Department of Food Preservation Engineering, Faculty of Technology Novi Sad, University of Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Republic of Serbia.
Sunflower Department, Institute of Field and Vegetable Crops, Maksima Gorkog 30, 21000 Novi Sad, Republic of Serbia.
J Sci Food Agric. 2021 Nov;101(14):5827-5833. doi: 10.1002/jsfa.11234. Epub 2021 Apr 18.
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.
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.
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. © 2021 Society of Chemical Industry.
葵花籽是世界上五种最丰富的油籽之一，葵花籽油也是食用油脂类的一种。最近，人们越来越关注冷榨葵花籽油，因为这种油的加工过程涉及较少，且不使用溶剂。本研究旨在调查 20 种新型葵花杂交种子的尺寸（长度、宽度、厚度）、硬度、常规特性（水分含量、壳含量、千粒重）、重量特性（真实密度、堆密度、孔隙率）和几何特性（等效直径、表面积、种子体积、球形度）。由于这个过程不需要很长时间或特殊设备，因此确定这些参数中的大多数参数的步骤都非常简单和容易。
主成分分析和聚类分析证实了油用型和食葵型杂交种子在上述特性上的差异。两组样品之间的主要区别之一是提取油产量。油用型杂交种子的机械提取油产量显著（P<0.05）更高（从 68.72±4.21%到 75.61±1.99%），而食葵型杂交种子的机械提取油产量较低（从 20.10±2.82%到 39.91±6.23%）。提取油产量值仅在提取油后才知道。
在进行机械提取之前了解提取油产量值，可以更好地管理该过程。通过应用人工神经网络方法，开发了一个最优的神经网络模型。该开发模型显示了良好的泛化能力，可以根据实验数据预测新型葵花杂交种子的机械提取油产量，这是本文的主要目标。 © 2021 英国化学学会。
