# Comparison of PSA to Moringa Oleifera Seed Protein as Sorbent in QuEChERS: A Response Surface Methodology Optimization for Extraction of Some Endocrine Disrupting Chemicals in Food

Authors: Thapelo Ramalepe; Heidi Richards; Hlanganani Tutu; Yannick Nuapia; Eutilério Chaúque; Imasiku Nyambe; Luke Chimuka
Journal: Journal of Chemistry
 (2022)

---

## Abstract

This work aimed at optimizing the QuEChERS method with PSA and then comparing it with Moringa Oleifera seed protein as a clean-up sorbent for the extraction of endocrine-disrupting chemicals. The response surface methodology approach was used in the optimization. A design of experiment (DoE) was used to investigate the effect of the sample mass (0.5–3 g), centrifuge speed (3400–4000 rpm) and time (5–20 min), the mass ofNaCl and MgSO41−3g, and solvent extraction volume (5–10 mL). The analysis was done using GC-ECD and GC × GC TOFMS. The PSA method which was later replaced with Moringa Oleifera seed protein presented optimal values of 3 g of sample, 150 mg PSA, 4000 rpm for 6 min centrifuge conditions, including 2 g NaCl and 2 g MgSO4 extracted in 10 mL methanol, respectively. Moringa Oleifera seed protein gave better selectivity, and the detection limits ranged between 0.16 and 1.77 μg kg−1 with RSD values ≤13.32%, respectively. Moreover, recoveries were between 76.2 ± 0.85% and 105.2 ± 2.24%. Application of the developed method in food samples detected some EDCs. This study has shown that Moringa Oleifera seed protein is a promising alternative to PSA in the clean-up of food-related samples using the QuEChERS approach.


---

## Body

## 1. Introduction

The clean-up activity of extracts for chromatographic analysis is the first and far-most critical step for accurate quantification of endocrine disrupting chemicals (EDCs) in fruits, vegetables, meat, and fish [1]. Over the past 40 years, endocrine-linked diseases and complications have been significantly increasing [2]. This is associated with the presence of many man-made EDCs in the environment that eventually make their way into foodstuff [3,4]. EDCs are categorized as man-made or naturally occurring [5,6]. Man-made EDCs include some chemicals from different classes like plasticizers, pesticides, pharmaceutical compounds, and preservatives often found in detected fruits and vegetables [7]. They have been linked to interfering with the reproductive system [8,9]. For example, exposure to DDT, which is man-made, contributes to an earlier start of puberty and as females get older, the exposure has the potential to extend menstrual cycles and also speed up menopause [10].In infants and children, the target organs for man-made EDCs are the brain and prostate glands [11]. As a result, it causes neurological and immune system defects [10,12,13]. Other man-made EDCs include bisphenol A (BPA), dioxins, polybrominated biphenyls (PBBs), and polychlorinated biphenyls (PCBs) [14–16]. Other endocrine-related health effects include infertility, attention deficit hyperactivity disorder (ADHD), obesity and diabetes, and cancer [17, 18]. Some of these chemical pollutants can affect the hormonal system and interfere with important developmental processes in humans [19].EDCs affect humans by mimicking the normal binding of different hormones in the body, e.g., melatonin hormone in the pineal gland, prolactin and oxytocin in the pituitary gland, insulin and glucagon in the pancreas, estrogen, and androgen in the female reproductive system, among others [8, 20]. The mimicry complicates the different natural body signals [21, 22]. This occurs by tricking the cell to accept the binding of the EDC to the hormone while causing an identical cellular response from the host as normal binding [20, 23].There are various analytical techniques for the extraction of EDCs in food including Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS), Soxhlet extraction, ultrasonic-assisted extraction (UAE), pressurized liquid extraction (PLE), pressurized hot water extraction (PHWE) and supercritical fluid (SFE) extraction [24, 25]. A conventional Soxhlet extraction has been used for the extraction of EDCs with acceptable recoveries [24]. However, the limitation of this technique is that it is time-consuming as the reflux must be done for 24 hours, and it requires a large amount of solvent which has negative disposal effects on the environment [26–28]. UAE technique overcomes the Soxhlet extraction disadvantages of time consumption and large volume usage; however, its limitation is the repeatability of the method as the ultrasound energy that transfers from the ultrasonic bath to the sample container is dependent on the properties of the bath. As a result, a probe is needed to control the ultrasound frequency and amplitude for acceptable reproducibility [29, 30]. The elevated temperatures used in PLE may degrade unstable EDCs [31, 32]. Also, the rate of diffusion of the solvent to the sample increases as the solvent volume is increased, which is not ideal when the solvent volume is increased above the optimized amount [33]. QuEChERS extraction technique is one of the alternatives for food because it is not labor intensive and also uses a less organic solvent to produce optimal recoveries [34, 35]. This technique has been reported in the literature for various analyte applications [36–39]. The common clean-up sorbents in QuEChERS have been synthetic ones sometimes used in combination to improve matrix clean-up in samples.Biosorbents though are receiving much attention in solid-liquid extraction in analytical chemistry [40] yet very little has been reported in the QuEChERS technique. It is anticipated that some of these biosorbents will offer as alternative commercial clean-up sorbents in the QuEChERS technique as demonstrated in this paper.This study aimed to optimize the QuEChERS method by keeping the processes greener, cheaper, and more selective than the existing traditional QuEChERS. This was done using a response surface methodology (RSM) as the optimization tool and using Moringa seed protein as biosorbent. Furthermore, the performance of the optimized PSA approach was compared with the water-soluble Moringa Oleifera seed protein as a selective clean-up sorbent. The protein was used as a cleaning sorbent instead of the seed powder because the protein is purer thus making it more selective towards unwanted matrix. The seed protein is also not soluble in organic solvents like methanol, unlike the seed powder that has soluble components.


## 2. Materials and Methods

### 2.1. Materials and Reagents

Seven EDCs: dimethyl phthalate, 4-nonylphenol,β−BHC, diethyl phthalate, 4,4’−DDT,4,4’−DDDand4,4’−DDE with purities of over 95% were purchased from Sigma-Aldrich, Johannesburg, South Africa. Stock and standard solutions were prepared using HPLC-grade methanol from Merck (Johannesburg, South Africa). Anhydrous magnesium sulfate and sodium chloride were purchased from Sigma-Aldrich. For clean-up, primary secondary amine (PSA) bonded with silica was purchased from Sigma-Aldrich (Johannesburg, South Africa). Nitrogen gas (99.999%) was used for the evaporation of extracts to the required volume. Other chemicals used were of analytical grade. Further, certified reference materials (dimethyl phthalate, diethyl phthalate, 4-nonylphenol, 4,4’-DDT, 4,4’-DDD, and 4,4’-DDE) were bought from Dr. Ehrenstorfer GmbH of LGC chemicals (Augsburg, Germany).


### 2.2. GC-ECD Conditions Used in the Optimization

Gas chromatography (GC) 7890A (Agilent Technologies, DE, USA) equipped with an electron capture detector (ECD) with a WCOT fused silica capillary column (30 × 0.25 mm ID, 0.25 m film thickness) was used in the optimization. The software used for analysis was Chemstation version B.04.03. The GC method was programmed to get the best separation of the EDCs of interest as follows: Oven 60°C for 5 min, increased to 150°C at a rate of10°C min-1, further increased to 200°C at a rate of 30°C min-1 then lastly to 300°C at 15°C min-1 for 10 min. Nitrogen gas was used for GC-ECD with a flow rate of 1 mL min−1. The make-up nitrogen flow in the detector was set at 25 mL min-1. The temperature of the injector operating in split less mode was held at 300°C and ECD temperature was 25°C. The volume injected was 10 μ L using manual injection.


### 2.3. GC × GC TOFMS Conditions Used in the Applications

A LECO GC-MS with a capacity for a GC × GC equipped with a TOFMS detector 7890B (LECO Corp., St Joseph, MI, USA) was employed for confirmation of the EDC identities. A 7683 series injector (Agilent Technologies, DE, USA) was used for the GC × GC TOFMS. ChromaTOF ® was used as the analysis software. The mass spectrometer was programmed with a transfer line temperature of 350°C; ion source temperature of 250°C and multiplier voltage of 1450 V. A programmed temperature vaporization injector operating in splitless mode was employed. The volume injected was 10μL, split flow 50mL min−1 and injection time: 0.50 min, injection flow: 100 ml min−1. Helium was used as carrier gas at a flow rate of 1 mL min−1. Ion trap mass detection was operated in full scan mode from 50 to 500 amu. The oven temperature was from 50°C increased to 150°C at 10°C ramp rate for 1 min followed by an increase to 300°C at a rate of 5°C for 1 min.


### 2.4. Preparation of Standards and Calibration Curve

Solutions of1000mg.L−1 of each EDC were prepared in 25mL volumetric flasks by dissolving 25 mg of each EDC in their respective volumetric flasks and filling it to the mark with methanol. The stock solutions were stored at −21°C. The 10mg.L−1each of working standard solution of the seven chemicals was prepared by withdrawing 100μL each prepared stock solution into a 10mL volumetric flask and diluting to the mark with methanol. The prepared mixed solutions were then stored at 4°C until analysis. From the 10 mg.L−1 working standard solution, calibration standard solutions of EDCs of different concentration ranging from 0.2 mg L−1 to 1.0 mg.L−1 was prepared. Five-point calibration standards were prepared.


### 2.5. Moringa Oleifera Seed Protein Extraction

Moringa Oleifera seed was collected from a Moringa farm in Limpopo, South Africa. The seed was manually deshelled and cleaned with distilled water to remove ground dirt. The seed was then left to dry in the oven overnight at 40°C. After drying, the seed was ground using a domestic blender and further powdered using a mortar and pestle, and sieved using a 0.20 mm sieve to obtain uniform particle size. The powder was then stored in the refrigerator at 4°C.The water-soluble protein was extracted using a modified method from the literature [41]. Briefly, the seed powder was first mixed with hexane and petroleum ether (37 w/v%) and stirred for 30 min using a magnetic stirrer to dissolve fats, oils, or waxes constituents. The protein was then extracted using distilled water. The protein was used as a cleaning sorbent instead of the seed powder because the protein is purer and more active, thus making it more selective towards unwanted matrix. The seed powder is also not soluble in organic solvents like methanol, unlike the protein. The experimental illustration of the various Moringa Oleifera products is shown (Figure 1).Figure 1
Schematic illustration ofMoringa Oleifera seed protein extracted product.


### 2.6. Response Surface Methodology (RSM)

#### 2.6.1. Design of Experiments: Screening and Optimization

To get the best recovery in the application of QuEChERS to food samples, the sample mass, cleaning sorbent mass, speed and time of centrifuge, amount ofNaCl and MgSO4, solvent type, and solvent extraction volume was screened using L8 (3 levels with 7 factors, 6 responses, and 21 runs) linear model. The selected parameters were optimized by a central composite orthogonal (CCO) design which is composed of a full or fractional factorial design (Quadratic model, 17 runs). The statistical tool used for the design of the experiment is MODDE Pro 13.0.1 (Sartorius Stedim Biotech, Malmö, Sweden). Three center points replicates were employed for the design and for each experimental run. The screened factors are displayed in Table 1.Table 1
Parameters screened using L8 linear model.
QuEChERS parameterMinimumMaximumFactor typeSample mass0.5 g3 gQuantitativeCentrifuge time5 min20 minQuantitativeCentrifuge speed3400 rpm4000 rpmQuantitativeAmount of NaCl1 g3 gQuantitativeAmount of MgSO41 g3 gQuantitativeSolvent volume5 mL10 mLQuantitativeSolvent typeMethanol, acetone and acetonitrileQualitative


#### 2.6.2. QuEChERS Extraction Method

The QuEChERS method was done using the procedure by [42], with modifications where necessary (Figure 2). The method was first performed following different screening conditions as displayed in Table 1 (a full factorial design was used as a screening tool). Each run was done following the combination shown in Table 2. Varying amounts of sample (Table 2) were weighed in a 50 mL centrifuge tube and spiked with 500 μL of 200 μg L−1 standard mixture to give varying concentrations in μg kg−1 . The spiked samples were allowed to stand for 30 min to allow the standards to integrate into the samples. Varying volumes of solvent were added according to Table 2, and the sample was vortex-shaken for 1 min. This was followed by addition of sodium chloride and anhydrous magnesium sulfate and the mixture was vortex-shaken vigorously for 1 min and then centrifuged for varying min under different speeds (Table 2). After the centrifuge, the supernatant was transferred into a second centrifuge tube for the clean-up process with anhydrous magnesium sulfate and PSA. PSA was used for screening purposes. The solution was further vortexed for 30 sec. The solution was then centrifuged for 5 min under the same conditions and filtered using first a 0.45 μm PTFE syringe filters followed by 0.22 μm PTFE syringe filters and injected into the GC-ECD for analysis.Figure 2
Schematic QuEChERS experimental procedure.Table 2
Experimental responses of independent factors for screening.
Run orderSample mass/gCentrifuge speed/rpmCentrifuge time/minNaCl mass/gMgSO4 mass/gSolvent volume/mLSolvent typeDMPDEP4-Nonylphenol4,4’-DDT4,4’-DDE4,4’-DDD50.534005115Methanol44.99123.368.8623.24123.5132.080.5370012.5225Acetone76.85132.588.6537.0496.6596.37180.5400020335Acetonitrile44.2472.6388.6276.8587.64130.721.7534005225Acetonitrile28.8868.8566.9655.67147.966.61121.75370012.5335Methanol32.6542.2584.5642.5576.68101.9151.75400020115Acetone67.65142.6136.6971.04119,7145.6103340012.5135Acetone98.89108.1123.2513.66176.6465.55213370020215Acetonitrile11.9264.56105.9865.9567.32127.6216340005325Methanol12.658.4675.5559.6746.95101.0590.53400203210Acetone45.6856,6945.9888.3748.1577.79200.5370051310Acetonitrile47.0664.5632.9774.91119.968.5510.5400012.52110Methanol16.9545.5645.575.6838.69140.2331.75340012.53110Acetonitrile14.6526.6538.6689.5152.2390.58191.753700201210Methanol22.8943.56105.4579.9871.2373.09141.75400052310Acetone105.2665.5989.6565.8754.4461.22433400202310Methanol26.6820.56102.2365.6669.9946.9673370053110Acetone56.6665.6458.6988.9636.9655.56113400012.51210Acetonitrile42.6332.2271.25108.6656.6875.6661.75370012.5227,5Methanol23.3649.6866.6959.6939.6446.96171.75370012.5227,5Methanol21.9933.6565.5855.2629.3886.66131.75370012.5227,5Methanol33.139.6587.8867.8536.6688.89Once all the parameters were screened and the important ones determined, the optimization process was done with reduced runs using the most important QuEChERS parameters. The optimization runs were reduced from 21 to 17 and the data is displayed in Table3. The optimization tool employed has the advantage to determine the interaction between the independent quantitative variables, namely sample mass, speed, and time of centrifuge. It then models the system mathematically. This saved time and cost by reducing the number of trials and experiments that could have been performed when using the traditional way of optimization. The traditional way entails dealing with one parameter at a time while keeping the rest of the parameters constant. At optimal PSA amounts, the PSA was then replaced with the same amount of Moringa oleifera protein.Table 3
Experimental design for the optimization model.
Exp NoRun orderSample mass/gCentrifuge time/minCentrifuge speed/rpmDimethyl phthalateDiethyl phthalate4-Nonylphenol4,4’-DDT4,4’-DDD4.4’-DDE140.55340042.1182.3660.4120.4171.3869.662635340049.8154.1141.9180.4157.8574.213120.520340016.9621.2298.4166.2164.2441.2248320340024.5871.1145.2788.2272.6477.71530.55400058.1155.2172.2961.6648.6596.3461135400028.9677.4185.6972.7785.2171.027150.520400041.2133.24114.2269.9474.2149.21817320400035.9186.9676.9636.66100.0548.769160.113370055.5481.2179.6812.166.2171.281093,413370058.64101.2371.2639.6588.5756.651121.752370035.6530.2171.2148.6561.2488.8512141.7523370012.7131.2480.2167.4161.2163.331311.7512.5330019.8922.2131.2886.2142.8858.8714101.7512.5410032.8854.2188.2752.4764.5271.261551.7512.5370034.5638.2771.4151.4837.5872.2416131.7512.5370033.2441.2763.4155.2139.2169.711771.7512.5370035.5950.4465.2250.2842.8172.36Appropriate quality assurance procedures were carried out to ensure reliability and reproducibility of the results such as spiking and replicate extraction of samples. Limit of detection was also calculated as a signal-to-noise ratio of three times. The CRMs were used for QuEChERS method validation. The material certificate of analysis was designed in accordance with ISO 17034 and ISO Guide 31.


## 2.1. Materials and Reagents

Seven EDCs: dimethyl phthalate, 4-nonylphenol,β−BHC, diethyl phthalate, 4,4’−DDT,4,4’−DDDand4,4’−DDE with purities of over 95% were purchased from Sigma-Aldrich, Johannesburg, South Africa. Stock and standard solutions were prepared using HPLC-grade methanol from Merck (Johannesburg, South Africa). Anhydrous magnesium sulfate and sodium chloride were purchased from Sigma-Aldrich. For clean-up, primary secondary amine (PSA) bonded with silica was purchased from Sigma-Aldrich (Johannesburg, South Africa). Nitrogen gas (99.999%) was used for the evaporation of extracts to the required volume. Other chemicals used were of analytical grade. Further, certified reference materials (dimethyl phthalate, diethyl phthalate, 4-nonylphenol, 4,4’-DDT, 4,4’-DDD, and 4,4’-DDE) were bought from Dr. Ehrenstorfer GmbH of LGC chemicals (Augsburg, Germany).


## 2.2. GC-ECD Conditions Used in the Optimization

Gas chromatography (GC) 7890A (Agilent Technologies, DE, USA) equipped with an electron capture detector (ECD) with a WCOT fused silica capillary column (30 × 0.25 mm ID, 0.25 m film thickness) was used in the optimization. The software used for analysis was Chemstation version B.04.03. The GC method was programmed to get the best separation of the EDCs of interest as follows: Oven 60°C for 5 min, increased to 150°C at a rate of10°C min-1, further increased to 200°C at a rate of 30°C min-1 then lastly to 300°C at 15°C min-1 for 10 min. Nitrogen gas was used for GC-ECD with a flow rate of 1 mL min−1. The make-up nitrogen flow in the detector was set at 25 mL min-1. The temperature of the injector operating in split less mode was held at 300°C and ECD temperature was 25°C. The volume injected was 10 μ L using manual injection.


## 2.3. GC × GC TOFMS Conditions Used in the Applications

A LECO GC-MS with a capacity for a GC × GC equipped with a TOFMS detector 7890B (LECO Corp., St Joseph, MI, USA) was employed for confirmation of the EDC identities. A 7683 series injector (Agilent Technologies, DE, USA) was used for the GC × GC TOFMS. ChromaTOF ® was used as the analysis software. The mass spectrometer was programmed with a transfer line temperature of 350°C; ion source temperature of 250°C and multiplier voltage of 1450 V. A programmed temperature vaporization injector operating in splitless mode was employed. The volume injected was 10μL, split flow 50mL min−1 and injection time: 0.50 min, injection flow: 100 ml min−1. Helium was used as carrier gas at a flow rate of 1 mL min−1. Ion trap mass detection was operated in full scan mode from 50 to 500 amu. The oven temperature was from 50°C increased to 150°C at 10°C ramp rate for 1 min followed by an increase to 300°C at a rate of 5°C for 1 min.


## 2.4. Preparation of Standards and Calibration Curve

Solutions of1000mg.L−1 of each EDC were prepared in 25mL volumetric flasks by dissolving 25 mg of each EDC in their respective volumetric flasks and filling it to the mark with methanol. The stock solutions were stored at −21°C. The 10mg.L−1each of working standard solution of the seven chemicals was prepared by withdrawing 100μL each prepared stock solution into a 10mL volumetric flask and diluting to the mark with methanol. The prepared mixed solutions were then stored at 4°C until analysis. From the 10 mg.L−1 working standard solution, calibration standard solutions of EDCs of different concentration ranging from 0.2 mg L−1 to 1.0 mg.L−1 was prepared. Five-point calibration standards were prepared.


## 2.5. Moringa Oleifera Seed Protein Extraction

Moringa Oleifera seed was collected from a Moringa farm in Limpopo, South Africa. The seed was manually deshelled and cleaned with distilled water to remove ground dirt. The seed was then left to dry in the oven overnight at 40°C. After drying, the seed was ground using a domestic blender and further powdered using a mortar and pestle, and sieved using a 0.20 mm sieve to obtain uniform particle size. The powder was then stored in the refrigerator at 4°C.The water-soluble protein was extracted using a modified method from the literature [41]. Briefly, the seed powder was first mixed with hexane and petroleum ether (37 w/v%) and stirred for 30 min using a magnetic stirrer to dissolve fats, oils, or waxes constituents. The protein was then extracted using distilled water. The protein was used as a cleaning sorbent instead of the seed powder because the protein is purer and more active, thus making it more selective towards unwanted matrix. The seed powder is also not soluble in organic solvents like methanol, unlike the protein. The experimental illustration of the various Moringa Oleifera products is shown (Figure 1).Figure 1
Schematic illustration ofMoringa Oleifera seed protein extracted product.


## 2.6. Response Surface Methodology (RSM)

### 2.6.1. Design of Experiments: Screening and Optimization

To get the best recovery in the application of QuEChERS to food samples, the sample mass, cleaning sorbent mass, speed and time of centrifuge, amount ofNaCl and MgSO4, solvent type, and solvent extraction volume was screened using L8 (3 levels with 7 factors, 6 responses, and 21 runs) linear model. The selected parameters were optimized by a central composite orthogonal (CCO) design which is composed of a full or fractional factorial design (Quadratic model, 17 runs). The statistical tool used for the design of the experiment is MODDE Pro 13.0.1 (Sartorius Stedim Biotech, Malmö, Sweden). Three center points replicates were employed for the design and for each experimental run. The screened factors are displayed in Table 1.Table 1
Parameters screened using L8 linear model.
QuEChERS parameterMinimumMaximumFactor typeSample mass0.5 g3 gQuantitativeCentrifuge time5 min20 minQuantitativeCentrifuge speed3400 rpm4000 rpmQuantitativeAmount of NaCl1 g3 gQuantitativeAmount of MgSO41 g3 gQuantitativeSolvent volume5 mL10 mLQuantitativeSolvent typeMethanol, acetone and acetonitrileQualitative


### 2.6.2. QuEChERS Extraction Method

The QuEChERS method was done using the procedure by [42], with modifications where necessary (Figure 2). The method was first performed following different screening conditions as displayed in Table 1 (a full factorial design was used as a screening tool). Each run was done following the combination shown in Table 2. Varying amounts of sample (Table 2) were weighed in a 50 mL centrifuge tube and spiked with 500 μL of 200 μg L−1 standard mixture to give varying concentrations in μg kg−1 . The spiked samples were allowed to stand for 30 min to allow the standards to integrate into the samples. Varying volumes of solvent were added according to Table 2, and the sample was vortex-shaken for 1 min. This was followed by addition of sodium chloride and anhydrous magnesium sulfate and the mixture was vortex-shaken vigorously for 1 min and then centrifuged for varying min under different speeds (Table 2). After the centrifuge, the supernatant was transferred into a second centrifuge tube for the clean-up process with anhydrous magnesium sulfate and PSA. PSA was used for screening purposes. The solution was further vortexed for 30 sec. The solution was then centrifuged for 5 min under the same conditions and filtered using first a 0.45 μm PTFE syringe filters followed by 0.22 μm PTFE syringe filters and injected into the GC-ECD for analysis.Figure 2
Schematic QuEChERS experimental procedure.Table 2
Experimental responses of independent factors for screening.
Run orderSample mass/gCentrifuge speed/rpmCentrifuge time/minNaCl mass/gMgSO4 mass/gSolvent volume/mLSolvent typeDMPDEP4-Nonylphenol4,4’-DDT4,4’-DDE4,4’-DDD50.534005115Methanol44.99123.368.8623.24123.5132.080.5370012.5225Acetone76.85132.588.6537.0496.6596.37180.5400020335Acetonitrile44.2472.6388.6276.8587.64130.721.7534005225Acetonitrile28.8868.8566.9655.67147.966.61121.75370012.5335Methanol32.6542.2584.5642.5576.68101.9151.75400020115Acetone67.65142.6136.6971.04119,7145.6103340012.5135Acetone98.89108.1123.2513.66176.6465.55213370020215Acetonitrile11.9264.56105.9865.9567.32127.6216340005325Methanol12.658.4675.5559.6746.95101.0590.53400203210Acetone45.6856,6945.9888.3748.1577.79200.5370051310Acetonitrile47.0664.5632.9774.91119.968.5510.5400012.52110Methanol16.9545.5645.575.6838.69140.2331.75340012.53110Acetonitrile14.6526.6538.6689.5152.2390.58191.753700201210Methanol22.8943.56105.4579.9871.2373.09141.75400052310Acetone105.2665.5989.6565.8754.4461.22433400202310Methanol26.6820.56102.2365.6669.9946.9673370053110Acetone56.6665.6458.6988.9636.9655.56113400012.51210Acetonitrile42.6332.2271.25108.6656.6875.6661.75370012.5227,5Methanol23.3649.6866.6959.6939.6446.96171.75370012.5227,5Methanol21.9933.6565.5855.2629.3886.66131.75370012.5227,5Methanol33.139.6587.8867.8536.6688.89Once all the parameters were screened and the important ones determined, the optimization process was done with reduced runs using the most important QuEChERS parameters. The optimization runs were reduced from 21 to 17 and the data is displayed in Table3. The optimization tool employed has the advantage to determine the interaction between the independent quantitative variables, namely sample mass, speed, and time of centrifuge. It then models the system mathematically. This saved time and cost by reducing the number of trials and experiments that could have been performed when using the traditional way of optimization. The traditional way entails dealing with one parameter at a time while keeping the rest of the parameters constant. At optimal PSA amounts, the PSA was then replaced with the same amount of Moringa oleifera protein.Table 3
Experimental design for the optimization model.
Exp NoRun orderSample mass/gCentrifuge time/minCentrifuge speed/rpmDimethyl phthalateDiethyl phthalate4-Nonylphenol4,4’-DDT4,4’-DDD4.4’-DDE140.55340042.1182.3660.4120.4171.3869.662635340049.8154.1141.9180.4157.8574.213120.520340016.9621.2298.4166.2164.2441.2248320340024.5871.1145.2788.2272.6477.71530.55400058.1155.2172.2961.6648.6596.3461135400028.9677.4185.6972.7785.2171.027150.520400041.2133.24114.2269.9474.2149.21817320400035.9186.9676.9636.66100.0548.769160.113370055.5481.2179.6812.166.2171.281093,413370058.64101.2371.2639.6588.5756.651121.752370035.6530.2171.2148.6561.2488.8512141.7523370012.7131.2480.2167.4161.2163.331311.7512.5330019.8922.2131.2886.2142.8858.8714101.7512.5410032.8854.2188.2752.4764.5271.261551.7512.5370034.5638.2771.4151.4837.5872.2416131.7512.5370033.2441.2763.4155.2139.2169.711771.7512.5370035.5950.4465.2250.2842.8172.36Appropriate quality assurance procedures were carried out to ensure reliability and reproducibility of the results such as spiking and replicate extraction of samples. Limit of detection was also calculated as a signal-to-noise ratio of three times. The CRMs were used for QuEChERS method validation. The material certificate of analysis was designed in accordance with ISO 17034 and ISO Guide 31.


## 2.6.1. Design of Experiments: Screening and Optimization

To get the best recovery in the application of QuEChERS to food samples, the sample mass, cleaning sorbent mass, speed and time of centrifuge, amount ofNaCl and MgSO4, solvent type, and solvent extraction volume was screened using L8 (3 levels with 7 factors, 6 responses, and 21 runs) linear model. The selected parameters were optimized by a central composite orthogonal (CCO) design which is composed of a full or fractional factorial design (Quadratic model, 17 runs). The statistical tool used for the design of the experiment is MODDE Pro 13.0.1 (Sartorius Stedim Biotech, Malmö, Sweden). Three center points replicates were employed for the design and for each experimental run. The screened factors are displayed in Table 1.Table 1
Parameters screened using L8 linear model.
QuEChERS parameterMinimumMaximumFactor typeSample mass0.5 g3 gQuantitativeCentrifuge time5 min20 minQuantitativeCentrifuge speed3400 rpm4000 rpmQuantitativeAmount of NaCl1 g3 gQuantitativeAmount of MgSO41 g3 gQuantitativeSolvent volume5 mL10 mLQuantitativeSolvent typeMethanol, acetone and acetonitrileQualitative


## 2.6.2. QuEChERS Extraction Method

The QuEChERS method was done using the procedure by [42], with modifications where necessary (Figure 2). The method was first performed following different screening conditions as displayed in Table 1 (a full factorial design was used as a screening tool). Each run was done following the combination shown in Table 2. Varying amounts of sample (Table 2) were weighed in a 50 mL centrifuge tube and spiked with 500 μL of 200 μg L−1 standard mixture to give varying concentrations in μg kg−1 . The spiked samples were allowed to stand for 30 min to allow the standards to integrate into the samples. Varying volumes of solvent were added according to Table 2, and the sample was vortex-shaken for 1 min. This was followed by addition of sodium chloride and anhydrous magnesium sulfate and the mixture was vortex-shaken vigorously for 1 min and then centrifuged for varying min under different speeds (Table 2). After the centrifuge, the supernatant was transferred into a second centrifuge tube for the clean-up process with anhydrous magnesium sulfate and PSA. PSA was used for screening purposes. The solution was further vortexed for 30 sec. The solution was then centrifuged for 5 min under the same conditions and filtered using first a 0.45 μm PTFE syringe filters followed by 0.22 μm PTFE syringe filters and injected into the GC-ECD for analysis.Figure 2
Schematic QuEChERS experimental procedure.Table 2
Experimental responses of independent factors for screening.
Run orderSample mass/gCentrifuge speed/rpmCentrifuge time/minNaCl mass/gMgSO4 mass/gSolvent volume/mLSolvent typeDMPDEP4-Nonylphenol4,4’-DDT4,4’-DDE4,4’-DDD50.534005115Methanol44.99123.368.8623.24123.5132.080.5370012.5225Acetone76.85132.588.6537.0496.6596.37180.5400020335Acetonitrile44.2472.6388.6276.8587.64130.721.7534005225Acetonitrile28.8868.8566.9655.67147.966.61121.75370012.5335Methanol32.6542.2584.5642.5576.68101.9151.75400020115Acetone67.65142.6136.6971.04119,7145.6103340012.5135Acetone98.89108.1123.2513.66176.6465.55213370020215Acetonitrile11.9264.56105.9865.9567.32127.6216340005325Methanol12.658.4675.5559.6746.95101.0590.53400203210Acetone45.6856,6945.9888.3748.1577.79200.5370051310Acetonitrile47.0664.5632.9774.91119.968.5510.5400012.52110Methanol16.9545.5645.575.6838.69140.2331.75340012.53110Acetonitrile14.6526.6538.6689.5152.2390.58191.753700201210Methanol22.8943.56105.4579.9871.2373.09141.75400052310Acetone105.2665.5989.6565.8754.4461.22433400202310Methanol26.6820.56102.2365.6669.9946.9673370053110Acetone56.6665.6458.6988.9636.9655.56113400012.51210Acetonitrile42.6332.2271.25108.6656.6875.6661.75370012.5227,5Methanol23.3649.6866.6959.6939.6446.96171.75370012.5227,5Methanol21.9933.6565.5855.2629.3886.66131.75370012.5227,5Methanol33.139.6587.8867.8536.6688.89Once all the parameters were screened and the important ones determined, the optimization process was done with reduced runs using the most important QuEChERS parameters. The optimization runs were reduced from 21 to 17 and the data is displayed in Table3. The optimization tool employed has the advantage to determine the interaction between the independent quantitative variables, namely sample mass, speed, and time of centrifuge. It then models the system mathematically. This saved time and cost by reducing the number of trials and experiments that could have been performed when using the traditional way of optimization. The traditional way entails dealing with one parameter at a time while keeping the rest of the parameters constant. At optimal PSA amounts, the PSA was then replaced with the same amount of Moringa oleifera protein.Table 3
Experimental design for the optimization model.
Exp NoRun orderSample mass/gCentrifuge time/minCentrifuge speed/rpmDimethyl phthalateDiethyl phthalate4-Nonylphenol4,4’-DDT4,4’-DDD4.4’-DDE140.55340042.1182.3660.4120.4171.3869.662635340049.8154.1141.9180.4157.8574.213120.520340016.9621.2298.4166.2164.2441.2248320340024.5871.1145.2788.2272.6477.71530.55400058.1155.2172.2961.6648.6596.3461135400028.9677.4185.6972.7785.2171.027150.520400041.2133.24114.2269.9474.2149.21817320400035.9186.9676.9636.66100.0548.769160.113370055.5481.2179.6812.166.2171.281093,413370058.64101.2371.2639.6588.5756.651121.752370035.6530.2171.2148.6561.2488.8512141.7523370012.7131.2480.2167.4161.2163.331311.7512.5330019.8922.2131.2886.2142.8858.8714101.7512.5410032.8854.2188.2752.4764.5271.261551.7512.5370034.5638.2771.4151.4837.5872.2416131.7512.5370033.2441.2763.4155.2139.2169.711771.7512.5370035.5950.4465.2250.2842.8172.36Appropriate quality assurance procedures were carried out to ensure reliability and reproducibility of the results such as spiking and replicate extraction of samples. Limit of detection was also calculated as a signal-to-noise ratio of three times. The CRMs were used for QuEChERS method validation. The material certificate of analysis was designed in accordance with ISO 17034 and ISO Guide 31.


## 3. Results and Discussion

### 3.1. Quality Assurance

The seven EDCs were separated within 29 min. The instrumental performance and precision for GC-ECD are given by the LOD, LOQ, and the retention times as shown in Table4 (LOQ is 10xLOD). The linearity of this method was performed using five data points for all the compounds ranging from 0.2 mg L−1 to 1.0 mg L−1 with at least three injection repetitions.Table 4
Limits of detection (LOD -μg kg−1), limit of quantification (LOQ −μg kg−1 and RSD (%) for GC-ECD analytical performance (Fish).
EDCr2Retention timeDetection limits (μg kg−1)Precision (RSD %)LODLOQn = 3β−BHC0.931810.8711.0310.38.55DimethylPhthalate0.925211.6430.616.14.39Diethylphthalate0.947912.8291.1811.84.884−n−Nonylohenol0.988713.2140.898.99.864,4′−DDD0.959917.3470.161.613.324,4′−DDE0.956718.2141.1211.24.444,4′−DDT0.963820.4221.7717.72.25The chromatographic representation of the individual certified reference materials (CRMs) under GC × GC TOFMS is represented in Appendix (FigureIa). Its recoveries after fortification are given in Table 5. The % recovery was 52.34%≤x≤114.69%. These recoveries are comparable to other previous studies [42–44] but low for diethyl phthalate in fish and beef samples (52–59%).(1)% Recovery=100∗amount in extractamount in sample.Table 5
Measures of CRMs concentration (μgkg−1) and their respective recoveries in vegetable, fish, and beef sample (n = 3, RSD% < 21.66).
EDCCabbageFishBeefAmount in extract (μg)Amount in sample (μg)Recovery %Amount in extract (μg)Amount in sample (μg)Recovery %Amount in extract (μg)Amount in sample (μg)Recovery %DMP0.1460.1690.1480.167%0.1820.155DEP0.1490.1670.1910.152%0.1680.1594-Nonylphenol0.0870.11140.0920.1108%0.0900.11114,4-DDE0.1410.1710.1830.154%0.1560.164


### 3.2. RSM Optimization of QuEChERS Conditions

#### 3.2.1. Screening Experiments

The parameters which affect the efficiency of the QuEChERS method (sample mass, speed and time of centrifuge, amount ofNaCl and MgSO4, solvent type, and solvent extraction volume) were screened using a linear L8 model. The L8 model was then fitted using multiple linear regression (MLR).R2 represents the percent of the variation of the response showing how well the model fits the data. The findings are shown in Table 6. A large R2 (close to 1) is a necessary condition for a good model. For this model, R2 values are in the range of 0.78–0.94.Table 6
MLR statistical analysis (n = 21).
EDCR2Q2RSD (%)Model validityReproducibilityDimethyl phthalate0.920.649.800.680.95Diethyl phthalate0.940.7711.270.750.944-Nonylphenol0.880.5812.180.880.784,4’-DDT0.890.609.650.710.914,4’-DDE0.840.5620.760.260.984,4’-DDD0.780.4918.330.940.41Some factors were deemed “nonessential” from the screening. They are the type and volume of solvent and amount of salt. However, the type of solvent used for extraction needs to satisfy several requirements for maximum recovery of the EDCs. The chosen solvent needs to have high affinity and high selectivity for the endocrine-disrupting chemicals of interest. It needs to have good chromatographic behavior and its interaction with water is also essential. The solvent needs to be immiscible with water and must be less dense than water [45].The solvent polarity, cost, toxicity, and selectivity as well as the structure of the target analyte were taken into consideration since the chosen solvent should be able to extract all the target compounds. Figure3 shows different peak concentrations when three solvent types are varied for the extraction of EDCs of interest. All fish samples were spiked with 106.67μg kg−1 of the EDCs. From Figure 3, it is essential to note the selectivity of methanol on the matrix in the presence of the target EDCs in the extraction of the hake fish sample.Figure 3
Effect of solvent type on QuEChERS extraction of a hake fish sample. (i) acetone (ii) Acetonitrile (iii) Methanol.The volume of the chosen solvent was also optimized. This is because extraction is based on the affinity differences between the analytes of the organic and aqueous phases. Generally, low solvent volumes are favored when considering enrichment factors. Low solvent volumes give higher enrichment factors. For this work, the final volume was reduced by nitrogen blowing. For the extraction, 10 mL of solvent was optimized to give the best extraction of the analytes while ensuring that the final volume is enough to be collected for analysis. The final volume was± 1 mL.The amount of NaCl andMgSO4 salts were also investigated as this is the step in QuEChERS where target EDCs are extracted. The amount added may have salting-out effects, salting-in effects, or no effect at all on the extraction of endocrine-disrupting chemicals [46]. The optimal amount of salt added was determined to be 2 g of NaCl and 2 g MgSO4. Increasing this amount further decreased the concentration of all seven EDCs as the solution viscosity was also increased, consequently.The fitted model shows cross-validated predictability of0.49≤Q2≤0.77 and total explained variance of 0.78≤R2≤0.94 where Q2 shows an estimation of the future prediction and R2 shows the model total variance. Figure 4 shows the linearity of the predicted versus observed values plot highlighting the validity of the model and its ability to predict the most important parameters to be optimized.Figure 4
Observed vs Predicted concentrations of EDCs.A study[47] did a similar RSM study and found that the predicted data of the response from the empirical model agrees with the observed ones in the range of the operating variables. The high value of their adjusted R2 (0.89) indicated that the model fit the observed data well. Other similar studies showed cross-validated predictability of 65% (Q2 = 0.65) and a total explained variance of 95% (R2 = 0.95). Some other studies found that verification experiments carried out for predicted and observed results show that the empirical models developed were within 6% error [48].The coefficient plots (Figure5) show the parameters that have a significant influence on the EDC extraction when the QuEChERS method is employed. It shows that centrifuge conditions and sample mass have significant influence on the extraction of DDT, DDD, DDE, and 4-nonylphenol. Acetone as the solvent is not essential as it is not the optimum solvent even though it influences the phthalates group significantly (Figure 5). The reasoning behind the significances is given in Section 3.2.2. Figure 5 further shows that acetonitrile as a solvent is very relevant to the extraction of 4-Nonylphenol. [49] found that acetonitrile solvent greatly impacts polyphenols extraction in their study. No other study has reported the effect of partition salts (NaCl and MgSO4 on QuEChERS extraction using chemometrics.Figure 5
Coefficient plots: Effect of different screening factors on the concentration of EDCs.


#### 3.2.2. Optimization (RSM) Using CCO Quadratic Design

The optimum values from the optimization are given in Table7 with their factor contributions. From the design employed, it is essential to note that the sample mass, centrifuge time, and centrifuge speed are the factors with the highest contributions. This is due to their large influence on the efficiency of QuEChERS extraction of EDCs over the other screened parameters.Table 7
RSM- Optimised values.
FactorRoleValueFactor contributionSample massFree352,8265Centrifuge timeFree624,3663Centrifuge speedFree400022,8072Centrifuge speed and time are essential parameters in QuEChERS extraction to get the target compound in the final extract from the solid. The centrifuge is used in two phases: first, to separate the supernatant that contains the extracted target analytes from the solid sample after addition of partition salts (NaCl andMgSO4); second, to separate the final extract from the sample matrix in the extract. In this work, Figure 6 shows the optimum extraction conditions obtained by using graphical and numerical analysis based on the response surface plots. The contour plot shows that at the optimal centrifuge speed of 4000 rpm, the optimal centrifuge time is 6 min. E.g., For 4,4’-DDE, increasing the centrifuge time from 6 to 17 min decreases the 4,4’-DDE concentration from 90 to 60 μg kg−1, corresponding to the coefficient plot at a 95% confidence interval (Figure 5).Figure 6
Contour plots of the interaction of the selected parameters on the concentrations of target endocrine disrupting chemicals.


#### 3.2.3. Optimization of PSA as Cleaning Sorbent Using Single Factor Experiments

The optimization of the amount of PSA was investigated using single-factor experiments. PSA amount was varied from 25 to 75 mg (25 mg increments) while keeping the other parameters constant at 1.5 g of sample, 4000 rpm for 6 min centrifuge, 1 g of each salt and 5 mL of methanol. With the increase of PSA mass from 25 to 75 mg, the extraction efficiency of most analytes gradually increased and then remained the same above 75 mg for most analytes (data not shown). Therefore, 75 mg of PSA was selected as the optimal clean-up amount. Further increasing the PSA mass past 75 mg did not have any effect on the quantification of EDCs or on the cleanliness of the chromatogram (FigureIb appendix).After the PSA optimization, each factor was scaled up by a factor of 2, consequently giving 150 mg Moringa Oleifera protein as the optimum cleaning sorbent mass to obtain an acceptable recovery which correlates to 3 g samples mass, 2 g NaCl mass, 2 gMgSO4 mass, and 10 mL solvent volume.


### 3.3. Comparison of PSA and Moringa Oleifera Seed Protein

#### 3.3.1. Selectivity

Figure7(a) shows the comparison of selectivity between PSA and Moringa Oleifera seed protein as clean-up sorbents in QuEChERS in the extraction of beef samples. In the same way, Figure 7(b) shows comparison of the same but for extraction of the fish sample. In both cases, chromatograms using Moringa Oleifera seed protein as clean-up show much selectivity compared to PSA.Figure 7
(a) GC × GC MSTOF chromatograms of (i) 1mg L−1 standard mixture of five EDCs standards, (ii) PSA-Cleaned beef sample spiked with 142.86 μg kg−1 standard mixture, (iii) Moringa Oleifera seed protein cleaned beef sample spiked with 142.86 μg kg−1 standard mixture (Peak markers: 1. dimethyl phthalate. 2: diethyl phthalate 3: 4-nonylphenol 4:4,4’-DDE, 5:4,4’-DDT). (b) GC-ECD chromatogram of fish samples extracted using QuEChERS and (i) PSA and (ii) Moringa Oleifera seed protein clean-up sorbent.
(a)(b)


#### 3.3.2. Comparison of the Recovery between PSA and Moringa Oleifera Seed Protein

Besides selectivity comparison between PSA and Moringa Oleifera seed protein in QuEChERS as clean-up sorbents, recovery was also compared. The results are given in Tables8 and 9. Overall, recoveries using Moringa Oleifera seed protein as clean-up sorbent were slightly higher than those from PSA. These recoveries in QuEChERS are comparable to those reported by [50].Table 8
Analytical spike recovery and standard deviation (SD) of five endocrine disrupting chemicals for different food items sold in Johannesburg open markets at 142.86μgkg−1 and 714.29 μgkg−1 fortification levels.
EDCBananaPotatoOnionCabbage142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1DMP87.8± 0.4189.0± 0.5285.9± 1.3681.3± 1.6993.9± 2.2099.3± 1.3976.3± 2.2374.2± 1.12DEP88.9± 0.3386.3± 0.38101.2± 1.21104,1± 0.9680.6± 1.0278.2± 2.8986.3± 2.5686.3± 2.234-n-OH83.9± 0.1785.9± 0.2394.3± 1.2386.6± 0.9687.4± 1.2784.4± 0.7887.4± 1.0486.8± 1.014,4’-DDE89.2± 0.8886.9± 1.2074.3±1.0874.1± 1.2678.0± 0.8580.1± 1.0586.1± 0.7687.2± 0.874,4’-DDT102.3± 0.2994.3± 0.09100.1± 0.2677.5± 0.2578.8± 1.5575.1± 0.9698.9± 1.23100.5± 0.22DMP: Dimethyl phthalate, DEP: Diethyl phthalate, SD: standard deviation.Table 9
Choice of clean-up sorbent: Recovery comparison between PSA and Moringa Oleifera-extracted Protein on selected food from Johannesburg open markets at 142.86μgkg−1 and 714.29 μgkg−1 fortification levels.
EDCFish: TilapiaBeef142.86μg kg-1714.29μg kg-1142.86μg kg-1714.29μg kg-1PSAProteinPSAProteinPSAProteinPSAProteinDMP89.7± 1.0488.2± 0.8581.0± 1.2089.3± 1.2690.7± 0.5393.6± 0.21104.5± 0.21105.1± 2.24DEP93.3± 1.3686.5± 0.56107.4± 0.8898.5± 0.8579.8±1.3576.2± 0.8580.8±2.3298.6± 0.454-Nonylphenol86.0± 0.8991.1± 1.1188.1± 1.0391.2± 0.6382.7±1.2290.9± 2.2385.2± 1.0197.7± 1.244,4’-DDE90.8± 1.01103.2± 0.6879.9±0.96101.6± 0.5288.4± 0.5388.8± 0.5280.7± 0.5292.1± 0.284,4’-DDT103.1± 0.9694.3± 0.8996.5± 1.0192.6± 0.7586.7± 0.2196.9± 1.1882.2± 0.3296.0± 0.96


### 3.4. Application of the Developed Method to Food Samples

The optimized Moringa Oleifera method was applied to real food samples collected from open markets in Johannesburg and Pretoria, South Africa. All EDCs monitored in this study were detected in Johannesburg food items in the order: DMP (8 samples) > 4-Nonylphenol (4 samples) > DEP (3 samples) > DDT (3 samples) > DDE (2 samples) (Table10). The lowest detected mean concentration in Johannesburg samples was 4,4’-DDT in cabbage samples (5.2±6.82μg kg−1), and the highest detected concentration was also 4,4’-DDT in Tilapia fish (188.9 ±8.89μg kg−1) and beef muscle (143.7 ±11.87μg kg−1) followed by DMP in banana (169.5 ±12.28μg kg−1). The high concentration of 4,4’-DDT in fish can be attributed to the extent of bioaccumulation of the pesticides in fish’ fat or lipid membranes. This is due to extensive pesticide use in farms in the past years since DDT was banned in the 1990s. Since 4,4’-DDT is highly persistent in nature, it eventually leaches into water bodies and aquatic environment in rainy seasons. Half the DDT in soil will only break down in 2–15 years [51]. The 4,4’-DDT detected in this study in fish and beef samples was generally comparable to the results reported by [52], who reported 4,4’-DDT in beef as 167.89 ±1.50μg kg−1 and 134.57 ±5.20μg kg−1, respectively, in Johannesburg open markets. The levels of 4,4’-DDT detected in this study were higher than the limits recommended by the FAO/WHO (2012). No target EDCs were detected in tomato samples.Table 10
Concentration of EDCs in food items (μg kg−1) in Johannesburg Market.
DMPDEP4-Nonylphenol4,4’-DDE4,4’-DDTSampleBanana169.5±12.2888.3± 9.96NdndndPotato155.9± 4.56NdNdndndCarrot45.6± 2.28NdndndndOnion37.2± 8.86Nd77.2± 7.23ndndCabbage8.1± 5.69Nd34.7± 6.39nd5.2±6.8Fish17.2± 10.1172.6± 5.5286.3± 8.56188.9±8.89Beef muscle21.2± 5.56Nd93.8± 4.5662.8± 8.86143.7±11.87Liver23.7± 6.6976.1± 6.63141.5± 7.68ndndnd: Not detected.The mean concentration of 4-nonylphenol varied from 34.7μg kg−1 to 141.5 μg kg−1, the highest being in the chicken liver. The closest published work working on nonylphenols in South Africa is reported by [53]. They analyzed a chicken liver sausage sample, targeting 4-nonylphenol. They reported its concentration at 13 μg kg−1. Their highest concentration is 18.5 μg kg−1 in potato samples. For this study, 4-nonylphenol was not detected in any potato samples. Nonylphenols accumulate in fat environments since they are hydrophilic, which correlates to the highest levels in this study being detected in beef and liver samples. However, some 4-nonylphenol was detected in onion and cabbage with minimal fat.The optimized method was also applied to Pretoria samples and the results are displayed in Table11. When comparing Johannesburg and Pretoria samples, Johannesburg samples generally report the highest detected endocrine-disrupting chemicals in fruits and vegetables. There could be too many possible sources of the EDCs around Johannesburg compared to Pretoria. Poorly monitored municipal wastewater treatment plants around Johannesburg could be a source of some of these chemicals in surface water used for farming purposes. Further studies are needed to link possible sources of these EDCs in surface water.Table 11
Concentration of EDCs in food items (μg kg−1) in Pretoria Market.
DMPDEP4-Nonylphenol4,4’-DDE4,4’-DDTSampleBanana210.6± 5.6573.2± 5.21ndndndPotato23.2± 3.69NdndndndCarrot46.7± 8.9695.8± 6.69ndndndOnion74.1± 1.10Nd67.2± 2.36ndndCabbage22.4± 6.69Nd28.8± 3.69ndndTomato34.6± 8.8575.8± 7.63ndndndnd: Not detected.


## 3.1. Quality Assurance

The seven EDCs were separated within 29 min. The instrumental performance and precision for GC-ECD are given by the LOD, LOQ, and the retention times as shown in Table4 (LOQ is 10xLOD). The linearity of this method was performed using five data points for all the compounds ranging from 0.2 mg L−1 to 1.0 mg L−1 with at least three injection repetitions.Table 4
Limits of detection (LOD -μg kg−1), limit of quantification (LOQ −μg kg−1 and RSD (%) for GC-ECD analytical performance (Fish).
EDCr2Retention timeDetection limits (μg kg−1)Precision (RSD %)LODLOQn = 3β−BHC0.931810.8711.0310.38.55DimethylPhthalate0.925211.6430.616.14.39Diethylphthalate0.947912.8291.1811.84.884−n−Nonylohenol0.988713.2140.898.99.864,4′−DDD0.959917.3470.161.613.324,4′−DDE0.956718.2141.1211.24.444,4′−DDT0.963820.4221.7717.72.25The chromatographic representation of the individual certified reference materials (CRMs) under GC × GC TOFMS is represented in Appendix (FigureIa). Its recoveries after fortification are given in Table 5. The % recovery was 52.34%≤x≤114.69%. These recoveries are comparable to other previous studies [42–44] but low for diethyl phthalate in fish and beef samples (52–59%).(1)% Recovery=100∗amount in extractamount in sample.Table 5
Measures of CRMs concentration (μgkg−1) and their respective recoveries in vegetable, fish, and beef sample (n = 3, RSD% < 21.66).
EDCCabbageFishBeefAmount in extract (μg)Amount in sample (μg)Recovery %Amount in extract (μg)Amount in sample (μg)Recovery %Amount in extract (μg)Amount in sample (μg)Recovery %DMP0.1460.1690.1480.167%0.1820.155DEP0.1490.1670.1910.152%0.1680.1594-Nonylphenol0.0870.11140.0920.1108%0.0900.11114,4-DDE0.1410.1710.1830.154%0.1560.164


## 3.2. RSM Optimization of QuEChERS Conditions

### 3.2.1. Screening Experiments

The parameters which affect the efficiency of the QuEChERS method (sample mass, speed and time of centrifuge, amount ofNaCl and MgSO4, solvent type, and solvent extraction volume) were screened using a linear L8 model. The L8 model was then fitted using multiple linear regression (MLR).R2 represents the percent of the variation of the response showing how well the model fits the data. The findings are shown in Table 6. A large R2 (close to 1) is a necessary condition for a good model. For this model, R2 values are in the range of 0.78–0.94.Table 6
MLR statistical analysis (n = 21).
EDCR2Q2RSD (%)Model validityReproducibilityDimethyl phthalate0.920.649.800.680.95Diethyl phthalate0.940.7711.270.750.944-Nonylphenol0.880.5812.180.880.784,4’-DDT0.890.609.650.710.914,4’-DDE0.840.5620.760.260.984,4’-DDD0.780.4918.330.940.41Some factors were deemed “nonessential” from the screening. They are the type and volume of solvent and amount of salt. However, the type of solvent used for extraction needs to satisfy several requirements for maximum recovery of the EDCs. The chosen solvent needs to have high affinity and high selectivity for the endocrine-disrupting chemicals of interest. It needs to have good chromatographic behavior and its interaction with water is also essential. The solvent needs to be immiscible with water and must be less dense than water [45].The solvent polarity, cost, toxicity, and selectivity as well as the structure of the target analyte were taken into consideration since the chosen solvent should be able to extract all the target compounds. Figure3 shows different peak concentrations when three solvent types are varied for the extraction of EDCs of interest. All fish samples were spiked with 106.67μg kg−1 of the EDCs. From Figure 3, it is essential to note the selectivity of methanol on the matrix in the presence of the target EDCs in the extraction of the hake fish sample.Figure 3
Effect of solvent type on QuEChERS extraction of a hake fish sample. (i) acetone (ii) Acetonitrile (iii) Methanol.The volume of the chosen solvent was also optimized. This is because extraction is based on the affinity differences between the analytes of the organic and aqueous phases. Generally, low solvent volumes are favored when considering enrichment factors. Low solvent volumes give higher enrichment factors. For this work, the final volume was reduced by nitrogen blowing. For the extraction, 10 mL of solvent was optimized to give the best extraction of the analytes while ensuring that the final volume is enough to be collected for analysis. The final volume was± 1 mL.The amount of NaCl andMgSO4 salts were also investigated as this is the step in QuEChERS where target EDCs are extracted. The amount added may have salting-out effects, salting-in effects, or no effect at all on the extraction of endocrine-disrupting chemicals [46]. The optimal amount of salt added was determined to be 2 g of NaCl and 2 g MgSO4. Increasing this amount further decreased the concentration of all seven EDCs as the solution viscosity was also increased, consequently.The fitted model shows cross-validated predictability of0.49≤Q2≤0.77 and total explained variance of 0.78≤R2≤0.94 where Q2 shows an estimation of the future prediction and R2 shows the model total variance. Figure 4 shows the linearity of the predicted versus observed values plot highlighting the validity of the model and its ability to predict the most important parameters to be optimized.Figure 4
Observed vs Predicted concentrations of EDCs.A study[47] did a similar RSM study and found that the predicted data of the response from the empirical model agrees with the observed ones in the range of the operating variables. The high value of their adjusted R2 (0.89) indicated that the model fit the observed data well. Other similar studies showed cross-validated predictability of 65% (Q2 = 0.65) and a total explained variance of 95% (R2 = 0.95). Some other studies found that verification experiments carried out for predicted and observed results show that the empirical models developed were within 6% error [48].The coefficient plots (Figure5) show the parameters that have a significant influence on the EDC extraction when the QuEChERS method is employed. It shows that centrifuge conditions and sample mass have significant influence on the extraction of DDT, DDD, DDE, and 4-nonylphenol. Acetone as the solvent is not essential as it is not the optimum solvent even though it influences the phthalates group significantly (Figure 5). The reasoning behind the significances is given in Section 3.2.2. Figure 5 further shows that acetonitrile as a solvent is very relevant to the extraction of 4-Nonylphenol. [49] found that acetonitrile solvent greatly impacts polyphenols extraction in their study. No other study has reported the effect of partition salts (NaCl and MgSO4 on QuEChERS extraction using chemometrics.Figure 5
Coefficient plots: Effect of different screening factors on the concentration of EDCs.


### 3.2.2. Optimization (RSM) Using CCO Quadratic Design

The optimum values from the optimization are given in Table7 with their factor contributions. From the design employed, it is essential to note that the sample mass, centrifuge time, and centrifuge speed are the factors with the highest contributions. This is due to their large influence on the efficiency of QuEChERS extraction of EDCs over the other screened parameters.Table 7
RSM- Optimised values.
FactorRoleValueFactor contributionSample massFree352,8265Centrifuge timeFree624,3663Centrifuge speedFree400022,8072Centrifuge speed and time are essential parameters in QuEChERS extraction to get the target compound in the final extract from the solid. The centrifuge is used in two phases: first, to separate the supernatant that contains the extracted target analytes from the solid sample after addition of partition salts (NaCl andMgSO4); second, to separate the final extract from the sample matrix in the extract. In this work, Figure 6 shows the optimum extraction conditions obtained by using graphical and numerical analysis based on the response surface plots. The contour plot shows that at the optimal centrifuge speed of 4000 rpm, the optimal centrifuge time is 6 min. E.g., For 4,4’-DDE, increasing the centrifuge time from 6 to 17 min decreases the 4,4’-DDE concentration from 90 to 60 μg kg−1, corresponding to the coefficient plot at a 95% confidence interval (Figure 5).Figure 6
Contour plots of the interaction of the selected parameters on the concentrations of target endocrine disrupting chemicals.


### 3.2.3. Optimization of PSA as Cleaning Sorbent Using Single Factor Experiments

The optimization of the amount of PSA was investigated using single-factor experiments. PSA amount was varied from 25 to 75 mg (25 mg increments) while keeping the other parameters constant at 1.5 g of sample, 4000 rpm for 6 min centrifuge, 1 g of each salt and 5 mL of methanol. With the increase of PSA mass from 25 to 75 mg, the extraction efficiency of most analytes gradually increased and then remained the same above 75 mg for most analytes (data not shown). Therefore, 75 mg of PSA was selected as the optimal clean-up amount. Further increasing the PSA mass past 75 mg did not have any effect on the quantification of EDCs or on the cleanliness of the chromatogram (FigureIb appendix).After the PSA optimization, each factor was scaled up by a factor of 2, consequently giving 150 mg Moringa Oleifera protein as the optimum cleaning sorbent mass to obtain an acceptable recovery which correlates to 3 g samples mass, 2 g NaCl mass, 2 gMgSO4 mass, and 10 mL solvent volume.


## 3.2.1. Screening Experiments

The parameters which affect the efficiency of the QuEChERS method (sample mass, speed and time of centrifuge, amount ofNaCl and MgSO4, solvent type, and solvent extraction volume) were screened using a linear L8 model. The L8 model was then fitted using multiple linear regression (MLR).R2 represents the percent of the variation of the response showing how well the model fits the data. The findings are shown in Table 6. A large R2 (close to 1) is a necessary condition for a good model. For this model, R2 values are in the range of 0.78–0.94.Table 6
MLR statistical analysis (n = 21).
EDCR2Q2RSD (%)Model validityReproducibilityDimethyl phthalate0.920.649.800.680.95Diethyl phthalate0.940.7711.270.750.944-Nonylphenol0.880.5812.180.880.784,4’-DDT0.890.609.650.710.914,4’-DDE0.840.5620.760.260.984,4’-DDD0.780.4918.330.940.41Some factors were deemed “nonessential” from the screening. They are the type and volume of solvent and amount of salt. However, the type of solvent used for extraction needs to satisfy several requirements for maximum recovery of the EDCs. The chosen solvent needs to have high affinity and high selectivity for the endocrine-disrupting chemicals of interest. It needs to have good chromatographic behavior and its interaction with water is also essential. The solvent needs to be immiscible with water and must be less dense than water [45].The solvent polarity, cost, toxicity, and selectivity as well as the structure of the target analyte were taken into consideration since the chosen solvent should be able to extract all the target compounds. Figure3 shows different peak concentrations when three solvent types are varied for the extraction of EDCs of interest. All fish samples were spiked with 106.67μg kg−1 of the EDCs. From Figure 3, it is essential to note the selectivity of methanol on the matrix in the presence of the target EDCs in the extraction of the hake fish sample.Figure 3
Effect of solvent type on QuEChERS extraction of a hake fish sample. (i) acetone (ii) Acetonitrile (iii) Methanol.The volume of the chosen solvent was also optimized. This is because extraction is based on the affinity differences between the analytes of the organic and aqueous phases. Generally, low solvent volumes are favored when considering enrichment factors. Low solvent volumes give higher enrichment factors. For this work, the final volume was reduced by nitrogen blowing. For the extraction, 10 mL of solvent was optimized to give the best extraction of the analytes while ensuring that the final volume is enough to be collected for analysis. The final volume was± 1 mL.The amount of NaCl andMgSO4 salts were also investigated as this is the step in QuEChERS where target EDCs are extracted. The amount added may have salting-out effects, salting-in effects, or no effect at all on the extraction of endocrine-disrupting chemicals [46]. The optimal amount of salt added was determined to be 2 g of NaCl and 2 g MgSO4. Increasing this amount further decreased the concentration of all seven EDCs as the solution viscosity was also increased, consequently.The fitted model shows cross-validated predictability of0.49≤Q2≤0.77 and total explained variance of 0.78≤R2≤0.94 where Q2 shows an estimation of the future prediction and R2 shows the model total variance. Figure 4 shows the linearity of the predicted versus observed values plot highlighting the validity of the model and its ability to predict the most important parameters to be optimized.Figure 4
Observed vs Predicted concentrations of EDCs.A study[47] did a similar RSM study and found that the predicted data of the response from the empirical model agrees with the observed ones in the range of the operating variables. The high value of their adjusted R2 (0.89) indicated that the model fit the observed data well. Other similar studies showed cross-validated predictability of 65% (Q2 = 0.65) and a total explained variance of 95% (R2 = 0.95). Some other studies found that verification experiments carried out for predicted and observed results show that the empirical models developed were within 6% error [48].The coefficient plots (Figure5) show the parameters that have a significant influence on the EDC extraction when the QuEChERS method is employed. It shows that centrifuge conditions and sample mass have significant influence on the extraction of DDT, DDD, DDE, and 4-nonylphenol. Acetone as the solvent is not essential as it is not the optimum solvent even though it influences the phthalates group significantly (Figure 5). The reasoning behind the significances is given in Section 3.2.2. Figure 5 further shows that acetonitrile as a solvent is very relevant to the extraction of 4-Nonylphenol. [49] found that acetonitrile solvent greatly impacts polyphenols extraction in their study. No other study has reported the effect of partition salts (NaCl and MgSO4 on QuEChERS extraction using chemometrics.Figure 5
Coefficient plots: Effect of different screening factors on the concentration of EDCs.


## 3.2.2. Optimization (RSM) Using CCO Quadratic Design

The optimum values from the optimization are given in Table7 with their factor contributions. From the design employed, it is essential to note that the sample mass, centrifuge time, and centrifuge speed are the factors with the highest contributions. This is due to their large influence on the efficiency of QuEChERS extraction of EDCs over the other screened parameters.Table 7
RSM- Optimised values.
FactorRoleValueFactor contributionSample massFree352,8265Centrifuge timeFree624,3663Centrifuge speedFree400022,8072Centrifuge speed and time are essential parameters in QuEChERS extraction to get the target compound in the final extract from the solid. The centrifuge is used in two phases: first, to separate the supernatant that contains the extracted target analytes from the solid sample after addition of partition salts (NaCl andMgSO4); second, to separate the final extract from the sample matrix in the extract. In this work, Figure 6 shows the optimum extraction conditions obtained by using graphical and numerical analysis based on the response surface plots. The contour plot shows that at the optimal centrifuge speed of 4000 rpm, the optimal centrifuge time is 6 min. E.g., For 4,4’-DDE, increasing the centrifuge time from 6 to 17 min decreases the 4,4’-DDE concentration from 90 to 60 μg kg−1, corresponding to the coefficient plot at a 95% confidence interval (Figure 5).Figure 6
Contour plots of the interaction of the selected parameters on the concentrations of target endocrine disrupting chemicals.


## 3.2.3. Optimization of PSA as Cleaning Sorbent Using Single Factor Experiments

The optimization of the amount of PSA was investigated using single-factor experiments. PSA amount was varied from 25 to 75 mg (25 mg increments) while keeping the other parameters constant at 1.5 g of sample, 4000 rpm for 6 min centrifuge, 1 g of each salt and 5 mL of methanol. With the increase of PSA mass from 25 to 75 mg, the extraction efficiency of most analytes gradually increased and then remained the same above 75 mg for most analytes (data not shown). Therefore, 75 mg of PSA was selected as the optimal clean-up amount. Further increasing the PSA mass past 75 mg did not have any effect on the quantification of EDCs or on the cleanliness of the chromatogram (FigureIb appendix).After the PSA optimization, each factor was scaled up by a factor of 2, consequently giving 150 mg Moringa Oleifera protein as the optimum cleaning sorbent mass to obtain an acceptable recovery which correlates to 3 g samples mass, 2 g NaCl mass, 2 gMgSO4 mass, and 10 mL solvent volume.


## 3.3. Comparison of PSA and Moringa Oleifera Seed Protein

### 3.3.1. Selectivity

Figure7(a) shows the comparison of selectivity between PSA and Moringa Oleifera seed protein as clean-up sorbents in QuEChERS in the extraction of beef samples. In the same way, Figure 7(b) shows comparison of the same but for extraction of the fish sample. In both cases, chromatograms using Moringa Oleifera seed protein as clean-up show much selectivity compared to PSA.Figure 7
(a) GC × GC MSTOF chromatograms of (i) 1mg L−1 standard mixture of five EDCs standards, (ii) PSA-Cleaned beef sample spiked with 142.86 μg kg−1 standard mixture, (iii) Moringa Oleifera seed protein cleaned beef sample spiked with 142.86 μg kg−1 standard mixture (Peak markers: 1. dimethyl phthalate. 2: diethyl phthalate 3: 4-nonylphenol 4:4,4’-DDE, 5:4,4’-DDT). (b) GC-ECD chromatogram of fish samples extracted using QuEChERS and (i) PSA and (ii) Moringa Oleifera seed protein clean-up sorbent.
(a)(b)


### 3.3.2. Comparison of the Recovery between PSA and Moringa Oleifera Seed Protein

Besides selectivity comparison between PSA and Moringa Oleifera seed protein in QuEChERS as clean-up sorbents, recovery was also compared. The results are given in Tables8 and 9. Overall, recoveries using Moringa Oleifera seed protein as clean-up sorbent were slightly higher than those from PSA. These recoveries in QuEChERS are comparable to those reported by [50].Table 8
Analytical spike recovery and standard deviation (SD) of five endocrine disrupting chemicals for different food items sold in Johannesburg open markets at 142.86μgkg−1 and 714.29 μgkg−1 fortification levels.
EDCBananaPotatoOnionCabbage142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1DMP87.8± 0.4189.0± 0.5285.9± 1.3681.3± 1.6993.9± 2.2099.3± 1.3976.3± 2.2374.2± 1.12DEP88.9± 0.3386.3± 0.38101.2± 1.21104,1± 0.9680.6± 1.0278.2± 2.8986.3± 2.5686.3± 2.234-n-OH83.9± 0.1785.9± 0.2394.3± 1.2386.6± 0.9687.4± 1.2784.4± 0.7887.4± 1.0486.8± 1.014,4’-DDE89.2± 0.8886.9± 1.2074.3±1.0874.1± 1.2678.0± 0.8580.1± 1.0586.1± 0.7687.2± 0.874,4’-DDT102.3± 0.2994.3± 0.09100.1± 0.2677.5± 0.2578.8± 1.5575.1± 0.9698.9± 1.23100.5± 0.22DMP: Dimethyl phthalate, DEP: Diethyl phthalate, SD: standard deviation.Table 9
Choice of clean-up sorbent: Recovery comparison between PSA and Moringa Oleifera-extracted Protein on selected food from Johannesburg open markets at 142.86μgkg−1 and 714.29 μgkg−1 fortification levels.
EDCFish: TilapiaBeef142.86μg kg-1714.29μg kg-1142.86μg kg-1714.29μg kg-1PSAProteinPSAProteinPSAProteinPSAProteinDMP89.7± 1.0488.2± 0.8581.0± 1.2089.3± 1.2690.7± 0.5393.6± 0.21104.5± 0.21105.1± 2.24DEP93.3± 1.3686.5± 0.56107.4± 0.8898.5± 0.8579.8±1.3576.2± 0.8580.8±2.3298.6± 0.454-Nonylphenol86.0± 0.8991.1± 1.1188.1± 1.0391.2± 0.6382.7±1.2290.9± 2.2385.2± 1.0197.7± 1.244,4’-DDE90.8± 1.01103.2± 0.6879.9±0.96101.6± 0.5288.4± 0.5388.8± 0.5280.7± 0.5292.1± 0.284,4’-DDT103.1± 0.9694.3± 0.8996.5± 1.0192.6± 0.7586.7± 0.2196.9± 1.1882.2± 0.3296.0± 0.96


## 3.3.1. Selectivity

Figure7(a) shows the comparison of selectivity between PSA and Moringa Oleifera seed protein as clean-up sorbents in QuEChERS in the extraction of beef samples. In the same way, Figure 7(b) shows comparison of the same but for extraction of the fish sample. In both cases, chromatograms using Moringa Oleifera seed protein as clean-up show much selectivity compared to PSA.Figure 7
(a) GC × GC MSTOF chromatograms of (i) 1mg L−1 standard mixture of five EDCs standards, (ii) PSA-Cleaned beef sample spiked with 142.86 μg kg−1 standard mixture, (iii) Moringa Oleifera seed protein cleaned beef sample spiked with 142.86 μg kg−1 standard mixture (Peak markers: 1. dimethyl phthalate. 2: diethyl phthalate 3: 4-nonylphenol 4:4,4’-DDE, 5:4,4’-DDT). (b) GC-ECD chromatogram of fish samples extracted using QuEChERS and (i) PSA and (ii) Moringa Oleifera seed protein clean-up sorbent.
(a)(b)


## 3.3.2. Comparison of the Recovery between PSA and Moringa Oleifera Seed Protein

Besides selectivity comparison between PSA and Moringa Oleifera seed protein in QuEChERS as clean-up sorbents, recovery was also compared. The results are given in Tables8 and 9. Overall, recoveries using Moringa Oleifera seed protein as clean-up sorbent were slightly higher than those from PSA. These recoveries in QuEChERS are comparable to those reported by [50].Table 8
Analytical spike recovery and standard deviation (SD) of five endocrine disrupting chemicals for different food items sold in Johannesburg open markets at 142.86μgkg−1 and 714.29 μgkg−1 fortification levels.
EDCBananaPotatoOnionCabbage142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1142.86μg kg−1714.29μg kg−1DMP87.8± 0.4189.0± 0.5285.9± 1.3681.3± 1.6993.9± 2.2099.3± 1.3976.3± 2.2374.2± 1.12DEP88.9± 0.3386.3± 0.38101.2± 1.21104,1± 0.9680.6± 1.0278.2± 2.8986.3± 2.5686.3± 2.234-n-OH83.9± 0.1785.9± 0.2394.3± 1.2386.6± 0.9687.4± 1.2784.4± 0.7887.4± 1.0486.8± 1.014,4’-DDE89.2± 0.8886.9± 1.2074.3±1.0874.1± 1.2678.0± 0.8580.1± 1.0586.1± 0.7687.2± 0.874,4’-DDT102.3± 0.2994.3± 0.09100.1± 0.2677.5± 0.2578.8± 1.5575.1± 0.9698.9± 1.23100.5± 0.22DMP: Dimethyl phthalate, DEP: Diethyl phthalate, SD: standard deviation.Table 9
Choice of clean-up sorbent: Recovery comparison between PSA and Moringa Oleifera-extracted Protein on selected food from Johannesburg open markets at 142.86μgkg−1 and 714.29 μgkg−1 fortification levels.
EDCFish: TilapiaBeef142.86μg kg-1714.29μg kg-1142.86μg kg-1714.29μg kg-1PSAProteinPSAProteinPSAProteinPSAProteinDMP89.7± 1.0488.2± 0.8581.0± 1.2089.3± 1.2690.7± 0.5393.6± 0.21104.5± 0.21105.1± 2.24DEP93.3± 1.3686.5± 0.56107.4± 0.8898.5± 0.8579.8±1.3576.2± 0.8580.8±2.3298.6± 0.454-Nonylphenol86.0± 0.8991.1± 1.1188.1± 1.0391.2± 0.6382.7±1.2290.9± 2.2385.2± 1.0197.7± 1.244,4’-DDE90.8± 1.01103.2± 0.6879.9±0.96101.6± 0.5288.4± 0.5388.8± 0.5280.7± 0.5292.1± 0.284,4’-DDT103.1± 0.9694.3± 0.8996.5± 1.0192.6± 0.7586.7± 0.2196.9± 1.1882.2± 0.3296.0± 0.96


## 3.4. Application of the Developed Method to Food Samples

The optimized Moringa Oleifera method was applied to real food samples collected from open markets in Johannesburg and Pretoria, South Africa. All EDCs monitored in this study were detected in Johannesburg food items in the order: DMP (8 samples) > 4-Nonylphenol (4 samples) > DEP (3 samples) > DDT (3 samples) > DDE (2 samples) (Table10). The lowest detected mean concentration in Johannesburg samples was 4,4’-DDT in cabbage samples (5.2±6.82μg kg−1), and the highest detected concentration was also 4,4’-DDT in Tilapia fish (188.9 ±8.89μg kg−1) and beef muscle (143.7 ±11.87μg kg−1) followed by DMP in banana (169.5 ±12.28μg kg−1). The high concentration of 4,4’-DDT in fish can be attributed to the extent of bioaccumulation of the pesticides in fish’ fat or lipid membranes. This is due to extensive pesticide use in farms in the past years since DDT was banned in the 1990s. Since 4,4’-DDT is highly persistent in nature, it eventually leaches into water bodies and aquatic environment in rainy seasons. Half the DDT in soil will only break down in 2–15 years [51]. The 4,4’-DDT detected in this study in fish and beef samples was generally comparable to the results reported by [52], who reported 4,4’-DDT in beef as 167.89 ±1.50μg kg−1 and 134.57 ±5.20μg kg−1, respectively, in Johannesburg open markets. The levels of 4,4’-DDT detected in this study were higher than the limits recommended by the FAO/WHO (2012). No target EDCs were detected in tomato samples.Table 10
Concentration of EDCs in food items (μg kg−1) in Johannesburg Market.
DMPDEP4-Nonylphenol4,4’-DDE4,4’-DDTSampleBanana169.5±12.2888.3± 9.96NdndndPotato155.9± 4.56NdNdndndCarrot45.6± 2.28NdndndndOnion37.2± 8.86Nd77.2± 7.23ndndCabbage8.1± 5.69Nd34.7± 6.39nd5.2±6.8Fish17.2± 10.1172.6± 5.5286.3± 8.56188.9±8.89Beef muscle21.2± 5.56Nd93.8± 4.5662.8± 8.86143.7±11.87Liver23.7± 6.6976.1± 6.63141.5± 7.68ndndnd: Not detected.The mean concentration of 4-nonylphenol varied from 34.7μg kg−1 to 141.5 μg kg−1, the highest being in the chicken liver. The closest published work working on nonylphenols in South Africa is reported by [53]. They analyzed a chicken liver sausage sample, targeting 4-nonylphenol. They reported its concentration at 13 μg kg−1. Their highest concentration is 18.5 μg kg−1 in potato samples. For this study, 4-nonylphenol was not detected in any potato samples. Nonylphenols accumulate in fat environments since they are hydrophilic, which correlates to the highest levels in this study being detected in beef and liver samples. However, some 4-nonylphenol was detected in onion and cabbage with minimal fat.The optimized method was also applied to Pretoria samples and the results are displayed in Table11. When comparing Johannesburg and Pretoria samples, Johannesburg samples generally report the highest detected endocrine-disrupting chemicals in fruits and vegetables. There could be too many possible sources of the EDCs around Johannesburg compared to Pretoria. Poorly monitored municipal wastewater treatment plants around Johannesburg could be a source of some of these chemicals in surface water used for farming purposes. Further studies are needed to link possible sources of these EDCs in surface water.Table 11
Concentration of EDCs in food items (μg kg−1) in Pretoria Market.
DMPDEP4-Nonylphenol4,4’-DDE4,4’-DDTSampleBanana210.6± 5.6573.2± 5.21ndndndPotato23.2± 3.69NdndndndCarrot46.7± 8.9695.8± 6.69ndndndOnion74.1± 1.10Nd67.2± 2.36ndndCabbage22.4± 6.69Nd28.8± 3.69ndndTomato34.6± 8.8575.8± 7.63ndndndnd: Not detected.


## 4. Concluding Remarks

Response surface methodology was found to be an easy way of QuEChERS optimization. Moringa Oleifera seed protein was found to be an alternative biosorbent for food sample clean-up in QuEChERS. The recovery was comparable with PSA and in some cases gave cleaner extracts. Application of the optimized method to various food samples found in Johannesburg and Pretoria, South Africa revealed the presence of some EDCs like dimethyl phthalate, diethyl phthalate, 4-nonylphenol, 4,4’-DDT, 4,4’-DDD, and 4,4’-DDE.



---

*Source: 7161318-2022-09-13.xml*