Pezzotti Giuseppe, Zhu Wenliang, Chikaguchi Haruna, Marin Elia, Boschetto Francesco, Masumura Takehiro, Sato Yo-Ichiro, Nakazaki Tetsuya
Ceramic Physics Laboratory, Kyoto Institute of Technology, Kyoto, Japan.
Department of Orthopedic Surgery, Tokyo Medical University, Tokyo, Japan.
Front Nutr. 2021 Jun 23;8:663569. doi: 10.3389/fnut.2021.663569. eCollection 2021.
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.
大米的营养品质取决于广泛的生化特性,这些特性本质上依赖于水稻基因组,但也受到生长/环境条件以及储存期间老化的极大影响。基因基础及相关基因鉴定已得到广泛研究,并合理地与谷物中微量营养素的积累相关联。然而,基因分类无法捕捉因年份间、环境及储存条件导致的品质波动。在此,我们提出一种基于拉曼光谱分析大米营养品质的定量光谱方法,并公开用于测定以下各项的分析算法:(i)支链淀粉和直链淀粉浓度,(ii)芳香族氨基酸,(iii)蛋白质含量及结构,以及(iv)化学残留。所提出的拉曼算法直接与谷物的分子组成相关联,并允许在最少样品制备的情况下快速/无损测定关键营养参数。基于分子水平的光谱信息,我们新提议用专门根据拉曼光谱定制的条形码来表示标记大米产品的营养品质。拉曼条形码可迅速存储在数据库中,通过条形码扫描仪可随时查询,它可与饮食应用程序(应用)相链接,以便基于直接分子筛选实现快速、真实且明确的产品识别。