Needles of J. rigida were collected from 11 regions covering all primary origins in China (Table 1, Figure 1). The voucher specimen was identified by the professor Dengwu Li and was deposited at Herbarium of the Northwest A&F University, Yangling, China. All materials were air-dried, powdered, and stored in the dark at −20°C until further analysis.

A HPLC analysis was conducted with an Agilent Series 1260 liquid chromatograph equipped with a quaternary gradient pump system and variable-wavelength detector system connected to a reversed-phase (RP) SB-C 18 column (5 μm, 4.6 × 250 mm, Agilent, USA). Data collection was performed using ChemStation software (Agilent, USA). Chromatograph-grade methanol and acetonitrile were purchased from Sigma-Aldrich Co. Ltd (St. Louis, Missouri, USA).

Folin-Ciocalteus's phenol reagent (Beijing Solarbio Co. Ltd, PR China), 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2-azino-bis (3-ethyl-benzothiazoline-6-sulphonic acid) diammonium salt (ABTS), 2,4,6-Tripyridyl-s-triazine (TPTZ), and 6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox, Sigma-Aldrich Co., St. Louis, Missouri, USA), gallic acid, quercetin, and acetic acid (Tianjin Bodi Chemical Holding Co. Ltd, PR China) were used. All solutions were filtered through 0.22 mm nylon filters before use. Reagents were of analytical grade and dissolved in deionized water (18 MΩ cm).

The extraction process was optimized using a response surface method considering the impact of various factors. Each powdered sample was treated as described in the optimized extraction process: extraction time (1 h), aqueous ethanol concentration (60%), extraction temperature (70°C), number of extractions (2), liquid-solid ratio (20:1). Filtrates were concentrated via rotary evaporation under vacuum to obtain the crude extracts and stored at −20°C in the dark until further use. All extractions were conducted in triplicate.

Antimicrobial activity was tested against nine bacterial strains provided by the Microbial Culture Collection Center of Guangdong Institute of Microbiology, China. Tests against four gram-positive bacteria (Staphylococcus aurous Rosen Bach ATCC6538, Pseudomonas aeruginosa ATCC29212, Bacillus subtilis ATCC6633, and Listeria monocytogenes ATCC19115) and five gram-negative bacteria (Klebsiella pneumoniae ATCC46117, Salmonella enteritidis ATCC14028, Salmonella typhimurium CMCC50115, Salmonella paratyphi CMCC50093, and Escherichia coli ATCC25922) were conducted. All strains were cultured at 37°C on Mueller-Hinton medium.

The Folin-Ciocalteau colorimetric method was adopted to determine the TPC, with minor modifications (Burcu et al., 2014; Mocan et al., 2014). The phenolic content of all samples was calculated from the gallic acid equivalent based on a calibration curve of gallic acid standard solutions (10, 20, 40, 60, 80, 100, 200, 300, and 400 μg/mL). All values were expressed as millimole gallic acid equivalent per 100 g of dry weight (mmol equiv. GAE/100 g). Data were expressed as means ± SD for three replicates.

The TFC was determined using the sodium borohydride/chloranil-based (SBC) assay developed by He et al. (2008). Sample solutions were diluted to a concentration of 20 mg/mL. A calibration curve was generated using different concentrations of quercetin (0.1–10.0 mM). The total flavonoid content of ethanol extracts of needles of J. rigida was expressed as millimoles of quercetin equivalents per 100 g dry weight (mmol equiv. QUE/100 g). Data were expressed as means± SD for three replicates.

The DPPH radical scavenging capacity was measured using the method described by Yen and Chen (1995) with some modifications. Trolox was used as the positive control, and all samples were tested in triplicate. A lower IC₅₀ value indicates higher DPPH radical scavenging activity.

The scavenging effects for the ABTS^•+ radical cation were monitored as described in the published literature, with some modifications (Yang et al., 2013; Mocan et al., 2015). Trolox and phosphate-buffered saline (PBS) solutions were used as the standard antioxidant and blank samples, respectively. Data were expressed in terms of micromoles of trolox equivalents per g dry extract weight (μmol eq. trolox/g). All determinations were performed in triplicate.

FRAP activity was determined using the protocol of Benzie and Strain (2008) with some modifications. Trolox was employed as the standard solution and FRAP results were expressed in terms of micromoles Trolox equivalent per gram dry extract weight (mmol equiv. Trolox/ g). All experiments were performed in triplicate.

As recommended by the Chinese pharmacopeia Committee, data analysis was performed using Computer Aided Similarity Evaluation System (CASES) software. The correlation coefficients of entire chromatographic patterns among samples were calculated and simulated mean chromatograms as well as characteristic peaks generated using CASES. Based on the correlation coefficient (median), the software was also used to conduct similarity analysis (SA) of different chromatograms. HCA, PCA, and DA were performed using SPSS software (SPSS for Windows 19.0, SPSS Inc., USA) (Duan et al., 2012). HCA was conducted to classify samples with regard to similarities of chemical properties, and the “average linkage between groups” method and cosine applied in the measurements. Three principal components obtained using PCA were applied to assess similarities and differences among the samples (Nyambaka and Ryley, 2004; Pierce et al., 2006). DA analysis facilitated the identification and assortment among the groups and confirmed the results of SA, HCA, and PCA, improving quality evaluation of J. rigida more acceptable.

All measurements were carried out in triplicate, and the results presented as mean values ± SD (standard deviation). Statistical analysis was performed via one-way analysis of variance (ANOVA) followed by Duncan's test. P < 0.05 were considered significant. Data were analyzed using SPSS 19.0 (SPSS Inc., Chicago, IL, USA) for Windows and figures generated using SigmaPlot 12.0 and Photoshop 8.0 for Windows.

Among J. rigida derived from the 11 districts, the total phenolic and total flavonoid contents varied significantly (Table 2). Total phenolic compounds were the most abundant in Fengcheng (S11) with contents of 31.64 mmol equiv. GAE/100 g, and lowest in Dabaozhen (S6) with contents of 13.34 mmol equiv. GAE/100 g. Consistent with total phenolic content data, the highest and lowest total flavonoid contents were detected in S11 and S6 (84.53 mmol equiv. QUE/100 g, 39.05 mmol equiv. QUE/100 g, respectively). Overall, abundance of total phenolic and flavonoid contents of J. rigida was in the order: Fengcheng (S11), Changbai Mountain (S10), Yijun (S3), Fugu (S2), Chongli (S5), Xiaosuangou (S7), Jinhua (S9), Datong (S4), Wulahada (S8), Helan Mountain (S1), Dabaozhen (S6). Therefore, among the diverse J. rigida samples, those from Fengcheng and Changbai Mountain were of the highest quality due to their rich phenolic contents. Variations in the total phenolic and flavonoid contents in J. rigida needles were potentially due to differences in the levels, properties, and proportions of their active ingredients, which, in turn, may be attributed to distribution in diverse geological zones, leading to different therapeutic properties of the same species from various growth regions.

RP-HPLC chromatograms of phenolic compounds in J. rigida needles were analyzed. Based on comparison of retention times with those of the standards, peaks 1, 2, 4, 5, 6, 7, 9, 10, 11, and 13 were identified as chlorogenic acid, catechin, cumaric acid, rutin, hyperoside, ellagic acid, podophyllotoxin, amentoflavone, formononetin, and galangal, respectively (Figure 2, G0). Total contents of the identified compounds varied significantly among the regions (p < 0.05, Table 3), the highest recorded in S2 (746.65 mg/g) followed by S6 (694.02 mg/g) and S3 (666.40 mg/g), and the lowest in S11 (415.27 mg/g) and S8 (411.74 mg/g). In all 11 J. rigida samples, chlorogenic acid, catechin, podophyllotoxin, and amentoflavone were the most abundant phenolic compounds. The contents of these four compounds varied from 53.32 ± 0.17 to 163.38 ± 0.17 mg/g DW, 112.52 ± 0.08 to 288.88 ± 0.38 mg/g DW, 71.11 ± 0.06 to 189.18 ± 0.04 mg/g DW and 57.68 ± 0.01 to 146.16 ± 0.05 mg/g DW, respectively. Ellagic acid and formononetin were the least abundant phenolic compounds, with contents ranging from 2.82 ± 0.02 to 3.01 ± 0.06 mg/g DW and 2.22 ± 0.01 to 5.68 ± 0.02 mg/g DW, respectively.

Chlorogenic acid, catechin, podophyllotoxin, and amentoflavone are well-known human health antioxidants (Harwood et al., 2007; Wang et al., 2013). Previous studies have shown that these active components are abundant in J. rigida and have significant potential for application as natural antioxidants in the food and drug industries. Since the contents of phenolic compounds in J. rigida needles from different geographical sources vary considerably, a standard protocol for effective quality evaluation of J. rigida appears essential.

The antioxidant activities of J. rigida needles are presented in Table 4. J. rigida derived from Yijun (S3) possessed the highest DPPH radical scavenging activity with the lowest mean IC₅₀ value (17.99 ± 0.23 μg/mL) while that from Chongli (S5) had the highest mean IC₅₀ value (55.17 ± 0.97 μg/mL), indicative of lowest DPPH radical scavenging activity. ABTS activity data were consistent with those of FRAP analysis. ABTS radical scavenging capacity and ferric reducing power of J. rigida derived from Yijun (S3, 4862.29 ± 326.73 μmol Trolox/g and 824.28 ± 45.49 μmol Trolox/g, respectively) were also the highest. J. rigida derived from Wulahada (S8) displayed the lowest antioxidant activity in both ABTS and FRAP assays with values of 3463.72 ± 102.01 μmol Trolox/g and 467.75 ± 3.61 μmol Trolox/g, respectively. These profiles clearly suggest that the antioxidant ability of J. rigida varies between locations, with samples from Fugu (S2) and Yijun (S3) exhibiting the strongest activities.

These results were consistent with the total contents of 10 phenolic compounds, supporting the theory that active ingredients, such as chlorogenic acid, catechin, podophyllotoxin, and amentoflavone, play important roles in the antioxidant activity of J. rigida. The strong antioxidant activities observed for samples from Fugu (S2) and Yijun (S3) may thus be attributed to the high abundance of chlorogenic acid, catechin and podophyllotoxin. In conclusion, variations in the antioxidant activities of plants from different localities are possibly attributable to the contents, properties, and proportions of their phenolic constituents. Furthermore, geographical factors, such as elevation, latitude, longitude, temperature, light, rainfall, and locations of high altitude and cold climatic conditions may be underlying causes of these variations.

The antibacterial activities of phenolic extracts of J. rigida were tested against nine strains of bacteria. Two narrow-spectrum antibiotics (penicillin, tetracycline) and one broad-spectrum antibiotic (chloramphenicol) were used as positive controls. DIZ values of phenolic extracts of J. rigida against all tested bacteria strains varied from 6.65 to 14.83 mm (Table 5). The largest DIZ was obtained for K. pneumonia, followed by L. monocytogenes, S. paratyphi, S. enteritidis, P. aeruginosa, and S. typhimurium, and the lowest for S. aurous. J. rigida phenolic extracts exerted antibacterial activity against both gram-positive and -negative bacteria. Among J. rigida from the 11 regions, those derived from Fengcheng (S11) and Changbai Mountain (S10) displayed the highest sensitivity against bacteria, with the greatest inhibition zone diameters for K. pneumonia (13.03 ± 0.12 mm, 12.17 ± 0.62 mm), followed by L. monocytogenes (12.23 ± 0.10 mm, 12.13 ± 0.35 mm), S. paratyphi (11.25 ± 0.26 mm, 10.56 ± 0.30 mm), P. aeruginosa (12.25 ± 1.35 mm, 10.25 ± 0.35 mm) and S. typhimurium (10.87 ± 0.17 mm, 9.00 ± 0.50 mm), clearly indicating strongest antibacterial activity of plants sourced from S11 and S10.

MIC and MBC values of the phenolic extracts were determined for further evaluation of J. rigida quality (Table 6). The MIC and MBC values for all tested bacterial strains ranged from 0.3125 to 10.00 mg/mL. Consistent with DIZ results, J. rigida derived from Fengcheng (S11) and Changbai Mountain (S10) exerted the strongest antibacterial effects with the lowest MIC and MBC values among the 11 J. rigida samples examined.

In conclusion, all the phenolic extracts of J. rigida showed broad-spectrum antibacterial activity, with samples from Fengcheng (S11) and Changbai Mountain (S10) exerting the strongest effects. These results were in keeping with the total phenolic and flavonoid contents of J. rigida, suggesting that antibacterial activity is closely related to the total phenolic constituent levels. Moreover, since J. rigida traditionally achieves its therapeutic effects through strong biological activities, including anti-inflammatory, anticancer, and antiviral properties, antibacterial activity may be considered to possess larger proportion in quality evaluation of J. rigida (Gordien et al., 2009; Ryu et al., 2010; Jeong et al., 2012). J. rigida derived from Fengcheng and Changbai Mountain were therefore of outstanding quality, supporting their utility as effective potential bacterial inhibitors and bactericidal agents.

The J. rigida samples collected from 11 different regions were analyzed to develop a standard fingerprint under the established HPLC conditions. Fourteen common peaks were selected as characteristic, and peak 7 (retention time of 30.71 min) as the reference standard peak (Figure 2). Species of the same origin derived from different areas were tentatively identified based on the chromatograms, and similarities assessed with CASES software. The correlation coefficients of the samples ranged from 0.85 to 0.98 (Table 7). A high degree of similarity was detected among Chongli (S5), Dabaozhen (S6), Xiaosuangou (S7), and Wulahada (S8) samples from Hebei province, suggesting that J. rigida from Hebei are of the same origin with similar chemical constituents. The chromatograms appeared characteristic of the specific phenolic components, supporting disparities among plants derived from different regions.

Prior to HCA, data were visually analyzed via overlapping tests using ChemStation software (Agilent, USA), and divided into three distinct patterns (G1, G2, and G3; Figure 3). Due to the subjective and non-quantitative nature of visual comparisons, the HCA assay was selected for quantitative assessment of J. rigida and relative peak areas (RPA) of the 14 common chromatographic peaks determined. A 14 × 11 matrix was formed for HCA using SPSS software, and a dendrogram acquired using average linkage between groups and the cosine method (Figure 3). The 11 samples were assorted into three quality clusters assuming a proper level of distance (14.5), consistent with the subjective results of visual classification. Group 1 included J. rigida derived from Fugu (S2), Yijun (S3), and Jinhua (S9) while Group 2 contained J. rigida from Fengcheng (S11) and Changbai Mountain (S10). Samples from the other six regions, including Helan Mountain (S1), Datong (S4), Chongli (S5), Dabaozhen (S6), Xiaosuangou (S7), and Wulahada (S8), were clustered in Group 3 owing to similarities in their chemical constituents. Group 2 possessed the highest contents of phenolic compounds while Group 1 exhibited the highest antioxidant activity. The quality of J. rigida derived from distinct regions differed due to a number of factors, such as altitude, latitude, longitude, and soil nutrients. Correlation coefficients of chromatograms within Groups 1, 2, and 3 corresponding to the simulated mean chromatograms were generated from the software.

The correlation coefficients of each chromatogram for G1, G2, and G3 are presented in Table 7. Chromatograms were similar within a particular group while significantly distinct between groups. The differences in similarity values for the three groups were consistent with data generated from HCA (Table 8). These results illustrate that HCA can effectively distinguish between J. rigida samples of the same origin from different districts and confirm classification based on morphological characteristics (Table 1).

Differences in chromatograms of samples mainly existed due to content variations in the common components. To evaluate the discrimination capacity of the common constituents, PCA was conducted using the RPAs of common peaks using HCA input data. The first two principal components contained the most information of all variables, accounting for 87.51% of total variability. The score plot of the first three principal components, PC1 and PC2, visually revealed a positive influence on quality evaluation of J. rigida from different regions (Figure 4). PCA results were consistent with HCA findings, indicative of different chemical profiles from distinct locations. Analysis of the loading plot of PC1 against PC2 (Figure 5) revealed that peaks contributing to PC1 (7, 9, 10, 11, and 13) influenced the cluster in a top-down order. These were the compounds that distinguished S2, S3 and S9 as Group 1. Peaks 1, 2, 3, 4, 5, 6, and 14 that mainly contributed to PC2 led to the classification of S10 and S11 as Group 3. The remaining samples comprised Group 2. In summary, since PC1 contributing more peaks than PC2, samples on the right side of Figure 5 were presumed to be of higher quality. Accordingly, we concluded that J. rigida collected from Shaanxi province (S2, S3) and Jinhua (S9) were of the highest quality. Data from HCA and PCA supported each other and validated the quality evaluation of J. rigida samples.

Discriminant analysis was employed to establish a predictive model to facilitate clustering and distinguishing of group members based on observed characteristics. The discriminant function generated on the basis of linear combinations of predictor variables was used to further discriminate and classify the unknown members. These functions were generated from samples with known membership and could be applied to new cases with known values for predictor variables but unknown group membership variables of unknown group membership.

Although 14 peaks were selected as variables from the patterns, not all the variables generated contributed to development of the discriminant function. Only valuable predictor variables may be selected to generate discriminant functions using this procedure. Two types of discriminant functions were acquired using SPSS software.

Canonical discriminant function: Y1=0.007X1-0.026X2+0.005X3+0.026X4-0.0.002X5+ 0.003X7+7.511Y2=-0.005X1+0.012X2-0.004X3+0.007X4+0.001X5+ 0.002X6+0.001X7-2.406 Discrimination standard:

Fisher's discrimination function: G1=-1.24X1+0.468X2-0.081X3-0.468X4+0.042X5- 0.005X6-0.051X7-203.055G2=-0.008X1+0.038X2+0.009X3-0.057X4+0.009X5- 0.014X6-0.005X7-34.659G3=-0.023X1+0.059X2-0.006X3-0.057X4+0.009X5- 0.004X6+0.003X7-36.699 Standard for discriminant: each sample was assigned to a group according to the highest of the three functional values. X represents the variable and G1, G2, and G3 denote samples from Groups 1, 2, and 3, respectively. The variables assigned to the areas of peaks 1–7 were adopted to develop the discriminant functions. The inputted values of the three variables were obtained to form discriminant standard values and classify unknown samples. High-resolution DA plots for the three groups are displayed in Figure 6. The seven variables employed were the most discriminating, so that analytes included in Groups 1, 2, and 3 could be divided with 100% accuracy.

The phenolic components differed significantly among different regions of origin of J. rigida according to HPLC fingerprint analysis. These results were consistent with SA, HCA, PCA, and DA data, clearly indicating that the techniques were effective for quality assessment of J. rigida. The quality of medicinal herbs may contribute to the integrated influence of composite factors, such as environmental or genetic factors, cultivation methods, and collection periods. Therefore, quality evaluation of J. rigida via HPLC fingerprint analysis combined with chemometrics methods may be of vital importance to optimize its future use. Further studies should focus on the collection of samples from various regions of China and other countries for analysis via HPLC fingerprint combined with chemometrics as well as examination of environmental and genetic factors.

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