Content area
Lonicera japonica Thunb. (LJT) is a clinically used bulk traditional Chinese medicine with high adaptability to grow in saline and alkaline environments. In this study, we established a quality evaluation method for LJT (Lonicerae Japonicae Flos [LJF] and Lonicera Japonica Leaves [LJL]) in saline and alkaline environments based on ultrahigh‐performance liquid chromatography (UPLC) technology, combined with fingerprinting, multivariate statistical analysis, and determination of multicomponent content. First, the fingerprint of LJF was established by UPLC, and a total of 13 peaks were calibrated, and nine chemical components were identified. The LJL fingerprint was established, a total of 16 peaks were calibrated, and 10 chemical components were identified and evaluated for similarity. Then, multivariate statistical analyses such as cluster heat map analysis, principal component analysis (PCA), orthogonal and partial least squares discriminant analysis (OPLS‐DA) were applied to classify LJT into “Yate 1” LJT and other LJT (“Yate 2” LJT and “Yate Liben” LJT). The other LJTs are further categorized into LJF (“Yate 2” LJF and “Yate Liben” LJF) and LJL (“Yate 2” LJL and “Yate Liben” LJL). LJF was further divided into “Yate 2” LJF and “Yate Liben” LJF. Different markers between the groups were screened. Finally, the content of 11 known components in 18 batches of LJT samples was determined. Differences in the content of “Yate 1” LJF, “Yate 2” LJF, and “Yate Liben” LJF were compared by t‐test. The results showed that the LJF content of the three varieties was significantly higher in saline than in nonsaline areas, or there was no significant difference. The quality evaluation method of saline LJT established in this study can effectively evaluate the quality of saline LJT, provide a reasonable reference for LJT cultivation in saline areas, and provide a scientific basis for the full application of different varieties of LJT herbs.
1. Introduction
Soil salinization is a global ecological issue, affecting approximately 20% of arable land worldwide [1, 2]. China is the third-largest country in terms of saline–alkali land distribution, with a total area of 185 million mu of saline–alkali land suitable for agricultural use. The Yellow River Delta is a typical example of coastal saline–alkali land and an important experimental site for exploring saline–alkali land remediation and comprehensive utilization [3]. Selecting and cultivating salt–alkali–tolerant medicinal plants serves a dual purpose of improving saline–alkali land and promoting comprehensive ecological utilization, thereby helping to fully tap the potential for ecological development and utilization of saline–alkali land. Lonicera japonica Thunb. (LJT) exhibits strong environmental adaptability, has a wide distribution, and can grow in soils with salt–alkali content below 3.5% [4], making it an ideal experimental variety. Lonicerae Japonicae Flos (LJF) is the medicinal part of LJT and is a commonly used bulk traditional Chinese medicine [5], widely applied in treating wind-heat colds, febrile diseases, and other conditions, offering significant economic and ecological benefits. Lonicera Japonica Leaves (LJL) share similar chemical compositions and pharmacological activities with LJF. Prior to the Song Dynasty, LJL was also one of the medicinal parts, with a longer history of medicinal use than LJF [6]. Experimental studies have shown that salt stress alters ion homeostasis in plant cells, leading to oxidative damage and subsequently affecting the accumulation of secondary metabolites. Phenolic acid compounds are secondary metabolites with high antioxidant capacity and play a crucial role in protecting plants from oxidative damage. Salt stress can induce the accumulation of phenolic acid components in LJT [7, 8]. Therefore, it is highly necessary to investigate the impact of saline–alkali soil cultivation environments on LJT quality and explore the feasibility of producing LJT in saline–alkali soil cultivation conditions.
LJF refers to the dried flower buds of LJT or those with newly opened flowers. Commercially available herbal medicines primarily use the “Erbai” and “Dabai” stages of LJF, with a small amount from the Sanqing stage. “Yate 1” LJT is a variant of LJT, scientifically known as LJT var. Chinensis. Its flower buds are purplish-red and are also referred to as Red LJF, commonly used to make flower tea. This study focuses on LJT cultivated in saline–alkali and non–saline–alkali environments. UPLC was used to establish fingerprint for LJF and LJL, followed by similarity evaluation and multivariate statistical analysis. The content of 11 components was also determined. The aim is to provide scientific basis for the quality evaluation and comprehensive utilization of LJT in saline–alkali environments.
2. Materials and Methods
2.1. Materials and Reagents
Neochlorogenic acid (purity 98%, batch number: D23GB172337), isochlorogenic acid A (purity 98%, batch number: P11D11L134209), cryptoclorogenic acid (purity 98%, batch number: M06GB147634), loganin (purity 98%, batch number: 20120427), rutin (purity 98%, batch number: T20N11Z131674), sweroside (purity 98%, batch number: P31M7F12286), and luteoloside (purity 98%, Batch number: Y19A8H34394) standard reference substances were all purchased from Shanghai Yuanye Biotechnology Co., Ltd. Secoxyloganin (purity 98%, batch number: ST20380110), isochlorogenic acid B (purity 98%, batch number: ST06590120), and isochlorogenic acid C (purity 98%, batch number: ST06600120) standard reference substances were all purchased from Shanghai Shidande Standard Technology Service Co., Ltd. Chlorogenic acid (purity 99.3%, batch number: 110,753–201,716) standard reference substance was purchased from the China Food and Drug Administration. Methanol, acetonitrile (product of DNK, United States of America, chromatographic grade), phosphoric acid (Tianjin Kemiou Chemical Reagent Co., Ltd., chromatographic grade), and ultra-pure water (prepared in the laboratory) were used.
The samples were identified by Dr. Lin Huibin of the Shandong Provincial Institute of Traditional Chinese Medicine as follows: “Yate 2” and “Yate Liben” were identified as LJT, while “Yate 1” was identified as a variant of LJT, LJT var. Chinensis. Flower buds (LJF) and leaves (LJL) of different LJT varieties were collected from both saline–alkali and non–saline–alkali cultivation environments, yielding a total of 18 LJT samples, which were dried and stored for later use. Sample source information is detailed in Table 1.
Table 1 The source of the 18 batches of LJT.
| No. | Assortment | Period | Collection time | Collection site |
| S1 | Yate 1 | Erbai | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S2 | Yate 1 | Dabai | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S3 | Yate 2 | Erbai | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S4 | Yate 2 | Dabai | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S5 | Yate Liben | Erbai | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S6 | Yate Liben | Dabai | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S7 | Yate 1 | Erbai | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
| S8 | Yate 1 | Dabai | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
| S9 | Yate 2 | Erbai | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
| S10 | Yate 2 | Dabai | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
| S11 | Yate Liben | Erbai | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-altitude Zone |
| S12 | Yate Liben | Dabai | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-altitude Zone |
| S13 | Yate 1 | leaves | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S14 | Yate 2 | leaves | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S15 | Yate Liben | leaves | 2022.5.27 | Shandong Yate Medicinal Plants Ecological Park |
| S16 | Yate 1 | leaves | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
| S17 | Yate 2 | leaves | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
| S18 | Yate Liben | leaves | 2022.5.23 | Saline land in Dongying Yellow Triangle Agricultural High-Altitude Zone |
2.2. Preparation of Samples and Reference Substance Solutions
Take approximately 0.5 g of sample powder (passed through a No. 4 sieve), weigh precisely, place in a stoppered conical flask, add precisely 50 mL of 75% methanol, weigh the flask, sonicate (power 512 W and frequency 40 kHz) for 30 min, cool to room temperature, weigh again, add 75% methanol to compensate for the weight loss, shake well, filter, take the filtrate, pass through a 0.22-μm microporous filter membrane, and the solution is obtained.
Take appropriate amounts of neochlorogenic acid, chlorogenic acid, cryptoclorogenic acid, loganin, sweroside, secoxyloganin, rutin, luteoloside, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C reference substances; weigh precisely; place in a volumetric flask; and add 75% methanol to prepare a reference substance stock solution containing 0.92, 2.08, 1.46, 2.14, 1.12, 2.04, 2.06, 0.09, 0.88, 0.96, and 2.64 mg per 1 mL. Take an appropriate amount of the 11 reference substance stock solutions, add 75% methanol, and prepare a mixed reference substance solution of a certain concentration.
2.3. UPLC Conditions of Fingerprint and Quantitative Analysis
An Agilent 1290 UPLC system (Agilent Technologies Inc., California, USA) was used to detect the analytes. Chromatographic separation was carried out with a Thermo Accucore C18 column (100 × 4.6 mm, 2.6 μm) using the mobile phase composed of acetonitrile (A) and 0.1% phosphoric acid (B) with the following gradient elution: 0–4 min, 8% A; 4–6 min, 8%–10% A; 6-7 min, 10% A; 7–10 min, 10%–14% A; 10–27 min, 14%–15% A; 27–35 min, 15%–25% A; 35–45 min, 25% A; 0.3 mL/min flow rate; 1 μL volume injection; and column set at 30°C. The detection wave length was 0–13.5 min, 324 nm; 13.5–18 min, 238 nm; 18–27 min, 354 nm; and 27–45 min, 324 nm.
2.4. Statistical Analysis
Data analysis was performed by the software Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2012). Cluster heat map was performed by Origin (Version 2018). PCA and OPLS-DA were performed by SIMCA (Version 14.1). T-test was performed by SPSS (Version 21.0).
3. Results and Discussion
3.1. Optimization of Chromatographic Conditions
About 0.1% phosphoric acid and acetonitrile were finally selected as mobile phases, column temperature was 30°C, and the flow rate was 0.3 mL/min. After a full-wavelength scan, it was found that the optimal absorption wavelength for phenolic acids is 324 nm, for iridoids is 241 nm, and for flavonoids is 354 nm. Therefore, the three absorption wavelengths of 324, 241, and 354 nm were selected, and detection was performed in segments based on peak elution times. This approach results in a shorter chromatogram, detection of multiple components, and high peak resolution.
3.2. UPLC Fingerprint
3.2.1. Method Validation of Fingerprint
Prepare one sample solution of LJT and perform six consecutive injections. The relative standard deviation (RSD) values for retention time and peak area are both less than 3%, indicating good instrument precision. Prepare six sample solutions of LJT and perform injections for detection. The RSD values for retention time and peak area are both less than 3%, indicating good reproducibility of the method. Prepare one sample solution of LJT and perform injections at 0, 2, 4, 8, 12, and 24 h. The RSD values for retention time and peak area are all less than 3%, indicating that the sample solution has good stability within 24 h.
3.2.2. UPLC Fingerprint Analysis
The results showed that the LJF fingerprint spectrum had 13 common peaks (Figure 1). The LJL fingerprint spectrum had 16 common peaks (Figure 2). Eleven standard reference substances were known, of which 10 were common peaks. Combining the common peaks of LJF and LJL and the known components, 14 peaks were selected for further analysis. Peaks 1, 2, 3, 4, 5, 6, 10, 11, 12, and 14 were identified as neochlorogenic acid, chlorogenic acid, cryptoclorogenic acid, loganin, sweroside, secoxyloganin, luteoloside, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C, respectively(Figure 3).
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3.2.3. Similarity Analysis
The results of the similarity analysis are shown in Table 2. Compared with the reference fingerprint of LJF, the similarity of LJF is 0.946–0.996. Compared with the reference fingerprint of LJL, the similarity of LJL is 0.937–0.978. This indicates that the quality of LJF and LJL is relatively stable.
Table 2 Similarity evaluation of 18 batches of LJT.
| No. | Similarity |
| Buds | |
| S1 | 0.967 |
| S2 | 0.956 |
| S3 | 0.989 |
| S4 | 0.996 |
| S5 | 0.979 |
| S6 | 0.979 |
| S7 | 0.946 |
| S8 | 0.952 |
| S9 | 0.963 |
| S10 | 0.977 |
| S11 | 0.984 |
| S12 | 0.989 |
| Leaves | |
| S13 | 0.959 |
| S14 | 0.939 |
| S15 | 0.937 |
| S16 | 0.964 |
| S17 | 0.976 |
| S18 | 0.978 |
3.3. Multivariate Statistical Analysis
3.3.1. Cluster Heat Map Analysis
The 14 common peak areas of 18 batches of LJT were brought into the Origin 2018 software for cluster heat map analysis, and the results are shown in Figure 4. The samples were divided into two major groups: Group I (S3, S4, S5, S6, S9, S10, S11, S12, S14, S15, S17, and S18) and Group II (S1, S2, S7, S8, S13, and S16), indicating that the “Yate 1” LJT was grouped together, while the other LJTs (“Yate 2” LJT and “Yate Liben” LJT) were grouped together. Group I can be further divided into Group Ia (S14, S15, S17, and S18) and Group Ib (S3, S4, S5, S6, S9, S10, S11, and S12), indicating that LJF (“Yate 2” LJF and “Yate Liben” LJF) are grouped together, while other LJL (“Yate 2” LJL and “Yate Liben” LJL) are grouped together. Group Ib can be further divided into Group Ib1 (S5, S6, S11, and S12) and Group Ib2 (S3, S4, S9, and S10), indicating that “Yate 2” LJF is grouped together, and “Yate Liben” LJF is grouped together.
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3.3.2. PCA Analysis
The 14 common peak areas of 18 batches of LJT were brought into the SIMCA 14.1 software for PCA analysis, and the results are shown in Figure 5. “Yate 1” LJT are distributed together, while other LJT (“Yate 2” LJT and “Yate Liben” LJT) are distributed together. Additionally, the LJF and LJL of other LJT show a tendency to be distinguishable. This indicates that there are differences between the “Yate 1” LJT and other LJT (the “Yate 2” LJT and “Yate Liben” LJT). There are also differences between the LJF and LJL of other LJT. The results of the PCA analysis are similar to those of the cluster analysis.
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3.3.3. OPLS-DA Analysis
The above analysis indicates that there are differences between “Yate 1” LJT and other LJTs (“Yate 2” LJT and “Yate Liben” LJT), between the LJF and LJL of other LJTs, and between “Yate 2” LJF and “Yate Liben” LJF. To further distinguish the differences between the three groups, OPLS-DA analysis was performed on each group. First, OPLS-DA analysis was performed on “Yate 1” LJT and other LJT (including “Yate 2” LJT and “Yate Liben” LJT). As shown in Figure 6(a), “Yate 1” LJT was distinguished from the other LJT (“Yate 2” LJT and “Yate Liben” LJT). The model was validated with 200 test runs. As shown in Figure 6(b), the regression line at Point Q2 intersects the vertical axis below the origin, indicating the model’s validity. As shown in Figures 6(c) and 6(d), based on the characteristics that VIP values > 1 and larger ion loading values in the loading plot indicate stronger intergroup separation ability, three differential markers were screened out: broken oxymatrine, Peak 7, chlorogenic acid, and isochlorogenic acid A. Then, OPLS-DA analysis was performed on other LJT samples (“Yate 2” LJT and “Yate Liben” LJT), dividing them into LJF and LJL groups. As shown in Figure 7(a), the LJF and LJL groups of other LJT samples were distinguished. As shown in Figure 7(b), the model validation was effective. As shown in Figures 7(c) and 7(d), three differential markers were screened based on the VIP plot and load plot, namely, chlorogenic acid, isochlorogenic acid A, and Peak 7. Finally, OPLS-DA analysis was performed on the LJF of other LJT, namely, “Yate 2” LJF and “Yate Liben” LJF. As shown in Figure 8(a), the “Yate 2” LJF and “Yate Liben” LJF are distinguished. As shown in Figure 8(b), the model validation is effective. As shown in Figures 8(c) and 8(d), four differential markers were screened based on the VIP plot and load plot, namely, Peak 7, chlorogenic acid, deoxygenated strychnos glycoside, and isochlorogenic acid A. This indicates that the differences between variants (“Yate 1” LJT vs. other LJTs) > differences between medicinal parts (LJF vs. LJL of other LJTs) > differences between varieties (“Yate 2” LJF vs. “Yate Liben” LJF) > differences between growing environments (saline–alkali soil vs. non–saline–alkali soil), which is consistent with the results of the cluster heat map and PCA analysis.
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3.4. Quantitative Analysis
3.4.1. Method Validation of Quantitative Analysis
The mixed reference solution is taken and injected six times consecutively. The RSD values for retention time and peak area are both less than 3%. Six LJT sample solutions are prepared, injected, and detected, and the RSD values for retention time and peak area of the 11 components are less than 3%. One LJT sample solution is prepared, injected, and detected at 0, 2, 4, 8, 12, and 24 h, and the RSD values for retention time and peak area of the 11 components are less than 3%. A standard curve is plot with the mass concentration of the reference standard as the x-axis (X) and the peak area as the y-axis (Y), linear regression is performed, and the regression equations and linear ranges are obtained for the 11 components, as shown in Table 3. Six LJT samples are weighed with known concentrations; a certain amount of each of the 11 reference standards is added to each sample; the sample solutions are prepared, injected, and detected them; and the recovery rates for neochlorogenic acid, chlorogenic acid, cryptoclorogenic acid, loganin, sweroside, secoxyloganin, rutin, luteoloside, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C being 101.83%, 100.24%, 101.65%, 100.76%, 96.36%, 98.99%, 97.60%, 98.79%, 103.05%, 98.12%, and 99.85%, respectively, were calculated with RSD values all below 3%. All results from the method validation indicate that the proposed method is reliable.
Table 3 Standard curves of 11 kinds of reference components.
| No. | Compounds | Regression equation | Linear range (μg/mL) | R2 |
| 1 | Neochlorogenic acid | Y = 17.528x − 16.601 | 1.25–311.55 | 0.9999 |
| 2 | Chlorogenic acid | Y = 18.651x + 60.482 | 7.00–210 0 | 0.9999 |
| 3 | Cryptochlorogenic acid | Y = 12.528x − 0.3421 | 0.51–15.38 | 0.9997 |
| 4 | Loganin | Y = 10.969x + 4.7057 | 1.51–301.13 | 1.0000 |
| 5 | Sweroside | Y = 10.981x + 1.8329 | 0.65–129.16 | 1.0000 |
| 6 | Secoxyloganin | Y = 9.4535x + 11.071 | 7.00–210 0.00 | 1.0000 |
| 7 | Rutin | Y = 10.469x + 2.3945 | 0.59–59.43 | 0.9999 |
| 8 | Luteoloside | Y = 17.879x + 5.476 9 | 0.46–92.13 | 0.9999 |
| 9 | Isochlorogenic acid B | Y = 21.197x − 3.6847 | 0.32–32.34 | 0.9998 |
| 10 | Isochlorogenic acid A | Y = 21.992x − 26.575 | 4.00–100 0.00 | 1.0000 |
| 11 | Isochlorogenic acid C | Y = 23.288x − 3.2175 | 1.37–136.54 | 0.9999 |
3.4.2. Quantitative Analysis of 18 Batches of LJT
Quantitative analysis of 18 batches of LJT samples using UPLC was conducted to determine the content of neochlorogenic acid, chlorogenic acid, cryptoclorogenic acid, loganin, sweroside, secoxyloganin, rutin, luteoloside, isochlorogenic acid B, isochlorogenic acid A, and isochlorogenic acid C. The results are shown in Table 4. A rough analysis revealed that there were significant differences in the compound content of the three types of LJT. The differences in LJT compound content between different planting environments (saline–alkaline soil and non–saline–alkaline soil) were relatively small.
Table 4 Contents of 18 batches of LJT.
| No. | Period | Mass fraction (mg/g) | ||||||||||
| Neochlorogenic acid | Chlorogenic acid | Cryptochlorogenic acid | Loganin | Sweroside | Secoxyloganin | Rutin | Luteoloside | Isochlorogenic acid B | Isochlorogenic acid A | Isochlorogenic acid C | ||
| S1 | Erbai | 0.41 | 20.06 | 0.2 | 0.88 | 0.7 | 19.34 | 1.66 | 0.94 | 0.23 | 6.91 | 1.64 |
| S2 | Dabai | 0.59 | 33.98 | 0.35 | 0.62 | 0.22 | 34.35 | 1.79 | 1.07 | 0.32 | 8.64 | 1.96 |
| S3 | Erbai | 1.08 | 40.87 | 0.43 | 1.77 | 1.45 | 6.57 | — | 0.44 | 0.44 | 15.56 | 2.25 |
| S4 | Dabai | 1.02 | 34.26 | 0.46 | 1.75 | 0.77 | 10.22 | — | 0.58 | 0.4 | 14.27 | 1.77 |
| S5 | Erbai | 0.83 | 30.37 | 0.37 | 1.29 | 2.14 | 2.21 | 1.19 | 0.41 | 0.4 | 13.66 | 2.16 |
| S6 | Dabai | 0.82 | 30.9 | 0.45 | 1.19 | 1.38 | 1.77 | 1.48 | 0.54 | 0.52 | 13.33 | 2.15 |
| S7 | Erbai | 0.65 | 40.39 | 0.6 | 0.82 | — | 46 | 2.4 | 1.15 | 0.47 | 10.97 | 2.59 |
| S8 | Dabai | 0.64 | 37.05 | 0.56 | 0.98 | — | 39.69 | 2.43 | 1.2 | 0.44 | 9.9 | 2.37 |
| S9 | Erbai | 1.11 | 37.33 | 0.38 | 2.32 | 1.03 | 5.78 | — | 0.51 | 0.47 | 19.48 | 2 |
| S10 | Dabai | 0.99 | 32.82 | 0.42 | 2.61 | 0.78 | 6.55 | — | 0.65 | 0.39 | 13.89 | 1.35 |
| S11 | Erbai | 1.03 | 33.65 | 0.56 | 1.54 | 1.38 | 7.12 | 1.79 | 0.44 | 0.72 | 16.73 | 3.03 |
| S12 | Dabai | 1.01 | 31.17 | 0.63 | 1.01 | 0.85 | 6.75 | 1.66 | 0.4 | 0.75 | 16.05 | 2.48 |
| S13 | Leaves | 1.04 | 47.12 | 0.38 | 1.41 | 0.33 | 54.1 | 5.26 | 9.22 | 0.57 | 26.43 | 5.7 |
| S14 | Leaves | 2.56 | 27.63 | 0.41 | 1.91 | 0.52 | 6.01 | 0.06 | 2.53 | 0.35 | 15.4 | 2.2 |
| S15 | Leaves | 1.89 | 21.5 | 0.44 | 1.15 | 0.85 | 4.41 | 1.02 | 1.63 | 0.76 | 26.28 | 4.22 |
| S16 | Leaves | 1.64 | 40.15 | 0.54 | 1.19 | 0.14 | 35.66 | 2.68 | 4.99 | 0.54 | 17.69 | 3.83 |
| S17 | Leaves | 2.18 | 26.77 | 0.36 | 1.68 | 0.37 | 7.65 | — | 1.66 | 0.3 | 18.75 | 2.47 |
| S18 | Leaves | 1.34 | 33.28 | 0.47 | 1.83 | 0.05 | 3.87 | 2.06 | 3.31 | 0.74 | 25.37 | 4.68 |
LJF is the medicinal part of LJT. To further investigate the quality differences in LJF, an independent t-test analysis was conducted on 11 known components. The results are shown in Table 5. The rutin content in LJF from saline–alkali soil “Yate 1” was significantly higher than that in LJF from non–saline–alkali soil “Yate 1,” while there were no significant differences in the content of the other 10 components. The loganin content in saline–alkali soil “Yate 2” LJF was significantly higher than that in non–saline–alkali soil “Yate 2” LJF, while there were no significant differences in the content of the remaining 9 components. The content of neochlorogenic acid, secoxyloganin, isochlorogenic acid B, and isochlorogenic acid A in the saline–alkali soil “Yate Liben” LJF was significantly higher than that in the non–saline–alkali soil “Yate Liben” LJF, while there were no significant differences in the content of the remaining 7 components. This indicates that the saline–alkali soil environment has a certain promotional effect on the increase in the content of certain components in LJF. This may be related to the fact that nonbiotic stress to some extent promotes the synthesis and accumulation of secondary metabolites. This suggests that saline–alkali soil can be used for the cultivation and production of LJF.
Table 5 T-test analysis of 12 batches of LJF.
| Compounds | “Yate 1” LJF | “Yate 2” LJF | “Yate Liben” LJF | ||||||
| Nonsaline (mg/g) | Saline soil (mg/g) | p value | Nonsaline (mg/g) | Saline soil (mg/g) | p value | Nonsaline (mg/g) | Saline soil (mg/g) | p value | |
| Neochlorogenic acid | 0.50 | 0.65 | 0.25 | 1.05 | 1.05 | > 0.99 | 0.83 | 1.02 | 3.3 × 10−3∗∗ |
| Chlorogenic acid | 27.02 | 38.72 | 0.24 | 37.57 | 35.08 | 0.60 | 30.64 | 32.41 | 0.30 |
| Cryptochlorogenic acid | 0.28 | 0.58 | 0.06 | 0.45 | 0.40 | 0.21 | 0.41 | 0.60 | 0.07 |
| Loganin | 0.75 | 0.90 | 0.43 | 1.76 | 2.47 | 0.04∗ | 1.24 | 1.28 | 0.91 |
| Sweroside | 0.46 | 0.00 | 0.20 | 1.11 | 0.91 | 0.63 | 1.76 | 1.12 | 0.30 |
| Secoxyloganin | 26.85 | 42.85 | 0.19 | 8.40 | 6.17 | 0.35 | 1.99 | 6.94 | 3.4 × 10−3∗∗ |
| Rutin | 1.73 | 2.42 | 9.2 × 10−3∗∗ | — | — | — | 1.34 | 1.73 | 0.13 |
| Luteoloside | 1.01 | 1.18 | 0.13 | 0.51 | 0.58 | 0.55 | 0.48 | 0.42 | 0.50 |
| Isochlorogenic acid B | 0.28 | 0.46 | 0.06 | 0.42 | 0.43 | 0.84 | 0.46 | 0.74 | 4.7 × 10−2∗ |
| Isochlorogenic acid A | 7.78 | 10.44 | 0.12 | 14.92 | 16.69 | 0.60 | 13.50 | 16.39 | 1.7 × 10−2∗ |
| Isochlorogenic acid C | 1.80 | 2.48 | 0.07 | 2.01 | 1.68 | 0.49 | 2.16 | 2.76 | 0.16 |
3.5. Discussion
LJT is commonly used in clinical bulk Chinese herbal medicine, and its chemical composition has been studied more. In recent years, the commonly used method for analyzing the chemical composition is high-performance liquid chromatography (HPLC) with single-wavelength detection, which detects fewer components [9–12]. In this study, UPLC was used for full wavelength scanning (190–400 nm) to select the optimal absorption wavelengths of each component. Phenolic acid’s optimal absorption wavelength is 324 nm, iridoids’ optimal absorption wavelength is 241 nm, and flavonoids’ optimal absorption wavelength is 354 nm. Therefore, three absorption wavelengths of 324, 241, and 354 nm were selected and detected in segments according to the peak appearance time. This allows as many components as possible to be detected under OE chromatogram. When methanol was used as the mobile phase, the peak shape of the chromatographic peaks was poor, the baseline was not flat, and the separation effect was not good, so acetonitrile was chosen as the mobile phase. The fingerprint of LJT and the method for the determination of the content of 11 components were established by the above study.
Studies on the evaluation of LJT quality have focused on the examination of single factors such as origin, authenticity identification, and varietal differences [13–17]. Studies addressing the evaluation of LJT quality in saline cultivation environments have not been reported. This study is the first to systematically compare the degree magnitude of the effects of varietal type, varietal differences, and saline cultivation environment on the quality of LJT. Three different varieties of LJT were involved in this experiment, of which “Yate 1” LJT was a variant of LJT. The fingerprints of LJF and LJL were established in this experiment, and the results of similarity analysis showed that the quality of LJF and LJL was relatively stable. Through multivariate statistical analysis, it was found that there were differences between LJTs grown in different germplasm and soils (saline and nonsaline), and the magnitude of the differences was as follows: differences between variants > differences between varieties > differences between planting soils, and the differences between “Yate 1” LJT and other LJTs (“Yate 2” LJT and “Yate Liben” LJT) > differences between“Yate 2” LJT and “Yate Liben” LJT > differences between saline and nonsaline LJTs. The saline environment had the least effect on LJT quality compared to the effect of variants and varieties on LJT quality. It shows that saline and alkaline land can be considered for LJT cultivation, which can help to promote the comprehensive development and utilization of saline and alkaline land.
LJF is the medicinal part of LJT. The content of neochlorogenic acid, secoxyloganin, isochlorogenic acid B, and isochlorogenic acid A in saline “Yate Liben” LJF was found to be higher than that of nonsaline “Yate Liben” LJF. This is consistent with the findings of Cai et al. [18–20], who found that salt stress could promote the accumulation of LJF active components. For example, the expression of genes related to the synthesis of phenylpropanoids, flavonoids, and iridoids was mostly dynamically upregulated under salt stress, which also promoted the accumulation of phenolic acid components. Through reviewing the literature, it is known that salinity affects the chemical composition and metabolite production in plants, which is related to the gene expression of the plant itself, as well as to the saline soil composition and microbial composition [21–24]. Therefore, the next step of our group will be to conduct relevant research on the inter-root microbial situation of LJT in saline environment. We will analyze the causes of salt stress tolerance in LJT from the microbial point of view. On the other hand, we will conduct relevant studies on the gene expression of salinity-tolerant plants and try to screen the genes for salt stress tolerance. This will provide new ideas for the screening of saline-tolerant plants. Reference for the development and utilization of saline and alkaline land was provided.
4. Conclusion
The UPLC fingerprinting, multivariate statistical analysis, and multicomponent content determination methods established in this study are stable and feasible for the quality evaluation of saline LJT. The results of cluster heat map analysis, PCA analysis, and OPLS-DA analysis were consistent, and the LJT was classified into two groups of “Yate 1” LJT and other LJT (“Yate 2” LJT and “Yate Liben” LJT), and other LJTs were further categorized into LJF (“Yate 2” LJF and “Yate Liben” LJF) and LJL (“Yate 2” LJL and “Yate Liben” LJL), and LJF is further categorized into “Yate 2” LJF and “Yate Liben” LJF. The results of content determination showed that the content of saline LJF was slightly better than that of nonsaline LJF. In conclusion, the present study established a new method to evaluate the quality of saline LJT, innovatively provided a preliminary evaluation of the quality of saline LJT, and provided a reference for the cultivation of LJT in saline areas. It provides a scientific basis for the quality control of saline LJT.
Data Availability Statement
The data used to support the findings of this study are included within the article.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
Shu Wang was responsible for data analysis and paper writing. Xichang Yu and Ruiqi Guo were responsible for data collation and literature review. Qiuchen Zhao reviewed the paper. Shengbo Li was responsible for resource acquisition. Huibin Lin and Renwei Guan were responsible for review, funding acquisition, and project management.
Funding
This work was supported by the Special funding for the Innovation Capability Enhancement Project for Technological Small and Medium Sized Enterprises (grant number: 2023TSGC0444), Cultivation of High-Level Talents in Traditional Chinese Medicine in Shandong Province (grant number: 2023143), National Traditional Chinese Medicine Characteristic Technology Inheritance Talent Training Project (grant number: 202396), Key Research and Development Projects of Linyi City (grant number: 2022026), National Administration of Traditional Chinese Medicine High-Level Key Discipline Development Project for Traditional Chinese Medicine (grant number: zyyzdxk-2023121), National Key Research and Development Program of China (grant number: 2017YFC1701500), Enhancement of Traditional Chinese Medicine Resource Guarantee Capability Project (grant number: 202319), and Taishan Industry Leading Talent Project (grant number: tscy20230623).
Acknowledgments
This work was supported by the Special funding for the Innovation Capability Enhancement Project for technological small- and medium-sized enterprises (grant number: 2023TSGC0444), cultivation of high-level talents in traditional Chinese medicine in Shandong Province (grant number: 2023143), National Traditional Chinese Medicine Characteristic Technology Inheritance Talent Training Project (grant number: 202396), key research and development projects of Linyi City (grant number: 2022026), National Administration of Traditional Chinese Medicine High-Level Key Discipline Development Project for Traditional Chinese Medicine (grant number: zyyzdxk-2023121), National Key Research and Development Program of China (grant number: 2017YFC1701500), Enhancement of Traditional Chinese Medicine Resource Guarantee Capability Project (grant number: 202319), and Taishan Industry Leading Talent Project (grant number: tscy20230623).
1 Goswami S. K., Kashyap A. S., Kumar R., Gujjar R. S., Singh A., and Manzar N., Harnessing Rhizospheric Microbes for Eco-Friendly and Sustainable Crop Production in Saline Environments, Current Microbiology. (2023) 81, no. 1, https://doi.org/10.1007/s00284-023-03538-z.
2 Liu L. L. and Wang B. S., Protection of Halophytes and Their Uses for Cultivation of Saline-Alkali Soil in China, Biology. (2021) 10, no. 5, https://doi.org/10.3390/biology10050353.
3 Hao W. F., Dong J., Lu Q. S., Ban X. C., Xi M., and Li J., Vegetation Selection for Saline-Alkaline Land Improvement, Anhui Agricultural Bulletin. (2020) 26, no. 22, 119–122.
4 Hu Y. T., Li T. X., Zang X. Y., Gou Q. X., Yan G. J., and Lu T. L., Research on Hedge Planting and Mechanical Harvesting Technology of Lonicera japonica in Saline and Alkaline Land, Chinese Materia Medica. (2021) 44, no. 3, 530–532.
5 Huang W. J., Xiong L. W., Zhang L. F. et al., Changes of Flavonoid Content During the Development of Honeysuckle From Different Germplasm, Chinese Herbal Medicine. (2022) 53, no. 10, 3156–3164.
6 He H. Y., Meng P. P., Yu B. Y., and Zhang H. R., Textual Research & General Situation of Modern Research on Medicinal Parts of Lonicera japonica Thunb, Journal of Shaoguan University. (2023) 44, no. 3, 68–72.
7 Yan K., Cui M. X., Zhao S. J., Chen X. B., and Tang X. L., Salinity Stress is Beneficial to the Accumulation of Chlorogenic Acids in Honeysuckle (Lonicera japonica Thunb.), Frontiers in Plant Science. (2016) 7, https://doi.org/10.3389/fpls.2016.01563, 2-s2.0-84992580018.
8 Yan K., Zhao S. J., Bian L. X., and Chen X. B., Saline Stress Enhanced Accumulation of Leaf Phenolics in Honeysuckle (Lonicera japonica Thunb.) Without Induction of Oxidative Stress, Plant Physiology and Biochemistry. (2017) 112, 326–334, https://doi.org/10.1016/j.plaphy.2017.01.020, 2-s2.0-85010422683.
9 Yang G. Y., Cheng M. H., Qin S. H., and Liu Y. J., Determination of Phenolic Acid Components in Lonicerae japonicae Flos From Zhaotong by HPLC Method, Yunnan Chemical Technology. (2025) 52, no. 5, 88–91.
10 Meng R., Yuan L. Y., Feng W. et al., Screening of High Phenolic Acid Salt Resistant Lonicera japonica Thunb. Varieties Based on HPLC Fingerprint, Journal of Hebei Agricultural Sciences. (2024) 28, no. 3, 69–73.
11 Yi L. W., Wu T. X., Mai Y. Q., Liu B. Z., Wang J. Q., and Li Y. Y., HPLC Determination of the Content of Strychnine in Different Developmental Stages of Honeysuckle “Beihua No. 1, Rural Economy and Science-Technology. (2024) 35, no. 7, 62–64.
12 Hao P. J., Zhang L. X., Jin W. J. et al., Grade Evaluation of Color Sorting Lonicerae japonicae Flos Based on HPLC Fingerprint and Index Components Combined With Multivariate Statistics, Chinese Journal of Modern Applied Pharmacy. (2023) 40, no. 19, 2694–2701.
13 Liu W., Tan H. Z., Jiang L. W., Du G. R., Li P., and Tang H., Nondestructive Identification of Lonicerae japonicae Flos and Flos Lonicerae With Near Infrared Spectroscopy and New Variable Selection-Partial Least Squares Discriminant Analysis, Spectroscopy and Spectral Analysis. (2025) 45, no. 6, 1605–1611.
14 Zhang S. Z., Zhang Y. P., Wei Y. J. et al., Quality Evaluation and Metabolomics Analysis of Different Varieties of Lonicerae japonicae Flos Cultivated in Gansu Province, Chinese Traditional and Herbal Drugs. (2025) 56, no. 6, 2135–2147.
15 Wang J. L., Cha S. H., Lian C. C., and Zhang H., Research on Rapid Identification of Lonicerae japonicae Flos and Lonicerae Flos on the Basis of Heracles Neo Ultra-Fast Gas Phase Electronic Nose, Journal of Food Safety & Quality. (2024) 15, no. 13, 142–147.
16 Xu X. B., Xu P., Si Z. M. et al., Evaluation of the Utilization Value of Different Germplasmoflonicera Japonica Thunb Branches and Leaves Basedon Phenolic Acid Components, Science and Technology of Food Industry. (2024) 45, no. 19, 247–255.
17 Liu T. L., Yang L. L., Dong C. M. et al., Study on HPLC Fingerprint of Lonicera japonica Flos From Different Areas Based on Chemical Pattern Recognition, Chinese Traditional and Herbal Drugs. (2022) 53, no. 15, 4833–4843.
18 Cai Z. C., Wang C. C., Chen C. H. et al., Comparative Transcriptome Analysis Reveals Variations of Bioactive Constituents in Lonicera japonica Flowers Under Salt Stress, Plant Physiology and Biochemistry. (2022) 173, 87–96, https://doi.org/10.1016/j.plaphy.2022.01.022.
19 Cai Z. C., Wang C. C., Chen C. H. et al., Omics Map of Bioactive Constituents in Lonicera japonica Flowers Under Salt Stress, Industrial Crops and Products. (2021) 167, no. 1, https://doi.org/10.1016/j.indcrop.2021.113526.
20 Cai Z. C., Liu X. H., Chen H. et al., Variations in Morphology, Physiology, and Multiple Bioactive Constituents of Lonicerae japonicae Flos Under Salt Stress, Scientific Reports. (2021) 11, no. 1, https://doi.org/10.1038/s41598-021-83566-6.
21 Ou Yang K., Tan S. Y., Zhang W. S., and Zhi Q. Q., Isolation and Identification of Rhizosphere Microorganisms and Endophytes in Okra, Journal of Zhejiang Agricultural Sciences. (2025) 66, no. 5, 1072–1077.
22 Yang Y. Z., Analysis of Rhizosphere Microbial Community Ofglycine Max Under Salt Stress and Screening Application of Salt-Resistant Growth-Promoting Strains, 2024, Chinese Academy of Agricultural Sciences.
23 Wang X. C., Han P., Wang Y. N., and Gao T., Research Progress on the Effects of Saline-Alkali Soil on Plant Physiology and Rhizosphere Soil Microorganisms, Seed Science & Technology. (2023) 41, no. 23, 22–24.
24 Sun R. B., Zhang F. L., Zong J. W. et al., Research Progress on the Role of Microorganisms in the Remediation of Saline-Alkali Land, Guangdong Agricultural Sciences. (2025) 52, no. 2, 14–30.
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