Quality control of herbal medicines through Hyperspectral imaging: Species discrimination and adulteration detection
INTRODUCTION
The quality control of herbal medicines is notoriously difficult due to the complex mixture of compounds present in plants. In addition, the variability in phytochemical profiles and toxicity issues associated with herbal products necessitates the development of reliable quality control methods. Analytical methods such as liquid chromatography coupled to mass spectrometry is time-consuming and requires expertise. HSI integrates conventional spectroscopy and imaging to obtain spectral and spatial information from a sample. Once a hyperspectral imaging (HSI) method has been developed, the visual results are rapidly obtained and easy to interpret. This application of HSI will illustrated through two examples: 1) Scutellaria lateriflora (skullcap) has a long history of use in the treatment of ailments such as insomnia and anxiety. Due to intentional or unintentional adulteration, cases of hepatotoxicity have been reported. This may be due to adulteration with the potentially hepatotoxic Teucrium canadense and T. chamaedrys (germander species). 2) S. tetrandra (hang fang ji) is a traditional chinese medicine used as a diuretic and antirheumatic medicine. It is mistakenly substituted and adulterated with the nephrotoxic A. fangchi (guang fang ji) which may cause severe health consequences. In this study, HSI in combination with multivariate image analysis methods was used to differentiate between raw plant materials. In addition, the potential of this technique to detect adulteration was also investigated.
EXPERIMENTAL
Hyperspectral images were captured using a shortwave infrared (SWIR) pushbroom imaging system in the wavelength range 920–2514 nm. Multivariate software (Evince® and Matlab®) was used to analyse the data. Principal component analysis was applied to the images to investigate chemical differences between the species. Partial least squares discriminant analysis models were constructed by assigning the clusters to classes. The classification models were used to predict the identity of raw material replicates inserted into the model as well as the levels of adulteration in spiked raw materials. UHPLC-MS as an independent analytical technique was used to confirm chemical differences between the species.
RESULTS AND DISCUSSION
Three distinct clusters, Skullcap and the two germander species, were obtained in the score scatter plot. A 92.3% variation in modelled data using PC1 and PC2 was correlated to chemical differences between the three species. The images of the spiked samples depicted the spatial orientation of contaminants within the powdered samples, and the percentage adulteration was accurately predicted at levels ≥ 40% adulteration based on pixel abundance. In the 'fang ji' example, the replicates for each plant species were predicted at a value > 99% for all the samples. Artificially adulterated samples were accurately predicted from as low as 10%. With the increasing need to regulate herbal products and ingredients, emerging technologies are providing fast and non-destructive alternatives that allow the holistic analysis of the samples. Hyperspectral imaging has been identified as an efficient quality control alternative technique offering analysis of raw materials and adulteration detection in the herbal medicines industries.