Classification of herbal medicines by using chemometric methods in the analysis of their Fourier transform infrared (FTIR) spectra

Project Number
RI 12/10 TTL

Project Duration
January 2011 - March 2016

Status
Completed

Abstract
Importance of herbal medicines in TCM: The control of quality of herbal products in traditional Chinese medicine (TCM) such as ginseng, lingzhi, and cordyceps, is important for assuring consumer safety and efficacy. Natural high quality herbal medicines are expensive as demand exceeds supply in an affluent society where TCM is gaining popularity. Adulteration and fakes have been reported as the commercial products are sold in powdered form, capsules softgels, and tea mixture. Therefore, the traditional means of authentication using smell, taste or physical appearance are not expected to be reliable. Limitations of current analytical methods: Currently, herbal medicines have beeninvestigated with the use of high performance liquid chromatography (HPLC), thin layer chromatography (TLC), and colorimeter. These methods are found to be expensive, time-consuming, labour-intensive, and requiring large quantity of organic solvents. Also, the results are inadequate for classification purposes because of the limited number of active chemical components that can be detected in what is a very complex system. Advantages of using Fourier transform infrared (FTIR) spectroscopic technique in TCM: Fourier transform infrared (FTIR) spectroscopic methods have many advantages for the classification of herbal medicines in terms of easy and direct usage of technique, non-destructiveness, small quantity of sample needed and short data acquisition time. Studies on herbal medicines using FTIR techniques are still in its infancy. Advantages of using chemometric methods in the analysis of FTIR spectra of herbal medicines: Furthermore, very importantly, FTIR spectra of herbal medicines consist of many overlaying absorption bands representing the different modes of vibration of a large number of molecular constituents in the compounds. These vibrational bands are sensitive to the physical and chemical stated of the compounds, and they can be detected at low levels. Therefore, suitable chemometric methods are required to analyse the FTIR spectra so as to discriminate and match the compositional differences and constituent interactions in the herbal samples. Studies on classification, discrimination, and identification of herbal medicines have used both supervised and unsupervised techniques for interpretation from multivariate data. These pattern recognition methods or chemometric techniques include the principal component analysis (LDA), clustering analysis (CA), and soft independent modeling of class analogies (SIMCA). Due to the complex composition of herbal medicine, other chemometric techniques based on nonlinear technology such as artificial neural networks (ANN), support vector machine (SVM) can also be explored for application to the classification of herbal medicines [3,6]. FTIR spectra are affected by both the chemical and physical properties of the herbal medicines, and the chemical composition much related to classification is considered to be cmall. To enhance te contribution of the chemical composition and to improve signal quality, different spectral pretreatment methods, such as multiplicative scatter correction (MSC), standard normal variates (SNV), Savitzky-Golay, first and second derivative, are to be compared for seeking the most suitable one. The spectral pretreatment method will work as an essential step before carrying on a successful classification.

Funding Source
NIE

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