Original articleRapid recognition of Chinese herbal pieces of Areca catechu by different concocted processes using Fourier transform mid-infrared and near-infrared spectroscopy combined with partial least-squares discriminant analysis
Graphical abstract
Near-infrared (NIR) and mid-infrared (MIR) technology combined with supervised pattern recognition based on partial least-squares discriminant analysis (PLSDA) was attempted to classify and verify six different concocted pieces of 600 Areca catechu L. samples and the influence of fingerprint information preprocessing methods on recognition performance was also investigated in this work. In conclusion, PLSDA can rapidly and effectively extract otherness of fingerprint information from NIR and MIR spectra to identify different concocted herbal pieces of A. catechu.
Introduction
The theory of traditional Chinese medicine processing plays an important role in the therapy using Traditional Chinese Medicine (TCM). The active ingredients and pharmacological efficacy of Chinese herbal pieces are greatly influenced by different concocted processing technologies. The consequences of these changes are complex and difficult to measure. Current methods are often based on the contents of a single or multiple principal components to identify different processed herbal pieces. These methods not only suffer from tedious pretreatments but also are unable to completely reflect all variations in the concocted herbal pieces. In contrast, Fourier transform mid-infrared (MIR) and near-infrared (NIR) as fast and nondestructive analytical techniques have been widely used in several scientific fields [1], [2], [3], [4], [5], [6]. However, the fingerprint information provided by MIR and NIR spectra is difficult to interpret. So it is vital to establish effective and robust chemometrics methods [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. In this study, a supervised pattern recognition based on partial least-squares discriminant analysis (PLSDA) algorithm [8], [9], [10], [11] with different fingerprint preprocessing of NIR and MIR spectral variables is adopted to extract and discriminate otherness of different processed Areca pieces.
Section snippets
Apparatus
Nicolet 6700 FT-IR, OMNIC 8.2 spectral collecting software (Thermo Fisher Scientific Inc., USA). Antaris II FT-NIR spectrometer, RESULT 3.0 spectral collecting software (Thermo Electron Co., USA).
Sample preparation and spectra acquisition
The samples of Areca pieces were produced by commercial herbal pieces, three kinds of self-processing herbal pieces and two kinds of commercial herbal pieces heated at different levels. Self-processing Areca pieces were processed by an auto-roller heating machine and a microwave oven (700 W) with
Results and discussion
Areca pieces are complex mixtures that contain many types of bioactive constituents. The average MIR and NIR spectra of each group are displayed to reflect the overlay fingerprint information in Fig. 1. A family of volatile pyridine containing alkaloids [13] is the main bioactive ingredients that exert the pharmacological effects by modulating the acetylcholine neurotransmission system [14]. The characteristic key vibrations of pyridine occur at 3075–3020 cm−1, 1620–1590 cm−1, 1500 cm−1 and 920–720
Conclusion
The results presented in this paper indicate that supervised pattern recognition based on PLSDA can rapidly and effectively extract otherness of fingerprint information from MIR and NIR spectra to identify herbal pieces of Areca catechu by different concocted processes. The MIR and NIR techniques combined with PLSDA as a feasible and promising method could be expected to discriminate more herbal pieces with different concocted processes.
Acknowledgments
This work was financially supported by the National Natural Science Foundation of China (Nos. 21205145, 21276006, 21036009), the Open Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics of Hunan University (No. 201111), the Special Fund for Basic Scientific Research of Central Colleges, South-Central University for Nationalities (Nos. CZZ10005 and CZQ11012), the ‘Five-twelfth’ National Science and Technology Support Program (No. 2012BAI27B00).
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