Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support...

Spatial frequency domain imaging has great potential in agricultural produce quality control due to its advantage of wide-field mapping of absorption (μ_a) and reduced scattering (μs') parameters. However, it is not widely adopted in real applications due to the large time cost during image acquisition and inversion calculation processes. In this study, a single snapshot technique was used to obtain ac and dc components (R_d_ac, R_d_dc) of diffuse reflectance of turbid media (phantoms and pears). The validation results for the snapshot method indicate that at the spatial frequency of 1000/3  m^-1, it achieved the optimal demodulation, by comparison with the results obtained by the commonly used time-domain amplitude demodulation method. Diffusion approximation, artificial neural network, least-squares support vector machine regression (LSSVR), and LSSVR combined with a genetic algorithm (LSSVR+GA) were then used to predict μ_a and μs' from the obtained R_d_ac, R_d_dc at the f_x of 1000/3  m^-1. Validation results indicated that the LSSVR method took the least time to calculate μ_a and μs' with high performance. The proposed imaging system and algorithm were implemented for the inspection of a pear bruise. Results indicated that the bruise, which is not obviously distinguishable in original gray maps, can show obvious contrast in calculated μ_a and μs' maps, especially in μ_a maps. Further, the contrast becomes more obvious with the passage of time. In summary, this study developed a low-cost spatial frequency imaging system and matching software that could realize fast detection of optical properties for a pear with the proposed snapshot and LSSVR algorithms.