Wandersleben, T., Morales, E., Burgos-Díaz, C., Barahona, T., Labra, E., Rubilar, M., Salvo-Garrido, H. (2018) Enhancement of functional and nutritional properties of bread using a mix of natural ingredients from novel varieties of flaxseed and lupine. LWT. Vol. 91; 48-54.
The use of supplements derived of natural sources as legumes and other grains have attracted great interest and it is highly desirable that the products used are free of genetetically modified organisms and low cost. However, due to the consumers’ preference for refined white bread, it is necessary to find a balance between nutrition and palatability in order to reach a larger population, therefore we tested three ingredients: lupine grit flour of a novel variety with the highest protein content available (AluProt-CGNA®, 60% of protein, dry matter), lupine hulls flour and flaxseed expeller flour (Kallfu-CGNA ®, good source dietary fibre). This work presents the results on the dough rheological properties of different combinations of the main ingredients with wheat flour and also a consumer’s acceptability test, conducted on 259 volunteers using the bread estimated as the best by its balance between rheology and nutritional value. The final bread had 125% more fibre and 55% more protein than the control bread; these increments were very similar to the ones obtained by previous reports but with half the amount of raw materials, due to the enhanced features of the grains used. The bread presented acceptability over 90% in all the aspects surveyed.
Osorio, C., Amiard, V., Aravena-Calvo, J., Udall, J., Doyle, J., Maureira-Butler, I. (2018) Chromatographic fingerprinting of Lupinus luteus L. (Leguminosae) main secondary metabolites: a case of domestication affecting crop variability. Genetic Resources and Crop Evolution. Vol. 65; 1281–1291
Secondary metabolites (SMs), such as alkaloids and raffinose oligosaccharides (RFOs), play important roles in plant physiology. Although alkaloid and RFO phenotypic variation has been reported for yellow lupin (Lupinus luteus L.), most evaluations have used a reduced number of accessions; thus, limiting the understanding of accumulation patterns and variation ranges. The main goal of this research was to assess alkaloid and RFO content in a diverse L. luteussample to understand possible SM accumulation patterns across this legume species. Eighteen yellow lupin accessions were analyzed using high performance thin layer chromatography to provide insights on seed and leaf RFO and alkaloid phenotypic variation. Co-dominant markers (170) were used to examine genetic relationships among L. luteus accessions and possible accumulation patterns across closely related genotypes. Significant differences were observed for seed and leaf RFOs. Total seed RFO accumulation ranged from 79.738 to 131.079 mg g−1. Raffinose, stachyose, and verbascose were observed in all genotypes’ seeds, but at different RFO concentrations. Raffinose was the only RFO detected in leaves (2.793–0.4224 mg g−1). Total alkaloid accumulation ranged from 0.22 to 15.12 and 0.00 to 8.007 mg g−1 for seeds and leaves, respectively. Lupinine, sparteine, and gramine were observed in seeds and leaves, and showed a wide range of variation. Neighbor-Joining (NJ) analysis showed an apparent pattern of seed alkaloid accumulation, most likely due to domestication events. However, high RFO accumulating accessions were scattered across the NJ tree. Alkaloid and RFO significant phenotypic variation will not only help to understand the roles of these SMs in L. luteus, but also to uncover the genetic basis behind their accumulation.
Labra, E., Torrecillas, C. (2018) Estimating dynamic Panel data. A practical approach to perform long panels. Revista Colombiana de Estadística. Vol. 41; 31-52
Panel data methodology is one of the most popular tools for quantitative analyses in the field of social sciences, particularly on topics related to economics and business. This technique allows us simultaneously addressing individual effects, numerous periods, and in turn, the endogeneity of the model or independent regressors. Despite these advantages, there are several methodological and practical limitations to perform estimations using this tool. Two types of models can be estimated with Panel data. While those of static nature have been the most developed, for performing dynamic models still remain some theoretical and practical constraints. This paper focus precisely on the latter, dynamics panel data, using an approach that combines theory and praxis, and paying special attention on estimations with macro database, that is to say, dataset with a long period of time and a small number of individuals, also called long panels.