article (Accepted Version)
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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), α-tocopherol, γ-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 rapese...ed quality traits.
Keywords:
mathematical modelling / machine learning / rapeseed / quality traits / fatty acids / tocopherolsSource:
Journal of Food Composition and Analysis, 2023, 115, 105020-Publisher:
- Elsevier
Funding / projects:
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200032 (Institute of Field and Vegetable Crops, Novi Sad) (RS-MESTD-inst-2020-200032)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200051 (Institute of General and Physical Chemistry, Belgrade) (RS-MESTD-inst-2020-200051)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200134 (University of Novi Sad, Faculty of Technology) (RS-MESTD-inst-2020-200134)
Note:
- This is a peer-reviewed accepted version of the published article http://fiver.ifvcns.rs/handle/123456789/3232 with embargoed access until 17 November 2023.
- This work was carried out as a part of the activities of the Centre of Excellence for Innovations in Breeding of Climate Resilient Crops–Climate Crops, Institute of Field and Vegetable Crops, Novi Sad, Serbia.
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FiVeRTY - JOUR AU - Rajković, Dragana AU - Marjanović-Jeromela, Ana AU - Pezo, Lato AU - Lončar, Biljana AU - Grahovac, Nada AU - Kondić-Špika, Ankica PY - 2023 UR - http://fiver.ifvcns.rs/handle/123456789/3233 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), α-tocopherol, γ-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. PB - Elsevier 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 SP - 105020 VL - 115 DO - 10.1016/j.jfca.2022.105020 ER -
@article{ author = "Rajković, Dragana and Marjanović-Jeromela, Ana and Pezo, Lato and Lončar, Biljana and Grahovac, Nada and Kondić-Špika, 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), α-tocopherol, γ-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.", publisher = "Elsevier", 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", pages = "105020", volume = "115", doi = "10.1016/j.jfca.2022.105020" }
Rajković, D., Marjanović-Jeromela, A., Pezo, L., Lončar, B., Grahovac, N.,& Kondić-Špika, 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 Elsevier., 115, 105020. https://doi.org/10.1016/j.jfca.2022.105020
Rajković D, Marjanović-Jeromela A, Pezo L, Lončar B, Grahovac N, Kondić-Špika 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;115:105020. doi:10.1016/j.jfca.2022.105020 .
Rajković, Dragana, Marjanović-Jeromela, Ana, Pezo, Lato, Lončar, Biljana, Grahovac, Nada, Kondić-Špika, 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):105020, https://doi.org/10.1016/j.jfca.2022.105020 . .