Prediction of meat spectral patterns based on optical properties and concentrations of the major constituents

A simulation method for approximating spectral signatures of minced meat samples was developed depending on concentrations and optical properties of the major chemical constituents. Minced beef samples of different compositions scanned on a near-infrared spectroscopy and on a hyperspectral imaging system were examined. Chemical composition determined heuristically and optical properties collected from authenticated references were simulated to approximate samples' spectral signatures. In short-wave infrared range, the resulting spectrum equals the sum of the absorption of three individual absorbers, that is, water, protein, and fat. By assuming homogeneous distributions of the main chromophores in the mince samples, the obtained absorption spectra are found to be a linear combination of the absorption spectra of the major chromophores present in the sample. Results revealed that developed models were good enough to derive spectral signatures of minced meat samples with a reasonable level of robustness of a high agreement index value more than 0.90 and ratio of performance to deviation more than 1.4.