Simultaneous determination of phenolic metabolites in Chinese citrus and grape cultivars

Introduction

Phenolic compounds are secondary metabolites widely distributed in fruits and vegetables, which are essential for the nutritional, commercial, and organoleptic qualities of the fruit. The growing interest in phenolic metabolites is mainly due to their biological activities, such as anti-inflammation, antioxidant, antimicrobial and anticancer (Gu et al., 2019; Lou & Ho, 2017; Rasines-Perea & Teissedre, 2017). Observational studies have shown that polyphenols significantly reduced the risk of hypertension and cardiovascular disease (CVD) (Rasines-Perea & Teissedre, 2017).

Grapes and citrus contain a considerable amount of different phenolic compounds in peels, leaves, pulp and seeds, contributing to high nutritional value (De Paula Menezes Barbosa, Ruviaro & Macedo, 2018; Ernawita et al., 2017; Liew et al., 2018). As a major bioactive compound in citrus and grape, it is significant to use phenolic compound content as a quality/nutritional evaluation criteria for different varieties of these fruits grown in different environmental conditions to provide a quantifiable classification of the quality and potential health benefits of each fruit variety. Besides, phenolic acids and flavonoids are the most abundant phenolic compounds in grapes and citrus (Da Silva Padilha et al., 2017; Wang et al., 2017).

Several methods have been reported for simultaneous determination of flavonoids and phenolic acids in citrus or grape. Wang et al. (2019) identified 254 flavonoid metabolites including eight isoflavone, 21 flavanone, 24 anthocyanins, 39 flavonol, 147 flavone and 15 polyphenol in the peels of five citrus varieties by UPLC-ESI-MS/MS method. Wang et al. (2017) investigated the flavonoids in the peels of 35 citrus varieties by li quid chromatography combined with electrospray ionization mass spectrometry (LC-ESI-MS/MS) and ultra-performance liquid chromatography combined with diode array detector (UPLC-DAD). In addition, RP-High-performance liquid chromatography (HPLC)/DAD was applied to determine the flavonoids and phenolic acids in grapes (Da Silva Padilha et al., 2017).

Many studies have investigated the analysis and detection of phenolic compounds in citrus and grape varieties, but little is known to the simultaneous determination of flavonoids and phenolic acids in the peel extracts of citrus and grape. Considering the similar phenolic groups in citrus and grapes, we hypothesized that a quantification and identification method could be established to simultaneously determine several phenolic compounds for citrus and grape. Therefore, this study aimed to develop a novel, direct, optimized and validated method for the simultaneous identification and quantification of phenolic compound in citrus and grape peel extracts using HPLC-DAD with a fast sample preparation. The study was also designed to apply principal component analysis (PCA) and hierarchical cluster analysis (HCA) as exploratory methods to analyze the phenolic compound profile, and to understand the differences between varieties of citrus and grape.

Materials and Methods

Results

HPLC method validation

HPLC chromatographic profiles

The chromatographic profiles of the standard solutions and the sample representatives monitored at 280 nm/330 nm were shown in Fig. 1. The HPLC condition obviously separated these compounds in the standard solutions and the sample representatives within the running time. Then the method was validated.

Linearity, LOD and LOQ

All results of method validation are summarized in Table 2. The linearity of each compound was created in a wide concentration ranging from 0.09 to 100 mg/mL for different compounds. All the correlation coefficients (r²) of the calibration curves were above 0.9976, indicating good linearity of the calibration curves. The LODs and LOQs of the 17 compounds were in the range of 0.03–1.83 µg/mL and 0.09–5.55 µg/mL, respectively, suggesting good sensitivity of the developed method.

Table 2:

Regression equation, correlation coefficient, linear range, limit of detection (LOD) and limit of quantification (LOQ) of the phenolic compositions.

Compound	Wavelength (nm)	m/z	Retention time (min)	Linearity		Linear range (μg/ml)	LOD (μg/ml)	LOQ (μg/ml)	Repeatability (n = 5)	Recovery (%)
				Calibration equationsa	Correlation coefficient
Gallic acid	280	170	4.93	y = 16486.96 x − 1935.75	0.9999	0.11–100	0.03	0.09	1.11	101.58
Catechin	280	290	19.2	y = 4362.63 x − 1075.23	0.9998	0.43–100	0.13	0.39	1.18	92.20
Chlorogenic acid	280	354	21.61	y = 11217.41 x − 3852.84	0.9997	0.18–100	0.05	0.15	1.66	95.56
Caffeic acid	330	180	24.29	y = 23730.91 x − 4696.96	0.9999	0.09–100	0.03	0.09	1.28	98.48
Syringic acid	280	198	27.12	y = 20842.32 x − 3904.67	0.9999	0.40–100	0.12	0.36	1.02	98.89
Epicatechin	280	290	30.99	y = 8484.05 x − 2672.05	0.9998	2.5–100	0.72	2.18	1.41	99.58
ρ-Coumaric acid	330	164	40.23	y = 36409.07 x − 5490.19	0.9999	0.86–100	0.26	0.79	1.24	99.22
Ferulic acid	330	194	52.16	y = 21047.21 x − 3361.75	0.9999	0.27–100	0.08	0.24	1.53	102.82
Benzoic acid	280	123	54.95	y = 3309.42 x − 99.98	0.9999	0.53–100	0.16	0.48	1.49	93.60
Salicylic acid	330	138	57.21	y = 4286.10 x − 2290.45	0.9991	2.5–100	1.19	3.61	1.90	94.30
Rutin	280	611	60.16	y = 4643.32 x + 1353.07	0.9976	0.52–100	0.15	0.45	1.44	92.20
Naringin	280	581	64.97	y = 12259.00 x − 1724.35	0.9999	0.38–100	0.11	0.33	1.22	96.34
Hesperidin	280	611	66.34	y = 11424.82 x − 2542.83	0.9998	0.50–100	0.15	0.45	1.20	98.54
Resveratrol	330	228	69.80	y = 26014.88 x − 4569.33	0.9999	0.22–100	0.07	0.21	1.37	96.09
Quercetin	330	302	76.52	y = 4515.99 x − 16433.83	0.9991	2.5–100	0.13	0.39	1.97	94.74
Nobiletin	330	403	93.08	y = 15335.05 x − 2413.05	0.9999	0.80–200	0.24	0.73	1.20	100.39
Tangeritin	330	373	97.09	y = 15914.66 x + 12562.01	0.9994	2.5–200	1.83	5.55	1.55	96.27

DOI: 10.7717/peerj.9083/table-2

Note:

a y is peak area and x is concentration of each standard (μg/mL).

Precision

The precision of the method was assessed by repeatability, which was evaluated by five consecutive injections of a sample representative (GG as citrus representative and JF as grape representative) in one day. RSDs of peak area for each compound were used to evaluate repeatability.

As indicated in Table 2, the RSDs of repeatability were 1.02–1.97%, which were less than 3.5% for all the compounds, revealing good precision of this method.

Recovery

The recoveries of all the 17 compounds were in the range of 92.2–102.82%, with RSD values less than 5%, indicating that the established method was accurate enough for the determination of the 17 compounds in citrus and grape.

Classification of citrus and grape cultivars

For citrus, PCA was generated with two significant principal components (PCs) explaining 52.50% and 38.56% of the variance, respectively (Data S2). The CPV of the two PCs therefore explained 92.14% of the total variance. The resulting data was then plotted to produce a 2D dimensional graphic of PCA scores (Fig. 2A). In general, the 15 citrus cultivars were clustered into three groups: Samples 1–7 (Duweiwendan, Hongmianmiyou, Huangjinyou, Guangximiyou, Huangjinyou, Huyou and Putaoyou) in Table 1 as group I, sample 12 (Maogujucheng) as group II and the other seven samples as group III. All of the samples were sorted into two main clusters using HCA analysis (A and B, Fig. 2B). Samples 1–7 and 12 were in cluster A, whereas the other samples were in cluster B. Both cluster A and B were further divided into subgroup A1: Samples 1–6, subgroup A2: samples 7 and 11, subgroup B1: Samples 9, 14 and 15 and subgroup B2: samples 10, 11 and 13. Though there was some difference, significant overlap in the clustering of results between PCA and HCA was found. Generally, Cluster B included all the citrus cultivars in group III classified by PCA (Figs. 2A vs. 2B); samples 1–7 were always in one group (group I or cluster A). The difference in grouping sample 12 may be due to the PCA method only considering two main PCs, while HCA considering all of the components.

For grape, three significant PCs explaining 43.89%, 21.95% and 18.66% of the variance, respectively, corresponding to a CPV of 83.10% of the total variance were taken for further PCA analysis (Data S2). A 3D graphic of PCA scores was generated, which is shown in Fig. 3A. Generally, the 12 grape cultivars were also classified into two groups (Fig. 3A). XH1, XH2, XH3, JH and HR were in group I and the other grape cultivars were in group II. When HCA was used to cluster samples, the 12 samples were also grouped into two main clusters with two subgroups for each cluster (Fig. 3B). Though HCA clustered DX, MM and JMD in one group and the other cultivars in another group, both HCA and PCA classified XH1, XH2, XH3, JH and HR in one group, indicating a strong consistency between these two methods in evaluating similar patterns of plant cultivars belonging to the same genus.

Discussion

In this study, HPLC method was used to verify that the established method has high sensitivity and good precision, which is sufficient to determine 17 compounds in citrus and grape. Peel extract rather than pulp extract was used due to its higher phenolic content in citrus (Sir Elkhatim, Elagib & Hassan, 2018). Flavonoids are classified into flavanone, flavonol, flavone, isoflavone, flavan-3-ols and anthocyanin, and hesperidin belongs to flavanone (Liew et al., 2018). The results showed that flavonoid hesperidin was the main flavonoid compound in all citrus samples, which was consistent with previous studies (Ernawita et al., 2017; Gomez-Mejia et al., 2019; Lou & Ho, 2017), where the authors concluded that hesperidin was a dominant flavonoid compound in citrus. Naringin (28.15–8,203.71 µg/g DW), quercetin (4.07–1,816.04 µg/g DW), nobiletin (not detectable (ND)-2,661.66 µg/g DW), tangeritin (ND-769.76 µg/g DW) and rutin (13.43–1,801.38 µg/g DW) were also abundant in citrus samples. These results were different from those reported by Zhang et al. (2014), in which naringin was an abundant compound. Among 16 samples, naringin was not detected in eight samples, while its concentrations were between 1,160 µg/mL and 13,610 µg/mL in the other eight samples (Zhang et al., 2014).

Generally, the concentrations of phenolic acids were lower than those of flavonoids, which is expected since methanol: DMSO (50:50, v/v) is more suitable for extracting flavonoids than phenolic acids, which has been reported by Magwaza et al. (2016). Our results differ from previous studies (Liew et al., 2018) which found that ferulic acid was the main phenolic acid in citrus peel extracts, and the abundance of phenolic acids in citrus varied in the following order: ferulic acid > caffeic acid > 4-hydroxybenzoic acid > protocatechuic acid. Zhang et al. (2014) also reported that ferulic acid was the most abundant phenolic acid in the peels of citrus fruits, with ferulic acid (1,613.34–7,780.17 µg/g DW) > caffeic acid (46.24–1,273.47 µg/g DW ) ∼ ρ-coumaric acid (154.55–834.77 µg/g DW) > sinapic acid (ND-342.84 µg/g DW). However, Xi et al. (2017) reported that caffeic acid was the main phenolic acid in the citrus species, with caffeic acid (9.31–741.4 μg/g DW) > chlorogenic acid (2.7–527.5 μg/g DW) > Gallic acid (ND–90.69 μg/g DW) > ferulic acid (ND-2.75 μg/g DW). In our study, the variation order of the phenolic acid contents observed was as follows: salicylic acid > benzoic acid > chlorogenic acid > ferulic acid > caffeic acid > ρ-coumaric acid > syringic acid > gallic acid. These differences resulted from the genetic difference of the citrus cultivars, and environmental and pedoclimatic factors (Zhang et al., 2014).

Similar to the flavonoid distribution in citrus, considerable differences in content of flavonoid compounds were detected among grape varieties. Findings in concentration difference among samples have been widely reported in the literatures (Colombo et al., 2019; Li & Sun, 2019; Tang et al., 2018), which was related to the variety of grape, environmental and pedoclimatic factors, and management of the vineyard. Despite that the order of abundance is different from the results by other authors, the abundance of rutin, quercetin, catechin and epicatechin in grape is the same (Tang et al., 2018). It is worth highlighting that the abundance of hesperidin in citrus has been widely reported (De Paula Menezes Barbosa, Ruviaro & Macedo, 2018; Gomez-Mejia et al., 2019), while it is rarely reported in grape. Our study provides an idea on the distribution of hesperidin in the peels of grapes. In addition, salicylic acid is also the most abundant phenolic acid in grapes. The difference between our results and the study by Liu et al. (2018), who mentioned that caffeic acid was the major phenolic acid in grape, was attributed to a more limited number of phenolic acid types determined by these authors.

Principal component analysis and HCA are powerful chemometric methods that have been widely used for classification studies (Shawky & Abou El Kheir, 2018). PCA involves unsupervised pattern recognition, which is the most frequently used method for reducing the dimensionality of numerical datasets in a multivariate space. It transforms the original set of variables to a new set of uncorrelated variables that are called PCs (Abegaz et al., 2019). PCA has been widely used for taxonomic discrimination, quality assessment or classification between plants from different geographic origins, and authenticity verification (for example, in Chinese herbal medicines) (Yan et al., 2020). Plotting the PCA scores highlighting the similarities and differences between groups. In order to map a PCA score, the initial eigenvalues were generated by inputting the average concentration of 16 determined phenolic components in the citrus or grape samples into SPSS software. PCs were selected to meet the requirement of cumulative percent variance (CPV) > 70–85% (Yi et al., 2015).

Hierarchical cluster analysis is a statistical method used for finding relatively homogeneous clusters of objects based on measured characteristics. Samples with the most similarities are clustered preferentially (Yan et al., 2020). To obtain the HCA dendrogram, the 16 phenolic compounds were set as variables, Ward’s method was selected as the cluster method, and squared Euclidean distance as the interval measurement (Yi et al., 2015; Zhao et al., 2017). PCA and HCA were used to analyze the chemical analysis of 15 citrus and 12 grape varieties. The results showed that citrus and grape were divided into two main groups by PCA and HCA with strong consistency.

Conclusions

A highly precise and accurate HPLC-DAD method was herein developed and validated for the first time for simultaneous determination of 16 phenolic compounds in the peel of different citrus and grape cultivars, which verified our hypothesis. The method was eventually applied to quantify the phenolic constituents in the fruits of different citrus species and grape varieties to monitor their quality. Among the 16 compounds, hesperidin and salicylic acid were used as representative compounds for quality control of citrus and grape cultivars. In addition, chemometric analysis, including PCA and HCA, were performed to cluster 15 citrus and 12 grape cultivars. The quantitative data of the 16 phenolic compounds in citrus and grape samples were determined by the established HPLC protocol. The results revealed that citrus and grape were clustered into two main groups by PCA and HCA with strong consistency. This study provides information on the distribution of phenolic compounds in different citrus or grape cultivars in Fujian China, and gives a raw idea about the genetic relationship of these cultivars.

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