Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model
Нема приказа
Аутори
Lužaić, TanjaRomanić, Ranko
Grahovac, Nada
Jocić, Siniša
Cvejić, Sandra
Hladni, Nada
Pezo, Lato
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
BACKGROUND Sunflower seeds are in the top five most abundant oilseeds in the world, as well as sunflower oil in the edible oils group. Recently, increasing attention has been paid to cold-pressed sunflower oil because less processing is involved and no solvent is used. The present study was carried out to investigate dimensions (length, width, thickness), firmness, general (moisture content and hull content, mass of 1000 seeds), gravimetric (true and bulk density, porosity) and geometric characteristics (equivalent diameter, surface area, seed volume, sphericity) of 20 new sunflower hybrid seeds. Steps to determine most of these parameters are quite simple and easy since the process does not require long time or special equipment. RESULTS Principal component analysis and cluster analysis confirmed differences in the mentioned characteristics between oily and confectionary sunflower hybrid seeds. One of the major differences between two groups of samples was in extraction oil yield. Mec...hanical extraction oil yield of the oily hybrid seeds was significantly (P ˂ 0.05) higher (from 68.72 ± 4.21% to 75.61 ± 1.99%) compared to confectionary hybrids (from 20.10 ± 2.82% to 39.91 ± 6.23%). Extraction oil yield values are known only after oil extraction. CONCLUSION Knowledge of the extraction oil yield value before the mechanical extraction enables better management of the process. By application of the artificial neural network approach, an optimal neural network model was developed. The developed model showed a good generalization capability to predict the mechanical extraction oil yield of new sunflower hybrids based on the experimental data, which was a main goal of this paper.
Кључне речи:
oil extraction / sunflower / sunflower oil / oil yield / artificial neural networksИзвор:
Journal of the Science of Food and Agriculture, 2021, 101, 14, 5827-5833Издавач:
- Wiley
DOI: 10.1002/jsfa.11234
ISSN: 1097-0010
WoS: 000640867900001
Scopus: 2-s2.0-85104415617
Колекције
Институција/група
FiVeRTY - JOUR AU - Lužaić, Tanja AU - Romanić, Ranko AU - Grahovac, Nada AU - Jocić, Siniša AU - Cvejić, Sandra AU - Hladni, Nada AU - Pezo, Lato PY - 2021 UR - http://fiver.ifvcns.rs/handle/123456789/3107 AB - BACKGROUND Sunflower seeds are in the top five most abundant oilseeds in the world, as well as sunflower oil in the edible oils group. Recently, increasing attention has been paid to cold-pressed sunflower oil because less processing is involved and no solvent is used. The present study was carried out to investigate dimensions (length, width, thickness), firmness, general (moisture content and hull content, mass of 1000 seeds), gravimetric (true and bulk density, porosity) and geometric characteristics (equivalent diameter, surface area, seed volume, sphericity) of 20 new sunflower hybrid seeds. Steps to determine most of these parameters are quite simple and easy since the process does not require long time or special equipment. RESULTS Principal component analysis and cluster analysis confirmed differences in the mentioned characteristics between oily and confectionary sunflower hybrid seeds. One of the major differences between two groups of samples was in extraction oil yield. Mechanical extraction oil yield of the oily hybrid seeds was significantly (P ˂ 0.05) higher (from 68.72 ± 4.21% to 75.61 ± 1.99%) compared to confectionary hybrids (from 20.10 ± 2.82% to 39.91 ± 6.23%). Extraction oil yield values are known only after oil extraction. CONCLUSION Knowledge of the extraction oil yield value before the mechanical extraction enables better management of the process. By application of the artificial neural network approach, an optimal neural network model was developed. The developed model showed a good generalization capability to predict the mechanical extraction oil yield of new sunflower hybrids based on the experimental data, which was a main goal of this paper. PB - Wiley T2 - Journal of the Science of Food and Agriculture T1 - Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model EP - 5833 IS - 14 SP - 5827 VL - 101 DO - 10.1002/jsfa.11234 ER -
@article{ author = "Lužaić, Tanja and Romanić, Ranko and Grahovac, Nada and Jocić, Siniša and Cvejić, Sandra and Hladni, Nada and Pezo, Lato", year = "2021", abstract = "BACKGROUND Sunflower seeds are in the top five most abundant oilseeds in the world, as well as sunflower oil in the edible oils group. Recently, increasing attention has been paid to cold-pressed sunflower oil because less processing is involved and no solvent is used. The present study was carried out to investigate dimensions (length, width, thickness), firmness, general (moisture content and hull content, mass of 1000 seeds), gravimetric (true and bulk density, porosity) and geometric characteristics (equivalent diameter, surface area, seed volume, sphericity) of 20 new sunflower hybrid seeds. Steps to determine most of these parameters are quite simple and easy since the process does not require long time or special equipment. RESULTS Principal component analysis and cluster analysis confirmed differences in the mentioned characteristics between oily and confectionary sunflower hybrid seeds. One of the major differences between two groups of samples was in extraction oil yield. Mechanical extraction oil yield of the oily hybrid seeds was significantly (P ˂ 0.05) higher (from 68.72 ± 4.21% to 75.61 ± 1.99%) compared to confectionary hybrids (from 20.10 ± 2.82% to 39.91 ± 6.23%). Extraction oil yield values are known only after oil extraction. CONCLUSION Knowledge of the extraction oil yield value before the mechanical extraction enables better management of the process. By application of the artificial neural network approach, an optimal neural network model was developed. The developed model showed a good generalization capability to predict the mechanical extraction oil yield of new sunflower hybrids based on the experimental data, which was a main goal of this paper.", publisher = "Wiley", journal = "Journal of the Science of Food and Agriculture", title = "Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model", pages = "5833-5827", number = "14", volume = "101", doi = "10.1002/jsfa.11234" }
Lužaić, T., Romanić, R., Grahovac, N., Jocić, S., Cvejić, S., Hladni, N.,& Pezo, L.. (2021). Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model. in Journal of the Science of Food and Agriculture Wiley., 101(14), 5827-5833. https://doi.org/10.1002/jsfa.11234
Lužaić T, Romanić R, Grahovac N, Jocić S, Cvejić S, Hladni N, Pezo L. Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model. in Journal of the Science of Food and Agriculture. 2021;101(14):5827-5833. doi:10.1002/jsfa.11234 .
Lužaić, Tanja, Romanić, Ranko, Grahovac, Nada, Jocić, Siniša, Cvejić, Sandra, Hladni, Nada, Pezo, Lato, "Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model" in Journal of the Science of Food and Agriculture, 101, no. 14 (2021):5827-5833, https://doi.org/10.1002/jsfa.11234 . .