ABSTRACT
The nutritional quality of rice is contingent on a wide spectrum of biochemical characteristics, which essentially depend on rice genome, but are also greatly affected by growing/environmental conditions and aging during storage. The genetic basis and related identification of genes have widely been studied and rationally linked to accumulation of micronutrients in grains. However, genetic classifications cannot catch quality fluctuations arising from interannual, environmental, and storage conditions. Here, we propose a quantitative spectroscopic approach to analyze rice nutritional quality based on Raman spectroscopy, and disclose analytical algorithms for the determination of: (i) amylopectin and amylose concentrations, (ii) aromatic amino acids, (iii) protein content and structure, and (iv) chemical residues. The proposed Raman algorithms directly link to the molecular composition of grains and allow fast/non-destructive determination of key nutritional parameters with minimal sample preparation. Building upon spectroscopic information at the molecular level, we newly propose to represent the nutritional quality of labeled rice products with a barcode specially tailored on the Raman spectrum. The Raman barcode, which can be stored in databases promptly consultable with barcode scanners, could be linked to diet applications (apps) to enable a rapid, factual, and unequivocal product identification based on direct molecular screening.
ABSTRACT
Analytical algorithms based on Raman spectroscopy are proposed for the determination of amylopectin and amylose concentrations in polished white rice, and applied to characterize and compare linear and branched polysaccharide structures in nine different types of Japanese rice. A selected algorithm used symmetric bending vibrations of the COC glycosidic linkage from a relatively narrow spectral zone between 830 and 895 cm-1. It specifically compared the intensity of Raman signals from two types of bending common to both starch components (C1-O-C5 and C1-O-C4 at 868 and 855 cm-1, respectively) and that at the branch point peculiar to amylopectin (C1-O-C6 at 844 cm-1). Raman data were confronted with data collected by conventional amylose-iodine colorimetry method. Consistency was found between Raman and colorimetric methods over the entire series of tested rice cultivars, thus validating the newly proposed spectroscopic algorithm. The amylose content of the tested rice species broadly varied between 1.2 and 20.4%. The proposed Raman algorithm allows fast and nondestructive determination of amylose content in rice with minimal sample preparation. These characteristics might be key in the development of portable Raman devices capable to promptly screen polysaccharides in different rice cultivars with respect to their interannual and plantation-related fluctuations.
