Comparison of Artificial Neural Network and Response Surface Methodology Performance on Fermentation Parameters Optimization of Bioconversion of Cashew Apple Juice to Gluconic Acid

Abstract

The study examined the impact and interactions of cashew apple juice (CAJ) concentration, pH, NaNO 3 concentration, inoculum size and time on gluconic acid (GA) production in a central composite design (CCD). The fermentation process and parameters involved were modeled and optimized using artificial neural network (ANN) and response surface methodology (RSM). The ANN model established the optimum levels as CAJ of 250 g/l, pH of 4.21, NaNO 3 of 1.51 g/l, inoculum size of 2.87% volume and time of 24.41 h with an actual GA of 249.99 g/l. The optimum levels predicted by RSM model for the five independent variables were CAJ of 249 g/l, pH of 4.6, NaNO 3 of 2.29 g/l, inoculum size of 3.95% volume, and time of 38.9 h with an actual GA of 246.34 g/l. The ANN model was superior to the RSM model in predicting GA production. The study demonstrated that CAJ could serve as the sole carbon source for GA production.

Optimization of Corn Steep Liquor Dosage and Other Fermentation Parameters for Ethanol Production by Saccharomyces cerevisiae Type 1 and Anchor Instant Yeast

“…Tesfaw and Assefa [46] also confirm the importance of the finding that lower inoculum size reduces the cost of production in ethanol fermentation. The mutually significant relationship between inoculum size and temperature was due to the use of a commercial strain (Anchor Instant Yeast) which operated within the specified temperature range (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) • C) in the design of the experiment. …”

“…RSM quantifies the effect of the control variables, alone or in combination, on a selected response. In addition to analyzing the effects of the independent variables, this method also generates a mathematical expression that describes the relationship between variables and a selected response [25]. Pereira et al [22] used a Placket-Burman design to initially select the critical nutrients for CSL and other low-cost nutrients for ethanol fermentation, followed by a Box-Behnken design for the optimization of the selected nutrients [22].…”

“…It is clear, however, that most of the critical nutrients selected for optimization may be redundant, as they are already contained in adequate quantities in CSL [26]; eliminating them may significantly improve the economics of the process, especially on an industrial scale. While the applicability of RSM to optimization studies has been demonstrated successfully [22,25,27], studies have also shown that its predictive capability may be limited [24,28]. Baş and Boyacı [24] reported that the second-order equation obtained from RSM did not show the interactive effects of pH and substrate concentration on the initial reaction rate of the enzymatic reaction in their study.…”

Statistical Optimization of Acetoin Production Using Corn Steep Liquor as a Low-Cost Nitrogen Source by Bacillus Subtilis CICC 10025

“…RSM defines the effect of the independent variables, alone or in combination, in the processes. In addition to analyzing the effects of the independent variables, this methodology also generates a mathematical model [23]. The applicability of RSM to optimization studies has been demonstrated successfully [23][24][25].…”

Acetylation of Amaranthus viridis starch: Modeling and process parameters optimization

“…Artificial Neural Network (ANN), which is a computational method that can mimics the neurological processing capability of the human brain, has also been applied to modeling of many food processing studies. These studies include gluconic acid (Osunkanmibi, Olowlabi & Betiku, 2015), ethanol (Betiku & Taiwo, 2015) and oxalic acid (Emeko et al, 2015) production processes as well as in enzymatic reaction catalyzed by amyloglucosidase (Bas & Boyaci, 2007). Many of these studies have demonstrated consistently that the predictive capability of ANN is stronger than RSM (Bas & Boyaci, 2007;Betiku & Taiwo, 2015;Emeko et al, 2015).…”