Determination of seed oil content and fatty acid composition in sunflower through the analysis of intact seeds, husked seeds, meal and oil by near-infrared reflectance spectroscopy
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- 作者：B. P茅rez-Vich (2)
L. Velasco (1)
J. M. Fern谩ndez-Mart铆nez (2) - 关键词：Key words Fatty acid composition ; Helianthus annuus ; husked ; seed samples ; intact ; seed samples ; NIRS ; near ; infrared reflectance spectroscopy ; meal samples ; oil content ; oil samples ; sunflower
- 刊名：Journal of the American Oil Chemists' Society
- 出版时间：May 1998
- 出版年：1998
- 期刊代码：pc356_0003-021x
- 类别：ch
- 卷：75
- 期：5
- 页码：547-555
- 数据来源：sp
摘要
A methodological study was conducted to test the potential of near-infrared reflectance spectroscopy (NIRS) to estimate the oil content and fatty acid composition of sunflower seeds. A set of 387 intact-seed samples, each from a single plant, were scanned by NIRS, and 120 of them were selected and further scanned as husked seed, meal, and oil. All samples were analyzed for oil content (nuclear magnetic resonance) and fatty acid composition (gas chromatography), and calibration equations for oil content and individual fatty acids (C16:0, C16:1, C18:0, C18:1, and C18:2) were developed for intact seed, husked seed, meal, and oil. For intact seed, the performance of the calibration equations was evaluated through both cross- and external validation, while cross-validation was used in the rest. The results showed that NIRS is a reliable and accurate technique to estimate these traits in sunflower oil (validation r 2 ranged from 0.97 to 0.99), meal (r 2 from 0.92 to 0.98), and husked seeds (r 2 from 0.90 to 0.97). According to these results, there is no need to grind the seeds to scan the meal; similarly accurate results are obtained by analyzing husked seeds. The analysis of intact seeds was less accurate (r 2 from 0.76 to 0.85), although it is reliable enough to use for pre-screening purposes to identify variants with significantly different fatty acid compositions from standard phenotypes. Screening of intact sunflower seeds by NIRS represents a rapid, simple, and cost-effective alternative that may be of great utility for users who need to analyze a large number of samples.