Abstract: In this study, time series spectroscopic, microbiological and sensory analysis data were obtained from minced beef samples stored under different packaging conditions (aerobic and modified atmosphere packaging) at 5 °C. These data were analyzed using machine learning and evolutionary computing methods, including partial least square regression (PLS-R), genetic programming (GP), genetic algorithm (GA), artificial neural networks (ANNs) and support vector machines regression (SVR) including different kernel functions [i.e. linear (SVRL), polynomial (SVRP), radial basis (RBF) (SVRR) and sigmoid functions (SVRS)]. Models predictive of the microbiological load and sensory assessment were calculated using these methods and the relative performance compared. In general, it was observed that for both FT-IR and Raman calibration models, better predictions were obtained for TVC, LAB and Enterobacteriaceae, whilst the FT-IR models performed in general slightly better in predicting the microbial counts compared to the Raman models. Additionally, regarding the predictions of the microbial counts the multivariate methods (SVM, PLS) that had similar performances gave better predictions compared to the evolutionary ones (GA-GP, GA-ANN, GP). On the other hand, the GA-GP model performed better from the others in predicting the sensory scores using the FT-IR data, whilst the GA-ANN model performed better in predicting the sensory scores using the Raman data. The results of this study demonstrate for the first time that Raman spectroscopy as well as FT-IR spectroscopy can be used reliably and accurately for the rapid assessment of meat spoilage.