Surfactant-Induced Aqueous Two-Phase System for the Green Preconcentration and Determination of Cobalt and Nickel in Food Samples

Surfactant-Induced Aqueous Two-Phase System for the Green Preconcentration and Determination of Cobalt and Nickel in Food Samples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Surfactant-Induced Aqueous Two-Phase System for the Green Preconcentration and Determination of Cobalt and Nickel in Food Samples Dilaine Suellen Caires Neves, Robson Silva França, Anderson Santos Souza, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7661147/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Surfactant-induced aqueous two-phase systems (ATPS) offer an environmentally friendly alternative for the separation and preconcentration of analytes, minimizing toxic waste generation and operational costs. In this work, we report for the first time the application of a surfactant-driven ATPS to the simultaneous extraction and preconcentration of cobalt and nickel from food matrices. The system was composed of Triton X-100 + Na2SO4 + H2O in the presence of 4-(2-Pyridylazo)resorcinol (PAR) as the complexing agent, followed by detection via flame atomic absorption spectrometry. Key parameters, including pH, PAR concentration, centrifugation time, and incubation time, were optimized through multivariate analysis based on a desirability function approach. Optimal conditions were pH 9.2, centrifugation time 10 min, thermostatic bath time 11 h, and PAR concentration 0.0750% w/w. Under these conditions, the limits of detection and quantification were 0.330 and 1.10 µg.kg − 1 for Co, and 0.0370 and 0.890 µg.kg − 1 for Ni, respectively, with enrichment factors of 20.2 and 16.7. The method showed good precision, with RSDs of 6.3% for Co and 7.4% for Ni, and accuracy verified using the certified reference material NIST 1515 (apple leaves), yielding recoveries of 97.6 ± 2.7% for Co and 97.9 ± 2.9% for Ni. In real food samples, recoveries ranged from 96% to 106%, further confirming the reliability of the approach. This novel methodology, by combining micellar extraction with the principles of green chemistry, provides a reliable, cost-effective, and sustainable strategy for trace metal monitoring in food safety applications. surfactant-based extraction food matrices trace metal analysis sample preparation Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION The definition of metal toxicity in the human body depends on its concentration, as any element can be considered potentially toxic at elevated levels (Arpa & Arıdaşır, 2019), including cobalt and nickel (Fioravanti et al., 2025). Cobalt is associated with vitamin B12 and plays a vital role in preventing pernicious anemia. Its deficiency has been linked to hepatic, pancreatic, and nervous system disorders, while excessive intake can lead to toxic effects such as vasodilation, hypothyroidism, neuropathy, auditory or visual impairment, and cardiomyopathy. Nickel, on the other hand, is present in the active sites of certain enzymes and, when accumulated in high concentrations, may cause stomach pain, kidney and cardiovascular diseases, contact dermatitis, and respiratory conditions such as lung lesions (Arpa & Arıdaşır, 2019; Elik et al., 2021; Simonsen et al., 2012). Cobalt and nickel are trace elements that, while necessary in small amounts, may be present in food due to environmental contamination or industrial activities (Demarquoy, 2025). Recent research indicates that these metals can accumulate in the food chain, posing potential health risks. Studies have found that cobalt and nickel exposure can affect soil microbiota and lead to bioaccumulation in organisms at higher trophic levels (Garza Amaya et al., 2023). Reliable quantification of these metals in food is crucial, as excessive intake can cause health issues such as liver and neurological problems with cobalt, and kidney damage and skin conditions with nickel (Sule et al., 2020). Thus, accurate and effective analytical techniques are essential to ensure food safety and protect public health (Bida et al., 2025). One of the most commonly used techniques for the determination of Co and Ni in various types of samples is flame atomic absorption spectrometry (FAAS). Although this technique is widely applied for the quantification of metal ions, it lacks sufficient sensitivity for detecting these analytes in food samples. Therefore, additional separation and preconcentration steps are necessary to achieve higher selectivity and sensitivity (de S. Dias et al., 2020a; Ghaedi et al., 2013). In this context, aqueous two-phase systems (ATPS) are excellent alternatives for sample preparation, as they offer several advantages, including ease of preparation, absence of organic solvents, high water content, low interfacial tension, high selectivity, and cost-effectiveness (Hamta & Dehghani, 2017; Yao et al., 2025; Zhang et al., 2024). The extraction of cobalt and nickel using ATPS has been well documented in the literature with satisfactory results (Rodrigues et al., 2012; Valadares et al., 2018). However, the simultaneous preconcentration of these metals using ATPS has not been reported, nor has their application to food samples. Moreover, none of the reported ATPS are surfactant-based. Surfactant-based ATPS enables a wider range of applications for metal extraction, as micelle formation allows for the efficient extraction of hydrophobic analytes, including various metal complexes (Neves et al., 2024). Additionally, these systems offer advantages such as lower interfacial tension compared to other ATPS compositions and require smaller amounts of inorganic salts to induce phase separation (Figueiredo et al., 2024). Triton X-100 is the most commonly used surfactant for various analytical applications, and its integration into ATPS has expanded the potential uses of these systems, from metal extraction (Silva et al., 2021) to biomolecule separation (Álvarez et al., 2025). Nevertheless, analytical applications of the ATPS-triton X-100 remain largely underexplored. Therefore, the objective of this study is to develop an accurate and reliable analytical method for the simultaneous preconcentration of cobalt and nickel in food samples using a surfactant-based aqueous two-phase system composed of Triton X-100. This approach aims to combine the benefits of micellar extraction with the environmentally benign characteristics of ATPS, contributing to the advancement of green analytical chemistry. To ensure optimal performance, key parameters such as pH, complexing agent concentration, equilibrium time, and centrifugation time were optimized using a multivariate experimental design based on the desirability function approach. This study contributes to expanding the analytical applications of surfactant-based ATPS, particularly in food safety and trace metal monitoring. 2. EXPERIMENTAL 2.1. Reagents The aqueous two-phase system was formed using Triton X-100 (Sigma–Aldrich, USA) and sodium sulfate (Na 2 SO 4 ), all supplied by Synth (Brazil). Deionized water (MILLI-Q, R ≥ 18 MΩ·cm⁻¹) was used throughout the experiments. Standard solutions of cobalt and nickel (1000 mg·kg⁻¹) were obtained from Sigma–Aldrich, USA. Potassium hydroxide (ISOFAR, Brazil) was employed for pH adjustment. Nitric acid (HNO 3 , 65%) (Sigma–Aldrich, USA) and hydrogen peroxide (H 2 O 2 , 35%) (Dinâmica, Brazil) were used for sample digestion. The complexing agent PAR (4-(2-pyridylazo)resorcinol) and the certified reference material (NIST 1515–Apple Leaves) were also purchased from Sigma–Aldrich, USA. All reagents were of analytical grade and used as received, without further purification. All glassware was pretreated with 10% nitric acid for 24 hours and thoroughly rinsed with deionized water. 2.2. Optimization Analytical Methodology 2.2.1. Metal ion extraction in ATPS A mixture was prepared by combining 2.09 g (Shimadzu, AUX220, uncertainty of ± 0.0001 g) of a 20.0% w/w Triton X-100 solution containing PAR, 8.75 g of an 18.0% w/w sodium sulfate solution, and 3.15 g of deionized water, with pH previously adjusted (Metrohm, 827 pH meter) (Liu et al., 2016). The system was agitated (TECNAL, TE0851) and centrifuged (Hettich, Rotofix 32A) at 4000 rpm to promote phase separation. After centrifugation, the tubes were incubated in an ultra-thermostated water bath (Marconi, MA184) at 293.15 K. Subsequently, an aliquot of the top phase (TP) was collected, diluted with deionized water, and the analytical signal intensity for cobalt and nickel was measured by flame atomic absorption spectrometry (FAAS) (Analitik Jenna, Nova 300). 2.2.2. Optimization Multivariate optimization of the experimental variables was carried out in two stages. In the first stage, a full factorial design 2 4 was applied to evaluate the influence of four factors on the extraction and preconcentration of cobalt and nickel in the ATPS (Table 1 ). The variables considered were pH, concentration of the complexing agent (PAR), incubation time in the thermostatted bath time (TBT), and centrifugation time (CT). In the second stage, a Box–Behnken design was employed to refine the optimization and determine the critical values (Table 2 ). All experiments were conducted in randomized order, and the results were analyzed using Statistica 6.0 software. In both stages, the concentrations of cobalt and nickel in the top phase were simultaneously optimized using the desirability function. This approach allows the combination of individual responses for each analyte into a single response (Vera Candioti et al., 2014). The individual desirability function ( d i ) and the global desirability ( D G ) were calculated according to Equations 1 and 2. \(\:{d}_{i}=\:\left\{\begin{array}{cc}0&\:\text{if\:\:}{y}_{i}{U}_{i}\end{array}\right.\) Eq. 1 \(\:{D}_{G}=\:\sqrt[2]{{d}_{i,\:Co\:\:\:}\bullet\:\:{d}_{i,Ni}}\) Eq. 2 where U i is the upper acceptable value for the response and L i is the lower, s is a power value named “weight”, which was set to 1, as nickel and cobalt were considered equally important. The mass ratio between the surfactant-rich and salt-rich phases, as well as the tie-line length (TLL), were investigated using a univariate approach. The phase ratio was evaluated in the range of 0.0770 to 0.210, and TLL values of 14.5, 18.9, 23.4, and 27.7% w/w were assessed. The extraction percentage (%E) was calculated as the ratio between the number of moles of analyte in the top phase and the total number of moles of analyte in the system, multiplied by 100. The selectivity of the proposed methodology was assessed by introducing various ions commonly found in food matrices alongside the target analytes into the ATPS. Potential matrix interferences were evaluated by comparing the analytical signals obtained in the presence and absence of these concomitant species. 2.2.3. Analytical Characteristics The analytical parameters required for method validation were determined using classical approaches. The limits of detection (LOD) and quantification (LOQ) were established based on the measurement of 10 blanks. The LOD was calculated as three times the standard deviation of the blank divided by the slope of the calibration curve, while the LOQ was determined as ten times the standard deviation of the blank divided by the slope of the calibration curve. Precision was assessed through eight replicate analyses at a concentration of 50.0 µg kg⁻¹ for both cobalt and nickel, and expressed as the relative standard deviation (RSD). Linearity was evaluated by constructing calibration curves ranging from 25.0 to 250 µg kg⁻¹ for nickel and from 50.0 to 250 µg kg⁻¹ for cobalt. The enrichment factor was calculated as the ratio between the slope of the preconcentrated calibration curve and the direct calibration curve. Accuracy was assessed using a certified reference material (NIST 1515 - Apple Leaves). Following optimization, the method was applied to the determination of cobalt and nickel in food samples, including peanuts, hazelnuts, shrimp, starch, and cornmeal. 2.2.4. Application to Real Samples Samples of peanuts, hazelnuts, shrimp, starch, and cornmeal were purchased from local markets. The samples were dehydrated and ground prior to analysis. For digestion, 0.200 g of each sample was weighed in triplicate, and 2.00 mL of concentrated HNO 3 , 1.00 mL of water, and 2.00 mL of H 2 O 2 were added. The mixtures were subjected to microwave-assisted (Provecto Analitica, DGT 100 plus) digestion using the following program: 160 W for 5 minutes, 300 W for 2 minutes, and 100 W for 3 minutes. After digestion, the pH of each sample was adjusted using KOH, and the volume was brought to 20.0 mL. A blank and the certified reference material (NIST 1515 – Apple Leaves) were digested following the same procedure, with the mass of the certified sample (0.500 g), which was digested with 4.00 mL of HNO 3 and 2.00 mL of H 2 O 2 . The digested samples were then used as the solvent for the preparation of the Triton X-100 and sodium sulfate solutions. 3. RESULTS AND DISCUSSION Triton X-100 is a nonionic surfactant with amphiphilic properties, making it highly suitable for the extraction of both hydrophobic and hydrophilic analytes. In an ATPS composed of Triton/salt/water, metal ions typically partition into the salt-rich phase (bottom phase (BP)). To preconcentrate these analytes in the surfactant-rich phase (top phase), it is necessary to reduce their hydrophilicity through complexation. Preliminary studies indicated that the combination of sodium sulfate as the salting-out agent and the complexing agent PAR resulted in the highest extraction efficiency for the analytes. The Co–PAR and Ni–PAR complexes exhibit greater hydrophobic character and can be solubilized within the micelles formed by Triton X-100. Accordingly, the ATPS composed of Triton X-100, Na 2 SO 4 , and H 2 O at a temperature of 293.15 K (Liu et al., 2016). In the presence of PAR, it was employed as a platform for the preconcentration of cobalt and nickel. 3.1. Optimization The first stage of the multivariate analysis was carried out using a two-level factorial design, in which the complexing agent concentration ([PAR]), pH, centrifugation time (CT), and thermostatic bath time (TBT) were evaluated. The results, expressed in absorbance, were analyzed, and the desirability values were calculated using Equations 3.1 and 3.2. Table 1 presents the two-level factorial design matrix with the individual desirability values for cobalt and nickel and the global desirability, while Fig. 1 displays the Pareto chart. Analysis of the results presented in the Pareto chart revealed that three variables were statistically significant: complexing agent concentration, thermostatic bath time, and pH. All exhibited positive effects, indicating a tendency toward the upper levels of the studied ranges. The variable with the greatest effect was the thermostatic bath time, evaluated between 2 and 6 hours. The positive effect suggests that a minimum time is necessary for the system to reach mass transfer equilibrium between the phases, thereby improving extraction efficiency. Table 1 2⁴ full factorial design matrix with experimental responses for Co and Ni. Exp. Variables ID a Cobalt ID a Nickel GD b [PAR] pH TBT CT 1 -1 + 1 -1 + 1 -1 + 1 -1 + 1 -1 + 1 -1 + 1 -1 + 1 -1 + 1 0 0 0 -1 -1 + 1 + 1 -1 -1 + 1 + 1 -1 -1 + 1 + 1 -1 -1 + 1 + 1 0 0 0 -1 -1 -1 -1 + 1 + 1 + 1 + 1 -1 -1 -1 -1 + 1 + 1 + 1 + 1 0 0 0 -1 -1 -1 -1 -1 -1 -1 -1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 0 0 0 0.00443 0 0 2 0.235 0.133 0.176 3 0.337 0.215 0.269 4 0.876 0.00717 0.079 5 0.162 0.200 0.180 6 0.598 0.322 0.439 7 0.969 0.932 0.950 8 0.861 0.946 0.902 9 0 0.0613 0 10 0.304 0.175 0.231 11 0.252 0.243 0.247 12 0.505 0.559 0.531 13 0.181 0.276 0.223 14 0.505 0.391 0.444 15 0.737 0.437 0.567 16 0.969 1.00 0.984 17 1.05 0.606 0.796 18 0.984 0.602 0.769 19 1.03 0.588 0.778 a ID: Individual Desiralility. b GD: Global Desiralility The pH is a critical factor in the formation of metal–ligand complexes between Co²⁺/Ni²⁺ ions and the PAR reagent. The pH range investigated for the digested samples was from 4 to 10. The positive effect observed for pH is attributed to the increased deprotonation of the PAR molecule at higher pH values, which enhances its ability to coordinate with metal ions due to a greater number of electron pairs available for Lewis acid–base interactions. The PAR concentration was evaluated between 0.00500 and 0.0200%wt. A positive effect was observed, with the highest concentration yielding better extraction efficiency. At higher concentrations, more PAR molecules are available to form metallic complexes, which promotes their transfer to the surfactant-rich top phase and improves extraction performance. Centrifugation time, in contrast, did not show a significant effect on the analytical response. Therefore, the lowest tested value (3.0 min) was used in subsequent experiments to ensure efficient separation without unnecessarily delaying the procedure. Following the identification of the significant variables, a Box-Behnken design was applied to determine their optimal conditions. In this step, the global desirability function was employed to enable the simultaneous optimization of both analytes. Table 2 presents the experimental matrix for the three-variable Box-Behnken design, along with the corresponding individual and global desirability values. Table 2 Box–Behnken design matrix with experimental results and desirability responses for Co and Ni. Exp. Variables ID a Cobalt ID a Nickel GD b pH [PAR] TBT 1 -1 -1 0 0 0 0 2 + 1 -1 0 0.250 0.287 0.268 3 -1 + 1 0 0.169 0.0700 0.109 4 + 1 + 1 0 0.412 0.776 0.565 5 -1 0 -1 0.199 0.0770 0.124 6 + 1 0 -1 0.265 0.196 0.228 7 -1 0 + 1 0.419 0.406 0.412 8 + 1 0 + 1 0.640 0.615 0.627 9 0 -1 -1 0.522 0.531 0.527 10 0 + 1 -1 0.699 0.629 0.663 11 0 -1 + 1 0.735 0.434 0.565 12 0 + 1 + 1 0.919 0.944 0.932 13 0 0 0 1.00 1.00 1.00 14 0 0 0 0.978 0.965 0.971 15 0 0 0 0.949 0.972 0.960 a ID: Individual Desiralility. b GD: Global Desiralility The outcomes of the optimization are depicted in the response surface plots shown in Fig. 2. The study ranges for the variables, thermostatic bath time (6.0–12.0 h) and PAR concentration (0.0200–0.100%wt), were extended from the maximum values used in the previous experimental design. In contrast, the maximum pH value was set at 10.0 (range: 8.0–10.0), as higher pH levels may lead to precipitation within the system. For the thermostatic bath time, an increase in analytical signal was observed up to approximately 10 hours (Fig. 2A–B), after which the response plateaued, indicating that equilibrium in mass transfer between the phases had been achieved. The D G reached maximum values around the central levels of PAR concentration (Fig. 2A and 2C). While moderate increases in PAR improve analyte extraction, excessive amounts may saturate the top phase, decreasing the extraction efficiency. Likewise, elevated pH levels promote the formation of metal hydroxides, leading to a reduced analytical signal. The optimal values for each variable were determined through response surface methodology based on the Box-Behnken matrix. Both linear and quadratic models were evaluated to identify the best mathematical fit to the DG data. The quality of the selected model was assessed via lack-of-fit testing. According to the analysis of variance (Table 3 ), the quadratic model exhibited no significant lack of fit (F calculated < F tabulated ), confirming its suitability for describing the system. Table 3 Analysis of variance (ANOVA) for the quadratic model adjusted to global desirability at the 95% confidence level. Variables SS df MS F P (1)pH L + Q 1.18 2 0.589 935 0.00107 (2)[ PAR] L + Q 0.262 2 0.131 208 0.00478 (3) TBT L + Q 0.157 2 0.0785 124 0.00795 Lack of Fit 0.0652 6 0.0109 17.3 0.0557 Erro Puro 0.00125 2 0.000629 Total SQ 1.58 14 F tab = 19.33. SS- sum of squres; df - degrees of freedom; MS- mean square. Based on the fitted quadratic model, Eq. 3 describes the behavior of the system as a function of the studied variables. The optimum conditions were determined using the Lagrange optimization criterion, resulting in a maximum predicted response at a PAR concentration of 0.0710% (w/w), an incubation time in the thermostatic bath of 10.9 hours, and a pH of 9.10. These conditions are consistent with the trends observed in the response surfaces presented in Fig. 2. GD = − 45.08(± 0.53) + 9.69(± 0.12)pH + 18.39(± 0.50)[PAR] + 0.23(± 0.01)TBT – 0.53(± 0.01)pH 2 – 129.57(± 4.06)[PAR] 2 – 0.011(± 0.00)TBT 2 Eq. 3 3.2. Evaluation of phase mass ratio and tie-line length The influence of the tie-line length and the mass ratio between the surfactant-rich and salt-rich phases on the extraction efficiency was evaluated and is shown in Fig. 3 for both cobalt and nickel. The results indicate no significant variation in the extraction percentage across the tested compositions of the ATPS, with %E values consistently exceeding 95.0%. This behavior suggests that, within the studied range, the system is robust and maintains its extraction performance independently of moderate changes in phase composition. Therefore, the shortest TLL was selected to reduce reagent consumption, namely, lower concentrations of sodium sulfate and Triton X-100, thus enhancing the system's sustainability and cost-effectiveness. A mass ratio of 0.160 was selected because it provided a suitable preconcentration factor while generating a sufficient volume of the extractant phase to facilitate experimental handling, thereby balancing analytical performance with operational practicality. 3.3. Assessment of potential matrix interferences on the analytical performance The selectivity of the proposed methodology was evaluated through interference studies to assess potential matrix effects on the developed method. Extraction efficiency was investigated in the presence of various common cations and anions, and tolerance limits were determined for each interfering ion. Potential interferences arise from competition between ions for the chelating agent and from impacts on the phase equilibrium of the ATPS. Table 4 presents the maximum molar ratios that caused up to a 5% variation in the analytical signal. The results demonstrate that no significant interference effects were observed. Therefore, it can be concluded that the extraction process under optimal conditions is selective for Ni(II) and Co(II), making it suitable for application in a wide range of food samples. Table 4 The maximum interferent concentration that does not affect the determination of the analyte concentration. Interferent Cobalt (mol/mol) Nickel (mol/mol) Recovery (%) CO 3 2− 1000 1000 94.0–96.0 Na+ 50.0 50.0 91.0–93.0 Ca 2+ 100 100 99.0-101 Fe 3+ 100 100 93.0–97.0 Al 3+ 1000 1000 95.0–98.0 Zn 2+ 100 100 97.0–99.0 NO 3 − 1000 1000 91.0–95.0 Mn 2+ 50.0 50.0 99.0-100 Mg 2+ 100 100 96.0% Li 2+ 50.0 50.0 95.0% PO 4 2− 1000 1000 95.0% 3.4. Analytical Performance Characteristics After optimization of the experimental conditions, the analytical figures of merit of the method were determined. Linearity was evaluated through calibration curves constructed over the ranges of 25.0 to 250 µg·kg − 1 for nickel and 50.0 to 250 µg·kg − 1 for cobalt. For the direct calibration curves, the equations obtained were y = 0.00101x + 0.0032 with R 2 = 0.998 for nickel, and y = 5.82×10 − 4 x + 0.00144 with R 2 = 0.999 for cobalt. For the preconcentrated calibration curves, the equations were y = 0.0169x + 0.00171 with R 2 = 0.999 for nickel, and y = 0.0118x − 0.00408 with R 2 = 0.999 for cobalt. Enrichment factors of 20.3 for cobalt and 16.7 for nickel were calculated from the slopes of the direct and preconcentrated curves, values that are consistent with preconcentration methods using FAAS reported in the literature (de S. Dias et al., 2020a). Precision, expressed as relative standard deviation (RSD), yielded values of 6.32% for cobalt and 7.37% for nickel. LOD were 0.330 µg·kg − 1 for cobalt and 0.0370 µg·kg − 1 for nickel, while LOQ were 1.10 µg·kg − 1 and 0.890 µg·kg − 1 , respectively. Method accuracy was confirmed by analyzing the certified reference material NIST1515 (apple leaves), which has certified values of 90.0 ± 0.011 µg·kg − 1 for cobalt and 936 ± 0.094 µg·kg − 1 for nickel. The obtained results were 87.6 ± 0.053 µg·kg − 1 for cobalt and 916 ± 0.028 µg·kg − 1 for nickel, with recoveries around 97%, demonstrating excellent agreement between measured and certified values. The Student’s test at a 95% confidence level indicated no statistically significant difference between the experimental and certified values, thus validating the developed methodology. The analytical figures of merit obtained with the proposed methodology are comparable or superior to those reported for other preconcentration approaches coupled with FAAS detection (Altunay et al., 2019; de S. Dias et al., 2020b; Lemos et al., 2007). 3.5. Application to Real Samples After method validation, the developed procedure was applied to determine cobalt and nickel ions in peanut, hazelnut, corn starch, corn flour, and shrimp samples. These samples were also spiked with known concentrations of the analytes (25.0 and 50.0 µg·kg − 1 ) to evaluate recovery. The results, along with recovery values for the fortified samples, are presented in Table 5 . All data represent the mean and standard deviation of three independent replicates. The recoveries obtained were satisfactory and showed no significant difference compared to the unspiked samples, as confirmed by Student’s test (α = 0.05). These results demonstrate the method’s robustness and reliability for cobalt and nickel determination in complex food matrices. Additionally, the method avoids the use of organic solvents, reducing environmental impact and exposure to toxic reagents. The procedure is straightforward, cost-effective, and compatible with routine laboratory analysis, highlighting its potential for widespread application in food quality control and environmental monitoring while adhering to green chemistry principles. Table 5 Measured concentrations of cobalt and nickel in different food matrices and corresponding recovery values obtained at two fortification levels. Sample Cobalt Nickel Added (µg·kg − 1 ) Found (µg·kg − 1 ) Recovery (%) Added (µg·kg − 1 ) Found (µg·kg − 1 ) Recovery (%) Peanut - 1.32 ± 0.21 - - 124 ± 0.52 - 25.0 26.7 ± 0.29 101 ± 1.02 25.0 158 ± 0.41 105.8 ± 1.8 50.0 50.9 ± 0.29 99.3 ± 1.02 50.0 176 ± 0.09 101 ± 1.36 Hazelnut - 3.63 ± 0.33 - - 217 ± 0.13 - 25.0 27.9 ± 0.22 97.4 ± 1.2 25.0 240 ± 0.28 99.2 ± 0.96 50.0 54.0 ± 0.22 100 ± 1.4 50.0 258 ± 0.83 96.6 ± 1.5 Corn starch - 1.20 ± 0.37 - - 95.9 ± 1.05 - 25.0 25.9 ± 0.73 98.8 ± 1.8 25.0 117 ± 0.15 97.4 ± 0.89 50.0 50.7 ± 0.48 99.0 ± 0.91 50.0 143 ± 0.52 98.5 ± 0.94 Corn flour - ND - - 189 ± 0.47 - 25.0 25.5 ± 0.69 102 ± 0.88 25.0 211 ± 1.1 98.3 ± 1.7 50.0 50.2 ± 0.87 100 ± 1.42 50.0 237 ± 0.13 98.9 ± 1.4 Shrimp - 1.43 ± 0.06 - - 116 ± 0.37 - 25.0 27.1 ± 0.76 102 ± 0.99 25.0 142 ± 0.26 100 ± 0.87 50.0 52.6 ± 0.91 102 ± 1.12 50.0 162 ± 0.45 97.4 ± 1.7 4. CONCLUSION This study presents the first report of a surfactant-driven aqueous two-phase system applied to food matrices for the simultaneous preconcentration and determination of cobalt and nickel. The method demonstrated high precision, accuracy, and enrichment factors while maintaining strong adherence to the principles of green analytical chemistry by avoiding the use of organic solvents. Key extraction parameters were fine-tuned using multivariate experimental design, leading to extraction efficiencies consistently above 95% for both target analytes. Application to various real food samples, including peanut, hazelnut, corn starch, corn flour, and shrimp, produced robust and reproducible outcomes. Beyond analytical robustness, the method presents important environmental advantages. By avoiding the use of organic solvents and relying instead on a surfactant-based phase separation, the procedure aligns with the principles of green chemistry and reduces the ecological impact commonly associated with sample preparation. Moreover, the method enhances the performance of flame atomic absorption spectrometry, a technique valued for its affordability and wide availability. These findings establish the methodology as a versatile and sustainable analytical platform for trace element determination in complex food matrices, with direct relevance to food safety and quality control. Declarations Conflict of Interest: The authors declare that they have no conflict of interest. Author Contribution Dilaine Suellen Caires Neves: Data curation, Writing- Original draft preparation, Investigation, MethodologyRobson Silva da França: Data curation, InvestigationAnderson Santos Souza: Writing- Reviewing and Editing, Conceptualization, MethodologyLeandro Rodrigues de Lemos: Writing- Reviewing and Editing, Conceptualization, Methodology, Funding acquisition, Project administration ACKNOWLEDGEMENTS The authors are grateful for the financial support provided by FAPEMIG (APQ-03088-21 and APQ-02223-24), Federal University of the Jequitinhonha e Mucuri Valleys and and Federal University of Bahia. LRL is grateful to CNPq (grant number 307450/2022-8). References Altunay, N., Elik, A., & Gürkan, R. (2019). Vortex assisted-ionic liquid based dispersive liquid liquid microextraction of low levels of nickel and cobalt in chocolate-based samples and their determination by FAAS. Microchemical Journal , 147 , 277–285. https://doi.org/10.1016/J.MICROC.2019.03.037 Álvarez, M. 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Mechanisms of Co, Ni, and Mn toxicity: From exposure and homeostasis to their interactions with and impact on lipids and biomembranes. Biochimica et Biophysica Acta (BBA) - Biomembranes , 1862 (8), 183250. https://doi.org/10.1016/J.BBAMEM.2020.183250 Valadares, A., Valadares, C. F., de Lemos, L. R., Mageste, A. B., & Rodrigues, G. D. (2018). Separation of cobalt and nickel in leach solutions of spent nickel-metal hydride batteries using aqueous two-phase systems (ATPS). Hydrometallurgy , 181 , 180–188. https://doi.org/10.1016/J.HYDROMET.2018.09.006 Vera Candioti, L., De Zan, M. M., Cámara, M. S., & Goicoechea, H. C. (2014). Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta , 124 , 123–138. https://doi.org/10.1016/J.TALANTA.2014.01.034 Yao, T., Feng, C., Zhou, Y., & Song, J. (2025). Thermo-sensitive magnetic fluid based aqueous two-phase system for the separation and purification of aloe anthraquinones. Journal of Food Composition and Analysis , 142 , 107459. https://doi.org/10.1016/J.JFCA.2025.107459 Zhang, X., Cai, Z., Wang, L., Xie, S., & Zong, W. (2024). Unlocking Liquid-Liquid Separation: Exploring the Marvels of Aqueous Two-Phase Systems. Microchemical Journal , 200 , 110445. https://doi.org/10.1016/J.MICROC.2024.110445 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":199780,"visible":true,"origin":"","legend":"\u003cp\u003ePareto chart from the 2⁴ factorial design showing the standardized effects of the studied variables\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7661147/v1/1bbfdf6a94d511cfd59d58a2.png"},{"id":93702419,"identity":"706af77e-b006-4c5e-901e-e0aa612d61a0","added_by":"auto","created_at":"2025-10-16 15:50:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":903188,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface plots obtained from the Box–Behnken design showing the combined effects of (a) PAR concentration and pH, (b) thermostatic bath time and PAR concentration, and (c) thermostatic bath time and pH on the global desirability for simultaneous extraction of Co and Ni.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7661147/v1/aca37ca3842db7ae20c968d2.png"},{"id":93702000,"identity":"36137b22-832e-4ef2-b02d-2fad1c763f4e","added_by":"auto","created_at":"2025-10-16 15:42:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59759,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7661147/v1/6ae68a21e5440a5723e3c121.png"},{"id":93705028,"identity":"0e9c073e-3bd2-4448-85c7-148a481c263e","added_by":"auto","created_at":"2025-10-16 16:14:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2138774,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7661147/v1/06f05999-0a87-4900-b189-0a9592d5cd10.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Surfactant-Induced Aqueous Two-Phase System for the Green Preconcentration and Determination of Cobalt and Nickel in Food Samples","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe definition of metal toxicity in the human body depends on its concentration, as any element can be considered potentially toxic at elevated levels (Arpa \u0026amp; Arıdaşır, 2019), including cobalt and nickel (Fioravanti et al., 2025). Cobalt is associated with vitamin B12 and plays a vital role in preventing pernicious anemia. Its deficiency has been linked to hepatic, pancreatic, and nervous system disorders, while excessive intake can lead to toxic effects such as vasodilation, hypothyroidism, neuropathy, auditory or visual impairment, and cardiomyopathy. Nickel, on the other hand, is present in the active sites of certain enzymes and, when accumulated in high concentrations, may cause stomach pain, kidney and cardiovascular diseases, contact dermatitis, and respiratory conditions such as lung lesions (Arpa \u0026amp; Arıdaşır, 2019; Elik et al., 2021; Simonsen et al., 2012).\u003c/p\u003e\u003cp\u003eCobalt and nickel are trace elements that, while necessary in small amounts, may be present in food due to environmental contamination or industrial activities (Demarquoy, 2025). Recent research indicates that these metals can accumulate in the food chain, posing potential health risks. Studies have found that cobalt and nickel exposure can affect soil microbiota and lead to bioaccumulation in organisms at higher trophic levels (Garza Amaya et al., 2023). Reliable quantification of these metals in food is crucial, as excessive intake can cause health issues such as liver and neurological problems with cobalt, and kidney damage and skin conditions with nickel (Sule et al., 2020). Thus, accurate and effective analytical techniques are essential to ensure food safety and protect public health (Bida et al., 2025).\u003c/p\u003e\u003cp\u003eOne of the most commonly used techniques for the determination of Co and Ni in various types of samples is flame atomic absorption spectrometry (FAAS). Although this technique is widely applied for the quantification of metal ions, it lacks sufficient sensitivity for detecting these analytes in food samples. Therefore, additional separation and preconcentration steps are necessary to achieve higher selectivity and sensitivity (de S. Dias et al., 2020a; Ghaedi et al., 2013).\u003c/p\u003e\u003cp\u003eIn this context, aqueous two-phase systems (ATPS) are excellent alternatives for sample preparation, as they offer several advantages, including ease of preparation, absence of organic solvents, high water content, low interfacial tension, high selectivity, and cost-effectiveness (Hamta \u0026amp; Dehghani, 2017; Yao et al., 2025; Zhang et al., 2024). The extraction of cobalt and nickel using ATPS has been well documented in the literature with satisfactory results (Rodrigues et al., 2012; Valadares et al., 2018). However, the simultaneous preconcentration of these metals using ATPS has not been reported, nor has their application to food samples. Moreover, none of the reported ATPS are surfactant-based.\u003c/p\u003e\u003cp\u003eSurfactant-based ATPS enables a wider range of applications for metal extraction, as micelle formation allows for the efficient extraction of hydrophobic analytes, including various metal complexes (Neves et al., 2024). Additionally, these systems offer advantages such as lower interfacial tension compared to other ATPS compositions and require smaller amounts of inorganic salts to induce phase separation (Figueiredo et al., 2024). Triton X-100 is the most commonly used surfactant for various analytical applications, and its integration into ATPS has expanded the potential uses of these systems, from metal extraction (Silva et al., 2021) to biomolecule separation (\u0026Aacute;lvarez et al., 2025). Nevertheless, analytical applications of the ATPS-triton X-100 remain largely underexplored.\u003c/p\u003e\u003cp\u003eTherefore, the objective of this study is to develop an accurate and reliable analytical method for the simultaneous preconcentration of cobalt and nickel in food samples using a surfactant-based aqueous two-phase system composed of Triton X-100. This approach aims to combine the benefits of micellar extraction with the environmentally benign characteristics of ATPS, contributing to the advancement of green analytical chemistry. To ensure optimal performance, key parameters such as pH, complexing agent concentration, equilibrium time, and centrifugation time were optimized using a multivariate experimental design based on the desirability function approach. This study contributes to expanding the analytical applications of surfactant-based ATPS, particularly in food safety and trace metal monitoring.\u003c/p\u003e"},{"header":"2. EXPERIMENTAL","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Reagents\u003c/h2\u003e\u003cp\u003eThe aqueous two-phase system was formed using Triton X-100 (Sigma\u0026ndash;Aldrich, USA) and sodium sulfate (Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e), all supplied by Synth (Brazil). Deionized water (MILLI-Q, R\u0026thinsp;\u0026ge;\u0026thinsp;18 MΩ\u0026middot;cm⁻\u0026sup1;) was used throughout the experiments. Standard solutions of cobalt and nickel (1000 mg\u0026middot;kg⁻\u0026sup1;) were obtained from Sigma\u0026ndash;Aldrich, USA. Potassium hydroxide (ISOFAR, Brazil) was employed for pH adjustment. Nitric acid (HNO\u003csub\u003e3\u003c/sub\u003e, 65%) (Sigma\u0026ndash;Aldrich, USA) and hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 35%) (Din\u0026acirc;mica, Brazil) were used for sample digestion. The complexing agent PAR (4-(2-pyridylazo)resorcinol) and the certified reference material (NIST 1515\u0026ndash;Apple Leaves) were also purchased from Sigma\u0026ndash;Aldrich, USA. All reagents were of analytical grade and used as received, without further purification. All glassware was pretreated with 10% nitric acid for 24 hours and thoroughly rinsed with deionized water.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Optimization Analytical Methodology\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Metal ion extraction in ATPS\u003c/h2\u003e\u003cp\u003eA mixture was prepared by combining 2.09 g (Shimadzu, AUX220, uncertainty of \u0026plusmn;\u0026thinsp;0.0001 g) of a 20.0% w/w Triton X-100 solution containing PAR, 8.75 g of an 18.0% w/w sodium sulfate solution, and 3.15 g of deionized water, with pH previously adjusted (Metrohm, 827 pH meter) (Liu et al., 2016). The system was agitated (TECNAL, TE0851) and centrifuged (Hettich, Rotofix 32A) at 4000 rpm to promote phase separation. After centrifugation, the tubes were incubated in an ultra-thermostated water bath (Marconi, MA184) at 293.15 K. Subsequently, an aliquot of the top phase (TP) was collected, diluted with deionized water, and the analytical signal intensity for cobalt and nickel was measured by flame atomic absorption spectrometry (FAAS) (Analitik Jenna, Nova 300).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Optimization\u003c/h2\u003e\u003cp\u003eMultivariate optimization of the experimental variables was carried out in two stages. In the first stage, a full factorial design 2\u003csup\u003e4\u003c/sup\u003e was applied to evaluate the influence of four factors on the extraction and preconcentration of cobalt and nickel in the ATPS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The variables considered were pH, concentration of the complexing agent (PAR), incubation time in the thermostatted bath time (TBT), and centrifugation time (CT). In the second stage, a Box\u0026ndash;Behnken design was employed to refine the optimization and determine the critical values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All experiments were conducted in randomized order, and the results were analyzed using Statistica 6.0 software.\u003c/p\u003e\u003cp\u003eIn both stages, the concentrations of cobalt and nickel in the top phase were simultaneously optimized using the desirability function. This approach allows the combination of individual responses for each analyte into a single response (Vera Candioti et al., 2014). The individual desirability function (\u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) and the global desirability (\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eG\u003c/em\u003e\u003c/sub\u003e) were calculated according to Equations 1 and 2.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{i}=\\:\\left\\{\\begin{array}{cc}0\u0026amp;\\:\\text{if\\:\\:}{y}_{i}\u0026lt;{L}_{i}\\\\\\:{\\left(\\frac{{y}_{i}-{L}_{i}}{{U}_{i}\\:-{L}_{i}}\\right)}^{s}\u0026amp;\\:\\text{if\\:}{L}_{i\\:}\\le\\:{y}_{i}\\le\\:{U}_{i}\\\\\\:1\u0026amp;\\:\\text{if}{\\:\\:y}_{i\\:}\u0026gt;{U}_{i}\\end{array}\\right.\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{G}=\\:\\sqrt[2]{{d}_{i,\\:Co\\:\\:\\:}\\bullet\\:\\:{d}_{i,Ni}}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the upper acceptable value for the response and \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the lower, \u003cem\u003es\u003c/em\u003e is a power value named \u0026ldquo;weight\u0026rdquo;, which was set to 1, as nickel and cobalt were considered equally important.\u003c/p\u003e\u003cp\u003eThe mass ratio between the surfactant-rich and salt-rich phases, as well as the tie-line length (TLL), were investigated using a univariate approach. The phase ratio was evaluated in the range of 0.0770 to 0.210, and TLL values of 14.5, 18.9, 23.4, and 27.7% w/w were assessed. The extraction percentage (%E) was calculated as the ratio between the number of moles of analyte in the top phase and the total number of moles of analyte in the system, multiplied by 100. The selectivity of the proposed methodology was assessed by introducing various ions commonly found in food matrices alongside the target analytes into the ATPS. Potential matrix interferences were evaluated by comparing the analytical signals obtained in the presence and absence of these concomitant species.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Analytical Characteristics\u003c/h2\u003e\u003cp\u003eThe analytical parameters required for method validation were determined using classical approaches. The limits of detection (LOD) and quantification (LOQ) were established based on the measurement of 10 blanks. The LOD was calculated as three times the standard deviation of the blank divided by the slope of the calibration curve, while the LOQ was determined as ten times the standard deviation of the blank divided by the slope of the calibration curve. Precision was assessed through eight replicate analyses at a concentration of 50.0 \u0026micro;g kg⁻\u0026sup1; for both cobalt and nickel, and expressed as the relative standard deviation (RSD). Linearity was evaluated by constructing calibration curves ranging from 25.0 to 250 \u0026micro;g kg⁻\u0026sup1; for nickel and from 50.0 to 250 \u0026micro;g kg⁻\u0026sup1; for cobalt. The enrichment factor was calculated as the ratio between the slope of the preconcentrated calibration curve and the direct calibration curve. Accuracy was assessed using a certified reference material (NIST 1515 - Apple Leaves). Following optimization, the method was applied to the determination of cobalt and nickel in food samples, including peanuts, hazelnuts, shrimp, starch, and cornmeal.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Application to Real Samples\u003c/h2\u003e\u003cp\u003eSamples of peanuts, hazelnuts, shrimp, starch, and cornmeal were purchased from local markets. The samples were dehydrated and ground prior to analysis. For digestion, 0.200 g of each sample was weighed in triplicate, and 2.00 mL of concentrated HNO\u003csub\u003e3\u003c/sub\u003e, 1.00 mL of water, and 2.00 mL of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e were added. The mixtures were subjected to microwave-assisted (Provecto Analitica, DGT 100 plus) digestion using the following program: 160 W for 5 minutes, 300 W for 2 minutes, and 100 W for 3 minutes. After digestion, the pH of each sample was adjusted using KOH, and the volume was brought to 20.0 mL. A blank and the certified reference material (NIST 1515 \u0026ndash; Apple Leaves) were digested following the same procedure, with the mass of the certified sample (0.500 g), which was digested with 4.00 mL of HNO\u003csub\u003e3\u003c/sub\u003e and 2.00 mL of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. The digested samples were then used as the solvent for the preparation of the Triton X-100 and sodium sulfate solutions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003eTriton X-100 is a nonionic surfactant with amphiphilic properties, making it highly suitable for the extraction of both hydrophobic and hydrophilic analytes. In an ATPS composed of Triton/salt/water, metal ions typically partition into the salt-rich phase (bottom phase (BP)). To preconcentrate these analytes in the surfactant-rich phase (top phase), it is necessary to reduce their hydrophilicity through complexation. Preliminary studies indicated that the combination of sodium sulfate as the salting-out agent and the complexing agent PAR resulted in the highest extraction efficiency for the analytes. The Co\u0026ndash;PAR and Ni\u0026ndash;PAR complexes exhibit greater hydrophobic character and can be solubilized within the micelles formed by Triton X-100. Accordingly, the ATPS composed of Triton X-100, Na\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, and H\u003csub\u003e2\u003c/sub\u003eO at a temperature of 293.15 K (Liu et al., 2016). In the presence of PAR, it was employed as a platform for the preconcentration of cobalt and nickel.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Optimization\u003c/h2\u003e\n \u003cp\u003eThe first stage of the multivariate analysis was carried out using a two-level factorial design, in which the complexing agent concentration ([PAR]), pH, centrifugation time (CT), and thermostatic bath time (TBT) were evaluated. The results, expressed in absorbance, were analyzed, and the desirability values were calculated using Equations 3.1 and 3.2. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the two-level factorial design matrix with the individual desirability values for cobalt and nickel and the global desirability, while Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays the Pareto chart.\u003c/p\u003e\n \u003cp\u003eAnalysis of the results presented in the Pareto chart revealed that three variables were statistically significant: complexing agent concentration, thermostatic bath time, and pH. All exhibited positive effects, indicating a tendency toward the upper levels of the studied ranges. The variable with the greatest effect was the thermostatic bath time, evaluated between 2 and 6 hours. The positive effect suggests that a minimum time is necessary for the system to reach mass transfer equilibrium between the phases, thereby improving extraction efficiency.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e2⁴ full factorial design matrix with experimental responses for Co and Ni.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003csup\u003ea\u003c/sup\u003e Cobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eNickel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[PAR]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"19\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"19\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"19\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"19\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eID: Individual Desiralility. \u003csup\u003eb\u003c/sup\u003eGD: Global Desiralility\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe pH is a critical factor in the formation of metal\u0026ndash;ligand complexes between Co\u0026sup2;⁺/Ni\u0026sup2;⁺ ions and the PAR reagent. The pH range investigated for the digested samples was from 4 to 10. The positive effect observed for pH is attributed to the increased deprotonation of the PAR molecule at higher pH values, which enhances its ability to coordinate with metal ions due to a greater number of electron pairs available for Lewis acid\u0026ndash;base interactions. The PAR concentration was evaluated between 0.00500 and 0.0200%wt. A positive effect was observed, with the highest concentration yielding better extraction efficiency. At higher concentrations, more PAR molecules are available to form metallic complexes, which promotes their transfer to the surfactant-rich top phase and improves extraction performance. Centrifugation time, in contrast, did not show a significant effect on the analytical response. Therefore, the lowest tested value (3.0 min) was used in subsequent experiments to ensure efficient separation without unnecessarily delaying the procedure.\u003c/p\u003e\n \u003cp\u003eFollowing the identification of the significant variables, a Box-Behnken design was applied to determine their optimal conditions. In this step, the global desirability function was employed to enable the simultaneous optimization of both analytes. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the experimental matrix for the three-variable Box-Behnken design, along with the corresponding individual and global desirability values.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBox\u0026ndash;Behnken design matrix with experimental results and desirability responses for Co and Ni.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExp.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003csup\u003ea\u003c/sup\u003e Cobalt\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eID\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eNickel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGD\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e[PAR]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTBT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eID: Individual Desiralility. \u003csup\u003eb\u003c/sup\u003eGD: Global Desiralility\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe outcomes of the optimization are depicted in the response surface plots shown in Fig. 2. The study ranges for the variables, thermostatic bath time (6.0\u0026ndash;12.0 h) and PAR concentration (0.0200\u0026ndash;0.100%wt), were extended from the maximum values used in the previous experimental design. In contrast, the maximum pH value was set at 10.0 (range: 8.0\u0026ndash;10.0), as higher pH levels may lead to precipitation within the system. For the thermostatic bath time, an increase in analytical signal was observed up to approximately 10 hours (Fig. 2A\u0026ndash;B), after which the response plateaued, indicating that equilibrium in mass transfer between the phases had been achieved. The D\u003csub\u003e\u003cem\u003eG\u003c/em\u003e\u003c/sub\u003e reached maximum values around the central levels of PAR concentration (Fig. 2A and 2C). While moderate increases in PAR improve analyte extraction, excessive amounts may saturate the top phase, decreasing the extraction efficiency. Likewise, elevated pH levels promote the formation of metal hydroxides, leading to a reduced analytical signal.\u003c/p\u003e\n \u003cp\u003eThe optimal values for each variable were determined through response surface methodology based on the Box-Behnken matrix. Both linear and quadratic models were evaluated to identify the best mathematical fit to the DG data. The quality of the selected model was assessed via lack-of-fit testing. According to the analysis of variance (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the quadratic model exhibited no significant lack of fit (F\u003csub\u003e\u003cem\u003ecalculated\u003c/em\u003e\u003c/sub\u003e \u0026lt; F\u003csub\u003e\u003cem\u003etabulated\u003c/em\u003e\u003c/sub\u003e), confirming its suitability for describing the system.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of variance (ANOVA) for the quadratic model adjusted to global desirability at the 95% confidence level.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1)pH L\u0026thinsp;+\u0026thinsp;Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(2)[ PAR] L\u0026thinsp;+\u0026thinsp;Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(3) TBT L\u0026thinsp;+\u0026thinsp;Q\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of Fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eErro Puro\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal SQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eF\u003csub\u003etab\u003c/sub\u003e = 19.33. SS- sum of squres; df - degrees of freedom; MS- mean square.\u003c/p\u003e\n \u003cp\u003eBased on the fitted quadratic model, Eq.\u0026nbsp;3 describes the behavior of the system as a function of the studied variables. The optimum conditions were determined using the Lagrange optimization criterion, resulting in a maximum predicted response at a PAR concentration of 0.0710% (w/w), an incubation time in the thermostatic bath of 10.9 hours, and a pH of 9.10. These conditions are consistent with the trends observed in the response surfaces presented in Fig.\u0026nbsp;2.\u003c/p\u003e\n \u003cp\u003eGD = \u0026minus;\u0026thinsp;45.08(\u0026plusmn;\u0026thinsp;0.53)\u0026thinsp;+\u0026thinsp;9.69(\u0026plusmn;\u0026thinsp;0.12)pH\u0026thinsp;+\u0026thinsp;18.39(\u0026plusmn;\u0026thinsp;0.50)[PAR]\u0026thinsp;+\u0026thinsp;0.23(\u0026plusmn;\u0026thinsp;0.01)TBT \u0026ndash; 0.53(\u0026plusmn;\u0026thinsp;0.01)pH\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 129.57(\u0026plusmn;\u0026thinsp;4.06)[PAR]\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 0.011(\u0026plusmn;\u0026thinsp;0.00)TBT\u003csup\u003e2\u003c/sup\u003e Eq. 3\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Evaluation of phase mass ratio and tie-line length\u003c/h2\u003e\n \u003cp\u003eThe influence of the tie-line length and the mass ratio between the surfactant-rich and salt-rich phases on the extraction efficiency was evaluated and is shown in Fig. 3 for both cobalt and nickel. The results indicate no significant variation in the extraction percentage across the tested compositions of the ATPS, with %E values consistently exceeding 95.0%. This behavior suggests that, within the studied range, the system is robust and maintains its extraction performance independently of moderate changes in phase composition. Therefore, the shortest TLL was selected to reduce reagent consumption, namely, lower concentrations of sodium sulfate and Triton X-100, thus enhancing the system\u0026apos;s sustainability and cost-effectiveness. A mass ratio of 0.160 was selected because it provided a suitable preconcentration factor while generating a sufficient volume of the extractant phase to facilitate experimental handling, thereby balancing analytical performance with operational practicality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Assessment of potential matrix interferences on the analytical performance\u003c/h2\u003e\n \u003cp\u003eThe selectivity of the proposed methodology was evaluated through interference studies to assess potential matrix effects on the developed method. Extraction efficiency was investigated in the presence of various common cations and anions, and tolerance limits were determined for each interfering ion. Potential interferences arise from competition between ions for the chelating agent and from impacts on the phase equilibrium of the ATPS. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the maximum molar ratios that caused up to a 5% variation in the analytical signal. The results demonstrate that no significant interference effects were observed. Therefore, it can be concluded that the extraction process under optimal conditions is selective for Ni(II) and Co(II), making it suitable for application in a wide range of food samples.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe maximum interferent concentration that does not affect the determination of the analyte concentration.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterferent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCobalt (mol/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNickel (mol/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecovery (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e2\u0026minus;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.0\u0026ndash;96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNa+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.0\u0026ndash;93.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.0-101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.0\u0026ndash;97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.0\u0026ndash;98.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZn\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.0\u0026ndash;99.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e\u0026minus;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.0\u0026ndash;95.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLi\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2+\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/sub\u003e\u003csup\u003e\u003cstrong\u003e2\u0026minus;\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Analytical Performance Characteristics\u003c/h2\u003e\n \u003cp\u003eAfter optimization of the experimental conditions, the analytical figures of merit of the method were determined. Linearity was evaluated through calibration curves constructed over the ranges of 25.0 to 250 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for nickel and 50.0 to 250 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for cobalt. For the direct calibration curves, the equations obtained were y\u0026thinsp;=\u0026thinsp;0.00101x\u0026thinsp;+\u0026thinsp;0.0032 with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.998 for nickel, and y\u0026thinsp;=\u0026thinsp;5.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003ex\u0026thinsp;+\u0026thinsp;0.00144 with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.999 for cobalt. For the preconcentrated calibration curves, the equations were y\u0026thinsp;=\u0026thinsp;0.0169x\u0026thinsp;+\u0026thinsp;0.00171 with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.999 for nickel, and y\u0026thinsp;=\u0026thinsp;0.0118x\u0026thinsp;\u0026minus;\u0026thinsp;0.00408 with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.999 for cobalt. Enrichment factors of 20.3 for cobalt and 16.7 for nickel were calculated from the slopes of the direct and preconcentrated curves, values that are consistent with preconcentration methods using FAAS reported in the literature (de S. Dias et al., 2020a). Precision, expressed as relative standard deviation (RSD), yielded values of 6.32% for cobalt and 7.37% for nickel. LOD were 0.330 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for cobalt and 0.0370 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for nickel, while LOQ were 1.10 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.890 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Method accuracy was confirmed by analyzing the certified reference material NIST1515 (apple leaves), which has certified values of 90.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for cobalt and 936\u0026thinsp;\u0026plusmn;\u0026thinsp;0.094 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for nickel. The obtained results were 87.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for cobalt and 916\u0026thinsp;\u0026plusmn;\u0026thinsp;0.028 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for nickel, with recoveries around 97%, demonstrating excellent agreement between measured and certified values. The Student\u0026rsquo;s test at a 95% confidence level indicated no statistically significant difference between the experimental and certified values, thus validating the developed methodology. The analytical figures of merit obtained with the proposed methodology are comparable or superior to those reported for other preconcentration approaches coupled with FAAS detection (Altunay et al., 2019; de S. Dias et al., 2020b; Lemos et al., 2007).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Application to Real Samples\u003c/h2\u003e\n \u003cp\u003eAfter method validation, the developed procedure was applied to determine cobalt and nickel ions in peanut, hazelnut, corn starch, corn flour, and shrimp samples. These samples were also spiked with known concentrations of the analytes (25.0 and 50.0 \u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to evaluate recovery. The results, along with recovery values for the fortified samples, are presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. All data represent the mean and standard deviation of three independent replicates. The recoveries obtained were satisfactory and showed no significant difference compared to the unspiked samples, as confirmed by Student\u0026rsquo;s test (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05). These results demonstrate the method\u0026rsquo;s robustness and reliability for cobalt and nickel determination in complex food matrices. Additionally, the method avoids the use of organic solvents, reducing environmental impact and exposure to toxic reagents. The procedure is straightforward, cost-effective, and compatible with routine laboratory analysis, highlighting its potential for widespread application in food quality control and environmental monitoring while adhering to green chemistry principles.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMeasured concentrations of cobalt and nickel in different food matrices and corresponding recovery values obtained at two fortification levels.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCobalt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNickel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdded (\u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFound (\u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdded (\u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFound\u003c/p\u003e\n \u003cp\u003e(\u0026micro;g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeanut\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazelnut\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e258\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorn starch\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorn flour\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eShrimp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThis study presents the first report of a surfactant-driven aqueous two-phase system applied to food matrices for the simultaneous preconcentration and determination of cobalt and nickel. The method demonstrated high precision, accuracy, and enrichment factors while maintaining strong adherence to the principles of green analytical chemistry by avoiding the use of organic solvents. Key extraction parameters were fine-tuned using multivariate experimental design, leading to extraction efficiencies consistently above 95% for both target analytes. Application to various real food samples, including peanut, hazelnut, corn starch, corn flour, and shrimp, produced robust and reproducible outcomes. Beyond analytical robustness, the method presents important environmental advantages. By avoiding the use of organic solvents and relying instead on a surfactant-based phase separation, the procedure aligns with the principles of green chemistry and reduces the ecological impact commonly associated with sample preparation. Moreover, the method enhances the performance of flame atomic absorption spectrometry, a technique valued for its affordability and wide availability. These findings establish the methodology as a versatile and sustainable analytical platform for trace element determination in complex food matrices, with direct relevance to food safety and quality control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDilaine Suellen Caires Neves: Data curation, Writing- Original draft preparation, Investigation, MethodologyRobson Silva da Fran\u0026ccedil;a: Data curation, InvestigationAnderson Santos Souza: Writing- Reviewing and Editing, Conceptualization, MethodologyLeandro Rodrigues de Lemos: Writing- Reviewing and Editing, Conceptualization, Methodology, Funding acquisition, Project administration\u003c/p\u003e\u003ch2\u003e ACKNOWLEDGEMENTS\u003c/h2\u003e\u003cp\u003eThe authors are grateful for the financial support provided by FAPEMIG (APQ-03088-21 and APQ-02223-24), Federal University of the Jequitinhonha e Mucuri Valleys and and Federal University of Bahia. LRL is grateful to CNPq (grant number 307450/2022-8).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAltunay, N., Elik, A., \u0026amp; G\u0026uuml;rkan, R. (2019). Vortex assisted-ionic liquid based dispersive liquid liquid microextraction of low levels of nickel and cobalt in chocolate-based samples and their determination by FAAS. \u003cem\u003eMicrochemical Journal\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e, 277\u0026ndash;285. https://doi.org/10.1016/J.MICROC.2019.03.037\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez, M. S., Deive, F. J., Rodr\u0026iacute;guez, A., \u0026amp; Longo, M. A. (2025). Designing biodegradable aqueous biphasic systems for the selective separation of enzymes. \u003cem\u003eSeparation and Purification Technology\u003c/em\u003e, \u003cem\u003e353\u003c/em\u003e, 128508. https://doi.org/10.1016/J.SEPPUR.2024.128508\u003c/li\u003e\n\u003cli\u003eArpa, \u0026Ccedil;., \u0026amp; Arıdaşır, I. (2019). 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Thermo-sensitive magnetic fluid based aqueous two-phase system for the separation and purification of aloe anthraquinones. \u003cem\u003eJournal of Food Composition and Analysis\u003c/em\u003e, \u003cem\u003e142\u003c/em\u003e, 107459. https://doi.org/10.1016/J.JFCA.2025.107459\u003c/li\u003e\n\u003cli\u003eZhang, X., Cai, Z., Wang, L., Xie, S., \u0026amp; Zong, W. (2024). Unlocking Liquid-Liquid Separation: Exploring the Marvels of Aqueous Two-Phase Systems. \u003cem\u003eMicrochemical Journal\u003c/em\u003e, \u003cem\u003e200\u003c/em\u003e, 110445. https://doi.org/10.1016/J.MICROC.2024.110445\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"surfactant-based extraction; food matrices, trace metal analysis, sample preparation","lastPublishedDoi":"10.21203/rs.3.rs-7661147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7661147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSurfactant-induced aqueous two-phase systems (ATPS) offer an environmentally friendly alternative for the separation and preconcentration of analytes, minimizing toxic waste generation and operational costs. In this work, we report for the first time the application of a surfactant-driven ATPS to the simultaneous extraction and preconcentration of cobalt and nickel from food matrices. The system was composed of Triton X-100\u0026thinsp;+\u0026thinsp;Na2SO4\u0026thinsp;+\u0026thinsp;H2O in the presence of 4-(2-Pyridylazo)resorcinol (PAR) as the complexing agent, followed by detection via flame atomic absorption spectrometry. Key parameters, including pH, PAR concentration, centrifugation time, and incubation time, were optimized through multivariate analysis based on a desirability function approach. Optimal conditions were pH 9.2, centrifugation time 10 min, thermostatic bath time 11 h, and PAR concentration 0.0750% w/w. Under these conditions, the limits of detection and quantification were 0.330 and 1.10 \u0026micro;g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Co, and 0.0370 and 0.890 \u0026micro;g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Ni, respectively, with enrichment factors of 20.2 and 16.7. The method showed good precision, with RSDs of 6.3% for Co and 7.4% for Ni, and accuracy verified using the certified reference material NIST 1515 (apple leaves), yielding recoveries of 97.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7% for Co and 97.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9% for Ni. In real food samples, recoveries ranged from 96% to 106%, further confirming the reliability of the approach. This novel methodology, by combining micellar extraction with the principles of green chemistry, provides a reliable, cost-effective, and sustainable strategy for trace metal monitoring in food safety applications.\u003c/p\u003e","manuscriptTitle":"Surfactant-Induced Aqueous Two-Phase System for the Green Preconcentration and Determination of Cobalt and Nickel in Food Samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 15:42:51","doi":"10.21203/rs.3.rs-7661147/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-24T09:36:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T01:31:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T11:15:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10290598101506665636787563979930350074","date":"2025-10-09T00:34:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338859821007055352760731248064824872516","date":"2025-10-07T11:59:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265375859532426914213746036043928520692","date":"2025-10-04T04:17:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T09:49:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T22:10:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T22:09:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Food Analytical Methods","date":"2025-09-19T20:51:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"food-analytical-methods","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Food Analytical Methods](https://www.springer.com/journal/12161)","snPcode":"12161","submissionUrl":"https://submission.nature.com/new-submission/12161/3","title":"Food Analytical Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f5492c51-557f-4ad4-99f9-7e7b18028790","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T22:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 15:42:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7661147","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7661147","identity":"rs-7661147","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}