Rapid Quantitative Analysis Of Anise Oil In Illicium Verum Hook. F. Using Near Infrared Reflectance Spectroscopy

The near infrared spectroscopy (NIRS) analysis technology is so popular in chemical analysis since the rapid development of computer technology, chemometrics and electronics. As the near infrared spectroscopy analysis technology is based on the frequency of organic media's chemical bond, the absorbance is weak and the spectroscopy is overlapped and complex. A powerful calibration method is needed to parse the near infrared spectra. Chemometrics is much suitable. However, the calibration of chemometrics is complex. It must be assisted with the computer technology. As near infrared spectroscopy analysis technology is nondestructive, rapid, low cost and muti-component analysis, it is greatly interested by researchers. However, near infrared spectroscopy technology is a indirect technology. The application of this technology is based on developing a credible calibration models. Therefore, the development of calibration model is a key process and this process is much complex. In this paper, the process of developing the calibration model for determining of the content of anise Oil in Illicium verum Hook. f. is investigated. In this paper, the background of Illicium verum Hook. f., anise oil, near infrared spectroscopy and chemometrics has been reviewed in the first chapter. In the second chapter, partial least square (PLS) was applied in modeling the relationship between near infrared spectra and the contents of anise oil in Illicium verum Hook. f.. The modeling processes has been Further studied. First, the initial partial least square model has been developed using original near infrared spectra and leave-one-cross-validation method has been employed to select the initial number of factors.During this process, the root mean square error of cross-validation (RMSECV) was employed as criterion. Second, Monte Carlo partial least square (MCPLS) was employed to recognize the outliers. The samples were divided into two sample sets depends their contents of anise oil and the messages of the spectra. Average smoothing method, Savitzky-Golay smoothing method, fast fourier transform (FFT), first order derivative, second order derivative and standard normalize transfer (SNV) were used for preprocessing the spectra respectively. The original spectra and the preprocessed spectra were used for modeling respectively. Each model has been optimized by selecting the effective wavelength variables using moving window partial least square method (MWPLS) and selecting the most suitable number of principal factors depending on RMSECV. And then, the optimum model has been developed. The contents of anise oil in Illicium verum Hook. f. has been predicted by the optimum model. The root mean square error of calibration set (RMSEC) was 0.215, and the root mean square error of prediction set (RMSEP) was 0.350. The coefficient between the predictive contents of anise oil and the reference contents in calibration set (Rc) was 0.99955, and coefficient between the predictive contents of anise oil and the reference contents in prediction set (Rp) was 0.9890. These results demonstrated that this method was precise, stabilized and reproducible. It will be popular in the pharmacy. A comprehensive summarization of this research has been presented in the last chapter.