University of Turin, Turin, Italy.
Lett Appl Microbiol. 2011 Feb;52(2):96-103. doi: 10.1111/j.1472-765X.2010.02961.x. Epub 2010 Dec 22.
A research was undertaken to explore the possibility to express with suitable mathematical models Biolog metabolic curves obtained for oenological yeasts and to use such models for monitoring yeast growth in alcoholic fermentation.
Experimental curves of metabolic activity in Biolog YT microplates, obtained in a previous work for various oenological yeast strains in pure cultures and mixed populations, at various cell concentrations, have been modelled with Gompertz's, Gompertz's modified and Lindstrom's mathematical equations. Lindstrom's model proved to be the most suitable to fit the curves of the oenological yeasts under study, providing the highest correlation coefficients between experimental and calculated data. The model made it possible to recognize, in mixed yeast populations, the presence of active dry yeasts used for guided fermentations. Model's constant parameters were used for a numerical characterization of yeast curves.
The application of the model to the experimental data resulted to be suitable for an early prediction of the successive evolution of yeast growth.
The results obtained indicate the possibility to develop protocols for monitoring yeast presence during alcoholic fermentation, with an early assessment of the correct evolution of their growth, especially when active dry yeasts are employed.
本研究旨在探索是否可以用合适的数学模型来表达葡萄酒酵母的生物代谢曲线,并将这些模型用于监测酒精发酵过程中酵母的生长情况。
在之前的工作中,用 Biolog YT 微孔板对各种葡萄酒酵母菌株在纯培养和混合培养中的不同细胞浓度下的代谢活性进行了实验,用 Gompertz 方程、Gompertz 修正方程和 Lindstrom 方程对实验曲线进行了建模。结果表明,Lindstrom 模型最适合拟合所研究的葡萄酒酵母的曲线,实验数据与计算数据之间的相关性最高。该模型使得在混合酵母群体中能够识别用于引导发酵的活性干酵母的存在。模型的常数参数用于对酵母曲线进行数值特征描述。
该模型在实验数据中的应用适合于对酵母生长的后续演变进行早期预测。
研究结果表明,有可能开发出监测酒精发酵过程中酵母存在的方案,从而可以早期评估其生长的正确演变,特别是在使用活性干酵母时。