Rapid and non-destructive spectroscopic method for classifying beef freshness using a deep spectral network fused with myoglobin information

A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and reflectance spectra over different freshness states. The reflectance spectra (480?920 nm) were measured from 78 beef samples for 17 days, and the datasets were sorted into three freshness classes based on their pH values. Myoglobin information showed statistically significant differences depending on the freshness; consequently, it was utilized as a crucial parameter for classification. The model exhibited improved performance when the reflectance spectra were combined with the myoglobin information. The accuracy of the proposed model improved to 91.9%, whereas that of the single-spectra model was 83.6%. Further, a high value for the area under the receiver operating characteristic curve (0.958) was recorded. This study provides a basis for future studies on the investigation of myoglobin information associated with meat freshness.

Automated summary

A novel spectroscopic method using deep spectral network was developed for beef-freshness classification, achieving an accuracy of 91.9% with improved performance by combining reflectance spectra and myoglobin information. The study provides a basis for further investigation into the relationship between myoglobin information and meat freshness.