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

• Component Analysis • Previous Articles     Next Articles

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

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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