Optimization of the hyperspectral imaging-based spatially-resolved system for measuring the optical properties of biological materials

Abstract

Hyperspectral imaging-based spatially-resolved technique provides a new means to determine the optical properties of biological materials. However, several critical technical issues must be properly addressed in order to achieve desired measurement accuracies. This paper reports on the optimization and assessment of a hyperspectral imaging-based spatially-resolved system for determination of the optical properties of biological materials over the wavelengths of 500-1,000 nm. Twelve model samples were created using three absorbing dyes and fat emulsion scatters to validate the hyperspectral imaging system, and their true values of absorption and reduced scattering coefficients [THESE SYMBOLS ARE NOT AVAILABLE IN THML, SEE PDF FOR COMPLETE VERSION] (&) were determined and then cross-validated using three commonly used methods (i.e., transmittance, integrating sphere, and empirical equation). The optical properties of the model samples were then extracted from the spatially-resolved reflectance profiles, acquired by the hyperspectral imaging system, based on the diffusion model with a nonlinear least squares inverse algorithm. Light beam and source-detector distance were quantified and optimized through Monte Carlo simulations and experiments. The optimized hyperspectral imaging system was evaluated for accuracy, precision/reproducibility and sensitivity. The results suggested that the optimal light beam should be of circular shape and Gaussian type with the diameter of less than 1 mm, the optimal minimum source-detector distance should be about 1.5 mm, and the optimal maximum source-detector distance should be 10-20 mean free paths [1 mean free path =[THESE SYMBOLS ARE NOT AVAILABLE IN THML, SEE PDF FOR COMPLETE VERSION]] or be determined by the minimum signal-to-noise ratio of 20 (or 150 CCD counts for the system). The hyperspectral imaging-based spatially-resolved system had average measurement errors of 23% and 7% for[THESE SYMBOLS ARE NOT AVAILABLE IN THML, SEE PDF FOR COMPLETE VERSION] and, respectively, for the model samples, which are smaller than, or comparable to those obtained using other techniques (i.e., frequency-domain, time-resolved, and spatially-resolved but with other sensing configurations). The research provides a systematic guide for the development and optimization of spatially-resolved technique to measure the optical properties of biological materials like food and agricultural products.