In this study, various yeasts (Kluyveromyces marxianus, Saccharomyces turicensis, Pichia fermentans) and lactic acid bacteria (Lactobacillus kefiranofaciens, Lactobacillus kefiri, Leuconostoc mesenteroides) were entrapped in 2 different microspheres using an entrapment ratio for the strains that was based on the distribution ratio of these organisms in kefir grains. The purpose of this study was to develop a new technique to produce kefir using immobilized starter cultures isolated from kefir grains. An increase in cell counts with fermentation cycles was observed for both the lactic acid bacteria (LAB) and yeasts, whereas the cell counts of kefir grains were very stable during cultivation. Scanning electron microscopy showed that the short-chain lactobacilli and lactococci occupied the surface of the LAB microspheres, whereas the long-chain lactobacilli were inside the microspheres. When the yeasts were analyzed, cells at a high density were entrapped in cracks on the surface and within the microspheres, where they were surrounded by the short-chain lactobacilli. The distribution of the LAB and yeast species in kefir produced from grains and microspheres showed that there was no significant difference between the kefirs produced by the 2 methods; moreover, Leu. mesenteroides and K. marxianus were the predominating microflora in both types of kefir. There was no significant difference in the ethanol and exopolysaccharide contents between the 2 kefirs, although the acidity was different.

Calcium gluconate (0.0 to 0.5%), sodium gluconate (0.0 to 1.0%), and N-acetylglucosamine (0.0 to 1.0%) were added to skim milk to retain the viability of Lactobacillus acidophilus and Bifidobacterium longum. To carry out response surface modeling, the regression method was performed on experimental results to build mathematical models. The models were then formulated as an objective function in an optimization problem that was consequently optimized using a genetic algorithm approach to obtain the maximum viability of the probiotics. The genetic algorithms (GAs) were examined to search for the optimal value. The results indicated that GAs were very effective for optimizing the activity of probiotic cultures.