Separation of Acylglycerides Obtained by Enzymatic Esterification Using Solvent Extraction

New avenues to add value to glycerol are currently being explored. One of them is the synthesis of structured lipids through glycerol esterification. In this work we have analyzed the recovery and purification of dicaprin obtained by esterification of glycerol with capric acid (C) in heptane, mediated by Lipozyme RM IM. This is an intermediate step to obtain lipids MLM. In the first stage, the diglyceride synthesis MGM (being G a central HC–OH) was carried out. When M = C, the diglyceride is CGC. Recovery of the diglyceride CGC is required to carry out the esterification of the sn-2 position with palmitic acid (P), thus obtaining the triglyceride CPC. Different solvents were evaluated using Ecofac 1.0 (a molecular design software solvent) through a theoretical approach to explore the best solvents for the acylglycerides separation. Then, the performance of the selected solvents to separate dicaprin from mono and tricaprin was experimentally studied in a liquid–liquid extraction process. Previously, the remaining fatty acid had been neutralized. With liquid–liquid extraction in three simple steps, using ethanol/water, 94 % of the dicaprin obtained by enzymatic esterification was recovered with a purity of 89 % (wt%). It was also possible to obtain dicaprin with a purity of 97 % but with a yield of 56 %.     

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from ?0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.     

Optimization of Sesame Oil Extraction by Screw-Pressing at Low Temperature

Box-Behnken designs were used to optimize a process for sesame oil extraction by screw-pressing at low temperature (50 °C). Experimental designs included seed moisture content (SMC), pressing speed (PS), and restriction die (RD) as the main processing parameters. Extractions at pilot plant scale showed a peak in oil recovery (OR, 71.1 ± 2.80%) at 12.3% SMC, 4 mm RD, and 20 rpm PS. Theoretical models were scanned against experimental data in order to scale up the proposed oil extraction process to industrial scale. A fitted model for OR showed a maximum predicted value similar to the highest experimental value (74.4 ± 1.23%) under the following conditions: 8.03% SMC, 10 mm RD, and 20 rpm PS. Chemical quality parameters of oils obtained at both pilot and industrial scales were in the ranges stated in Codex (FAO/WHO) standards for non-refined sesame oil.     

Automatic word naming recognition for an on-line aphasia treatment system

One of the most common effects among aphasia patients is the difficulty to recall names or words. Typically, word retrieval problems can be treated through word naming therapeutic exercises. In fact, the frequency and the intensity of speech therapy are key factors in the recovery of lost communication functionalities. In this sense, speech and language technology can have a relevant contribution in the development of automatic therapy methods. In this work, we present an on-line system designed to behave as a virtual therapist incorporating automatic speech recognition technology that permits aphasia patients to perform word naming training exercises. We focus on the study of the automatic word naming detector module and on its utility for both global evaluation and treatment. For that purpose, a database consisting of word naming therapy sessions of aphasic Portuguese native speakers has been collected. In spite of the different patient characteristics and speech quality conditions of the collected data, encouraging results have been obtained thanks to a calibration method that makes use of the patients’ word naming ability to automatically adapt to the patients’ speech particularities.     

The effect of co-encapsulation of Lactobacillus rhamnosus GG ATCC 53103 with inulin on alginate/chitosan matrix: the viability in fermented soy blend and simulated digestive system

This research verified the ability of Lactobacillus rhamnosus encapsulated with inulin to tolerate the simulated digestive system and their viability in a soy blend. Probiotic encapsulated in alginate-chitosan matrix without inulin presented a better encapsulation efficiency (80.92%) than encapsulation with inulin (57.39%). On the 28th day, the count of probiotics decreased by 3.42 and 1.99 logarithmic cycles of free and encapsulated cells without inulin, respectively. In contrast, the microorganisms encapsulated with inulin showed an increase of 1.26 logs CFU g⁻¹. During gastrointestinal simulation, cell counts decreased by 0.78, 1.55 and 1.95 CFU g⁻¹ logs for encapsulated cells without inulin, free and encapsulated with inulin, respectively. Sensory panellists liked the fermented soy blend with encapsulated lactobacilli, and this result shows the possibility to create new probiotic foods of plant origin. Therefore, the alginate/chitosan matrix can be considered adequate for the encapsulation of L. rhamnosus. The inulin reduces the encapsulation efficiency and increases the cell loss in gastrointestinal simulation. Considering cellular losses, the best option for preparing a fermented soy blend is to use L. rhamnosus encapsulated without inulin.