The rapid characterisation of natural products and herbal medicines by near-infrared spectroscopy

Although the use of herbal medicines has increased in popularity, there is very little quality control carried out on them. Thus, in the industry, potentially lethal problems such as misidentification and adulteration can occur. Traditional methods of analysis, including chromatography and microscopy, can be destructive and time-consuming. Near-infrared spectroscopy (NIRS), however, would be advantageous in that it is rapid, easy, and requires little or no sample preparation. The potential for NIRS to analyse natural products was explored. Results showed that the technique could be used to successfully identify different samples within a family (e.g. Umbelliferae) as well as within a genus (e.g. Digitalis). It was also possible to identify various parts of the same plant, as well as detect adulteration of a sample with a different plant part. NIRS could also be used to successfully discriminate between samples of the same species of different geographical origins, such as Cannabis saliva. The use of NIRS in the food industry was also briefly examined, and it was found that it could be used to discriminate between various tea blends. NIRS was also effective in measuring the moisture content of samples. This is advantageous over other techniques in that being rapid and non-destructive, it could easily be used on-line in the production process. Various statistical and chemometric methods were investigated to allow for optimum power of identification and sample discrimination. The Maximum Distance in Wavelength Space method was the most successful one, although others, such as Correlation in Wavelength Space and correlation coefficients also had limited success. Manual techniques, such as the two-wavelength method and the Polar Qualification method were also investigated. Overall, it could be said that NIRS could be a powerful tool for the analysis of natural products, with its speed and non-destructiveness being a major advantage