Response surface optimization of conditions for culturing <i>Azotobacter chroococcum</i> in <i>Agaricus bisporus</i> industrial wastewater

Abstract

In the present study, the conditions for Azotobacter chroococcum fermentation using Agaricus bisporus wastewater as the culture medium were optimized. We analyzed the total number of living A. chroococcum in the fermentation broth, using multispectral imaging flow cytometry. Single-factor experiments were carried out, where a Plackett-Burman design was used to screen out three factors from the original six processing factors wastewater solubility, initial pH, inoculum size, liquid volume, culture temperature, and rotation speed that affected the total number of viable A. chroococcum. The Box-Behnken response surface method was used to optimize the interactions between the three main factors and to predict the optimal fermentation conditions. Factors significantly affecting the total number of viable A. chroococcum, including rotation speed, wastewater solubility, and culture temperature, were investigated. The optimum conditions for A. chroococcum fermentation in A. bisporus wastewater were a rotation speed of 200 rpm, a solubility of 0.25%, a culture temperature of 26∞C, an initial pH of 6.8, a 5% inoculation volume, a culture time of 48 h, and a liquid volume of 120 mL in a 250 mL flask. Under these conditions, the concentration of total viable bacteria reached 4.29 ± 0.02 ¥ ¥ ¥ ¥ ¥ 10 7 Obj/mL A. bisporus wastewater can be used for the cultivation of A. chroococcum.

Purification, Fermentation Optimization, and Antibacterial Activity of Pyrrole-2-carboxylic Acid Produced by an Endophytic Bacterium, Bacillus cereus ZBE, Isolated from Zanthoxylum bungeanum

“…The peak of the response surface could be used to analyze and forecast the results. 33 The linear coefficient (X 2 , X 3 ) and the quadratic term coefficient (X 3 2 ) exhibited a highly significant effect (P < 0.01), while the P-values of the interaction term coefficient (X 1 X 3 ) and the quadratic term coefficient (X 1 2) were all less than 0.05. These statistical results of the quadratic polynomial model indicated that the relationship between the variables and the response were not simply linear.…”

“…The Box−Behnken design was performed to study the individual and mutual interaction effects of the three selected parameters on PCA yields and determine the optimum level. 33 The BBD matrix and the results of PCA yield are presented in Table 1. After multiple regression analysis on the BBD data, the quadratic polynomial model was obtained, as given by eq 3…”

“…The Box–Behnken design was performed to study the individual and mutual interaction effects of the three selected parameters on PCA yields and determine the optimum level . The BBD matrix and the results of PCA yield are presented in Table .…”

“…These results revealed that the response equation could provide a good fit between the measured and predicted PCA production. The peak of the response surface could be used to analyze and forecast the results . The linear coefficient ( X 2 , X 3 ) and the quadratic term coefficient ( X 3 2 ) exhibited a highly significant effect ( P < 0.01), while the P -values of the interaction term coefficient ( X 1 X 3 ) and the quadratic term coefficient ( X 1 2 ) were all less than 0.05.…”