4000 3500 3000 2500 2000 1500 1000 500 Wavenumber (cm4)

Fig. 4.15 FT-IR spectra of two unrelated yeast strains (redrawn from Timmins et al., 1998).

overcoming the fundamental issue that PyMS exhibits poor long-term (>30 days) reproducibility. It is clear that such developments can only encourage the development and application of PyMS in microbiology.

Although primarily applied to medical microbiology, this technology has been applied to the differentiation of yeasts such as Ascomcetes (PyGC - Viljoen & Kock, 1991), Saccharomyces/Rhodosporidium/Filobasidium (PyMS - Windig et al., 1981/2), Saccharomyces species (PyGC - Jones, 1984). Using PyMS, Gutteridge and Priest (1996) were able to differentiate 51 yeast strains isolated from what was clearly a heavily contaminated 'home-brew product', into brewing yeasts, contaminant ('wild') Saccharomyces and wild non-Saccharomyces strains.

Despite its rapidity, PyMS has yet to be 'taken on board' as a route to yeast strain differentiation and identification. An early report (Quain, 1988) briefly described the opportunities that PyMS may offer the brewing industry. In the first detailed study, Timmins et al. (1998), demonstrated the power of PyMS in broadly discriminating 22 production brewing yeasts into four main clusters and a 'single member cluster' (Fig. 4.14). As these strains have also been characterized by RFLP DNA fingerprinting (Schofield et al., 1995; Wightman et al., 1996), this work has allowed the direct comparison of phenotypic (including FT-IR below) and genotypic approaches to the differentiation of strains. Intriguingly, Timmins et al., (1998) conclude 'that these phenetic approaches mirror the known genotype (and brewing phenotype) of these organisms'. However, despite such promising observations, the cost and limited availability of PyMS is likely to hamper development and application of this technology within brewing. Other approaches.

• Fourier transform infrared spectroscopy - like PyMS, FT-IR is a physico-chemical method that provides a 'snap-shot' of the whole cell phenotype (DNA/ RNA, proteins, carbohydrates, etc.). The wave number v, the reciprocal of the wavelength, is used as the physical unit for FT-IR spectroscopy. Typically the 'middle' infrared (v = 4000-200 cm *) is used to differentiate and identify yeasts. Like PyMS, FT-IR data should be used with multivariate statistical techniques, as direct analysis of spectra reveals few differences (see Fig. 4.15 for spectra of two distinct strains of S. cerevisiae). The various peaks in the spectra represent the vibrations of bonds within functional groups, for example the peak at 1000-100 cm 1 is mainly due to carbohydrate C-O vibrations.

FT-IR is finding growing application in the differentiation of yeasts. An early study used FT-IR to reveal the interrelationships between eight yeast species and hybrid crosses between seven of them and S. diastaticus (Hopkinson et al., 1988). FT-IR was also used by Timmins et al. (1998) in their study of the differentiation of 22 production brewing yeasts. In addition to successfully discriminating between strains, FT-IR shows the same clusters of related strains as Py-MS, see Fig. 4.16.

A substantial paper from Kummerle et al. (1988) generated a FT-IR spectral library from 332 different food related yeasts representing 74 species of 18 genera. When challenged with 772 unknown yeasts from the food industry, FT-IR identified 699 yeasts (97.5%) correctly as validated by conventional physiological and morphological tests. These results suggest that FT-IR may well find wider application in the future, particularly as identification is achieved within 24 hours of colony isolation.

Discriminant function 1

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