In the present manuscript it was presented whether spreading of antibiotic resistant bacterial groups in environment could be monitored by our newly developed method by enumerating antibiotic resistant bacterial groups in various biological wastes and composts. Although the numbers were not so high, diverse kinds of colistin resistant bacteria (25 mg•L −1 ) were included in row cattle feces (1.78 × 10 4 MPN g −1 ) and cattle feces manure (>3.84 × 10 4 MPN g −1 ). Compost originated from leftover food (>44.8 × 10 4 MPN g −1 ) and shochu lee (>320 × 10 4 MPN g −1 ) included higher numbers of chlortetracycline resistant Pseudomonas sp., (25 mg•L −1 ), and row cattle feces included higher numbers of chlortetracycline resistant Enterobacteriacea (15.7 × 10 4 MPN g −1 ), which mostly consisted from Pantoea sp. or Xenorhobdus doucetiae. Numbers of multi drug resistant bacteria, resistant to 25 mg•L −1 of ciprofloxacin, streptomycin, chloramphenicol, and ampicillin, were the highest in row cattle feces (>143.6 × 10 4 MPN g −1 ), followed by cattle feces manure (4.19 × 10 4 MPN g −1 ), and shochu lee (0.36 × 10 4 MPN g −1 ), which included diverse kinds of bacterial group. The present results indicated that higher numbers of multi drug resistant bacteria were typically found in row cattle feces, and the method was found suitable to enumerate and identify them. These results suggested that the method might become their environmental risk evaluation method.

Lactic acid bacteria have not only been used to produce various kinds of fermented food, but also used as probiotic products. As lactic acid bacterial group was consisted from diverse genera, a simple inspection method by which numbers and contained microorganisms could be automatically analyzed without any preliminary information was required to use them more effectively. In this manuscript, lactic acid bacterial groups in commercial products of kimuchi, komekouji-miso, and yoghurt were identified and enumerated by our newly developed method [1]-[3], to evaluate whether the method could be used as an inspection method of various food samples. In kimuchi, numerically dominant bacteria were Lactobacillus sakei, and L. casei (1.4 × 10 4 MPN g −1) and Leuconostoc spp. (l.4 × 10 4 MPN). In kouji-miso, numerically dominant bacteria was Bacillus spp. (3 × 10 3 MPN), which mainly included B. subtilis group and B. cereus group. Lactic acid bacteria such as Lactobacillus spp., or Lactococcus spp., included in the komekouji-miso, could be enumerated after 3 days incubation (1.24 × 10 4 MPN), but not detected after 7 days incubation. In yoghurt A and C, Lactococcus lactis was detected as numerically dominant lactic acid bacteria (3.0 × 10 5 MPN). In yoghurt B, Lactobacillus spp., or Lactococcus spp., was detected not only by a culturebased method but also by an unculture-based method, although there was a difference between the both estimated numbers. The present results suggested that the method might become useful as a simple inspection method of food microorganisms, because time and labor of the analysis could be reduced by using an unculture-based method and MCE-202 MultiNA. In this study, Bifi-* Corresponding author. K. Matsumoto et al. 164 dobacteriium spp. was not detected in B and C yoghurt, in spite of indicating their existence, and numbers of lactic acid bacteria were lower than the level of the daily product regulation, because 16S rDNA of Bifidobacteriium spp. might not be amplified by the used PCR condition. The PCR condition must be changed so as to amplify Bifidobacterium spp., before the method will be used as an inspection method for lactic acid bacteria.

The method to analyze both eukaryotic and prokaryotic microorganisms without preliminary microbial information of sample seemed to be useful not only for research and investigation of microorganisms but also for industry using microorganisms. In the present manuscript, preparation of a new DNA primers, new reference database for 18S rDNA for our newly developed method [1]-[3], and analyses of eukaryotic and prokaryotic microorganisms in fermentation products were presented. In komekouji, Aspergillus spp., was enumerated to be 46.5 × 10 6 MPN g −1 , and Penicillium spp., was enumerated to be 1.5 × 10 6 MPN g −1 . In dry yeast, Saccharomyces group, were enumerated to be 8600 × 10 6 MPN g −1 . In komekouji-miso, no eukaryotic microorganism was detected, while the other Bacillus spp., was numerically dominant (21.5 × 10 6 MPN g −1 ) as prokaryotic mi- croorganisms, followed by B. subtilis group (4.65 × 10 6 MPN g −1 ), and the other Firmicutes (3.7 × 10 6 MPN g −1 ). The komekouji-miso included lower number of Actinobacteria (0.15 × 10 6 MPN g −1 ), Burkhokderia sp. (1.5 × 10 6 MPN g −1 ), and the other α,β,γ-proteobacteria (0.12 × 10 6 MPN g −1 ). In sake-kasu, both prokaryote and eukaryote were not detected by the method. Present results indicated that using both universal primers for eukaryotic and prokaryotic microorganisms, each groups of prokaryotic and eukaryotic microorganisms were enumerated without any preliminary information nor setting up standard curve, which were required for real time PCR.

Analyses of microbial properties in soil and manure had always included the problem that there was no available standard method to evaluate microbial property. The one of the major problems was the vast diversity and the enormous population of soil microorganisms [1], the other was an existence of numerically dominant unculturable microorganisms which comprise 99% of soil habitat [2]. We evaluated whether our newly developed method, by which taxonomies and their number of each bacterial groups were estimated, could be used as evaluation method of microbial properties of soils and manures. In the forest soil, β-Proteobacteria, which included Burkholderia sp., Ralstonia sp., and Alcaligenes sp., was numerically dominant bacteria (3.64 × 10 6 MPN g −1 dry soil), followed by γ-Proteobacteria (1.32 × 10 6 MPN), δ-Proteobacteria (0.006 × 10 6 MPN), and the other gram negative bacteria (0.006 × 10 6 MPN). In the commercial manure, Actinobacteria, which included Streptoverticillium salmonis, Mycrococcus sp., Streptomyces bikiniensis, and Microbacterium ulmi, was numerically dominant bacterial group (30.8 × 10 6 MPN), followed by α-Proteobacteria (26.0 × 10 6 MPN), β-Proteobacteria (17.1 × 10 6 MPN), δ-Proteobacteria (11.2 × 10 6 MPN), the other Firmicutes (1.71 × 10 6 MPN), γ-Proteobacteria (0.5 × 10 6 MPN), and the other gram negative bacteria (0.05 × 10 6 MPN). In the upland field, the other Firmicutes, which included Paenibacillus sp., was numerically dominant bacteria (4.41 × 10 6 MPN), followed by Actinobacteria (2.14 × 10 6 MPN), Bacillus sp. (2.14 × 10 6 MPN), and γ-Proteobacteria (0.35 × 10 6 MPN). Although the precision of the affiliations became lower because of higher diversity of samples and the number of some Antinobacteria and Firmicutes might be underestimated by the used PCR condition, the method was found suitable as a candidate of a new evaluation system of soil and manure.