Development of rapid method to assess microbial quality of minimally processed pomegranate arils using FTIR

Abstract

Fourier transform infrared spectroscopy (FTIR) spectra were correlated with microbial quality of minimally processed pomegranate (Punica granatum) arils stored at 10 °C using chemometrics. FTIR data processed in three ways i.e. FTIR spectrum, first derivative for FTIR spectrum and peak integrated data of FTIR spectrum was used as independent variables for preparing regression models by partial Least Square Regression (PLS-R) and artificial neural networks (ANN) for predicting the total viable count (TVC) and yeast and mold count (Y&M). Models built with both ANN and PLS-R using FTIR data demonstrated a high correlation value of R2 > 0.85. Analysis of PLS-R results suggested the production of alcohols and acids with utilization of sugars during storage. This is a first report demonstrating use of FTIR as a nondestructive rapid method for microbial quality analysis of minimally processed fruits.