Production of Citric Acid from the Fermentation of Pineapple Waste by Aspergillus niger
Augustine. O. Ayeni1, *, Michael O. Daramola3, Olugbenga Taiwo2, Omowonuola I. Olanrewaju1, Daniel T. Oyekunle1, Patrick T. Sekoai4, Francis B. Elehinafe1
Identifiers and Pagination:Year: 2019
First Page: 88
Last Page: 96
Publisher Id: TOCENGJ-13-88
Article History:Received Date: 04/03/2019
Revision Received Date: 03/05/2019
Acceptance Date: 07/05/2019
Electronic publication date: 31/07/2019
Collection year: 2019
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citric acid, aside its uses as a cleaning agent, has varied applications in the chemical, pharmaceutical, and food industries. A biotechnological fermentation process is one of the easiest ways to satisfy the demands for this useful commodity.
The fermentation of pineapple waste by Aspergillus niger for the production of citric acid was investigated in this study. STATISTICA 8 release 7 (Statsoft, Inc. USA) statistical software was used for the design of experiments, evaluation, and optimization of the process using the central composite design (CCD), a response surface methodology approach. Lower-upper limits of the design for the operating parameters were temperature (25-35 oC), fermentation time (35-96 h), pH (3-6), methanol concentration (1-7%) and glucose (15-85 g/L). Twenty-seven duplicated experimental runs were generated for the CCD route.
Results & Conclusion:
The optimal operating conditions were validated at 38 g/L of glucose concentration, 3% (v/v) of methanol, 50 h of fermentation time, pH of 4.3 and temperature of 30 oC which yielded15.51 g/L citric acid. The statistical significance of the model was evaluated using a one-way analysis of variance. The validated predicted response values obtained from the statistical model showed close relationships with the experimental data.