Nondestructive determination of optical properties of a pear using spatial frequency domain imaging combined with phase-measuring profilometry

Applications of optical property measurement for quality evaluation of agri-food products: a review

Spectroscopic techniques coupled with chemometric approaches have been widely used for quality evaluation of agricultural and food (agri-food) products due to the nondestructive, simple, fast, and easy characters. However, these techniques face the issues or challenges of relatively weak robustness, generalizability, and applicability in modeling and prediction because they measure the aggregate amount of light interaction with tissues, resulting in the combined effect of absorption and scattering of photons. Optical property measurement could separate absorption from scattering, providing new insights into more reliable prediction performance in quality evaluation, which is attracting increasing attention. In this review, a brief overview of the currently popular measurement techniques, in terms of light transfer principles and data analysis algorithms, is first presented. Then, the emphases are put on the recent advances of these techniques for measuring optical properties of agri-food products since 2000. Corresponding applications on qualitative and quantitative analyses of quality evaluation, as well as light transfer simulations within tissues, were reviewed. Furthermore, the leading groups working on optical property measurement worldwide are highlighted, which is the first summary to the best of our knowledge. Finally, challenges for optical property measurement are discussed, and some viewpoints on future research directions are also given.

Spatial-Frequency Domain Imaging: An Emerging Depth-Varying and Wide-Field Technique for Optical Property Measurement of Biological Tissues

Measurement of optical properties is critical for understanding light-tissue interaction, properly interpreting measurement data, and gaining better knowledge of tissue physicochemical properties. However, conventional optical measuring techniques are limited in point measurement, which partly hinders the applications on characterizing spatial distribution and inhomogeneity of optical properties of biological tissues. Spatial-frequency domain imaging (SFDI), as an emerging non-contact, depth-varying and wide-field optical imaging technique, is capable of measuring the optical properties in a wide field-of-view on a pixel-by-pixel basis. This review first describes the typical SFDI system and the principle for estimating optical properties using the SFDI technique. Then, the applications of SFDI in the fields of biomedicine, as well as food and agriculture, are reviewed, including burn assessment, skin tissue evaluation, tumor tissue detection, brain tissue monitoring, and quality evaluation of agro-products. Finally, a discussion on the challenges and future perspectives of SFDI for optical property estimation is presented.

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

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.

Spectral refractive index assessment of turbid samples by combining spatial frequency near-infrared spectroscopy with Kramers-Kronig analysis

A practical algorithm for estimating the wavelength-dependent refractive index (RI) of a turbid sample in the spatial frequency domain with the aid of Kramers-Kronig (KK) relations is presented. In it, phase-shifted sinusoidal patterns (structured illumination) are serially projected at a high spatial frequency onto the sample surface (mouse scalp) at different near-infrared wavelengths while a camera mounted normally to the sample surface captures the reflected diffuse light. In the offline analysis pipeline, recorded images at each wavelength are converted to spatial absorption maps by logarithmic function, and once the absorption coefficient information is obtained, the imaginary part (k) of the complex RI (CRI), based on Maxell's equations, can be calculated. Using the data represented by k, the real part of the CRI (n) is then resolved by KK analysis. The wavelength dependence of n  (  λ  )   is then fitted separately using four standard dispersion models: Cornu, Cauchy, Conrady, and Sellmeier. In addition, three-dimensional surface-profile distribution of n is provided based on phase profilometry principles and a phase-unwrapping-based phase-derivative-variance algorithm. Experimental results demonstrate the capability of the proposed idea for sample's determination of a biological sample's RI value.