ABSTRACT
This study was aimed to establish a method for sensorial color digitalization of Chinese herbal medicines (CHMs) with the application of spectrocolorimeter. The discussion was focused on difficulties of distinguishing surface and section color of CHMs. Based on uniform color space system of CIE1976L*a*b*, two methods for determination of section and surface color were constructed with two different kinds of spectrocolorimeters taking Glycyrrhizae Radix et Rhizoma as the experimental objective. In this paper, different kinds of sample preparation methods were used. Based on results, the method of scraping and grinding was proposed to prepare samples for section color determination. The method of wet pressing and peeling was proposed to prepare samples for surface color determination. Besides, RSD and dE*ab were served as evaluation indexes. This paper provided a simple, rapid and reliable analysis method for the color determination of CHMs. It also gave insight to future research on digitalization and modernization of CHMs' organoleptic characteristics based on traditional macroscopic identification.
ABSTRACT
This study was aimed to apply the electronic nose (E-nose) in the research of traditional Chinese medicine (TCM). The discussion was made on difficulties of using E-nose. The solution plan was proposed and the discrimination model was established. It provided a simple, rapid and effective analysi method in the identification of TCM. It also provided new ideas for the research and application of gas sensor arrays. E-nose was used in the ex-traction of TCM scent characteristics. Based on ion mobility spectrometry of MOS sensor, the fingerprint of TCM scent was established. The maximum response value of the sensor was used as analysis index. According to the diffi-culties of identification, two solution plans were proposed. Firstly, different detectors were employed to complete the classification. Secondly, radial basis function (RBF) and random forests (RF) were combined and then a cascade classifier was constructed in order to achieve the maximum of information obtained in conditions where the number of measurements, metal oxide semiconductor sensors in E-nose was limited. The results showed that both plans were accurate and practical with relatively high upper correct judge rate and better cross-validation (The highest upper correct judge rates were 95% and 100%, 96% and 80%, respectively). It was concluded that this study firstly ap-plied cascade classifier in the establishment of TCM identification by E-nose. With limited amount of sensors, the maximum information was received through data mining. Using E-nose in the identification of TCM was rapid and accurate. The established pattern recognition method was maneuverable with accurate identification rate and stability compared to conventional sensory identification method. It provided a simple and rapid analysis method for the iden-tification of TCM.