title: Enhanced Raman spectroscopic discrimination of the geographical origins of rice samples via transmission spectral collection through packed grains.
abstract: Transmission Raman spectroscopy has been effectively utilized for the discrimination of rice samples according to geographical origin. Since the constituents of rice are heterogeneously distributed and/or localized in a grain, the collection of Raman spectra providing a better compositional representation of packed rice grains is an essential requirement for accurate analysis. The optimal packing thickness yielding the most reproducible transmission spectra was initially determined. Internal propagation of radiation was more sensitively influenced by random packing when a packing was thinner; while, a thicker packing largely attenuated transmitting Raman signal and eventually degraded the signal-to-noise ratio of collected spectra. At the determined packing thickness, transmission spectra of all rice samples were collected, and discrimination into two different geographical origins was performed using principal component analysis (PCA) combined with linear discriminant analysis (LDA). For comparison, back-scattering Raman spectra of the same samples were also collected. The discrimination accuracy was improved when Raman spectra collected directly through the packed rice grains were used. Since the constituents of rice were not homogeneously distributed in a grain as confirmed using Raman microscopy, the transmission measurement enabling transversal sampling across a packing of rice grains was better for compositional representation of individual grains in the packing and able to recognize minute spectral differences between two groups, ultimately leading to more accurate discrimination of geographical origin.
authors.affiliation: Analytical Spectroscopy Lab, Department of Chemistry and Research Institute for Natural Sciences, Hanyang University, Seoul 133-791, Republic of Korea.
authors.forename: Jinyoung
authors.initials: J
authors.lastname: Hwang
authors.forename: Sukwon
authors.initials: S
authors.lastname: Kang
authors.forename: Kangjin
authors.initials: K
authors.lastname: Lee
authors.forename: Hoeil
authors.initials: H
authors.lastname: Chung
mesh_terms: D005843:Geography; D012275:Oryza / Q000145:classification*; D025341:Principal Component Analysis; D013059:Spectrum Analysis, Raman / Q000379:methods*