A Longitudinal Multimodal Dataset of Rainbow Trout Fillets During Refrigerated Storage

This dataset supports the development of non-invasive methods for assessing the freshness of rainbow trout (Oncorhynchus mykiss) fillets by combining digital images, gas sensor responses, environmental measurements, and microbiological reference data. Data was collected from 80 fillet portions during seven days of refrigerated storage at 4 °C. Of these, 59 fillets were longitudinally monitored, while the remaining 21 fillets were used for destructive Total Viable Count (TVC) analysis. The dataset contains 820 RAW images and their corresponding JPEG derivatives, together with 210 sensor acquisition sessions comprising 120 time points from five MQ-series gas sensors and two environmental sensors. Based on the daily mean TVC and a threshold of 7 log10 CFU/g, days 1–5 were classified as Fresh and days 6–7 as Spoiled. These classifications represent storage-day microbiological status. The dataset can support computer vision, electronic-nose analysis, and multimodal learning.