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Development of a Nuclear Magnetic Resonance Method and a Near Infrared Calibration Model for the Rapid Determination of Lipid Content in the Field Pea (Pisum sativum)

Pisum sativum is a leguminous crop suitable for cultivation worldwide. It is used as a forage or dried seed supplement in animal feed and, more recently, as a potential non-traditional oilseed. This study aimed to develop a low-cost, rapid, and non-destructive method for analyzing pea lipids with no...

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Detalles Bibliográficos
Autores principales: Addo, Philip Wiredu, Ossowski, Philip, MacPherson, Sarah, Gravel, Andrée E., Kaur, Rajvinder, Hoyos-Villegas, Valerio, Singh, Jaswinder, Orsat, Valérie, Dumont, Marie-Josée, Lefsrud, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911919/
https://www.ncbi.nlm.nih.gov/pubmed/35268743
http://dx.doi.org/10.3390/molecules27051642
Descripción
Sumario:Pisum sativum is a leguminous crop suitable for cultivation worldwide. It is used as a forage or dried seed supplement in animal feed and, more recently, as a potential non-traditional oilseed. This study aimed to develop a low-cost, rapid, and non-destructive method for analyzing pea lipids with no chemical modifications that would prove superior to existing destructive solvent extraction methods. Different pea accession seed samples, prepared as either small portions (0.5 mm(2)) of endosperm or ground pea seed powder for comparison, were subjected to HR-MAS NMR analyses and whole seed samples underwent NIR analyses. The total lipid content ranged between 0.57–3.45% and 1.3–2.6% with NMR and NIR, respectively. Compared to traditional extraction with butanol, hexane-isopropanol, and petroleum ether, correlation coefficients were 0.77 (R(2) = 0.60), 0.56 (R(2) = 0.47), and 0.78 (R(2) = 0.62), respectively. Correlation coefficients for NMR compared to traditional extraction increased to 0.97 (R(2) = 0.99) with appropriate correction factors. PLS regression analyses confirmed the application of this technology for rapid lipid content determination, with trends fitting models often close to an R(2) of 0.95. A better robust NIR quantification model can be developed by increasing the number of samples with more diversity.