Contactless and non-destructive differentiation of microstructures of sugar foams by hyperspectral scatter imaging

A hyperspectral scatter imaging system was developed for the contactless acquisition of spatially resolved diffuse reflectance profiles for the optical characterization of turbid food products in the wavelength range from 500 to 960 nm. To investigate the potential of this concept sugar foams with different microstructures, but with similar chemical compositions, have been prepared by applying different mixing times to the same mixtures of sugar and albumin. Hyperspectral scatter images have been acquired from these samples and the absorption and reduced scattering coefficients have been derived from spatially resolved reflectance profiles based on the diffusion approximation of the radiative transfer equation describing the light propagation in turbid media. The estimated reduced scattering coefficients mu(s)' spectra clearly reflected the effect of the different mixing times on the foam microstructure. On the other hand, similar absorption coefficient spectra were observed, confirming the identical chemical composition of the sugar-albumin matrix. These results indicate that the hyperspectral scatter imaging technique has potential as a non-contact and rapid method for online quality control and process monitoring of foamed food products. Industrial relevance: The presented non-contact and non-destructive hyperspectral scatter imaging technique has potential for many applications in food industry domain. The technique is suitable for on-line scanning of food samples owing to the line-scanning operation in which all the wavebands are recorded simultaneously by the CCD camera. Such non-contact online screening ability makes the hyperspectral technique become highly desirable for industrial application in sorting and grading of food products by means of optical properties related to food quality attributes. A hyperspectral scatter imaging system can be easily integrated into industrial conveyor belt system for on-line food quality assessment and monitoring of industrial processes. (C) 2013 Elsevier Ltd. All rights reserved.