Gaussian process regression coupled with MPT-AES for quantitative determination of multiple elements in ginseng

A comprehensive understanding of spectral information is the most reliable approach to identify ginseng characteristics and quality. More data is produced due to the use of a high resolution spectrometer, which in turn requires high-efficiency computational capability. To well address such problems, chemometrics plays a significant part and shows satisfactory results. In this paper, a Gaussian process regression (GPR) is proposed to couple microwave plasma torch-atomic emission spectrometry (MPT-AES) for quantitative determination of multiple elements in ginseng. GPR as a probabilistic method is capable of dealing with massive data and illustrating the outputs with probabilistic meanings. With high-dimension input variables involved, the principal component analysis (PCA) is proposed to reduce computational amount and improve variable representativeness. The results show that GPR is superior to the conventional approach compared with support vector regression (SVR) by evaluation indexes. All the work is performed based on real ginseng spectral data. Taking into account the computational amount caused by large datasets of scales, three approximation methods (subset of datasets (SD), subset of regressors (SR) and projected process (PP)) are applied to address this problem, which in a way shrink the training set with a certain similar result. It is concluded that the Gaussian process performs well in spectral analysis and has the potential for further practical applications.