Optimization of extracellular lipase production by halotolerant Bacillus sp. VITL8 using factorial design and applicability of enzyme in pretreatment of food industry effluents

Abstract

The effect of macro and micronutrients of media components on lipase production by Bacillus sp. VITL8 was investigated using classical as well as statistical methods. Initially, the carbon source, nitrogen source, inducer and metal ions that affect lipase production were selected using the classical approach. Subsequently, selected nutrients along with other key factors (such as pH, agitation rate, gum acacia and tween 80) were investigated using Placket Burman design. Finally, three significant factors, viz., olive oil, peptone and tween 80 were studied using a 2² full factorial central composite design. Under optimized condition [6% (v/v) of olive oil, 0.7% peptone, 0.9% tween 80 and 25 h of incubation], the enzyme production was found to be 2.2 times higher with an overall enzyme production of 325.0 ± 1.4 U mL⁻¹. Laboratory scale experiment proved that the enzyme could be utilized for pretreatment of food industry effluent rich in fat and oil (dairy, bakery and poultry). The enzyme was capable of hydrolyzing more than 50% of the initial fat present in all these effluents and enabled the reduction in the levels of biological oxygen demand (BOD) and chemical oxygen demand (COD) of the effluent samples. The study thus reveals the utility of the lipase produced by the halotolerant bacterium Bacillus sp. VITL8 in the pretreatment of industrial effluents contaminated with oil and fat.

Highlights

• Lipase production was enhanced by 2.2-fold using statistical methods

• One of the few reports on lipase production by a halotolerant bacterium, especially by Bacillus sp.

• Production of 325.0 ± 1.4 U mL⁻¹ lipase within 25 h by a halotolerant bacterium

• Pretreatment of food industry effluents using Bacillus sp. VITL8 lipase

• Improvement in effluent quality within 8 h of treatment

Keywords:

• Bacillus sp.
• halotolerant
• lipase
• pretreatment of effluents
• statistical optimization

Acknowledgments

B. Lavanya is a recipient of UGC-CSIR Fellowship [09/844(0010)/2012 EMR-I]. C. Jabeena Thaz is a recipient of a UGC-MAN Fellowship (201011-MANF-MUS-AND-1551). The research facility provided by the VIT University (Vellore, India) is gratefully acknowledged.

Additional information

Funding

The present work was financially supported by UGC-Moulana Azad National Fellowship and Council of Scientific & Industrial Research, India.