Differentiation frequency distribution of morphological traits
In this study, the‘Shiqian Taicha’ tea tree resources were all shrub types. The leaf morphological characteristics of 52 tea trees were investigated, and the frequency distribution of 16 observed leaf morphological traits showed different degrees of genetic differentiation according to the standardization of the data in Table 2. Leaf size, petiole color, leaf color, leaf base and leaf tip exhibited two phenotypes. The leaf sizes were mainly of the lobular type, with a few being of the middle lobe type and no large leaf types were observed. Leaf growth state, leaf shape, leaf body, leaf color, leaf texture, leaf tooth density, leaf tooth depth, leaf tooth sharpness and leaf margin had 3 phenotypes. The leaf growth state was mainly upward oblique, the leaf shape was mainly oval, the leaf color was green and dark green, the leaf edges were mostly flat, the leaf tips were mostly acuminate, and a few were blunt. There were four types of hair in the one bud and two leaves: no hair (1.9%), little hair (23.1%), moderate hair (51.9%) and more hair (22.1%). There were 5 phenotypes in terms of one bud and two leaves’s color, which were yellowish green, light green, green, purple‒green and purple, and green was the main type, accounting for 50.0%. According to the studies of Yu (1990) and Jiang (2010), 96.7% of the leaf types of shrubbery tea tree varieties are medium leaflets, and the other 3.3% are large leaves, indicating that the two traits of shrubbery tree type and medium leaf type often coexist, which is consistent with the results of this study.
Morphological variation and diversity
Plant phenotypic traits are affected by both genotype and the ecological environment, and have stability and variability(Yang 1991), while phenotypic variation is an important part of genetic diversity research. Phenotypic variation can facilitate thesimple and rapid scientific evaluation of genetic resources and has been widely used in the evaluation of tea tree resources (Li et al. 2016; Ma et al. 2018; Wu et al. 2023). The leaf morphological traits and genetic diversity indices of 52 tea plants were analyzed, and the results are shown in Table 3. The CVs of the 21 morphological traits ranged from 7.95–45.67%, with an average of 28.37%, which was higher than that of the Yunnan plants (22.11%) (Jiang 2013). Leaf surface area was the largest (45.67%), followed by leaf initiation state (41.22%) and leaf shape (40.49%), and the lateral vein was the smallest (7.95%). Leaf surface, leaf growth state, leaf shape, leaf body, leaf margin, bud and leaf hairs, leaf area, leaf tooth sharpness, leaf base and leaf size had CVs greater than 30%.
Table 3
Phenotypic traits and diversity indices (H') of 52 tea varieties
| Phenotypic traits | Min | Max | Mean | SD | CV(%) | Diversity index(H') |
| Petiole length | 0.09 | 0.70 | 0.37 | 0.11 | 29.99 | 1.96 |
| Leaf length | 3.73 | 11.32 | 6.59 | 1.25 | 18.96 | 2.06 |
| Leaf width | 1.80 | 6.11 | 3.07 | 0.51 | 16.65 | 1.96 |
| Lateral vein number | 4.00 | 9.00 | 6.75 | 0.54 | 7.95 | 1.98 |
| Leaf area | 5.04 | 43.33 | 14.64 | 5.05 | 34.49 | 1.90 |
| Leaf size | 1.00 | 3.00 | 1.13 | 0.34 | 30.08 | 0.40 |
| Petiole color | 1.00 | 2.00 | 1.65 | 0.48 | 28.77 | 0.65 |
| Leaf growth state | 1.00 | 3.00 | 1.27 | 0.52 | 41.22 | 0.52 |
| Leaf shape | 1.00 | 5.00 | 2.54 | 1.03 | 40.49 | 0.67 |
| Leaf color | 3.00 | 4.00 | 3.27 | 0.44 | 13.57 | 0.58 |
| Leaf cross section | 1.00 | 3.00 | 1.71 | 0.69 | 40.25 | 0.80 |
| Leaf upper surface | 1.00 | 3.00 | 1.62 | 0.74 | 45.67 | 1.06 |
| Leaf texture | 1.00 | 3.00 | 1.94 | 0.30 | 15.69 | 0.37 |
| Density of leaf serration | 1.00 | 3.00 | 2.75 | 0.48 | 17.29 | 0.61 |
| Sharpness of leaf serration | 1.00 | 3.00 | 1.73 | 0.59 | 34.21 | 0.88 |
| Depth of leaf serration | 1.00 | 3.00 | 1.79 | 0.49 | 27.60 | 0.71 |
| Leaf base shape | 1.00 | 2.00 | 1.15 | 0.36 | 31.27 | 0.43 |
| Leaf apex | 2.00 | 3.00 | 2.35 | 0.48 | 20.28 | 0.65 |
| Leaf margin undulation | 1.00 | 3.00 | 1.35 | 0.51 | 38.23 | 0.78 |
| Bud leaf hairs | 0.00 | 3.00 | 1.94 | 0.72 | 36.98 | 1.08 |
| Bud leaf color | 2.00 | 6.00 | 4.00 | 1.05 | 26.20 | 1.37 |
| Average | | | | | 28.37 | 1.02 |
The H'of the morphological traits ranged from 0.37 to 2.06, with the average value of 1.02, which was higher than the average H' of domestic tea tree resources (0.96) (Qiao 2010). The H' of the bud leaf color, bud leaf hairs and leaf surface were greater than 1.0, and the H' of the other 13 morphological traits were lower than 1.0, indicating that the genetic variation in the bud color, bud hairs and leaf surface of ‘Shiqian Taicha’ tea was high. The H' and CV results showed that there was abundant genetic variation in leaf morphology, leaf surface area, leaf area, leaf shape, bud hairs, bud color and leaf tooth acuity, but the number of lateral veins, leaf shape, leaf texture and leaf hue were conserved, and the variation was small.
Biochemical component diversity analysis
The analysis of the biochemical components of 52 different tea germplasm resources showed (Table 4) that the CVs of 14 biochemical components ranged from 3.73–54.05%, with an average of 20.35%. Water extract (WE) had the smallest effect, and C had the largest effect. The CVs of the water extract, tea polyphenols (TP), caffeine (CAF), ester catechins, total catechins, EGCG and free amino acids were 3.73%, 10.91%, 11.56%, 12.34%, 12.86%, 13.26% and 16.88%, respectively. The H' was 1.21 ~ 2.09, the average was 1.86, the smallest was the water extract, and the largest was gallic acid (GA). The diversity index was lower than the average of 1.86 for the water extract, tea polyphenols, catechin total, EGCG and ester catechin biochemical components, which were 1.21, 1.78, 1.84, 1.77 and 1.85, respectively. The CVs and H' of 14 biochemical components of 52 tea trees from Shiqian showed that water extract, tea polyphenols, caffeine, total catechin, EGCG and ester catechin were relatively stable, while C, GA, EGC, EC and ECG showed greater variation.
Table 4
Statistical analysis and diversity indices (H') of the biochemical components of 52 tea resources
| Biochemical components | Content/% | Mean/% | SD | CV/% | Diversity index(H') |
| WE | 41.35 ~ 52.04 | 49.15 | 1.84 | 3.73 | 1.21 |
| TP | 13.42 ~ 26.32 | 22.03 | 2.40 | 10.91 | 1.78 |
| TAA | 1.80 ~ 4.90 | 3.22 | 0.54 | 16.88 | 1.88 |
| CAF | 2.32 ~ 4.42 | 3.76 | 0.43 | 11.56 | 1.93 |
| GA | 0.20 ~ 1.07 | 0.65 | 0.19 | 28.54 | 2.09 |
| TC | 9.52 ~ 22.42 | 17.53 | 2.25 | 12.86 | 1.84 |
| EGC | 1.34 ~ 4.62 | 2.85 | 0.82 | 28.65 | 1.99 |
| C | 0.39 ~ 6.47 | 2.08 | 1.12 | 54.05 | 1.92 |
| EC | 0.48 ~ 1.75 | 0.98 | 0.23 | 23.98 | 2.01 |
| EGCG | 4.89 ~ 11.63 | 9.72 | 1.29 | 13.26 | 1.77 |
| ECG | 1.01 ~ 3.88 | 1.92 | 0.50 | 26.14 | 1.88 |
| NETC | 2.68 ~ 9.33 | 5.90 | 1.28 | 21.69 | 2.00 |
| ETC | 5.90 ~ 14.43 | 11.64 | 1.44 | 12.34 | 1.85 |
| Average | | | | 20.35 | 1.86 |
The CV and H' of the water extract were the smallest, at 3.73% and 1.21, respectively. The water extract ranged from 41.35–52.04%, with an average value of 49.15%, which was higher than that of Chongqing (35.35%) (Zhai et al. 2021), Shanxi (45.42%) (Ding et al. 2019), Menghai County in Yunnan Province (44.82%) (Ma et al. 2018), Liannan County in Guangdong Province (47.48%) (Mo et al. 2017), and Mingcong in Wuyi County in Fujian Province (43.98%) (Wang et al. 2015), etc., which were all high, indicating that the water extract content of ‘ShiqianTaicha’ was relatively stable and rich. EGCG is the main component of catechins, the key core component of tea polyphenols that exhibits physiological activity and wide application, and is one of the key factors affecting the quality of tea. The selection and product development of tea trees with a high EGCG content have enormous market prospects. Li et al. (2016) selected 11 tea tree resources with high EGCG content, whose average annual EGCG content was greater than 13%, using 45 varieties and strains. Jiang et al. (2010) determined the EGCG content of 83 tea tree varieties and selected 28 tea tree varieties with high levels of EGCG. Ma et al. (2005) selected 5 tea tree resources with high levels of EGCG(≥ 10.00%) using 96 tea tree resources as materials. Lin et al. (2023) identified high levels of EGCG in tea tree resources,with contents ≥ 10.00%. In this study, the EGCG content of tea tree resources in ‘ShiqianTaicha’ tea ranged from 4.89–11.63%, with an average of 9.72%, and the highest EGCG content reached 11.63%. There were 25 resources with EGCG contents higher than 10%, accounting for 48.07%, and the CV and diversity index were 13.26% and 1.77, respectively. All of these values were lower than the average value, indicating that the genetic variation in the EGCG content in ‘Shiqian Taicha’ tea tree resources was smalland relatively stable, and these plants have the potential to be used for developing and utilizing high-EGCG tea tree resources. ‘ShiqianTaicha’ has important application potential to develop tea products or deep-processed extracts with high levels of EGCG.
Correlation analysis of morphological traits and biochemical components
Figure 1 shows the pearson correlation analysis of morphological traits and biochemical components of different tea germplasm resources. There was a significant positive correlation between LC and TC (p < 0.05); LT was positively correlated with WE, TP, TC, EGCG and ECG (p < 0.01), positively correlated with GA and EC (p < 0.05), and negatively correlated with BLH and TAA (p < 0.01). SLS was significantly positively correlated with TC and EGCG (p < 0.01), and was positively correlated with TP and C (p < 0.05), and was negatively correlated with TAA (p < 0.05). The LBS was positively correlated with the TAA (p < 0.05). BLH was negatively correlated with WE (p < 0.05) and strongly negatively correlated with TP (p < 0.01). TAA was significantly negatively correlated with ECG, EC, EGCG, EGC, TP, TC and WE, indicating that the higher the TAA content was, the lower the TP and WE were, which was consistent with the results of previous studies.
These results indicated that LC, LT, SLS, LBS and TT had significant effects on TP, TAA and WE and could be used to evaluate tea plant resources based on leaf morphological characteristics. The morphological traits of tea germplasm resources are related to biochemical components to a certain extent, and regional and environmental differences affect phenotypic traits such as tea leaf color, leaf surface uplift and leaf edge state and ultimately affect the biochemical components of tea trees. The results of this study are similar to those of Ding et al. (2019) and Tian et al. (2017). The strong association of one trait with other traits can be used indirectly to evaluate resources, which take considerable time and money to evaluate. In addition to saving money, this technique can accelerate domestication and breeding processes (Khorshidi et al. 2020a, b; Zare et al. 2023).
Principal component analysis of morphological traits and biochemical components
Principal component analysis was carried out on 21 phenotypic trait and 11 biochemical component indicators of 41 tea tree resources (Tables 5 and 6), and 11 principal components were extracted basedeigenvalues greater than 1, with a cumulative contribution rate of 81.05%, which contained most of the information of the original variables and most of the information of the 32 survey indicators. It can be used to comprehensively evaluate the tea resources of ‘Shiqian Taicha’.Water extract, tea polyphenols, total catechin, gallic acid and EGCG were strongly correlated with biochemical components, while leaf length, leaf width, leaf area, leaf growth state, leaf quality, leaf margin, leaf tooth density, leaf tooth acuity and bud leaf color were strongly correlated with morphological traits. These 14 traits represented 81.05% of the 33 traits investigated. This is the main factor that causes differences in tea tree resource morphology and biochemical components.
Table 5
Principal component characteristic values and contribution rates of morphological and biochemical indices
| Principal component | Characteristic value | Contribution rate(%) | Accumulative contribution rate(%) |
| 1 | 5.31 | 16.59 | 16.59 |
| 2 | 5.00 | 15.62 | 32.22 |
| 3 | 3.17 | 9.91 | 42.13 |
| 4 | 2.44 | 7.61 | 49.74 |
| 5 | 1.85 | 5.80 | 55.54 |
| 6 | 1.78 | 5.56 | 61.10 |
| 7 | 1.61 | 5.03 | 66.13 |
| 8 | 1.50 | 4.69 | 70.82 |
| 9 | 1.14 | 3.55 | 74.37 |
| 10 | 1.09 | 3.39 | 77.76 |
| 11 | 1.05 | 3.29 | 81.05 |
Table 6
Principal components of morphological traits and biochemical components
| Trait type | Principal component |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
| Petiole length | 0.43 | 0.57 | -0.20 | -0.23 | -0.02 | 0.13 | -0.16 | -0.40 | 0.00 | 0.01 | -0.03 |
| Leaf length | 0.59 | 0.73 | 0.00 | 0.06 | -0.04 | -0.04 | 0.05 | 0.12 | 0.11 | -0.06 | -0.10 |
| Leaf width | 0.61 | 0.53 | 0.32 | 0.32 | 0.24 | 0.03 | 0.06 | 0.02 | 0.09 | 0.14 | -0.09 |
| Leaf area | 0.62 | 0.67 | 0.18 | 0.22 | 0.09 | -0.03 | 0.07 | 0.09 | 0.14 | 0.02 | -0.09 |
| Leaf size | 0.39 | 0.58 | 0.19 | 0.25 | 0.04 | -0.28 | 0.17 | 0.17 | 0.16 | -0.11 | 0.19 |
| Petiole color | -0.09 | -0.12 | -0.03 | 0.31 | -0.42 | -0.54 | -0.04 | -0.40 | 0.04 | 0.11 | 0.26 |
| Leaf growth state | 0.04 | -0.42 | 0.04 | 0.08 | 0.10 | 0.52 | 0.28 | 0.09 | -0.07 | -0.22 | 0.42 |
| Leaf shape | 0.04 | 0.42 | -0.52 | -0.39 | -0.42 | -0.05 | 0.09 | 0.10 | 0.11 | -0.13 | 0.07 |
| Lateral vein number | 0.31 | 0.61 | 0.07 | -0.17 | -0.03 | -0.16 | 0.06 | 0.21 | -0.19 | 0.31 | -0.13 |
| Leaf color | -0.07 | -0.03 | -0.40 | 0.14 | 0.01 | 0.66 | 0.18 | 0.08 | 0.23 | -0.05 | -0.17 |
| Leaf cross section | 0.20 | 0.33 | 0.13 | -0.32 | 0.26 | 0.29 | -0.42 | -0.39 | 0.23 | 0.04 | -0.03 |
| Leaf upper surface | 0.19 | 0.14 | 0.61 | 0.09 | 0.26 | 0.18 | -0.02 | -0.12 | 0.07 | 0.11 | 0.42 |
| Leaf texture | -0.08 | -0.01 | 0.14 | 0.50 | -0.45 | -0.10 | -0.11 | -0.21 | 0.35 | -0.11 | 0.03 |
| Density of leaf serration | -0.20 | -0.11 | 0.48 | -0.31 | 0.13 | -0.10 | 0.54 | 0.01 | 0.18 | -0.22 | -0.16 |
| Sharpness of leaf serration | 0.12 | 0.20 | -0.31 | 0.55 | -0.04 | 0.20 | -0.17 | -0.08 | -0.41 | -0.10 | 0.15 |
| Depth of leaf serration | 0.36 | -0.15 | 0.12 | -0.39 | 0.02 | -0.12 | 0.38 | -0.23 | 0.17 | -0.15 | 0.16 |
| Leaf base shape | -0.33 | -0.30 | 0.40 | 0.01 | 0.39 | -0.19 | -0.18 | 0.31 | -0.04 | 0.23 | 0.14 |
| Leaf apex | -0.16 | -0.48 | 0.42 | 0.42 | 0.30 | -0.11 | -0.13 | -0.05 | 0.06 | 0.03 | -0.29 |
| Leaf margin undulation | 0.15 | 0.50 | -0.09 | 0.13 | 0.13 | 0.17 | -0.06 | 0.31 | -0.16 | -0.09 | 0.35 |
| Bud leaf hairs | -0.09 | 0.02 | -0.21 | -0.60 | 0.29 | 0.00 | 0.00 | -0.30 | 0.00 | 0.38 | 0.19 |
| Bud leaf color | -0.07 | -0.09 | 0.12 | -0.12 | -0.46 | 0.21 | 0.03 | 0.55 | 0.19 | 0.40 | -0.03 |
| WE | 0.72 | -0.56 | -0.06 | -0.11 | 0.10 | -0.08 | -0.01 | 0.13 | 0.07 | -0.12 | -0.02 |
| TP | 0.79 | -0.40 | -0.07 | 0.08 | 0.10 | 0.00 | -0.16 | -0.08 | -0.13 | -0.12 | -0.10 |
| AA | -0.57 | 0.39 | 0.34 | 0.03 | 0.09 | 0.12 | -0.04 | -0.05 | 0.26 | -0.33 | -0.01 |
| CAF | 0.08 | -0.24 | 0.30 | -0.10 | -0.29 | 0.28 | -0.60 | 0.10 | 0.34 | 0.05 | 0.06 |
| GA | 0.48 | -0.29 | 0.52 | -0.05 | -0.34 | 0.04 | 0.30 | -0.10 | -0.15 | 0.10 | 0.10 |
| TC | 0.81 | -0.51 | -0.02 | -0.14 | -0.06 | -0.04 | -0.12 | 0.09 | 0.02 | -0.06 | 0.01 |
| EGC | 0.26 | -0.23 | -0.57 | -0.05 | 0.31 | -0.33 | -0.12 | 0.26 | 0.33 | -0.02 | 0.21 |
| C | 0.43 | -0.16 | 0.50 | -0.16 | -0.38 | 0.20 | -0.02 | -0.05 | -0.12 | 0.09 | 0.10 |
| EC | 0.32 | -0.43 | -0.51 | 0.36 | 0.13 | -0.03 | 0.17 | -0.04 | 0.34 | 0.22 | 0.16 |
| EGCG | 0.63 | -0.38 | 0.08 | -0.26 | -0.03 | -0.13 | -0.28 | 0.15 | -0.13 | -0.33 | -0.13 |
| ECG | 0.45 | -0.35 | -0.20 | 0.32 | 0.06 | 0.28 | 0.33 | -0.24 | 0.02 | 0.30 | -0.23 |
Clustering analysis of morphological trait
The square UPGMA method was used to cluster the morphological traits of the tea tree resources of ‘ShiqianTaicha’ tea plants, and the results are shown in Fig. 2. Fifty-two tea tree resources were divided into 3 groups at 18 Euclidean distances. Group I contained the majority of tea tree resources, with a total of 35 species. The main characteristics of group I plants were mainly leaflets to middle leaflets, elliptic and oblong leaf shapes, and green and purple‒green leaf colors. The second group consisted of 10 resources, the main characteristics of which were middle class, a small leaf size,a nearly round leaf shape, a green leaf color, a blunt leaf tip, and a flat leaf margin. There were 7 resources in the third group, and the main characteristics were middle class, oblique leaf growth, elliptic leaf shape, dense tooth density, and a slight to protruding leaf surface.
Clustering heatmap analysis of biochemical components
The cluster heatmap can visually show the differences in biochemical components among tea resources.As shown in Fig. 3 and Table 7, 52 tea tree resources fromShiqian were divided into four groups: I, II, III and IV. Group I, which included most of the resources surveyed, had the highest caffeine content of 43 resources, and the other 9 biochemical components were moderate in the four groups.SWXD005, SLD005, SWD-G, SWD004, SPP003 and SPL003 belonged to group II, and the contents of 8 biochemical components—tea polyphenols, gallic acid, total catechin, EGC, DL-C, EGCG, ECG and caffeine—in this group were the highest and were significantly greater than those in the other three groups. The content of free amino acids was significantly lower in group II than in the other three groups, which were characterized by high levels of tea polyphenolsand catechinsand low levels of free amino acids. Group III included two resources, SRD-M2 and SRD-M3. The ECG content of this group was significantly lower than that of the other three groups, while the contents of tea polyphenols, total catechins, DL-C, EGCG, ECG and caffeine were significantly greater than those of group I and significantly lower than those of group I and group II. The contents of tea polyphenols, total catechins, DL-C, EGCG, ECG and caffeine ofin SRD-M1 were significantly lower in group IV than those ofin the other three groups.
Table 7
Comparative analysis of biochemical components in the 4 groups
| Biochemical composition | ClusterⅠ | Cluster Ⅱ | Cluster Ⅲ | Cluster Ⅳ |
| content range | Average value | content range | Average value | content range | Average value | content | Average value |
| TP | 16.21 ~ 25.00 | 22.14 ± 0.16 B | 23.84 ~ 26.32 | 24.72 ± 0.05 A | 15.24 ~ 17.00 | 16.12 ± 0.36 C | 13.42 | 13.42 ± 0.71D |
| GA | 0.36 ~ 1.07 | 0.66 ± 0.01 A | 0.55 ~ 0.97 | 0.78 ± 0.04 A | 0.36 ~ 0.38 | 0.37 ± 0.08 B | 0.2 | 0.11 ± 0.08 B |
| TC | 13.98 ~ 19.69 | 17.56 ± 0.06 B | 19.46 ~ 22.42 | 20.56 ± 0.34 A | 12.38 ~ 11.35 | 11.86 ± 0.49 C | 9.52 | 9.52 ± 0.18 D |
| EGC | 1.34ཞ4.62 | 2.86 ± 0.01 B | 2.19 ~ 4.16 | 3.16 ± 0.03 A | 1.39 ~ 2.39 | 1.89 ± 0.15 D | 2.41 | 2.41 ± 0.11 C |
| C | 0.39ཞ3.82 | 1.99 ± 0.01 B | 0.92 ~ 6.47 | 3.34 ± 0.11 A | 0.65 ~ 1.09 | 0.87 ± 0.07 C | 0.53 | 0.53 ± 0.04 D |
| EC | 0.48ཞ1.75 | 0.98 ± 0.04 | 0.67 ~ 1.27 | 1.05 ± 0.06 | 0.64 ~ 0.98 | 0.81 ± 0.31 | 0.69 | 0.69 ± 0.08 |
| EGCG | 7.98ཞ11.33 | 9.80 ± 0.02 B | 10.13 ~ 11.63 | 10.91 ± 0.01 A | 6.56 ~ 7.39 | 6.97 ± 0.10 C | 4.89 | 4.89 ± 0.16 D |
| ECG | 1.35ཞ3.88 | 1.94 ± 0.01 B | 1.62 ~ 2.80 | 2.11 ± 0.01 A | 1.28 ~ 1.37 | 1.33 ± 0.05 C | 1.01 | 1.01 ± 0.08 D |
| TAA | 2.41ཞ4.00 | 3.17 ± 0.02 B | 1.84 ~ 3.72 | 2.96 ± 0.01 C | 4.13 ~ 4.89 | 4.51 ± 0.01 A | 4.45 | 4.45 ± 0.11 A |
| CAF | 3.11 ~ 4.42 | 3.80 ± 0.04 A | 2.32 ~ 4.33 | 3.78 ± 0.04 A | 2.92 ~ 3.84 | 3.38 ± 0.02 B | 2.82 | 2.82 ± 0.23 C |
Heatmap analysis can also clearly show the biochemical composition characteristics of different tea tree resources. Among the tea tree resources tested, SWXD005, SLD005, SWD-G, SWD004, SPP003 and SPL003 in group II were high-EGCG resources (contents greater than or equal to 10%). SWXD005 and SWD-G were also high polyphenol content tea resources.
Cluster analysis can be used to classify tea variety resources and can quickly and accurately determine the similarities between resources and reflect the comprehensive traits between resources (Tang et al. 2023).In this study, the results of cluster analysis based on morphological traits and biochemical components were not completely consistent. The 52 resources were divided into 3 categories based on phenotypic trait analysis, while the tested resources were divided into 4 categories based on biochemical component analysis.Class I, based on morphological traits and biochemical components, included most of the tested resources, but Class II and Class III were different, indicating that there is a certain correlation between morphological traits and biochemical components and that there is a specificity of resources, which is consistent with the correlation analysis results.Morphological traits are influenced by both genetic and environmental factors (Zhang 2016), and the variation in biochemical components is significantly altered by long-term natural selection and artificial domestication (Qi 2015). ‘ShiqianTaicha’ is a historical tribute tea that has been cultivated for thousands of years and has undergone natural and human selection for a long time, which has resulted in relatively rich genetic diversity and regional uniqueness in the tea tree resources of ‘ShiqianTaicha’ tea.
Screening specific tea tree resources
According to the relevant indicators of the Standard for Evaluation of Excellent Germplasm Resources of Crops - Tea Tree (NY/T 2031 − 2011), 25 tea tree resources with high EGCG (≥ 10%) and 2 high tea polyphenol (≥ 25%) contents were selected from 52 tea tree resources, namely, SWD-G and SLD005, according to the results of biochemical component analysis. The four most abundant catechin resources (≥ 20%) were SPL003, SWD004, SWD-G and SWXD005 (Table 8).These resources have the potential to be used for cultivating tea tree varieties with high levels of tea polyphenols, catechins and EGCG.
Table 8
Screening of specific tea tree resources
| No. | Germplasm name | TP(%) | TC(%) | EGCG(%) |
| 1 | SWXD006 | 22.81 ± 1.24 | 18.32 ± 0.10 | 10.01 ± 0.03 |
| 2 | SPP006 | 22.62 ± 0.81 | 18.22 ± 0.47 | 10.02 ± 0.03 |
| 3 | SPP004 | 23.82 ± 0.17 | 18.04 ± 0.27 | 10.04 ± 0.20 |
| 4 | SPL003 | 24.34 ± 0.13 | 20.09 ± 0.37 | 10.13 ± 0.14 |
| 5 | SWX006 | 22.57 ± 0.68 | 18.01 ± 0.45 | 10.21 ± 0.06 |
| 6 | SWXD-HH1 | 21.82 ± 0.38 | 18.82 ± 0.24 | 10.23 ± 0.11 |
| 7 | SPL005 | 24.00 ± 0.16 | 18.75 ± 0.51 | 10.23 ± 0.11 |
| 8 | SWXD008 | 20.91 ± 0.82 | 18.47 ± 0.28 | 10.24 ± 0.04 |
| 9 | SWD004 | 24.62 ± 0.38 | 20.67 ± 0.31 | 10.27 ± 0.08 |
| 10 | SWXD007 | 23.84 ± 0.96 | 19.01 ± 0.31 | 10.28 ± 0.04 |
| 11 | SPL004 | 23.37 ± 0.76 | 18.35 ± 0.48 | 10.30 ± 0.04 |
| 12 | SWD002 | 22.59 ± 0.45 | 18.03 ± 0.10 | 10.35 ± 0.08 |
| 13 | SWD003 | 22.63 ± 0.45 | 19.07 ± 0.23 | 10.54 ± 0.17 |
| 14 | SPP003 | 23.84 ± 0.75 | 19.95 ± 1.36 | 10.63 ± 0.07 |
| 15 | SPC002 | 21.43 ± 0.31 | 17.27 ± 0.13 | 10.74 ± 0.06 |
| 16 | SWXD009 | 22.55 ± 0.79 | 18.86 ± 1.15 | 11.02 ± 0.10 |
| 17 | SPP005 | 23.00 ± 0.17 | 17.95 ± 0.20 | 11.08 ± 0.18 |
| 18 | SWXD011 | 23.12 ± 0.28 | 17.36 ± 0.72 | 11.15 ± 0.08 |
| 19 | SPL002 | 21.83 ± 0.30 | 17.29 ± 0.57 | 11.15 ± 0.11 |
| 20 | SWD005 | 23.63 ± 0.91 | 19.69 ± 0.27 | 11.22 ± 0.08 |
| 21 | SWX001 | 21.51 ± 1.51 | 18.10 ± 0.31 | 11.28 ± 0.06 |
| 22 | SWD-G | 26.32 ± 0.49 | 20.76 ± 0.31 | 11.29 ± 0.18 |
| 23 | SWX007 | 22.13 ± 0.40 | 17.33 ± 1.02 | 11.33 ± 0.07 |
| 24 | SWXD005 | 24.07 ± 0.25 | 22.42 ± 0.55 | 11.49 ± 0.04 |
| 25 | SLD005 | 25.13 ± 1.29 | 19.46 ± 0.91 | 11.63 ± 0.07 |