Optimization of extraction yield and chemical characterization of optimal extract from Juglans nigra L. leaves

https://doi.org/10.1016/j.cherd.2020.03.002Get rights and content

Abstract

The extraction yield of Juglans nigra L. leaves was assessed at different ethanol concentrations (0–96% (v/v)) and solvent-to-solid ratios (5–20 kg kg−1). The response surface methodology (RSM) and artificial neural network with genetic algorithms (ANN-GA) were developed to optimize the extraction variables. The RSM and ANN-GA models determined 50% (v/v) ethanol concentration and 20 kg kg−1 solvent-to-solid ratio as optimal conditions, ensuring an extraction yield of 27.69 and 27.19 g 100 g−1 of dry leaves. The phenolic compounds in optimal extract were quantified: 3-O-caffeoylquinic acid (2.27 mg g−1of dry leaves), quercetin-3-O-galactoside (10.99 mg g−1 of dry leaves) and quercetin-3-O-rhamnoside (15.07 mg g−1of dry leaves) using high-performance liquid chromatography (HPLC). The minerals in optimal extract were quantified: macro-elements (the relative order by content was: K > Mg > Ca) using inductively coupled plasma optical emission spectrometry (ICP-OES) and micro-elements (the relative order by content was: Zn > Rb > Mn > I>Sr > Ni > Cu > Co > V > Ag > Se) using inductively coupled plasma mass spectrometry (ICP-MS). The extraction coefficients for minerals were determined and were highest for K (64.3%) and I (53.5%). Optimization of extraction process resulted in high extraction yield from J. nigra leaves and optimal extract containing different phytochemical compounds.

Introduction

The genus Juglans L. contains about 20 species (Nicese et al., 1998) and among them, Juglans regia L. is most commonly studied. However, Camara and Schlegel (2016) promoted that due to the chemical composition of its fruit, J. nigra (Black Walnut) has multiple biological significance similar to J. regia as well as other nuts. Additionally, some other studies highlighted the medical importance of J. nigra fruit (Amarowicz et al., 2008; Camara and Schlegel, 2016). Rorabaugh et al. (2011) have shown that J. nigra nuts protect low-density lipoprotein against oxidation in vitro, however, resistance to oxidation in vivo was not proved. Fitschen et al. (2011) have shown a relationship between nuts consumption and reduced risks for heart disease due to its effect on blood lipids. J. nigra nuts intake led to an approximately 1% reduction of total cholesterol. Effects of J. nigra nuts were gender-dependent: the total cholesterol decreased in men for 4.5% and increased in women for 1.8% (Fitschen et al., 2011). Thus the majority of chemical research was focused on the fresh kernel of J. nigra (Rorabaugh et al., 2011; Nwosu et al., 2015). However, despite a very diverse phytochemical composition of J. nigra leaves (Gavrilović et al., 2018; Ponder et al., 1979, 2005), there is no detailed chemical analysis.

Gavrilović et al. (2018) optimized total phenolic extraction from J. nigra leaves in order to examine their antioxidant potential. The use of mathematical modeling significantly improved extraction process optimization from Juglans species (Nour et al., 2016; Vieira et al., 2017). The models such as response surface methodology (RSM) and artificial neural network with genetic algorithm (ANN-GA) are often utilized for extraction processes modeling and optimization (Alara et al., 2018; Simić et al., 2016). RSM uses the model equation to represent the dependence of the extraction yield on the used operational extraction parameters (Said and Amin, 2015). ANN works on the principle of biological neural networks, and its limitations in the optimization of various processes are overcome by combining with genetic algorithm (GA) (Rajković et al., 2015).

Nowadays, interest in functional food and herbal products is increasing. However, there are no available data on extraction optimization from J. nigra leaves, relative to total extractive substances. Accordingly, the aim of this research was to optimize variables for the extraction of total extractive substances from J. nigra leaves. Therefore, specific objectives included: (i) optimization of operational variables (ethanol concentration, solvent-to-solid ratio) using RSM and ANN–GA; ii) chemical characterization of J. nigra leaves extract obtained under optimal conditions. Within chemical characterization of extract, high-performance liquid chromatography (HPLC) analysis was used for the determination of phenolic composition, whereas inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) were used for mineral composition assessment.

Section snippets

Sample collection and extraction of Juglans nigra leaves

Juglans nigra leaves were collected during summer at Aleksinac locality, the southeast region of Serbia (located at 43° 32̕̕′ 11̕̕̕̕″N/, 21° 42′ 11′′E). The voucher specimen was deposited at the Herbarium of the Department of Botany, University of Belgrade-Faculty of Pharmacy (HFF) under the number 3906HFF. Plant material was identified by the botanist, prof. Branislava Lakušić (Department of Botany, University of Belgrade – Faculty of Pharmacy). The identification was made according to the

RSM modeling and optimization

The experimental matrix of operating variables with extraction yield is represented in Table 4. The RSM model describing the relationship between the extraction yield (y) and two variables, namely ethanol concentration (x1) and solvent-to-solid ratio (x2), is presented by Eq. (2):y=9.635+0.214x1+1.147x2+0.002x1x20.003x120.028x22

ANOVA was used to analyze the statistical significance of the equation model, the operating variables, their interactions and the validity of the fitting (Table 5).

Conflict of interests

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.

Acknowledgements

This study was supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (Grant Nos. 175034, 173020 and 173021).We thank Zoran Paunovic for kindly providing us leaf sample and prof. Branislava Lakušić for identification of plant material.

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