4.7 Article

Identification and evaluation of Polygonatum kingianum with different growth ages based on data fusion strategy

Journal

MICROCHEMICAL JOURNAL
Volume 160, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.microc.2020.105662

Keywords

Polygonatum kingianum; Growth ages; Polysaccharide; Partial least squares discriminant analysis; Feature variable extraction; Data fusion

Funding

  1. national survey of traditional Chinese medicine resources project of national administration of traditional Chinese medicine [GZY-KJS-2018-004]
  2. major science and technology projects in Yunnan Province [2018ZF010]
  3. science and technology planning project in Yunnan Province [2017RA001]
  4. pilot project of science and technology innovation and achievement transformation of Yunnan Academy of Agricultural Sciences [202002AE320007-01]

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The study focused on analyzing the polysaccharide accumulation in Polygonatum kingianum with different growth ages using chemometric methods, discussing correlations with 13 agronomic traits, and successfully identifying the growth age through data fusion strategy. The polysaccharide content showed negative correlations with other plant traits, and the data fusion models of ATR-FTIR and UV-Vis spectra achieved 100% accuracy.
Polysaccharide, the main component of Polygonatum kingianum, has been proven to have many pharmacological effects, such as antioxidant, anti-aging and anti-bacteria. The accumulation of polysaccharides was greatly influenced by the growth age of P. kingianum. Therefore, a fast and low-cost chemometric method is needed to identify the growth age. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) and ultraviolet visible (UV-Vis) spectroscopy of P. kingianum with four growth ages (80 samples) were collected and analyzed by chemometric methods. The correlation between 13 agronomic traits and polysaccharide accumulation was discussed. A data fusion strategy was used to identify the growth age of P. kingianum by extracting the latent variables (LVs) and principal components (PCs) of the spectra. The results founded that the polysaccharide content showed a negative correlation the fibrous root number, leaf length, leaf width, stem diameter and fresh weight on the ground part. The ATR-FTIR and UV-Vis spectra in P. kingianum with different growth ages were similar. The regions of 1750-1500 cm(-1), 1000-750 cm(-1) and 200-250 nm, 330-360 nm are important variables for discriminating four growth ages of P. kingianum. The classification effect of data fusion models was better than that of single spectra models with 100% accuracy as well as perfect sensitivity and specificity. Hierarchical cluster analysis shown that the samples were divided into two categories: one 5-year-old and the other 2, 3 and 4-year-old. In summary, the polysaccharide content in 4-year-old P. kingianum by seed reproduction was the highest. Data fusion of ATR-FTIR and UV-Vis spectra combined with PLS-DA models could be used to accurately distinguish P. kingianum in different growth ages, which was valuable for the identification and evaluation of other medicinal and edible homologous plants. Meanwhile, it also provides a theoretical basis for the harvesting age and cultivation conditions of P. kingianum.

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