title: Quantification of the optical properties of two-layer turbid materials using a hyperspectral imaging-based spatially-resolved technique.
abstract: Recent research has shown that a hyperspectral imaging-based spatially-resolved technique is useful for determining the optical properties of homogenous fruits and food products. To better characterize fruit properties and quality attributes, it is desirable to consider fruit to be composed of two homogeneous layers of skin and flesh. This research was aimed at developing a nondestructive method to determine the absorption and scattering properties of two-layer turbid materials with the characteristics of fruit. An inverse algorithm along with the sensitivity coefficient analysis for a two-layer diffusion model was developed for the extraction of optical properties from the spatially-resolved diffuse reflectance data acquired using a hyperspectral imaging system. The diffusion model and the inverse algorithm were validated with Monte Carlo simulations and experimental measurements from solid model samples of known optical properties. The average errors of determining two and four optical parameters were 6.8% and 15.3%, respectively, for Monte Carlo reflectance data. The optical properties of the first or top layer of the model samples were determined with errors of less than 23.0% for the absorption coefficient and 18.4% for the reduced scattering coefficient. The inverse algorithm did not give acceptable estimations for the second or lower layer of the model samples. While the hyperspectral imaging-based spatially-resolved technique has the potential to measure the optical properties of two-layer turbid materials like fruits and food products, further improvements are needed in determining the optical properties of the second layer.
authors.affiliation: Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, Michigan 48824, USA.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Cen
authors.forename: Renfu
authors.initials: R
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D005504:Food Analysis / Q000379:methods*; D005638:Fruit / Q000737:chemistry*; D009391:Nephelometry and Turbidimetry / Q000379:methods*