食品科学 ›› 2019, Vol. 40 ›› Issue (12): 176-181.doi: 10.7506/spkx1002-6630-20180621-412

• 生物工程 • 上一篇    下一篇

基于GC-MS技术对不同产地稻米的代谢组学分析

富天昕1,冯玉超1,张丽媛1,2,李 雪1,王长远1,2,*   

  1. 黑龙江八一农垦大学食品学院,黑龙江省农产品加工与质量安全重点实验室,黑龙江 大庆 163319
  • 出版日期:2019-06-25 发布日期:2019-06-28
  • 基金资助:
    黑龙江省农垦总局农业技术试验示范专项(HNK135-05-01);黑龙江省博士后基金资助项目(LBH-Z15217);黑龙江八一农垦大学校博士科研启动计划项目;黑龙江八一农垦大学研究生创新科研项目(YJSCX2017-Y49)

Metabonomics Study on Rice from Different Geographical Areas Based on Gas Chromatography-Mass Spectrometry

FU Tianxin1, FENG Yuchao1, ZHANG Liyuan1,2, LI Xue1, WANG Changyuan1,2,*   

  1. Key Laboratory of Agro-Products Processing and Quality Safety of Heilongjiang Province, College of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, China
  • Online:2019-06-25 Published:2019-06-28

摘要: 基于气相色谱-质谱(gas chromatography-mass spectrometry,GC-MS)联用技术对建三江和响水地区的60份稻米样本进行代谢组学研究。利用主成分分析和正交偏最小二乘-判别分析等多元统计分析方法,对建三江和响水的稻米样本进行检测,在样本中共检测出143 个峰,鉴定出包括氨基酸、脂肪酸、核苷酸、有机酸、多元醇、糖类等在内的39 个代谢产物。实验考察产地对水稻代谢组的影响,结果表明建三江地区和响水地区稻米代谢产物在数量和含量上均存在显著差异。对显著变化(P<0.05,VIP>1)的差异代谢物进行鉴定,共筛选出16 种差异代谢产物。实验表明,利用GC-MS技术,结合多元统计分析的方法,可用于不同地区稻米代谢产物差异的研究,为稻米产地溯源提供理论依据。

关键词: 稻米, 气相色谱-质谱联用, 代谢组学, 多元统计分析, 差异代谢

Abstract: Based on gas chromatography-mass spectrometry (GC-MS), metabolomics studies were conducted on 60 rice varieties growing in Jiansanjiang and Xiangshui, Heilongjiang. By applying the multivariate statistical analysis methods principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), we examined rice samples collected from the two growing areas. A total of 143 peaks were detected in the samples, and a total of 39 metabolites including amino acids, fatty acids, nucleotides, organic acids, polyols, sugars were identified. The effect of the geographical origin on rice metabolome was evaluated. The results showed significant differences in both the kinds and quantities of metabolites in rice samples from different growing areas. A total of 16 differential metabolites with significant changes (P < 0.05, variable importance in the projection (VIP) > 1) were identified. The results showed that GC-MS combined with multivariate statistical analysis could be used to study the differences in the metabolite profiles of rice samples collected from different growing regions. This study provides the basis for studying the geographical traceability of rice.

Key words: rice, gas chromatography-mass spectrometry (GC-MS), metabolomics, multivariate statistical analysis, differential metabolites

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