Effect of cold storage on culture viability and some rheological properties of fermented milk prepared with yogurt and probiotic bacteria

We examined the effect of storage time on culture viability and some rheological properties (yield stress, storage modulus, loss modulus, linear viscoelastic region, structural recuperation and firmness) of fermented milk made with Lactobacillus delbrueckii ssp. bulgaricus, Lactobacillus acidophilus (LA) and Bifidobacterium animalis ssp. lactis in coculture with Streptococcus thermophilus (ST). Acidification profiles and factors that affect viability (postfermentation acidification, acidity and dissolved oxygen) were also studied during 35 days at 4C. Fermented milk prepared with a coculture of ST and Bifidobacterium lactis gave the most constant rheological behavior and the best cell viability during cold storage; it was superior to ST plus LA for probiotic fermented milk production.

Optimization of the rheological properties of probiotic yoghurts supplemented with milk proteins

This study aimed to optimize the rheological properties of probiotic yoghurts supplemented with skimmed milk powder (SMP) whey protein concentrate (WPC) and sodium caseinate (Na-Cn) by using an experimental design type simplex-centroid for mixture modeling It Included seven batches/trials three were supplemented with each type of the dairy protein used three corresponding to the binary mixtures and one to the ternary one in order to increase protein concentration in 1 g 100 g(-1) of final product A control experiment was prepared without supplementing the milk base Processed milk bases were fermented at 42 C until pH 4 5 by using a starter culture blend that consisted of Streptococcus thermophilus Lactobacillus delbrueckii subsp bulgaricus and Bifidobacterium (Humans subsp lactis The kinetics of acidification was followed during the fermentation period as well the physico-chemical analyses enumeration of viable bacteria and theological characteristics of the yoghurts Models were adjusted to the results (kinetic responses counts of viable bacteria and theological parameters) through three regression models (linear quadratic and cubic special) applied to mixtures The results showed that the addition of milk proteins affected slightly acidification profile and counts of S thermophilus and B animal`s subsp lactis but it was significant for L delbrueckii subsp bulgaricus Partially-replacing SMP (45 g/100 g) with WPC or Na-Cn simultaneously enhanced the theological properties of probiotic yoghurts taking into account the kinetics of acidification and enumeration of viable bacteria (C) 2010 Elsevier Ltd All rights reserved

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.