Growth Prediction of the Food Spoilage Yeast Debaryomyces Hansenii using Multivariate Data Analysis

Abstract

The main aim of the present study was to predict the growth of the food spoilage yeast Debaryomyces hansenii by multivariate data analysis (MVDA) using temperature, pH and NaCl concentration as growth parameters. Growth of five strains of D. hansenii (DHI, DHII, DHIII, DHIV and DHV) was measured as optical density at 620 nm (OD620) at different values of temperature, pH and NaCl concentrations. It was found that salt was the most important factor, which affects yeast growth followed by temperature. The growth of all yeast strains was reduced by increasing salt concentration and decreasing temperature. On the other hand, pH was found to have a little effect on the growth of D. hansenii. Strain DHII was the most salt-tolerant strains among the five yeast strains investigated. Partial least squares (PLS) prediction model was created out using pH, temperature and NaCl concentration to predict the growth of D. hansenii. The model was acceptable with a correlation of 0.86. The developed PLS model will help in optimizing the food process conditions that will prevent food spoilage by D. hansenii.