Real-time monitoring of food consumer quality remains challenging due to diverse bio-chemical processes taking place in/at the food matrices and requires accurate analytical methods. Thresholds to determine spoiled food are often difficult to set. Here, we study the dynamics of meat spoilage by electronic nose (e-nose) for digitizing the smell associated with volatile spoilage markers of meat, and by assessing the microbiome composition. We apply the time series analysis to follow dynamic changes in the gas profile extracted from the e-nose responses and identify the change-point window of the meat state. The obtained e-nose features correlate with changes in the microbiome composition and with representative gas sensors towards hydrogen, ammonia, and alcohol vapors with R2 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.