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Rationale: Non‐Saccharomyces yeasts are widespread microorganisms that nowadays have gained importance for their ability to produce volatile compounds which in alcoholic beverages improve aromatic complexity and therefore the overall quality. Their rapid identification and differentiation in fermentation processes is vital for timely decision making. Methods: A total of 19 strains of Pichia kluyveri isolated from mezcal, tejuino and cacao fermentations were analyzed with rep‐PCR fingerprinting using the primer (GTG)5 and matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) on a Microflex LT mass spectrometer using Biotyper 3.1 software (Bruker Daltonics). Results: The comparative analysis between MS spectra and rep‐PCR patterns obtained from these strains showed a high similarity between both methods. However, minimal differences between the obtained rep‐PCR and MALDI‐TOF MS clusters could be observed, especially by the presence and/or absence of one or more discriminating peaks even when they have similarities in their main spectra projection, observing that isolates from the same fermentative process were grouped into the same sub‐cluster based on their MALDI‐TOF MS profiles. Conclusions: The data shown suggests that MALDI‐TOF MS is a promising alternative technique for rapid, reliable and cost‐effective differentiation of native yeast strains isolated from different traditional fermented foods and beverages. [ABSTRACT FROM AUTHOR]

Abstract: The application of in situ near‐infrared spectroscopy monitoring of xylose metabolizing yeast such as Pichia stipitis for ethanol production with semisynthetic media, applying chemometrics, was investigated. During the process in a bioreactor, biomass, glucose, xylose, ethanol, acetic acid, and glycerol determinations were performed by a transflection probe immersed in the culture broth and connected to a near‐infrared process analyzer. Wavelength windows in near‐infrared spectra recorded between 800 and 2200 nm were pretreated using Savitzky–Golay smoothing, second derivative and multiplicative scattering correction in order to perform a partial least squares regression and generate the calibration models. These calibration models were tested by external validation (78 samples). Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. Moreover, regressions coefficients (β) and variable influence in the projection plots were used to assess the results. A novelty is the use of β versus VIP dispersion plots to determine which vectors have more influence on the response in order to improve process comprehension and operability. Validated models were used in a real‐time monitoring during P. stipitis NRRL Y7124 semisynthetic media fermentations. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]