FTIR-based rapid microbial quality estimation of fresh-cut jackfruit (Artocarpus heterophyllus) bulbs

FTIR in combination with chemometric tools was utilised to evaluate the microbial counts for fresh cut jackfruit samples stored at 4 and 10 degrees C. Predictive models were prepared for total viable counts and yeast & mould counts using partial least square regression (PLS-R) and artificial neural networks (ANN) from FTIR data. Raw FTIR data and its first derivative were exploited for model building. Models built with both ANN and PLS-R using FTIR data demonstrated a high correlation value of R-2 > 0.85 for 10 degrees C stored samples. Variable importance projection score obtained from PLS-R models suggested production of acids after utilization of sugars due to microbial activity during storage. Feasibility of utilising FTIR as a rapid non-destructive methodology for estimation of microbial counts for fresh cut jackfruit is demonstrated.