食品科学 ›› 2021, Vol. 42 ›› Issue (6): 250-255.doi: 10.7506/spkx1002-6630-20200324-358

• 成分分析 • 上一篇    下一篇

红糖、白砂糖、赤砂糖与黑糖的HPLC指纹图谱与化学模式识别

蔡玮琦,左雯雯,陆杨,黄胜良,李存玉,郑云枫,彭国平   

  1. (1.南京中医药大学药学院,江苏 南京 210023;2.江苏融昱药业有限公司,江苏 淮安 223200;3.江苏省中药资源产业化过程协同创新中心,江苏 南京 210023)
  • 出版日期:2021-03-25 发布日期:2021-03-29
  • 基金资助:
    国家中医药管理局中药标准化项目(ZYBZH-C-JS-34)

HPLC Fingerprinting and Chemical Pattern Recognition of Brown Sugar, White Granulated Sugar, Red Granulated Sugar and Black Sugar

CAI Weiqi, ZUO Wenwen, LU Yang, HUANG Shengliang, LI Cunyu,, ZHENG Yunfeng,, PENG Guoping   

  1. (1. School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210023, China;2. Jiangsu Rongyu Pharmaceutical Co. Ltd., Huai’an 223200, China;3. Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Nanjing 210023, China)
  • Online:2021-03-25 Published:2021-03-29

摘要: 建立红糖、黑糖、赤砂糖与白砂糖的高效液相色谱指纹图谱,并对其进行模式识别。采用Durashell C18-AM亲水性色谱柱(250 mm×4.6 mm,5 μm)进行分析,以乙腈-三氟乙酸为流动相,流速1.0 mL/min,柱温25 ℃,进样量10 μL,检测波长210 nm。并对结果进行化学模式识别(聚类分析、主成分分析和正交最小二乘法判别分析)。结果表明,10 批红糖样品指纹图谱中共有峰为13 个,7 批白砂糖中共有峰为1 个,10 批赤砂糖中共有峰为7 个,3 批黑糖中共有峰为17 个。正交最小二乘法判别分析出6 个主要差异性成分。该方法稳定可靠,可将红糖、黑糖、赤砂糖与白砂糖进行明显区分,从而用于该类食品的质量控制与评价。

关键词: 红糖;白砂糖;赤砂糖;黑糖;指纹图谱;聚类分析;主成分分析;正交最小二乘法判别分析

Abstract: In this study, high-performance liquid chromatography (HPLC) fingerprints of brown sugar, black sugar, red granulated sugar, and white granulated sugar were established, and pattern recognition was performed on them. A Durashell C18-AM hydrophilic column (250 mm × 4.6 mm, 5 μm) was used for the analysis. Acetonitrile-trifluoroacetic acid water was used as the mobile phase at a flow rate of 1.0 mL/min. The column temperature was set at 25 ℃. The injection volume was 10 μL. The detection wavelength was 210 nm. The obtained results were analyzed by cluster analysis (CA), principal component analysis (PCA), and orthogonal partial least squares-discriminant analysis (OPLS-DA). The results showed that 13 peaks were shared among the fingerprints of 10 batches of brown sugar samples, 1 peak among 7 batches of white granulated sugar samples, 7 peaks among 10 batches of red granulated sugar samples, and 17 peaks among 3 batches of black sugar samples. Six differential components were identified by OPLS-DA. In conclusion, the method is stable and reliable, and can clearly distinguish among brown sugar, black sugar, red granulated sugar and white granulated sugar, enabling it to be used for quality control and evaluation of these foods.

Key words: brown sugar; white granulated sugar; red granulated sugar; black sugar; fingerprint; cluster analysis; principal component analysis; orthogonal partial least squares-discriminant analysis

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