Suchergebnisse

The kinetics of growth, acid and solvent production in batch culture of Clostridium pasteurianum DSMZ 525 were examined in mixed or mono-substrate fermentations. In pH-uncontrolled batch cultures, the addition of butyric acid or glucose significantly enhanced n-butanol production and the ratio of butanol/1,3-propanediol. In pH-controlled batch culture at pH = 6, butyric acid addition had a negative effect on growth and did not lead to a higher n-butanol productivity. On the other hand, mixed substrate fermentation using glucose and glycerol enhanced the growth and acid production significantly. Glucose limitation in the mixed substrate fermentation led to the reduction or inhibition of the glycerol consumption by the growing bacteria. Therefore, for the optimal growth and n-butanol production by C. pasteurianum, a limitation of either substrate should be avoided. Under optimized batch conditions, n-butanol concentration and maximum productivity achieved were 21 g/L, and 0.96 g/L × h, respectively. In comparison, mixed substrate fermentation using biomass hydrolysate and glycerol gave a n-butanol concentration of 17 g/L with a maximum productivity of 1.1 g/L × h. In terms of productivity and final n-butanol concentration, the results demonstrated that C. pasteurianum DSMZ 525 is well suitable for n-butanol production from mixed substrates of biomass hydrolysate and glycerol and represents an alternative promising production strain. © 2014 The Author(s). ; European Union Seventh Framework Program

ABSTRACTPartial least squares path modeling (PLS‐PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully benefit from the predictive capabilities of PLS‐PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance of standard PLS‐PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. We use Monte Carlo simulation to study this question under various sample sizes, effect sizes, item loadings, and model setups. Specifically, we explore whether, and when, the in‐sample measures such as the model selection criteria can substitute for out‐of‐sample criteria that require a holdout sample. Such a substitution is advantageous when creating a holdout causes considerable loss of statistical and predictive power due to an overall small sample. We find that when the researcher does not have the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error, the in‐sample model selection criteria, in particular the Bayesian Information Criterion (BIC) and Geweke–Meese Criterion (GM), are useful substitutes for out‐of‐sample criteria. When a holdout sample is available, the best performing out‐of‐sample criteria include the root mean squared error (RMSE) and mean absolute deviation (MAD). We recommend against using standard the PLS‐PM criteria (R2, Adjusted R2, and Q2), and specifically the out‐of‐sample mean absolute percentage error (MAPE) for prediction‐oriented model selection purposes. Finally, we illustrate the model selection criteria's practical utility using a well‐known corporate reputation model.

ABSTRACTManagement researchers often develop theories and policies that are forward‐looking. The prospective outlook of predictive modeling, where a model predicts unseen or new data, can complement the retrospective nature of causal‐explanatory modeling that dominates the field. Partial least squares (PLS) path modeling is an excellent tool for building theories that offer both explanation and prediction. A limitation of PLS, however, is the lack of a statistical test to assess whether a proposed or alternative theoretical model offers significantly better out‐of‐sample predictive power than a benchmark or an established model. Such an assessment of predictive power is essential for theory development and validation, and for selecting a model on which to base managerial and policy decisions. We introduce the cross‐validated predictive ability test (CVPAT) to conduct a pairwise comparison of predictive power of competing models, and substantiate its performance via multiple Monte Carlo studies. We propose a stepwise predictive model comparison procedure to guide researchers, and demonstrate CVPAT's practical utility using the well‐known American Customer Satisfaction Index (ACSI) model.