ABSTRACT
The production of second-generation bioethanol has several challenges, among them finding cheap and efficient enzymes for a sustainable process. In this work, we analyzed two native fungi, Cladosporium cladosporioides and Penicillium funiculosum, as a source of cellulolytic enzyme production, and corn stover, wheat bran, chickpeas, and bean straw as a carbon source in two fermentation systems: submerged and solid fermentation. Corn stover was selected for cellulase production in both fermentation systems, because we found the highest enzymatic activities when carboxymethyl cellulase activity (CMCase) was assessed using CMC as substrate. C. cladosporioides showed the highest CMCase activity (1.6 U/mL), while P. funiculosum had the highest filter paper activity (Fpase) (0.39 U/mL). The ß-glucosidase activities produced by both fungi were similar in submerged fermentation using corn stover as substrate. Through in-gel zymography, three polypeptides with cellulolytic activities were identified in each fungus: with molecular weights of ~ 38, 45 and 70 kDa in C. cladosporioides and ~ 21, 63 and 100 kDa in P. funiculosum. The best results for saccharification (10.11 g/L of reducing sugars) of diluted acid pretreated corn stover were obtained after 36 h of the hydrolytic process at pH 5 and 50 °C using the enzyme extract of P. funiculosum. This is the first report of cellulase identification in C. cladosporioides and the saccharification of corn stover using enzymes of this fungus. Enzymatic extracts of C. cladosporioides and P. funiculosum obtained from low-cost lignocellulosic biomass have great potential for use in the production of second-generation bioethanol.
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 (ß) and variable influence in the projection plots were used to assess the results. A novelty is the use of ß 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
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.
