Hyperspectral imaging and predictive microbiology for the non-invasive evaluation of the growth probability of Staphylococcus aureus in sliced dry-cured ham

Abstract

This work aimed to explore the potential of a non-invasive approach, consisting of the use of hyperspectral imaging (HSI) together with predictive microbiology, for dry-cured ham safety evaluation. The growth probability of Staphylococcus aureus in salt-reduced or not sliced dry-cured ham stored at room temperature and the effect of image preprocessing on this evaluation were analysed. To this end, predictive models for aw were developed (RMSEP = 0.013) and later used to calculate aw chemical images, aw frequency polygons and predicted aw at different percentiles, which were later used as predictive microbiology model inputs. This innovative approach showed no differences in product aw between salting groups and in the associated growth probability of S. aureus. Due to the high variability of aw between samples inside the same salting batch, the growth probability of S. aureus ranged from 19 to 46 % when using pH and aw values at percentile 75th. Image preprocessing was able to remove image artifacts (specular highlights and fat streaks), and the different image preprocessing thresholds used did not influence the predicted aw values at different percentiles.