Abstract
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 -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 99·1 %. 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 98·2 %. 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.
Original language | English |
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Pages (from-to) | 783-791 |
Number of pages | 9 |
Journal | Journal of Applied Microbiology |
Volume | 109 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2010 |
Keywords
- -PCR
- DHPLC
- FTIR spectroscopy
- Saccharomyces cerevisiae
- artificial neural network
- microbial feed additives
- probiotics
- strain typing