Geographical origin traceability of rice using a FTIR-based metabolomics approach†

IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Molecular omics Pub Date : 2023-04-20 DOI:10.1039/D2MO00317A
Weifeng Xue, Qi Wang, Xuemei Li, Mei Wang, Zhenlin Dong, Haitao Bian and Fang Li
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引用次数: 0

Abstract

Infrared spectroscopy is a crucial tool to achieve the origin traceability of rice, but it is constrained by data mining. In this study, a novel infrared spectroscopy-based metabolomics analytical method was proposed to discriminate rice products from 14 Chinese cities by seeking ‘wave number markers’. Principal component analysis (PCA), cluster analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to separate all rice groups. The S-plot, permutation test and variable importance in projection (VIP) are used to screen eligible ‘markers’, which were further verified by a pairwise t-test. There are 55–265 ‘markers’ picked out from 14 rice groups, with their characteristic wave number bands to be 2935.658–3238.482, 3851.846–4000.364, 3329.136–3518.160, 1062.778–1213.225, 1161.147–1386.819, 3348.425–3560.594, 3115.038–3624.245, 2567.254–2872.007, 3334.923–3560.594, 3282.845–3543.235, 3338.780–3518.160, 3197.977–3560.594, 3163.258–3267.414 and 3292.489–3477.655 cm−1, respectively. All but No. 5 rice groups show significantly low absorbance on their ‘marker’ bands. A mixed rice containing congenial No. 5 and No. 6 rice (80 : 20, m/m) was employed to test the validity of the method, and found that the ‘marker’ band of the mixed rice is the range of 1170.791–1338.598 cm−1, implying the existence of considerable discrepancy between the mixed rice and other rice. The results indicate that infrared spectroscopy coupled with metabolomics analysis is competent for origin traceability of rice; thus, it provides a novel and workable approach for the accurate and rapid discrimination of rice from different geographical origins, and a distinctive perspective of metabolomics to explore infrared spectroscopy and beyond, especially not confined in the field of origin traceability.

Abstract Image

基于ftir代谢组学方法的水稻地理来源可追溯性研究
红外光谱技术是实现大米溯源的重要手段,但受到数据挖掘的限制。本研究提出了一种新的基于红外光谱的代谢组学分析方法,通过寻找“波数标记”来区分中国14个城市的大米产品。采用主成分分析(PCA)、聚类分析和正交偏最小二乘判别分析(OPLS-DA)进行分组。使用s图、排列检验和投影变量重要性(VIP)筛选符合条件的“标记”,并通过两两t检验进一步验证。从14个水稻类群中共筛选出55 ~ 265个“标记”,特征波数波段分别为2935.658-3238.482、3851.846-4000.364、3329.136-3518.160、1062.778-1213.225、1161.147-1386.819、3348.425-3560.594、3115.038-3624.245、2567.254-2872.007、3334.923-3560.594、3282.845-3543.235、3338.78 - 3518.160、3197.977-3560.594、3163.258-3267.414和3292.489-3477.655 cm−1。除5号水稻组外,其余水稻组在其“标记”波段上的吸光度都很低。以5号和6号杂交水稻(80∶20,m/m)为样本,对该方法的有效性进行了检验,发现该杂交水稻的“标记”波段为1170.791 ~ 1338.598 cm−1,表明该杂交水稻与其他水稻存在较大差异。结果表明,红外光谱结合代谢组学分析可用于水稻的原产地溯源;因此,它为准确和快速区分不同地理产地的水稻提供了一种新颖可行的方法,并为探索红外光谱及其他领域的代谢组学提供了独特的视角,特别是不局限于原产地溯源领域。
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来源期刊
Molecular omics
Molecular omics Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
5.40
自引率
3.40%
发文量
91
期刊介绍: Molecular Omics publishes high-quality research from across the -omics sciences. Topics include, but are not limited to: -omics studies to gain mechanistic insight into biological processes – for example, determining the mode of action of a drug or the basis of a particular phenotype, such as drought tolerance -omics studies for clinical applications with validation, such as finding biomarkers for diagnostics or potential new drug targets -omics studies looking at the sub-cellular make-up of cells – for example, the subcellular localisation of certain proteins or post-translational modifications or new imaging techniques -studies presenting new methods and tools to support omics studies, including new spectroscopic/chromatographic techniques, chip-based/array technologies and new classification/data analysis techniques. New methods should be proven and demonstrate an advance in the field. Molecular Omics only accepts articles of high importance and interest that provide significant new insight into important chemical or biological problems. This could be fundamental research that significantly increases understanding or research that demonstrates clear functional benefits. Papers reporting new results that could be routinely predicted, do not show a significant improvement over known research, or are of interest only to the specialist in the area are not suitable for publication in Molecular Omics.
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