Impact of nitrogen supply on glucosinolate content and phenolic acids in cauliflower varieties

ABSTRACT

Application of nitrogen (N) is a common practice used to achieve profitable yields in horticultural crops and N application can be used as a tool to manipulate the enhancement of phytochemicals in vegetable crops to address consumer-oriented quality production. Our previous findings recommended 90 kg ha⁻¹ for certain types of cauliflower varieties without compromising yields. Thus, this study was aimed to investigate the effect of N application on glucosinolates and phenolic acids, at harvest, in varieties ‘Largardo’, ‘Eskimo’ and ‘CF-744’ grown in the field. N was applied as ammonium nitrate (NH₄NO₃) at concentrations from 0, 60, 90, 120, 150 to 180 kg ha⁻¹. Variety ‘CF-744’ was more sensitive to N supply and at 180 kg ha⁻¹ N it showed the highest accumulation of glucosinolates (sinigrin, glucoiberin, progoitrin, 4-methoxyglucobrassicin) at harvest. However, 90 kg ha⁻¹ N supply demonstrated the highest accumulation of majority of the glucosinolates in varieties ‘Largardo’ and ‘Eskimo’. Different varieties responded differently to N supply and glucosinolate levels in cauliflowers. Also, different varieties responded differently to N supply and antioxidant property. In all three varieties, the N supply at 120 kg ha⁻¹ showed the highest accumulation of protocatechoic acid, 4-hydroxy benzoic acid, ρ-coumaric acid and caffeic acid.

KEYWORDS:

• Antioxidant property
• bioactive compounds
• fertiliser application
• Glucoiberin
• sinigrin
• vegetable crop

Introduction

Cauliflower is particularly rich in vitamin C and polyphenols (Picchi et al. 2012). Brassica plants are of beneficial interest due to their high glucosinolate content, and cauliflower (Brassica oleracea var. botrytis L.) is a member of this genus. Currently, the nutritional and health-promoting values of vegetables are equally appreciated by the consumers (Verkerk et al. 2009). However, one way to increase the intake of health-promoting compounds would be to boost their content in fresh vegetables by exploiting crop production practices (Krumbein et al. 2001).

Nitrogen (N) application can affect the ascorbic acid (Vitamin C) content in Brassica crops (Lisiewska and Kmiecik 1996). The same authors demonstrated that increasing N supply from 80 to 120 kg N ha⁻¹ reduced the vitamin C content in cauliflower by 7%. Research findings of Xu et al. (2010) showed that N application rate at 300–400 kg ha⁻¹ (higher concentrations) significantly decreased the ascorbic acid content. Lisiewska and Kmiecik (1996) also showed a remarkable reduction of total phenolic content by increasing the concentration of N supply in mustard leaf (Brassica juncea).

Glucosinolates are classified into three groups based on the structure of different amino acid precursors: (i). aliphatic glucosinolates obtained from methionine, isoleucine, leucine or valine, (ii). aromatic glucosinolates obtained from phenylalanine or tyrosine, and (iii). indole glucosinolates derived from tryptophan (Radojčić et al. 2008).

N and sulphur (S) elements are important for the biosynthesis of amino acids such as methionine, phenylalanine, tyrosine and tryptophan. These amino acids also act as a precursor for the synthesis of phenolic acids and flavonoids (De Pascale et al. 2007; Groenbaek et al. 2014). Sinapic acid and kaempferol are known as main phenolic compounds in cauliflower (Picchi et al. 2012), and a low N supply increased the biosynthesis of flavonoid glycosides (Scheible et al. 2004) in Brassica rapa subsp. nipposinica var. chinoleifera and B. juncea (Fallovo et al. 2011).

The breakdown products of certain glucosinolates (GLSs) such as glucoraphanin, sinigrin, gluconapin, and gluconasturtiin in vegetables have shown anti-cancerous properties and also are associated with the bitterness and pungency of certain vegetables (Verkerk et al. 2009).

The health benefits of indolylic GLS glucobrassicin and neoglucobrassicin were reported due to their hydrolysis products N-indole-3-carbinol and methoxyindole-3-carbinol respectively (Mithen et al. 2000). Concentrations of GLSs are influenced by the type of soil, fertiliser application, climate, and the genotype (Verkerk et al. 2009). Higher N application rates were shown to increase the levels of progoitrin in Brassica napus and at the same time decreased the sinigrin levels (Zhao et al. 1994). Also, N application rate of 300–400 kg ha⁻¹ decreased the glucoraphanin concentrations in broccoli florets (Xu et al. 2010). GLS components and the concentration can vary between the varieties. For example, white cauliflower was reported to contain glucoiberin and sinigrin whilst green cauliflower contained glucoraphanin and progoitrin (Schreiner 2005).

While bioactive compunds in fresh produce can be enriched by manipulating the agronomy practices (Schreiner 2005), the marketable yield and the head size should not be compromised. Application of N fertilisers are important to improve plant growth (yield) and bio-mass (Albornoz 2016).

In our earlier studies it was clear that increasing N application rates negatively affected the colour and ascorbic acid content in cauliflower varieties but positively influenced curd fresh and dry mass and size in varieties ‘CF-744’ and ‘Eskimo’ whilst in var. ‘Lagardo’, increasing N application rates did not affect curd fresh and dry mass and size (Mashabela et al. 2018). Fallovo et al. (2011) reported that the concentration of N supply was identified as the key factor affecting the phytochemical composition in Brassica vegetables.

Therefore, the aim of the present investigation was to study the effect of different N applications on the GLS, phenolic acid content, and mineral composition in cauliflower varieties ‘CF-744’, ‘Eskimo’ and ‘Lagardo’.

Materials and methods

Growing conditions

Cauliflower seedlings were planted on the same day during winter growing seasons (May/July 2015 and May/July 2016) at the experimental fields of the Agricultural Research Council – Vegetable and Ornamental Plants (ARC – VOP), Roodeplaat Experimental Farm in Gauteng Province, South Africa (lat. 25°59’ S, long. 28°35’ E and 1200 m.a.s.l.) Seedling production and soil mineral composition were reported previously in detail by Mashabela et al. (2018). The experiment was laid out in a randomised complete block design (RCBD) with four replicates, using three cauliflower varieties (‘Largardo’, ‘Eskimo’ and ‘CF – 744’) and six N levels (0, 60, 90, 120, 150 and 180 kg ha⁻¹) as treatments. The commercial standard of 120 kg ha⁻¹ N was included as a positive control. The experiment included a set of 72 plots with plot area of 5 m². The cauliflower seedlings were planted in 2 rows per plot with 50 × 50 cm in-row and inter-row spacings. Drip irrigation was adopted every two days after transplanting. Granular superphosphate (10.5% P) was used as source of phosphorus (20 kg ha⁻¹) and incorporated into the soil beds before planting. Limestone ammonium nitrate (28% N) was used as source of N applied as basal dressing before transplanting and as top dressing four weeks after transplanting (Mashabela et al. 2018). All the fertilizers were supplied by Hygrotech Ltd. (Gauteng, Pretoria, South Africa). Standard crop management practices were adopted to maintain plant growth. The curds from the plants were harvested at commercial maturity, i.e. 108–110 days after transplanting. Four replicate samples per cultivar per N application were used for the analysis of glucosinolates and phenolic compounds. The average monthly rainfall, minimum and maximum temperature and relative humidity during the experimental seasons is given in Table 1.

Table 1. Average monthly rainfall, minimum and maximum temperature and relative humidity during the experimental seasons.

Month	Rainfall (mm)	Temperature (°C)		Relative humidity (%)
		Maximum	Minimum	Maximum	Minimum
May	1.50−1.76	22.5−23.2	7.0−7.4	89.7−90.4	32−633.3
June	0.38−0.41	20.95−21.8	4.5−4.7	88.5−89.6	28.7−29.5
July	0.05−0.07	20.5−21.6	2.0−2.5	85.7–86.3	22.8−23.9

Extraction and determination of glucosinolate and phenolic compounds

A set of 30 curds per N application treatment was snap frozen soon after harvest at the farm in liquid nitrogen. In order to avoid any variation, 10 curds per N treatment were pooled together and extracted as one sample. Likewise, for each N treatment, there were three samples each obtained from the 10 curds available for the analysis. Glucosinolates were extracted and quantified by adopting the previously described method of Wang et al. (2012) using the freeze-dried cauliflower curds (100 mg) in triplicates per treatment. The samples were boiled in 10 mL of methanol/water mixture (v/v = 7:3) at 70°C for 10 min and thereafter centrifugated at 5000 x g for 5 min. The supernatants were collected and purified using a 250 µL DEAE-Sephadex A-25 ion-exchanger (Sigma Aldrich, Johannesburg, South Africa). Aryl sulphatase (Sigma-Aldrich, Johannesburg, South Africa) was added to convert the GLS to their desulpho analogues at 25°C for 12 h and eluted with water. Thereafter, the desulfo compounds were flushed with 5 mL of deionised water and the analysis was performed using reversed-phase HPLC (high-performance liquid chromatography) Model Flexar^TM 89173-556 (PerkinElmer, Waltham, Massachusetts, USA) equipped with a UV PDA (photodiode array) detector. A C₁₈ column (4.6 × 150 mm, 3 µm, 300 Å) and a flow rate of 0.75 mL min⁻¹ and a column temperature of 40°C with an acetonitrile–water gradient were used as described by Fallovo et al. (2011). Allyl GLS (sinigrin) in methanol (5 mM, 200 µL) was included (Fallovo et al. 2011) and the detection and quantification were performed at 229 nm. The GLSs were identified by the retention times of the authentic standards and quantified using internal standards (sinigrin, 4-methoxyglucobrassicin, glucoiberin, gluconapin, glucobrassicin, 4-hydroxyglucobrassicin, glucoerucin, gluconasturtiin, and progoitrin). The GLS concentrations were expressed as mg kg⁻¹ on dryweight basis.

For the determination of phenolic compounds, the methanol was removed from the supernatant that was collected after centrifugation at 50°C under reduced pressure using a rotary evaporator and the resulting residue was reconstituted with distilled water to make 1 mL. Subsequently, the concentrated extract 35 μL was filtered via a hydrophilic nylon syringe filter (0.22 µm pore size) and 10 μL injected three times using high-performance liquid chromatography (HPLC) [with an photodiode array detector (PDA), C18 column (100 × 4.6 mm; 5 µm particle size)], Altus™ PerkinElmer, Waltham, Massachusetts, USA. The mobile phase conditions were maintained according to Zaghdoud et al. (2016) using mixtures of water and trifluoro acetic acid (99.9: 0.1 v/v) and acetonitrile and trifluoro acetic acid (99.9: 0.1 v/v) and a flow rate 1 mL min⁻¹ in a linear gradient and set as described by Zaghdoud et al. (2016). Identification and quantification of phenolic compounds were carried out at wavelengths of 265, 280, 320 and 330 nm for phenolic acids by comparing peak retention times with those of authentic standards (protocatechuic acid, ρ-hydroxybenzoic acid, ρ-coumaric acid, ferulic acid, chlorogenic acid, and caffeic acid).

Antioxidant property

Ferric reducing antioxidant power (FRAP) assay was carried out according to the method described by Malejane et al. (2018) without modification using FRAP solution [0.3 mM sodium acetate (pH 3.6), 10 mM 2,4,6-tripyridyl-2-triazine (TPTZ) and 20 mM FeCl₃ (10:1:1 v/v/v)]. The calibration curve was obtained using the Trolox solution at concentrations from 10 to 250 mg/L for quantification of FRAP antioxidant activity and the results were expressed as µmol Trolox equivalent antioxidant capacity (TEAC) per 100 g fresh weight.

Statistical analysis

The two-years of data (2015 and 2016) were pooled and subjected to analysis of variance in GenStat® ver. 11.1 (VSN International Ltd, Hemel Hempstead, UK). If interactions were significant, they were used to explain the results. If interactions were not significant, means were separated using Fisher’s protected t-test least significant difference (LSD). The polynomial model procedure was tested in GenStat.

Results and discussion

Effect of N application on GLS content in cauliflower varieties

Concentrations of GLSs (sinigrin, progoitrin, glucoiberin, glucobrassicin, 4-methoxyglucobrassicin, and glucohirsutin) and phenolic acids (protocatechoic acid, 4-hydroxybenzoic acid, ρ-coumaric acid, ferulic acid, chlorogenic acid and caffeic acid) were affected by the N rate in all three varieties during both years. The interactions between year and N application rate (Y × N), year and cultivar (Y × C) and year by N application by cultivar (Y × N × C) were not significant. Different responses to the N supply and biosynthesis of GLSs were noted with respect to different varieties in this study. Aliphatic GLSs; sinigrin (prop-3-enyl glucosinolate), progoitrin, glucoiberin, indole GLSs; glucobrassicin, 4-methoxyglucobrassicin, aromatic GLSs, and glucohirsutin were detected in these three varieties of cauliflower (Table 2). Among the three varieties, CF-744 revealed higher concentrations of GLSs (Table 2). Variety ‘CF-744’ at 180 kg ha⁻¹ N supply showed the highest accumulation of sinigrin, glucoiberin, and progoitrin. However, var. ‘CF-744’ accumulated the highest concentrations of 4-methoxy glucobrassicin at 150 and 180 kg ha⁻¹ N supply (Table 2). Variety ‘Eskimo’ showed the highest levels of glucoiberin at 90 kg ha⁻¹ N supply, but 120, 150 and 180 kg ha⁻¹ N supply negatively affected its accumulation (Table 2). Whereas in var. ‘Largardo’ N supply from 90, 120, and 150 kg ha⁻¹ showed the highest increase in glucohirsutin levels and at 180 kg ha⁻¹ N supply the levels dropped significantly (Table 2). Levels of glucobrassicin in var. ‘Largardo’ increased significantly at 90 kg ha⁻¹ N supply (Table 2). Sinigrin is reported to be the predominant GLS in cauliflowers (Hanschen and Schreiner 2017). The GLS levels and profiles in Brassica plants were reported to be affected by temperature, irradiation, nutrition, and irrigation (Verkerk et al. 2009). Furthermore, the relationships between N and phytochemical content (GLSs) in cauliflower varieties fitted the polynomial model; specific regression differed in the level of significance and degree of association (Table 3). Also, the correlation between N application rates and the phytochemicals are important to select desirable genotype that is less sensitive (not depended) on N application. In Table 3, higher R² indicates a close relationship between the N application rates and the glucosinolate concentrations in all three varieties (‘CF-744’ and ‘Eskimo’, ‘Largardo’). Based on this explanation, increasing N application rates resulted in a strong and moderate positive relationship with levels of glucohirsutin (R² = 0.94) and progoitrin (R² = 0.61) respectively in var. ‘Largardo’ (Table 3). Variety ‘Eskimo’ showed a weak relationship between the N application rates and the accumulation of sinigrin (R² = 0.08), progoitrin (R² = 0.39), glucoiberin (R² = 0.31), 4-glucobrassicin (R² = 0.34), and 4-methoxyglucobrassicin (R² = 0.26), (Table 3). However, var. ‘Eskimo’ showed a strong relationship between the N application rates and the concentrations of glucohirsutin (R² = 0.70) (Table 3). Also, in var. ‘CF-744’, progoitrin and glucoiberin showed a moderate (R² = 0.44) and strong (R² = 0.86) relationship between the N application rates (Table 3).

Table 2. Influence of different N application rate on major glucosinolates in cauliflower varieties.

Nitrogen concentration	Sinigrin (µg/g DW)	Glucoiberin (µg/g DW)	Glucohirsutin (µg/g DW)	Progoitrin (µg/g DW)	Methoxy 4 Glucobrassicin (µg/g DW)	Glucobrassicin (µg/g DW)
0	96.5c ± 0.21	50.7e ± 0.34	62.3d ± 0.07	9.9c ± 0.12	20.8 d ± 0.12	90.3d ± 0.06
60	62.1d ± 0.14	32.3f ± 0.72	68.9c ± 0.12	10.8c ± 0.84	20.4d ± 0.06	100.2c ± 0.03
90	193.1b ± 0.07	157.2a ± 0.05	85.1ab ± 0.04	22.9 ± 0.22	33.7a ± 0.08	234.2a ± 0.16
120	52.7d ± 0.12	65.4d ± 0.12	89.9a ± 0.21	23.6 ± 0.05	18.5d ± 0.17	76.7e ± 0.15
150	33.6e ± 0.08	80.2c ± 0.04	83.8b ± 0.12	4.3 d ± 0.03	23.6c ± 0.38	111.6b ± 0.08
180	235.6a ± 0.03	106.3b ± 0.02	80.8b ± 0.04	31.8a ± 0.16	27.9 b ± 0.24	58.4 f ± 0.18
P < F	<.0001	<.0001	<.0001	<.0001	<.0001	<.0001
Largardo	87.5c ± 0.15	53.5c ± 1.2	89.4a ± 0.08	10.5b ± 0.02	18.9b ± 0.05	139.9a ± 0.23
Eskimo	111b ± 0.18	66.4b ± 0.76	85.3b ± 0.05	6.6c ± 0.04	16.4c ± 0.36	51.4b ± 0.43
CF-744	138.2a ± 0.12	126.0a ± 0.65	60.7c ± 0.02	34.4a ± 0.16	37.2a ± 0.04	144.2a ± 0.09
P < F	<.0001	<.0001	<.0001	<.0001	<.0001	<.0001
Interactions Variety x nitrogen
Largardo × 0	14.5g ± 0.03	7.2k ± 0.12	47.1gf ± 0.15	1.2 l ± 1.2	2.4i ± 0.46	36.5ij ± 0.07
Largardo × 60	81.9e ± 0.02	36.4ih ± 0.97	93.1b ± 0.09	23.4ed ± 0.78	29.7d ± 0.87	214.4c ± 0.24
Largardo × 90	243.7b ± 0.05	165.7c ± 0.63	108.8a ± 0.04	31.2c ± 0.43	37.1c ± 0.76	404.7a ± 0.01
Largardo×120	38.1fg ± .04	16.4j ± 0.72	102.2a ± 0.05	2.4 klj ± 0.09	6.7 h ± 0.04	20.7k ± 0.45
Largardo×150	19.5g ± 0.12	12.8kj ± 1.2	104.3a ± 1.2	1.8kl ± 0.14	7.7gh ± 0.05	34.3ij ± 0.21
Largardo × 80	127d ± 0.21	82.4f ± 0.78	80.6c ± 1.4	2.8klij ± 0.03	29.5d ± 0.96	129.1g ± 0.32
Eskimo × 0	76.5e ± 0.01	59.7g ± 0.64	76.1dc ± 0.3	6.6g ± 0.08	13.1f ± 0.04	53.4 h ± 0.56
Eskimo × 60	61.2fe ± 0.8	41.7h ± 0.03	71.8de ± 0.02	4.3hij ± 0.32	20e ± 0.03	39.6ij ± 0.05
Eskimo × 90	196.5c ± 0.17	215.5a ± 1.5	91.6b ± 0.10	13.2f ± 0.04	36.7c ± 0.17	151.2f ± 0.07
Eskimo × 120	78.2e ± 0.12	31.9i ± 0.43	89.8b ± 0.09	6.7 g ± 0.06	7.1 h ± 0.06	31.95i ± 0.19
Eskimo × 150	43.6fg ± 0.07	35.5ih ± 0.54	92.8b ± 0.13	5.5hg ± 0.15	14.4 f ± 0.73	20.7k ± 0.28
Eskimo × 180	210.3c ± 0.09	14.2kj ± 0.43	89.6b ± 0.69	3.4kij ± 0.26	6.7 h ± 0.82	28.2 kj ± 0.05
CF-744 × 0	198.7c ± 0.11	85.2fe ± 0.31	63.7e ± 0.18	21.8e ± 0.34	46.9a ± 0.05	180.5e ± 0.41
CF-744 × 60	43.3fg ± 0.15	18.9j ± 0.05	41.8 g ± 0.04	4.5hi ± 0.47	11.4gf ± 0.16	46.3ih ± 0.73
CF-744 × 90	139d ± 0.02	90.3e ± 0.08	54.9f ± 0.07	24.2d ± 0.63	27.1d ± 0.13	146.5f ± 0.06
CF-744 × 120	41.8fg ± 0.13	147.7d ± 0.03	77.5dc ± 0.16	61.5b ± 0.05	50.0 b ± 0.34	194.2d ± 0.17
CF-744× 150	37.7fg ± 0.91	192.2b ± 0.07	54.3f ± 0.07	5.5hg ± 0.02	48.6a ± 0.05	279.8b ± 0.57
CF-744 × 180	368.8a ± 0.21	222.2a ± 0.04	72.2d ± 0.28	89.4a ± 1.2	47.8 a ± 0.34	17.9k ± 0.31
P < F	<.0001	<.0001	<.0001	<.0001	<.0001	<.0001

Different small letters indicate significant differences at P < 0. 0001.

Table 3. Regression equations for glucosinolate content in cauliflower varieties fertilised with nitrogen (0−180 kg ha⁻¹) in soil.

Glucosinolates (GLSs)	Cultivar	Regression equation	R^2
Aliphatic GLSs
Sinigrin	Lagardo	y = −9,3054×^2 + 69,986x − 16,37	0.09
	Eskimo	y = 0,003×^2 − 0,246x + 87,343	0.08
	CF-744	y = 0,0268×^2 − 4,3846x + 214,86	0.38
Progoitrin	Lagardo	y = −2,4929×^2 + 15,004x − 4,24	0.61
	Eskimo	y = −0,7036×^2 + 4,385x + 1,94	0.39
	CF-744	y = 3,6286×^2 − 14,591x + 30,52	0.44
Glucoiberin	Lagardo	y = −5,8857×^2 + 45,654x − 17,04	0.09
	Eskimo	y = −12,452×^2 + 74,885x − 6,83	0.31
	CF-744	y = 6,6768×^2 − 10,672x + 62,17	0.86
Indole GLSs
Glucobrassicin	Lagardo	y = −0,0198×^2 + 3,3804x + 69,068	0.24
	Eskimo	y = −0,0073×^2 + 1,1386x + 46,197	0.34
	CF-744	y = −0,0042×^2 + 0,653x + 135,23	0.03
4-Methoxyglucobrassicin	Lagardo	y = −20,041×^2 + 127,11x − 0,97	0.16
	Eskimo	y = −6,8732×^2 + 39,485x + 20,22	0.26
	CF-744	y = −12,445×^2 + 85,261x + 34,53	0.12
Aromatic GLSs
Glucohirsutin	Lagardo	y = −7,1946×^2 + 55,92x + 2,75	0.94
	Eskimo	y = −1,1018×^2 + 11,39x + 62,13	0.70
	CF-744	y = 0,9607×^2 − 3,7936x + 59,44	0.21

Therefore, based on this results mentioned in Table 2, N supply at 90 kg ha ⁻¹ will benefit varieties ‘Eskimo’ and ‘Largardo’ in the accumulation of GLSs such as sinigrin, glucoiberin, glucohirsutin progoitrin, 4-methoxy glucobrassicin and glucobrassicin, whilst var. ‘CF-744’ needed 180 kg ha ⁻¹ N supply to increase the levels of all the GLSs mentioned in this study except glucobrassicin. On this note ‘CF-744’ can be regarded as a sensitive variety towards N application than the other two varieties.

Glucoraphanin and glucoiberin concentrations were influenced by N supply in broccoli and other Brassica crops (Krumbein et al. 2001; Gerendas et al. 2009). Furthermore, Kim et al. (2002) reported that the indole GLS concentrations in turnip rape increased with increasing N supply. Schonhof et al. (2007) showed a strong influence of N supply on the glucosinolate concentrations in broccoli (variety not mentioned). On the other hand, reports of Schonhof et al. (2007) stated that the levels of GLSs increased under decreasing N supply. But Neugart et al. (2018) in their review specifically stated that N supply decreased the alkyl and indole GLS levels in kale cv. ‘Winterbor’. It is clear from our findings that different genotypes can respond differently to N supply and GLS levels in cauliflowers. However, a balance between N and S fertiliser application with respect to GLSs and different cauliflower varieties is also important.

Effect of N application on phenolic acid compounds in cauliflower varieties

Effects of N supply and biosynthesis of phenolic acids in the three different cauliflower varieties are shown in Table 4. Chlorogenic and protocatechoic acids were the predominant phenolic acids in the cauliflower varieties (Table 4). In all three varieties, the N supply at 120 kg ha⁻¹ resulted in a higher accumulation of the above mentioned phenolic acids (Table 4). Accumulation of phenolic acids in all three cauliflower varieties declined or reached a plateau with increasing N supply from 150 to 180 kg ha⁻¹ (Table 4).

Table 4. Influence of different N application rate on major phenolic acids in cauliflower varieties.

	Phenolic acids (mg kg^−1)						Antioxidant property
Nitrogen concentration	Protocatechoic acid	4-Hydroxy Benzoic acid	P_Coumaric acid	Ferulic acid	Chlorogenic acid	Caffeic acid	FRAP (µmol TEAC 100 g^−1)
0	303.9de ± 0.78	692c ± 0.04	8.1b ± 0.05	36.7c ± 0.34	1976.8e ± 0.16	2.8d ± 0.12	106.63 d ± 0.09
60	239.9e ± 0.12	64.7b ± 0.26	4.1d ± 0.17	22.6d ± 0.52	4033.9d ± 0.05	3.7dc ± 0.34	112.71 c ± 0.45
90	437c ± 0.41	5.6a ± 0.31	6.7c ± 0.04	82.7a ± 0.13	6655.4b ± 0.74	5.9b ± ± 0.42	117.71 b + 0.58
120	812.5a ± 0.08	79.3a ± 0.15	10a ± 0.05	77.7a ± 0.09	8397.1a ± 0.12	8.7a ± 0.43	125.14 a ± 0.16
150	504.6b ± 0.15	58.8b ± 0.46	4.5d ± 0.16	58.4b ± 0.62	5285.8c ± 0.43	4.7c ± 0.04	122.50 a ± 0.87
180	307.4d ± 0.26	58.4b ± 0.03	4.5d ± 0.04	21.4d ± 0.04	1647.5e ± 0.04	4.7c ± 0.87	114.90 bc ± 0.26
P < F	<.0001	<.0001	<.0001	<.0001	<.0001	<.0001	<.0001
Largardo	432.4a ± 0.05	60.283a ± 0.05	5.3b ± 0.68	50.4ab ± 0.26	5087.6a ± 0.09	4.7b ± 0.34	119.85 a ± 0.75
Eskimo	434.7a ± 0.56	59.1a ± 0.34	6.9a ± 0.01	51.6a ± 0.16	4785.2ab ± 0.41	5.7a ± 0.02	118.93 a ± 0.21
CF-744	435.5a ± 0.16	61.9a ± 0.07	6.7a ± 0.15	47.7b ± 0.05	4644.0b ± 0.53	4.8b ± 0.49	111.23b ± 0.11
P < F	0.9904	0.4397	0.0022	0.0936	0.0746	0.0220	0.250
Interactions Variety x nitrogen
Largardo × 0	358.1ced ± 0.13	29.4f ± 0.54	6.1efgh ± 0.03	48.6e ± 0.03	36,658.1h ± 0.54	2.5i ± 0.61	101.44 jk ± 0.33
Largardo×60	212.1f ± 0.09	53.8e ± 0.12	2.6j ± 0.83	19.4g ± 0.17	4287.3gh ± 0.73	3.2fig ± 0.79	101.44 defgh ± 0.41
Largardo×90	438.2bc ± 0.17	75.1b ± 0.27	5.7 fghi ± 0.52	76.8bc ± 0.05	6437.1cd ± 0.41	6bc ± 0.6	128.10 a ± 0.54
Largardo×120	770a ± 0.16	75.4b ± 0.05	9.8abc ± 0.72	74 c ± 0.48	8121.7ab ± 0.03	9.1a ± 03	128.46 a ± 0.69
Largardo×150	499.8b ± 0.27	59.3ed ± 0.17	3.6ji ± 0.22	62.4d ± 0.73	5444.6fe ± 0.63	4.4f cdegh ±	127.00 ab ± 0.73
Largardo×180	316.2efd ± 0.14	68.4bcd ±	4.3ghij ± 0.07	21.3g ± 0.50	1147 j ± 0.02	4.0eghi ±	118.47 cdefg ± 0.52
Eskimo × 0	238.4f ± 0.05	25.1f ± 0.03	7.6cdef ± 0.73	30.7f ± 0.07	1655.9ji ± 0.06	2.9ih ±	95.77 k ± 0.43
Eskimo × 60	292.6ef ± 0.76	72.8bc ± 0.26	4.6ghij ± 0.05	23.7gf ± 0.32	3669.5h ± 0.60	4.8bcefg + 0.06	108.21 jhi ± 0.72
Eskimo × 90	448.2bc ± 0.54	73.1bc ± 0.04	6.3 defg 0.04	87.8a ± 0.58	7286.0bc ± 0.07	6.3 b ± 0.17	110.68 ghi ± 0.45
Eskimo × 120	817.2a ± 0.03	75b ± 0.06	11.9a ± 0.29	88a ± 0.04	8412.8a ± 0.12	8.2a + 0.07	124.45 abc ± 0.17
Eskimo × 150	517b ± 0.07	54.6e ± 0.08	5.7 ghi ± 0.52	57ed ± 0.62	5677.1de ± 0.05	5.0 bcdef ± 0.51	118.18 cdefg ± 0.23
Eskimo × 180	294.9ef ± 0.15	53.7e ± 0.48	5.1 ghi ± 0.69	22.5gf ± 0.78	2009.7 ji ± 0.48	5.9dcd ± 0.84	106.18 ji ± 0.06
CF-744 × 0	315.3def ± 0.48	22.4f ± 0.72	10.5ab ± 0.74	30.8f ± 0.39	2297.7i ± 0.62	3.0ghi ± 0.63	119.95 bcdef ± 0.43
CF-744 × 60	215.1f ± 0.06	67.6bc ± 0.09	5.0 ghi ± 0.04	24.6gf ± 0.06	4144.9hg ± 0.02	3.1 ghi ± 0.72	114.30 fgh ± 0.31
CF-744 × 90	424.4bcd ± 0.82	78.5ba ± 0.52	8.0 cde ± 0.08	83.5ba ± 0.54	6243.1de ± 0.45	5.5bcde ± 0.57	114.34 efgh ± 0.53
CF-744 × 120	850.3a ± 0.56	87.6a ± 0.56	8.4 bcd ± 0.48	71.3c ± 0.08	8412.8a ± 0.04	8.2a + 0.05	122.51 abcd ± 0.03
CF-744 × 150	496.9b ± 0.07	62.4cde ± 0.06	4.2 ghij ± 0.73	56ed ± 0.74	4735.7fg ± 0.36	4.5bcdefg ± 0.41	122.32 abcde ± 0.21
CF-744 ×180	311.1efd ± 0.39	53.7e ± 0.07	4 hij ± 0.06	20.3g ± 0.02	1785.7ji ± 0.01	4.5bcdefg ± 0.56	120.05 bcdef ± 0.05
P < F	0.4909	0.0025	0.0062	0.0001	0.0480	0.8520	0.0001

Different small letters indicate significant differences at P < 0.4909 or 0.0025 or 0.0480 or 0.0001 or 0.0480 or 0.8520.

Accumulation of 4-hydroxybenzoic acid and chlorogenic acid in all three cauliflower varieties (Table 5) demonstrated a strong relationship (R² ≥ 70) with increasing N supply (Table 4). However, protocatechoic and caffeic acids in ‘Largardo’ showed a strong (R² = 0.84) and moderate (R² = 0.62) relationships with increasing N supply respectively (Table 5). Concentration of phenolic acids in vegetables determines their quality because they act as substrates for enzymatic browning (Plazas et al. 2013). Among the phenolic acids, chlorogenic acid is known as the browning substrate in many vegetables (Plazas et al. 2013). It is interesting to note that N supply at 120 kg ha⁻¹ increased the chlorogenic concentration in all three varieties.

Table 5. Regression equations for phenolic acid content in cauliflower varieties fertilised with nitrogen (0–180 kg ha⁻¹) in soil.

Phenolic acids	Cultivar	Regression equation	R^2
Protocatechoic acid	Lagardo	y = −101,53×^2 + 839,21x − 1082,1	0.84
	Eskimo	y = −57,227×^2 + 438,44x − 231,87	0.63
	CF-744	y = −47,836×^2 + 370,57x − 135,98	0.42
4-Hydroxybenzoic acid	Lagardo	y = −4,0375×^2 + 34,314x + 1,37	0.82
	Eskimo	y = −5,8179×^2 + 43,305x − 4,28	0.74
	CF-744	y = −7,3911×^2 + 56,023x − 21,95	0.90
p-Coumaric acid	Lagardo	y = −0,2893×^2 + 1,9707x + 2,84	0.09
	Eskimo	y = −0,35×^2 + 2,3471x + 3,96	0.13
	CF-744	y = −0,0411×^2 − 0,6982x + 9,75	0.48
Ferulic acid	Lagardo	y = −5,9911×^2 + 41,643x − 4,47	0.41
	Eskimo	y = −9,2482×^2 + 66,426x − 40,61	0.68
	CF-744	y = −7,9339×^2 + 56,38x − 29,25	0.67
Chlorogenic acid	Lagardo	y = 2161,8×^2 − 20058x + 47,766	0.70
	Eskimo	y = −960,96×^2 + 6981,5x − 5075,7	0.91
	CF-744	y = −840,84×^2 + 5925,4x − 3382,8	0.86
Caffeic Acid	Lagardo	y = −0,6339×^2 + 4,8432x − 2,47	0.62
	Eskimo	y = −0,425×^2 + 3,475x − 0,2	0.71
	CF-744	y = −0,4446×^2 + 3,5239x − 0,79	0.56
Antioxiant property
	Lagardo	y = −2,0878×^2 + 20,342x + 79,2	0.81
	Eskimo	y = −2,8066×^2 + 22,381x + 74,816	0.85
	CF-744	y = 0,2862×^2 − 1,0679x + 118,31	0.26

Generally, high N supply negatively affected the accumulation of phenolic compounds in plant tissues (Zhao et al. 2009). N application rates did not show any correlation between individual phenolic acids in pac choi leaves (Zhao et al. 2009). Higher N application rates were shown to reduce phenylalanine ammonia-lyase (PAL) activity (Ibrahim et al. 2011) known as the enzyme that mediates the conversion of L-phenylalanine to ammonia and trans-cinnamic acid which is the initial step of the phenyl propanoid pathway that is responsible for the production of polyphenols (Hahlbrock and Scheel 1989). Furthermore, a high N supply was reported to reduce the level of phenolics in tronchuda cabbage (Sousa et al. 2008). Zhao et al. (2009) reported that phenolic compounds demonstrate more effective antioxidant activity than ascorbic acid (Zhao et al. 2009).

Effect of N application on antioxidant property in cauliflower varieties

Influence of different N treatments on antioxidant property is shown in Table 4. In var. ‘CF-744’, the antioxidant activity did not vary significantly with respect to different rates of N supply (Table 4). In var. ‘Lagardo’ antioxidant activity was significantly highest at N application rates of 90–150 kg ha⁻¹ and thereafter, it declined at 180 kg ha⁻¹ (Table 4). However, in var. ‘Eskimo’, antioxidant activity was significantly highest at 120 and 150 kg ha⁻¹ N supply (Table 4). Although all three varieties showed a strong relationship with N supply and antioxidant property, the relationship of N application on the antioxidant property of varieties, ‘Largardo’ (R² = 0.81) and ‘Eskimo’ (R²= 0.85) was very strong (Table 5). Previously, the antioxidant properties of GLSs were determined using DPPH and ABTS (Cabello-Hurtado et al. 2012). The same authors stated that the correlation was very weak and recommended the use of ORAC assays and superoxidase radical scavenging activity. Indole GLS glucobrassin in cauliflower was the most potent antioxidant (Cabello-Hurtado et al. 2012). At the same time, glucoiberin was also reported to show higher antioxidant properties and the authors concluded that the compounds probably act as proton doners. In this study, it was determined that the antioxidant assays using the FRAP method and GLS and phenolic compounds could have contributed towards the observed antioxidant properties. Antioxidant properties in food play an important role in human health (Saikat and Chakraborty 2011).

In conclusion, differential responses were noted with respect to N application and biosynthesis of glucosinolates among the three cauliflower varieties. However, the response to the accumulation of phenolic acids was similar in these varieties. Growers must select cultivars that are rich in phytochemical content and use N application optimally to benefit health-conscious consumers. At the same time growers need to cater the vegetables the meet the quality criteria. On this note, N application rate at 120 kg ha⁻¹ can be recommended in order to retain the quality, the antioxidant property in cauliflower varieties ‘Eskimo’ and ‘CF-744’ whilst, N application rate at 90 kg ha⁻¹ can be recommended for var. ‘Largardo.’

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Madonna N. Mashabela obtained a doctoral degree and this manuscript emanated from her research findings that she carried out in her experimental work.

Martin Maboko, Ph.D., researcher in the field of horticulture. His fields of interest include the influence of nitrogen application rates on morphological parameters of vegetables grown in soil and hydroponic systems.

Puffy Soundy, Ph.D. His research interest is on the agronomy of vegetable crops.

Dharini Sivakumar, Ph.D. Her work is involved in the quality analysis, phytochemical and nutritional analysis.

Additional information

Funding

This study is based on research that was funded by the National Research Foundation (NRF) of South Africa (grant number 98352).