How to identify ‘Material basis - Quality markers’ more accurately in Chinese herbal medicines from modern chromatography-mass spectrometry data-sets: Opportunities and challenges of chemometric tools

Abstract Modern chromatography - mass spectrometer (MS) technology is an essential weapon in the exploration of traditional Chinese medicines (TCMs) which is based on the ‘effectiveness-material basis-quality markers (Q-markers)’. Nevertheless, the hardware bottleneck and irregular operation will limit the accuracy and comprehensiveness of test results. Chemometrics was thereby used to solve the existing problems: 1) The method of ‘design-modeling-optimization’ can be adopted to solve the multi-factor and multi-level problems in sample preparation/ parameter setting; 2) The approaches of signal processing can be used to calibrate the deviation from retention time (rt) dimension and mass-to-charge ratio (m/z) dimension in different types of instruments; 3) The methods of multivariate calibration and multivariate resolution can be utilized to analyze the co-eluting peaks in complex samples. When the researchers need to capture essential information on raw data sets extracting the higher level of information on essential features, 1) The significant components which affects the drug properties/efficacy can be find by the pattern recognition and variable selection; 2) Fingerprint-efficacy modeling is explored to clarify the material basis, or to screen out the Q-markers of biological significance; 3) Chemometric tools can apply to integrate chemical (metabolic) fingerprints with network pharmacology, bioinformatics, omics and others from a multi-level perspective. Under these programs, the qualitative/quantitative works will achieve in chemical (metabolic) fingerprint and metabolic trajectories, which leads to an accurate reflection of ‘material basis and Q-markers’ in TCMs. Likewise, an in-depth hidden information can be disclosed, so that the components of drug properties/efficacy will be found. More importantly, multidimensional data can be integrated with fingerprints to acquire more hidden information.