The quality evaluation of 30 Asparagus officinalis L. varieties

Abstract Asparagus, a vital economic contributor, is a well‐liked vegetable grown around the globe, and some secondary metabolites in its spear are beneficial to human health. Asparagus spears possess a significant quantity of nutrients and phytochemicals; however, the difference in these chemical compositions among various varieties has not been sufficiently studied. This work aimed to detect the chemical compositions of 30 varieties of asparagus and to assess them by principal component analysis (PCA). The results showed that the contents of these chemical compositions varied in varieties. Selenium (Se, 1.12–2.9 μg/100 g dry‐weight [DW]) was abundant in asparagus, with an average dry matter content of 8.25%. Free amino acids (5.60–9.98 g/100 g DW) and polyphenols (6.34–8.67 mg/g DW) were both present in high amounts, along with flavonoids (4.218–8.22 mg/g DW) and protodioscin (0.44–1.96 mg/g DW). Correlation analysis, PCA, and hierarchical cluster analysis were used to conduct a comprehensive evaluation of asparagus. Atlas, Appolo, Jinggang 111, Jingke 2, and WS‐1 were the top five varieties with comprehensive scores. This study provided valuable data for the breeding, quality improvement, processing, and utilization of asparagus varieties in the future.

Organization of the United Nations (2023) statistics, in 2021, the asparagus cultivation area in China was 1,441,009 ha (approximately 91% of the world's total area) and the total production reached 7,344,390 tons (approximately 88% of the global total production).
Shandong Province is the place where the majority of Chinese asparagus is produced and exported (Liu et al., 2022).
The main edible part of asparagus is the spear, which is delicious and rich in nutrients such as various types of amino acids, minerals, and phytochemicals such as steroids, flavonoids, and other bioactive ingredients (Fuentes-Alventosa et al., 2009;Yi et al., 2019).
Flavonoids are important components in asparagus, which may provide antiallergic, anti-inflammatory, antiviral, and antioxidant activities to human body (Ferreyra et al., 2012;Franca et al., 2023;Li et al., 2023;Silva et al., 2023;Xu et al., 2023;Yang et al., 2023;Zhang et al., 2023).Rutin comprises the largest proportion of flavonoids in asparagus (Fuentes-Alventosa et al., 2007).Another important component, steroidal saponins have been reported to affect cholesterol metabolism and protect the liver (Huang & Kong, 2006;Roman et al., 1995;Zhu et al., 2011).The most common steroidal saponins in asparagus is protodioscin (Huang & Kong, 2006;Liu et al., 2022).The physiological functions of asparagus (Jiménez-Sánchez et al., 2021;Lee et al., 2014;Zhang et al., 2019), such as antitumor, antifatigue, hypoglycemic, hypolipidemic anti-inflammatory, antioxidant, and protecting liver cells, mainly come from flavonoids and steroidal saponins.Besides, asparagus also has strong ecological values.Its strong salt-alkali resistance property enables asparagus to grow normally in soil with less than 0.3% salt content (Chen et al., 2014;Gao et al., 2021;Zhang et al., 2020Zhang et al., , 2022)), and its sophisticated root system makes it effective in preventing soil erosion (Li et al., 2020).As people's requirements for a healthy diet are gradually increasing, asparagus has a promising prospect as a nutritious and economical vegetable.The production of high-quality asparagus is far from meeting the market demand (Li & Lin, 2022).The variety of asparagus is the most important factor that determines the yield and quality.Previous studies of some asparagus varieties mainly focused on agronomic traits, active component functions, and multiomics (Huang & Kong, 2006;Jiménez-Sánchez et al., 2021;Lee et al., 2014;Liu et al., 2022;Yi et al., 2019;Zhang et al., 2019Zhang et al., , 2022)).However, it is still short of research on the active component comparison which may be essential for the screening of excellent varieties.In this study, seven chemical compositions, including dry matter, free amino acids, polyphenols, flavonoids, rutin, protodioscin, and the mineral selenium of 30 asparagus varieties in Shandong Province, China, were determined.The relationship between quality properties and varieties was investigated, and a comprehensive model for evaluating the quality of asparagus was established.

| Plant materials and reagents
The asparagus varieties were obtained from an asparagus planting base in Yuncheng County, Shandong, China.Their number, variety, and stem color are shown in Table 1.Samples were collected on April 27, 2023, and immediately placed in a portable cooler box and transported to the laboratory.The asparagus samples were cleaned with deionized water, crushed, mixed, and quartered after snap-freezing in liquid nitrogen.A quarter of the samples were freeze-dried at −70°C.The resulting lyophilizates were ground using a mixer and stored at −80°C until further analysis.
Acetonitrile and methanol (high-performance liquid chromatography [HPLC] grade) were purchased from Merck (Darmstadt, Germany).Deionized water was prepared by a Milli-Q water purification system (Millipore, France).Formic acid was obtained from Rhawn (Shanghai, China).The Folin-Ciocalteu reagent was purchased from Sangon Biotech (Shanghai, China).The standard samples of gallic acid, leucine, protodioscin, and rutin were brought

| Determination of the dry matter content, total phenols, total flavonoids, and free amino acids
The dry matter content of fresh asparagus was determined by heating 10 g of chopped samples at 105°C for 6 h (Liu et al., 2022).The total phenols were determined using the Folin-Ciocalteu method at 765 nm according to GB/T 8313 (National Standards, n.d.); the concentration was calculated from the calibration curve using gallic acid as the standard.The total flavonoids were analyzed according to Industry Standard -Agriculture, NY/T 1295-2007 (n.d.), and the concentration was calculated from a calibration curve using rutin as the standard.The free amino acids were determined by the ninhydrin colorimetry method using leucine as the standard.Concentrations of total phenols and total flavonoids were expressed as mg/kg on a dry-weight basis (DW).The concentration of free amino acids was expressed as g/100 g DW.

| High-performance liquid chromatography analysis of rutin and protodioscin
Rutin and protodioscin were determined by a modified HPLC-UV method according to Yi et al. (2019).Briefly, the powdered sample (0.1 g) was extracted with 5 mL of 70% methanol at 30°C for 2 h.After filtration using a 0.22μm syringe filter (Woongki Science, Seoul, Korea), the filtrate was used for analysis.The sample was separated on a C18 column (250 mm × 4.6 mm, particle size 5 μm; Shimadzu, Kyoto, Japan) using a Prominence HPLC system (Shimadzu, Kyoto, Japan) equipped with a photo-diode array detector.Solvent A was water (formic acid 0.1%) and solvent B was acetonitrile (formic acid 0.1%).The flow rate was 0.7 mL/min, the injection volume was 10 μL, and the column temperature was maintained at 40°C.The analytical wavelengths were 205 and 330 nm.Initially, the concentration of solvent B was 12%, which was then increased to 30% and 80% at 20 and 50 min, respectively.Concentrations of rutin and protodioscin were expressed as mg/kg DW.

| Determination of mineral contents
The determination of the mineral contents was carried out using ICP emission spectroscopy (inductively coupled plasma).Each sample was digested in 3 mL of concentrated nitric acid (HNO 3 ) and 3 mL of concentrated hydrochloric acid (HCl) on a digestion block at 95°C for 2 h; then made up to a final volume of 40 mL with deionized water.
Each sample was digested in triplicate.High purity single element standards were used for quantitation.All results were expressed as mg/100 g DW.

| Data processing and statistical analysis
All analyses were repeated three times, and the data were expressed as means and standard deviations.

| The contents of seven chemical compositions in each asparagus variety
By analyzing the content of dry matter, free amino acids, total phenols, flavonoids, rutin, protodioscin, and the mineral selenium of different asparagus varieties (Figure 1), we found that the top two varieties in amino acid content were Zhefeng 801 (9.98 g/100 g DW) and Hangyu 6 (9.32 g/100 g DW), while the lowest was in Jinglvlu 3 (5.60 g/100 g DW).The highest content of total phenols was in Jingke 2, followed by Jinggang 111, which had the highest content of flavonoids and rutin.The asparagus variety with the highest content of protodioscin was Atlas, 1.96 mg/g DW, which was 4.45 times as many as the lowest variety Jingzilu2.The highest content of the mineral selenium was in Walker noble, which was far more than the average value.Detailed values can be found in the Table S1.
Therefore, asparagus varieties with a low dry matter content, like Jingzilu 2, and Guelpin Millennium, will taste more hydrated.
Asparagus varieties rich in saponins and selenium may have more antitumor effects (Kim et al., 2021;Peng et al., 2023), such as Atlas and Appolo, Walker noble, and Jinglvlu 3, which are the top two varieties in protodioscin and the mineral selenium, respectively.

| Correlation analysis
Pearson correlation analysis was carried out on the quality indicators of asparagus, including dry matter, free amino acids, total phenols, flavonoids, rutin, protodioscin, and the mineral selenium, and results are shown in Table 2.There was little correlation (r < .5) between dry matter and the other six indicators (Table 2).Free amino acids were negatively correlated with total phenols, flavonoids, rutin, protodioscin, and the mineral selenium, respectively; however, it had the strongest correlation with total phenols (r = −.569,p < .01).In higher plants, most phenolic compounds come from the shikimic acid pathway (Lu & Chen, 2012).
The aromatic amino acids-phenylalanine and methionine that are synthesized through the shikimic acid pathway are the first molecules for the biosynthesis of phenylpropanoids, while flavonoids are synthesized by phenylpropane biosynthetic pathway (Ferreyra et al., 2012;Lu & Chen, 2012).This revealed the negative correlation of free amino acids among total phenols, flavonoids, and rutin.
The correlation coefficient between rutin and protodioscin was 0.345, which was consistent with the previous research results that genetic factors and light conditions affected the content of rutin and protodioscin in asparagus (Lee et al., 2010;Liu et al., 2022;Yi et al., 2019).The correlation between the mineral selenium and the other six indicators was also low (r < .5).

| PCA and OPLS-DA
The PCA method has been widely used to evaluate the quality of agricultural products such as kiwifruit, jujubes, foxtail millet, and jasmine tea (An et al., 2023;Peng et al., 2023;Singh et al., 2023;Zhao et al., 2023).By reducing the dimensionality of the data and eliminating overlapping information from numerous sources, the evaluation process was simplified, making it faster and more accurate than a single evaluation.At the same time, it avoided the correlation between traits that may affect the evaluation results.
Considering the seven quality indicators comprehensively, the PCA was performed on the standardized data to explore the differences in OPLS-DA is a supervised statistical method of discriminant analysis that differs from PCA (An et al., 2023;Lin et al., 2023).It can be seen that there were different distribution areas between asparagus samples with different stem colors (Figure 3a), indicating that stem color had a significant impact on the quality of asparagus.However, the relationship between series and quality was not significant, and the spatial distribution of different samples in the same series was also different (Figure 3b).

| HCA
The HCA classifies the research objects according to their degree of affinity in quality indicators to analyze the similarity and difference between the quality of the samples (An et al., 2023).Therefore, HCA (Figure 4) was further conducted on the average contents of seven substances in 30 asparagus samples.

| Comprehensive quality evaluation model
To quantitatively evaluate the quality of these asparagus, a com-  1. PCA, principal component analysis.

F I G U R E 3
The score diagrams of OPLS-DA: (a) was classed by stem color and (b) was classed by series.The varieties of each number are shown in Table 1.
components were extracted according to the total eigenvalue of the correlation coefficient matrix and the variance contribution rates of each principal component, as shown in Table 3.The cumulative variance contribution rate of these three principal components reached 81.87%, which meant these three principal components could represent 81.87% of the information of all detected quality indicators.Therefore, it was feasible to use these three principal components for the comprehensive evaluation of different asparagus varieties.
To further explain the relationship between the quality indicators of asparagus and the principal component factors, the functional expressions of the three principal components F1, F2, and F3 were obtained as follows, and X1, X2, X3, X4, X5, X6, and X7 represent the content of dry matter, free amino acid, total phenol, flavonoids, rutin, protodioscin, and selenium, respectively.
Based on the variance contribution rates of the principal components, the comprehensive evaluation model was built as F = 0.598 F 1 + 0.211 F2 + 0.191 F3, and the scores of principal components and comprehensive scores are shown in Table 4. Atlas, Appolo, Jinggang 111, Jingke 2, and WS-1 were the top five varieties of comprehensive score in turn, revealing that the comprehensive quality of these five asparagus varieties was relatively better.The left numbers stand for asparagus samples, and the top numbers were distance markers.The cluster analysis was performed by using the intergroup linkage method and the measurement interval used a square Euclidean distance.HCA, hierarchical cluster analysis.

TA B L E 3
Variance contribution rates of the principal components of nutritional quality.

ACK N OWLED G M ENTS
We are grateful to the asparagus planting base for kindly providing the original materials used in this work.We also want to thank

CO N FLI C T O F I NTE R E S T S TATE M E NT
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

DATA AVA I L A B I L I T Y S TAT E M E N T
The asparagus cultivation area and total production can be acquired from FAO statistics (Food and Agriculture Organization of the United Nations).

E TH I C S S TATEM ENT
Not applicable.

F
the quality of different varieties of asparagus from an overall perspective.The score diagram of PCA is shown in Figure 2a, in which each dot represents an asparagus sample.The spatial distribution of these dots indicated the difference in the chemical compositions of each sample.It can be seen that 30 asparagus varieties were distributed in different regions, indicating that these chemical composition contents were significantly different among these samples.The loading diagram of PCA .05. **p < .01. is shown in Figure 2b.Total phenols, flavonoids, and rutin had a high proportion in principal component 1, and selenium, free amino acid, and protodioscin comprised the largest proportion of principal component 2. Meanwhile, dry matter and protodioscin composed a majority of principal components 3.These two diagrams visually presented the quality and also revealed that different varieties or genotypes had important effects on the quality of the asparagus.
prehensive evaluation model was established.Through PCA, seven quality indicators (dry matter, free amino acid, total phenol, flavonoids, rutin, protodioscin, and selenium) of 30 asparagus samples were transformed into three principal components.The principal F I G U R E 2 The score diagram (a) and loading diagram (b) of PCA.The varieties of each number are shown in Table

the
Shandong Academy of Agricultural Sciences and Science and Technology Department of Shandong Province.FU N D I N G I N FO R M ATI O N This work was supported by the Agricultural Scientific and Technological Innovation Project of the Shandong Academy of Agricultural Sciences (nos.CXGC2022E05 and CXGC2022B04) and the Key R&D Plan of Shandong Province (no.LJNY202120).
The radar map was made by Origin 2015.Experiment data were analyzed using a one-way analy-
TA B L E 4 Scores of principal components and comprehensive scores.