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Table 3 ANOVA for hydrogen production by T. aotearoense SCUT27/ Δldh with SCB hydrolysates as substratea

From: Optimization of key factors affecting hydrogen production from sugarcane bagasse by a thermophilic anaerobic pure culture

Factors Sum of squares Degrees of freedom Mean square F-value P-value  
Model 18368.05 5 3673.61 13.97 0.0016 significant
X 1 740.37 1 740.37 2.82 0.1373  
X 2 4796.85 1 4796.85 18.24 0.0037  
X 1 X 2 150.06 1 150.06 0.57 0.4747  
X 1 2 4654.76 1 4654.76 17.70 0.0040  
X 2 2 3230.99 1 3230.99 12.29 0.0099  
Residual 1840.87 7 262.98    
Lack of fit 1656.83 3 552.28 12.00 0.0181 significant
Pure error 184.04 4 46.01    
Cor total 20208.92 12     
  1. aCoefficient of determination (R2) = 0.9089. A model with an F-value of 13.97 implies that the model is significant. There is only a 0.16% chance that a model F-value this large could occur due to noise. Values of “Prob>F” less than 0.0500 indicate that model terms are significant. In this case B, A2, B2 are significant model terms. The “Lack of fit F-value” of 12.00 implies that the lack of fit is significant. There is only a 1.81% chance that a lack of fit F-value this large could occur due to noise. The “Pred R-Squared” of 0.3684 is not as close to the “Adj R-Squared” of 0.8438 as one might normally expect. This may indicate a large block effect or a possible problem with a model and/or data. Things to consider are model reduction, response transformation, and outliers, among others. “Adeq Precision” measures the signal-to-noise ratio. A ratio greater than 4 is desirable. A ratio of 10.962 indicates an adequate signal. This model can be used to navigate the design space.