Microbial quality assessment of minimally processed pineapple using GCMS and FTIR in tandem with chemometrics

Vanshika Adiani,Sumit Gupta,Prasad S Variyar

doi:10.1038/s41598-020-62895-y

Abstract

Microbial quality is the critical parameter determining the safety of refrigerated perishables. Traditional methods used for assessing microbial quality are time consuming and labour intensive. Thus rapid, non-destructive methods that can accurately predict microbial status is warranted. Models using partial least square regression (PLS-R) from chemical finger prints of minimally processed pineapple during storage obtained by Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry (HS-SPME-GCMS), Fourier Transform Infrared (FTIR) spectroscopy and their data fusion are developed. Models built using FTIR data demonstrated good prediction for unknown samples kept under non-isothermal conditions. FTIR based models could predict 87 and 80% samples within ±1 log CFU/g for TVC and Y&M, respectively. Analysis of PLS-R results suggested the production of alcohols and esters with utilization of sugars due to microbial spoilage.

Highlights

Microbial quality is the critical parameter determining the safety of refrigerated perishables
Samples stored at 10 °C demonstrated a rapid increase in total viable count (TVC) and yeast and mould count (Y&M) with counts reaching to 7.92 ± 0.32 and 7.91 ± 0.15 Log CFU/g, respectively by the end of the storage period of 7 days (Fig. 1B)
Models prepared were tested for efficiency by prediction of microbial quality of samples from different batch kept under non-isothermal conditions to simulate market conditions

Summary

Introduction

Microbial quality is the critical parameter determining the safety of refrigerated perishables. Several non-destructive techniques have been proposed that offers online rapid monitoring of product microbial quality when integrated with multivariate chemometric tools such as imaging[2] in meat samples, spectroscopic[3,4,5] in beef, jackfruit and pineapple, hyperspectral imaging[6,7], e-nose[8] in several agro-food products and acoustic[9] for fruit juices These techniques have offered reduced time of analysis along with informative results, but their suitability has been demonstrated in microbial quality assessment on subset of same batch of sample used for training chemometric models. ) count of minimally processed stored pineapple. (A) Samples stored at 4 °C (B) Samples stored at 10 °C and (C) unknown set stored at non-isothermal conditions with periodic 24 h cycle of 16 h at 10 °C and 4 h at 15 °C for 4 h at 20 °C

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Conclusion