Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages

Abstract

To precisely determine heme and non-heme iron contents in meat product, the feasibility of combining spectral with texture features extracted from multispectral imaging data (405–970nm) was assessed. In our study, spectra and textures of 120 pork sausages (PSs) treated by different temperatures (30–80°C) were analyzed using different calibration models including partial least squares regression (PLSR) and LIB support vector machine (Lib-SVM) for predicting heme and non-heme iron contents in PSs. Based on a combination of spectral and textural features, optimized PLSR models were obtained with determination coefficient (R2) of 0.912 for heme and of 0.901 for non-heme iron prediction, which demonstrated the superiority of combining spectra with texture data. Results of satisfactory determination and visualization of heme and non-heme iron contents indicated that multispectral imaging could serve as a feasible approach for online industrial applications in the future.