Fusion of spectra and texture data of hyperspectral imaging for prediction of myoglobin content in nitrite-cured mutton

Abstract

The amount and chemical state of myoglobin are often relevant to meat color, which is one of the important characteristics of meat freshness and wholesomeness. To precisely determine OxyMb and MetMb contents in mutton, the feasibility of optimization detection technology of spectral image fusion was explored. Three methods were used to select characteristic wavelengths while textural features of muscle tissue were derived. Compared among all models, LSSVM models coupled with spectra and textural parameters all performed better than these PLSR models. The iVISSA-COR-LSSVM model for OxyMb prediction achieved a good performance of R2C = 0.9525, RMSEC = 2.2955, R2P = 0.8956, and RMSEP = 3.0036. The more effective prediction performances emerged from VCPA-IRIV-COR-LSSVM model with R2C = 0.8809, RMSEC = 2.2009, R2P = 0.8353, and RMSEP = 3.2096 for MetMb. In final, the pseudo color maps of OxyMb and MetMb distribution were generated. This study demonstrated that the data fusion method furnishes a new approach to rapidly evaluate myoglobin content.