Comparison of Growth Characteristics and Active Compounds of Cultivated Hovenia dulcis under Different Environments in South Korea

Abstract

The growth characteristics and active compounds of plants are influenced by various environmental factors, including light, temperature, soil, and precipitation. These factors interact with each plant in a unique way. In this study, we collected fruit and soil samples from 17 cultivation sites in October 2020 to investigate the relationship among environmental factors, growth characteristics, and active compounds of H. dulcis seed and fruit. We developed an optimized method for quantifying active compounds using ultra-performance liquid chromatography and performed correlation analyses with the surveyed environmental factors. Results showed that the size and fresh weight of H. dulcis fruits were positively correlated with exchangeable Mg, Na, and silt texture proportion in the soil. The temperature was positively correlated with sugar content in the fruits but negatively correlated with fruit weight, and precipitation was negatively correlated with fruit size. Results also showed a positive correlation between the active compounds and growth characteristics such as fruit length, seed number per fruit, and fresh weight. The results of this study can be used as basic data for the cultivation and quality control of H. dulcis fruits.

1. Introduction

Hovenia dulcis Thunberg, belonging to the Rhamnaceae family, is a deciduous tree species growing naturally from East Asia (China, Korea, and Japan) to the Himalayan region. It is known as Korean, Chinese, Japanese Raisin Tree, and Coral tree [1,2]. It is a rapid-growth tree, preferentially when in a sunny position on well-drained, moist sandy or loamy soils, and reaches 20–30 m in height [3]. H. dulcis blooms in summer and matures in late summer/autumn in East Asia. This infructescence is composed of globose capsules (fruits) with contorted peduncles which have a sweet taste due to the high contents of sucrose [4].

Fruits, fruit stalks, leaves, and stembark of H. dulcis have a long history as foods, nutraceuticals, and folk remedies in East Asia [3]. Extracts of H. dulcis are helpful in liver disease and alcoholic poisoning in Chinese Traditional Medicine [5]. Particularly, fruits, peduncles, and seeds of H. dulcis are used for several diseases like febrifuge, antispasmodic, laxative, and hangover syndrome [6,7,8]. Recent studies have proved that extracts of H. dulcis have detoxification on alcohol poisoning [9,10,11], hepatoprotective [10,12], antioxidant [13,14], antidiabetic [15], antiobesity [16], antiallergic [17], and anticancer activities [18,19,20]. Because of these medicinal properties, there is a trial for functional foods and natural health products by using extracts of H. dulcis [21]. It is also used as fermented fruit vinegar, jelly, cookies, and wine [22,23,24,25].

These pharmacological effects used by humans can be related to the various secondary metabolites in plants [26]. This evidence is convincing results for the potential health benefits of plant-derived compounds, commonly known as active compounds. It is assumed that there are approximately one million active compounds, and only a few compounds have been tested on the basis of their ancient knowledge for current clinical uses [27,28]. Chemical constitution research on H. dulcis has been conducted, and various active compounds such as triterpenoids, flavonoids, alkaloids, polysaccharides, and organic acids have been listed [9,29]. The listed active compounds isolated from the fruit, branches, bark, and peduncles showed pharmacological effects in vitro and in vivo [7]. Among these active compounds, principal components of H. dulcis fruits have been reported as ampelopsin (dihydromyricetin), taxifolin, myricetin, and quercetin [30]. Ampelopsin is known for the most abundant active compound in H. dulcis [7], providing inhibitory effects on alcohol-induced muscular relaxation, alcohol intoxication, and hepatoprotective activity [10,11,31,32]. Taxifolin has antioxidant activity through ARE-dependent gene regulation and has efficiency in antiallergic and anti-inflammatory [17,33,34]. Myricetin has antiallergic, antioxidant, and hypoglycemic effects [35,36,37]. Quercetin has antibacterial and anticancer activities and reduced body fat accumulation [38,39,40].

Worldwide trends in medicinal plant research were a continuous increase with the increasing demand for herbal drugs, natural health products, and secondary metabolites of medicinal plants [41]. The global market for medicinal plants is currently estimated at over USD 60 billion annually, and it is growing continuously [42]. These medicinal plants are equally considered conventional medicines with higher effectiveness on chronic diseases without side effects [43]. It is assumed that over 80% of the world’s population uses medicinal plants [44]. To meet this demand, the development of medicinal-plant-based cultivation research are now required [45,46]. Usually, the contents of active compounds in plants have an important function for growth and development that are affected by numerous factors, such as environmental, genetic, and agronomic factors [47,48]. Recent studies were conducted to increase the content of useful active compounds for the production of high-quality medicinal plants [49,50,51,52]. Several research articles on H. dulcis have been published about breeding cultivars for high yields and active properties in different maturity stages [6,53]. Therefore, we conducted a correlation research between the environmental factors and growth characteristics. The growth characteristics vary depending on the active compounds in cultivated H. dulcis.

Therefore, fruits and soils of H. dulcis were collected from 17 cultivation sites in 14 regions in Korea. A statistical analysis using one-way analysis of variance (ANOVA) with Duncan’s multiple-range test, cluster analysis, and correlation analysis was adopted. The correlation analyses between (1) the environmental factors and growth characteristics and (2) growth characteristics and active compounds from different cultivation sites were conducted. From this research, we try to determine the factors that affect the active compounds of H. dulcis fruits for medicinal resources.

2. Materials and Methods

2.1. Plant Materials and Chemicals

Between October to November 2020, a total of 51 plant materials of H. dulcis fruits were collected from 17 cultivation sites in 14 regions across South Korea (Figure 1). The samples were randomly collected at each site with three replicates and authenticated by researcher Hyun-Jun Kim of the National Institute of Forest Science, Yeongju, Korea. The collected samples were immediately frozen and stored at −20 °C after measuring growth characteristics. Standard ampelopsin was purchased from TGI (Chuo, Tokyo, Japan). Taxifolin, myricetin, and quercetin were purchased from Sigma-Aldrich (Burlington, MA, USA). Methanol, ethanol, acetonitrile, and distilled water were purchased from J.T. Baker (Easton, PA, USA) with HPLC grades.

2.2. Growth Characteristics

The growth characteristics of H. dulcis fruits were measured, such as weight, length, width of fruit, number of fruit stalks per fruit, weight, length, width of the peduncle, number of seeds per peduncle, weight of 10 fruit peduncles, sugar contents of peduncle by using digital calipers (500-182-30, Mitutoyo, Kanagawa, Japan), electronic scale (HS3200S, HANSUNG instrument Co., Gyeonggi, Republic of Korea), and refractometer (PR-101α, ATAGO Co., Ltd., Tokyo, Japan).

2.3. Samples and Standard Preparation

The samples of H. dulcis seeds were washed with sterilized distilled water and dried in a freeze dryer (PVTFD 20R, Ilshin Lab. Co., Ltd., Gyeonggi, Republic of Korea). The dried seeds were pulverized and sieved through an 80-mesh standard sieve. Each sample was weighed at 5 g and extracted with 100 mL 70% EtOH at room temperature by ultrasonication (JAC-5020, KODO Co., Ltd., Hwaseong, Republic of Korea) for 24 h. After that, samples were centrifuged at 3000 rpm for 15 min using a centrifuge instrument (Labogene UM-1238, Bio-Medical Science Co., Ltd., Seoul, Republic of Korea). The supernatant was filtered through a 0.2 µm membrane filter (Whatman Co., Maidstone, UK) before injection. Standard (ampelopsin, taxifolin, myricetin, and quercetin) stock solutions for UPLC were prepared by diluting the stock solutions in methanol to obtain concentration ranges of 10–100 µg/mL for four active compounds.

2.4. UPLC Conditions

Analysis data were obtained using a Waters Alliance UPLC (Waters Co., Milford, MA, USA) with a UV detector. The analytical conditions for recording chromatograms of the four compounds were as follows. Qualitative and quantitative analysis were carried out using an ACQUITY UPLC HSS T3 (2.1 × 100 mm, 1.7 μm, Waters Co., Milford, MA, USA) at 30 °C. The mobile phase consists of (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. Under this condition, the gradient concentrations were as follows: 0–1 min, 10% B; 1–4 min, 20% B; 4–10 min, 25% B; 10–12 min, 30% B; 12–14 min, 90% B; 14–17 min, 90% B; 17–17.1 min, 10% B; 17.1–20 min, 10% B. The flow rate and sample injection volume were 0.3 mL/min and 2 μL, respectively, and the detection wavelength was 355 nm. Each sample was analyzed in triplicate and expressed as a mean value.

2.5. Method Validation

Linearity, limits of detection (LOD), limit of quantification (LOQ), precision, and accuracy validation were performed with the UPLC–UV method as follows. Linearity was evaluated using the linear correlation coefficient calibration (r² > 0.999) with six different pointed concentration ranges as follows: 20–800 μg/mL for ampelopsin and taxifolin, 5–400 μg/mL for myricetin and quercetin. The LOD of each compound was calculated based on a signal-to-noise ratio of 3.3 and LOQ set to 10. The intra- and inter-day precision were estimated by triplicates of three different concentration ranges, 40–400 μg/mL, and expressed as the relative standard deviation (RSD). Each procedure was conducted within 1 day for intra-day and 3 consecutive days for inter-day. The accuracy (%) was determined from the triplicate experiments of a recovery assay by measuring the percentage between peak areas of standard stock solution and seed extracts.

2.6. Soil Sample Analysis

Soil samples were collected within the root zone of the H. dulcis at cultivation sites. The collected samples were dried at room temperature (20 °C) and passed through a 2 mm sieve. Analysis of soil texture, which can measure the content of sand, silt, and clay in soil, was classified by the United States Department of Agriculture (USDA) textural triangle. Analysis of soil properties was performed following the standard analysis manual of the Rural Development Administration (RDA) in Korea. Soil pH and electrical conductivity (EC) were measured by pH meter (HI2020, Hanna instruments Inc., Smithfield, RI, USA) and EC meter (HI2030, Hanna instruments Inc., Smithfield, RI, USA). Organic matter (OM) was measured using Walkley–Black method [54]. Available phosphate was measured using a spectrophotometer and 1-amino-2-naphthol-4-sulfonic acid according to the Lancaster extraction method, and total nitrogen (TN) was measured by the Kjeldhal method after digesting the sample using sulfuric acid and block digester. Cation exchange capacity (CEC) was measured using 1N NH4OAc and the Kjeldhal method, and exchangeable cations were measured by inductively coupled plasma optical emission spectrometry (ICP-OES).

2.7. Data on Climate Factors

The annual mean temperature, annual mean highest temperature, annual mean lowest temperature, annual highest temperature, annual lowest temperature, total precipitation, and duration of sunshine for the sampling areas were obtained from the Korea Meteorological Administration’s open portal (data.kma.go.kr).

2.8. Data Analysis

Data analysis using Statistical Package for the Social Science (SPSS) software (version 26.0, IBM Inc., Chicago, IL, USA) were expressed as means ± standard deviation (S.D.). The differences among cultivation sites were shown using a one-way analysis of variance (ANOVA) with Duncan’s multiple-range test; the level of significance was set at p < 0.05. Correlation among active compounds, growth characteristics, and environmental factors were confirmed by using Pearson’s correlation coefficient. Hierarchical clustering analysis (HCA) was also performed for the comparison of quantitative data by Ward’s method with squared Euclidean distance [55].

3. Results and Discussion

3.1. Growth Characteristics

The average morphological traits of each fruit are expressed as a means ± standard deviation (minimum–maximum values) with alphabet superscript indicating significant differences based on Duncan’s test (Table S1). In terms of fruit bunches, the average length and width of fruit bunch values were 52.70 ± 10.20 (22.33–91.76) and 49.52 ± 7.64 (23.44–81.42) mm, respectively. In terms of peduncles, the average length, width, diameter, and fresh weight of peduncle values were 19.28 ± 2.96 (8.91–33.98), 35.96 ± 5.64 (13.95–57.31), 5.40 ± 0.91 (1.96–12.09) mm, and 1.28 ± 0.42 (0.29–3.76) g, respectively. The average number of seeds and sugar contents per peduncle values were 2.72 ± 0.88 (0–6) and 37.62 ± 4.23 (13.2–45.4) °Bx, respectively. The growth characteristics of H. dulcis fruit varied significantly among the different cultivation sites. Among these cultivation sites, Nonsan (Site 12) showed the highest values for fruit, peduncle size, fresh weight, and the number of seeds per peduncle, while Hadong1 (Site 4) showed the lowest values for fruit, peduncle size, and fresh weight. There are various reasons affecting fruit quality, such as environmental, genetic, and agronomic factors in apples [47]. These complex factors interact during the growth of plants at the same time, and no climatic factors can decide the physiological performance [56]. When comparing this result to well-grown wild types of H. dulcis collected from natural habitats in South Korea, the average length (52.70 ± 10.20 mm) and width (49.52 ± 7.64 mm) of fruit bunches and the number of peduncles per fruit (5.88 ± 1.66) for cultivated H. dulcis were superior to the length (33.37 ± 0.33 mm) and width (44.05 ± 0.32 mm) of fruits and the number of peduncles per fruit (2.81 ± 0.04) for wild types [57].

3.2. Validation

The optimized identification and calibration method of the UPLC method for the four active compounds were validated (Figure 2). Calibration curves examined within the test range showed good linearity (r² > 0.9999) for each active compound (Table 1). LOD and LOQ values of ampelopsin, taxifolin, myricetin, and quercetin were 0.27 and 0.89 μg/mL, 0.32 and 1.06 μg/mL, 0.02 and 0.06 μg/mL, and 0.02 and 0.06 μg/mL, respectively. It indicates that this method has a high degree of sensitivity. Intra- and inter-day variations were selected to process the precision and accuracy of the method for each compound (Table 2). Precision values were 0.04 to 0.93% for intra-day and 0.08 to 0.79% for inter-day (Table 2). Average recovery values were 98.72 to 104.49%, and RSD values were 0.10 to 0.82% (Table 3). The recovery of the active compounds was calculated within the range of 90–110%, and RSD values less than 5% can be considered acceptable [58]. In this result, simultaneous quantitation of active compositions in H. dulcis seeds was developed and validated. These validation results for linearity, LOD, LOQ, precision, and accuracy can provide the analytical method with reproducible and reliable data for H. dulcis seeds.

3.3. Quantitative Analysis of Active Compounds and HCA Analysis

The UPLC–UV method was applied to simultaneously determine 4 active compounds, namely ampelopsin, taxifolin, myricetin, and quercetin, in H. dulcis seeds from 17 different cultivation sites. These four active compounds were identified by comparing retention time and UV spectra chromatograms of the peaks with those of the standards in the UPLC chromatogram. Each active compound was detected at a retention time of 5.81, 8.09, 10.80, and 15.05 min, respectively (Figure 3). The total content of ampelopsin, taxifolin, myricetin, and quercetin ranged from 0.007 ± 0.007 to 2.513 ± 0.638, 0.004 ± 0.003 to 4.587 ± 0.833, 0.003 ± 0.002 to 0.082 ± 0.029, and 0.001 ± 0.000 to 0.321 ± 0.158%, respectively (Table 4). The highest contents of ampelopsin and myricetin were observed in Nonsan (Site 12). The highest contents of taxifolin and quercetin were observed in Gwangyang (Site 8). The total contents of four active compounds were found in Gwangyang (Site 8). In a previous study, four flavonoids in H. dulcis fruit were observed as major compounds, which showed different concentration ratios among the samples [30]. This difference also showed similar trends in this study collected from 17 cultivation sites.

Because of the content differences among active compounds, we performed an HCA analysis (Figure 4). The observed constitution of each active compound (ampelopsin, taxifolin, myricetin, and quercetin) was divided into two clades. Clade 1 is composed of ampelopsin and myricetin, except for Hadong1 (Site 4) with taxifolin. Clade 2 is composed of three subclades. Subclade 1 is mainly composed of taxifolin and quercetin. Subclade 2 is composed of complex contents of four active compounds. Subclade 3 is composed of taxifolin and quercetin (Figure 4). Lee and Kyunn (2015) reported that the origin of H. dulcis showed different constitutions of active compounds, with Korean H. dulcis containing higher amounts of taxifolin and Chinese H. dulcis containing higher amounts of ampelopsin [59]. The origin of seeds was also an important factor in determining the concentration of flavonoid compounds in Pinus sylvestris [60]. However, not all the sites follow this tendency in the constitution of active compounds among the cultivation sites. It may be due to the environmental factors which affect the biosynthesis and accumulation of active compounds in medicinal plants [51,61].

3.4. Soil Characteristics and Climate Factors

The soil properties of each cultivation site were analyzed for 13 properties (Tables S2 and S3). The soil texture properties were found to be consistently classified as sandy loam across all cultivation sites. In terms of soil chemical properties, OM and TN contents showed similar patterns of values. Specifically, Hadong1 (Site 4) showed the highest OM and TN values at 10.53 ± 0.59 and 0.48 ± 0.09%, respectively, while Hongseong (Site 15) showed the lowest values at 1.29 ± 0.29 and 0.09 ± 0.01%, respectively. Available P₂O₅ contents showed significantly different among cultivation sites ranging from 54.26 ± 30.59 to 1855.04 ± 337.64 mg/kg. CEC was recorded in the range of 11.07 ± 1.33 to 23.17 ± 1.99 cmol⁺/kg. The level of soil pH was measured as acidic to neutral ranging from 4.43 ± 0.20 to 6.99 ± 0.69. In the case of exchangeable cations, K⁺ content was recorded in the range of 0.33 to 1.64 cmol⁺/kg, Ca²⁺ of 2.78 to 20.14 cmol⁺/kg, Mg²⁺ of 0.68 to 3.91 cmol⁺/kg, and Na⁺ of 0.01 to 0.11 cmol⁺/kg. Overall, the soil chemical properties of the cultivation sites were found to be within an adequate range of the general soil quality index (SQI) for tree growth, with the exception of appropriate levels of soil pH (5.5~6.5) and available phosphate (≥100) [62]. These soil factors are related to key factors for the growth, productivity, and reproductive process of plants by controlling the availability of essential nutrients for root uptake through fixation and release [63]. The decline in soil fertility is a major limiting factor for the productivity of fruit crops [64].

The climate factors of each cultivation site were collected for seven factors (Table S4). The annual mean temperature was in the range of 11.0 to 15.1 °C. The annual mean maximum and minimum temperatures were 17.1 to 20.7 and 5.3 to 11.4 °C, respectively. The annual maximum and minimum temperatures were 32.6 to 37.8 and −20.3 to −7.0 °C, respectively. The monthly duration of sunshine and precipitation were 173.44 to 213.10 hr/month and 103.21 to 178.07 mm/month, respectively. H. dulcis is known for its adaptability to various environments and is widely distributed across all continents [65]. According to Köppen’s classification, the most suitable climatic zone for H. dulcis is the Cfa climate type [66]. When comparing this climate type to this result, the average temperatures of winter and summer are above −3 to 22 °C that ranges are suitable for cultivation sites. Additionally, minimum temperatures of cultivation sites are not determining factors for H. dulcis, and previous research has shown that it has some drought resistance [67,68]. This wide range of climate conditions can be contributed to guidelines for suitable cultivation sites, which are also important factors in determining the growth and quality of fruits [69].

3.5. Correlation between Environmental Factors and Growth Characteristics

The results of the correlation analysis between growth characteristics and soil factors of H. dulcis fruits are presented in Table 5. The chemical properties in cultivation sites generally showed a positive correlation with the growth characteristics of H. dulcis. The size of fruits showed a positive correlation with Na⁺ (0.308, p < 0.05). In the peduncle size, Mg²⁺ (0.329, p < 0.05), Na⁺ (0.350, p < 0.05), and the level of pH (0.309, p < 0.05) showed a positive correlation, while OM (−0.296, p < 0.05), CEC (−0.296, p < 0.05), and EC (−0.371, p < 0.01) showed a negative correlation with peduncle growth of H. dulcis. In particular, fresh weight and number of seeds per peduncle showed a significant positive correlation with soil chemical properties except for Na⁺ (−0.395, p < 0.01). In the soil texture properties, silt contents showed a positive correlation, while sand showed a negative correlation with the growth characteristics of H. dulcis. Generally, OM and TN are well known as largely responsible factors for the variation in soil quality and crop productivity by promoting soil nutrient availability [70,71]. However, the fruit size with high contents of three major chemical properties was exceptionally small in Hadong1 (Site 4). This site showed a significantly low soil pH of 4.69 ± 0.32 and a different proportion of soil textures compared to Nonsan (Site 12). When the soil pH is less than 6.0, forms of Mg, Ca, P, and Mo become less available, and when soil pH is less than 5.5, the uptake of N, K, and S becomes hindered, leading to a shortage of nutrient availability for plant roots [72]. In addition, differences in soil texture properties between Hadong1 (Site 4) and Nonsan (Site 12) could possibly affect the fruit growth of H. dulcis. Suitable soil texture is an important indicator that presents a vital criterion in predicting hydraulic conductivity, fertility, and productivity variability in many species [73]. Thus, the soil pH and texture properties could be the determining factors in the fruit growth of H. dulcis when other soil nutrients are sufficient.

The results of the correlation analysis between the growth characteristics and climate factors are shown in Table 6. Temperature factors showed a positive correlation with sugar contents and showed a negative correlation with fresh weight and number of seeds per peduncle. Monthly precipitation showed a negative correlation with the width of the fruit (−0.371, p < 0.01), the number of peduncles per fruit (−0.346, p < 0.05), and the width of peduncles (−0.339, p < 0.05). Duration of sunshine showed a positive correlation with the width of the peduncles (0.376, p < 0.01) and the number of peduncles per fruit (−0.298, p < 0.05). Altitude showed a positive correlation with the number of peduncles per fruit (0.530, p < 0.01) and a negative correlation with the length of the fruit (−0.294, p < 0.05). Overall, climate factors interacted with fruit growth in a complex way. These complex growth responses according to climate factors could differ depending on the crop species throughout their life cycle [74]. The size of the fruits and peduncles in H. dulcis did not show obvious differences by climate factors in this study, but the peduncle numbers per fruit and sugar contents showed a significant correlation with climate factors. The sugar content in fruits can be caused by temperature, which is an important factor for the growth and maturity of fruits by regulating sucrose synthase, acid invertase enzymes, and controlling sugar transport [75,76]. Morales et al. (2017) reported that peduncle maturation is accompanied by an increase in soluble sugar contents [6].

3.6. Correlation between Growth Characteristics and Contents of Active Compounds

The results of the correlation analysis between active compounds and growth characteristics of H. dulcis seeds are presented in Table 7. As a result, ampelopsin and myricetin showed a positive correlation with the fruit length (0.298, p < 0.05; 0.320, p < 0.05), the number of seeds per peduncle (0.431, p < 0.01; 0.372, p < 0.01), and the fresh weight of 10 peduncles (0.313, p < 0.05; 0.332, p < 0.025), while they showed negative correlation with the number of peduncles per fruit (−0.334, p < 0.05; −0.298, p < 0.05), respectively. Quercetin showed a negative correlation with the width of the fruit bunch (−0.315, p < 0.05). The total contents of four active compounds were positively correlated with the number of seeds per peduncle (0.387, p < 0.01). In the correlation between fruit growth and active compounds, the fresh weight of 10 peduncles is positively correlated with ampelopsin and myricetin. This trend is dissimilar to previous studies that showed a negative correlation between plant growth and active compounds. Lee et al. (2022) reported that three marker compounds (shisandrin, gomisin A, and gomisin N) were significantly negatively correlated with the fresh weight of Schisandra chinensis fruits [77]. Park et al. (2020) also reported that four active compounds (nodakenin, decursin, and decursinol angelate) were negatively correlated with the dry weight of Angelica gigas roots [51]. The seed number was significantly correlated with ampelopsin, myricetin, and total contents in H. dulcis. There is no or few further research on the correlation between the number of seeds and active compounds in H. dulcis. Seeds are an important storage place for active compounds in plants. The seeds of Ziziphus jujube were an important factor in the determination of active compounds [78]. Seed numbers in H. dulcis showed major factors affected by environmental factors in this research. The higher seed number in fruits could be sufficient to guarantee high-quality fruit growth in passion fruit trees and showed higher contents of phenolic compounds [79,80].

4. Conclusions

In this study, The H. dulcis fruits and soil samples were collected in 17 cultivation sites in South Korea. The simultaneous analysis of active compounds of H. dulcis seed was developed by using the UPLC–UV. The correlation analyses were conducted between the environmental factors and growth characteristics, growth characteristics, and active compounds. As a result, most of the soil physico-chemical and climate properties showed a positive influence on the growth characteristics of H. dulcis. Specifically, adequate soil pH, texture properties, and average temperature were important factors for seed numbers per peduncle and fresh weight, which in turn positively correlated with active compounds of H. dulcis. In this result, specific sites showed remarkable characteristics of fruit growth and active compound contents. This obtained data can be used for cultural practices and the production of high-quality medicinal resources in the near future.

Author Contributions

Conceptualization, D.H.L. and H.-J.K.; methodology, D.H.L. and H.-J.K.; software, Y.S. and D.H.L.; validation, D.H.L. and J.-H.J.; formal analysis, D.H.L.; investigation, Y.P., J.A.K., G.H.P. and H.-J.K.; resources, Y.S., D.H.L. and H.-J.K.; data curation, H.-J.K. and D.H.L.; writing—original draft preparation, Y.S.; writing—review and editing, H.-J.K. and S.-Y.L.; visualization, Y.S.; supervision, H.-J.K.; project administration, H.-J.K.; funding acquisition, H.-J.K. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are available under permission from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. Map of sampling collection sites of Hovenia dulcis.

Figure 2. Structure of active compounds in Hovenia dulcis.

Figure 3. UPLC chromatogram of active compounds of the standard mixture (A) and Hovenia dulcis sample (B). Ampelopsin (1), taxifolin (2), myricetin (3), and quercetin (4).

Figure 4. Dendrograms resulting from hierarchical cluster analysis based on four active compounds of Hovenia dulcis from different cultivation sites.

Table 1

Linear regression, LOD, LOQ of four active compounds.

Compound	Regression Equation	Correlation Coefficient (r2)	Range (µg/mL)	LOD (µg/mL)	LOQ (µg/mL)
Ampelopsin	Y = 897.71X − 3436.4	0.9999	20–800	0.27	0.89
Taxifolin	Y = 811.56X + 378.98	1.0000	20–800	0.32	1.06
Myricetin	Y = 8134.9X + 21650	0.9999	5–400	0.02	0.06
Quercetin	Y = 13583X − 10778	1.0000	5–400	0.02	0.06

Table 2

Intra- and inter-day precision of four active compounds.

Compound	Concentration(µg/mL)	Intra-Day (n = 3) 1		Inter-Day (n = 3) 2
		Concentration Found (µg/mL)	RSD (%)	Concentration Found (µg/mL)	RSD (%)
Ampelopsin	20	19.6	0.89	19.7	0.27
	100	101.1	0.06	101.0	0.13
	400	400.8	0.30	402.0	0.43
Taxifolin	20	19.3	0.56	19.4	0.63
	100	98.5	0.18	98.8	0.51
	400	397.9	0.04	401.4	0.79
Myricetin	20	20.7	0.49	20.7	0.38
	100	99.9	0.08	100.0	0.08
	400	409.0	0.18	409.3	0.06
Quercetin	20	19.1	0.93	19.2	0.59
	100	97.0	0.21	97.6	0.55
	400	399.6	0.46	400.2	0.78

¹ Sample was analyzed three times on 1 day, n = 3; ² Sample was analyzed each day for three consecutive days, n = 3.

Table 3

Recoveries of four active compounds.

Compound	Concentration (µg/mL)	Recovery (%) (n = 3)	RSD (%)
Ampelopsin	10	98.95	0.82
	20	98.72	0.36
	100	102.84	0.35
Taxifolin	10	100.06	0.37
	20	99.74	0.68
	100	99.94	0.13
Myricetin	10	101.63	0.29
	20	103.38	0.36
	100	104.49	0.10
Quercetin	10	103.63	0.22
	20	101.18	0.40
	100	101.27	0.21

Table 4

Composition of four active compounds in Hovenia dulcis from 17 different cultivation sites.

CultivationSites(n = 3)	Ampelopsin (%)	Taxifolin (%)	Myricetin (%)	Quercetin (%)	Total (%)
1	2.008 ± 0.346 b	ND *	0.064 ± 0.018 ab	ND	2.071 ± 0.363 bc
2	0.074 ± 0.052 g	0.925 ± 0.242 cd	0.003 ± 0.002 e	0.061 ± 0.022 cd	1.062 ± 0.269 cde
3	1.496 ± 0.134 cd	0.004 ± 0.003 d	0.048 ± 0.014 bc	0.001 ± 0.000 d	1.548 ± 0.145 bcde
4	1.800 ± 0.594 bc	0.718 ± 1.243 cd	0.054 ± 0.027 bc	0.025 ± 0.043 cd	2.597 ± 0.751 b
5	1.767 ± 0.190 bc	ND	0.053 ± 0.019 bc	0.001 ± 0.001 d	1.821 ± 0.196 bc
6	ND	0.352 ± 0.152 d	ND	0.028 ± 0.011 cd	0.380 ± 0.163 e
7	ND	3.951 ± 1.228 a	0.005 ± 0.001 e	0.166 ± 0.065 b	4.122 ± 1.291 a
8	0.007 ± 0.007 g	4.587 ± 0.833 a	0.005 ± 0.005 e	0.321 ± 0.158 a	4.920 ± 0.951 a
9	1.208 ± 0.277 de	0.879 ± 0.887 cd	0.033 ± 0.016 cd	0.021 ± 0.031 cd	2.147 ± 0.698 bc
10	0.742 ± 0.178 ef	0.298 ± 0.437 d	0.018 ± 0.006 de	0.027 ± 0.031 d	1.070 ± 0.445 cde
11	1.523 ± 0.213 bcd	ND	0.049 ± 0.007 bc	ND	1.572 ± 0.208 bcde
12	2.513 ± 0.638 a	ND	0.082 ± 0.029 a	ND	2.595 ± 0.667 b
13	0.809 ± 0.278 ef	ND	0.017 ± 0.005 de	ND	0.826 ± 0.283 cde
14	1.718 ± 0.139 bc	ND	0.035 ± 0.002 cd	ND	1.754 ± 0.140 bcd
15	0.461 ± 0.210 fg	ND	0.009 ± 0.003 e	ND	0.470 ± 0.213 de
16	ND	1.871 ± 0.416 bc	ND	0.073 ± 0.036 cd	1.944 ± 0.451 bc
17	ND	2.525 ± 1.731 b	ND	0.104 ± 0.071 bc	2.628 ± 1.802 b

Different letters in each column indicate statistically significant differences (p < 0.05) among the treatments according to Duncan’s test. * ND: not detected.

Table 5

Pearson’s correlation coefficient between growth characteristics and soil physico-chemical composition of cultivation sites.

	Correlation Coefficient (r) *
	OM	TN	Avail. P2O5	Exchangeable Cations				CEC	pH	EC	Sand	Silt	Clay
				K+	Ca2+	Mg2+	Na+
Length of fruit bunch	0.187	0.192	0.130	0.120	0.146	0.092	−0.059	0.109	0.064	0.261	−0.054	0.133	−0.115
	(0.188)	(0.176)	(0.362)	(0.400)	(0.307)	(0.520)	(0.678)	(0.447)	(0.657)	(0.065)	(0.707)	(0.353)	(0.420)
Width of fruit bunch	−0.116	−0.041	−0.218	0.211	−0.053	0.214	0.308 *	−0.095	0.023	−0.114	−0.245	0.369 **	−0.180
	(0.419)	(0.774)	(0.125)	(0.138)	(0.709)	(0.131)	(0.028)	(0.507)	(0.872)	(0.426)	(0.083)	(0.008)	(0.206)
No. of peduncles per fruit	−0.010	0.030	0.100	0.500 **	−0.034	0.201	0.235	−0.039	0.102	−0.039	−0.168	−0.013	0.267
	(0.944)	(0.834)	(0.483)	(0.000)	(0.814)	(0.158)	(0.097)	(0.787)	(0.478)	(0.785)	(0.237)	(0.929)	(0.058)
Length of peduncle	−0.147	−0.091	−0.323 *	−0.130	0.010	0.034	0.350 *	−0.053	0.053	−0.089	−0.309 *	0.464 **	−0.226
	(0.304)	(0.525)	(0.021)	(0.361)	(0.946)	(0.815)	(0.012)	(0.713)	(0.712)	(0.533)	(0.028)	(0.001)	(0.111)
Width of peduncle	−0.103	0.003	−0.028	0.250	0.236	0.329 *	0.288*	−0.129	0.309 *	0.013	−0.216	0.128	0.130
	(0.473)	(0.984)	(0.846)	(0.077)	(0.096)	(0.019)	(0.041)	(0.367)	(0.028)	(0.926)	(0.128)	(0.369)	(0.363)
Diameter of peduncle	−0.296 *	−0.219	−0.159	−0.136	−0.118	0.043	0.084	−0.296 *	0.133	−0.371 **	−0.273	0.262	0.018
	(0.035)	(0.122)	(0.266)	(0.341)	(0.409)	(0.763)	(0.556)	(0.035)	(0.352)	(0.007)	(0.053)	(0.063)	(0.899)
Fresh weight of peduncle	0.199	0.283 *	0.148	0.095	0.332 *	0.433 **	−0.141	0.134	0.313 *	−0.013	−0.250	0.329 *	−0.114
	(0.161)	(0.045)	(0.300)	(0.508)	(0.017)	(0.002)	(0.323)	(0.347)	(0.025)	(0.930)	(0.077)	(0.018)	(0.424)
No. of seeds per peduncle	0.416 **	0.387 **	0.323 *	0.011	0.280 *	0.145	−0.395 **	0.379 **	0.073	0.164	−0.283 *	0.373 **	−0.130
	(0.002)	(0.005)	(0.021)	(0.940)	(0.046)	(0.311)	(0.004)	(0.006)	(0.612)	(0.251)	(0.044)	(0.007)	(0.363)
Sugarcontents	−0.027	−0.002	0.005	0.177	0.003	−0.019	0.288 *	−0.021	−0.116	0.097	0.275	−0.347 *	0.104
	(0.851)	(0.990)	(0.973)	(0.215)	(0.982)	(0.893)	(0.040)	(0.885)	(0.419)	(0.497)	(0.051)	(0.013)	(0.470)
Fresh weight of 10 peduncle	0.244	0.308 *	0.081	0.051	0.379 **	0.430 **	−0.098	0.136	0.280 *	−0.003	−0.252	0.392 **	−0.203
	(0.085)	(0.028)	(0.574)	(0.723)	(0.006)	(0.002)	(0.492)	(0.342)	(0.047)	(0.984)	(0.074)	(0.004)	(0.153)

* Correlation coefficient (r) written is significantly correlated between the variables compared. Positive values denote positive correlation, and negative values denote negative correlation. Values in brackets indicate p-values (** p < 0.01, * p < 0.05).

Table 6

Pearson’s correlation coefficient between growth characteristics and meteorological factors of cultivation sites.

	Correlation Coefficient (r) *
	Annual Mean Temp.	Annual Mean Max Temp.	Annual Mean Min Temp.	Annual Max Temp.	Annual Min Temp.	Monthly Precipitation	SunshineDuration	Altitude
Length of the fruit bunch	−0.146	−0.166	−0.121	−0.152	−0.090	0.008	−0.026	−0.019
	(0.307)	(0.243)	(0.397)	(0.287)	(0.528)	(0.995)	(0.854)	(0.896)
Width of the fruit bunch	0.061	−0.199	0.141	−0.235	0.025	−0.371 **	0.376 **	−0.034
	(0.670)	(0.162)	(0.323)	(0.097)	(0.859)	(0.007)	(0.007)	(0.812)
No. of pedunclesper fruit	0.255	0.187	0.239	0.367 **	0.051	−0.346 *	0.298 *	0.530 **
	(0.071)	(0.190)	(0.091)	(0.008)	(0.723)	(0.013)	(0.034)	(0.000)
Length of peduncle	0.007	−0.233	0.104	−0.347 *	0.045	−0.175	0.269	−0.294 *
	(0.962)	(0.099)	(0.468)	(0.013)	(0.756)	(0.219)	(0.057)	(0.036)
Width of peduncle	0.045	−0.033	0.066	0.005	−0.036	−0.339 *	0.150	0.035
	(0.755)	(0.816)	(0.645)	(0.972)	(0.804)	(0.015)	(0.293)	(0.809)
Diameter of peduncle	0.058	−0.122	0.144	−0.229	0.043	−0.273	0.253	−0.260
	(0.684)	(0.393)	(0.427)	(0.106)	(0.765)	(0.053)	(0.074)	(0.065)
Fresh weight of peduncle	−0.282 *	−0.215	−0.261	−0.142	−0.230	−0.239	−0.179	−0.208
	(0.045)	(0.131)	(0.065)	(0.320)	(0.104)	(0.091)	(0.209)	(0.143)
No. of seeds per peduncle	−0.279 *	−0.222	−0.281 *	−0.168	−0.178	−0.017	−0.156	−0.046
	(0.047)	(0.117)	(0.046)	(0.239)	(0.210)	(0.908)	(0.273)	(0.747)
Sugar contents of peduncle	0.396 **	0.446 **	0.337 *	0.260	0.385 **	0.166	0.019	−0.176
	(0.004)	(0.001)	(0.016)	(0.065)	(0.005)	(0.245)	(0.897)	(0.216)
Fresh weight of 10 peduncles	−0.304 *	−0.265	−0.266	−0.238	−0.239	−0.253	−0.177	−0.267
	(0.030)	(0.060)	(0.059)	(0.093)	(0.091)	(0.073)	(0.215)	(0.058)

* Correlation coefficient (r) written is significantly correlated between the variables compared. Positive values denote positive correlation, and negative values denote negative correlation. Values in brackets indicate p-values (** p < 0.01, * p < 0.05).

Table 7

Pearson’s correlation coefficient between active compounds and growth characteristics of Hovenia dulcis.

	Correlation Coefficient (r) *
	Length of Fruit Bunch	Width of Fruit Bunch	No. of Peduncles per Fruit	Length of Peduncle	Width of Peduncle	Diameter of Peduncle	Fresh Weight of Peduncle	No. of Seeds per Peduncle	Sugar Contents of Peduncle	Fresh Weight of 10 Peduncles
Ampelopsin	0.298 *	0.084	−0.334 *	0.093	−0.056	−0.046	0.271	0.431 **	−0.228	0.313 *
	(0.034)	(0.560)	(0.016)	(0.515)	(0.694)	(0.750)	(0.054)	(0.002)	(0.108)	(0.025)
Taxifolin	−0.133	−0.201	0.259	−0.116	0.007	−0.072	−0.140	0.077	−0.028	−0.182
	(0.351)	(0.156)	(0.066)	(0.418)	(0.959)	(0.617)	(0.329)	(0.590)	(0.844)	(0.200)
Myricetin	0.320 *	0.035	−0.298 *	0.040	−0.001	−0.079	0.308 *	0.372 **	−0.222	0.332 *
	(0.022)	(0.808)	(0.033)	(0.783)	(0.996)	(0.581)	(0.028)	(0.007)	(0.117)	(0.017)
Quercetin	−0.263	−0.315 *	0.111	−0.084	−0.052	0.068	−0.123	−0.010	0.023	−0.167
	(0.062)	(0.024)	(0.438)	(0.560)	(0.720)	(0.635)	(0.389)	(0.947)	(0.872)	(0.242)
Total	0.031	−0.202	0.082	−0.079	−0.033	−0.112	0.015	0.387 **	−0.189	−0.010
	(0.829)	(0.155)	(0.566)	(0.583)	(0.819)	(0.435)	(0.914)	(0.005)	(0.184)	(0.947)

* Correlation coefficient (r) written is significantly correlated between the variables compared. Positive values denote positive correlation, and negative values denote negative correlation. Values in brackets indicate p-values (** p < 0.01, * p < 0.05).

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/d15080905/s1, Table S1: growth characteristics of Hovenia dulcis in 17 different cultivation sites, Table S2: soil properties data of 17 different cultivation sites of Hovenia dulcis, Table S3: soil properties data of 17 different cultivation sites of Hovenia dulcis, Table S4: climate factors of 17 different cultivation sites of Hovenia dulcis.

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