Abstract
Spine grape (Vitis davidii Foëx), an important wild grape species in South China, has gained attention because of its health-promoting effects and use in the wine industry. Fruit quality plays an important role in determining the quality of wine; however, a suitable evaluation system to monitor its fruit quality has not been established. The fruit quality characteristics (phenolics and aromas) of 15 spine grapes grown in China were evaluated using a combination of principal component and cluster analyses. The total sugar, organic acid, and phenolic content ranged from 81.80 to 154.89 mg·g−1, 8.02 to 15.48 mg·g−1, and 5.58 to 20.12 mg·g−1, respectively. The comprehensive assessment by principal component analysis revealed that ‘Red xiangzhenzhu’ had the highest quality and ‘Hongjiangci10’ and ‘Ziluolan’ the lowest quality. Cluster analysis using k-means grouped the cultivars into three clusters based on their quality: Cluster 1 grouped those with inferior quality (‘Hongjiangci09’, ‘Hongjiangci10’, ‘Hongjiangci11’, and ‘Hongjiangci07’, etc.), Cluster2 grouped those with average quality (‘Ciputao3#,’ ‘Ziluolan’, and ‘Xiangci4#’), and Cluster3 grouped those with superior quality (‘Red xiangzhenzhu’ and ‘Green xiangzhenzhu’). A combination of principal component analysis and cluster analysis provides a comprehensive and objective evaluation system for determining the quality of grape cultivars. This study is important for the systematic evaluation and utilization of spine grape resources.
The spine grape (Vitis davidii Foëx), native to China, is an East Asian species widely distributed in Fujian and southern Hunan Province (Meng et al., 2012a; Yue et al., 2015). Its berries are rich in anthocyanins and other polyphenolic compounds, are commonly used in wine and juice making, and have broad market prospects (Meng et al., 2012b).
Fruit quality is a critical factor determining the quality of the wine. In spine grape, fruit quality can be divided into two major groups: extrinsic and intrinsic. The extrinsic quality of the fruit is affected by color, size, hardness, and so on, whereas the internal quality is the fruit texture and flavor, among other attributes (Chen et al., 2015; Granato et al., 2016; King et al., 2000). Fruit flavors are determined by acids, sugars, aromatic substances, and phenols (Zheng et al., 2015). The external quality of the fruit influences consumer preference through perception, whereas internal quality is the central factor that determines the quality of the fruit. The sugar–acid fraction directly influences the taste and sweetness of the berry and is also a necessary raw substance for the synthesis of other components such as acids, pigments, and amino acids (Guo et al., 2017; Lecourieux et al., 2014; Nowicka et al., 2019; Zuanazzi et al., 2019). Phenolic substances regulate the aroma, taste, and color of grapes and are significant indicators of grape quality (Gervasi et al., 2016; Li et al., 2021). Thus, the analysis and evaluation of grape berries is a prerequisite for their further understanding and utilization.
Several statistical and mathematical analysis methods applied in the research on fruit flavor quality have effectively revealed quantitative relations and tendencies between quality parameters. Statistical analyses such as principal component analyses, cluster analyses, and linear regression analyses have been used to study systems, criteria, and fresh grape fruit quality evaluation indices. Using these methods, key databases on fresh grape quality have been developed and established (Pan, 2019). Previous studies have reported the physical, chemical, and nutritional properties of several grape cultivars distributed in China. (Jiang et al., 2017; Ju et al., 2021; Li et al., 2019a). However, the scientific evaluation of spine grape has not been systemically documented in terms of fruit quality.
The present study aimed to identify the most promising spine grape genotypes with better fruit quality and high health-promoting components to develop a selection procedure suitable for a spine grape breeding program in China. The study also attempted to establish a mathematical and statistical analysis–based evaluation system to determine the fruit quality of wild grapes.
Materials and Methods
Plant material.
All plant materials used in this study were collected from the China National Germplasm Resources Repository of Grapes, Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences (lat. 34°16'N, long. 112°42'E). This study included the fruit of 15 spine grape (Table 1). Three grapevines were maintained for each genotype. The grapevines were supplied with the equivalent fertilization, pruning, water management, weeding, and similar protocols to control pests. The berries were harvested at physiological maturity. Select three vines for each genotype to collect berries, and 100 berries were randomly collected from each tree. Three biological replicates were used for each genotype (three replicates of 100 berries each) were stored at –80 °C for subsequent analyses.
Participating germplasm.
Determination of fruit shape index.
Forty-five berries of each variety were randomly taken and divided into three groups for the determination of fruit shape index, and the average value of each group was calculated to obtain three biological replicates.
Determination of single fruit weight.
Thirty berries of each variety were randomly taken and divided into three groups for the determination of fruit weight, and the average value of each group was calculated to obtain three biological replicates.
Determination of chromatic aberration and soluble solids.
Three bunches of each germplasm were randomly selected, 15 berries were taken from the top, middle, and bottom of each bunches, and a total of 45 fruits were taken from each germplasm, and L* (brightness), a* (redness), b* (yellowness), C* (chromaticity), and E (combined deviation of color difference) were determined by a portable colorimeter (CR-10; Konica Minolta, Japan) for the color difference, and soluble solids values were determined for each fruit using a handheld PAL-1 digital refractometer (ATAGO Co., Ltd., Japan).
Determination of titratable acidity and pH.
Thirty berries of each germplasm were randomly taken, the seeds were carefully removed, and the enriched grape juice was separated using a blender (JYL-C01S; Joyoung Co., Jinan, China). TA was determined by titration to pH 8.1 with 0.1 N NaOH using an automated pH titration system (Titralab 1000; HACH, Loveland, CO). It was expressed as a percentage of tartaric acid while determining the fruit pH using an automated pH titration system (Titralab 1000, HACH). Soluble solids content (SSC)/total acid (TA) ratio was calculated based on measured data.
Determination of sugars and organic acids.
The extracted juice was centrifuged at 10,000 gn for 15 min. The supernatant (1 mL) was aspirated, added to a centrifuge tube, and diluted by adding 9 mL water. The diluted juice was then filtered through a 0.22-μm pipette filter tip (XB-LQ-13022; XB-BIO, China), transferred to the injection vial, and analyzed using a Waters 2695 high-performance liquid chromatograph (Li et al., 2019b).
The chromatographic conditions for sugar determination were as follows: 2414 parallax detector, Waters Sugar-pack1 column (6.5 mm × 300 mm), mobile phase: 50 mg·L−1 calcium disodium EDTA solution, flow rate: 0.5 mL·min−1, column temperature 80 °C.
The chromatographic conditions for the determination of organic acids were as follows: ultraviolet detector, Ultimate AQ-C18 column (250 mm × 4.6 mm, 5 µm), mobile phase: 0.02 mol·L−1 diammonium hydrogen phosphate solution, pH 2.4, flow rate: 1.0 mL·min–1, column temperature: 30 °C, detection wavelength 210 nm. Each sample was injected three times, and the corresponding organic acid or sugar content was calculated using the obtained standard curve. The average value and the standard deviation were calculated. The standard curve and detection range are shown in Table 2.
Determination of phenolic content.
Phenolic compounds were extracted using the method described by Makris et al. (2007) with some modifications. Fruits with uniform size, consistent ripeness, and free-from mechanical injuries and pests and diseases were selected, frozen, and ground into powder. Then methanol extract (containing 0.5% hydrochloric acid) was added following a material-to-liquid ratio of 1:30 (m/v), and the extraction was assisted by ultrasonication for 30 min. The extract was then centrifuged at 12,000 gn for 15 min at 4 °C. The extraction was repeated three times, and the supernatant was combined and filtered through a 0.22-μm pipette filter head (XB-LQ-13022).
Polyphenols were determined using high-performance liquid chromatography (HPLC) analysis. The following chromatographic conditions were used: column—Platisil ODS column (4.6 mm × 250 mm, 5 μm); mobile phase—water-to-acetic acid (98:2 v/v) for phase A, acetonitrile for phase B; elution gradient—0 to 10 min, phase A: 90%, phase B: 10%; 10 to 15 min, phase A: 90% to 80%, phase B: 10% to 20%; 15–26 min, phase A: 90% to 80%, phase B: 10% to 20%; 15–26 min, phase A: 80% to 60%, phase B: 20% to 40%; 26–30 min, phase A: 60% to 0%, phase B: 40% to 100%; 30–35 min, phase A: 0% to 90%, phase B: 100% to 10%; 35–40 min, phase A: 90%, phase B: 10%. Column temperature was 30 °C, injection volume was 2 μL, flow rate was 0.4 μL·min−1, and detection wavelength was 280 nm. The standard curve and detection range are shown in Table 3.
Standard curve, detection limits, and retention time of the sugar and acid.
Standard curve, detection limits and retention time of seven phenols.
Physicochemical characteristics of different spine grapes.
Determination of aroma.
The aroma compounds were determined following the method described by Xie et al. (2021) with some modifications. In an experimental environment of 20 ± 0.5 °C, 10 mL sample was put in a 40 mL headspace chromatography sample bottle and left for 10 min to ensure that the gas in the bottle was saturated. The analysis was performed using the following conditions: presampling time: 20 s; sampling interval: 3 s; flushing time: 80 s; zero-adjustment time: 5 s; measurement time: 120 s; flow rate: 300 mL·min−1. Five parallel samples were analyzed for each of the 15 spine grapes germplasms. Each sample was repeated three times, and the average value was used for further analyses to eliminate measurement errors.
Statistical analyses.
Data are reported as mean ± standard deviation of triplicate experiments and analyzed using SPSS 20.0 (IBM). One-way analysis of variance (ANOVA) and Duncan’s multiple range tests were used to determine the significance of the differences among samples, with a significance level of 0.05. A two-tailed Pearson’s correlation test was conducted to determine the correlations among the means. Statistical and principal component analyses (PCA) were performed using Origin 2019 software.
Results and Discussions
Physicochemical characteristics.
External indicators can be directly determined by fruit weight (FW) and fruit shape index (FI). FI is mainly determined by genetic characteristics and varies depending on the cultivation conditions and germplasms (Zhang et al., 2016). The FW of most grape germplasms depends on their fruit-ripening stage and crop load (Drake and Elfving, 2002; Serrano et al., 2009; Usenik et al., 2010). All the germplasms evaluated in this study were medium and small fruit grapes, and their average FW and FI were smaller than the average FW and FI of the table grapes.
It is well known that grape fruit appearance is closely related to the FI and appears to be a key factor influencing preference and acceptability by consumers (Lecourieux et al., 2014). In Hunan and other regions, spine grapes are eaten as a fresh variety because of their large fruit shape, good color and rich taste. The study demonstrated that the fruit shape of six germplasms (40%) were round (shape index <1.10), and the remaining germplasms had an elliptical fruit shape with a longitudinal diameter slightly larger than the transverse diameter (shape index between 1.10 and 1.30). As shown in Table 4, the average FW of the 15 germplasms ranged from 2.22 g (TM3) to 4.82 g (HJC9). The FWs of ‘TM3’, ‘HJC10’, ‘HJC11’, and ‘FAC’ did not differ significantly (P > 0.05), with an average weight of ≈2.35 g. However, the FW of ‘HJC9’ (4.82 g) was significantly higher than that of the other germplasms. The fruit shape index of wild grapes is smaller than that of common germplasms, due to the selection of cultivated grapes (Jiang et al., 2017).
The color of the fruit is an important indicator of the quality and freshness of the fruit (Wang et al., 2010). Therefore, there is a strong interest in breeding new varieties with different colors, rich in anthocyanins and improving the nutritional quality of the fruit. The color characteristics of the spine grape germplasms are reported in Table 4. The L* values of grape berries indicate the degree of lightness and darkness of the berries, with L* = 0 representing all black berries and L* = 100 representing all white berries. The a* values are red and green values, with positive numbers being red and negative numbers being green and larger values indicating a redder sample color. The b* values are yellow and blue values, with positive numbers being yellow and negative numbers being blue, and larger values indicating more yellow color. ANOVA showed that most L* values reached significant levels. ‘XC4#’ had the highest L* value (40.55), whereas ‘HJC10’ had the lowest (28.98). The a* value was the highest in ‘FAC’ (5.49) and the lowest in ‘HJC7’ (–0.21). ‘HJC10’ had the highest b* value (–0.88), and ‘HJC7’ had the lowest (–4.46). C* that represents the saturation of the color difference (chroma) was the lowest in ‘HJC5’. The E values (color difference) of ‘XC4#’, ‘GS1#’, and ‘HJC7’ were significantly different from those of ‘GXZZ’, ‘GS2#’, ‘HJC10’, and ‘HJC5'. Different degrees of variation were represented by different coefficients of variation among the samples. The highest degree of variation among the germplasms was observed for the skin color (a* value) with a coefficient variant (CV) of 80%, followed by the saturation of the skin color (C*) with a CV of 61.32%. These results suggest that the germplasms tested here are genetically diverse and can potentially be used in the breeding program of spine grapes in China.
Sugars and acids content.
The grape fruit flavor is closely related to sugar–acid content, and the sugar-to-acid ratio reflects fruit taste (Guo et al., 2017). Previous studies have concluded that sugars in wild grapes were mainly divided into glucose and fructose, with total sugar content ranging from 91.92 to 146.71 mg·mL−1, glucose content ranging from 37.57 to 64.22 mg·mL−1, fructose content ranging from 53.16 to 84.25 mg·mL−1, and sucrose detected only in V. rotundifolia (Jiang et al., 2017). This study showed that the sugars in the fruits of the 15 germplasms of the spine grapes mainly comprised glucose and fructose, whereas sucrose was not detected. Glucose content ranged from 38.28 mg·mL−1 (HJC9) to 70.35 mg·mL−1 (RXZZ), and fructose content ranged from 43.53 to 84.55 mg·mL−1, which is consistent with previous studies (Jiang et al., 2017; Liu et al., 2006).
The organic acid fraction of all the 15 germplasms was dominated by tartaric acid and ranged from 6.19 to 12.67 mg·mL−1. ‘ZQ’ had the highest tartaric acid content, and ‘HJC7’ had the lowest. This was followed by malic acid, which ranged from 0.80 (‘HJC7’) to 4.19 (‘TM3’) mg·mL−1. Citric acid content accounted for 0.44 to 1.77 mg·mL−1, and oxalic acid content was the lowest at 0–1.14 mg·mL−1 (Table 5). The tartaric, malic, and citric acid contents were all higher than those previously reported (Jiang et al., 2017). This may be due to differences in the germplasm of the tested spine grapes. Tartaric acid is the main unfermentable soluble acid in grape berries and has an important influence on the flavor, color, and stability of wine. Compared with tartaric acid, malic acid is softer and more pleasant, but high levels of malic acid can lead to higher lactic acid content in wine, which can affect quality (Liu et al., 2006). Citric and oxalic acids, although low in grapes, both have important effects on fruit flavor.
Comparison of sugar and acid quality in different spine grape germplasms.
Comparison of phenols in different spine grape germplasms.
Factor loading matrix, variance contribution rate, and initial eigenvalues.
The flavor of the fruit depends on both the absolute values of soluble sugars and organic acids and the sugar-to-acid ratio of the fruits. The sugar-to-acid ratio was the lowest in ‘GS2#’ (6.36) and highest in ‘GXZZ’ (14.50). The SSC varied from 11.17 (HJC10) to 16.00 (XC4#) °Brix, whereas the TA values ranged from 0.609% (HJC9) to 0.879% (ZQ) of tartaric acid (Table 5). It is known that the flavor intensity of grapes, which is mainly linked to the SSC/TA ratio (Guo et al., 2017), is a fundamental factor that influences consumer acceptability and preference. It is known that the flavor intensity of a wine is mainly related to the SSC/TA ratio of the berries (Jiang et al., 2017; Kyraleou et al., 2020), and wines made from grapes with higher acidity will have better body and stability (Liu et al., 2006). In this study, ‘XC4#’ and ‘CPT3#’ had significantly higher (P < 0.05) SSC/TA ratios (20.54 and 20.13, respectively) compared with those of ‘HJC5’ and ‘GS1#’ (14.61 and 12.67, respectively). These results indicated that ‘XC4#’ and ‘C3#’ fruits are sweeter than ‘HJC5’ and ‘GS1#’.
Phenolics.
Phenols can affect the color, aroma, and taste of wine and are important indicators of the quality of grapes (Kyraleou et al., 2016, 2020). The phenol concentration in berries varies widely depending on the genotypes. The total phenolic concentrations in the germplasms tested here ranged from 5.58 (HJC10) to 20.12 (RXZZ) mg·g−1. The total phenolic content of the spine grapes tested here were comparable with the previous findings, which reported that the total phenolic content of 15 spine grape samples (Junzi1#, Gaoshan2#, 5044, 5049, etc.) ranged from 3.78 to 21.07 (FW) mg·g−1 (Ferrandino et al., 2019; Ju et al., 2021). The varietal differences in the total phenolic content of the spine grapes suggested that the tastes of these spine grapes might vary.
Among the seven polyphenol components of spine grapes, epicatechin had the highest content (average of 8.47 mg·g−1) and the lowest content of gallocatechin (0.03 mg·g−1). Except for ‘FAC’ and ‘CPT3#’, all other germplasms contained epigallocatechin, ‘HJC5’ having the highest content, which was significantly higher than other spine grapes (Table 6). There was no significant difference between the epigallocatechin content of ‘RXZZ’, ‘GXZZ’, and ‘XC4#’; only ‘TM3’, ‘HJC7’, and ‘ZQ’ contained gallocatechin. Furthermore, quantitative analysis of the proanthocyanidins (PA) showed that the concentration of PA in spine grapes ranged from 0.11 (HJC10) to 6.87 (RXZZ) mg·g−1. All seven polyphenols tested in this study were flavan-3-ol monomers and dimeric phenolics associated with wine bitterness, and this sensory perception defines red wine quality (Ju et al., 2021).
Aromas.
The electronic nose provides a new nondestructive way to detect the volatile aroma components of fruits. It can detect the volatile components produced by fruits rapidly and nondestructively and obtain the “fingerprint data” of the samples (Chang et al., 2014; Zhang et al., 2008), thus determining the intrinsic quality of fruits. Therefore, in this study, the volatile aroma components of different spine grapes were analyzed using an electronic nose (Fig. 1).
(A) Range and distribution of the concentration of aromas and (B) the proportion of aromas [W1C (), W5S (
), W3C (
), W6S (
), W5C (
), W1S (
), W2S (
), W2W (
), W1W (
) and W3S (
)] in 15 spine grape cultivars. W1C, W5S, W3C, W6S, W5C, W1S, W2S, W2W, W1W, and W3S correspond to sensitive to aromatic components, sensitive to nitrogen oxides, sensitive to ammonia and aromatic compounds, selectively sensitive to hydrogen, sensitive to alkanes and aromatic compounds, sensitive to methyl groups, sensitive to ethanol and aromatic compounds, sensitive to organic sulfides, sensitive to inorganic sulfides, and sensitive to alkanes, respectively.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
(A) Range and distribution of the concentration of aromas and (B) the proportion of aromas [W1C (), W5S (
), W3C (
), W6S (
), W5C (
), W1S (
), W2S (
), W2W (
), W1W (
) and W3S (
)] in 15 spine grape cultivars. W1C, W5S, W3C, W6S, W5C, W1S, W2S, W2W, W1W, and W3S correspond to sensitive to aromatic components, sensitive to nitrogen oxides, sensitive to ammonia and aromatic compounds, selectively sensitive to hydrogen, sensitive to alkanes and aromatic compounds, sensitive to methyl groups, sensitive to ethanol and aromatic compounds, sensitive to organic sulfides, sensitive to inorganic sulfides, and sensitive to alkanes, respectively.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
(A) Range and distribution of the concentration of aromas and (B) the proportion of aromas [W1C (), W5S (
), W3C (
), W6S (
), W5C (
), W1S (
), W2S (
), W2W (
), W1W (
) and W3S (
)] in 15 spine grape cultivars. W1C, W5S, W3C, W6S, W5C, W1S, W2S, W2W, W1W, and W3S correspond to sensitive to aromatic components, sensitive to nitrogen oxides, sensitive to ammonia and aromatic compounds, selectively sensitive to hydrogen, sensitive to alkanes and aromatic compounds, sensitive to methyl groups, sensitive to ethanol and aromatic compounds, sensitive to organic sulfides, sensitive to inorganic sulfides, and sensitive to alkanes, respectively.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
The electronic nose shows aroma composition distinguishing different spine grape germplasms, mainly through W5S, W1S, and W2S sensors. Other sensors have response values for spine grape aroma, but there is less variability between samples, and intuitively they cannot differentiate between samples. The W5S sensor response values for the ‘RXZZ’ sample were significantly higher than those of the other 14 species, indicating that the ‘RXZZ’ sample probably contained the most nitrogen oxide-like substances. ANOVA revealed that the CV of W6S, W1W, and W3S among germplasms were small, indicating the narrow diversity among the germplasms for W6S, W1W, and W3S. The CV of W5S was the largest, up to 33.21%, indicating a considerable variation in W5S content among the germplasms.
Principal component analysis.
Thirty-five fruit quality indicators of 15 germplasms were measured, and the CV between the qualities were calculated. The CV reflects the degree of dispersion among the data (Risticevic et al., 2009). Among the fruit quality indicators of different accessions, the CV of fruit (gallocatechin, catechin, procyanidin B1, procyanidin B2, and epicatechin-3-O-gallate ) were higher, indicating that the differences between fruit coefficients of different grapes were greater than those between other indicators.
However, the comprehensive evaluation of different exploitable qualities of spine grape germplasms is not systematic and precise (Ju et al., 2021; Meng et al., 2012). Currently, PCA and cluster analysis are increasingly used to evaluate fruit quality for multiple samples with multiple indicators (Guo et al., 2017; Jiang et al., 2017; Li et al., 2021). In this study, the fruit quality indicators of different spine grapes were subjected to PCA. Six principal components (PCs) with characteristic roots greater than two were obtained with a cumulative contribution of 82.691%, which is in line with the analysis requirements (Table 7). Therefore, we selected PC1 and PC2 as two principal component factors and created a distribution point map of the PCs of the different grape germplasms on the PC1-PC2 plane (Fig. 2).
Principal component analysis of fruit quality in spine grapes. Spatial distribution of 15 spine grapes in principal component (PC)1 and PC2. The proportion in parentheses is the variance of the various components.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
Principal component analysis of fruit quality in spine grapes. Spatial distribution of 15 spine grapes in principal component (PC)1 and PC2. The proportion in parentheses is the variance of the various components.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
Principal component analysis of fruit quality in spine grapes. Spatial distribution of 15 spine grapes in principal component (PC)1 and PC2. The proportion in parentheses is the variance of the various components.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
The proportion of the variance contribution of each of the six principal components was used as the weight to calculate the comprehensive evaluation scores of the different germplasms. The scores of the different accessions were calculated and ranked according to the comprehensive evaluation model; F = 0.355F1 + 0.121F2 + 0.104F3 + 0.977F4 + 0.824F5 + 0.671F6, with higher comprehensive scores indicating better overall quality (Li et al., 2019b). The ranking scores in show that the overall quality scores of ‘ZQ’, ‘TM3’, and ‘XC4#’ fruits were higher than those of the others (Table 8). The overall quality score of ‘ZQ’ was the highest, wherein ‘HJC9’ detected the lowest score, indicating that its overall quality was inferior to those of the other 14 spine grape germplasms. The study demonstrated that ‘RXZZ’ had a higher overall score with high polyphenol content and sugar-to-acid ratio and better overall fruit quality. These evaluation results are basically consistent with the actual taste, proving that PCA is scientific and practical in fruit quality evaluation (Nowicka et al., 2019).
Composite score of principal components.
Cluster analysis.
Systematic cluster analysis classified the 15 spine grape germplasms into three clusters (Fig. 3). GXZZ and RXZZ fruits with high sugar-to-acid ratio and polyphenol content were grouped as the first cluster (Fig. 2; green). The second cluster contained three germplasms, representing 20% of the spine grapes offered for testing, all of which contained low amounts of soluble sugars (Fig. 3; red). The third cluster contained 10 grapes. The overall quality of grapes in this cluster was lower than those in the other clusters, with relatively low sugar, acid, and polyphenol contents (Fig. 3; blue). These results reflect, to some extent, the differences in fruit quality between spine grape germplasms.
Circular cluster plot visualizing the clustering of the 15 varieties of spine grape in this study based on their fruit-quality.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
Circular cluster plot visualizing the clustering of the 15 varieties of spine grape in this study based on their fruit-quality.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
Circular cluster plot visualizing the clustering of the 15 varieties of spine grape in this study based on their fruit-quality.
Citation: HortScience 57, 10; 10.21273/HORTSCI16702-22
The main purpose of PCA is to synthesize and simplify evaluation indicators, whereas cluster analysis aims to classify research objects or variables according to their affinity in nature. The combination of both tools has become one of the main methods for comprehensive fruit quality evaluation and formed important theoretical support for the selection and breeding, rational processing, and utilization of specialized fruit and vegetable germplasms (Hossain et al., 2011; Nie et al., 2000).
Because PCA and cluster analysis are based on the original fruit quality data, and the 35 major fruit-quality traits selected in this study are not comprehensive, there is a need to select targeted quality traits and establish dominant production areas in a targeted manner, considering the differences in different spine grape production areas. Therefore, further exploration is needed in future studies.
Conclusions
This study evaluated the fruit quality parameters and chemical attributes of 15 spine grape germplasms. The study demonstrated considerable variability in the physicochemical characteristics of the germplasms, indicating their potential use in grape breeding programs. ‘RXZZ’ had the highest sugar content, and ‘HJC9’ had the lowest. Tartaric acid was the principal organic acid in all germplasms, followed by malic acid, citric acid, and oxalic acid. Furthermore, we compared the levels of bioactive compounds relevant to fruit quality, including aroma and phenolic compounds. Epicatechin was the main phenolic compound in all accessions, followed by epicatechin-3-O-gallate, epigallocatechin, and procyanidin B1. The highest aroma content was found in ‘RXZZ’. Our results revealed similar qualitative profiles of the germplasms; however, they differ for their aroma and phenolic contents. Further, the evaluation of 35 fruit quality indicators using a combination of PCA and cluster analysis identified ‘RXZZ’ contained high polyphenols and sugar-to-acid ratio and had better fruit quality. Collectively, the findings suggest that a combination of PCA and cluster analysis can be used as a comprehensive and objective evaluation system for determining the quality of grape germplasms.
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