NIR and MIR spectral data fusion for rapid detection of Lonicera japonica and Artemisia annua by liquid extraction process

Abstract

Abstract Lonicera japonica and Artemisia annua are two widely used plants that are typically combined together as botanical dietary supplements or herbal medicines. In this study, the liquid extraction process of various mixtures of these two plants was investigated with near-infrared (NIR) and mid-infrared (MIR) spectroscopy for rapid detection of important effective compounds. Eight main effective compounds of Lonicera japonica and Artemisia annua mixtures were modeled and selected as quality attributes: neochlorogenic acid (5-CQA), cryptochlorogenic acid (4-CQA), chlorogenic acid (3-CQA), caffeic acid (CA), isochlorogenic acid B (ICAB), isocholorogenic acid A (ICAA), isocholorogenic acid C (ICAC), and secoxyloganin (SL). First, partial least squares (PLS) was adopted to build quantitative models by combining spectra from MIR and NIR instruments. Two multiblock PLS methods were applied using NIR and MIR data matrix in which concatenated PLS (C-PLS) and sequential and orthogonalized PLS (SO-PLS) were utilized to optimize model performance. The results showed that models based on single NIR and MIR spectroscopy had close capacity for detection of the eight indexes. However, performance of the fusion methods C-PLS and SO-PLS both achieved better results than the results of single NIR or MIR PLS models, with SO-PLS being superior to C-PLS. This indicates that the multiblock methodology can be used to optimize rapid detection of indexes from complex industrial processes of plants using NIR and MIR spectroscopy.