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Beverages 2019, 5(3), 42; https://doi.org/10.3390/beverages5030042

Article
Optimization of Bioactive Substances in the Wastes of Some Selective Mediterranean Crops
1
Department of Chemical Engineering, Engineering Faculty, Istanbul University-Cerrahpaşa, Avcilar, 34320 Istanbul, Turkey
2
Department of Chemistry, Engineering Faculty, Istanbul University-Cerrahpaşa, Avcilar, 34320 Istanbul, Turkey
3
Green Processes & Biorefinery Group, School of Agricultural Sciences, University of Thessaly, 43100 Karditsa, Greece
*
Author to whom correspondence should be addressed.
Received: 10 April 2019 / Accepted: 11 June 2019 / Published: 1 July 2019

Abstract

:
Production of added products from industrial byproducts is a challenge for the current natural product industry and the extraction field more generally. Therefore, the aim of this study is to valorize the selected Mediterranean crops that can be applied as antioxidants, natural chelating agents, or even as biosolvents or biofuels after special treatment. In this study, the wastes of popular Mediterranean plants were extracted via homogenizer-assisted extraction (HAE) by applying response surface methodology (RSM) to examine the effects of process parameters on the total biophenolic contents (TBCs) of their residues. Box–Behnken design model equations calculated for each system were found significant (p < 0.0001) with an adequate value of determination coefficient (R2). Olive leaf had the highest TBC content (58.62 mg-GAE/g-DW with 0.1 g sample, 42.5% ethanol at 6522.2 rpm for 2 min), followed by mandarin peel (27.79 mg-GAE/g-DW with 0.1 g sample, 34.24% ethanol at 8772 rpm for 1.99 min), grapefruit peel (21.12 mg-GAE/g-DW with 0.1 g sample, 42.33% ethanol at 5000 rpm for 1.125 min) and lemon peel (16.89 mg-GAE/g-DW with 0.1 g sample, 33.62% ethanol at 5007 rpm for 1.282 min). The antioxidant activities of the extracts were measured by several in vitro studies. The most prominent biophenols of the wastes were quantified by high performance liquid chromatography (HPLC). Fourier-transform infrared-attenuated total reflectance (FTIR-ATR) and atomic force microscopy (AFM) techniques were also used for characterization.
Keywords:
Mediterranean crops; waste; valorization; optimization; FTIR-ATR; AFM

1. Introduction

Food waste production covers the whole food life cycle from agricultural and industrial production and processing, to retail and domestic consumptions. In developed countries, 42% of food waste is produced during domestic consumption, while 39% is from the food manufacturing industry, 14% is from the food services sector and 5% is from the retail and distribution sectors [1]. Nowadays, industrial ecology concepts have been evaluated as a leading principle of eco-innovation that targets the zero waste economy, where waste is used as a raw material for new products and applications. Large quantities of waste generated by food industries cause serious problems both economically and environmentally, as well as resulting in a great loss of high-added value compounds. Moreover, most of these residues have reusable potential in other production systems.
The wastes of fruit and vegetable processes are the most important resources of various types of antioxidants and dietary fibers. The reason for this is that the corresponding byproducts are made from soft tissue that is rich in both components, allowing simultaneous extraction into two separate streams [2]. Citrus is the largest fruit crop in the world [3,4], with a global production of approximately 115 million tons per year [5]. Since their water content is less than half the total weight of the fruits, their main byproducts are their peels after processing [6]. The concerned wastes are traditionally evaluated for animal feed, pectin and fuel production [5,7]. Recent studies evaluating these wastes have suggested that some fruit or vegetable byproducts may be natural antioxidant sources. Orange peel has been used for the recovery of phenolic materials, flavonoids, essential oils and carotenoids [5,8,9]. Karsheva et al. obtained extracts from mandarin peels, and examined the level of biophenols in their extract content [6]. Singanusong et al. also found the antioxidant capacity of mandarin peels by various methods, and analyzed their total biophenolic substances [10]. Ateş et al. extracted phenolic antioxidants by various advanced separation methods (microwave-assisted extraction and supercritical-CO2 extraction), using mandarin peel as a raw material [11]. Lemon peel was also used as raw material for pectin and flavonoid production [3,4]. Li et al. extracted lemon peels by enzyme-assisted extraction to get bioactive ingredients [12]. Furthermore, Guimarães et al. compared the water content of the lemon peel in terms of antioxidants, and observed a higher level (about eight times) of phenolic material in lemon peels compared to that of water [13]. Moreover, the same research group compared grapefruit juice and peels with respect to phenolic material. They observed that the peel had an approximately six times higher biophenol level compared to that of grapefruit water. Li et al. recycled grapefruit peels for biologically active materials by means of enzyme-assisted extraction [12].
On the other hand, olive trees are one of the most important fruit trees in Mediterranean countries, covering eight million hectares, which corresponds to about 98% of the world crop. This output demonstrates the economic and social importance of this crop [14]. Olive leaves, which are byproducts of this crop, also represent 10% of the total weight of the harvested olives, but this residue remains agricultural waste if it is not assessed [15]. Since olive leaf is a rich source of bioactive substances that have been proven many times to possess health effects, a wide variety of studies have been carried out in this regard [16]. Şahin et al. obtained extract rich in biofenol and flavonoid by using ultrasound-assisted extraction [17]. Xynos et al. [18] and Putnik et al. [19] applied pressurized liquid extraction as an environmentally friendly technology to extract olive leaf for its biophenol substances. Şahin et al. utilized solvent-free microwave extraction to attain olive leaf extract [20]. Mourtzinos et al. [21] and Athanasiadis et al. [22] obtained a rich extract of antioxidants using environmentally friendly and novel solvents, respectively. Khemakhem et al. applied novel separation methods (microfiltration, ultrafiltration and nanofiltration) to acquire extract with a high level of oleuropein (the main ingredient of the olive leaf) [23]. To our knowledge, the number of studies of citrus peels in terms of polyphenol level is quite inadequate, although there have been many studies in which olive leaf has been examined for its natural antioxidants. For this reason, evaluation of these resources, and comparison of the related wastes, is carried out in this study. On the other hand, the concerned waste samples have been extracted with homogenizer-assisted extraction (HAE), which is an extremely simple system with extremely minimal time and investment cost requirements. As the rotary blade spins at a very high speed, the texture is rapidly reduced in size by a combination of excessive shear, cavitation and scissor-like mechanical shear in the narrow gap between the rotor and the stator. Since most rotor stator homogenizers have an open configuration, the product is recirculated repeatedly. Depending on the processing speed and hardness of the tissue sample, the desired results are usually obtained in 15–120 s. The goal of the present study is to optimize the conditions of HAE for obtaining the selected raw materials, depending on their phenolic content, and to identify the richest waste byproduct with respect to bioactive properties.

2. Materials and Methods

2.1. Plant and Chemical Materials

Olive leaf samples were supplied from Özgün Olive, Olive Oil Co in the Aegean part of Turkey (Ayvalik, Balikesir). Citrus fruits were provided by the Bati Akdeniz Agricultural Research Institute (BATEM) in Antalya, Turkey. The samples were dried at ambient conditions. The dried leaves and peels were ground by a grinder (Moulinex Super Blender Grinder, LM209041, Paris, France), and screened through a 22-mesh sieve.
Ethanol (>99.5%) and methanol (>99.8%) were from Merck (Darmstadt, Germany), while sodium carbonate, Folin–Ciocalteu, 2,2-diphenyl-1-picrylhydrazyl (DPPH•), 2,9-dimethyl-1,10-phenanthroline (neocuproine), 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (trolox), gallic acid, oleuropein, naringin, formic acid and acetonitrile were from Sigma-Aldrich (St. Louis, MO, USA).

2.2. Homogenizer-Assisted Extraction

Residue samples were extracted three times by an ethanol-water solution (v/v) of different concentrations by means of an IKA T25 (ULTRA-TURRAX, Staufen, Germany) brand homogenizer. The homogenization was adjusted to several conditions (Table 1). Before analysis, extracts were filtered through a syringe filter (0.45 μm) and kept in dark at −20 °C.

2.3. Spectrophotometric Analyzes

Total biophenolic content (TBC) determination of the extracts was carried out spectrophotometrically (PG Instruments, T60/Leicestershire, Leicester, England) depending on the Folin–Ciocalteu method at a wavelength of 765 nm [24]. The findings were expressed as a gallic acid equivalent on a dried base (mg-GAE/g-DW). Scavenging activity of the ABTS radical was measured following the procedure of Re et al. with slight modifications [25]. The wavelength was selected as 734 nm. Inhibition of ABTS was given as mg trolox equivalent antioxidant activity on a dried base (mg-TEAC/g-DW). Free-radical scavenging activity against the DPPH radical was also achieved by following the report of Yu et al. [26] with some modifications [27]. The wavelength was selected as 517 nm. Inhibition of DPPH was given as mg-TEAC/g-DW. Moreover, cupric ion reducing antioxidant capacity (CUPRAC) assay was applied to measure the antioxidant activity of the residues [28]. Maximum absorbance was observed at 450 nm. The antioxidant activity of the samples was also stated as mg-TEAC/g-DW.

2.4. Chromatographic Analysis

Individual phenolic quantification was performed by high-performance liquid chromatography (HPLC). The main ingredients of the selected wastes have been investigated in the literature. After determination of the prominent compounds of the samples, the relevant compounds were provided as standards. Then, standard solutions were prepared in several concentrations to draw a calibration curve. After measuring the absorbance of the samples, the concentrations were determined using the calibration curve. Conditions of HPLC are given in Table 2.

2.5. Atomic Force Microscopy

The nanostructural morphologies and height profiles of the lemon peels and olive leaves were examined with an atomic force microscopy (AFM) instrument, which was provided by Nanomagnetics Instruments. It was operated in tapping mode at room temperature using silicon probes coated with the aluminum (PPP–NCLR nanosensors). Samples were scanned before and after extraction processes utilizing a 10 µm/s scanning rate and a 256 × 256 pixel resolution. The statistical parameters were evaluated from AFM images using the image analysis software NMI Viewer 2.0.7.

2.6. Fourier-Transform Infrared-Attenuated Total Reflectance

The chemical characterization of the extracts was made using a Bruker Alpha-T DRIFT spectrometer with a 528/D model through OPUS 6.5 software (Bruker Optics Inc., Coventry, UK).

2.7. Statistical Experimental Design

Box–Behnken design was applied into the selected HAE process as a three-level factorial design for the optimization of four process parameters (Table 1). Since there are relatively many independent variables, Box–Behnken design was selected in order to be more economical in a more effective way [29]. Furthermore, Box–Behnken design, along with response surface methodology (RSM) provides an evaluation of the effects of process parameters and their interactions with the relevant system. In this study, Design-Expert (Stat-Ease, Minneapolis, MN, USA) software version 10.0.4 was used.
The quadratic model of response is described the equation given below:
Y   =   β 0 + i = 1 3 β i X i + i = 1 3 β ii X i 2 + i = 1 3 j = i + 1 3 β ij X i X j + e ,
where β0 is the constant, βi is the linear and βii is the quadratic (i and j = 4) interaction coefficient. Xi (i = 1–4) is the non-coded factor, while Y represents the dependent parameter, known as the response.
An analysis of variance (ANOVA) test is utilized to assess the model fitting, as well as to determine the interaction between the variables using the same software. A lack of fit test was further applied to the independent and dependent variables for verification of the model fitting.

2.8. Statistical Analysis

Analysis of variance (ANOVA) statistical testing was utilized through Tukey’s test of InStat software (GraphPad, San Diego, CA, USA) to analyze the means of three replicate outputs.

3. Results and Discussions

TBC findings of the olive leaf, grapefruit, lemon and mandarin peel extracts attained by HAE through Box–Behnken design are given in Table 3, which details their EtOH concentration, solid mass, extraction time and speed.

3.1. Modeling and Optimization by Box–Behnken Design

Quadratic polynomial models derived for the TBCs extracted from the relevant residues of each crop are given in Table 4. Coefficient of determination (R2) values also indicate that the equations calculated for the four responses were adequate to explain the relationship between the dependent and independent variables (R2 > 0.89).
Table 5 summarizes the statistical results of each extract system. The adequacy of the derived models obtained by Box–Behnken design were verified and found to be significant (p < 0.0001) to the experimental findings (Table 3). Regarding olive leaf, time for HAE was the most effective variable of the TBC yield, followed by solvent concentration (p < 0.0001). Time effect was also found to be statistically the most significant parameter in the extraction of anthocyanin from red raspberries by Chen et al. [30]. Amount of solid mass was the most significant parameter for the extraction of TBC from grapefruit peel (p < 0.0001). Similarly, Jeganathan et al. observed solid mass as an effective parameter for the solvent extraction of polyphenols from red grapes by applying Box–Behnken design [31]. Quadratic power of ethanol concentration was statistically (p < 0.0001) the most important parameter of all for the lemon peel extraction by HAE (p < 0.0001). Bilgin et al. also reported a second power of ethanol concentration in the HAE of TBC from Sideritis montana L. [32]. Second power of extraction speed was the most influential process parameter affecting the HAE of mandarin peels. This finding is in agreement with that of Şahin et al., where HAE was used for solid-liquid extraction to enrich sunflower oil with polyphenols [33].
Another statistical parameter to determine the adequacy of the proposed models for the experimental data is the value of lack of fit. The model proposed for grapefruit peel extraction had a non-significant lack of fit value (p > 0.05), meaning that the model is in good agreement with the experimental output. However, the remaining models had significant values for lack of fit, showing similarity with the other reports [33,34,35,36,37,38]. Kittisuban et al. declared that lack of fit with a significant value might be acceptable if there are lots of data in the relevant process system [37].

3.2. Effects of Independent Variables on the TBC Yields

Solid mass had a negative effect on each system (Figure 1a, Figure 2a, Figure 3a and Figure 4a). This result is to be expected from a mass transfer point of view [31]. By increasing the solid mass, the liquid extract was saturated with the target components, which unfavored the rate of mass transfer by preventing the diffusion of the biophenols into the solvent [39]. On the other hand, increasing the solvent concentration in water favored the extraction at first (Figure 1b, Figure 2b, Figure 3b and Figure 4b). Later, it started to decrease after the composition reached to a value of ≈50% (v/v). Similar observations have also been attained in other reports, where bioactive ingredients were extracted from several natural sources [40,41,42]. A decrease in the amount of ethanol amount in water might be hypothesized by the denaturation property of the ethanol [32]. Regarding extraction time, there was a markable increase in each system (Figure 1b, Figure 2b, Figure 3b and Figure 4b). Zhong and Wang [43], Silva et al. [44] and Ramić et al. [45] also had observed similar results for the time effect on the extraction of various natural products. Mixing speed had a slight effect on both olive leaf and lemon peel extraction (Figure 1c and Figure 3c). As for grapefruit peel and mandarin peel extractions, speed of the homogenizer decreased the TBC yields up to a certain value, at which point the yield began to rise. This might be attributable to the initial degradation of the biophenol-degrading enzymes, which in turn caused these bioactive substances to precipitate [46].

3.3. Verification of the Suggested Conditions

Table 6 summarizes the optimum HAE conditions for TBC from each residue to achieve the greatest yield with the verification results. The second-order polynomial models proposed for the relevant systems have been confirmed to be satisfactory to estimate the suggested conditions depending on the small and acceptable levels of error rate (%).

3.4. Evaluation of the Bioactive Ingredients in the Extracts

Table 7 demonstrates the quantitative results of the most prominent phenolic compounds of each item. When the spectrophotometric analysis is assessed (Table 8), the related biophenols have proven to be the most contributing compounds to bioactive properties. A mathematical statement can be formed to confirm the concerned relationship by means of correlation coefficients between the relevant dependent variables (oleuropein/naringin and TBC; oleuropein/naringin and DPPH/CUPRAC/ABTS; TBC and DPPH/CUPRAC/ABTS). When the coefficients of correlation (0.8517) are checked, a strong relationship between the individual and total biophenols is observed. Furthermore, the relationships (>0.94) between the total biophenols and each antioxidant activity assay finding are also extremely satisfactory for verifying the contribution of total biophenols to the antioxidant capacity of the selected waste products.
On the other hand, selected antioxidant activity methods also showed positive correlations with each other. The correlation coefficients between DPPH and CUPRAC, CUPRAC and ABTS, and DPPH and ABTS were found as 0.9955, 0.9052 and 0.8764, respectively.

3.5. Infrared Spectra of the Extracts

A large variety of samples such as powders, films, liquids and solids can be studied using this characterization technique which clarifies the functional groups in the samples studied. Figure 5 demonstrates the FTIR spectra of the selected waste extracts obtained at optimum conditions, which comprises the range between 4000 and 500 cm−1. The peaks ranging from 3010 to 3670 cm−1 correspond to the O–H stretch of the hydroxyl group, comprising biophenols and alcohols [47]. The sharper peaks observed between 3409 and 1733 cm−1 are assigned to C=O and O–H stretchings, which are attributable to oleuropein, naringin and other phenolic compounds of the leaf and peel extracts [48]. The band located at 500–800 cm−1 characterizes C–H, denoting the alkanes and aromatics [49].

3.6. Nanostructural Morphologies of the Extracts

AFM is a suitable technique to explore the parameters of average surface roughness, homogeneity and particle size distribution for biomaterials with high resolution topographic images [50]. Evaluation of the interfacial behavior of the surface is crucial to understanding the interactions of the selected systems. In this regard, AFM was conducted to realize the morphological and nanostructure changes of the extracted materials. This technique provided a visualization of the deposited materials with a nanometric resolution. Figure 6a,b demonstrates two-dimensional (2D) AFM images of the olive leaf samples obtained before and after the extraction processes, respectively. From the 2D images, cross-section profiles and histograms, it is revealed that untreated leaves have more grains and particles when compared to those of treated samples. The significant difference observed between these two structures is due to the extraction process. As can be seen in Figure 6a, the heterogeneously distributed particles of various sizes cover all of the surfaces. The chemical composition of the olive leaves, consisting of the minerals, chlorophylls, fatty acids and phenolic substances, are removed after the extraction process. The histogram plots clarified that the average size distribution was about 750 nm for the untreated samples, while it was nearly 250 nm for the treated leaves.
Figure 7a,b represents the lemon peel before and after extraction processes. It is noteworthy that the lemon peel had a spikier structure after the extraction process compared with the randomly distributed particles shown in Figure 7a. The spaces and the valleys in the sample obtained after the extraction process were higher than the untreated lemon peels. According to the histogram plots, the average heights of the particles are different, while their size distributions are nearly the same. After the extraction process, compact, spiky and porous structures were observed, indicating some biophenolic substances were removed from the peel matrix.

4. Conclusions

Olive leaf was found to possess the highest phenolic ingredients (58.62 mg-GAE/g-DW with 0.1 g sample, 42.5% ethanol at 6522.2 rpm for 2 min), followed by mandarin peel (27.79 mg-GAE/g-DW with 0.1 g sample, 34.24% ethanol at 8772 rpm for 1.99 min), grapefruit peel (21.12 mg-GAE/g-DW with 0.1 g sample, 42.33% ethanol at 5000 rpm for 1.125 min) and lemon peel (16.89 mg-GAE/g-DW with 0.1 g sample, 33.62% ethanol at 5007 rpm for 1.282 min). The quadratic models proposed by Box–Behnken design were found satisfactory, depending on the statistical results (p < 0.0001 and R2 > 0.89). The convincing correlation coefficients (>0.94) between the phenolic ingredients and antioxidant activity tested by each assay proved that polyphenols in the selected waste products were the most contributing substances of bioactive properties. Hereby, the present findings will be guidance for science researchers, consumers and commercial entities (cosmetic, pharmaceutical and food industries) with a green process developed for the extraction of the most popular Mediterranean crops. On the other hand, additional studies are necessary to determine whether there is a link between possible prominent compounds in the extracts related to health benefits. In short, the need for newly developed natural additives is very clear, but the most important issue is to be careful that the product is reliable.

Author Contributions

Conceptualization, S.S.; Data curation, E.K.; Methodology, S.S.; Software, E.K.; Supervision, S.S.; Validation, E.K.; Visualization, S.Y.; Writing—original draft, S.S.; Writing—review & editing, D.M. and S.S.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare that there is no conflict of interest in writing upon submission of the manuscript.

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Figure 1. Response surface plot for the TBC of olive leaf extract (a) as a function of solvent concentration to solid mass (extraction time = 2 min and mixing speed = 6517.41 rpm); (b) as a function of solvent concentration to time (solid mass = 0.1 g and mixing speed = 6517.41 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.1 g and extraction time = 2 min); (d) as a function of solid mass to extraction time (solvent concentration = 34.31 and mixing speed = 6517.41 rpm).
Figure 1. Response surface plot for the TBC of olive leaf extract (a) as a function of solvent concentration to solid mass (extraction time = 2 min and mixing speed = 6517.41 rpm); (b) as a function of solvent concentration to time (solid mass = 0.1 g and mixing speed = 6517.41 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.1 g and extraction time = 2 min); (d) as a function of solid mass to extraction time (solvent concentration = 34.31 and mixing speed = 6517.41 rpm).
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Figure 2. Response surface plot for the TBC of grapefruit peel extract (a) as a function of solvent concentration to solid mass (extraction time = 1.125 min and mixing speed = 5000 rpm); (b) as a function of solvent concentration to time (solid mass = 0.1 g and mixing speed = 5000 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.1 g and extraction time = 1.125 min); (d) as a function of solid mass to extraction time (solvent concentration = 42.32 and mixing speed = 5000 rpm).
Figure 2. Response surface plot for the TBC of grapefruit peel extract (a) as a function of solvent concentration to solid mass (extraction time = 1.125 min and mixing speed = 5000 rpm); (b) as a function of solvent concentration to time (solid mass = 0.1 g and mixing speed = 5000 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.1 g and extraction time = 1.125 min); (d) as a function of solid mass to extraction time (solvent concentration = 42.32 and mixing speed = 5000 rpm).
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Figure 3. Response surface plot for the TBC of lemon peel extract (a) as a function of solvent concentration to solid mass (extraction time = 1.34 min and mixing speed = 8999.99 rpm); (b) as a function of solvent concentration to time (solid mass = 0.1 g and mixing speed = 8999.99 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.1 g and extraction time = 1.34 min); (d) as a function of solid mass to extraction time (solvent concentration = 48.09 and mixing speed = 8999.99 rpm).
Figure 3. Response surface plot for the TBC of lemon peel extract (a) as a function of solvent concentration to solid mass (extraction time = 1.34 min and mixing speed = 8999.99 rpm); (b) as a function of solvent concentration to time (solid mass = 0.1 g and mixing speed = 8999.99 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.1 g and extraction time = 1.34 min); (d) as a function of solid mass to extraction time (solvent concentration = 48.09 and mixing speed = 8999.99 rpm).
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Figure 4. Response surface plot for the TBC of mandarin peel extract (a) as a function of solvent concentration to solid mass (extraction time = 0.70 min and mixing speed = 6372.15 rpm); (b) as a function of solvent concentration to time (solid mass = 0.154 g and mixing speed = 6372.15 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.154 g and extraction time = 0.70 min); (d) as a function of solid mass to extraction time (solvent concentration = 30.80 and mixing speed = 6372.15 rpm).
Figure 4. Response surface plot for the TBC of mandarin peel extract (a) as a function of solvent concentration to solid mass (extraction time = 0.70 min and mixing speed = 6372.15 rpm); (b) as a function of solvent concentration to time (solid mass = 0.154 g and mixing speed = 6372.15 rpm); (c) as a function of solvent concentration to mixing speed (solid mass = 0.154 g and extraction time = 0.70 min); (d) as a function of solid mass to extraction time (solvent concentration = 30.80 and mixing speed = 6372.15 rpm).
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Figure 5. FTIR spectra of the relevant residues.
Figure 5. FTIR spectra of the relevant residues.
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Figure 6. 2D AFM- atomic force microscopy images, cross-section profiles and histograms of the olive leaf samples (a) before and (b) after extraction.
Figure 6. 2D AFM- atomic force microscopy images, cross-section profiles and histograms of the olive leaf samples (a) before and (b) after extraction.
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Figure 7. 2D AFM images, cross-section profiles and histograms of the lemon peel samples (a) before and (b) after extraction.
Figure 7. 2D AFM images, cross-section profiles and histograms of the lemon peel samples (a) before and (b) after extraction.
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Table 1. Summary of the HAE-homogenizer-assisted extraction parameters with their units, symbols and levels.
Table 1. Summary of the HAE-homogenizer-assisted extraction parameters with their units, symbols and levels.
Process ParametersUnitsSymbol of the ParametersLevels with the Codes
−101
Solvent Concentration%, v/vX1205080
Solid MassgX20.10.1750.25
Extraction timeminX30.51.252
SpeedrpmX4500070009000
Table 2. Analyzing conditions of individual phenols along with the gradient program applied.
Table 2. Analyzing conditions of individual phenols along with the gradient program applied.
HPLC ConditionsProgram
Model: Agilent 1260 (Agilent, Waldbronn, Germany)Time (min)A (%)B (%)
Colon: Agilent Eclipse Plus C18 RRHD 18 (3 × 5 mm; 1.8 µm)0.01000
Mobile phase: A = Water + % 0.1 formic acid (v/v)0.51000
B = Acetonitrile + % 0.1 formic acid (v/v)7.06040
Detection wavelength: 276 nm7.10100
Flow rate: 1 mL/min8.60100
Column temperature: 40 °C8.71000
Injection volume: 20 µL101000
Table 3. Effects of the independent variables on the TBC-total biophenolic content extraction of the relevant wastes *.
Table 3. Effects of the independent variables on the TBC-total biophenolic content extraction of the relevant wastes *.
X1 (%. v/v)X2 (g)X3 (min)X4 (rpm)TBC (mg-GAE/g-DW)
Olive LeafGrapefruit PeelLemon PeelMandarin Peel
800.11.25700033.06 ± 0.00016.06 ± 0.0014.23 ± 0.00224.06 ± 0.001
200.1750.5700034.41 ± 0.00212.03 ± 0.0026.80 ± 0.00120.70 ± 0.001
500.1750.5500027.94 ± 0.00112.03 ± 0.0017.18 ± 0.00118.80 ± 0.001
500.1752900048.80 ± 0.00014.13 ± 0.00113.37 ± 0.00222.99 ± 0.000
500.11.25900042.56 ± 0.00115.06 ± 0.00316.23 ± 0.00321.90 ± 0.002
800.1751.25900033.37 ± 0.00010.70 ± 0.00114.22 ± 0.00117.18 ± 0.001
500.1751.25700041.05 ± 0.00012.22 ± 0.00213.02 ± 0.00116.89 ± 0.001
500.10.5700026.06 ± 0.00114.06 ± 0.00015.23 ± 0.00015.56 ± 0.002
500.1751.25700040.98 ± 0.00013.50 ± 0.00113.54 ± 0.00115.63 ± 0.001
500.1751.25700042.67 ± 0.00012.65 ± 0.00111.20 ± 0.00115.98 ± 0.002
500.11.25500046.90 ± 0.00122.40 ± 0.00212.40 ± 0.00118.90 ± 0.001
500.12700061.25 ± 0.00215.06 ± 0.00013.23 ± 0.00219.73 ± 0.003
500.250.5700030.02 ± 0.0009.36 ± 0.0017.02 ± 0.0011.93 ± 0.001
500.251.25500035.96 ± 0.00111.09 ± 0.00210.16 ± 0.00114.62 ± 0.001
200.11.25700046.73 ± 0.00117.06 ± 0.00212.23 ± 0.00224.23 ± 0.002
200.1751.25900043.65 ± 0.00112.99 ± 0.00110.60 ± 0.00117.65 ± 0.001
500.1751.25700043.60 ± 0.00111.36 ± 0.00112.65 ± 0.00217.23 ± 0.002
800.1750.5700025.27 ± 0.0019.56 ± 0.0021.46 ± 0.00117.18 ± 0.001
500.1750.5900035.37 ± 0.0018.41 ± 0.0007.18 ± 0.00225.37 ± 0.001
500.1751.25700042.41 ± 0.00110.25 ± 0.00111.18 ± 0.00016.32 ± 0.001
800.251.25700029.09 ± 0.0009.62 ± 0.0013.69 ± 0.00113.69 ± 0.002
200.1751.25500035.56 ± 0.00113.75 ± 0.0018.89 ± 0.00120.13 ± 0.001
800.1752700036.89 ± 0.0019.56 ± 0.0022.80 ± 0.00117.56 ± 0.001
800.1751.25500027.46 ± 0.00213.56 ± 0.0013.65 ± 0.00215.84 ± 0.000
500.251.25900041.09 ± 0.00314.29 ± 0.00210.56 ± 0.00121.62 ± 0.001
200.1752700045.37 ± 0.00113.37 ± 0.0009.75 ± 0.00117.75 ± 0.001
200.251.25700038.56 ± 0.00112.02 ± 0.00110.36 ± 0.0007.96 ± 0.002
500.1752500044.32 ± 0.00213.18 ± 0.00110.99 ± 0.00125.46 ± 0.001
500.252700045.09 ± 0.00111.49 ± 0.00110.49 ± 0.00120.09 ± 0.001
* Data are given as the mean (n = 3) ± standard deviation.
Table 4. Model equations with coded factors derived by Box–Behnken design through RSM-Response Surface Methodology.
Table 4. Model equations with coded factors derived by Box–Behnken design through RSM-Response Surface Methodology.
ResponseEquationR2
TBC (mg-GAE/g-DW)Olive leaf42.14 − 4.93X1 − 3.06X2 + 8.55X3 + 2.23X4 + 1.05X1X2 + 0.17X1X3 − 0.55X1X4 − 5.03X2X3 + 2.37X2X4 − 0.74X3X4 − 5.51X12 + 0.36X22 − 1.60X32 − 1.31X420.9233
Grapefruit peel12.00 − 1.01X1 − 2.65X2 + 0.94X3 − 0.86X4 − 0.35X1X2 − 0.33X1X3 − 0.52X1X4 + 0.28X2X3 + 2.63X2X4 + 1.14X3X4 − 0.16X12 + 2.00X22 − 1.17X32 + 1.25X420.9219
Lemon peel12.32 − 2.38X1 − 1.77X2 + 1.31X3 + 1.57X4 + 0.33 X1X2 − 0.40X1X3 + 2.21X1X4 + 1.37X2X3 − 0.86X2X4 + 0.60X3X4 − 4.35X12 + 0.29X22 − 2.25X32 + 0.24X420.8934
Mandarin peel16.29 − 1.12X1 − 1.06X2 +2.37X3 +2.49X4 − 1.64X1X2 +0.45X1X3 + 0.52X1X4 − 2.82X2X3 + 1.00X2X4 + 0.46X3X4 − 2.24X12 + 1.12X22 + 3.50X32 + 2.33X420.9055
Table 5. Analysis of variance test using Design-Expert 10.0.4 for the HAE of TBC in the relevant wastes.
Table 5. Analysis of variance test using Design-Expert 10.0.4 for the HAE of TBC in the relevant wastes.
SourceSum of SquaresdfMean SquareF Valuep-Value
Prob > F
Olive leafModel1687.7414120.5512.04<0.0001
X1-Solvent concentration291.401291.4029.11<0.0001
X2-Solid Mass112.591112.5911.250.0047
X3-Extraction time877.761877.7687.69<0.0001
X4-Speed59.43159.435.940.0288
X1X24.4114.410.44060.5176
X1X30.111110.11110.01110.9176
X1X41.2011.200.11980.7344
X2X3101.171101.1710.110.0067
X2X422.40122.402.240.1568
X3X42.1812.180.21770.6480
X12196.741196.7419.650.0006
X220.840110.84010.08390.7763
X3216.33116.331.630.2223
X4211.21111.211.120.3079
Residual140.141410.01
Lack of Fit135.121013.5110.760.0175
Pure Error5.0241.26
Cor Total1827.8828
Grape fruitModel203.891414.5611.80<0.0001
X112.33112.339.990.0069
X284.48184.4868.45<0.0001
X310.69110.698.660.0107
X49.0519.057.330.0170
X1X20.490010.49000.39700.5388
X1X30.444410.44440.36010.5580
X1X41.1011.100.88930.3617
X2X30.321110.32110.26020.6179
X2X427.74127.7422.480.0003
X3X45.2215.224.230.0588
X120.173510.17350.14060.7133
X2225.91125.9120.990.0004
X328.8518.857.170.0180
X4210.13110.138.210.0125
Residual17.28141.23
Lack of Fit11.08101.110.71530.6969
Pure Error6.2041.55
Cor Total221.1728
Lemon peelModel410.791429.348.380.0001
X157.89157.8916.540.0012
X254.19154.1915.480.0015
X317.10117.104.890.0442
X429.73129.738.490.0113
X1X20.340310.34030.09720.7598
X1X30.655310.65530.18720.6718
X1X436.00136.0010.290.0063
X2X37.4717.472.130.1661
X2X44.3514.351.240.2835
X3X40.226810.22680.06480.8028
X12126.781126.7836.22<0.0001
X2215.87115.874.530.0515
X3227.58127.587.880.0140
X426.3316.331.810.1999
Residual49.00143.50
Lack of Fit47.39104.7411.770.0148
Pure Error1.6140.4025
Cor Total459.7928
Mandarin peelModel390.341427.889.58<0.0001
X114.94114.945.130.0399
X213.50113.504.640.0492
X367.65167.6523.240.0003
X474.42174.4225.570.0002
X1X210.80110.803.710.0746
X1X30.818610.81860.28120.6042
X1X41.0911.090.37340.5509
X2X331.79131.7910.920.0052
X2X44.0014.001.370.2607
X3X40.837210.83720.28760.6002
X1232.63132.6311.210.0048
X228.2018.202.820.1154
X3279.47179.4727.300.0001
X4235.33135.3312.140.0037
Residual40.76142.91
Lack of Fit39.24103.9210.370.0187
Pure Error1.5140.3784
Cor Total431.0928
Table 6. Verification results of the optimum conditions for the HAE of each residue.
Table 6. Verification results of the optimum conditions for the HAE of each residue.
ItemOptimum Extraction ConditionsMaximum
TBC (mg-GAE/g-DW)
ExperimentalPredictedError%
Olive leafX1 (%. v/v)34.2458.6259.160.92
X2 (g)0.100
X3 (min)2.00
X4 (rpm)6522.2
Grapefruit peelX142.3321.1221.461.61
X20.100
X31.125
X45000
Lemon peelX133.6216.8917.071.07
X20.100
X31.282
X45007
Mandarin peelX142.527.7928.944.14
X20.100
X31.99
X48772
Table 7. Major bioactive compounds derived from the selected byproducts under optimal extraction conditions *.
Table 7. Major bioactive compounds derived from the selected byproducts under optimal extraction conditions *.
ItemRt (min)Major BiophenolsConcentration (mg/g-DW)
Olive leaf5.910Oleuropein79.26 ± 0.001 a
Grapefruit peel5.400Naringin36.10 ± 0.001 b
Lemon peel5.400Naringin10.33 ± 0.002 c
Mandarin peel5.400Naringin25.56 ± 0.001 d
* Data are given as the mean (n = 3) ± standard deviation. Lines not sharing a common (a, b, c and d) letter indicate significant differences at p < 0.001.
Table 8. Antioxidant properties of the relevant residues *.
Table 8. Antioxidant properties of the relevant residues *.
Item.TBC (mg-GAE/g-DW)DPPH (mg-TAEC/g-DW)CUPRAC (mg-TAEC/g-DW)ABTS (mg-TAEC/g-DW)
Olive leaf16.86 ± 0.004 a20.39 ± 0.001 a64.01 ± 0.003 a60.12 ± 0.001 a
Grapefruit peel5.39 ± 0.002 b1.78 ± 0.001 b20.61 ± 0.001 b555.02 ± 0.002 b
Lemon peel3.44 ± 0.001 c2.36 ± 0.001 c24.05 ± 0.002 c69.71 ± 0.002 c
Mandarin peel8.96 ± 0.003 d5.58 ± 0.002 d32.34 ± 0.003 d326.76 ± 0.004 d
* Data are given as the mean (n = 3) ± standard deviation. Lines not sharing a common letter (a, b, c and d) indicate significant differences at p < 0.001.

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