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Variations in Microstructural and Physicochemical Properties of Candelilla Wax/Rice Bran Oil-Derived Oleogels Using Sunflower Lecithin and Soya Lecithin.

ABSTRACT: Candelilla wax (CW) is a well-known oleogelator that displays tremendous oil-structuring potential. Lecithin acts as a crystal modifier due to its potential to alter the shape and size of the fat crystals by interacting with the wax molecules. The proposed work is an attempt to understand the impact of differently sourced lecithin, such as sunflower lecithin (SFL) and soya lecithin (SYL), on the various physicochemical properties of CW and rice bran oil (RBO) oleogels. The yellowish-white appearance of all samples and other effects of lecithin on the appearance of oleogels were initially quantified by using CIELab color parameters. The microstructural visualization confirmed grainy and globular fat structures of varied size, density, packing, and brightness. Samples made by using 5 mg of SFL (Sf5) and 1 mg of SYL (Sy1) in 20 g showed bright micrographs consisting of fat structures with better packing that might have been due to the improvised crystallinity in the said samples. The FTIR spectra of the prepared samples displayed no significant differences in the molecular interactions among the samples. Additionally, the slow crystallization kinetics of Sf5 and Sy1 correlated with better crystal packing and fewer crystal defects. The DSC endotherm displayed two peaks for melting corresponding to the melting of different molecular components of CW. However, all the formulations showed a characteristic crystallization peak at ~40 °C. The structural reorganization and crystal growth due to the addition of lecithin affected its mechanical property significantly. The spreadability test among all prepared oleogels showed better spreadable properties for Sf5 and Sy1 oleogel. The inclusion of lecithin in oleogels has demonstrated an enhancement in oleogel properties that allows them to be included in various food products.

SUBMITTER: Sahu D

PROVIDER: S-EPMC8628760 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.

Dataset Information

Variations in Microstructural and Physicochemical Properties of Candelilla Wax/Rice Bran Oil-Derived Oleogels Using Sunflower Lecithin and Soya Lecithin.

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