Differentiation of probiotic and environmental Saccharomyces cerevisiae strains in animal feed

作者：Buechl N R; Hutzler M; Mietke Hofmann H; Wenning M; Scherer S^*

来源：Journal of Applied Microbiology, 2010, 109(3): 783-791.

DOI：10.1111/j.1365-2672.2010.04705.x

• artificial neural network
• DHPLC
• FTIR spectroscopy
• microbial feed additives
• probiotics
• Saccharomyces cerevisiae
• strain typing
• delta-PCR

Aims:
To establish an identification system for probiotic Saccharomyces cerevisiae strains based on artificial neural network (ANN)-assisted Fourier-transform infrared (FTIR) spectroscopy to improve quality control of animal feed.
Methods and Results:
The ANN-based system for differentiating environmental from probiotic S. cerevisiae strains comprises five authorized feed additive strains plus environmental strains isolated from different habitats. A total of 108 isolates were used as reference strains to create the ANN. DHPLC analysis and delta-PCR were used as reference methods to type probiotic yeast isolates. The performance of the FTIR-ANN was tested in an internal validation using unknown spectra of each reference strain. This validation step yielded a classification rate of 99 center dot 1 %. For an external validation, a test data set comprising 965 spectra of 63 probiotic and environmental S. cerevisiae isolates unknown to the ANN was used, resulting in a classification rate of 98 center dot 2 %.
Conclusions:
Our results demonstrate that probiotic S. cerevisiae strains in feed can be differentiated successfully from environmental isolates using both genotypic approaches and ANN-based FTIR spectroscopy.
Significance and Impact of the Study:
FTIR-based artificial neural network analysis provides a rapid and inexpensive technique for yeast identification both at the species and at the strain level in routine diagnostic laboratories, using a single sample preparation.

• 出版日期2010-9