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6 May 2025

11 Pages

Determination of Multiple Active Components in Mume Fructus by UPLC-MS/MS

,
and
1
Wuhu Institute of Technology, Wuhu 241009, China
2
Life and Health Engineering Research Center of Wuhu, Wuhu 241009, China
3
Dali University, Dali 671000, China
*
Author to whom correspondence should be addressed.
Metabolites2025, 15(5), 312;https://doi.org/10.3390/metabo15050312
This article belongs to the Special Issue Advances in Secondary Metabolites: Phytochemical Analysis and Bioactivity Assays
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Abstract

Background: This study presents a sensitive method for the simultaneous determination of organic acids, flavonoids, and amino acids in Mume Fructus (MF) using ultra-performance liquid chromatography coupled with triple-quadrupole linear ion-trap tandem mass spectrometry (UPLC-QTRAP-MS/MS). Methods: Analysis was performed on a UPLC system (Shimadzu, Kyoto, Japan) equipped with a quaternary pump solvent management system, an online degasser, a triple-quadrupole mass detector, and an autosampler. An Agilent ZORBAX SB-C18 column (3.0 mm × 100 mm, 1.8 µm) was used for chromatographic analyses. The mobile phase was distributed between 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a velocity of 0.2 mL/min. The gradient evolution protocol was 0–2 min at 90–70% B; 3–7 min at 70–50% B; 7–10 min at 50–20% B; 10–14.5 min at 20–90% B; and 14.5–17 min at 10% B. Results: The method was validated for matrix effects, linearity, limits of detection/quantification, precision, repeatability, stability, and recovery of target components. It effectively determined all target compounds in 12 MF batches from different drying methods. Conclusions: Principal component analysis (PCA) of 47 active components was conducted to evaluate MF quality comprehensively. The proposed method serves as a reliable approach for assessing the consistency of MF’s quality and therapeutic efficacy.

1. Introduction

Mume Fructus (MF) is the dried immature fruit of Prunus mume (Sieb.) Sieb. et Zucc, and its whole herb was listed in the Chinese Pharmacopoeia [1]. It is commonly called a sour plum in China and grown in temperate areas like Yunnan, Fujian, and Sichuan [2,3]. In traditional Chinese medicine (TCM), MF has been widely used to treat gastrointestinal disease like ulcerative colitis [4,5]. Nowadays, more and more studies have shown that the active compounds of MF have various pharmacological actions, including anti-oxidative [6], anti-cancer [7], analgesic [8], anti-inflammatory [9], neuroprotective [10] and antibacterial actions [11]. The hundreds of chemical components of MF result in its biological activities. MF contains different kinds of compounds, including organic acids, sugars, flavonoids, terpenes, amino groups, nucleosides, and other active chemical components [12].
Because of its significant medical value, MF and its relevant products are used in large amounts. According to the literature, the production of MF is nearly hundreds of thousands of tons every year. So, it is necessary to develop a rapid method for the simultaneous determination of multiple compounds as well as for the quality control of MF. Through a continuous research program addressing the isolation, structural characterization, and pharmacological evaluation of natural products, over 100 chemical compositions have been isolated and identified from the fruits and flower heads of the plants of MF [13,14,15]. At present, the organic acid, flavonoid, and amino acid composition is used as the index to determine the quality of medicinal ingredients, such as citric acid, rutin, and aspartic acid [16,17,18]. Many methodologies have been reported to control the quality of MF. For instance, high-performance liquid chromatography with an ultraviolet detector (HPLC-UV) has been used for the determination of organic acid [19], and HPLC with an evaporative light scattering detector (HPLC-ELSD) was employed to quantitate saponins and iridoid glucosides [20]. However, there are several shortcomings such as the relatively low sensitivity of ELSD and the inaccuracy of chromatographic peaks only determined by retention time. With the development of analytical technology, HPLC-DAD–electrospray ionization mass spectrometry (ESI-MS) was proposed to analyze multiple types of bioactive constituents. Ultrafast LC (UFLC) combined with quadrupole/linear ion trap (QTRAP) and tandem MS (MS/MS) uses a multiple-reaction monitoring (MRM) mode to scan the chemical constituents of the fragments, providing high sensitivity and selectivity and allowing for analyses to be performed rapidly [21].
Recently, we successfully identified chemical components from MF using UPLC-Q-TOF technology [22]. Therefore, a reliable, sensitive and selective LC-MS/MS method was successfully established for the simultaneous quantification of 47 active components (10 organic acids, 4 flavonoids, 1 cyanoside, 1 anthocyanin, 1 carbohydrate, 20 amino acids, and 10 nucleosides) in one run cycle. Moreover, from the results of cluster analysis, it can be concluded that the different processing methods have a significant impact on the chemical compositions in MF. The proposed approach could be readily utilized as a comprehensive approach for determining the consistency of the quality and therapeutic efficacy of MF.

2. Materials and Methods

2.1. Chemicals, Reagents, and Materials

Forty-seven chemical standards were used, including the following: benzoic acid (1), quercitrin (2), caffeic acid (3), fumaric acid (4), chlorogenic acid (5), protocatechuic acid (6), succinic acid (7), quininic acid (8), rutin (9), citric acid (10), malic acid (11), kaempferol (12), ursolic acid (13), amygdalin (14), cyanidin-3-O-glucoside chloride (15), apigenin (16), hydroxymethylfurfural (17), L-alanine (18), L-serine (19), L-aspartic acid (20), L-asparagine (21), L-valine (22), L-glutamic acid (23), L-isoleucine (24), L-methionine (25), L-arginine (26), L-histidine (27), L-threonine (28), L-phenylalanine (29), L-leucine (30), L-cystine (31), L-hydroxyproline (32), L-tyrosine (33), L-tryptophan (34), L-proline (35), L-lysine hydrochloride (36), γ-aminobutyric acid (37), guanine (38), adenine (39), uracil (40), hypoxanthine (41), thymidine (42), guanosine (43), inosine (44), uridine (45), adenosine (46), and cytidine (47). The purity of all standard components was ≥ 98%. L-alanine, L-serine, L-aspartic acid, L-asparagine, L-valine, L-glutamic acid, L-isoleucine, L-methionine, L-arginine, L-histidine, L-threonine, L-phenylalanine, L-leucine, L-cystine, L-hydroxyproline, L-tyrosine, L-tryptophan, L-proline and L-lysine hydrochloride were purchased from National Institutes for Foods and Drugs (Beijing, China). The remainder were obtained from Shanghai Yuanye Biotechnology (Shanghai, China). Chromatography-grade methanol and acetonitrile were purchased from Merck (Darmstadt, Germany). Ultrapure water was obtained using a Milli-Q™ purification system (Millipore, Billerica, MA, USA).
Samples were collected in 2021, and samples S1–S12 samples were smoked with pine wood, at a temperature of 60–80° for 72 h. S13–S18 were dried in an oven at a low temperature of 60 °C for 30 h. Sample information can be found in Table 1.
Table 1. Information of tested MFf and MFd samples.

2.2. Preparation of Standard Solutions

Forty-seven standard substances were prepared by dissolution in ultrapure water, and their concentrations (in mg/mL) were as follows (numbers in parentheses correspond to chemical number as listed in Section 2.1: (1) 0.12, (2) 0.11, (3) 1.21, (4) 2.64, (5) 1.30, (6) 1.31, (7) 2.40, (8) 2.69, (9) 1.41, (10) 12.31, (11) 6.12, (12) 0.41, (13) 2.01, (14) 0.50, (15) 1.21, (16) 2.58, (17) 0.98, (18) 0.99, (19) 1.26, (20) 2.80, (21) 1.56, (22) 1.08, (23) 0.80, (24) 0.28, (25) 0.11, (26) 0.20, (27) 0.95, (28) 0.61, (29) 0.15, (30) 0.35, (31) 0.55, (32) 0.40, (33) 1.05, (34) 0.91, (35) 0.36, (36) 0.16, (37) 0.66, (38) 0.25, (39) 0.37, (40) 0.18, (41) 0.87, (42) 0.46, (43) 0.21, (44) 0.37, (45) 0.68, (46) 0.19, and (47) 0.13. A mixed standard stock solution containing all 47 standard substances was serially diluted with ultrapure water to the required concentration for the establishment of calibration curves. All solutions were stored at 4 °C and then passed through a 0.22 μm membrane.

2.3. Preparation of Sample Solutions

A total of 1.0 g of sample powder (powder, sieve 3) was taken, and 30 mL of ultrapure water was added, followed by weighing the mass and sonication for 30 min at room temperature. The same solution was used to replace the extraction system after solar event loss data changed to volatile. The supernatant was removed, centrifuged (13,000 r/min) for 10 min, filtered through 0.22 μm filter membrane, and then the continued filtrate was taken [22]. The extraction method we used aimed to maximize the extraction of active components from Mume Fructus while minimizing potential losses and interferences. The ultrasound extraction method ensures that the samples are fully dissolved and effectively releases the active components, simplifying the subsequent analytical steps.

2.4. UPLC–MS/MS Instrumentation and Conditions

Analysis was performed on a UPLC system (Shimadzu, Kyoto, Japan) equipped with a quaternary pump solvent management system, an online degasser, a triple-quadrupole mass detector, and an autosampler. An Agilent ZORBAX SB-C18 column (3.0 mm × 100 mm, 1.8 µm) was used for chromatographic analyses. The mobile phase was distributed between 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a velocity of 0.2 mL/min. The gradient evolution protocol was 0–2 min at 90–70% B; 3–7 min at 70–50% B; 7–10 min at 50–20% B; 10–14.5 min at 20–90% B; and 14.5–17 min at 10% B. The optimized parameters for MS for the 47 target components are shown in Table 2. All MS data were analyzed using Analyst 1.6.2 (AB SCIEX).
Table 2. Retention times and related mass spectrometry (MS) data for the target compounds.

2.5. Multivariate Statistical Analyses

PCA was used to visualize the similarity or differences in multivariate data. PCA represents an unsupervised pattern recognition technology that can be used to transfer multiple variables through linear transformations to select a few important variables. To observe the classifications of experimental samples, PLS-DA, supervised by Simca-p 14.1, was conducted to perform PCA using data from 47 analytes, to discover the different chemical compositions of each sample.

3. Results

3.1. MS Condition Optimization

In the UPLC-MS experiment, we identified the compounds using databases such as the Human Metabolome Database (HMDB) and PubChem, in conjunction with mass spectrometry fragmentation patterns and retention times. Additionally, we used Progenesis QI software (2.1.2) for data analysis to assist in confirming and identifying the compound names. Separate solutions (about 100 ng/mL) of all standard compounds were detected using electrospray ionization (ESI) sources, in both positive and negative ion modes, and the retention times (t) of each compound, the precursor and product ions, the cluster voltage (DP), and the collision energy (CE) were analyzed. In order to effectively distinguish isomers by high-resolution mass spectrometry and secondary mass spectrometry fragments, the individual solutions of all standard compounds (100 ng/mL in 70% (v/v) acetonitrile) were injected into the ESI source in the positive and negative ion modes to obtain more suitable declustered voltage (DP) and collision energy (CE) parameters. The most abundant fragment ions were chosen as MRM transition from MS/MS spectrum; after trial-and-error inspection, most constituents had a good response in the negative ion mode. The chromatogram, in multi-reaction monitoring (MRM) mode, is shown in Figure S1.

3.2. Verification of Analytical Methods

Table 3 lists the verification results obtained using this method. A standard calibration curve indicated that the determination coefficients for all analytes were good (r > 0.9990). The limit of detection (LOD) with the signal to noise ratio (S/N) was about 3. The limit of quantitation (LOQ) with the signal to noise ratio (S/N) was about 10. The limit of detection (LOD) and the limit of quantitation (LOQ) were 0.15–3.37 ng/mL and 0.49–13.77 ng/mL, respectively. Within days, 47 analytes were identified with relative standard deviation (RSD) values of 1.28–3.29% and 1.60–2.89%, respectively. The repetitive stability test value for all 47 components was less than 4, and the average recovery was between 94.89% and 105.42%, with an RSD% of less than 3.89% and the slope ratio of the matrix curve to the pure solution curve was 0.93–1.06, supporting the validity of the proposed method.
Table 3. Regression equations, detection limits (LODs), quantity limits (LOQs), intrinsic and differential precision, stability, reproducibility recovery, and matrix effects of 47 components.
The quantitative determinations are shown in Table S1. In the current Chinese Pharmacopoeia, citric acid is considered to be the most characteristic component of MF. Based on the analysis of 18 batches of samples, the citric acid contents were found to be similar to those reported by the pharmacopeia standard, and 47 analytes, identified in 18 batches of samples, were analyzed using the verified analytical method. The contents of all 47 analytes are summarized in Table 4. The data from all samples indicate that MFd (178,111.72 µg/g) > MFf (163,284.84 µg/g). The contents of organic acids and polysaccharides (5-hydroxymethylfurfural) in MFd were higher than those in MFf. However, the contents of flavonoids (2597.40 µg/g) and amino acids (9947.03 µg/g) in the MFf were higher than those in the MFd (flavonoids: 2265.69 µg/g; amino acids: 6692.86 µg/g). By comparing these parameters, we observed that the contents and compositions of the MFd and MFf samples were quite different.
Table 4. Contribution rate of principal components.

3.3. Principal Component Analysis (PCA)

PCA is a common data dimensionality reduction technique that can be applied to distinguish between samples and visualize clusters and outliers. According to the contents of the 47 components identified in the samples, PCA was used to distinguish between the samples of MF processed with different drying methods. After the raw data were standardized, SPSS, v. 23.0, was used to perform PCA. The eigenvalues and variance contributions of the principal components (PCs) can be found in Table 4. Among these, eight eigenvalues greater than 1 were identified, and the cumulative contribution rate was 84.63. The cube was simplified into a three-dimensional data set using three eigenvalues (PC1: 30.41%; PC2: 13.16%; and PC3; 9.59%). As shown in Figure 1, the samples can be divided into two categories (drying and fumigation), and the results show that the composition and contents of the dried samples differed greatly from those for fumigated samples.
Figure 1. Scatter plot showing the score of PCA-processed data, acquired from the samples. Each of the blue circles represents a batch of MFf, whereas each green circle represents a batch of MFd.

3.4. Partial Least Squares Discriminant Analysis (PLS-DA) of the Samples

To identify potential chemical markers with significant impacts on sample identification, PLS-DA and variable importance in predictive trials (VIP) were used. The PLS-DA score and VIP values are shown in Figure 2a,b. Samples processed by drying and fumigation were also divided into two groups using these analyses, indicating significant differences in the chemical compositions of samples processed by MFd and MFf. The OPLS-DA results show that R2Y was 0.991, indicating that the model fitted well, and Q2 = 0.951 > 0.5 indicated that the model predicted well.
Figure 2. The MFf and MFd data were PLS-DA-treated and visualized on a scatter plot (a), followed by VIP (b) processing. Each blue circle indicates a batch of MFf, whereas each green circle indicates a batch of MFd. The VIP abscissa indicates the compounds (see Table 1 for the name of each compound). (Red represents VIP >1, green represents <1).
The VIP results describe the overall contribution of each variable to the model, with the threshold typically set to VIP greater than 1 to identify important variables. In this experiment, VIP values were obtained using the PLS-DA-processed data. Variables with VIP greater than 1 can be viewed as potential markers that contribute significantly to these sample classifications. For example, 5-hydroxymethylfurfural (17), L-aspartic acid (20), L-asparagine (21), uridine (45), thymidine (42), kaempferol (12), L-cystine (31), L-tryptophan (34), caffeic acid (3), L-proline (35), γ-aminobutyric acid (37), benzoic acid (1), L-phenylalanine (29), and rutin (9).
From Figure 2b, we can see that Compound 17 (5-hydroxymethylfurfural) has the highest VIP value. The Chinese Pharmacopoeia edition, only defines a limit for 5-hydroxymethylfurfural in glucose injections, which should not exceed 0.02% of the calculated mass fraction. The levels of 5-hydroxymethylfurfural identified in samples prepared using different processing methods in this study were much higher than this limit. In addition, the content of 5-hydroxymethylfurfural in MF was also much higher than the concentration used in cells, which demonstrated weak toxicity in previous studies and changed some indices in animal models [23,24]. Plums are used as both food and medicine in daily life, and the European Commission for Food Safety has established a maximum recommended intake of 1.6 mg of 5-hydroxymethylfurfural substances per person per day. However, 5-hydroxymethylfurfural can be widely found in various foods, resulting in the daily average intake being much higher than this recommended limit higher. Despite this intake, no strong evidence has supported 5-hydroxymethylfurfural as a health hazard. Therefore, the potential harm and limits of 5-hydroxymethylfurfural in Chinese medicinal materials, such as MF, requires further study.

4. Discussion

In this study, the UPLC-MS/MS technology achieved significant results by identifying 47 components in negative ion mode. We found that many compounds (such as apigenin, luteolin, and quercetin) exhibited higher sensitivity in negative ion mode, which is consistent with the characteristic of these compounds having acidic functional groups that easily undergo proton loss for ionization. Previous studies have also indicated that flavonoids have higher ionization efficiency in negative ion mode [25]. Furthermore, we effectively distinguished multiple pairs of isomers (such as L-leucine and L-isoleucine) using specific fragment information from high-resolution mass spectrometry and tandem mass spectrometry, further validating the reliability of the optimized ESI parameters (DP and CE values). The MRM chromatograms in Figure S1 demonstrate the good signal response and retention time separation of different compounds, indicating that the multiple-reaction monitoring mode used can effectively detect multiple target compounds simultaneously. A validated analytical method is fundamental for the efficient detection of chemical components in traditional Chinese medicine. The validation results of the methods in this study show that all analytes had a wide linear range (R2 > 0.9990), with limits of detection (LODs) and quantification (LOQs) reaching 0.15–3.37 ng/mL and 0.49–13.77 ng/mL, respectively, demonstrating sensitivity superior to many methods reported in the literature [26,27]. Additionally, the matrix effect test (0.93–1.06) and sample recovery rates (94.89–105.42%) indicated that the method is accurate and has a significant advantage of being unaffected by matrix interference. This result further confirms that the proposed method has high precision, stability, and reproducibility, making it widely applicable for the analysis of similar samples. The analysis of 18 batches of MEf (dried plum samples processed differently) revealed that the total organic acid content in dried samples (MFd) was significantly higher than that in fumigated samples (MFf), while the latter had higher levels of flavonoids and amino acids. This may be related to the impact of processing methods on the chemical composition of dried plums. For example, the drying process may lead to the partial degradation of flavonoids and amino acids, while fumigation heating may promote the production of certain metabolites [28]. Notably, the citric acid content in these results meets the standards set by the “Chinese Pharmacopoeia,” confirming its applicability as a quality marker and indicating that different processing methods significantly influence the composition distribution of medicinal materials. HMF is an important product in the processing of food and medicinal plants, and its content is only defined in the Chinese Pharmacopoeia for glucose injection (0.02%). However, the HMF levels in all samples of this study were far above this standard (Figure 2b). Although animal model studies suggest that HMF may have certain toxicity at higher concentrations, current conclusions regarding its safety are inconsistent, with the European Food Safety Authority recommending a daily intake of 1.6 mg. However, considering the widespread presence of HMF in dried plums and other edible medicinal materials, its long-term accumulation effects and actual impact on human health require further investigation. Additionally, this study proposes the potential of HMF as a marker for processing, but there is a lack of unified standard limits for HMF in traditional Chinese medicinal materials, highlighting the need to address this issue in the future standardization process of traditional Chinese medicine.

5. Conclusions

In this study, a UPLC-QTRAP-MS/MS method was established and validated as a rapid, convenient, and sensitive way for the simultaneous determination of 47 compounds in Mume Fructus, within 14 min. It was successfully applied to the quantification of the 18 samples of different drying methods of MF.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/metabo15050312/s1, Figure S1: MRM chromatograms of forty-seven components.; Table S1: Determination of each sample (n = 3).

Author Contributions

Conceptualization, N.L. and J.Y.; methodology, N.L.; software, N.L.; validation, N.L., J.Y. and R.W.; formal analysis, N.L.; investigation, N.L.; resources, N.L.; data curation, N.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

2021 Anhui Provincial Natural Science Research Project (KJ2021A1335), University-level Research Team for the High-Value Utilization of Traditional Chinese Medicine Resources (Wzykytd202207); Research on the Chemical Constituents of Dendrobium Orchid Based on “Quality” and “Efficacy” and Exploration of Its Health Functions (2024AH052023).

Conflicts of Interest

All authors declare that they have no conflicts of interest.

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