Artificial neural network and random forest regression models for modelling fatty acid and tocopherol content in oil of winter rapeseed

被引:27
作者
Rajkovic, Dragana [1 ,4 ]
Jeromela, Ana Marjanovic [1 ]
Pezo, Lato [2 ]
Loncar, Biljana [3 ]
Grahovac, Nada [1 ]
Spika, Ankica Kondic [1 ]
机构
[1] Inst Field & Vegetable Crops, Novi Sad 21000, Serbia
[2] Inst Gen & Phys Chem, Belgrade 11000, Serbia
[3] Univ Novi Sad, Fac Technol Novi Sad, Novi Sad 21000, Serbia
[4] Maksima Gorkog 30, Novi Sad 21000, Serbia
关键词
Mathematical modelling; Machine learning; Rapeseed; Quality traits; Fatty acids; Tocopherols; REMOTELY-SENSED DATA; NAPUS L. CULTIVARS; ALPHA-TOCOPHEROL; SOIL PROPERTIES; OLIVE OIL; PREDICTION;
D O I
10.1016/j.jfca.2022.105020
中图分类号
O69 [应用化学];
学科分类号
081704 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 57 条
[1]   Weather Conditions Influence on Lavandin Essential Oil and Hydrolate Quality [J].
Acimovic, Milica ;
Loncar, Biljana ;
Jeremic, Jovana Stankovic ;
Cvetkovic, Mirjana ;
Pezo, Lato ;
Pezo, Milada ;
Todosijevic, Marina ;
Tesevic, Vele .
HORTICULTURAE, 2022, 8 (04)
[2]   Different Processing Practices and the Frying Life of Refined Canola Oil [J].
Adjonu, Randy ;
Zhou, Zhongkai ;
Prenzler, Paul D. ;
Ayton, Jamie ;
Blanchard, Christopher L. .
FOODS, 2019, 8 (11)
[3]  
American Oil Chemists' Society (AOCS), 2009, CE162 AOCS
[4]   Artificial Neural Network Methods for the Solution of Second Order Boundary Value Problems [J].
Anitescu, Cosmin ;
Atroshchenko, Elena ;
Alajlan, Naif ;
Rabczuk, Timon .
CMC-COMPUTERS MATERIALS & CONTINUA, 2019, 59 (01) :345-359
[5]  
[Anonymous], 2011, FDA DEP HLTH HUMAN S, V3
[6]  
[Anonymous], 2005, Design and Analysis of Experiments
[7]   Artificial Neural Networks (ANNs) as a Novel Modeling Technique in Tribology [J].
Argatov, Ivan .
FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND, 2019, 5
[8]   Rapid determination of alpha tocopherol in olive oil adulterated with sunflower oil by reversed phase high-performance liquid chromatography [J].
Bakre, S. M. ;
Gadmale, D. K. ;
Toche, R. B. ;
Gaikwad, V. B. .
JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE, 2015, 52 (05) :3093-3098
[9]   Artificial neural network model in predicting yield of mechanically transplanted rice from transplanting parameters in Bangladesh [J].
Basir, Md Samiul ;
Chowdhury, Milon ;
Islam, Md Nafiul ;
Ashik-E-Rabbani, Muhammad .
JOURNAL OF AGRICULTURE AND FOOD RESEARCH, 2021, 5
[10]   Insights into temperature effects on the fatty acid composition of oilseed rape varieties [J].
Baux, A. ;
Colbach, N. ;
Allirand, J. M. ;
Jullien, A. ;
Ney, B. ;
Pellet, D. .
EUROPEAN JOURNAL OF AGRONOMY, 2013, 49 :12-19