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.