A Simple and Rapid Computer‐assisted Technique for the Identification of some Selected Bacillus Species using Biochemical Tests

Abstract

A computer‐assisted probabilistic identification technique was developed to identify species of the genus Bacillus known to be potentially pathogenic and/or frequently found as contaminants in pharmaceutical preparations. An identification matrix on 18 species of the genus Bacillus was constructed. Twenty‐four biochemical tests were used. The reaction profile of each species was quantified in probability terms and called up on a computer. The computer method was used on 30 test cultures. In each case, the most likely identification, according to the computer method, was the species to which the strain had already been assigned. In 29 of the 30 cases, the identification was with a probability level of one; in seven of these, however, more than one species tied for first choice. Only one strain was identified with a lower level of probability. The technique developed is comparatively easy to use and would seem to constitute a useful diagnostic tool within the pharmaceutical field.

Identification of Bacillus Strains Using the API System

“…The keys of Gordon et al, (1973) and Bonde (1978, and personal communication) for example, use 27 and 22 characters and recognize only 21 and 10 taxa respectively. The computer-assisted identification system described by Willemse-Collinet et al (1980), and based upon studies of 30 strains from culture collections, used 24 tests, taking up to 7 d for the identification of only 18 species, and was unable to achieve a satisfactory diagnosis in several instances, although misidentification did not occur. Our results enable the recognition of 38 taxa and the availability of results for a large number of tests (Table 1) allows the identification of atypical and intermediate strains.…”

Numerical taxonomy of Bacillus isolated from orally administered drugs

“…Members of this genus are the most common microorganism found in these products (Mosso et at. 1977;Devleeschoower & Dony 1979;Willemse-Collinet et al 1980;Garcia Arribas et al 1981) and sometimes alter their properties. These organisms are not usually included in the routine work of a control laboratory because of the difficulties involved.…”

New Strategies for the Selective Isolation of Industrially Important Bacteria

“…Probabilistic identification schemes have also been constructed using data less comprehensive than those provided by numerical classifications. They include systems for the identification of Bacillus (Willemse-Collinet, Tromp and Huizinga, 1980) and Micrococcus species (Feltham and Sneath, 1982). Improved identification systems based on numerical taxonomies are available for actinobacteria (Seiler, 1983) and nearly all of the recognized species of Bacillus (Logan and Berkeley, 1984).…”