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

Giuseppe Pezzotti,Wenliang Zhu,Haruna Chikaguchi,Elia Marin,Yo-Ichiro Sato,Francesco Boschetto,Tetsuya Nakazaki,Takehiro Masumura

doi:10.3389/fnut.2021.663569

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.

Highlights

The need for a reliable method capable of screening the nutritional characteristics of food emerges from a growing customers’ interest about food composition, processing, and dietetic impact [1]
In which I represents the integrated areas of the sub-bands with maxima at frequencies given in subscript, while δ ( = 7.15 and 17.16 for mochikome and uruchimai, respectively, as obtained from the calibration curves shown in Supplementary Figure 2B) is a numerical calibration factor that takes into account the different cross-sections of the Raman signals
In the case of aromatic amino acids, similar to the case of amylose contents, we found significant differences among different rice cultivars, which might be associated with the production conditions; with the highest amount of phenylalanine and tryptophan being recorded in Koshihikari (Niigata) and Genmai (Kyoto), respectively

Summary

INTRODUCTION

The need for a reliable method capable of screening the nutritional characteristics of food emerges from a growing customers’ interest (and concerns) about food composition, processing, and dietetic impact [1]. The Raman method enables multiple analyses within a single measurement, allowing the concurrent and highly efficient evaluation of a range of key nutritional targets [16,17,18,19,20,21,22,23] Such a multivalent information is encrypted in complex spectral features, whose deconvolution and quantitative interpretation require constructed algorithms, whose development is yet in its infancy. We shall discuss analytical algorithms for the determination of amylopectin and amylose concentrations, phenolic compounds and grain protein contents, which directly represent the glycemic, antioxidative, and nutritional characteristics of rice cultivars, respectively. The advantage of the Raman barcode with respect to a conventional barcode is that the information contained in the former, once appropriately certified, could serve as factual and data-driven source for customers, while providing science-consolidated branding strategies to producers

EXPERIMENTAL PROCEDURES

RESULTS

DISCUSSION

DATA AVAILABILITY STATEMENT