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.

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The helical flutes in drills are usually made by grinding cylindrical rods to form helically swept grooves. In order to maintain the quality of the fluting process for microdrills, grinding wheels must be trued and dressed regularly. It is therefore quite important to examine the profile accuracy of trued and dressed wheels. With the aid of machine vision, this paper presents an image-based method for examining the profile accuracy of grinding wheels used for microdrill fluting. Using thin plate specimens ground to yield two-dimensional contours for duplicating the topographical profiles of inspected grinding wheels, digital images of the ground contours can be captured in order to detect their coordinate data by edge detection. A contour matching method is then developed to calculate the relative deviations between the theoretical and detected inspected contours, and the profile accuracy of the inspected grinding wheel can thus be indirectly evaluated. To test the proposed method, a machine vision system was built, and experiments examining diamond grinding wheels used for machining helical flutes in microdrills were conducted. The results showed that the proposed contour matching method could achieve sufficient repeatability in the examination of wheel contours.