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 (405-970 nm) 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 (R(2)) 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.

Keywords: Heme iron; Multispectral imaging; Non-heme iron; Pork sausage; Texture; Visualization.