Assessment of Mineral Nutrient Impact on Metabolites Accumulation in Kale (Brassica Oleracea Var. Sabellica)

Abstract

Green leafy kale (Brassica oleracea var. sabellica) has huge scientific attention because of its health-promoting functionality. In the present study the impact of NPK, energen, biostim and humate on flavonoid, phenolic compounds, vitamin C, carotenoids, malondialdehyde (MDA), protein, proline and soluble sugar in kale was investigated. The mineral nutrients mostly increased but in some cases maintained the studied metabolites. The stimulatory effect of applied mineral nutrients on the phytochemicals analyzed varied with the different combinations of macro and microelements. Lipid peroxidation was minimized in leaves treated with mineral nutrients hence a reduction in MDA levels. Contrary to the correlation between nitrogen deficiency and increase in polyphenol and vitamin C content in plants, NPK and biostim did not reduce phenolic compound levels. The results of this study showed that NPK maximized the synthesis of vitamin C and proline; energen - phenolic compounds, carotenoids and sugar; biostim – phenolic compounds, proteins and sugar; humate – flavonoids and sugar in curly kale. Therefore, the type of macronutrient and micronutrients combination increases phytochemicals in differently. To enhance the synthesis of phenolic compounds and vitamins, the most promising additives are those containing humic acids (humate and energen), and biostim proved to be more effective for the synthesis of proteins.
 Background. The understanding of how diet affects the incidence or treatment of disease has led to a rise in consumer’s demand for functional foods as well as created the market for natural sources of health benefitting compounds rather than the synthetic sources. Curly kale has gained scientific attention as a functional food because it contains higher levels of phytochemicals than most vegetables. These phytochemicals have shown antioxidant, antimutagenic, cytotoxic, antifungal, and antiviral activities. However, the content levels of these metabolites are influenced by not only genetic but environmental factors. It was of interest to evaluate how various mineral nutrients can elicit the accumulation of these compounds that minimize the risk of chronic diseases or aid in their treatment.
 Purpose. Evaluate how the mineral nutrients, NPK, energen, biostim and humate affect the content of metabolites (proteins, sugars, flavonoids, phenolic compounds, vitamin C, carotenoids, MDA and proline) in curly kale (Brassica oleracea var. sabellica).
 Materials and methods. Sprouts from kale seed kept wet in a Petri dish for 7 days were transferred to the field. At 6 weeks old four mineral nutrients (NPK, energen, humate and biostim) were added to the soil. Control variants were treated with water. A week later, the leaves were harvested after which, the phenolic compound, flavonoid, protein, sugar, vitamin C, carotenoid, MDA and proline contents were determined using spectrophotometric methods.
 Results. It was shown that humate fertilizer elicited the highest accumulation of flavonoids. Kale plants fertilized with energen were observed to have the highest phenolic compound content. NPK, energen and humate caused a similarly positive effect on vitamin C content in leaves, unlike biostim whose effect did not significantly differ from control plants. Energen treated kale had the highest increment of carotenoids. A varied reduction of MDA levels in plants treated with all four mineral nutrients was observed in kale leaves. Plants fertilized with biostim accrued the highest protein content in leaves. Proline content increased under the influence of all fertilizers studied. Sugar levels for all kale plants treated with the studied mineral nutrients were enhanced equally
 Conclusion. Macro and microelements supplied by mineral nutrients differentially boost the biosynthesis of health-promoting metabolites in curly kale, thereby enhancing its quality.

Phytochemical composition of Humulus Lupulus L. in ontogeny under different treatments

“…Some studies have shown that the treatment of plants with solutions and mixtures of mineral salts such as NPK increased the accumulation of polyphenolic compounds several times over in comparison with test plants [16,17]. On the other hand, there is evidence of a significant decrease in the amount of soluble phenolic compounds and flavonoids in different plants under the influence of NPK [18,19].…”

Effects of humate and neodymium on phytochemical levels in kale at different ontogenetic stages

“…where C is the amount of MDA (mmol/g dry weight); ΔD is the difference in optical density of the sample at 532 and 600 nm; 155 -extinction coefficient of MDA at 532 nm, mM -1 cm -1 ; X -dilution, i.e. the total volume of the reaction mixture was divided by the amount of the introduced extract sample; V -the volume of the extract (mL); m -the mass of the wet sample (g); Δm -the ratio of dry and wet weight; 1 -optical path length (cm) (Anteh et al, 2021).…”

“…The concentration of vitamin C was determined using a calibration curve. The vitamin C content was calculated using the formula: C = (K * V * X)/(m * Δm * L), where C is the content of vitamin C (μg/g wet weight); K -the concentration of vitamin C (μg/mL); V -the total volume of the extract (mL); X -the dilution of the extract in the reaction mixture; L -the length of the optical path (cm); m -the mass of the wet sample (g); Δm -the ratio of dry weight to wet weight (Anteh et al, 2021).…”

“…The concentration of pigments is calculated using the equations: where D -experimentally obtained values of optical density at the corresponding wavelengths; C car , C chla and C chlb -respectively, the sum of the concentration of carotenoids and the concentration of chlorophylls a and b (mg/L). To determine the content of carotenoids in plant materials (mg/g) of wet weight was calculated by the formula: M = C * (V/P), where M is the pigment content in plant raw materials (mg/g); C -the concentration of pigments (mg/L); V -the volume of the pigment extract (mg/L); P -the sample of plant material (grams) (Anteh et al, 2021).…”

Big Data Application in the Neyman-Pearson Regression and Deep Bernoulli and Boltzmann for Iot Based Soil Quality Prediction

“…In [20], deep reinforcement model was applied for crop yield prediction. Spectrophotometric methods were introduced in [21] to improve the quality of natural sources. Environmental and economic efficiency protective afforestation was introduced in [22] to achieve the quality of soil and climatic conditions.…”