# Statistical Optimization of Culture Conditions for Enhanced Biomass Yield of Lactobacillus acidophilus CM1 Using Plackett–Burman and Response Surface Methodology

## TL;DR

This study improves the growth of Lactobacillus acidophilus by optimizing key factors like pH and temperature, leading to higher biomass yield.

## Contribution

A novel integration of Plackett–Burman and RSM-CCD methods for efficient biomass yield optimization in probiotic production.

## Key findings

- pH, temperature, NaCl, and inoculum size significantly affect L. acidophilus CM1 growth (p < 0.05).
- Optimization increased biomass yield by 1.45-fold to 1.948 g/100 mL.
- The model showed strong predictive capability with R² of 0.9689 and adequate precision of 52.77.

## Abstract

This study is aimed at enhancing the biomass yield of Lactobacillus acidophilus CM1 by identifying and optimizing critical growth parameters. Using the Plackett–Burman design (PBD), 11 physical and chemical variables were screened, of which pH, temperature, NaCl concentration, and inoculum size were found to significantly influence cell growth (p < 0.05). These statistically significant factors were subsequently optimized using response surface methodology (RSM) with a central composite design (CCD). Optimization led to a 1.45-fold increase in biomass yield, achieving a maximum of 1.948 g/100 mL. ANOVA confirmed model validity with an R2 of 0.9689 and adequate precision of 52.77, indicating a strong predictive capability. The integration of PBD and RSM-CCD proved efficient for minimizing experimental runs while maximizing output, supporting the development of cost-effective cultivation strategies for probiotic production. This approach offers a scalable model for bioprocess optimization in industrial fermentation.