ORIGINAL RESEARCH article

Front. Chem., 29 April 2025

Sec. Analytical Chemistry

Volume 13 - 2025 | https://doi.org/10.3389/fchem.2025.1578126

Discrimination of poisonous and medicinal plants with similar appearance (Asarum heterotropoides vs. Cynanchum paniculatum) via a fusion method of E-nose, E-tongue, LC-HR-Q-TOF-MS/MS, and electrochemical fingerprint spectra

  • 1. Key Laboratory for Identification and Quality Evaluation of Traditional Chinese Medicine of Liaoning Province, Liaoning University of Traditional Chinese Medicine, Dalian, China

  • 2. Key Laboratory of Ministry of Education for TCM Viscera-State Theory and Applications, Liaoning University of Traditional Chinese Medicine, Shenyang, China

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Abstract

Introduction:

The similarity in appearance of poisonous and medicinal plants, such as Asarum heterotropoides (AH) and Cynanchum paniculatum (CP), poses safety risks due to frequent confusion. Since AH contains toxic ingredients, the traditional methods of olfactory and gustatory identification cannot be used to distinguish AH from CP.

Methods:

To differentiate them systematically, we proposed a novel strategy based on dual electronic sensors (DES) and dual fingerprint spectra (DFS). The DES included two intelligent sensors, namely the E-nose and E-tongue, which differentiated AH and CP based on odor and taste, respectively. DFS comprised chemical fingerprint spectra obtained through LC-HR-Q-TOF-MS/MS and electrochemical fingerprint spectra derived from the Belousov-Zhabotinsky reaction, differentiating AH and CP by their specific and overall compositions, respectively. To our knowledge, this was the first time that the E-nose, E-tongue, LC-HR-Q-TOF-MS/MS, and the Belousov-Zhabotinsky reaction were combined to identify AH and CP.

Results and discussion:

With the E-nose, we identified 25 major odor components in AH and 12 odor components in CP in a single run of 140 s. Using the E-tongue, bitterness and astringency were identified as their primary taste differences. Furthermore, 91 compounds in AH and 90 compounds in CP were identified through LC-HR-Q-TOF-MS/MS. Both AH and CP shared nitrogenous compounds, volatile oils, organic acids, and lignans. However, AH uniquely contained coumarins and flavonoids, while CP contained steroidal compounds and saccharides. Notably, AH also possessed distinct toxic components, specifically aristolactam I, aristolochic acid D, and safrole. Based on the Belousov-Zhabotinsky reaction, we obtained the electrochemical fingerprint spectra of AH and CP, thereby facilitating further distinction between these two herbs. Through the combination of electrochemical fingerprint spectra with principal component analysis (PCA) or orthogonal partial least squares-discriminant analysis (OPLS-DA), the accuracy of this method reached 100%. Through the fusion strategy, the odors, tastes, components, and electrochemical properties of AH and CP have been systematically analyzed.

1 Introduction

Confusion and misuse frequently occur among medicinal plants with highly similar appearances (Xin et al., 2022). This phenomenon not only impacts the efficacy of medications but also poses potential threats to patients’ health. The underground parts of Asarum heterotropoides (AH) and Cynanchum paniculatum (CP), as two herbal medicines with remarkable medicinal value and highly similar appearances, serve as typical examples of such issues. AH is widely used to treat symptoms such as colds, rhinitis, and coughs, while CP can effectively alleviate stomachaches and toothaches (Zhang et al., 2021). Given their significant differences in pharmacological functions, misusing one for the other can lead to severe consequences. Notably, AH contains poisonous components such as aristolochic acid-like ingredients which has been classified as a Group I cancer-causing agent by the World Health Organization. Its misuse or overdosage can trigger a series of adverse reactions or even lead to life-threatening conditions. Additionally, due to AH’s significantly higher market price compared to CP, some unethical merchants may intentionally adulterate AH with CP for sale. This further poses challenges to the authentication of these two medicinal plants. To effectively prevent the confusion and misuse, there is an urgent need to adopt modern technologies and establish reliable strategies to discriminate them from multiple angles.

Electronic sensory technologies have demonstrated unique advantages in the identification of medicinal plants. Electronic nose (E-nose) and electronic tongue (E-tongue) are two representative electronic sensory technologies (Tibaduiza et al., 2024; Wang S. et al., 2022). The E-nose perceives and analyzes volatile odors by simulating the human olfactory system. Using E-nose, Zhang et al. differentiated raw Magnolia officinalis and ginger-processed M. officinalis and identified 16 possible odor components (Zhang et al., 2022). Lu et al. employed E-nose combined with gas chromatography-mass spectrometry to identify 40 aroma components from chamomile (Lu et al., 2024). In another example, the adulterants and geographical origins of Ziziphi Spinosae were successfully identified by E-nose and headspace gas chromatography-mass spectrometry (Zhang et al., 2023). Similarly, as an intelligent taste recognition tool, the E-tongue has also been widely applied in the field of medicinal plants. For example, Lei et al. conducted comprehensive evaluations of the aroma and taste of bear bile powder and its common counterfeit by E-nose and E-tongue technologies (Lei et al., 2023). Xing et al. determined the taste characteristics of Polygonum multiflorum using E-tongue and revealed the relationship between tastes and components (Xing et al., 2021). Wang et al. studied the correlation between the fragrance, taste, and effective components of Gastrodiae Rhizoma by E-nose and E-tongue (Wang B. et al., 2022). In summary, the rapid development of E-nose and E-tongue technologies provides a new approach for the identification of morphologically similar medicinal plants.

Chemical fingerprint spectra based on liquid chromatography-mass spectrometry (LC-MS) is one of the effective strategies for the analysis of chemical components in medicinal plants (Chen et al., 2021; Liang et al., 2022). In recent years, this technique has been increasingly and widely applied in this field. For instance, Bao et al. revealed at least 18 different chemical components in Coptidis Rhizoma by using UPLC-Q/TOF-MS (Bao et al., 2024). Mei et al. identified 50 components in Spatholobi Caulis by LC-Triple TOF-MS (Mei et al., 2021). Batsukh et al. utilized LC-IT-TOF-MS/MS in conjunction with multivariate statistical analysis to identify 30 compounds from Divaricate Saposhnikoviae (Batsukh et al., 2020). With this method, researchers can obtain detailed fingerprint spectra and abundant information on chemical components. Although LC-MS is effective in the identification of medicinal plants, it comes with drawbacks like expensive equipment and lengthy data analysis. In recent years, electrochemical fingerprint spectra has emerged and developed rapidly (Lan et al., 2023). Compared to LC-MS, electrochemical fingerprint spectra offers advantages including cheap instrumentation, simple sample treatment and short detection time, making it an effective complement to LC-MS. Furthermore, it can intuitively reflect the overall characteristic information of medicinal plants. The principle indicates that during the electrochemical reaction process, the chemical components in different medicinal plants will elicit unique changes, leading to characteristic fingerprint spectra. Zeng et al. utilized fingerprint spectra on the basis of three-electrode system to differentiate Coptidis Rhizoma from its adulterants (Zeng and Jiang, 2022). Tarighat et al. used fingerprint spectra based on cyclic voltammetry to classify and identify Lamiaceae herbs such as mint and lavender (Tarighat et al., 2023). Liu et al. discovered significant differences in the fingerprint spectra of Astragali Radix from various provinces through differential pulse voltammetry (Liu and Yan, 2023). However, electrochemical fingerprint spectra commonly identify medicinal plants from the overall components, lacking specificity for individual components. It seems that the combination of LC-MS and electrochemical fingerprint spectra is an ideal method for the analysis of medicinal plants. Currently, there are few reports on the combination of the two methods.

In this study, a novel strategy combining dual electronic sensors (DES) and dual fingerprint spectra (DFS) was proposed for differentiating AH and CP. This strategy emphasized the integration of electronic sensory technology and fingerprint spectra analysis. On the one hand, E-nose and E-tongue were utilized to differentiate AH and CP from the perspectives of odor and taste, respectively. On the other hand, chemical fingerprint spectra obtained through LC-HR-Q-TOF-MS/MS and electrochemical fingerprint spectra derived from the Belousov-Zhabotinsky reaction were employed to differentiate AH and CP, focusing on specific chemical components and overall characteristic information, respectively. Furthermore, the electrochemical fingerprint spectra was combined with PCA and OPLS-DA to ensure a 100% accurate differentiation between AH and CP. Through the implementation of the DES and DFS strategy, a comprehensive and systematic differentiation of AH and CP from multiple angles was achieved.

2 Materials and methods

2.1 Reagents and materials

Seven different batches of AH were purchased from the regional medicinal herb trading center of Anguo City, Hebei Province (batches: 07230307, 07230401, 07230504, 07230604, 07230702, 07230801, 07230905). Seven different batches of CP were purchased from the regional medicinal herb trading center of Lu’an City, Anhui Province (batches: 23060201, 23070304, 23080302, 23090203, 23100402, 23110501, 23120204). The chemical standards including asarinin (PS010871), methyl eugenol (PS001191), (1R)-(+)-α-pinene (PS230925-10), (+)-3-carene (PS230926-01), eucalyptol (PS020906), carvacrol (PS230925-13), α-terpineol (PS020226), paeonol (PS000281), hesperidin (PS010632), chlorogenic acid (PS010694), o-hydroxyacetophenone (PS230925-14), p-hydroxyacetophenone (PS020038), palmitic acid (PS020930), and oleic acid (PS020507) were all purchased from Chengdu Push Bio-technology Co., Ltd. Vanillic acid (MUST-23012113), caffeic acid (MUST-23061118), and (−)-β-pinene (MUST-2392216) were purchased from Chengdu Must Biotechnology Co., Ltd. The purity of all the above compounds was above 98%. Purified water was purchased from Wahaha Group Co., Ltd. (Hangzhou, China). H2SO4 (20111014) was purchased from Sinopharm Chemical Reagent Co., Ltd. CH2(COOH)2 (M813041) was purchased from Macklin Co., Ltd. (NH4)2SO4·Ce(SO4)2 (20230601) was purchased from Tianjin Damao Chemical Reagent Co., Ltd. KBrO3 (20160107) and LC-grade methanol (20241101) were purchased from Tianjin Kermel Chemical Reagent Co., Ltd. LC-grade acetonitrile (JB145430) and MS-grade formic acid (20171008) were purchased from Merck (Darmstadt, Germany).

2.2 Sample preparation

The roots and rhizomes of AH and CP were powdered and passed through a sieve. Then 0.5 g of sample was weighed and mixed with 10 mL of methanol for a 40-min ultrasonic extraction (F-050 type, Fuyang ultrasonic cleaner). After centrifugation (LC-LX-H185C type, Lichen Co., Ltd.) at 14,000 rpm for 5 min, the supernatant was used for LC-HR-Q-TOF-MS/MS analysis. Another 0.5 g of sample powder was weighed and mixed with 10 mL of deionized water for a 30-min ultrasonic extraction. The extract was filtered and diluted tenfold for E-tongue analysis. The powders of medicinal plants were directly used for electrochemical analysis. Prior to E-nose analysis, both dried AH and CP samples were processed into uniform small segments (1 cm in length) to ensure morphological standardization. Each headspace vial was filled with 1.0 g of the processed sample material. This standardization procedure aimed to unify both morphology and mass, thereby reducing variations in the detection of volatile components.

2.3 Setup and conditions of E-nose

The E-nose (Alpha MOS SA Heracles NEO) was employed for analysis, equipped with an automatic sampling device, an ultra-fast gas chromatography (GC) unit, two flame ionization detectors (FID), and two columns of different polarities (MXT-5 and MXT-1701). The volume of a headspace vial was 20 mL and the sample weight was 1.0 g. Seven batches of samples were prepared and each sample was subjected to three replicate measurements. The injection volume was set at 4,000 μL, with an incubation temperature of 60°C and an incubation time of 20 min. The injection speed was 125 μL/s, lasting for 45 s. The inlet temperature was maintained at 200°C, while the trap temperature was set at 40 °C. Hydrogen was used as the carrier gas at a flow rate of 1.0 mL/min. The trap time was 50 s, and the final temperature of the trap was 240°C. The initial column temperature was 50°C. The temperature was programmed to ramp up from 0.5°C/s to 90°C, followed by an increase of 4°C/s to 250°C, where it was held for 15 s. The acquisition time was 137 s, and the FID gain was set at 12. A mixture of n-alkanes (C6–C16) was used as the chemical reference.

2.4 Setup and conditions of E-tongue

The sensors (AAE, CT0, CA0, C00, AE1) and reference electrodes of the E-tongue (INSET Intelligent Sensor Technology, Inc. Taste Sensing System SA402B) were separately immersed in the reference solution (30 mmol/L potassium chloride and 0.3 mmol/L tartaric acid) and 3.33 mol/L potassium chloride solution for 24 h for activation. Calibration was performed using the reference solution, followed by the measurement of the umami, saltiness, sourness, bitterness, astringency, and richness of seven batches of samples at a room temperature of 25°C. After a brief rinse with the reference solution, sensors C00 and AE1 were used to determine residual tastes (bitter and astringent aftertaste). The data acquisition time was 30 s, with a total of 4 cycles collected. Due to significant fluctuations in the data from the first cycle, this cycle’s data was excluded from the analysis. Data from the second to fourth cycles were retained. Each batch was analyzed in triplicate, and the mean of triplicate measurements was adopted for subsequent analysis.

2.5 Conditions of LC-HR-Q-TOF-MS/MS

The chemical compositions of AH and CP were analyzed using an Agilent LC-6500 series Q-TOF liquid chromatography-mass spectrometry system. The separation column was an Agilent Phenyl-Hexyl column (4.6 × 50 mm, 3.5 μm). The mobile phase consisted of 0.1% formic acid in water (A) and acetonitrile (B). The flow rate was set at 0.5 mL/min. The injection volume was 1 μL. Gradient elution was performed as follows: 0–8 min, 5%–5% B; 8–20 min, 5%–20% B; 20–40 min, 20%–60% B; 40–45 min, 60%–95% B; 45–50 min, 95%–95% B. The Q-TOF-MS/MS system was used for analysis in positive or negative ion mode. The operational parameters were set as follows: drying gas temperature at 200°C, drying gas flow rate at 11 L/min, nebulizer gas pressure at 35 psi, sheath gas temperature at 350°C, sheath gas flow rate at 8 L/min, capillary voltage at 4,000 V, m/z range from 100 to 1,000, nozzle voltage at 1,000 V, fragmentation voltage at 120 V, and collision energy at 30 eV. Auto MS/MS was used for data acquisition. Compounds with available chemical reference standards could be accurately identified by comparing their retention times, molecular ions, and secondary fragment ions with those of the reference standards. For unknown compounds, a combined approach that utilized both self-built compound libraries and public compound databases was employed for structural elucidation. On one hand, a mass spectrometry database for AH and CP was compiled from relevant literature, encompassing chemical formulas, molecular ions, fragment ions, and other related parameters (Wang et al., 2023; Wen et al., 2014; Zhang et al., 2021; Mao et al., 2017; Gao et al., 2019; Chen et al., 2023; Hu et al., 2025; Yu et al., 2016). On the other hand, public databases such as PubChem, METLIN, ChemSpider, and mzCloud spectral library were used for the structural comparison of the compounds.

2.6 Setup and conditions of electrochemical fingerprint spectra

The Belousov-Zhabotinsky reaction was conducted in a continuously stirred reactor (85–2 type, Changzhou Yuexin Instrument Manufacturing Co., Ltd.). A graphite electrode was used as the reference electrode, and a platinum electrode was used as the indicator electrode. To the reactor, 0.4 g of sample powder, 24 mL of H2SO4 solution (3 mol/L), 12 mL of CH2(COOH)2 solution (1 mol/L), and 6 mL of (NH4)2SO4·Ce(SO4)2 solution (0.1 mol/L) were added. The temperature of the reaction system was controlled at 310 K. After stirring at a constant speed of 600 r/min for 5 min, 6 mL of KBrO3 solution (0.2 mol/L) was rapidly injected through a syringe to initiate the reaction. Immediately, the data acquisition program was started to record the electrochemical fingerprint spectrum until the oscillation of electric potential disappeared.

2.7 Statistical analysis

To analyze Q-TOF data, Agilent MassHunter was employed. The Heracles NEO E-nose was controlled via Alpha Soft, wherein principal component analysis (PCA) and discriminant factor analysis (DFA) were implemented for data processing. The AroChemBase database was used to identify volatile compounds and obtain sensory description. Origin 2021 was utilized to generate fingerprint spectra for E-nose, radar charts for E-tongue, and electrochemical fingerprint spectra for Belousov-Zhabotinsky reaction. GraphPad Prism 6 was applied to generate box plots. SIMCA was utilized to perform PCA and orthogonal partial least squares discriminant analysis (OPLS-DA).

3 Result and discussion

3.1 Technical route

AH and CP are commonly used but easily confused medicinal plants due to their highly similar appearance. Due to the presence of toxic ingredients in AH, the conventional methods of identification through smell and taste cannot be employed to differentiate AH from CP. To achieve this, a four-step technical route utilizing dual electronic sensors (DES) and dual fingerprint spectra (DFS) was proposed for the first time (Figure 1). Firstly, an E-nose was applied to capture characteristic gas information, with PCA and DFA adopted to distinguish them further. Secondly, an E-tongue was utilized to obtain characteristic tastes, with radar charts and box plots used to analyze their taste differences. The strategy of DES could overcome the shortcomings of the traditional methods of olfactory and gustatory identification which could not be used for the identification of toxic medicinal plants. Thirdly, LC-HR-Q-TOF-MS/MS was employed to analyze the differences in chemical compositions between AH and CP, yielding their chemical fingerprint spectra. Fourthly, the Belousov-Zhabotinsky reaction was utilized to acquire electrochemical fingerprint spectra, differentiating them from the perspective of electrochemical properties. By integrating E-nose, E-tongue, chemical fingerprint spectra, and electrochemical fingerprint spectra, a systematic and multi-angled differentiation between AH and CP was achieved.

FIGURE 1

FIGURE 1

A four-step technical route for differentiation of AH and CP.

3.2 E-nose analysis

Using the Heracles NEO ultra-fast gas-phase E-nose, odor chromatograms for AH and CP were established on two types of chromatographic columns: MXT-5 and MXT-1701 (Figure 2). It was evident that each sample could be analyzed within 140 s, demonstrating remarkable efficiency. Through comparison with the Arochembase database, 25 odor components were identified in AH and 12 odor components in CP. Table 1 provides detailed information on the compounds and their odor descriptions. Five compounds, including camphene, tridecane, 2,2,4-trimethylpentane, limonene, and acetaldehyde, were found to be shared by AH and CP. These compounds were associated with a range of odor descriptions, such as freshly cut grass, fruity, aromatic, spicy, alkane-like, acidic, and petrol-like notes. The unique components in AH, such as pyridine, butylbutanoate, o-chlorotoluene, pentadecane, and butan-2-one, were primarily characterized by notes of freshly cut grass, alkane-like qualities, and spicy aromas. CP contained unique components like gamma-decalactone, hexane, (Z)-3-hexenal, alpha-ionone, and cymen-8-ol, which exhibited petrol-like, greasy, and sweet odors. While there were similarities in the odor descriptions of AH and CP, they also possessed distinct characteristics that set them apart.

FIGURE 2

FIGURE 2

Odor chromatograms of AH on MXT-5 (A) and MXT-1701 (B). Odor chromatograms of CP on MXT-5 (C) and MXT-1701 (D). PCA (E) and DFA (F) for distinguishing between AH and CP based on E-nose.

TABLE 1

Molecular formula Reserved parameter Possible compound Correlation index Sensory description AH CP
MXT-5 MXT-1701
1 C11H24 1098 1097 Terpinolene 98.92 Star anise; oranges; fresh fruits; herbaceous plant; pine tree, plastic, sweet, woody scent +
2 C10H18O2 1474 1688 Gamma-decalactone 98.76 Coconut; greasy; fresh; fruity (dry); lactones; greasy; oily (fresh); peach; sweet; candlesmell +
3 C15H32 1486 1488 Pentadecane 96.78 Alkane; heteroalcohols; freshly mowed +
4 C12H24O2 1417 1488 Methy lundecanoate 96.24 Brandy; greasy; fruits; greasy; sweet; thesmellofcandles; wine +
5 C10H22 953 963 4-ethyl-octane 95.84 +
6 C12H26 1146 1130 Decane 95.47 Oak; apple; greasy; fruits; grass; freshly mowed; luxuriant +
7 C6H10O 802 893 (Z)-3-hexenal 95.41 +
8 C10H16O 1147 1288 Camphor 94.61 Greasy; freshly mowed; greenpepper; mushrooms; pepper; butter + +
9 C13H20O 1405 1559 Alpha-ionone 94.43 Fragrant with oil or spices; cedar; floral or botanical; fruits; dovetail; sweet; tropical; the violet; warm; woody scent +
10 C5H10O 664 740 2-methyl butanal 94.32 Almonds; apple; charred; burning (strong); asphyxiating; coco; coffee; fermented or brewed; fruits; freshly mowed; iodoform; malt; musty smell; nutty; powerful; agreasy smell of incense; acidity +
11 C13H28 1307 1286 Tridecane 94.27 Alkane; oranges; fruits; heteroalcohols; hydrocarbon + +
12 C4H8O 600 693 Butan-2-one 94.16 Acetone; butter; cheese; chemistry; chocolate; the atmosphere; aromatic; fruits; gaseous; cheerful; spicy; sharp; sweet +
13 C5H8O2 943 1097 4-pentanolide 93.90 Fennel; coco; herbaceous; sweet; tobacco; warm; woody scent +
14 C3H6O 459 559 Propanal 93.85 Acetaldehyde; coco; earthy; the atmosphere; nutty; plastics; spicy; solvent +
15 C10H14O 1182 1338 Cymen-8-ol 93.60 Cherry; oranges; coumarin; floral or botanical; fruits; fruity (sweet); musty smell; sweet +
16 C8H18 683 681 2,2,4-trimethyl pentane 93.27 Gasoline + +
17 C6H14O 802 893 2-hexanol 93.05 Cauliflower; chemistry; greasy; fruits; terpene; wine +
18 C7H10O3 1190 1445 5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone 92.35 Brown sugar; butterscotch; caramel; fruits; fruity (sweet); maple; nutty; condiments; spicy; sweet +
19 C10H16 1037 1078 Limonene 91.55 Oranges; freshly mowed; pine tree + +
20 C5H12 518 485 Pentane 87.72 Alkane; gasoline +
21 C8H18 769 789 3-methylheptane 86.20 Green plants; sweet +
22 C10H10O2 1407 1560 (E)-methyl cinnamate 83.67 +
23 C13H26O2 1533 1607 Methyl dodecanoate 83.01 Coconut; creamy; greasy; floral or botanical; fruits; mushrooms; soap; sweet; the smell of candles; waxy +
24 C10H12O2 1244 1358 Ethyl phenylacetate 82.62 Fennel; cinnamon; coco; floral or botanical; fruits; honey; rose; spicy; sweet; candlesmell +
25 C2H4O 433 499 Acetaldehyde 81.05 Aldehyde group; the atmosphere; fresh; fruits; cheerful; piquant + +
26 C10H16 995 1069 Alpha-phellandrene 73.02 Orange; freshly cut grass scent; mint flavor; spicy; terpene aroma; pine resin; woody scent +
27 C10H16 995 1069 Myrcene 72.96 Sesame oil aroma; spice fragrance; airy; fruity; geranium; lemon; metallic; musty; plastic; pleasant; resinous; soapy; spicy; sweet; woody scent +
28 C10H16 995 1069 (+)-alpha-phellandrene 72.65 Dill flavor +
29 C15H24 1577 1627 1-phenyl-nonane 77.81 +
30 C9H20 928 873 Nonane 76.80 Alkane; heteroalcohols; gasoline +
31 C10H18O 1235 1358 3-decen-2-one 68.65 Unctuous +
32 C3H6O2 484 596 Methy lacetate 52.09 Blackcurrant; the atmosphere; aromatic; fruits; fruity (sweet); cheerful; solvent; sweet +

Possible compounds and sensory descriptions of AH and CP.

To further distinguish AH from CP, the chromatographic peaks obtained by the E-nose were used as influencing factors for PCA (Figure 2E) and DFA (Figure 2F). In the PCA model, the first principal component (PC1) contributed 94.183%, while the second principal component (PC2) contributed 4.729%. The cumulative contribution rate of the principal components reached 98.912%, indicating that AH and CP could be well distinguished. In the DFA model, the horizontal and vertical coordinates represented the first discriminant factor (DF1) and the second discriminant factor (DF2), respectively. The DF1 in Figure 2F was 100%, suggesting that DFA could better distinguish AH and CP samples based on odor characteristics. The result demonstrated that the E-nose combined with DFA was effective to distinguish AH from CP from the perspective of odor. The efficiency of this method was demonstrated in two aspects. On the one hand, plant samples used for E-nose analysis did not require grinding and extraction, thus offering significant advantages in sample pretreatment. On the other hand, the single analysis time for each plant sample was 140 s, which significantly shortened the analysis time compared to traditional methods.

3.3 E-tongue analysis

The taste values of AH and CP samples were measured using an E-tongue, and radar charts were constructed based on the signals collected by the sensors (Figures 3A,B). While an initial observation suggested a similar overall shape in the radar charts, a closer analysis revealed significant differences (P < 0.05) between AH and CP in terms of bitterness, astringency, sourness, aftertaste-A, and richness, as shown in Figures 3C,D and Supplementary Figure S1. The taste response range of the E-tongue encompasses the following: sourness (−13 to 12), bitterness (0–25), astringency (0–25), and saltiness (−6–19). Importantly, only values falling within these ranges could reflect the corresponding taste. Notably, the bitterness and astringency of AH were significantly higher than those of CP, suggesting that these two tastes could serve as key discriminators between the two samples.

FIGURE 3

FIGURE 3

Electronic tastes of AH (A) and CP (B). Comparison of bitterness (C) and astringency (D) between AH and CP.

PCA and OPLS-DA were employed to further differentiate between AH and CP. As shown in Figures 4A,B, the samples of AH and CP could be clearly separated from each other. The PCA model achieved R2 and Q2 values of 0.869 and 0.607, respectively. Furthermore, the OPLS-DA model demonstrated R2X, R2Y, and Q2 values of 0.93, 0.936, and 0.895, respectively. These parameters confirm the reliability of the results. To further validate the credibility of the model, a permutation test was performed (Figure 4C). Ideally, the R2Y intercept and Q2Y intercept of a valid model should not have exceeded 0.4 and 0.05, respectively (Wang et al., 2024). In this case, the R2Y intercept and Q2Y intercept were 0.113 and −0.666 respectively, indicating the results were credible. A VIP value greater than 1 was considered a criterion for differential variables (Wang et al., 2024). As shown in Figure 4D, the VIP values for bitterness and astringency exceeded this threshold, which was consistent with the previous findings. Consequently, the results suggested that the E-tongue could effectively differentiate between AH and CP from the perspective of tastes. The necessity of employing E-tongue analysis was underscored by two aspects: (1) AH contained toxic components such as aristolochic acid-like ingredients, thus traditional taste-testing methods could lead to poisoning; (2) The taste of CP was unpleasant and nauseating, making taste-testing methods unsuitable as well.

FIGURE 4

FIGURE 4

PCA (A) and OPLS-DA (B) of AH and CP based on electronic tastes. The permutation test (C) and VIP values (D) of OPLS-DA.

3.4 Chemical fingerprint spectra based on LC-HR-Q-TOF-MS/MS

3.4.1 Identification of chemical compositions

The chemical compositions of AH and CP were analyzed using LC-HR-Q-TOF-MS/MS. As a result, 91 compounds were identified in AH, comprising 32 nitrogen-containing compounds, 28 volatile oils, 11 organic acids, 6 coumarins, 5 flavonoids, 3 lignans, and 6 other compounds. Notably, ortho-hydroxyacetophenone, 4-hydroxyacetophenone, vanillic acid, and asarinin were confirmed by comparison with their respective chemical standards. The total ion chromatogram (TIC) of AH in positive ion mode is presented in Figure 5A, with detailed compound information listed in Table 2. For negative ion mode, the TIC and compound information are shown in Supplementary Figure S2 and Supplementary Table S1, respectively. Similarly, 90 compounds were identified from CP, including 22 steroidal compounds, 24 nitrogen-containing compounds, 14 volatile oils, 10 organic acids, 8 saccharides, 2 lignans, and 10 other compounds. Among these, paeonol was positively identified by comparison with its chemical standard. The TIC of CP extract in positive ion mode is depicted in Figure 5B, and the corresponding compound information is presented in Table 3. For negative ion mode, the TIC and compound information are shown in Supplementary Figure S3 and Supplementary Table S2, respectively. The discussion on the MS/MS fragmentation patterns of compounds in AH and CP is as follows.

FIGURE 5

FIGURE 5

Total ion chromatograms of AH (A) and CP (B) in positive ion mode.

TABLE 2

No. tR (min) m/z (Error,ppm) Formula Fragmentions (m/z) Identification
1 0.959 175.1194 (-2.57)H C6H14N4O2 130.0955,116.0700,112.0863 L-arginine
2 1.072 138.0553 (-2.52)H C7H7NO2 123.0653,122.4088 Anthranilic acid
3 1.123 137.0600 (-2.16)H C8H8O2 120.0783,121.0817 Ortho-hydroxyacetophenonea
4 1.508 137.0600 (-2.16)H C8H8O2 120.0297,121.0733 4-hydroxyacetophenonea
5 1.553 180.1019 (0.03)NH4 C10H10O2 150.0546,124.0504,110.0365 Safrole
6 1.586 166.0863 (-0.27)H C9H11NO2 122.0690,107.0485,151.1898 Phenylalanine
7 1.602 152.0704 (1.36)H C8H9NO2 110.0338,135.0296 Acetaminophen
8 1.681 121.0648 (-0.07)H C8H8O 107.0724,103.0543 4-methylbenzaldehyde
9 2.182 180.1019 (0.03)NH4 C10H10O2 124.0508,110.0624 Isosafrole
10 2.339 229.0319 (-1.34)H C5H4N6O5 138.9636,122.0160,111.8917 6,8-dinitro-3,5-dihydrotetrazolo [1,5-a]pyridin-5-ol
11 2.668 166.0863 (-0.27)H C9H11NO2 122.0855,108.0424,107.0496 Dimethylanthranilate
12 3.413 353.0847 (5.7)H C16H16O9 177.0058,160.9132,118.9029 4-methylumbelliferyl glucuronide
13 5.033 200.0478 (-5.05)H C12H7O3 157.0412,129.0443 6-formylnaphthalene-2-carboxylate
14 5.625 205.0969 (1.25)H C11H12N2O2 146.8926,132.0806,118.0647 Tryptophan
15 5.691 188.0707 (-0.51)H C11H9NO2 188.0707,118.0647 3-indoleacrylic acid
16 6.856 136.0617 (0.53)H C5H5N5 120.0381,107.0746 Adenine
17 11.657 330.1699 (0.26)H C19H23NO4 207.0798,177.0787,164.8723,150.0901 Reticuline
18 13.885 379.1000 (6.24)H C18H18O9 217.8755,189.8638,161.8689,185.0418,171.9442 Geshoidin
19 14.000 177.0545 (0.68)H C10H8O3 151.0544,111.0370,134.0348 Hymecromone
20 14.113 147.0440 (0.38)H C9H6O2 118.0407,102.0479 2-benzofurancarboxaldehyde
21 14.403 273.0757 (0.18)H C15H12O5 181.0626,155.0234,153.0177,147.0438,137.9719 (2S)-naringenin
22 14.447 314.1758 (-2.32) C19H24NO3+ 209.0954,167.0830,179.0888,153.0692 Magnocurarine
23 14.683 344.1856 (0.10)H C20H25NO4 192.1013,162.0670,138.0625,108.0558 Cilomilast
24 14.906 314.1758 (-2.32) C19H24NO3+ 209.0950,167.0799,179.0884,153.0698 Lotusine
25 15.251 177.0545 (0.68)H C10H8O3 121.0261,109.9687,105.0331 7-methoxycoumarin
26 16.461 236.1643 (0.87)H C14H21NO2 165.0687,121.0644 Spectraban
27 16.694 344.1856 (0.10)H C20H25NO4 207.0785,177.0749,147.8670,139.9561 Laudanine
28 17.584 231.0626 (-0.43)H C9H6N6O2 148.9010,110.9477 4-[5-(Pyridin-3-yl)-1,2,4-oxadiazol-3-yl]-1,2,5-oxadiazol-3-amine
29 17.881 353.1207 (6.80)H C17H20O8 207.9798,177.8574,164.8723,146.8601 RhytidchromoneD
30 18.153 314.1758 (-2.32) C19H24NO3 209.0950,167.0832,179.0903,153.0691 (R)-oblongine
31 19.196 236.1643 (0.87)H C14H21NO2 123.0438,107.0485 Meprylcaine
32 19.398 201.1634 (1.88)H C15H20 187.1392,159.1155,145.0995,131.0846 3,4-dihydrocadalene
33 19.479 268.1328 (1.51) C17H18NO2 251.1069,219.0783,236.0822,191.0844 Unknown
34 20.165 435.1289 (-0.75)H C21H22O10 273.0748,181.0637,153.0179 (2S)-naringenin-5-O-beta-D-glucopyranoside
36 20.238 273.0757 (0.18)H C15H12O5 181.0615,155.0236,153.0179,147.0444,137.8857 (2R)-naringenin
35 20.255 597.1824 (-1.68)H C27H32O15 435.1288,273.0756 (2R)-naringenin5,7-di-O-glucoside
37 20.467 278.1746 (1.70)H C16H23NO3 128.8733,112.9887,152.9022 Cordypyridone B
38 22.487 215.0678 (0.34)Na C11H12O3 175.0678,144.0570,114.9631 Myristicin
39 22.836 304.1883 (0.05)Na C16H27NO3 222.0666,179.0847,165.0698,205.0649 Scalusamide A
40 23.237 304.1883 (0.05)Na C16H27NO3 248.8163,164.0674 3,3-dimethyl-1-[(2S)-2-pentanoylpyrrolidin-1-yl]pentane-1,2-dione
41 23.814 282.1488 (0.21)NH4 C18H16O2 265.1211,250.0973,235.0750,219.0797 Unknown
42 23.839 265.1223 (0.02)H C16H18O2 153.0688,108.9738,122.9137 1,2-bis(3-methylphenoxy)ethane
43 24.071 304.1883 (0.05)Na C16H27NO3 206.8651,164.0700,136.8765 3-acetyl-5-hydroxy-4,5-dimethyl-1-octyl-2-pyrrolone
44 24.696 304.1883 (0.05)Na C16H27NO3 231.8412,180.8673 Ethyl1-(3-cyclopentylpropanoyl)piperidine-4-carboxylate
45 25.939 336.1229 (0.40)H C20H17NO4 320.0908,292.0961,184.9388 N-(biphenyl-4-ylmethyl)-3-hydroxy-6-methyl-4-oxo-4H-pyran-2-carboxamide
46 26.254 387.1413 (6.55)H C21H22O7 302.8310,276.8630,202.8263,176.0397 Sen-byakangelicol
47 26.749 291.1295 (1.42)H C10H18N4O6 247.0652,203.0687,159.0353 L-argininosuccinic acid
48 27.021 387.1413 (6.55)H C21H22O7 289.1100,188.8615,161.0210 Edultin
49 27.426 226.1799 (1.13)H C13H23NO2 144.8945,100.9319 Cyclohexyln-cyclohexylcarbamate
50 27.615 183.1012 (2.04)H C10H14O3 168.0745,153.0537,125.0594,137.0592,152.0812 3,4,5-trimethoxytoluene
51 27.665 205.0832 (0.17)H C8H8N6O 137.9020,122.9628,106.9811 2-[(e)-(2H-tetrazol-5-ylhydrazinylidene)methyl]phenol
52 27.688 168.0778 (1.77)H C9H11O3 152.0611,109.0283,137.0060 (3,4-dimethoxyphenyl)methanolradical
53 28.561 179.0700 (1.52)H C10H10O3 135.9491,108.9598,121.0283 Trans-4-methoxycinnamic acid
54 28.646 308.0557 (-1.14)H C17H9NO5 222.0650,278.0566,250.0593,280.0596,252.0642 17-hydroxy-3,5-dioxa-11-azapentacyclo [10.7.1.02,6.08,20.014,19]icosa-1(19),2(6),7,12(20),13,15,17-heptaene-9,10-dione
55 29.223 183.1013 (1.49)H C10H14O3 168.0745,153.0537,125.0594,137.0592,152.0812 2,4,6-trimethoxytoluene
56 29.616 228.1955 (1.34)H C13H25NO2 158.0950,144.0575,100.9321 Cyclohexyl-carbamic acidhexylester
57 29.634 250.1774 (0.28)H C11H19N7 166.1208,155.8579,112.8977 Metazine
58 30.108 209.0809 (-0.31)H C11H12O4 176.0447,161.0231 2-methoxyl-methylenedioxypropiophenone
59 30.182 308.0557 (-1.14)H C17H9NO5 222.0637,278.0567,250.0595,280.0616,252.0634 7-hydroxy-3,5-dioxa-11-azapentacyclo [10.7.1.02,6.08,20.014,19]icosa-1(20),2(6),7,12,14,16,18-heptaene-9,10-dione
60 30.815 338.0665 (-1.74)H C18H11NO6 294.0460,265.0489,250.0261,206.0595 4-[(z)-[2-(1,3-benzodioxol-5-yl)-5-oxo-1,3-oxazol-4-ylidene]methyl]benzoic acid
61 30.983 318.3005 (-0.72)H C18H39NO3 192.8404,164.8297,136.9307 Phytosphingosine
62 31.506 205.0969 (1.25)H C11H12N2O2 176.0463,122.0709 Ethotoin
63 31.512 195.0652 (-0.08)H C10H10O4 167.0330,138.9622,123.0408 Kakuol
64 32.667 294.0760 (0.29)H C17H11NO4 279.0521,251.0571,264.0656,236.0693 Aristolactam I
65 33.292 318.3005 (-0.72)H C18H39NO3 192.8434,164.8287,136.9309 2-aminooctadecane-1,3,4-triol
66 33.462 302.3052 (0.52)H C18H39NO2 246.8158,176.9090,106.0860 Sphinganine
67 34.616 222.1850 (1.09)H C14H23NO 101.9493,152.1066,191.0324 N-isobutyl-2E,4E,8Z-decatrienamide
68 35.844 219.1741 (1.11)H C15H22O 178.0759,150.0992,122.0691,163.1103,123.0798 Nootkatone
69 36.445 250.2165 (0.16)H C16H27NO 140.8710,112.9899,100.0754 (2E,4E)-1-(pyrrolidin-1-yl)dodeca-2,4-dien-1-one
70 36.558 219.1741 (1.11)H C15H22O 191.0859 Longiverbenone
71 36.564 224.2013 (-1.83)H C14H25NO 167.0813 Pellitorine
72 37.175 249.2077 (4.09)H C16H26NO 178.1300,151.1344,155.1157 N-methylmeptazinol
73 37.334 248.2014 (-2.06)H C16H25NO 167.8590,152.1068 N-isobutyl-2E,4E,8Z,10E-dodecatetraenamide
74 37.576 337.1075 (-1.34)H C20H16O5 321.0957,267.0612,237.0545 Psoralidin
75 37.911 248.2014 (-2.06)H C16H25NO 167.1264,152.1066 N-isobutyl-2E,4E,8Z,10Z-dodecatetraenamide
76 38.392 250.2165 (0.16)H C16H27NO 153.1093,127.0939,116.0588 Dodeca-2E,4E,8Z-trienoic acidisobutylamide
77 38.522 248.2014 (-2.06)H C16H25NO 167.8582,152.1066 N-isobutyl-2E,4Z,8Z,10E-dodecatetraenamide
78 38.714 284.1986 (-1.37)H C16H27O4 171.8515,116.0529,128.8711 Monododecylmaleate
79 39.258 274.2171 (-2.05)H C18H27NO 120.0886,107.0853 8-acetyl-2-(dipropylamino)tetralin
80 39.620 296.1987 (-1.66)H C17H27O4 196.7978,153.9014,127.0712 (E)-5-cyclohexyl-2-[2-[(2-methylpropan-2-yl)oxy]-2-oxoethyl]pent-2-enoate
81 40.125 252.2326 (-1.63)H C16H29NO 154.1219,112.0753,128.1425,102.0904 (2E,4E)-N-isobutyl-2,4-dodecadienamide
82 40.198 274.2171 (-2.05)H C18H27NO 120.0534,107.0491 7-(N,N-Dipropylamino)-5,6,7,8-tetrahydronaphtho (2,3-b)dihydro-2,3-furan
83 40.509 276.2324 (-0.76)H C18H29NO 176.1107,146.0701,107.0850 (1S,2R)-5-methoxy-1-methyl-N,N-dipropyl-1,2,3,4-tetrahydronaphthalen-2-amine
84 42.875 359.1265 (3.59)H C23H18O4 345.1991,253.1547,177.9723,147.0110 7-(benzyloxy)-3-(4-methoxyphenyl)-4H-chromen-4-one
85 44.208 415.0429 (4.69)H C23H10O8 268.0054,241.9654,165.0678,149.0265 5-[4-[(1,3-dioxo-2-benzofuran-5-yl)oxy]benzoyl]-2-benzofuran-1,3-dione

Identification of compounds in AH by LC-HR-Q-TOF-MS/MS.

Na, [M + Na]+; H, [M + H]+; NH4, [M + NH4]+.

a

The compounds were identified by comparing with reference substances.

TABLE 3

No. tR (min) m/z (Error,ppm) Formula Fragmentions (m/z) Identification
1 1.007 469.2150 (2.58)H C23H48O8 207.0872,181.1031 2-[(E)-4-(2-hydroxy-2-tricyclo [9.4.0.03,8]pentadeca-1 (15),3,5,7,9,11,13-heptaenyl)but-2-enyl]tricyclo [9.4.0.03,8]pentadeca-1 (15),3,5,7,9,11,13-heptaen-2-ol
2 1.087 398.1664 (-1.79)H C14H21N8O6 180.0641,164.0709 Methyl 3-o-(2-acetamido-2-deoxy-b-D-galactopyranosyl)-a-D-galactopyranoside
3 1.158 365.1061 (-1.95)Na C12H22O11 186.9692,203.0519 Melibiose
4 1.399 365.1061 (4.77)H C20H43NO4 179.1147,164.0699,150.0893 2-[(2S,3R,4R,5R,6R)-4,5-diacetyloxy-6-(acetyloxymethyl)-3-hydroxyoxan-2-yl]oxyacetic acid
5 1.535 268.1045 (-1.76)H C10H13N5O4 136.0617,121.0752 Adenosine
6 1.551 182.0814 (-1.27)H C9H11NO3 136.0755,119.0734 D-Thr-OH
7 1.648 294.1547 (0.10)H C12H23NO7 234.9316,147.0539,117.9572 1,2-O-dimethyl-4-[2,4-dihydroxy-butyramido]-4,6-dideoxy-alpha-D-mannopyranoside
8 1.681 276.1420 (-0.13)H C12H21NO6 190.0614,148.9078 Triethanolaminetriacetate
9 1.936 420.198 (-0.82)H C17H29N3O9 258.1306,198.1222,126.0581 Ethyl(2S,4R,5R)-5-azido-4-(methoxymethoxy)-6-[5-(methoxymethoxy)-2-methyl-1,3-dioxan-4-yl]oxane-2-carboxylate
10 1.945 201.0732 (-0.68)H C5H8N6O3 158.0701,128.9388,113.9639 2-[(E)-[amino-(4-amino-1,2,5-oxadiazol-3-yl)methylidene]amino]oxyacetamide
11 2.145 298.1396 (0.50)H C13H15N3O5 179.0685,122.0610 Hippuryl-glycyl-glycine
12 2.256 420.1980 (-0.82)H C17H29N3O9 288.1544,203.0967,159.0642 2-[2-[bis(carboxymethyl)amino]ethyl-[2-[carboxymethyl-(3-methyl-2-oxobutyl)amino]ethyl]amino]acetic acid
13 2.321 283.1402 (-5.16)H C11H22O8 223.1172,163.0966,103.0537 (2R,5R)-3,4-bis(methoxymethoxy)-5-(methoxymethoxymethyl)oxolan-2-ol
14 2.530 214.1186 (6.38)H C11H17O4 174.8792,116.9289 2-o-allyl-3,4-O-isopropylidenearabinopyranosylradical
15 4.342 253.1294 (-4.48)H C10H20O7 179.9909,149.9065,123.0985 2,3-butanediolglucoside
16 5.240 200.0478 (-5.05)H C12H7O3 156.0382,128.0163 2-naphthalen-1-yl-2-oxoacetate
17 5.658 188.0706 (0.03)H C11H9NO2 171.0617,143.0721,118.0645,104.0489 3-indoleacrylic acid
18 5.723 297.1557 (-4.41)H C12H24O8 203.9758,149.0712 Caryophyllose
19 11.239 273.1915 (2.27)H C12H24N4O3 174.8691,131.0996,130.0973 4-amino-1-[(3-amino-propyl)-isopropyl-carbamoyl]-pyrrolidine-3-carboxylic acid
20 12.490 313.1249 (-8.31)H C22H16O2 236.8742,144.8656,128.8721 6-(4-hydroxy-phenyl)-1-phenyl-naphthalen-2-ol
21 14.174 362.2407 (1.20)H C16H27N9O 169.9317,140.9187,211.8761,126.9461 2-[[4-[2-(dimethylamino)ethylamino]-6-ethyl-1,3,5-triazin-2-yl]amino]-N-ethyl-3-methylimidazole-4-carboxamide
22 15.304 483.1475 (-7.61)H C29H22O7 229.0671,257.0607,215.0571,171.0257 2-oxopropane-1,3-diylbis (3-phenoxybenzoate)
23 15.810 437.2351 (0.78)H C16H32N6O8 219.8877,191.0225,147.9832 2-[[1-[2-[1,1-bis(carboxymethylamino)ethyl-methylamino]ethyl-methylamino]-1-(carboxymethylamino)ethyl]amino]acetic acid
24 19.387 399.1408 (0.86)H C18H18N6O5 311.0778,178.0652,148.8566,134.9326 N6-methoxy-2-[(2-pyridinyl)ethynyl]adenosine
25 19.965 399.1408 (0.86)H C18H18N6O5 353.1377,220.8733,206.1003 N-[3-[4-(hydroxycarbamoyl)phenoxy]propyl]-6-oxo-2-pyrazol-1-yl-1h-pyrimidine-5-carboxamide
26 21.655 701.4939 (6.85)H C42H68O8 557.0080,412.9373 5-[[(1S,3aS,5aR,5bR,7aR,9S,11aR,11bR,13aR,13bR)-9-(5-hydroxy-3-methyl-5-oxo-pentanoyl)oxy-1-isopropyl-5a,5b,8,8,11a-pentamethyl-1,2,3,4,5,6,7,7a,9,10,11,11b,12,13,13a,13b-hexadecahydrocyclopenta [a]chrysen-3a-yl]methoxy]-3-methyl-5-oxo-pentanoic acid
27 23.492 475.3258 (1.57)H C25H46O8 279.8175,221.9363 (5-acetyloxy-3,4-diheptoxy-6-methoxyoxan-2-yl)methylacetate
28 23.814 219.1008 (3.93)H C12H27NO2 165.1682,137.0588,120.9529 Chuanxiongol
29 24.180 297.2206 (2.34)H C21H28O 221.1308,185.1308,169.1003 Phenol,2,4-bis(1,1-dimethylethyl)-6-(phenylmethyl)-2,4-di-tert-butyl-6-benzylphenol
30 24.197 679.3301 (-0.13)Na C33H52O13 679.3290,517.2774,312.0463,297.2187 Cynapanoside G
31 25.532 308.2213 (2.34)H C18H29NO3 251.1494,193.1422,138.0851,123.0668,109.0517 Betaxolol
32 26.747 291.1297 (0.73)H C10H18N4O6 247.0651,176.1423,160.0386 L-argininosuccinic acid
33 27.038 443.1671 (1.28)Na C22H28O8 291.4742,260.8591,230.0843,146.1015,154.0119 (−)-lyoniresinol
34 27.469 266.1721 (1.07)H C17H17N2O 180.9167,154.0762,152.8657 (2S,4S)-4-azido-1-((S)-2,6-diaminohexanoyl)pyrrolidine-2-carbonitrile
35 27.806 167.0702 (0.43)H C9H10O3 125.0588,111.0394,137.0425 Paeonol*
36 27.973 262.0157 (5.24)H C6H9NO9 218.1872,202.9778,144.9735 Glycolatenitrogen
37 28.094 167.0702 (0.43)H C9H10O3 153.0692,137.0221,121.0642,111.0388 Isopaeonol
38 28.163 167.0702 (0.43)H C9H10O3 153.0674,123.0697,109.0275 Ethylparaben
39 29.077 979.4513 (-0.40)Na C47H72O20 979.4491,817.0969,673.3171 Komaroside O
40 29.226 250.1773 (0.68)H C12H25O5 193.0993,136.0314 Metazine
41 29.538 250.1773 (0.68)H C12H25O5 168.8645,141.0679,113.9636 [4,6-bis(ethylamino)-1,3,5-triazin-2-yl]-propan-2-ylcyanamide
42 29.604 228.1953 (2.23)H C13H25NO2 130.8979,116.9627,102.9469 4-nonanoylmorpholine
43 29.827 250.1773 (0.68)H C12H25O5 168.9394,141.8710,113.9628 Ethyl-(4-ethylamino-6-isopropylamino-[1,3,5]triazin-2-yl)-cyanamide
44 30.084 250.1773 (0.68)H C12H25O5 235.8168,151.9062 8-(6-aminohexyl)-amino-adenine
45 30.379 285.2894 (2.25)H C17H36N2O 173.9206,117.0710 Tetrabutylurea
46 30.665 274.2742 (-0.53)H C16H35NO2 230.2460,106.0859 N-lauryldiethanolamine
47 30.921 979.4513 (-0.40)Na C47H72O20 979.4499,817.3955,673.3178,299.0703 Komaroside U
48 30.990 318.3003 (-0.09)H C18H39NO3 164.8291,150.1119,106.0649 2,2'-((2-(dodecyloxy)ethyl)imino)bisethanol
49 31.416 993.4648 (1.82)Na C48H74O20 933.4630,833.4395 Marstenacisside A3
50 31.607 817.4020 (-4.92)Na C41H62O15 673.3181,383.1164 Glaucoside D
51 31.672 979.4513 (-0.40)Na C47H72O20 979.4501,817.3950,673.3165,299.0700 Achyranthoside C
52 32.170 316.2842 (1.33)H C18H37NO3 246.8668,176.0696,162.8314 N,N-bis(2-hydroxypropyl)dodecanamide
53 32.883 304.2632 (0.96)H C20H33NO 191.1254,149.0471,248.2005 Fenpropimorph
54 33.147 831.4144 (-0.81)Na C42H64O15 655.3060,297.1291 (+)-divaroside
55 33.341 817.4020 (-4.92)Na C41H62O15 673.3206,543.2544 Cynapanoside C
56 33.376 963.4573 (-1.38)Na C41H62O15 801.4019,657.3241,299.0692 Cynatratoside D
57 33.448 302.3051 (0.85)H C18H39NO2 302.3051,260.2358,246.1843,232.1683,218.1529,190.1215 Sphinganine
58 33.580 963.4573 (-1.38)Na C47H72O19 657.3232,299.0692 Cynatratoside E
59 33.653 817.4020 (-4.92)Na C41H62O15 673.3181,543.2543,383.1164 Cynapanoside F
60 34.094 817.4020 (-4.92)Na C41H62O15 673.3181,543.2543,383.1164 Glaucoside C
61 34.109 977.4718 (-0.16)Na C48H74O19 917.4468,817.4185 Marstenacisside A2
62 34.151 335.219 (0.00)H C16H26N6O2 265.0644,249.1262,233.0831,177.8611 2-(6-(isobutylamino)-2-(pentylamino)-9H-purin-9-yl)acetic acid
63 34.454 817.4020 (-4.92)Na C41H62O15 673.3181,543.2543,383.1164 Hirundigoside C
64 35.168 831.4144 (-0.81)Na C42H64O15 671.3278 Cynapanoside E
65 35.321 437.1936 (2.10)H C23H20N10 356.2191,210.9564 3-(1-Methylpyrazol-4-yl)-6-[1-[5-(1-methylpyrazol-4-yl)triazolo [4,5-b]pyrazin-3-yl]ethyl]quinoline
66 35.481 831.4144 (-0.81)Na C42H64O15 655.3441,435.2209 Deoxoglycyrrhizin
67 35.843 303.0629 (7.57)H C19H10O4 199.0295,158.8591,130.9570 3-benzoylnaphtho [1,2-b]furan-4,5-dione
68 35.939 831.4144 (-0.81)Na C42H64O15 441.2081,329.1572 Gitaloxin
69 36.932 801.4041 (-1.18)Na C41H62O14 657.3234,527.2597,383.1818 Cynanoside K
70 37.12 277.1436 (3.35)H C16H22O4 263.7807,235.9707,149.0153,121.0289,105.0333 1,2-benzenedicarboxylic acid
71 37.591 366.3366 (0.15)H C23H43NO2 212.0646,212.0646,117.0681 Semiplenamide A
72 37.690 801.4041 (-1.18)Na C41H62O14 657.3234,527.2597,383.1818 Cynanoside J
73 37.768 279.2317 (0.56)H C18H30O2 199.8904,159.9928,131.0850 Linolenic acid
74 38.656 815.4195 (-0.85)Na C42H64O14 755.3959,715.3654,655.3442 3-O-S2-11α-O-acetyl-l2β-O-tigloyl-tenacigenin B
75 38.730 277.2159 (1.11)H C18H28O2 237.9916,183.0341,143.0845 Stearidonic acid
76 39.010 295.2265 (0.92)H C18H30O3 167.8593,141.9124 13-keto-9Z,11E-octadecadienoic acid
77 39.483 295.2265 (0.92)H C18H30O3 295.2265,238.8623,208.9628,151.0278 2-{2-[4-(1,1,3,3-tetramethylbutyl)phenoxy]ethoxy}ethanol
78 40.394 301.141 (-0.83)H C14H16N6O2 244.9672,164.9589,148.9675 8-amino-2-furan-2-yl-[1,2,4]triazolo [1,5-a]pyrazine-6-carboxylic acidbutylamide
79 41.801 291.1297 (0.73)H C10H18N4O6 247.1354,231.1099,160.1096,189.0018 (2S)-2-[[amino-[[(4S)-4-amino-4-carboxybutyl]amino]methylidene]amino]butanedioic acid
80 42.503 425.2152 (4.23)H C22H32O8 266.0295,211.0616,152.1410 Didrovaltrate
81 42.598 282.2794 (-0.92)H C18H35NO 158.0583,102.0910 Oleamide
82 42.807 359.1259 (5.27)H C23H18O4 333.1705,257.9660,213.9605 2-allyl-4,6-dibenzoylresorcinol
83 43.548 284.295 (-0.74)H C18H37NO 228.3951,158.9754,116.0496 Octadecanamide
84 44.068 415.0432 (3.97)H C23H10O8 268.0060,177.9745,149.0278 5-[4-[(1,3-Dioxo-2-benzofuran-5-yl)oxy]benzoyl]-2-benzofuran-1,3-dione
85 44.796 423.3241 (3.92)H C29H42O2 337.1505,255.1688,215.0875,201.1629 (3R,4S,4aR,6aR,6bS,14aR,14bR)-4-(hydroxymethyl)-4,6a,6b,11,12,14b-hexamethyl-1,2,3,4a,5,6,7,8,14,14a-decahydropicen-3-ol
86 45.406 291.1297 (0.73)H C10H18N4O6 247.0667,189.1625,160.0363 Argininosuccinate
87 45.606 471.106 (3.07)H C27H18O8 310.1218,177.1196,162.0399 Methyl 4-[bis(4-hydroxy-2-oxochromen-3-yl)methyl]benzoate
88 49.667 291.1297 (0.73)H C10H18N4O6 247.0628,231.1112,160.0414 (N (omega)-L-arginino)succinic acid

Identification of compounds in CP by LC-HR-Q-TOF-MS/MS.

Na, [M + Na]+; H, [M + H]+.

*The compounds were identified by comparing with reference substance.

3.4.2 Fragmentation patterns of main compositions in AH

Safrole (Figure 6A) was taken as an example of volatile oils for illustration. The quasi-molecular ion at m/z 180 initially underwent methoxy group cleavage to eliminate a molecule of CH2O, generating an ion at m/z 150. This process might have involved the formation of an allylic carbocation intermediate. Subsequently, the ion at m/z 150 underwent rearrangement within the conjugated double bond system, eliminating C2H2 to yield an ion at m/z 124. Finally, this fragment underwent either cleavage of the aromatic ring side-chain CH2 group or exocyclic rearrangement to form the stable terminal product ion at m/z 110. This fragmentation pathway revealed the stepwise dissociation characteristics of the methoxy group, conjugated double bonds, and aromatic ring structure in the safrole molecule.

FIGURE 6

FIGURE 6

Compound cracking pathways for safrole (A) asarinin (B) (2S)-naringenin (C) aristolactam I (D) aristolochic acid (E) glaucoside C (F) and melibiose (G).

Asarinin (Figure 6B) was taken as an example of lignans for illustration. Initially, it started from its quasi-molecular ion at m/z 353 and lost a molecule of CH2O, generating an ion at m/z 323. It then lost another molecule of CH2O, forming an ion at m/z 293. Subsequently, the ion at m/z 293 lost a molecule of C10H6O2, producing an ion at m/z 135. Ultimately, the ion at m/z 135 underwent another fragmentation, resulting in the loss of a molecule of CH3 and yielding an ion at m/z 120. In addition, there was another fragmentation pathway that started from the quasi-molecular ion at m/z 353, where it lost a molecule of C7H4O2, generating an ion at m/z 233 (Hu et al., 2025).

(2S)-naringenin (Figure 6C) was taken as an example of flavonoids for illustration. It started from its quasi-molecular ion at m/z 273. By losing a molecule of C6H4O, this ion transformed into a fragment ion at m/z 181. Subsequently, this fragment ion further fragmented and lost a molecule of CO, generating an ion at m/z 153. Immediately thereafter, the ion at m/z 153 lost an OH group, forming an ion at m/z 137. Additionally, there was another fragmentation pathway that started from the fragment ion at m/z 181, where it directly lost a molecule of C2H2, producing an ion at m/z 155 (Wen et al., 2014).

Aristolactam I (Figure 6D) was taken as an example of amides for illustration. It started from its quasi-molecular ion at m/z 294. By losing a molecule of CH3, it generated an ion at m/z 279. Subsequently, it lost a molecule of CO, resulting in an ion at m/z 251. In addition, there was another fragmentation pathway that started from the quasi-molecular ion at m/z 294. In this pathway, the ion lost a molecule of CH2O, producing an ion at m/z 264. Finally, this ion at m/z 264 lost a molecule of CO, yielding an ion at m/z 236 (Mao et al., 2017).

Aristolochic acid (Figure 6E) was taken as an example of phenanthrenes for illustration. It began with the quasi-molecular ion at m/z 356. By losing a molecule of NO2 and a molecule of COO, it generated an ion at m/z 266. Subsequently, this ion at m/z 266 further fragmented and lost a molecule of CH2O, forming an ion at m/z 236. Additionally, there was another fragmentation pathway that began with the quasi-molecular ion at m/z 356. In this pathway, the ion lost a molecule of COOH, a molecule of CO2, and a molecule of NO2, producing an ion at m/z 221 (Yu et al., 2016).

3.4.3 Fragmentation patterns of major types of compounds in CP

Glaucoside C (Figure 6F) was taken as an example of steroids for illustration. The initial quasi-molecular ion at m/z 817 underwent cleavage by losing a molecule of C7H12O3, generating an ion at m/z 673. This likely corresponded to the rupture of a glycosidic bond or an ester bond in the molecule, resulting in the detachment of a saccharide or ester group containing 7 carbon atoms, 12 hydrogen atoms, and 3 oxygen atoms. Subsequently, the ion at m/z 673 underwent further fragmentation by eliminating a molecule of C6H10O3, producing an ion at m/z 543. This step might similarly have involved the cleavage of another saccharide unit or related functional group. Following this, the ion at m/z 543 underwent additional fragmentation through the loss of another C6H10O3 molecule, yielding a terminal ion at m/z 383. The fragments lost at each step were structural glycosyl units, and these fragmentation processes gradually revealed the structural information of the molecule.

Melibiose (Figure 6G) was taken as an example of saccharides for illustration. The quasi-molecular ion at m/z 365 underwent cleavage at the α-1,6-glycosidic bond, primarily through two distinct fragmentation pathways. In the first pathway, glycosidic bond cleavage was accompanied by elimination of a hexose unit (C6H11O6), resulting in a dehydrated monosaccharide fragment at m/z 185. In the second pathway, direct elimination of the C6H11O6 moiety occurred without hydroxyl group removal, yielding a hydroxyl-retained monosaccharide fragment at m/z 202. These observations suggested that heterolytic cleavage of hydrogen bonds played a critical role in differentiating the fragmentation pathways. Additionally, the intermediate ion observed at m/z 349 (formed via deoxygenation) indicated the loss of a hydroxyl oxygen atom from the sugar ring, generating an unsaturated structure. This structural rearrangement likely facilitated fragmentation pathway branching through intracyclic double bond reorganization.

3.4.4 Component comparison of AH and CP

By comparing the chemical compositions of AH and CP, we could observe significant differences as well as shared components between them. AH primarily comprised nitrogenous compounds, volatile oils, organic acids, coumarins, flavonoids, and lignans. Notably, AH contained unique coumarins and flavonoids that were rare in CP, which exhibited a broad spectrum of pharmacological activities. For example, 7-methoxycoumarin ameliorated hepatotoxicity in rats induced by carbon tetrachloride and spatial memory impairment in ovariectomized Wistar rats induced by scopolamine (Sancheti et al., 2013; Zingue et al., 2018). Naringenin alleviated non-alcoholic fatty liver disease by suppressing the NLRP3/NF-κB pathway and prevented cardiomyopathy through targeting HIF-1α in mice (Wang et al., 2020; Pan et al., 2024). Furthermore, some components in AH exhibited potent toxicity, including aristolactam I, aristolochic acid D, and safrole. Studies demonstrated that aristolactam I accumulated extensively in renal cells and induced nephrotoxicity (Au et al., 2023), while aristolochic acid D triggered lymphocyte infiltration and renal fibroproliferation (Xian et al., 2021). Additionally, safrole exerted hepatotoxicity through the cytochrome P450 enzyme CYP1A2 (Hu et al., 2019). In contrast, the chemical composition of CP mainly included steroidal compounds, nitrogenous compounds, volatile oils, organic acids, saccharides, and lignans. Among them, CP contained unique steroidal compounds and saccharides that were absent in AH, exemplified by glaucoside C and melibiose. Glaucoside C alleviated atopic dermatitis by inhibiting the mitogen-activated protein kinase (Fleitas et al., 2022), while melibiose ameliorated cerebral ischemia/reperfusion injury through regulating autophagic flux (Wu et al., 2021). Despite the chemical differences between AH and CP, they shared common components, such as L-argininosuccinic acid and sphinganine. The results indicated that LC-HR-Q-TOF-MS/MS could differentiate AH and CP from the perspective of chemical compositions.

3.5 Electrochemical fingerprint spectra based on Belousov-Zhabotinsky reaction

Although LC-HR-Q-TOF-MS/MS was utilized for analyzing the components of medicinal plants, it also had limitations. On the one hand, it was impossible to identify all the components in medicinal plants. On the other hand, complex data analysis required a considerable amount of time. Therefore, it was necessary to establish a simpler method from the perspective of holistic chemistry, namely, electrochemical fingerprint spectra based on the Belousov-Zhabotinsky reaction. The principle, influencing factors, and model accuracy of this method were as follows.

3.5.1 Principle of electrochemical reactions

Electrochemical fingerprint spectra, as a part of nonlinear chemistry, was capable of characterizing the overall chemical properties of medicinal plants. It arose from oscillations in autocatalytic reactions, revealing fluctuations in the concentrations of certain substances. The principle of this reaction encompassed the consumption of bromide ions (Br), the oxidation of cerium ions (Ce3+), and the regeneration of bromide ions (Br). The cycle of bromide ion consumption and regeneration drove the oscillatory system (Wang et al., 2024). The whole components in medicinal plants influenced these reactions, offering novel representations of their chemical properties. For instance, the distinct redox-active components in AH and CP (e.g., ortho-hydroxyacetophenone and paeonol) could influence the oxidation process of Ce3+. To ensure the integrity of the phytochemical components, the plant powder was directly involved in the reaction without prior extraction.

3.5.2 Factors influencing Belousov-Zhabotinsky reaction

The effects of sample mass, rotation speed, and temperature on the Belousov-Zhabotinsky oscillation reaction were investigated. In Figure 7A, the electrochemical fingerprint spectra of AH powder with varying masses (0.2g, 0.3g, 0.4g, 0.5g, 0.6g) are presented. The characteristic parameters of these spectra were summarized in Supplementary Table S3. Notably, as the mass of the AH powder increased, a discernible trend emerged: the oscillation time gradually decreased, accompanied by a reduction in amplitude. The electrochemical fingerprint spectra of AH powder at stirring speeds ranging from 200 to 1200 r/min (in increments of 200 r/min) are shown in Figure 7B. The characteristic parameters of these spectra were summarized in Supplementary Table S4. As the stirring speed increased, the oscillation time shortened progressively, while the amplitude decreased correspondingly. In Figure 7C, the electrochemical fingerprint spectra of AH at experimental temperatures ranging from 302 to 318 K (in increments of 4 K) were illustrated. The characteristic parameters of these spectra are listed in Supplementary Table S5. As the experimental temperature increased, the oscillation time shortened progressively, while the amplitude decreased correspondingly. It could be seen that the AH powder caused regular changes in the Belousov-Zhabotinsky oscillation reaction. The rotation speed and temperature had a significant influence on this reaction, which should be strictly controlled during the experiment.

FIGURE 7

FIGURE 7

The effects of sample mass (A) rotation speed (B) and temperature (C) on the Belousov-Zhabotinsky oscillation reaction. Electrochemical fingerprint spectra of AH and CP under the same condition (D). Principal component analysis (E) and orthogonal partial least squares discriminant analysis (F) of AH and CP.

3.5.3 Electrochemical fingerprint spectra of AH and CP

The comparison between the electrochemical fingerprint spectra of AH and CP is illustrated in Figure 7D. It could be observed that the oscillation time of AH was significantly longer than that of CP, whereas the maximum amplitude of CP was notably larger than that of AH. To further differentiate the two medicinal plants, the PCA method was employed. The scatter plot is presented in Figure 7E, which shows the separation of AH and CP. The R2X and Q2 values of this model, at 0.687 and 0.591 respectively, indicated the reliability of the model. Furthermore, the OPLS-DA method was utilized to differentiate between these two medicinal plants (Figure 7F). The result was consistent with that obtained from PCA. The R2X, R2Y, and Q2 values of this model, standing at 0.568, 0.924, and 0.751 respectively, demonstrated the reliability of the outcomes. To assess the accuracy of the model, four unknown samples were analyzed, including two distinct AH samples and two distinct CP samples that had each been independently prepared. The results showed that the unknown samples could be accurately classified into their designated areas, demonstrating a 100% accuracy rate. Compared to LC-HR-Q-TOF-MS/MS, electrochemical fingerprint spectra exhibited the following significant advantages: (1) it allowed for direct analysis of plant powder without extraction, thus simplifying the operation; (2) the analysis time was short, and the data processing was simple. Therefore, it can be concluded that electrochemical fingerprint spectra can be effectively utilized to distinguish between AH and CP.

3.6 Integrated analysis of data and methods

AH and CP had very similar appearances, and they were often confused in the market. Given that AH contained toxic ingredients, and both AH and CP had irritating odors and tastes, traditional sensory identification methods, such as nose-sniffing and mouth-tasting, could not accurately distinguish between them. Furthermore, these methods might cause discomfort to the human body. Therefore, we used E-nose and E-tongue to distinguish between the two poisonous and medicinal plants. The E-nose provided the shortest analysis time among all technologies, enabling it to rapidly complete sample testing within 140 s. More importantly, it did not require extraction of samples and the plants could be directly used for analysis, greatly simplifying the operation process. In the PCA and DFA models, the reliability of the E-nose reached 98.912% and 100%, respectively, fully demonstrating its accuracy. The E-nose further disclosed that both AH and CP contained unpleasant ingredients. Specifically, AH included terpinolene, alpha-phellandrene, and camphor, which imparted flavors of anise, plastic, spiciness, and pepper. These ingredients might induce headaches and discomfort. On the other hand, CP contained camphor with a distinct, stimulating peppery taste that could also cause discomfort. At the same time, the E-tongue also revealed that the tastes of components in these two plants were bitter and astringent. In the PCA model, the R2 and Q2 values of the E-tongue were 0.869 and 0.607, respectively, indicating that the model had good predictive ability and stability. The R2X, R2Y, and Q2 values of the OPLS-DA model were 0.93, 0.936, and 0.895, respectively, further confirming the reliability of the results. Through LC-Q-TOF MS, we found that the bitter and astringent components in AH might be asarinin, N-isobutyl-2E,4E,8Z,10E-dodecatetraenamide, etc., while the bitter and astringent components in CP might be paeonol, etc. Due to the different components of AH and CP, their effects on the Belousov-Zhabotinsky reaction were also different. Based on the electrochemical fingerprint of the reaction, we achieved 100% accurate differentiation between AH and CP. By integrating data from E-nose, E-tongue, LC-HR-Q-TOF-MS/MS, and electrochemical fingerprint spectra, this study provided a diverse perspective based on odor, taste, and chemical composition, thereby providing powerful technical support for accurately distinguishing between AH and CP. It should be noted that the current study focused specifically on AH samples from Anguo City and CP samples from Lu’an City, which represented the mainstream sources of these medicinal plants in the Chinese herbal market. This study was based on a market survey revealing an adulteration practice in which AH (Anguo City) was adulterated with CP (Lu’an City) for illicit profit. Given that the quality of medicinal plants is influenced by geographical origins, growth stages, and plant parts, the impacts of these factors on the current methodology require further systematic and in-depth investigation.

In the field of medicinal plant identification, current techniques such as microscopic identification, DNA barcoding, and near-infrared spectroscopy exhibited distinct characteristics and inherent limitations when applied individually. Microscopic identification enabled rapid and cost-effective differentiation, but some microscopic characteristics lacked sufficient specificity to support accurate identification (Xu et al., 2015). Although DNA barcoding provided specific genetic information, it suffered from low resolution in distinguishing closely related species (Zhu et al., 2022). Near-infrared spectroscopy required minimal sample preparation, but its accuracy was susceptible to interference from factors such as moisture content and particle size (Yin et al., 2019). These limitations highlighted the inadequacy of a single method to address the complex demands of medicinal plant identification. In this study, E-nose, E-tongue, LC-HR-Q-TOF-MS/MS, and electrochemical fingerprint spectra were combined to distinguish the visually similar plants AH and CP. Actually, each method possessed distinct strengths and limitations. E-nose analysis required no sample extraction and could be completed within 3 minutes. However, its detectable targets were restricted to volatile compounds. E-tongue could substitute for human sensory evaluation in detecting the taste of toxic plants, but it was unable to distinguish specific taste components. LC-HR-Q-TOF-MS/MS could resolve chemical components, but data processing required a considerable amount of time. Electrochemical fingerprint spectra offered simple data processing with high accuracy. However, it could only reflect the plant’s electrochemical properties from a holistic perspective. Therefore, through complementary integration of these technologies, the limitations of individual methods were mitigated, and their strengths synergistically enhanced.

4 Conclusion

A novel strategy, incorporating dual electronic sensors (DES) and dual fingerprint spectra (DFS), was proposed for the authentication and differentiation of the highly similar poisonous and medicinal plants, AH and CP. The E-nose was utilized to identify 25 odor components in AH and 12 in CP within 140 s, effectively distinguishing the aroma profiles of the two plants. The E-tongue, combined with chemometrics, revealed that bitterness and astringency were the key differentiating tastes. Through the use of LC-HR-Q-TOF-MS/MS for chemical fingerprint spectra, 91 compounds in AH and 90 compounds in CP were identified. To further differentiate AH and CP, electrochemical fingerprint spectra based on the Belousov-Zhabotinsky reaction were established, achieving a 100% accuracy rate. In summary, this study represented the first instance of integrating E-nose, E-tongue, LC-HR-Q-TOF-MS/MS, and Belousov-Zhabotinsky reaction for the authentication and differentiation of highly similar poisonous and medicinal plants.

Statements

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Ethics statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

X-RZ: Methodology, Software, Writing – original draft, Writing – review and editing. Y-HC: Funding acquisition, Methodology, Supervision, Writing – original draft. J-NZ: Validation, Writing – review and editing. W-YW: Formal Analysis, Writing – review and editing. R-BS: Validation, Writing – review and editing. Z-XD: Writing – review and editing, Software. HZ: Writing – review and editing, Funding acquisition, Methodology. MX: Supervision, Writing – review and editing. T-GK: Writing – review and editing, Supervision. H-PS: Conceptualization, Supervision, Funding acquisition, Investigation, Writing – original draft.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by National Natural Science Foundation of China (82173935), Liaoning Natural Science Foundation (2024-MSLH-297), Shenyang Youth Science and Technology Innovation Talent Cultivation Project - U35 Top Youth Project (RC230846), Liaoning Provincial Education Department project (JYTMS20231826 and JYTQN2023455), and Youth Scientific and Technological Innovation Team Project (2024-JYTCB-080).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fchem.2025.1578126/full#supplementary-material

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Summary

Keywords

medicinal plants, electronic nose, electronic tongue, mass spectrometry, Belousov-Zhabotinsky reaction

Citation

Zhang X-R, Chen Y-H, Zhang J-N, Wang W-Y, Sun R-B, Ding Z-X, Zhang H, Xie M, Kang T-G and Song H-P (2025) Discrimination of poisonous and medicinal plants with similar appearance (Asarum heterotropoides vs. Cynanchum paniculatum) via a fusion method of E-nose, E-tongue, LC-HR-Q-TOF-MS/MS, and electrochemical fingerprint spectra. Front. Chem. 13:1578126. doi: 10.3389/fchem.2025.1578126

Received

17 February 2025

Accepted

10 April 2025

Published

29 April 2025

Volume

13 - 2025

Edited by

Anna Borioni, National Institute of Health (ISS), Italy

Reviewed by

Yulong Zhu, Anhui University of Chinese Medicine, China

Maria Cristina Gaudiano, National Institute of Health (ISS), Italy

Updates

Copyright

*Correspondence: Yue-Hua Chen, ; Hui-Peng Song,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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