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Determination of the optical properties of biological materials based on steady-state spatially resolved diffuse reflectance imaging is a complicated inverse problem-solving process. An effective inverse algorithm is first necessary and validated. This article proposed a Fourier series expansion (FSE) coupled with least squares support vector machine (LS-SVM) as an inverse algorithm for determining the absorption coefficient (μa) and the reduced scattering coefficient () of biological materials. A hyperspectral imaging system was used to acquire scattering images and steady-state spatially resolved diffuse reflectance profiles of liquid phantoms. Experiment results demonstrated that the hyperspectral imaging system coupled with this inverse algorithm effectively improved prediction accuracy of both μa and of liquid phantoms. Tests on liquid phantoms showed that the mean relative errors of this inverse algorithm to be 11.03% for the absorption coefficient and 7.16% for the reduced scattering coefficient when corresponding Fourier coefficients of liquid phantoms were used to develop the prediction model. To further study the method, the Fourier coefficients calculated from normalized Monte Carlo simulation data were used to develop the prediction model for determining the optical properties of 36 liquid phantoms. The prediction errors were 15.96 and 10.91% for μa and , respectively. For all liquid phantoms, it was found that the prediction values of both μa and were generally in good agreement with their actual values. Therefore, the FSE-LS-SVM method provides an effectively means for improving the prediction accuracy of optical properties of biological materials. [ABSTRACT FROM AUTHOR]

Milk is a vulnerable target for economically motivated adulteration. In this study, a line-scan high-throughput Raman imaging system was used to authenticate milk powder. A 5 W 785 nm line laser (240 mm long and 1 mm wide) was used as a Raman excitation source. The system was used to acquire hyperspectral Raman images in a wave number range of 103–2881 cm–1 from the skimmed milk powder mixed with two nitrogen-rich adulterants (i.e., melamine and urea) at eight concentrations (w/w) from 50 to 10,000 ppm. The powdered samples were put in sample holders with a surface area of 150 ×100 mm and a depth of 2 mm for push-broom image acquisition. Varying fluorescence signals from the milk powder were removed using a correction method based on adaptive iteratively reweighted penalised least squares. Image classifications were conducted using a simple thresholding method applied to single-band fluorescence-corrected images at unique Raman peaks selected for melamine (673 cm–1) and urea (1009 cm–1). Chemical images were generated by combining individual binary images of melamine and urea to visualise identification, spatial distribution and morphological features of the two adulterant particles in the milk powder. Limits of detection for both melamine and urea were estimated in the order of 50 ppm. High correlations were found between pixel concentrations (i.e., percentages of the adulterant pixels in the chemical images) and mass concentrations of melamine and urea, demonstrating the potential of the high-throughput Raman chemical imaging method for the detection and quantification of adulterants in the milk powder. [ABSTRACT FROM AUTHOR]

Spatially offset Raman spectroscopy (SORS) is a technique that can obtain subsurface layered information by collecting Raman spectra from a series of surface positions laterally offset from the excitation laser. Currently optical fiber probes are used as major tools in SORS measurement, which are either slow (single fiber probe with mechanical movement) or restricted in selecting offset range and interval (fiber probe array). This study proposes a new method to conduct SORS measurement based on a newly developed line-scan hyperspectral Raman imaging system. A 785-nm point laser was used as an excitation source. A detection module consisting of an imaging spectrograph and a charge-coupled device camera was used to acquire line-shape SORS data in a spectral region of −592 to 3015 cm−1. Using a single scan, the system allowed simultaneous collection of a series of Raman spectra in a broad offset range (e.g. 0-36 mm in two sides of the incident laser) with a narrow interval (e.g. 0.07 mm). Four layered samples were created by placing butter slices with thicknesses of 1, 4, 7, and 10 mm on top of melamine powder, providing different individual Raman characteristics to test the line-scan SORS technique. Self-modeling mixture analysis (SMA) was used to analyze the SORS data. Raman spectra from butter and melamine were successfully retrieved for all four butter-on-melamine samples using the SMA method. The line-scan SORS measurement technique provides a flexible and efficient method for subsurface evaluation, which has potential to be used for food safety and quality inspection. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]