Intelligent packaging technologies have a central importance in preserving and improving the quality of packed food products and thus in reducing food waste. The development of these packaging technologies is not possible without a comprehensive understanding of food quality and the numerous factors that affect it. However, the methods of food science are usually both time-consuming and labor-intensive, which is why the generation of extensive and useful food-related datasets is a major bottleneck in the whole development process. Since traditional food science has limited means of responding to this problem, new and innovative methods are needed. Hence, the aim of the proposed study is to develop data-driven solutions for improving the use of food quality data in the development of intelligent packaging technologies. The study combines various methods of food science (food microbiology and chemistry) and data science (statistical analysis, mathematical modelling, computer science) in a novel way in order to gain a deeper understanding of seafood quality and spoilage, as well as to develop methodologies for boosting packaging technology development. Overall, the study explores the boundaries and future prospects of an emerging scientific field called food informatics.