# Untargeted Metabolomic Profiling of _Fructus Chebulae_ and _Fructus Terminaliae Billericae_
## 3 Results
### Metabolite Detection  
Untargeted metabolomics analysis was conducted on FC and FTB to further explore their differences. The horizontal coordinate represents the retention time. The ordinate represents the response signal value (peak area); the positive and negative ions were tested for 30 min. In total, 571 signal values were measured in positive ion mode, and 220 signal values were measured in negative ion mode(See Supplementary Tables S2 and S3 for details). FC and FTB produced highly similar metabolite peak patterns as visualized on total ion chromatograms (TICs) but with significantly different peak values (Figure 2). FC reached a peak value of 3 \(\times\) 10\({}^{3}\) in positive ion mode and \(>\)3 \(\times\) 10\({}^{3}\) and \(<\)3.5 \(\times\) 10\({}^{3}\) in negative ion mode, whereas FTB had a peak value of 4 \(\times\) 10\({}^{3}\) in positive ion mode and 2.4 \(\times\) 10\({}^{3}\) in negative ion mode. The peak intensity of various peaks fluctuated relative to each other. For example, the peak at about 7 min in positive ion mode was significantly higher in FC than FTB, and the peak at 17.6 min was significantly lower in FC than FTB. This indicates a notable disparity in metabolite levels between FC and FTB. Metabolite Identification and PCA Analysis  
The identities of metabolites in the samples were determined by matching the characteristics of the metabolites, such as molecular mass (\(\pm\)10 ppm), secondary fragmentation spectra, and retention time, to those in the custom-built MS/MS database. A grand total of 558 metabolites were detected, with 155 in the positive ion mode and 403 in the negative ion mode. The classifications of the metabolites are visualized in pie charts in Figure 3. The filters according to vip value and _p_-value are as follows: 6-Aminocaproic acid; L-Alanine; DL-Alanine; L-Arginine; L-Phenylalanine; D-(+)Phenylalanine; DL-Phenylalanine; Phenethyl alcohol; 2-Phenylethanol; Quassin. Testosterone is a major contributor to PC. Various metabolite classes are depicted by the colors in the pie charts, while the sizes of the sections indicate the relative amounts of metabolites. The majority of identified metabolites were terpenoids, flavonoids, phenols, phenylpropionic acids, or alkaloids. terpenoids are an essential class of compounds in medicinal herbs with expectorant, antitussive, wind-dispolling, analgesic, and various other physiological activities. Natural terpenoids have been shown to exert antitumor activities by regulating host biological functions [15]. Flavonoids are widely spread substances that have a wide range of biological properties, including protection for the heart, antibacterial and antiviral effects, anticancer properties, antioxidant capabilities, anti-inflammatory properties, pain relief, and liver protection [16]. Phenolic compounds, which are secondary metabolites, can be found in various parts of plants such as fruits, skins, roots, and leaves, with their makeup differing between plant species. One of the most important physiological activities of phenolic compounds is the inhibition of free radicals and delay of damage [17]. Phenylpropionic acids, an important class of phenylpropanoids, are naturally occurring organic acids containing C6-C3 units, including cinnamic acids and phenyllactic acids. Many phenylpropionic acids have antioxidant, bacteriostatic, immune-enhancing, antitumor, antiviral, anti-inflammatory, and lipid-lowering activities as well as therapeutic effects in cardiovascular diseases [18]. There are many compounds with medicinal values among alkaloids. For example, matrine has cardiotonic and anti-arrhythmic functions, and aconitine can dilate the coronary artery and blood vessels in the extremities. Berberine is an anti-inflammatory alkaloid used for the treatment of encephalomyelitisitis, lobar pneumonia, and lung abscess. Furthermore, matrine, oxymatrine, and harringtonines have been shown to exhibit antitumor activities in the treatment of tumors [19]. Principal component analysis (PCA) is a statistical method