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

•Microbial quality of minimally processed jackfruit was investigated during storage.•Changes in headspace volatile composition during storage were evaluated by SPME-GCMS.•GC spectral data and total mass spectral data were correlated with microbial quality of stored samples using partial least squares regression analysis.•High correlation coefficients (R > 0.9) were obtained between GC spectral data and total mass spectral data with microbial counts.•Thus, SPME-GCMS integrated with chemometrics allowed rapid evaluation of microbial quality of minimally processed fruits.

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 °C and 10 °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.