Abstract
The present study substantiates the application of natural sunflower oil for the calibration of pulsed NMR analyzers, as well as for the identification and quality assessment of oilseeds and their products using the NMR method. Being the essential characteristic of sunflower seeds, the oil content is mandatory for the certification of agricultural products and their cost assessment. The first calibration option includes the measurement of oil NMR characteristics with the oil content determined using the Soxhlet exhaustive extraction. The second option involves the calibration of a NMR analyzer using sunflower oil, obtained by pressing identical cultivars and hybrids, as well as that acquired at a retail network. A comparison of the obtained calibration dependencies showed their similar nature. The application of sunflower oil for the calibration of NMR analyzers provides no increase in the oil content measurement error as compared to the first calibration option. Therefore, using natural oil for the calibration of NMR analyzers can considerably simplify the calibration process, reduce the calibration duration from 3–4 days to 3–4 h, as well as to exclude toxic solvents and additional high-value equipment from the process without essential variations in the oil content measurement error.
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Notes
- 1.
GSO 3107-84 Reference materials of the approved type of oil content and moisture content of sunflower seeds (set). Available via FIF EUM. www.fgis.gost.ru/fundmetrology/registry/19/items/393823. Accessed 12 September 2022 (In Russ.).
- 2.
The minispec Oil content and moisture in seeds and nuts. Available via: URL: http://spectrante.ru/images/pdf_series/Zernovie.pdf. Accessed 12 September 2022 (In Russ.).
- 3.
Measurement of oil component in dried palm mesocarp. Available via: Oxford Instruments. https://nmr.oxinst.com/assets/uploads/18_Measurement_of_Oil_Content_in_Dried_Palm_Mesocarp.pdf. Accessed 12 September 2022.
- 4.
Measurement of oil and water in seeds according to ISO 10565. Available via Oxford Instruments. https://nmr.oxinst.com/assets/uploads/3_3344_MR_Oilseeds_App%20Note_Web.pdf. Accessed 12 September 2022.
- 5.
Sunflower varieties of VNIIMK breeding, Russia:
UMNIK, oil content 30–35%. Available via VNIIMK. https://vniimk.ru/products/belosnezhnyy-sq/. Accessed 12 September 2022 (In Russ.).
DZhIN, oil content 44–46%. Available via VNIIMK. https://vniimk.ru/products/dzhinn/. Accessed 12 September 2022 (In Russ.).
IMIDZh, oil content 48%. Available via VNIIMK. https://vniimk.ru/about/fgup/Кaтaлoг%20BHИИMК.pdf. Accessed 12 September 2022 (In Russ.).
SPK, oil content 46–47%. Available via VNIIMK. https://vniimk.ru/products/spk/. Accessed 12 September 2022 (In Russ.).
BELOSNEZhNYI, oil content 30–35%. Available via VNIIMK. https://vniimk.ru/products/belosnezhnyy-sq/. Accessed 12 September 2022 (In Russ.).
Abbreviations
- RM:
-
Reference material
- TAG:
-
Triacylglycerol
- NMR spectroscopy:
-
Nuclear magnetic resonance spectroscopy
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Acknowledgments
No financial support in the form of a grant from any organization in the public, commercial, or non-profit sector was obtained for the research. All measurements were carried out using the equipment of the V. S. Pustovoit All-Russian Research Institute of Oil Crops.
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The authors have contributed equally.
Conflict of interest
The article was prepared on the basis of a report presented at the V International Scientific Conference “Reference materials in measurements and technologies” (Yekaterinburg, September 13–16, 2022). The article was admitted for publication after the abstract was revised, the article was formalized and the review procedure was carried out.
The version in the Russian language is published in the journal “Measurement Standards. Reference Materials” 2023;19(2):61–71 (In Russ.). https://doi.org/10.20915/2077-1177-2023-19-2-61-71
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Agafonov, O.S., Prudnikov, S.M. (2024). Determination of Sunflower Seed Oil Content Using Natural Sunflower Oil: Calibration of a Pulsed NMR Analyzer. In: Sobina, E.P., et al. Reference Materials in Measurement and Technology . RMMT 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-49200-6_9
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