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• W2945647325 abstract "Spatial frequency domain imaging has great potential in agricultural produce quality control due to its advantage of wide-field mapping of absorption (&#x03BC;a) and reduced scattering (&#x03BC;s&#x2032;) 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 (Rd_ac, Rd_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&#x2009;&#x2009;m&#x2212;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 &#x03BC;a and &#x03BC;s&#x2032; from the obtained Rd_ac, Rd_dc at the fx of 1000/3&#x2009;&#x2009;m&#x2212;1. Validation results indicated that the LSSVR method took the least time to calculate &#x03BC;a and &#x03BC;s&#x2032; 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 &#x03BC;a and &#x03BC;s&#x2032; maps, especially in &#x03BC;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." @default.
• W2945647325 created "2019-05-29" @default.
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• W2945647325 date "2019-05-20" @default.
• W2945647325 modified "2023-10-16" @default.
• W2945647325 title "Fast estimation of optical properties of pear using a single snapshot technique combined with a least-squares support vector regression model based on spatial frequency domain imaging" @default.
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• W2945647325 doi "https://doi.org/10.1364/ao.58.004075" @default.
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