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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.

Raman Molecular Fingerprints of Rice Nutritional Quality and the Concept of Raman Barcode