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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 2 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The quantification of microbes, particularly live bacteria, is of utmost importance in assessing the quality of meat products. In the context of meat processing facilities, prompt identification and removal of contaminated carcasses or surfaces is crucial to ensuring the continuous production of safe meat for human consumption. The plate count method and other traditional detection methods are not only labour-intensive but also time-consuming taking 24–48 h. In this report, we present a novel isotachophoretic quantification method utilizing two nucleic acid stains, SYTO9 and propionic iodide, for the detection of total viable bacteria. The study employed E. coli M23 bacteria as a model organism, with an analysis time of only 30 min. The method demonstrated a limit of detection (LOD) of 184 CFU mL−1 and 14 cells mL−1 for total viable count and total cell count, respectively. Furthermore, this new approach is capable of detecting the microbial quality standard limits for food contacting surfaces (10 CFU cm−2) and meat (1.99 × 104 CFU cm−2) by swabbing an area of 10 × 10 cm2. In contrast to the culture-based methods usually employed in food processing facilities, this isotachophoretic technique enables easy and rapid detection (<30 min) of microorganisms, facilitating crucial decision-making essential for maintaining product quality and safety. [Display omitted] • This paper reports an ITP method to detect viable bacteria, with the potential to replace traditional plate count method. • The method identifies contaminated surfaces within 30 minutes, mitigating the risk of foodborne illness outbreaks. • The method can be adapted to various settings with modifications to detect total viable count. [ABSTRACT FROM AUTHOR]