Enhanced production of acetic acid through bioprocess optimization employing response surface methodology and artificial neural network

Apoorva Upadhyay; Andrey A Kovalev; Elena A Zhuravleva; Nidhi Pareek; Vivekanand Vivekanand

doi:10.1016/j.biortech.2023.128930

Enhanced production of acetic acid through bioprocess optimization employing response surface methodology and artificial neural network

Bioresour Technol. 2023 May:376:128930. doi: 10.1016/j.biortech.2023.128930. Epub 2023 Mar 20.

Authors

Apoorva Upadhyay¹, Andrey A Kovalev², Elena A Zhuravleva³, Nidhi Pareek⁴, Vivekanand Vivekanand⁵

Affiliations

¹ Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur, Jaipur 302017, Rajasthan, India.
² Federal State Budgetary Scientific Institution, "Federal Scientific Agroengineering Center VIM", 1st Institutskiy Proezd 5, 109428 Moscow, Russia.
³ Federal Research Center "Fundamentals of Biotechnology" of the Russian Academy of Sciences, Leninsky Prospekt 33, 2, 119071 Moscow, Russia.
⁴ Department of Sports Bio-Sciences, School of Sports Sciences, Central University of Rajasthan, Ajmer 305817, India.
⁵ Centre for Energy and Environment, Malaviya National Institute of Technology Jaipur, Jaipur 302017, Rajasthan, India. Electronic address: vivekanand.cee@mnit.ac.in.

PMID: 36940877
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.

Keywords: Acetic acid bacteria; Apple waste; Artificial neural network; Fruit waste; Response surface methodology.

MeSH terms

Acetic Acid*
Fruit
Glucose
Neural Networks, Computer
Refuse Disposal*

Substances

Acetic Acid
Glucose