SPME-GCMS integrated with chemometrics as a rapid non-destructive method for predicting microbial quality of minimally processed jackfruit (Artocarpus heterophyllus) bulbs

SPME-GCMS in combination with chemometrics was employed to correlate volatile headspace composition with microbial quality of minimally processed jackfruit (Artocarpus heterophyllus) bulbs stored at 4 degrees C and 10 degrees C. Predictive models of the total viable count (TVC) and yeast and mold count (Y&M) were prepared by Partial Least Square Regression (PLS-R) using total ion current (TIC) and total mass spectral data as independent variables. All PLS-R models correlating microbial quality with GC spectral data and total mass spectral data demonstrated high regression coefficient (R > 0.93). Models generated using TIC performed better in comparison with models prepared with total mass spectral data against test data. Ethanol, ethyl acetate and 3-methyl-1-butanol were identified as major compounds responsible for the observed correlations. The possibility of using GCMS as a nondestructive method for rapid assessment of microbial quality of minimally processed fruits is demonstrated here for the first time. (C) 2014 Elsevier B.V. All rights reserved.