In Mexican alcohol distilleries using sugarcane molasses, one reason for low alcoholic fermentation efficiency is the use of inferior yeast cultures. The objective of the present study was to isolate and select yeast strains from alcoholic fermentations of natural sources (sugarcane molasses, grape juice, cane juice and pineapple) from Veracruz city market and Mexican distilleries, and to evaluate their performance under laboratory conditions in an effort to select superior strains for industrial fermentations. Ethanol production, glucose composition, growth rate, “Killer” activity, ethanol and glucose tolerance of the most promising strains were monitored on synthetic and molasses media. A total of 112 yeast strains were isolated by their capacity to produce ethanol, and from these, only 58 were selected on the basis of best ethanol theoretical yields (88–96%). These strains were exposed several times to high concentrations of glucose and ethanol in order to select ethanol‐ and glucose‐tolerant yeast; 10 were obtained that adapted best to these conditions and that showed “Killer” activity. Of these strains, U3‐11, M11, JC10 and U2‐10 (obtained from grape juice, sugarcane molasses and cane juice) demonstrated the highest adaptation to both ethanol (5–7% w/v) and glucose (20% w/v). The maximum yield obtained was 0.46 g/g (90% theoretical yield) in a 20‐L bioreactor with cane molasses under nonsterile conditions. PRACTICAL APPLICATIONS The selected yeasts could be introduced into industrial processes in Mexican distilleries using sugarcane molasses in order to improve productivity and diminish contamination problems.

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The application feasibility of in-situ or in-line monitoring of S. cerevisiae ITV01 alcoholic fermentation process, employing Near-Infrared Spectroscopy (NIRS) and Chemometrics, was investigated. During the process in a bioreactor, in the complex analytical matrix, biomass, glucose, ethanol and glycerol determinations were performed by a transflection fiber optic probe immersed in the culture broth and connected to a Near-Infrared (NIR) process analyzer. The NIR spectra recorded between 800 and 2,200 nm were pretreated using Savitzky-Golay smoothing and second derivative in order to perform a partial least squares regression (PLSR) and generate the calibration models. These calibration models were tested by external validation and then used to predict concentrations in batch alcoholic fermentations. The standard errors of calibration (SEC) for biomass, ethanol, glucose and glycerol were 0.212, 0.287, 0.532, and 0.296 g/L and standard errors of prediction (SEP) were 0.323, 0.369, 0.794, and 0.507 g/L, respectively. Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:510-517, 2016.