FT-NIRS to Assess Shelf Life of Meat Stored in a Low-Oxygen Master Bag System

Packaging plays a pivotal role in food waste reduction, by increasing product shelf life. Among active packaging systems, oxygen scavengers have been developed to improve quality and stability of meat packaged in low-oxygen atmospheres. This work aims at developing a FT-NIR system for the storage time assessment of case-ready beef slices in master bag with oxygen-depleted atmospheres. In details, the packaging system consisted of one self-absorbent expanded polystyrene tray containing meat slices (230g) overwrapped with a PVC film and inserted into a gas barrier master bag with modified atmosphere (30% CO2 and 70% N2) and O2 scavengers. After 6, 8, 13 and 15 days of storage (0-1°C), master bags were opened and case-ready units were stored at 4°C for the blooming process; afterwards, the trays were exposed for two days on an illuminated display case at 4±2°C, simulating real shelf life conditions. Three replicates were performed. Meat FT-NIR spectra (12.500-4.500 cm-1, res.: 12 cm-1; scan: 64) were recorded by a fiber optic probe directly through the PVC film. After different spectral pre-treatments and variable selection, LDA was performed independently on the three replicates’ datasets to assess master bag opening time and shelf life progress; models were validated both internally and externally. Good models were obtained to distinguish samples according to anoxia storage time, with correct classification rates in prediction ranging from 75 to 100%. Then, for each anoxia storage time, LDA models were built to assess the shelf life progress in the display case. Not all the calculated models gave satisfactory results (correct classification in prediction: 64-100%); however, the misclassification could be explained by similarity in-between samples collected at close sampling points. The proposed approach paves the way for a non-destructive meat shelf life assessment during storage in an innovative packaging system, which could overcome meat waste at retailer level.