Determination of metals in Pork Lard: A rapid and low cost sample preparation by RP-DLLME

doi:10.21203/rs.3.rs-1821371/v1

Download PDF

Research Article

Determination of metals in Pork Lard: A rapid and low cost sample preparation by RP-DLLME

https://doi.org/10.21203/rs.3.rs-1821371/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

ABSTRAT In this work, a new method was proposed for determination of Cu, Fe, Mg, Mn and Ni in pork lard by flame atomic absorption spectrometry (F AAS) was developed using reverse phase dispersive liquid-liquid microextraction (RP-DLLME) for sample preparation. The parameters such as type, volume and concentration of dispersing (n-propanol and isopropanol) and extracting (HNO3 and HNO3 + HCl (1:1 v/v) solvents, ultrasound and centrifugation time were evaluated. For the determination of analytes by F AAS, external using aqueous reference solutions was performed for the metals evaluated in the range from 0.25 to 1.5 mg L-1. The limits of quantification for Cu, Fe, Mg, Mn and Ni were 0.0106; 0.0136; 0.0042; 0.0169 and 0.0124 µg Kg-1, respectively. The accuracy was estimated to be between 95.64 to 104.42% and precision (RSD) less than 1% for all analytes. The method was applied to different brands of pork lard (n = 5), where the recovery was between 90.2 and 103.2% and RSD between 0.024 and 0.094. After that, a risk characterization was performed, in which it was obtained for Fe 0.43 to 2.79% and for Mg 0.21 to 4.22% in relation to the recommended daily intake and for Ni 5.13 to 25.83% to the tolerable upper intake level. The risk quotient was less than 1 x 105 so that even sensitive populations are unlikely to suffer adverse effects.

Reversed-phase dispersive liquid–liquid microextraction

determination of minerals

risk assessment

toxicological safety

pork fat

Pork lard is a substance obtained by the fusion of fresh adipose tissues of pigs (Marikkar and Yanty 2014); being used worldwide for frying, bakery products, and meat products, mainly due to its functional properties (Engelmann et al. 2020).

In Brazil, pork lard is regulated by the Regulation of Industrial and Sanitary Inspection of Products of Animal Origin (RIISPOA), which defines it as "the product obtained by the fusion of fresh adipose tissues of pigs, with the addition or not of technology adjuvants" (Brazil 2017). In its elaboration, the use of technology adjuvants is authorized as clarification, filtration, and catalysis agents, consisting of magnesium silicate, nickel, mixtures based on chromium, manganese, and copper oxide, among others (Brazil 2021).

In general, lard is composed of saturated and unsaturated fatty acids, the latter being susceptible to oxidative rancidity. Among the pro-oxidants, transition metals stand out, such as copper, iron, nickel, and manganese (Campbell-Platt 2015; Srinivasan and Kirk 2019). These components can be found in food by being common constituents of ingredients, water, packaging materials, and even technological adjuvants and act through one of the pathways of decomposition of hydroperoxides into free radicals, catalyzing oxidative rancidity (Srinivasan and Kirk 2019).

Thus, the determination of metals in oils and fats is a topic under constant investigation and, several techniques, GFAAS (Leal et al. 2022), ICP-MS (De Souza et al. 2022), ICP-OES (Kalschne et al. 2020; Mohajer et al. 2020) and F AAS (Guimarães et al. 2022; Da Mata Cerqueira et al. 2022; Roveda and Raposo 2019), have already been reported for the determination of metals in these matrices. However, due to the high content of organic compounds present and the high viscosity observed in these matrices, associated with the low levels of metals concentration in the samples, the investigation of metals in these matrices is an evident challenge, being possible many times, only after a careful sample preparation protocol.

Different sample preparation procedures employing microwave radiation, wet and dry digestion, and extraction protocols have been reported for this purpose (Carneiro et al. 2020, Mirón et al. 2020). However, these procedures and protocols are laborious, prone to contamination and loss of analytes (Godoy-Caballero et al. 2013).

The reverse-phase dispersive liquid-liquid microextraction (RP-DLLME) method has been successfully applied to the preparation of biodiesel, fish oil, and hydrogenated cocoa butter samples to determine metals. The RP-DLLME method is based on the extraction of metals from organic matrices in a small volume of an aqueous phase as the extracting solvent. Thus, the RP-DLLME has some advantages over other preparation procedures, such as the use of an aqueous phase in the extraction step (environmentally friendly), possibility of high enrichment factor (gain in sensitivity), shorter extraction time, and simplicity of operation and low cost (Kalschne et al. 2020; López-García et al. 2014; Lourenço et al. 2019; Rezaee et al. 2006).

Thus, this study proposes the development of a method for the determination of copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), and nickel (Ni) in pork lard using RP-DLLME and subsequent determination by flame atomic absorption spectrometry (F AAS). After method validation, commercial pork lard was analyzed and from the quantification, exposure and health risk for the total population and children were estimated.

Reagents, standards, equipment, and materials

Analytical grade reagents and solvents were used: HPLC grade isopropanol (2-propanol, 100%, Chromasolv®, Honeywell, Riedel-deHaen®, Germany, ref. 34863) and n-propanol ACS (≥ 99%, Sigma Aldrich, United States of America, ref. 402893) as a dispersing agent. Hydrochloric acid (HCl) PA/ACS (37%, d = 1.2 g mL^− 1, Sigma Aldrich, United States of America, ref. 84378) and nitric acid (HNO₃) P.A/ ACS (65.0–67%, d = 1.37–141 g mL^− 1, Sigma-Aldrich, United States of America, ref. 84378) as extracting solvent. Nitric acid (0.5% v.v^− 1) was also used to quench the solution volume, and xylene (≥ 98.5%, Sigma Aldrich, United States of America) was used as a solvent to prevent the solidification of the lard.

The standards for the calibration curves were prepared from the individual reference solutions containing 1000 mg L^− 1 of Ni, Mg, Fe, Mn, and Cu (SpecSol®, Brazil, ref. AANI 1000V, AAMG 1000V, AAFE 1000V, AAMN 1000V, AACU 1000V, respectively) by serial dilutions. For the recovery tests in pork lard samples, was used the individual standard solution containing 100 µg g^− 1 Cu, Fe, Mg, Mn, and Ni, based on certified reference mineral oil (SpecSol®, Brazil).

Ultrapure water was used for cleaning the glassware, preparing solutions and dilutions (resistivity less than 18.5 MΩ.cm^− 1, Master System Gehaka, São Paulo, Brazil),.

The quantification and determination of Cu, Fe, Mg, Mn, and Ni were performed by flame atomic absorption spectrometry (F AAS) equipment equipped with a Deuterium background correction system (model AA 240-FS, Varian, Australia), with continuous aspiration flow (5.0 mL min^− 1) and hollow cathode lamps of Cu, Fe, Mg, Mn, and Ni, using acetylene air (White Martins, purity 99.8%) as fuel and purified compressed air. The operating conditions for F AAS were used followingthe equipment manufacturer.

For the RP-DLLME procedure, a vortex (LSM56/4-220, Logen Scientific, Brazil), ultrasonic bath (Elmasonic P 60 H, Elma, China) with a frequency ranging from 37–80 kHz and a capacity of 5.75 L was used. The centrifugation was performed in the centrifuge (CT-5000R, Cientec, Brazil) with a capacity for 12 tubes of 15 mL and a maximum speed of 6000 rpm.

In the sample preparation and extraction steps, polypropylene (PP) tubes with conical bottom and screw cap with 15 mL capacity were used (Sarstedt, Germany, ref 62.554.205).

Samples

The samples were purchased in local commerce in the western region of Paraná, Brazil, packaged in a thermal box and transported to the chemical analysis laboratory of the UTFPR - Medianeira. The samples were stored protected from light and refrigerated at 10 ºC until the moment of the analyses. For all preliminary tests aiming the methodology adjustments, the same brand of pork lard was used (called Lard A), and in the step of metals quantification, different brands of lard were acquired from A to F. The lard B, C, and D had the inspection seal of the Inspection Service of Parana/Animal Origin Products (SIP/POA), being allowed the intercity commercialization, while the brand’s A, E, and F were inspected by the Federal Inspection Service (SIF) and could be commercialized internationally.

RP-DLLME Optimization

All RP-DLLME optimizations were performed using the spike with 0.5 µg g^− 1 of the analytes for recovery tests and the control without the added analytes. For the RP-DLLME procedure optimizations, the following were evaluated: the dispersing solvent (isopropanol and n-propanol); the extracting solvent (HNO₃ (1.0 mol L^− 1), and HNO₃ + HCl solution (v v^− 1 1.0 mol L^− 1)); the concentration of the extracting solvent (0.5, 0.75, 1.0 mol L^− 1 and compared to the assay using only ultrapure water); the ratio of the dispersing solvent: extracting solvent solution (50:50%, 60:40%, 70:30% and 80: 20% v v^− 1); the volume of the dispersant solvent: extractant solution (0.5, 1.0, 1.5 and 2.0 mL); length of stay ultrasound (0, 5 and 10 min) whose parameters temperature (75 ºC) and frequency (37 kHz) were kept constant and the centrifugation (5, 10 and 15 min) the other parameters of the equipment were fixed (rotation 6000 rpm, acceleration 30 s, braking 30 s, and temperature 25°C). All tests were performed in quintuplicates.

RP-DLLME procedure and optimized parameters

The RP-DLLME methodology was performed according to Kalschne et al. (2020), with some modifications (Fig. 1). The sample was heated to a temperature of 30°C for complete melting, and 5 g of pork lard was weighed directly into PP tubes (15 mL). Subsequently, RP-DLLME was performed in the same tube with the sample, following the steps: (1) addition of 5 mL of xylene solvent to keep the sample liquefied and reduce its viscosity; (2) maintenance of the sample in a water bath for 20 min at 75 ºC; (3) addition of 1.0 mL of the mixture containing the dispersant: extractant solvents (isopropanol: HNO₃ + HCl 1:1 v.v^− 1 1 mol L^− 1), in the proportion 60: 40, in the sample; (4) agitation for 1 min in the vortex; (5) ultrasound application for 10 min without agitation (75 ºC and 37 kHz); (6) centrifugation for the period of 5 min (rotation 6000 rpm, acceleration 30 s, braking 30 s and temperature 25°C); (7) removal of the supernatant (oily phase) with a micropipette; (8) removal of the aqueous phase with a micropipette and transfer to a PP tube (15 mL) and; (9) volume adjustment to 5 mL with 0.5% (v.v^− 1) HNO₃.

To ensure the reliability of the results acquired, the parameters of merit were determined: precision, accuracy, the limit of detection (LOD), and limit of quantification (LOQ) according to the methodology of INMETRO - DOQ-CGCRE-008 (2020), being determined from measurements of the blank (n = 10) for all analytes. The blank was prepared in a PP tube using 1.0 mL of dispersant: extractant solvent solution in 5 mL of 0.5% (v.v^− 1) HNO₃.

Determination of Cu, Fe, Mg, Mn, and Ni by F ASS

For the calibration of the equipment, external calibration was used from aqueous reference solutions of Cu, Fe, Mg, Mn, and Ni prepared with the addition of 0.5% (v.v^− 1) HNO3. The mixed solutions were divided from 1 to 5, and the metal was added to each solution at the corresponding concentration, as in Table 1. The blank was made only with 0.5% (v.v^− 1) HNO3.

Risk assessment

The estimated daily intake (EDI) of Cu, Fe, Mg, Mn, and Ni was calculated using intake data and average body weight available from IBGE (2010) for three (3) different age groups among male and female sexes (Eq. 1) (Barone et al. 2020).

\(EDI= \frac{C x IR}{WAB}\)

(1)

Where:

C: is the concentration of the element in the samples studied;

IR: is the daily intake rate (370 mg day^− 1);

WAB: is the average body weight of children aged 5 to 12 years (males 29.84 kg and females 30.51 kg);

WAB: is the average body weight of adults aged 13 to 17 years (Male 55.98 kg and Female 51.58 kg);

WAB: is the average body weight of adults aged 18 to 75 years and older (males 70.69 kg and females 60.87 kg).

The SDIs were compared to the recommended daily allowance (DRI 2006) for Fe and Mg and Ni compared to the upper tolerable intake level (DRI 2006), which represents an estimate of the amount of substance that can be ingested over a lifetime without significant health risk to the consumer.

The hazard quotient (HQ) was calculated using Eq. 2 (Barone et al. 2020).

\(HQ= \frac{EFr x ED x ID x C}{ WABx AT}\)

(2)

Where:

EFr: is the exposure frequency (365 days year^− 1);

ED: is the duration of exposure (70 years), equivalent to the average life span;

ID: is the dietary ingestion rate of lard (0.0037 kg day^− 1);

C: is the metal concentration;

WAB: is the average body weight of the child 5 to 12 years old (Male 29.84 kg and Female 30.51 kg);

WAB: is the average body weight of an adult 13 to 17 years old (Male 55.98 kg and Female 51.58 kg);

WAB: is the average body weight of the adult age group 18 to 75 years and older (Male 70.69 kg and Female 60.87 kg);

AT: is the average exposure time for non-carcinogens (25,550 days (365 days year^− 1 x ED));

RfD: is the oral reference dose (Cu 4.0 x 10^− 2 mg kg^− 1, Fe 7.0 x 10^− 1 mg kg^− 1, Mn 1.4 x 10^− 1 mg kg^− 1, Ni 2.0 x 10^− 2 mg kg^− 1 (EPA 2021) and Mg 1.1 x 10¹ mg kg ^− 1 (DEQ 2015),

The hazard index (HI) determines the potential risk triggered by the metals collectively and is calculated as the sum of the QR, as per Eq. 3 (Barone et al. 2020), where If the obtained value of IP is less than "1", an adverse effect is out of the question in terms of human health

\(HI= \sum {HQ}_{Ni}+{HQ}_{Mg}+{HQ}_{Fe}\dots\)

(3)

Statistical analysis

The results were submitted to analysis of variance and, when significant differences were detected at 5% probability level, to Tukey's test, using the Statistica software version 7.0 (StatSoft,).

RP-DLLME Optimization

In the RP-DLLME, isopropanol and n-propanol were evaluated as dispersing solvents in a mixture with HNO₃. Initial experiments were performed using 1.0 mL of this mixture, containing 70% (v v^-1) dispersing solvent (isopropanol or n-propanol) and 30% (v v^-1) 1 mol L^-1 HNO₃ as extracting solvent. For both dispersant solvents evaluated when injected into the sample, the formation of a turbid phase was observed, followed by the appearance of microdroplets, which characterizes the solvents as effective for the study matrix. However, isopropanol showed a lower standard deviation compared to n-propanol. Therefore, isopropanol was selected as the dispersant in the subsequent experiments.

For the RP-DLLME methodology, where metals are to be extracted from organic samples, the use of extracting solvents is mandatory. These solvents must have low solubility in the model, higher density, and provide extraction and preconcentration capacity of the metal ions (López-García et al 2014). Thus, when evaluating the solvents HNO₃ and the mixture HNO₃ + HCl, the best results were obtained by employing the mixture HNO₃ + HCl.

The ratio of the dispersant: extractant solvents should be optimized since this is one of the parameters that determine the efficiency of the extraction. At small volumes of dispersing solvent, the formation of microdroplets may not be effective, thus reducing the efficiency of the extracting solvent. With excess dispersing solvent volume, two competing processes can occur: a) increased solubility of analytes in the aqueous phase, which leads to reduced partitioning of polar compounds into the extractant microdroplets, and b) increased extraction efficiency generated by the increased volume of the isopropanol phase (Marciel 2013).

The proportions 50/50, 60/40, and 70/30 (v v^-1) did not differ significantly at the 5% level. However, the proportion of 60/40, showed a lower standard deviation which determined the choice for all metals and the subsequent analyses.

The effect of the concentration of the extracting solvent is essential because it favors the displacement of metal cations that present themselves in different forms in the oil sample, either as free ions or bound to organic molecules, increasing the transfer to the aqueous phase (Pereira et al. 2013; Pereira et al. 2014).

The results of the tests are shown in Figure 2, where it is possible to verify that the interaction between concentration and metal has a significant difference (p≤ 0.05), which enabled the comparison of the means test (Tukey test with a confidence level of 95%).

In the present work, when ultrapure water was employed as extracting solvent, percent recoveries lower than 40% were obtained for all metals under study and significantly different (p<0.05) from the solvent HNO₃ + HCl (Figure 2). Lourenço (2016), in turn, obtained a recovery of 105% with an RSD of 0.8% for Mg when he employed ultrapure water as extracting solvent in biodiesel. On the other hand, Pereira et al. (2013), when changing the concentration of HNO₃ for Cu and Mn, did not obtain a significant variation of the signals with the interpretation of concentration; however, there was a need to acidify the solvent. These results indicate that the metals evaluated could be free or bound through weak interactions with the organic molecules present in biodiesel (Pereira et al. 2014). For the pork lard, unlike what occurred for biodiesel, the cations were possibly strongly linked with the organic molecules, thus the need to use the extracting solvent at a concentration of 1.0 mol L^-1.

For all subsequent tests, the concentration of the solution used as an extractant was set at 1.0 mol L^-1 for all metals evaluated.

Defining the amount of volume in the extraction is essential because the dispersing solvent allows the interaction between the oily and aqueous phases. In contrast, the extracting solvent aims to transfer the metals from the oily phase to the aqueous phase. If the amount of the solution is more minor than necessary, incomplete displacement of the metal cations from the organic structures can occur; on the other hand, in excess, further dilution should be performed before going to determine; however, in this process, it can generate the loss of metals that are present in small concentrations (Al-Dalahmeh et al. 2019).

For all metals, as the volume of the solution increased from 1.5 mL onwards, the recovery percentage decreased (Figure 3), which may indicate that the excess solution generated a loss of the metals in the matrix studied. The volumes 0.5 and 1.0 mL did not differ (p<0.05); however, the volume of 1.0 mL was set as the appropriate volume for further tests because it showed the lowest standard deviation between the means.

Priego-Capote and Castro (2004) point out that ultrasound can perform extractions involving transfers from the organic phase to the aqueous phase without the need for additional emulsifying agents, which can be observed from the results obtained in this work. In other studies using the RP-DLLME methodology, some authors reported the use of ultrasound and kept a positive influence on its use, increasing the extraction efficiency (Hashemi et al. 2010; Liu et al. 2013).

The likely mechanism of action correlates with the fact that the ultrasound employed results in the formation of smaller microdroplets, increasing the contact and interaction surface of solvents (Lopez-Garcia et al. 2014; Lopez-Garcia et al. 2015).

Also, some oily samples need a longer time for interaction with the extractant solution. Ultrasound could facilitate the access of the acid solution to all parts of the oily sample's interior and facilitate the separation of the aqueous solution from the oily phase (Al-Dalahmeh et al. 2019).

When evaluating the results (Figure 4), it can be observed that there was a significant difference between the tests with 10 min and without the use of ultrasound for the analytes evaluated (p<0.05), and the use of ultrasound has a positive influence on the microextraction results of these metals for lard, except for Mg, which showed no significant difference (p<0.05) with and without the use of ultrasound, a result similar to that observed by Lourenço et al. (2019) in the microextraction of this analyte in biodiesel. For 5 and 10 min, there was no significant difference at the 5% level between the analytes evaluated; however, the lowest standard deviations were obtained with 10 min of ultrasound, and to standardize for all metals, including Mg, the analyses were continued with 10 min of ultrasound.

The centrifugation time was evaluated to obtain the complete separation of the aqueous phase containing the analytes from the organic phase.

The 5 min centrifugation time, for each metal separately, differed significantly from the other centrifugation times (10 and 15 min), with a higher percentage of recovery and lowered standard deviations, except for Mn, which for this analyte, there is no significant difference at the 5% level between the times of 5 and 10 min. When evaluating the standard deviation, the 10 min time is lower (Figure 5).

For the lard, as the centrifugation time increased, a small slight decrease in the percentage of analyte recovery was observed (Figure 5). A possible explanation lies in the instability of the interface between the polar and apolar phases over time, which favors phase coalescence reducing the extraction percentage (Takashima 2017). In contrast, Lourenço et al. (2016) observed that an increase in centrifugation time (5 to 15 minutes) favored the percentage recovery of analytes for the biodiesel matrix. In turn, Ni extraction was better for vegetable fat when 10 min of centrifugation was employed (Kalschne et al. 2020).

The 5 min centrifugation time was standardized for all analytes because it obtained the highest percent recovery, including for Mn.

RP-DLLME procedure and optimized parameters

To evaluate the analytical and linear range, calibrations of the equipment were performed with aqueous multi-element reference solutions (Cu, Fe, Mg, Mn, and Ni) prepared with 0.5% (v.v^-1) HNO₃ at different concentrations. The linear correlation coefficient (R²), straight-line equation, and linear range data are shown in Table 2.

Table 3 shows the values of LODs and LOQs for the analytes evaluated, which were lower than the limits indicated in the Codex Alimentarius for Cu, Fe, and Ni analysis (ISO 8294 1994), suggests the determination of these in concentrations lower than 0.2 ppm, 1.0 ppm, and 1.0 ppm, respectively, which makes the application of the proposed method feasible.

The accuracy and precision of the method for determining Cu, Fe, Mg, Mn,and Ni in lard were determined by recovery tests and their respective RSD (n=5). 0.3 and 0.5 mg kg^-1 of the analytes were added to the samples. The results are presented in Table 4. After adding 0.3 and 0.5 mg kg^-1 of analytes, the recoveries were 97.52 and 104.42% and 95.64 and 99.16%, respectively, and RSD was less than 7%. According to the Association of Analytical Chemists (AOAC) Guidelines for Standard Method Performance Requirements (2016) and a study conducted by Gonzalez and Herrador (2007), when the analyte concentration in the sample ranges from 100 ppb to 10 ppm, the acceptable recovery range is 80 to 110%, and maximum RSD was less than 11%. Thus, the proposed method showed excellent accuracy.

Determination of Cu, Fe, Mg, Mn, and Ni in pork lard by F AAS after RP-DLLME

The proposed method was applied to samples of pork lard of different brands commercialized in the western region of Paraná, Brazil, to verify the proposed method's applicability to possible variations in the sample matrix.

For the recovery tests, samples were separated with the addition of analyte (test samples), and samples were prepared without the addition of analyte for the determination and quantification of Cu, Fe, Mg, Mn, and Ni. The results are shown in Table 5.

The percent recovery for all analytes remained between 90.2 and 103.2%, and the RSD was between 0.41 and 8.524 (Table 5). These recovery values are acceptable as they are within the 80 to 110% range, and maximum RSD is less than 11% (Gonzalez and Herrador 2007).

The percent quantification of the analytes Cu and Mn were below the LOD of the analytical procedure. For Fe, half of the samples showed a value below the quantification limit, and those that were quantified showed a limit below the maximum described in the Codex Alimentarius (1999), which is 1.5 ppm. As for Ni, the only sample above the quantification limit was below the limit established by the Chinese standard (1.0 ug g^-1) (China 2018).

As for the percentage of quantification of Mg, the samples that presented the highest values were F, followed by C and B. This element maybe because it is used as an adjuvant in the form of magnesium silicate in the lard clarification process. According to RDC nº. 466 of February 10, 2021, there is no defined amount of this coadjuvant, with the maximum limit defined as "quantum satis" as ideal (Brazil 2021); as described in the standard, the presence may occur in technically unavoidable amounts as long as they do not pose risks to human health.

Risk assessment

Knowledge regarding the content of metals in food is of enormous importance for the assessment of risk to humans. For essential metals, it is necessary to their presence in trace amounts in the body, because they play an important role in the maintenance of numerous physiological processes (Barone et al. 2020). For the metals Cu and Mn it was not possible to perform the risk characterization, because they were below the quantification limit (Table 5).

Regarding the three groups of metals evaluated (Fe, Mg, and Ni), inspection of the consumption data (Table 6) indicated that the consumption of pork lard provided 1.93 to 2.79 % for children, 0.61 to 1.08 % for adolescents, and 0.43 to 1.18 % for adults of the mineral Fe; in relation to Mg for children 0.87 to 4.22 %, adolescents 0.28 to 1.63 %, and for adults 0.21 to 1.55 % in relation to RDA (Recommended Daily Allowance). These results show that for Fe and Mg, pork lard cannot supply the entire need for these minerals, making it necessary to ingest them through other means in the diet. For Ni, there is no RDA, and the UL (Tolerable Upper Intake Level) was compared, as shown in Table 6. For this mineral, the values obtained for children ranged from 19.81 to 25.83 %, for adolescents 6.48 to 8.97 %, and for adults 5.13 to 7.60 %. These results revealed that the intake of Ni from the ingestion of lard was high, especially for children, contributing significantly to greater care regarding the consumption of foods that may contain the mineral Ni in order not to exceed the tolerable amount.

The IDE values obtained were used to estimate the risk of non-cancer by means of the HQ, which evaluates the potential risk of adverse health effects of toxic substances to indicate the long-term evaluation. As shown in Table 7, for the three groups evaluated, the results obtained from the HQ were less than 1.0 x 10^-5, a value that shows that even populations sensitive to Fe, Mg, and Ni are unlikely to suffer adverse health effects. With the HQ we calculated the HI (Table 7), which expresses the cumulative value of the effects of different mixtures of metals, and the value obtained was less than 1 for all lard and for the three age groups evaluated.

However, it is important to emphasize that a single ingestion rate was employed for the three groups evaluated (0.37 g day^-1), a parameter that significantly influences the results of an exposure assessment. In this paper, it is important to note that even though the values obtained for HQ of Ni for children are unlikely results that they will suffer any adverse health effects at % UL, it shows cause for concern for the children (19.81 to 25.83 %) when they may be exposed to this metal by other routes.

However, when assessing exposure, there are a number of uncertainties that must be taken into consideration such as: technical (sampling procedures, sample preparation, the variations in consumption by regions in the same country, packaging of the lard) as well as biological factors (natural variability in an individual's response, bioavailability of minerals after food intake, nutritional status), which need to be recognized as they may lead to biases in the results (Barone et al. 2020).

The RP-DLLME methodology for sample preparation is characterized for being simple, because it involves few steps of easy operation and economical due to the small amounts of solvents used, compared to other techniques used for the preparation of lard samples, which generally employ time-consuming decomposition steps with oxidizing acids concentrated in large volumes and the use of expensive equipment (microwave digestion).

In this study, a new method for the determination of Cu, Fe, Mg, Mn, and Ni was developed based on the RP-DLLME methodology for the lard matrix, allowing simultaneous extraction and pre-concentration of the analytes, as well as F AAS determination.

For the proposed sample preparation procedure, a solvent mixture of isopropanol (dispersant, 600 µL) and HNO₃ + HCl (1 mol) (extractant, 400 µL) was injected (1 mL) into 5 g sample with xylene preheated at 75 ºC and then ultrasound for 10 min at 75 ºC. Next, the sample was centrifuged (5 min at 6000 rpm), the sample phase was then partially removed using a pipette and the aqueous phase was transferred to a clean vial for subsequent calibration with 0.5% (v.v^− 1) HNO₃ and elemental determination by F AAS.

As for the analytical characteristics of the method, one can highlight the satisfactory values for LOD and LOQ, accuracy (95 to 104%) and precision of less than 1%. Thus, the feasibility of applying the proposed method for routine analysis of pork lard samples for the determination of Cu, Fe, Mg, Mn and Ni by F AAS can be proved.

Regarding risk characterization, the estimated intake values showed that the metal intake from pork lard intake was within their recommended daily intake limits for Fe and Mg and within the limits for tolerable intake for Ni, for all lard and groups tested. For HQ, the values of the metals individually indicate no health risk for the groups evaluated, as do their combined effects (HI) with values less than 1. The present study indicates the need to monitor Ni contamination in this product on a regular and larger scale due to the presence of approximately 26% of the UL for children.

DECLARATION OF INTEREST

I would like to declare on behalf of my co-authors that there is no conflict of interest in the submission of this manuscript, and all authors approve the manuscript for publication. The described was original research that has not been published previously and is not being considered for publication elsewhere, in whole or in part. All listed authors have approved the manuscript that is attached.

AUTHOR CONTRIBUTIONS

This work was part of the master's work of Mayara Patricia de Oliveira Santos, who collected the samples, validated the RP-DLLME methodology, performed the analysis by F ASS, treated the data, and wrote the paper; Nicoli Andressa Carboni as undergraduate students helped with sample preparation; Eder Lisandro de Moraes Flores, Cristiane Canan and Oldair Donizeti Leite helped with guidance and performing the quantitative analysis by F ASS; Deisy Alessandra Drunker contributed with guidance and management of sample selection and collection, as well as ideas, writing, and discussion of the results of the entire work.

AVAILABILITY STATEMENT

Data supporting the findings of this study are available from the corresponding author, Mayara Santos, upon request.

FUNDING

This study was partially financed by the Coordination for the Improvement of Higher Education Personnel (CAPES) Finance Code 001, the National Council of Technological and Scientific Development (CNPq) and the Araucária Foundation.

Acknowledgments

The authors acknowledge the Post-Graduate Program in Food Technology (PPGTA-UTFPR), and the Federal University of Technology–Paraná (UTFPR). The authors acknowledge the CEANMED - Central Analítica Mulltiusuário da Universidade Tecnológica Federal do Paraná - Campus Medianeira, Paraná State, Brazil, by the assays performed

Conflict of Interest

Mayara Patricia de Oliveira Santos declares that she has no conflict of interest. Nicoli Andressa Carboni declares that she has no conflict of interest. Éder Lisandro de Moraes Flores declares that he has no conflict of interest. Cristiane Canan declares that she has no conflict of interest. Oldair Donizete Leite declares that he has no conflict of interest. Deisy Alessandra Drunker declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Al-Dalahmeh Y, Al-Swaidan HM, Al-Ghamdi AH (2019) Combination of ultrasonication and induced emulsion breaking for eficiente extraction of wear metals from lubricating oils with inductively Coupled Plasma – Mass Spectrometry Determination. J Anal Chem 74: 71–80. doi: https://doi.org/10.1134/S1061934819010039
Associação dos Químicos Analíticos Oficiais (AOAC) (2016) Diretrizes para Requisitos de Desempenho do Método Padrão; AOAC Official Methods of Analysis. http://www.eoma.aoac.org/app_f.pdf Accessed 20 June 2022
Barone G, Storelli A., Quaglia, NC, Garofalo R, Meleleo D, Busco A, Storelli, MM (2021) Trace metals in pork meat products marketed in Italy: Occurrence and health risk characterization. Biol Trace Elem Res 199: 2826–2836. doi: https://doi.org/10.1007/s12011-020-02417-z
Brasil. Ministério da Agricultura, Pecuária e Abastecimento (MAPA) (2017). Regulamentos Técnicos de Identidade e Qualidade de Produtos de Origem Animal. https://www.gov.br/agricultura/pt-br/assuntos/suasa/regulamentos-tecnicos-de-identidade-e-qualidade-de-produtos-de-origem-animal-1 Accessed 20 June 2022
Brasil. Ministério da Saúde Agência Nacional de Vigilância Sanitária (ANVISA) (2021). Resolução da Diretoria Colegiada – RDC nº 466, de 10 de fevereiro de 2021, dispõe sobre os coadjuvantes autorizados para uso na produção de alimentos e ingredientes na função de solventes de extração e processamento. Diário Oficial da União, Poder Executivo, Brasília, DF. https://portal.in.gov.br/web/dou/-/resolucao-rdc-n-466-de-10-de-fevereiro-de-2021-303765551 Accessed 26 May 2022
Campbell-Platt G (2015) Ciência e Tecnologia de Alimentos. Editora Manole, São Paulo.
Carneiro AF, Carneiro CN, Pires LN, Teixeira LSG, Azcarate SM, Dias FS (2020) D-optimal mixture design for the optimization of extraction induced by emulsion breaking for multielemental determination in edible vegetable oils by microwave-induced plasma optical emission spectrometry. Talanta. 219.doi: https://doi.org/10.1016/j.talanta.2020.121218
Codex Alimentárius (1999) Códex Standard for Named Animal Fats. https://www.fao.org/3/y2774e/y2774e05.htm Accessed 15 June 2022
da Mata Cerqueira UMF, Valasques GS, de Souza CT, Araujo SA., Bezerra MA, Novaes CG (2022) Extraction Induced by Emulsion Breaking for Ca, Fe, Mg, and Zn Determination in Edible Oils Using High-Resolution Continuous Source Flame Atomic Absorption Spectrometry. Anal Alimentar Métodos 15:1098–1106. doi: https://doi.org/10.1007/s12161-021-02216-9
De Souza RM, Toloza CAT, Aucélio RQ (2022) Fast determination of trace metals in edible oils and fats by inductively coupled plasma mass spectrometry and ultrasonic acidic extraction. J Trace Eleme and minerals 1.doi: https://doi.org/10.1016/j.jtemin.2022.100003
DEQ (2015) Chemical update worksheet, Michigan. https://www.michigan.gov/documents/deq/deq-rrd-chem-MagnesiumDatasheet_527861_7.pdf Accessed 20 June 2022
DRI (2006) Dietary Reference Intake: The essenttial guide to nutriente requirements. Institute of Medicine. The National Academies Press, 1344. https://www.nap.edu/download/11537 Accessed 20 May 2022:
Elgelmann JL, Peres PP, Igansi AV, Monte ML.; Pohndorf, RS, Cadaval JR TRS, Crexi VT, Pinto LAA (2020) Structured lipids of swine lard and oils from by products of skipjack tuna ando f common carp. Food Process. Preserv 45. doi: https://doi.org/10.1111/jfpp.15154
EPA (United States Environmental Protection Agency) (2021). Risk-based concentration table. Filadélfia. https://archive.epa.gov/region9/superfund/web/pdf/master_sl_table_run_june2015_rev.pdf Accessed 20 June 2022
Gonzaléz AG, Herrador MÁ (2007) A practical guide to analytical method validation, including measurement uncertainty and accuracy profiles. Trends Anal. Chem 26: 227–238. doi: https://doi.org/10.1016/j.trac.2007.01.009
Guimarães LB, Romero JC, Queiroz SMS, Da Silva EGP, Lima DC, Coutinho JP, Dias FS, Bezerra MA, Amorim FAC (2022). A New Method for Determination of Mg, Ca, Zn, and Na in Cocoa Butter by FAAS Employing Extraction Induced by Emulsion Breaking and Multivariate Optimization. Food Anal. Methods 15: 458–467. doi: https://doi.org/10.1007/s12161-021-02123-z
Hashemi P, Raeisi F, Ghiasvand, AR, Rahimi A (2010) Reversed-phase dispersive liquid–liquid microextraction with central composite design optimization for preconcentration and HPLC determination of oleuropein. Talanta 80: 1926–1931. doi: https://doi.org/10.1016/j.talanta.2009.10.051
Instituto Brasileiro de Geografia e Estatística (2010) - IBGE –. Pesquisa de Orçamentos Familiares 2008–2009 – POF. Rio de Janeiro. https://biblioteca.ibge.gov.br/visualizacao/livros/liv47307.pdf Accesse 03 May 2022
Kalschne DL, Canan C, Beato MO, Leite OD, Flores ELM (2020) A new and feasible analytical method using reversed-phase dispersive liquid-liquid microextraction (RP-DLLME) for further determination of Nickel in hydrogenated vegetable fat. Talanta 208: 120409. doi: https://doi.org/10.1016/j.talanta.2019.120409
Leal GC, Rovasi F, Maziero M, Nascimento PC, Carvalho LM, Viana C (2022) Emulsion breaking-induced extraction of Cd and Pb from oily dietary supplements followed by graphite furnace atomic absorption spectrometry detection. J. Food Compos. Anal. 112. doi: https://doi.org/10.1016/j.jfca.2022.104651
Liu S, Xie Q, Chen J, Sun J, He H, Zhang X (2013) Development and comparison of two dispersive liquid–liquid microextraction techniques coupled to high performance liquid chromatography for the rapid analysis of bisphenol A in edible oils. J. Chromatogr. A 1295: 16–23.doi: https://doi.org/10.1016/j.chroma.2013.04.054
López-García I, Briceño M, Vicente-Martínez Y, Hernández-Córdoba M (2015) Rapid screening of water soluble arsenic species in edible oils using dispersive liquid–liquid microextraction. Food Chem 167: 396–401. doi: https://doi.org/10.1016/j.foodchem.2014.06.121
López-García I, Briceño M, Vicente-Martínez Y, Hernández-Córdoba M (2014) Determination of cadmium and lead in edible oils by electrothermal atomic absorption spectrometry after reverse dispersive liquid–liquid microextraction. Talanta 124: 106–110. doi: https://doi.org/10.1016/j.talanta.2014.02.011
Lourenço EC (2016) Desenvolvimento de método empregando microextração líquido-líquido dispersiva em fase reversa para determinação de Na, K, Ca e Mg em Biodiesel. Dissertação, Universidade Tecnológica Federal do Paraná.
Lourenço EC, Eyng E, Bittencourt PRS, Duarte FA, Picoloto RS, Flores ELM (2019) A simple, rapid and low cost reversed-phase dispersive liquid-liquid microextraction for the determination of Na, K, Ca and Mg in biodiesel. Talanta, 199: 1–7. doi: https://doi.org/10.1016/j.talanta.2019.02.054
Marikkar JMN, Yanty NAM (2014) Effect of Chemical and enzymatic modifications on the identity characteristics of lard: A review. Int. J. Food Prop 17: 321–330. doi: https://doi.org/10.1080/10942912.2011.631251
Mirón C, Sánche R, Prats S, Todoli JL (2020) Total polyphenol content and metals determination in Spanish virgin olive oils by means of a dispersive liquid-liquid aerosol phase extraction method and ICP-MS. Anal. Chim. Acta 1094: 34–46.doi: https://doi.org/10.1016/j.aca.2019.10.009
Níveis máximos da China para contaminantes em alimentos, 2018. https://apps.fas.usda.gov/newgainapi/api/report/downloadreportbyfilename?filename=China%20Releases%20the%20Standard%20for%20Levels%20of%20Contaminants%20in%20Foods%20_Beijing_China%20-%20Peoples%20Republic%20of_5-9-2018.pdf Accessed 20 June 2022
Pereira FM, Zimpeck RC, Brum DM, Cassella RJ (2014) Extraction inducced by emulsion breaking as a tool for Ca and Mg determination in biodiesel by fast sequential flame atomic absorption spectrometry (FS-F AAS) USING Co as internal stand. Microchem. J. 117: 172–177. doi: https://doi.org/10.1016/j.microc.2014.06.026
Pereira FM, Zimpeck RC, Brum DM, Cassella RJ (2013) Novel extaction induced by emulsion breaking as tool for the determination of trace concentrations of Cu, Mn and Ni in biodiesel by electrothermal atomic absorption spectrometry. Talanta, 117: 32–38. doi: https://doi.org/10.1016/j.talanta.2013.08.042
Priego-Capote F, Castro LMD (2004) Analytical uses of ultrasound II. Detectors and detection techniques. TrAC Trends Anal. Chem. 23: 10–11. doi: https://doi.org/10.1016/j.trac.2004.07.013
Rezaee M, Assadi Y, Hosseini MM, Aghaee E, Fardin A, Berijani S (2006) Determination of organic compounds in water using dispersive liquid–liquid microextraction. J. Chromatogr. A 1116: 1–9. doi: https://doi.org/10.1016/j.chroma.2006.03.007
Roveda LM, Raposo JL (2019) Internal Standardization in Dispersion Systems: na Efficient application to determine Mg in crude vegetable oils by FS-FAAS. Food Anal. Methods 12: 1111–1120. doi: https://doi.org/10.1007/s12161-019-01439-1
Srinivasan D, Kirk P (2019) Química de Alimentos de Fennema. 5. ed. Artmed. Porto Alegre.
Takashima GMS (2017) Microextração líquido-líquido dispersive em fase reversa para a determinação de cobre, cromo e níquel em biodiesel por espectrometria de absorção atômica com atomização eletrotérmica. Dissertação, Universidade Federal de Santa Catarina.

Table 1 - Concentrations for calibration of F AAS equipment for Cu, Fe, Mg, Mn and Ni

Metals	Concentrations mg L^-1
Metals	1	2	3	4	5
Cu	0.30	0.60	0.80	1.00	1.50
Fe	0.25	0.50	0.75	1.00	1.50
Mg	0.30	0.40	0.60	0.80	1.00
Mn	0.25	0.50	0.75	1.00	1.50
Ni	0.30	0.60	0.80	1.00	1.50

Table 2 - Linear correlation coefficient, straight line equation and linear range of the calibration curve for Cu, Fe, Mg, Mn and Ni

Analyte	R²	Equation of the line	Linear range, mg L^-1
Cu	0.9982	y = 0.0938x – 0.0009	0.3 – 1.0
Fe	0.9988	y = 0.0709x – 0.0015	0.25 – 1.5
Mg	0.9985	y = 1.1967x + 0.0178	0.3 – 1.0
Mn	0.9918	y = 0.1873x – 0.0074	0.25 – 0.75
Ni	0.9968	y = 0.0847x – 0.0009	0.3 – 1.5

Table 3 - Parameters - LOQ and LOD - for the determination of Cu, Fe, Mg, Mn and Ni in lard samples by F AAS using RP-DLLME

Analyte	LOD mg Kg^-1	LOQ mg Kg^-1
Cu	0.0032	0.0106
Fe	0.0041	0.0136
Mg	0.0012	0.0042
Mn	0.0051	0.0169
Ni	0.0037	0.0124

Table 4 - Recovery tests at different concentrations (0.3 and 0.5 mg kg-1) of Cu, Fe, Mg, Mn and Ni in pork lard samples (n=5) submitted to RP-DLLME and quantified using F AAS

Analyte (mg kg^-1)	Cu		Fe		Mg		Mn		Ni
Analyte (mg kg^-1)	R (%)	RSD (%)	R (%)	RSD (%)	R (%)	RSD (%)	R (%)	RSD (%)	R (%)	RSD (%)
0.5	95.64	3.09	98.4	2.52	99.02	3.33	99.16	2.86	96.43	6.46
0.3	97.52	2.24	100.32	4.34	104.42	2.83	99.49	3.05	99.17	7.12

Legend: R: Recovery - RSD: Relative standard deviation

Table 5 - Assays for recovery and quantification of Cu, Fe, Mg, Mn and Ni in lard samples, applying the RP-DLLME methodology by F AAS (n=5)

Recovery
Lard	Cu		Fe		Mg		Mn		Ni
Lard	R (%)**	RSD (%)	R (%)**	RSD (%)	R (%)**	RSD (%)	R (%)**	RSD (%)	R (%)**	RSD (%)
A	99.5^a	3.98	95.1^a	0.41	103.4^a	8.524	98.3^a	7.318	97.6^a	6.432
B	96.5^a	4.96	102.5^a	4.46	101.5^a	2.393	98.8^a	6.676	95.5^a	4.928
C	97.7^a	4.96	99.5^a	6.80	101.2^a	6.001	91.5^b	5.252	95.2^a	3.826
D	90.2^b	4.63	98.0^a	7.47	100.6^a	7.385	96.8^{a b}	6.847	93.2^a	6.429
E	92.3^b	5.07	103.2^a	2.41	99.1^a	5.453	98.6^a	6.138	94.3^a	3.943
F	91.0^b	4.81	95.4^a	4.99	102.8^a	5.696	97.9^a	5.703	92.9^a	3.417
Quantification
Lard	Cu		Fe		Mg		Mn		Ni
Lard	mg k^-1	SD	mg k^-1	SD	mg k^-1	SD	mg k^-1	SD	mg k^-1	SD
A	< LOQ	-	^ab 0.0151	± 0.001	^a 0.1730	± 0.024	< LOQ	-	^a 0.0098^*	± 0.001
B	< LOQ	-	^a 0.0131^*	± 0.002	^c0.4037	± 0.011	< LOQ	-	^a 0.0099^*	± 0.001
C	< LOQ	-	^a 0.0131^*	± 0.005	^d 0.5284	± 0.053	< LOQ	-	^ab0.0111^*	± 0.001
D	< LOQ	-	^b0.0180	± 0.002	^a 0.1712	± 0.010	< LOQ	-	^ab 0.0109^*	± 0.001
E	< LOQ	-	^a 0.0127^*	± 0.001	^b0.2904	± 0.073	< LOQ	-	^a 0.0103^*	± 0.001
F	< LOQ	-	^ab 0.0175	± 0.001	^e 0.8172	± 0.049	< LOQ	-	^b 0.0125	± 0.001
LOD method	0.0032		0.0041		0.0012		0.0051		0.0037
LOQ method	0.0106		0.0136		0.0042		0.0169		0.0124

Legend: *: Analytes below the method's LOQ; **: Recovery; Letters a, b represent Tukey's Test at 95% confidence level. The test was performed for recovery and quantification for the metal groups separately.

Table 6 - Estimated dietary intake for Fe, Mg and Ni (mg day^-1) through the consumption of pork lard for the population of children, adolescents and adults

Children (5 to 12 years)
Lard	Fe			Mg			Ni
Lard	*IDE (m)**	*IDE (f)**	% RDA **	*IDE (m)**	*IDE (f)**	% RDA **	*IDE (m)**	*IDE (f)**	% UL***
A	0,19	0.18	2.34 (m); 2.29 (f)	2.15	2.10	0.89 (m); 0.87 (f)	0.12	0.12	20.25 (m); 19.81 (f)
B	0.16	0.16	2.03 (m); 1.99 (f)	5.01	4.90	2.09 (m); 2.04 (f)	0.12	0.12	20.46 (m); 20.01 (f)
C	0.16	0.16	2.03 (m); 1.99 (f)	6.55	6.41	2.73 (m); 2.67 (f)	0.14	0.13	22.94 (m); 22.44 (f)
D	0.22	0.22	2.79 (m); 2.73 (f)	2.12	2.08	0.88 (m); 0.87 (f)	0.14	0.13	22.53 (m); 22.03 (f)
E	0.16	0.15	1.97 (m); 1.93 (f)	3.60	3.52	1.50 (m); 1.47 (f)	0.13	0.12	21.29 (m); 20.82 (f)
F	0.22	0.21	2.71 (m); 2.65 (f)	10.13	9.91	4.22 (m); 4.13 (f)	0.15	0.15	25.83 (m); 25.26 (f)
Adolescents (13 to 17 years)
A	0.10	0.11	0.91 (m); 0.72 (f)	1.14	1.24	0.28 (m); 0.34 (f)	0.06	0.07	6.48 (m); 7.03 (f)
B	0.09	0.09	0.79 (m); 0.63 (f)	2.67	2.90	0.65 (m); 0.80 (f)	0.07	0.07	6.54 (m); 7.10 (f)
C	0.09	0.09	0.79 (m); 0.63 (f)	3.49	3.79	0.85 (m); 1.05 (f)	0.07	0.08	7.34 (m); 7.96 (f)
D	0.12	0.13	1.08 (m); 0.86 (f)	1.13	1.23	0.28 (m); 0.34 (f)	0.07	0.08	7.20 (m); 7.82 (f)
E	0.08	0.09	0.76 (m); 0.61 (f)	1.92	2.08	0.47 (m); 0.58 (f)	0.07	0.07	6.81 (m); 7.39 (f)
F	0.12	0.13	1.05 (m); 0.84 (f)	5.40	5.86	1.32 (m); 1.63 (f)	0.08	0.09	8.26 (m); 8.97 (f)
Adults (18 to 75 or more years)
A	0.08	0.09	0.99 (m); 0.51 (f)	0.91	1.05	0.22 (m); 0.33 (f)	0.05	0.06	5.13 (m); 5.96 (f)
B	0.07	0.08	0.86 (m); 0.44 (f)	2.11	2.45	0.50 (m); 0.77 (f)	0.05	0.06	5.18 (m); 6.02 (f)
C	0.07	0.08	0.86 (m); 0.44 (f)	2.77	3.21	0.66 (m); 1.00 (f)	0.06	0.07	5.81 (m); 6.75 (f)
D	0.09	0.11	1.18 (m); 0.61 (f)	0.90	1.04	0.21 (m); 0.33 (f)	0.06	0.07	5.71 (m); 6.63 (f)
E	0.07	0.08	0.83 (m); 0.43 (f)	1.52	1.77	0.36 (m); 0.55 (f)	0.05	0.06	5.39 (m); 6.26 (f)
F	0.09	0.11	1.14 (m); 0.59 (f)	4.28	4.97	1.02 (m); 1.55 (f)	0.07	0.08	6.54 (m); 7.60 (f)

Legend: The letters (m) male and (f) female; IDE estimated daily intake (mg day^-1); % RDA ** recommended daily allowance (DRI, 2006) (Fe, children 8 (m) and 8 mg day^-1(f), adolescents 11 (m) and 15 mg day^-1(f) and for adults 8 (m) and 18 mg day^-1(f). Mg, children 240 (m) and 240 mg day^-1(f), adolescents 410 (m) and 360 mg day^-1(f) and for adults 420 (m) and 320 mg day^-1(f); %UL** Tolerable upper intake level (DRI, 2006) (Ni, child 0.6 mg day^-1, adolescent and adults 1.0 mg day^-1.

Table 7 - Hazard quotient (HQ) and hazard index (HI) of metals Fe, Mg and Ni from the consumption of lard for adolescent and adult childrenLegend: The letters (m) male and (f) female; HQ hazard quotient; HI hazard index.

Children (5 to 12 years)
Banha	Fe		Mg		Ni		HI (m)	HI (f)
Banha	HQ (m)	HQ (f)	HQ (m)	HQ (f)	HQ (m)	HQ (f)	HI (m)	HI (f)
A	1.87E-06	1.83E-06	2.15E-05	2.10E-05	1.22E-06	1.19E-06	2.45E-05	2.40E-05
B	1.62E-06	1.59E-06	5.01E-05	4.90E-05	1.23E-06	1.20E-06	5.29E-05	5.17E-05
C	1.62E-06	1.59E-06	6.55E-05	6.41E-05	1.38E-06	1.35E-06	6.85E-05	6.70E-05
D	2.23E-06	2.18E-06	2.12E-05	2.08E-05	1.35E-06	1.32E-06	2.48E-05	2.43E-05
E	1.57E-06	1.54E-06	3.60E-05	3.52E-05	1.28E-06	1.25E-06	3.89E-05	3.80E-05
F	2.17E-06	2.12E-06	1.01E-04	9.91E-05	1.55E-06	1.52E-06	1.05E-04	1.03E-04
Adolescents (13 to 17 years)
A	9.98E-07	1.08E-06	1.14E-05	1.24E-05	6.48E-07	7.03E-07	1.31E-05	1.42E-05
B	8.66E-07	9.40E-07	2.67E-05	2.90E-05	6.54E-07	7.10E-07	2.82E-05	3.06E-05
C	8.66E-07	9.40E-07	3.49E-05	3.79E-05	7.34E-07	7.96E-07	3.65E-05	3.96E-05
D	1.19E-06	1.29E-06	1.13E-05	1.23E-05	7.20E-07	7.82E-07	1.32E-05	1.44E-05
E	8.39E-07	9.11E-07	1.92E-05	2.08E-05	6.81E-07	7.39E-07	2.07E-05	2.25E-05
F	1.16E-06	1.26E-06	5.40E-05	5.86E-05	8.26E-07	8.97E-07	5.60E-05	6.08E-05
Adults (18 to 75 or more years)
A	7.90E-07	9.18E-07	9.06E-06	1.05E-05	5.13E-07	5.96E-07	1.04E-05	1.20E-05
B	6.86E-07	7.96E-07	2.11E-05	2.45E-05	5.18E-07	6.02E-07	2.23E-05	2.59E-05
C	6.86E-07	7.96E-07	2.77E-05	3.21E-05	5.81E-07	6.75E-07	2.89E-05	3.36E-05
D	9.42E-07	1.09E-06	8.96E-06	1.04E-05	5.71E-07	6.63E-07	1.05E-05	1.22E-05
E	6.65E-07	7.72E-07	1.52E-05	1.77E-05	5.39E-07	6.26E-07	1.64E-05	1.91E-05
F	9.16E-07	1.06E-06	4.28E-05	4.97E-05	6.54E-07	7.60E-07	4.43E-05	5.15E-05

Legend: The letters (m) male and (f) female; HQ hazard quotient; HI hazard index.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Determination of metals in Pork Lard: A rapid and low cost sample preparation by RP-DLLME

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Statistical analysis

Results And Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1