| Title |
Enhanced production of acetic acid through bioprocess optimization employing response surface methodology and artificial neural network |
| ID_Doc |
7479 |
| Authors |
Upadhyay, A; Kovalev, AA; Zhuravleva, EA; Pareek, N; Vivekanand, V |
| Title |
Enhanced production of acetic acid through bioprocess optimization employing response surface methodology and artificial neural network |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.biortech.2023.128930 |
| Abstract |
In this study, acetic acid bacteria (AAB) are isolated from fruit waste and cow dung on the basis of acetic acid production potential. The AAB were identified based on halo-zones produced in the Glucose-Yeast extract-Calcium carbonate (GYC media) agar plates. In the current study, maximum acetic acid yield is reported to be 4.88 g/100 ml from the bacterial strain isolated from apple waste. With the help of RSM (Response surface methodology) tool, glucose and ethanol concentration and incubation period, as independent variable showed the significant effect of glucose concentration and incubation period and their interaction on the AA yield. A hypothetical model of artificial neural network (ANN) was also used to compare the predicted value from RSM. Acetic acid production through the biological route can be the sustainable and clean approach to utilizing food waste in circular economy approach |
| Author Keywords |
Fruit waste; Acetic acid bacteria; Artificial neural network; Response surface methodology; Apple waste |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001030452800001 |
| WoS Category |
Agricultural Engineering; Biotechnology & Applied Microbiology; Energy & Fuels |
| Research Area |
Agriculture; Biotechnology & Applied Microbiology; Energy & Fuels |
| PDF |
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