Monitoring of meat quality and change-point detection by a sensor array and profiling of bacterial communities.

BACKGROUND

Real-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in the food matrices, and hence it requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. The existing analytical methods are too complicated for rapid in situ screening of foodstuff.

RESULTS

We have studied the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, comparing the results with changes in the microbiome composition of the spoiling meat samples. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and to identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition such as increase in the proportion of Brochothrix and Pseudomonas spp. and disappearance of Mycoplasma spp., and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R values of 0.98, 0.93, and 0.91, respectively. Integration of e-nose and computer vision into a single analytical panel improved the meat state identification accuracy up to 0.85, allowing for more reliable meat state assessment.

SIGNIFICANCE

Accurate identification of the change-point in the meat state achieved by digitalizing volatile spoilage markers from the e-nose unit holds promises for application of smart miniaturized devices in food industry.