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Article

Elucidating Flavonoid and Antioxidant Activity in Edible and Medicinal Herbs Woodwardia japonica (L.f.) Sm. Based on HPLC-ESI-TOF-MS and Artificial Neural Network Model: Response to Climatic Factors

by
Xin Wang
1,2,
Jianguo Cao
3,
Lin Tian
4,
Baodong Liu
1,
Yawen Fan
1 and
Quanxi Wang
1,3,5,*
1
College of Life Science and Technology, Harbin Normal University, Harbin 150025, China
2
Heilongjiang Research Center of Genuine Wild Medicinal Materials Germplasm Resources, Harbin 150025, China
3
College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
4
Changchun Institute of Technology, College of Water Conservancy and Environmental Engineering, Changchun 130012, China
5
Shanghai Key Laboratory of Plant Functional Genomics and Resources, Chinese Academy of Sciences, Shanghai Chenshan Botanical Garden, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
Molecules 2023, 28(4), 1985; https://doi.org/10.3390/molecules28041985
Submission received: 6 February 2023 / Revised: 14 February 2023 / Accepted: 15 February 2023 / Published: 20 February 2023
(This article belongs to the Special Issue Analyses and Applications of Phenolic Compounds in Food)
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Abstract

:
Woodwardia japonica is a kind of great potential edible and medicinal fern. In a previous study, it was found that flavonoid and antioxidant activity of W. japonica from different sites were different. However, the cause of the differences has still been unclear, which has restricted the utilization of W. japonica. In this paper, flavonoid and antioxidant activity of W. japonica from nine different regions were determined with the method of a colorimetric assay with UV-VIS spectrophotometry and HPLC-ESI-TOF-MS, and the effects of climate factors on flavonoids and antioxidant activities were evaluated by mathematical modeling and statistical methods. The results showed: (1) total flavonoid content (TFC) of W. japonica from Wuyi Mountain (Jiangxi) was the highest, which might be related to the low temperature; (2) the differences of antioxidant activities of W. japonica might be related to precipitation; (3) five flavonols, two flavones and one isoflavone were tentatively identified in W. japonica; (4) flavonol and isoflavone might be affected by sunshine duration, and flavones were probably related to temperature. In conclusion, the effects of climate factors on flavonoids and antioxidants are significant, which would provide an important basis for further exploring the mechanism of climate affecting secondary metabolites.

1. Introduction

As a minority part of vegetable consumption in many communities across the globe, edible ferns are gradually attracting attention [1,2]. Young fronds, rhizomes, stems and tubers of ferns can be eaten as fresh greens, cooked as vegetables or processed for starch, and there are about 52 species of edible ferns traditionally consumed as food throughout the country at present [3,4]. In recent years, more and more research has reported the food uses of pteridophytes in different parts of the world.
Ferns are rich in flavonoids [5,6], which applied in the prevention and managing of various diseases such as cancers [7], diabetes [3,8], HIV and so on [9]. W. japonica is an important edible and medicinal fern in China, South Korea, Japan and other countries. In a previous report, we found that total flavonoid contents (TFC) of W. japonica from different habitats were obviously different. However, so far, the cause of the obvious differences remains unclear.
Climate factors were an important and common factor that affects secondary metabolism, especially flavonoids [10,11,12,13]. Therefore, it is critical to adopt effective analytical methods to understand the response of flavonoid and antioxidant activity of W. japonica to climatic factors for species to accomplish their full potential and guide cultivation.
The Artificial Neural Networks method (ANN) is one of the most widely methods used for studying the response of hydrological factors to climate, multi-objective optimization of material selection for sustainable products, evaluation of an artificial neural network retention index model for chemical structure identification in nontargeted metabolomics and so on [14,15,16]. In recent years, ANN modeling methods have grown in popularity due to their excellent capability to learn and model complex non-linear relationships, widely used in ecology and agronomy. Herein, GAs (genetic algorithms) are one of the most efficient global search methods. A BP network is one of the artificial neural network algorithms, a multi-layer feed forward training method based on error back propagation.
In this paper, flavonoid and antioxidant activity of W. japonica from different habitats were determined with HPLC-ESI-TOF-MS, ANN was used to evaluate the effects of climate factors on flavonoids and antioxidant activities, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed, as well as Pearson correlation coefficients were used for verifying the evaluation.

2. Results and Discussion

2.1. TFC and Antioxidant Activities of W. japonica from Nine Main Production Areas

As edible and medicinal ferns, TFCs of W. japonica have been reported several times, which shows W. japonica is rich in flavonoids [5,17,18,19]. In this paper, TFCs of W. japonica from nine main producing areas were different, but all were more than 114.55 mg/g in Figure 1. TFC of W. japonica from Wuyi Mountain (Jiangxi Province) was the highest (345.4 mg/g), and that of W. japonica from Tianmu Mountain was the lowest (114.5 mg/g).
Antioxidant activities of W. japonica from different districts were obviously different. IC50 (including scavenging activities of DPPH·, ABTS·, O2−·) and reducing force on Fe3+ of W. japonica from different districts were showed in Figure 2. W. japonica from Wuyi Mountain (Jiangxi Province) showed the strongest scavenging to DPPH radicals and reducing force on Fe3+, but weaker on eliminating ABTS radicals and superoxide anion radicals. However, W. japonica from Fengyang Mountain showed the strongest radical scavenging activity to ABTS radicals and superoxide anion radicals.
TFC and antioxidant activity of plants are two important evaluation indices to selection of plant for healthcare company. Numerous physiological processes and some environmental factors occasionally produce oxygen centered free radicals, which may lead to diseases including cancer, atherosclerosis, cirrhosis and so on [20]. Therefore, the search for antioxidants in industrial plants is a very active field of research [21]. The results in this paper confirmed that W. japonica with strong antioxidant activity has the potential to be applied to healthcare companies and deserved further exploiting.

2.2. Flavonoids of W. japonica from Different Districts

By means of HPLC-ESI-TOF-MS, accurate mass, mass match and isotope pattern match contributed to the identification, and eight flavonoids were tentatively identified on the basis of the characteristic fragments present in the spectra and reference spectra available in Figure 3. Rt (retention time), UVλmax, the molecular ions and so on are listed in Table 1. Tentatively identified flavonoids contained five flavonols (isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, quercitrin), two flavones (luteolin 6-C-glucoside and luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside) and one isoflavone (genestein G 2).
With UVλmax at 283 nm, molecular ions at m/z [M-H]−463.0882, [M+H]+465.1026 and [2M-H]−927.1837, C21H20O12 was automatically matched to by the software, and peak 6 was tentatively identified as isotrifolin [22]. Peak 7 with UVλmax at 265, 285 and 350 nm, and molecular ions at m/z [M-H]−609.1461, [M+H]+611.1621. C27H30O16 was automatically matched to and tentatively identified as Rutin [23]. With the negative ESI-MS spectrum, peak 9 was automatically matched to a molecular ion at m/z [M-H]−463.0882, the characteristic absorption was at 270, 280 and 355 nm. So peak 9 was tentatively identified as myricetin deoxyhexoside [24]. Peak 10 had UVλmax at 270, 285 and 335 nm, m/z [M-H]−609.1461, was tentatively determined as quercetin-3-rutinoside [25]. Peak 11 had UVλmax at 270, 280 and 340 nm, which was similar to luteolin 6-C-glucoside, molecular ions at m/z [M-H]−447.0933 and automatically matched to C21H20O11 [26]. For peak 12, a molecular ion at m/z [M-H]−447.0933 was observed in negative ESI-MS spectrum, the UV spectrum (245, 265 and 350 nm) which indicated this compound might be flavonol and tentatively identified as quercitrin [27]. The molecular ions of peak 13 were at m/z [M-H]−575.1768 in the negative ESI-MS spectrum, and automatically matched to C28H32O13, with the UVλmax at 235 and 285 nm, it was tentatively identified as genestein G2 [28]. For peak 14, the UVλmax at 235, 280 and 335 nm, and m/z [M-H]−609.0257, so it was tentatively identified as luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside [29].
The results showed that samples from Longquan were rich in isotrifolin, and samples from Wuyi Mountain (Jiangxi Province) were rich in rutin and myricetin deoxyhexoside. Samples from Tianmu Mountain were rich in quercetin-3-rutinoside, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside and genestein G2, but samples from Wuyuan County were rich in luteolin 6-C-glucoside.

2.3. Evaluating for the Effects of Climate Factors on Flavonoids and Antioxidant Activities by the Method of ANN, the Correlation Coefficient Matrix and Hierarchical Cluster Analysis (HCA)

The results of the sensitivity analysis showed that a non-significant difference existed between the climate factors and total flavonoid content, but the effects of climate factors on O2− radicals scavenging activity and reducing power of Fe3+ of W. japonica were more obvious than other antioxidant activities.
According to the ANN, total flavonoid content was the most sensitive to MIT (Average minimum temperature), followed AP (Average precipitation) and ARH (Average relative humidity) in Figure 4. While O2− radicals scavenging activity was the most sensitive to MAT (Maximum temperature) and ARH (Relative humidity), reducing power of Fe3+ was the most sensitive to MIT (Average minimum temperature) and AP (Average precipitation), MIT (Average minimum temperature) and AP (Average precipitation) were the main sensitive factors to scavenging activity of DPPH radicals. The key factors affecting ABTS radicals scavenging activity were not obvious, but relatively, low temperature had a slightly larger impact on ABTS radicals scavenging activity in Figure 5. Basically, it showed that low temperature and relative humidity were the key climate factors to influencing total flavonoid content. Decreasing the temperature on a small scale could increase the flavonoid content [30], which explained the above results, while the antioxidant activity was most sensitive to temperature, followed by relative humidity and precipitation.
The Pearson correlation coefficient was used to measure of the strength of the linear relationship between two variables and ranges from −1 to 1 [31]. A linear correlation between the total flavonoid content and antioxidant activity of W. japonica and climate factors was listed in Table S2 (Supplementary Material). The results showed that there was a non-significant difference between the climate factors and total flavonoid content and antioxidant activity of W. japonica in Figure 6. However, the influence of climate on the total flavonoids and antioxidant activity did exist. Average daytime precipitation per month and average maximum temperature per month showed a low positive correlation with total flavonoid content, and average sunshine duration per month showed a low negative correlation with total flavonoid content.
Average daytime precipitation per month showed a medium negative correlation with DPPH radical scavenging activity. ABTS radical scavenging activity had a medium positive correlation with maximum temperature and a medium negative correlation with average daytime precipitation. The relationship between O2− radical scavenging activity and average relative humidity was a medium negative correlation. In addition, the low positive correlation between average maximum temperature and the reducing force on Fe3+ was detected.
HCA was performed with a heatmap visualizing to illustrate the differences among samples. According to HCA in Figure 7, the total flavonoid content and antioxidant activity were more affected by average relative humidity and sunshine duration than other climate factors. Among the nine main producing areas, the areas with higher average overnight precipitation went along with relatively higher total flavonoids content, stronger scavenging DPPH radical activity and reducing power of Fe3+. However, the producing area with the highest average temperature per month had higher scavenging activity of ABTS radicals and O2− radicals.
The above results showed that there was a non-significant difference between the climate factors and total flavonoid content, but temperature, precipitation and relative humidity did affect the total flavonoid content of W. japonica. Antioxidant activity was confirmed to be relative to precipitation, followed by temperature. Through the statistical validation, the evaluating by the method of ANN for the effects of climate factors on flavonoids and antioxidant activities was accurate and reasonable.

2.4. Response of Flavonoid Type of W. japonica to Climatic Factors with ANN, Pearson Correlation Coefficient, PCA and HCA

The results of the sensitivity analysis (ANN) showed that sunshine duration was the main and key influential factors on flavonoid type of W. japonica in Figure 8. Besides sunshine duration, temperature was also effective influential factors on quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside.
In addition, genestein G2 and luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside were also sensitive to average precipitation. Based on this, it was evaluated that flavones and flavonols were most sensitive to sunshine and temperature, but isoflavone was sensitive to sunshine, temperature and precipitation. In addition, the accumulation of rutin, myricetin deoxyhexoside, quercetin-3-rutinoside and quercitrin would be also affected by average maximum temperature.
The Pearson correlation coefficients were listed in Table S3 (Supplementary Material). Quercetin-3-rutinoside showed high negative correlation with average minimum temperature per month, and medium negative correlation with average precipitation per month. Luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside was moderately negatively correlated with average precipitation. Myricetin deoxyhexoside and quercitrin were moderately negatively correlated with average sunshine duration. Isotrifolin was moderately positively correlated with average minimum temperature and average precipitation, but moderately negatively correlated with average sunshine duration. Rutin and luteolin 6-C-glucosidewere both moderately negatively correlated with average minimum temperature. Genestein G2 was positively correlated with average sunshine duration and relative humidity. In a word, the content of isoflavone was positively correlated with average sunshine duration and relative humidity. The content of flavonol was related to sunshine duration, overnight precipitation and low temperature.
Principal component analysis (PCA) was a common mathematical procedure, which was inclined to reduce high-dimensional data by expressing them with a set of linearly uncorrelated orthogonal basis. According to PCA score plots in Figure 9, it was found that PC1 mainly reflects the information of average precipitation per month, average minimum temperature per month and overnight precipitation, and PC2 mainly reflects the information of daytime precipitation per month. The score of isotrifolin and quercetin-3-rutinoside (flavonol) on PC1 was higher than other compounds. Therefore, it could be speculated that the content of isotrifolin and quercetin-3-rutinoside (flavonol) were mainly influenced by overnight precipitation and minimum temperature, especially for the content of isotrifolin.
Conversely, the score of rutin, myricetin deoxyhexoside, quercitrin, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside on PC2 was higher than other compounds. It followed that the content of rutin, myricetin deoxyhexoside, quercitrin, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside was mainly affected by daytime precipitation, especially for the content of myricetin deoxyhexoside.
The dendrogram and heatmap of hierarchical cluster analysis clearly and visually show the influence of climate factors on different flavonoid type in Figure 10. The positive influence of overnight precipitation and minimum temperature on the content of isotrifolin was obvious, but excessive sunshine duration might inhibit the producing of isotrifolin. The content of rutin was related to sunshine duration. Main factors influencing the content of myricetin deoxyhexoside were overnight precipitation, average relative humidity and sunshine duration. Average relative humidity was the key climate factor of genestein G2 (isoflavone). The contents of quercetin-3-rutinoside and luteolin 6-C-glucoside might be reduced by low temperature and overnight precipitation. The effect of maximum temperature on quercitrin was significant, but excessive sunshine could inhibit the producing of quercitrin. luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside mainly be positive impacted by sunshine duration and relative humidity, but negative impacted by overnight precipitation.
The above results showed that flavonol and isoflavone in the tentatively identified components, except for quercetin-3-rutinoside, were mainly affected by sunshine duration, and flavones were probably more related to temperature. Through the statistical validation, the evaluating by the method of ANN for the effects of climate factors on flavonoids type was basically consistent with our results except for quercetin-3-rutinoside.
Secondary metabolites were considered to be related to abiotic factors [32,33]. Sunshine had certain impact on plant morphological, plant development and metabolites [11]. Combined with the above analysis results, it was found that sunshine duration did have a great influence on flavonols and isoflavone. The result further confirmed the effect of sunshine on plant metabolism.
It was reported that temperature was an important factor affecting flavonol accumulation [34], low temperature would promote the accumulation of flavonols [35], and during 30–40 °C, flavonol synthesis was inhibited [36,37]. In this result, the accumulation of rutin, myricetin deoxyhexoside, quercetin-3-rutinoside and quercitrin (flavonols) were relative to average maximum temperature per month. However, low temperature could not promote the accumulation of all flavonol compounds. Only the accumulation of isotrifolin was found to be significantly relative to low temperature in this paper.
A little information was reported on the effects of relative humidity on the contents of flavonol in fruits and berries. Yang et al. [38] reported that flavonol glycosides of red currants were little relative to humidity variables. In this paper, relative humidity was negative correlation to O2− radical scavenging activity.
At present, no significant correlation was observed between the precipitation and the contents of each secondary metabolite. Kalinova et al. [39] reported that no correlations between epicatechin (flavanol) and precipitation were statistically significant. Ribeiro et al. [40] indicated that precipitation could reflect a stronger in vitro antioxidant activity of Secondatia floribunda. Dong et al. [41] found that there seems to be no direct relationship between the flavonoid content of Eucommia ulmoides and the precipitation, which was consistent with our result. In this paper, it was elucidated that the precipitation was no significantly associated with flavonoids, but significantly affected antioxidant activity. Through building ANN, the effects of climate factors on flavonoids and antioxidant activities were evaluated, and the results have been verified to be effective.

3. Materials and Methods

3.1. Plant Materials

W. japonica as a represent species in Blechnaceae, distributed in the south of the Yangtze River in China. The height of W. Japonica is 0.8–1.2 m. The rhizome is stout, recumbent and dark. The sporangia group is linear, on both sides of the main vein narrow long mesh in Figure 11.
Nine main production areas of W. japonica were showed in Figure 12. Meteorological data (August in 2015) were derived from National Meteorological Information Center, China Meteorological Administration. Temperature, precipitation, sunshine duration and relative humidity were collected from nine climate stations (Shangrao, Hongjia, Yushan, Yuhuan, Qimen, Lishui, Liuhe, Jianyang, Jianyang and Wuyi Mountain) (Table S1) (Supplementary Material). About 5–10 samples were randomly selected from each sample site. Marked specimens (SNU-2015-8) were stored in College of Life science, Shanghai Normal University.

3.2. Chemicals

Rutin (purity > 99.0%), DPPH (2, 2-diphenyl-1-picrylhydrazyl), ABTS (2, 21-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid), NBT (Nitrotetrazolium blue chloride), PMS (Phenazine methosulfate), NADH (Nicotinamide adenine dinucleotide) and TPTZ (2,4,6-tri-2-pyridyl-s-triazine), were purchased from Sigma Co. (Shanghai, China).

3.3. Preparation of Plant Extracts

The whole plants of W. japonica were separately placed in the shade for 72 h, and then dried in an oven for 48 h (75 °C) and crushed. After filtering through a 40-mesh screen, collected respectively. One gram powder of the sample was respectively extracted twice with 60% ethanol, and the constant volume was 50 mL. The extract was stored at 4 °C respectively.

3.4. Determination of TFCs

The methods for determination of flavonoids was similar to previous reports [42]. The concentration of rutin standard solution was 20 µg/mL. The calibration curves were y = 10.655x − 0.0076 and R2 = 0.99869. Absorbance was read on a TU-1810 UV-spectrophotometer (Beijing, China) at 510 nm.
Step 1: With the method of modified colorimetric assay, rutin was taken as the standard. By the way of drawing standard curve, 5 mL different concentration rutin (0.02 mg/mL, 0.04 mg/mL, 0.06 mg/mL, 0.08 mg/mL, 0.1 mg/mL) was added 0.3 mL 5% NaNO2 (6 min), 0.3 mL 5% Al(NO3)3 (6 min), 4.4 mL 4% NaOH (12 min) in turn, and then optical density (OD) at 510 nm were recorded respectively. According to the rutin standard curve, the linear equation (y = Bx + A) of one variable is synthesized.
Step 2: 2 mL extract from different collecting sites was also added NaNO2, Al(NO3)3 and NaOH in turn, the method was same with the step 1. The formula used was as follows:
TFC (%) = [(OD1 + OD2 + OD3)]/3 − A]/B × 10/2 × volume/1000 × 100%

3.5. Antioxidant Activity

3.5.1. DPPH· Scavenging Assay

Briefly, 1 mL 0.1 mM DPPH and 1 mL different concentrations of 60% ethanol extract of W. japonica were mixed and then incubated for 30 min in the dark [43]. The absorbance was recorded at 517 nm. Methanol as the control was substituted for sample. The scavenging percentage (%) was calculated by the following equation (All the determinations were performed in triplicate and found to be reproducible within the experimental error):
DPPH scavenging percentage (%) = (1 − Asample517/Acontrol517) × 100

3.5.2. ABTS· Scavenging Assay

The mixture (7 mM ABTS and 2.45 mM potassium persulfate) was in the dark for 12 h before use. When the absorbance at 734 nm reached to 0.7 ± 0.01, different concentration extracts were mixed with ABTS solutions for 6 min [43]. The absorbance at 734 nm was recorded, and 60% methanol was taken as the control. The scavenging activity (%) was calculated by the following equation (all the determinations were performed in triplicates and found to be reproducible within the experimental error):
ABTS scavenging activity (%) = (1 − Asample734/Acontrol734) × 100

3.5.3. Superoxide Anion (O2−) Scavenging Assay

One mL of NBT (150 µM), 1.0 mL of NADH (468 µM) dissolved in sodium phosphate buffer (100 mM, pH = 7.4) and 1.0 mL of PMS (60 µM) dissolved in purified water were added with 1.0 mL different concentration extracts, respectively [42]. After incubating at 25 °C for 5 min, the absorbance at 560 nm was recorded. The scavenging activity (%) was calculated by the following equation (all the determinations were performed in triplicates and found to be reproducible within the experimental error):
Superoxide anion scavenging activity (%) = (1 − Asample560/Acontrol560) × 100

3.5.4. Reducing Force on Fe3+ Assay

One mL extract of different concentrations dissolved in 2.5 mL phosphate buffer (pH = 6.0) mixed with 2.5 mL of potassium ferricyanide (1%, w/v) at 50 °C. The reaction was terminated with 10% TCA after 20 min. The mixture centrifuged at 3000 rpm for 10 min were divided into two parts, one part (part A) of which were added 2.5 mL of distilled water and 0.5 mL of 0.1% ferric chloride in turn, and the other part (part B) mixed with 3 mL of distilled water. The absorbance was read at 700 nm, respectively. Part B was taken as blank [42]. (All the determinations were performed in triplicates and found to be reproducible within the experimental error).

3.6. HPLC-ESI-TOF-MS Analysis

The conditions of chromatographic gradient separation and the gradient elute settings of mobile phase were as same as previous paper [43].

3.7. Model Construction

3.7.1. Sample Organization

Measured parameters/sample variables include: average temperature per month, maximum temperature per month, minimum temperature per month, average precipitation per month, daytime precipitation per month, overnight precipitation per month, sunshine duration per month, average relative humidity per month. The effect of each observed parameter on the content of compound 1–8 was evaluated. Subsequently, samples include environmental factors were organized on the basis of the GAs. Meanwhile, the key factors affecting the content of compound 1–8 were identified.

3.7.2. The Selection of Parameter

On the basis of an improved BP algorithm, network training combined the optimized initial weight of the GAs with the limited supervised adjustment of learning rates [44]. In order to improve the BP net learning algorithm avoiding local defects, BP net was optimized by the genetic algorithm. Firstly, optimizing the weights and threshold of BP net, and then assigned to optimized BP net. This method can effectively avoid the original local defects of BP net, meanwhile with the characteristics of fast convergence and high accuracy. The hidden layer node of the BP net is determined as 30–20 under multiple trials. An S-type logsig function was used for transferring the net, the topology structure of which was 8-50-30-1. All the input of the samples was between 0.10 and 0.90.
All the assays were repeated three times, and the average output error was less than 0.02. The error estimation formula to be:
ε < (Δ)2 × N (where N is sample size)
where N is sample size.

3.7.3. Sensitivity Analysis

Seven environmental factors influence on nine flavonoids content were analyzed and discussed with GAS.

3.8. Statistical Analysis

After normalization of the data, PCA and HCA as well as the Pearson correlation coefficients were used for statistical analysis. The mean of three independent experiments was recorded, and the main statistical software was R-project (Auckland, New Zealand), Origin 7.5 (Northampton, MA, USA) and matlab 7.0 (Natick, MA, USA).

4. Conclusions

In current research, the differences of total flavonoid contents and antioxidant activities of W. japonica from different regions were significant. W. japonica from Wuyi Mountain (Jiangxi Province) showed the highest total flavonoid contents (345.4 mg/g) and the strongest scavenging to DPPH radicals and reducing force on Fe3+. However, W. japonica from Fengyang Mountain showed the strongest radical scavenging activity to ABTS radicals and superoxide anion radicals. In addition, five flavonols, two flavones and one isoflavone were tentatively identified in W. japonica with HPLC-ESI-TOF-MS. Further results showed that the response of flavonoids and antioxidant activity to climatic factors was complex, combined ANN and statistical analysis, but it was certain that sunshine duration was the key factor on flavonol and isoflavone, and temperature was the key factor on flavones. All of the above results illustrate that the effects of climate factors on flavonoids and antioxidants was significant, which would provide an important basis for further exploring the mechanism of climate affecting secondary metabolites.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules28041985/s1, Table S1: The Climate Factors of Woodwardia japonica Collected from Nine Main Production Areas; Table S2: Pearson Correlation Coefficients Between TFC, Antioxidant Activity of W. japonica and Climate Factors a; Table S3: Pearson Correlation Coefficients between Flavonoid Type of W. japonica and Climate Factors a.

Author Contributions

Conceptualization and study design, X.W. and Q.W. Data acquisition, and targeted method development and validation, L.T. and X.W. Data processing and analysis, interpretation of results, compound identification and original draft preparation, X.W. Supervision and manuscript revision, Y.F. and Q.W. Resources, project administration, J.C. and B.L. Funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

The authors are grateful for financial support from the Startup Foundation for Doctors of Harbin Normal University.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available from the corresponding author on requested.

Acknowledgments

We are grateful to Chengjian Zheng from Second Military Medical University for excellent technical assistance.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chettri, S. Nutrient and Elemental Composition of Wild Edible Ferns of the Himalaya. Am. Fern. J. 2018, 108, 95–106. [Google Scholar] [CrossRef]
  2. Nekrasov, E.V.; Svetashev, V.I. Edible Far Eastern Ferns as a Dietary Source of Long-Chain Polyunsaturated Fatty Acids. Foods 2021, 10, 1220. [Google Scholar] [CrossRef] [PubMed]
  3. Xiao, J.B. Dietary flavonoid aglycones and their glycosides: What show better biological benefits? Crit. Rev. Food Sci. 2017, 57, 1874–1905. [Google Scholar]
  4. Liu, Y.; Wujisguleng, W.; Long, C. Food uses of ferns in China: A review. Acta Soc. Bot. Pol. 2012, 81, 263–270. [Google Scholar] [CrossRef]
  5. Wang, X.; Wang, M.L.; Cao, J.G.; Wu, Y.H.; Xiao, J.B.; Wang, Q.X. Analysis of flavonoids and antioxidants in extracts of ferns from Tianmu Mountain in Zhejiang Province (China). Ind. Crop. Prod. 2017, 97, 137–145. [Google Scholar] [CrossRef]
  6. Cao, H.; Chai, T.T.; Wang, X.; Morais-Braga, M.F.B.; Yang, J.H.; Wong, F.C.; Wang, R.B.; Yao, H.K.; Cao, J.G.; Cornara, L.; et al. Phytochemicals from fern species: Potential for medicine applications. Phytochem. Rev. 2017, 16, 379–440. [Google Scholar] [CrossRef] [PubMed]
  7. Cao, J.G.; Xia, X.; Chen, X.F.; Xiao, J.B.; Wang, Q.X. Characterization of flavonoids from Dryopteris erythrosora and evaluation of their antioxidant, anticancer and acetylcholinesterase inhibition activities. Food Chem. Toxicol. 2013, 51, 242–250. [Google Scholar] [CrossRef]
  8. Cao, H.; Ou, J.Y.; Chen, L.; Zhang, Y.B.; Szkudelski, T.; Delmas, D.; Daglia, M.; Xiao, J.B. Dietary polyphenols and type 2 diabetes: Human Study and Clinical Trial. Crit. Rev. Food Sci. 2019, 59, 3371–3379. [Google Scholar] [CrossRef]
  9. Andrae-Marobela, K.; Ghislain, F.W.; Okatch, H.; Majinda, R.R. Polyphenols: A diverse class of multi-target anti-HIV-1 agents. Curr. Drug Metab. 2013, 14, 392–413. [Google Scholar] [CrossRef]
  10. Deng, B.; Cao, Y.N.; Fang, S.Z.; Shang, X.L.; Yang, W.X.; Qian, C.Y. Variation and stability of growth and leaf favonoid content in Cyclocarya paliurus across environments. Ind. Crop. Prod. 2015, 76, 386–393. [Google Scholar] [CrossRef]
  11. Xu, Y.; Wang, G.B.; Cao, F.L.; Zhu, C.C.; Wang, G.Y.; EI-Kassaby, Y.A. Light intensity affects the growth and flavonol biosynthesis of Ginkgo (Ginkgo biloba L.). New Forest 2014, 45, 765–776. [Google Scholar] [CrossRef]
  12. Herrera, J.C.; Hochberg, U.; Degu, A.; Sabbatini, P.; Lazarovitch, N.; Castellarin, S.D.; Fait, A.; Alberti, G.; Peterlunger, E. Grape metabolic response to postveraison water deficit is affected by interseason weather variability. J. Agric. Food Chem. 2017, 65, 5868–5878. [Google Scholar] [CrossRef]
  13. Zheng, T.; Sun, J.Q.; Shi, X.J.; Liu, D.L.; Sun, B.Y.; Deng, Y.J.; Zhang, D.L.; Liu, S.M. Evaluation of climate factors affecting the quality of red huajiao (Zanthoxylum bungeanum maxim.) based on UPLC-MS/MS and MaxEnt model. Food Chem. X 2022, 16, 100522. [Google Scholar] [CrossRef]
  14. Zhou, C.C.; Yin, G.F.; Hu, X.B. Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach. Mater. Des. 2009, 30, 1209–1215. [Google Scholar] [CrossRef]
  15. Samaraweera, M.A.; Hall, L.M.; Hill, D.W.; Grant, D.F. Evaluation of an artificial neural network retention index model for chemical structure identification in nontargeted metabolomics. Anal. Chem. 2018, 90, 12752–12760. [Google Scholar] [CrossRef] [PubMed]
  16. Jahirul, M.I.; Rasul, M.G.; Brown, R.J. Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN). Renew. Energ. 2021, 168, 632–646. [Google Scholar] [CrossRef]
  17. Xia, X.; Cao, J.G.; Zheng, Y.X.; Wang, Q.X.; Xiao, J.B. Flavonoid concentrations and bioactivity of flavonoid extracts from 19 species of ferns from China. Ind. Crop. Prod. 2014, 58, 91–98. [Google Scholar] [CrossRef]
  18. Takuli, P.; Khulbe, K.; Kumar, P.; Parki, A.; Syed, A.; Elgorban, A.M. Phytochemical profiling, antioxidant and antibacterial efficacy of a native Himalayan Fern: Woodwardia unigemmata (Makino) Nakai. Saudi J. Biol. Sci. 2020, 27, 1961–1967. [Google Scholar] [CrossRef] [PubMed]
  19. Ding, Z.T.; Fang, Y.S.; Tai, Z.G.; Yang, M.H.; Xu, Y.Q.; Li, F.; Cao, Q.E. Phenolic content and radical scavenging capacity of 31 species of ferns. Fitoterapia 2008, 79, 581–583. [Google Scholar] [CrossRef]
  20. Halliwell, B.; Gutteridge, J.M. Oxygen toxicity, oxygen radicals, transition metals and disease. Biochem. J. 1984, 219, 1–4. [Google Scholar] [CrossRef]
  21. Kim, M.Y.; Seguin, P.; Ahn, J.K.; Kim, J.J.; Chun, S.C.; Kim, E.H.; Seo, S.H.; Kang, E.Y.; Kim, S.L.; Park, Y.J.; et al. Phenolic compound concentration and antioxidant activities of edible and medicinal mushrooms from Korea. J. Agric. Food Chem. 2008, 56, 7265–7270. [Google Scholar] [CrossRef] [PubMed]
  22. Bafor, E.E.; Eze, C.; Omoruyi, O.; Elvis-Offiah, B.U.; Viegelmann, C.; Edrada-Ebel, R.A. Green tea inhibits uterine contractility in ex vivo (non-pregnant) mice models. Trop. J. Nat. Prod. Res. 2018, 2, 254–261. [Google Scholar] [CrossRef]
  23. Nguyen, T.T.; Parat, M.O.; Hodson, M.P.; Pan, J.; Shaw, P.N.; Hewavitharana, A.K. Chemical characterization and in vitro cytotoxicity on squamous cell carcinoma cells of Carica papaya leaf extracts. Toxins 2016, 8, 7. [Google Scholar] [CrossRef] [PubMed]
  24. D’Urso, G.; Sarais, G.; Lai, C.; Pizza, C.; Montoro, P. LC-MS based metabolomics study of different parts of myrtle berry from Sardinia (Italy). J. Berry Res. 2017, 7, 217–229. [Google Scholar] [CrossRef]
  25. Console, L.; Giangregorio, N.; Cellamare, S.; Bolognino, I.; Palasciano, M.; Indiveri, C.; Incampo, G.; Campana, S.; Tonazzi, A. Human mitochondrial carnitine acylcarnitine carrier: Molecular target of dietary bioactive polyphenols from sweet cherry (Prunus avium L.). Chem. Biol. Interact. 2019, 307, 179–185. [Google Scholar] [CrossRef] [PubMed]
  26. Zia-ur-Rehman; Rasheed, H.M.; Bashir, K.; Gurgul, A.; Wahid, F.; Che, C.T.; Shahzadi, I.; Khan, T.; Rasheed, H. UHPLC-MS/MS-GNPS based phytochemical investigation of Dryopteris ramosa (Hope) C. Chr. and evaluation of cytotoxicity against liver and prostate cancer cell lines. Heliyon 2022, 8, e11286. [Google Scholar] [CrossRef] [PubMed]
  27. Maia, I.R.D.O.; Trevisan, M.T.S.; Silva, M.G.D.V.; Klika, K.D.; Brito, E.D.; Silva, L.M.A.E.; Pinto, F.D.C.L.; Breuer, A.; Owen, R.W. Characterization and quantitation of polyphenolic compounds in Senna splendida from the northeast of Brazil. Nat. Prod. Commun. 2018, 13, 1934578X1801300614. [Google Scholar]
  28. Hu, J.F.; Wunderlich, D.; Sattler, I.; Thiericke, R.; Grabley, S.; Feng, X.Z. New 2-O-methylrhamno-isoflavones from Streptomyces sp. Nat. Prod. Res. 2003, 17, 451–458. [Google Scholar] [CrossRef]
  29. Ma, W.Y.; Ma, L.P.; Yi, B.; Zhang, M.; Feng, S.X.; Tian, L.P. Antidiabetic activity of Callicarpa nudiflora extract in type 2 diabetic rats via activation of the AMPK-ACC pathway. Asian Pac. J. Trop. Bio. 2019, 9, 456. [Google Scholar]
  30. Caldwell, C.R.; Britz, S.J.; Mirecki, R.M. Effect of temperature, elevated carbon dioxide, and drought during seed development on the isoflavone content of dwarf soybean [Glycine max (L.) Merrill] grown in controlled environments. J. Agric. Food Chem. 2005, 53, 1125–1129. [Google Scholar] [CrossRef]
  31. Neugebauer, A.; Schieberle, P.; Granvogl, M. Characterization of the key odorants causing the musty and fusty/muddy sediment off-flavors in olive oils. J. Agric. Food Chem. 2021, 69, 14878–14892. [Google Scholar] [CrossRef] [PubMed]
  32. Cope, K.R.; Bugbee, B. Spectral effects of three types of white light-emitting diodes on plant growth and development: Absolute versus relative amounts of blue light. HortScience 2013, 48, 504–509. [Google Scholar] [CrossRef]
  33. Berger, J.M.; Itagaki, Y.; Nakanishi, K. The effect of ultraviolet-depleted light on the flavonol contents of the cactus species Opuntia wilcoxii and Opuntia violacea. Chem. Biodivers 2007, 4, 1525–1532. [Google Scholar] [CrossRef]
  34. Ubi, B.E.; Honda, C.; Bessho, H.; Kondo, S.; Wada, M.; Kobayashi, S.; Moriguchi, T. Expression analysis of anthocyanin biosynthetic genes in apple skin: Effect of UV-B and temperature. Plant Sci. 2006, 170, 571–578. [Google Scholar] [CrossRef]
  35. Bhatia, C.; Pandey, A.; Gaddam, S.R.; Hoecker, U.; Trivedi, P.K. Low temperature- enhanced flavonol synthesis requires light-associated regulatory components in Arabidopsis thaliana. Plant Cell Physiol. 2018, 59, 2099–2112. [Google Scholar] [CrossRef] [Green Version]
  36. Yan, Y.F.; Song, C.Z.; Falginella, L.G.; Castellarin, S.D. Day temperature has a stronger effect than night temperature on anthocyanin and flavonol accumulation in ‘Merlot’ (Vitis vinifera L.) grapes during ripening. Front. Plant Sci. 2020, 11, 1095. [Google Scholar] [CrossRef]
  37. Pastore, C.; Allegro, G.; Valentini, G.; Muzzi, E.; Filippetti, I. Anthocyanin and flavonol composition response to veraison leaf removal on Cabernet Sauvignon, Nero d’Avola, Raboso Piave and Sangiovese Vitis vinifera L. cultivars. Sci. Hortic-Amst. 2017, 218, 147–150. [Google Scholar] [CrossRef]
  38. Yang, B.; Zheng, J.; Laaksonen, O.; Tahvonen, R.; Kallio, H. Effects of latitude and weather conditions on phenolic compounds in currant (Ribes spp.) cultivars. J. Agric. Food Chem. 2013, 61, 3517–3532. [Google Scholar] [CrossRef]
  39. Kalinova, J.; Vrchotova, N. The influence of organic and conventional crop management, variety and year on the yield and flavonoid level in common buckwheat groats. Food Chem. 2011, 127, 602–608. [Google Scholar] [CrossRef]
  40. Ribeiro, D.A.; Camilo, C.J.; Nonato, C.F.A.; Rodrigues, F.F.G.; Menezes, I.R.A.; Ribeiro-Filho, J.; Xiao, J.B.; Souza, M.M.A. Influence of seasonal variation on phenolic content and in vitro antioxidant activity of Secondatia floribunda A. DC. (Apocynaceae). Food Chem. 2020, 315, 126277. [Google Scholar] [CrossRef]
  41. Dong, J.E.; Ma, X.H.; Wei, Q.; Peng, S.B.; Zhang, S.C. Effects of growing location on the contents of secondary metabolites in the leaves of four selected superior clones of Eucommia ulmoides. Ind. Crop. Prod. 2011, 34, 1607–1614. [Google Scholar] [CrossRef]
  42. Wang, X.; Cao, J.G.; Wu, Y.H.; Wang, Q.X.; Xiao, J.B. Flavonoids, antioxidant potential, and acetylcholinesterase inhibition activity of the extracts from the gametophyte and archegoniophore of Marchantia polymorpha L. Molecules 2016, 21, 360. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Wang, X.; Ding, G.H.; Liu, B.D.; Wang, Q.X. Flavonoids and antioxidant activity of rare and endangered fern: Isoetes sinensis. PLoS ONE 2020, 15, e0232185. [Google Scholar] [CrossRef] [PubMed]
  44. Tian, L.; Li, H.Y.; Li, F.P.; Li, X.B.; Du, X.Q.; Ye, X.Y. Identification of key influence factors and an empirical formula for spring snowmelt-runoff: A case study in mid-temperate zone of northeast China. Sci. Rep. 2018, 8, 1–12. [Google Scholar] [CrossRef] [Green Version]
Molecules 28 01985 g001 550
Figure 1. TFCs of Woodwardia japonica from different districts (mg/g) (w/w).
Figure 1. TFCs of Woodwardia japonica from different districts (mg/g) (w/w).
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Figure 2. IC50 (including scavenging activities of DPPH, ABTS, O2−) and reducing force on Fe3+ of W. japonica.
Figure 2. IC50 (including scavenging activities of DPPH, ABTS, O2−) and reducing force on Fe3+ of W. japonica.
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Figure 3. Flavonoids of W. japonica from different habitats (Sample sites shown in Table S1, in Supplementary Materials).
Figure 3. Flavonoids of W. japonica from different habitats (Sample sites shown in Table S1, in Supplementary Materials).
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Figure 4. Response of total flavonoid content to climate factors based on the Artificial Neural Network Model. AT: Average temperature per month; MAT: Maximum temperature per month; MIT: minimum temperature per month; AP: Average precipitation per month; DP: Daytime precipitation per month; OP: Overnight precipitation per month; SD: Sunshine duration per month; ARH: Average relative humidity per month.
Figure 4. Response of total flavonoid content to climate factors based on the Artificial Neural Network Model. AT: Average temperature per month; MAT: Maximum temperature per month; MIT: minimum temperature per month; AP: Average precipitation per month; DP: Daytime precipitation per month; OP: Overnight precipitation per month; SD: Sunshine duration per month; ARH: Average relative humidity per month.
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Figure 5. Response of antioxidant activity to climate factors.
Figure 5. Response of antioxidant activity to climate factors.
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Figure 6. The correlation coefficient matrix between TFC, antioxidant activity of W. japonica and climate factors.
Figure 6. The correlation coefficient matrix between TFC, antioxidant activity of W. japonica and climate factors.
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Figure 7. Dendrogram and heatmap of hierarchical cluster analysis of climate factors, TFC and antioxidant activity among nine main producing areas of W. japonica. Concentrations are illustrated by a color gradient from blue (high) to red (low).
Figure 7. Dendrogram and heatmap of hierarchical cluster analysis of climate factors, TFC and antioxidant activity among nine main producing areas of W. japonica. Concentrations are illustrated by a color gradient from blue (high) to red (low).
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Figure 8. Compound 1–8: isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside.
Figure 8. Compound 1–8: isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside.
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Figure 9. PCA score plots using flavonoid types with different climate factors (n = 3). 1–8: isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside. The blue arrows refer to different climate factors.
Figure 9. PCA score plots using flavonoid types with different climate factors (n = 3). 1–8: isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside. The blue arrows refer to different climate factors.
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Figure 10. Dendrogram and heatmap of hierarchical cluster analysis of climate factors among eight flavonoids of W. japonica. Concentrations are illustrated by a color gradient from blue (high) to red (low). Compound 1–8: isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside.
Figure 10. Dendrogram and heatmap of hierarchical cluster analysis of climate factors among eight flavonoids of W. japonica. Concentrations are illustrated by a color gradient from blue (high) to red (low). Compound 1–8: isotrifolin, rutin, myricetin deoxyhexoside, quercetin-3-rutinoside, luteolin 6-C-glucoside, quercitrin, genestein G2, luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside.
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Figure 11. Morphological characteristics of W. japonica.
Figure 11. Morphological characteristics of W. japonica.
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Figure 12. The distribution of nine main producing areas of W. japonica.
Figure 12. The distribution of nine main producing areas of W. japonica.
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Table
Table 1. Qualitative Analysis of Compounds of Woodwardia japonica with HPLC-ESI-TOF-MS.
Table 1. Qualitative Analysis of Compounds of Woodwardia japonica with HPLC-ESI-TOF-MS.
No.RT(Min)Compound FormulaMolar MassExperimental
m/z		ErrorUV (nm)
λmax		Identification
(ppm)(mDa)
16.74C7H6O4154.0269[M-H]−153.0193, [M+H]+155.0394−1.59−0.24280Protocatechuate
214.76C16H18O9354.0954[M-H]−353.0877, [2M-H]−707.1832
[M+H]+355.1027		−0.79−0.28285,325Chlorogenic acid
319.45C32H36O16676.1999[M-H]−675.1931, [M-C7H12O2]−547.1466
[M-C7H12O2-C10H10O5]−338.0996		0.690.47283,313Unknown
421.50C16H16O8336.0845[M-H]−335.07720.170.06230,280Unknown
525.04C18H26O8370.1626[M-H]−369.15550.50.19230,285Unknown
625.87C21H20O12464.0954[M-H]−463.0882, [M+H]+465.1026
[2M-H]−927.1837		−0.22−0.1283Isotrifolin
(flavonol)
729.58C27H30O16610.1527[M-H]−609.1461, [M+H]+611.16211.160.71265,285,350Rutin (flavonol)
831.17C33H48O16700.2945[M-H]−699.287−0.42−0.29230,280Unknown
931.7C21H20O12464.0954[M-H]−463.0882, [2M-H]−827.1829
[M+H]+465.1041		0.080.04270,280,355Myricetin deoxyhexoside
(flavonol)
1032.35C27H30O16610.1533[M-H]−609.1461, [M+H]+611.16130.150.09270,285,335Quercetin-3-rutinoside (flavonol)
1135.28C21H20O11448.1011[M-H]−447.0933, [M+H]+449.1095
[2M-H]−895.1918		−1.26−0.57270,280,340Luteolin 6-C-glucoside
(flavone)
1237.44C21H20O11448.101[M-H]−447.0933, [2M-H]−895.1927
[M+H]+449.1094		−1.02−0.46240,265,350Quercitrin
(flavonol)
1345.69C28H32O13576.1841[M-H]−575.177, [M+H]+577.19120.380.22235,285Genestein G 2 (isoflavone)
1446.16C30H26O14610.1326[M-H]−609.125, [M+H]+611.1388−0.57−0.35235,280,335Luteolin-4′-O-(6″-trans-caffeoyl)-β-d-glucopyranoside
(flavone)
1546.59C20H36O12468.2209[M-H]−467.2134−0.58−0.27235,280Unknown
1647.84C22H38O12494.2372[M-H]−493.2291−1.8−0.89238,280Rhodioloside B
1749.04C28H46O8510.3189[M-H]−509.3120.710.36245,280Pladienolides F/Pladienolides G
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Wang, X.; Cao, J.; Tian, L.; Liu, B.; Fan, Y.; Wang, Q. Elucidating Flavonoid and Antioxidant Activity in Edible and Medicinal Herbs Woodwardia japonica (L.f.) Sm. Based on HPLC-ESI-TOF-MS and Artificial Neural Network Model: Response to Climatic Factors. Molecules 2023, 28, 1985. https://doi.org/10.3390/molecules28041985

AMA Style

Wang X, Cao J, Tian L, Liu B, Fan Y, Wang Q. Elucidating Flavonoid and Antioxidant Activity in Edible and Medicinal Herbs Woodwardia japonica (L.f.) Sm. Based on HPLC-ESI-TOF-MS and Artificial Neural Network Model: Response to Climatic Factors. Molecules. 2023; 28(4):1985. https://doi.org/10.3390/molecules28041985

Chicago/Turabian Style

Wang, Xin, Jianguo Cao, Lin Tian, Baodong Liu, Yawen Fan, and Quanxi Wang. 2023. "Elucidating Flavonoid and Antioxidant Activity in Edible and Medicinal Herbs Woodwardia japonica (L.f.) Sm. Based on HPLC-ESI-TOF-MS and Artificial Neural Network Model: Response to Climatic Factors" Molecules 28, no. 4: 1985. https://doi.org/10.3390/molecules28041985

APA Style

Wang, X., Cao, J., Tian, L., Liu, B., Fan, Y., & Wang, Q. (2023). Elucidating Flavonoid and Antioxidant Activity in Edible and Medicinal Herbs Woodwardia japonica (L.f.) Sm. Based on HPLC-ESI-TOF-MS and Artificial Neural Network Model: Response to Climatic Factors. Molecules, 28(4), 1985. https://doi.org/10.3390/molecules28041985

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